Anticancer Drug Utilization and Prescription Practices in a Saudi Cancer Center (2014-2022)

Ahmed Badheeb; Manea Al Munjem; Faisal Ahmed; Huda Aljedaani; Nouf Assiri; Akrm Abdulaziz; Abdullah Abubakar; Mohammad AlQurayshah; Mohamed Badheeb

doi:10.12688/f1000research.147910.2

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Research Article

Revised

Anticancer Drug Utilization and Prescription Practices in a Saudi Cancer Center (2014-2022)

[version 2; peer review: 1 approved, 2 approved with reservations]

Ahmed Badheeb ^1,2, Manea Al Munjem³, Faisal Ahmed⁴, [...] Huda Aljedaani³, Nouf Assiri³, Akrm Abdulaziz³, Abdullah Abubakar⁵, Mohammad AlQurayshah⁶, Mohamed Badheeb⁷

Ahmed Badheeb ^1,2, Manea Al Munjem³, [...] Faisal Ahmed⁴, Huda Aljedaani³, Nouf Assiri³, Akrm Abdulaziz³, Abdullah Abubakar⁵, Mohammad AlQurayshah⁶, Mohamed Badheeb⁷

PUBLISHED 24 Jul 2025

Author details Author details

¹ Department of Oncology, King Khaled Hospital, Najran, Saudi Arabia
² Oncology, Hadhramout University, Al Mukalla, Hadhramaut Governorate, Yemen
³ Pharmacy, King Khaled Hospital, Najran, Saudi Arabia
⁴ Urology, Ibb University, Ibb, Ibb Governorate, Yemen
⁵ Ophthalmology, King Khaled Hospital, Najran, Saudi Arabia
⁶ Department of Internal Medicine, College of Medicine, Najran University, Najran, Saudi Arabia
⁷ Internal Medicine, Yale New Haven Health System, New Haven, Bridgeport, USA

Ahmed Badheeb
Roles: Conceptualization, Data Curation, Formal Analysis, Funding Acquisition, Investigation, Methodology, Project Administration, Resources, Software, Supervision, Validation, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing

Manea Al Munjem
Roles: Conceptualization, Data Curation, Formal Analysis, Investigation, Methodology, Project Administration, Resources, Supervision, Validation, Writing – Original Draft Preparation

Faisal Ahmed
Roles: Conceptualization, Formal Analysis, Investigation, Methodology, Project Administration, Software, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing

Huda Aljedaani
Roles: Conceptualization, Data Curation, Formal Analysis, Funding Acquisition, Investigation, Methodology, Resources, Supervision, Validation, Writing – Original Draft Preparation

Nouf Assiri
Roles: Conceptualization, Data Curation, Funding Acquisition, Investigation, Project Administration, Resources, Software, Writing – Review & Editing

Akrm Abdulaziz
Roles: Conceptualization, Data Curation, Formal Analysis, Funding Acquisition, Investigation, Methodology, Project Administration, Software

Abdullah Abubakar
Roles: Conceptualization, Investigation, Resources, Software, Supervision, Validation, Visualization, Writing – Original Draft Preparation

Mohammad AlQurayshah
Roles: Conceptualization, Formal Analysis, Investigation, Project Administration, Resources, Software, Supervision, Visualization, Writing – Review & Editing

Mohamed Badheeb
Roles: Conceptualization, Data Curation, Formal Analysis, Funding Acquisition, Investigation, Methodology, Project Administration, Resources, Software, Supervision, Validation, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing

OPEN PEER REVIEW

REVIEWER STATUS

This article is included in the Oncology gateway.

Abstract

Background

Effective cancer management include comprehensive evaluation of treatment patterns to ensure optimal resource utilization. This is a single center study review anticancer drug utilization, with an emphasis on adherence to the World Health Organization Essential Medicines List (WHO EML) and regional prescribing trends amid increasing cancer incidence.

Methods

We conducted a retrospective cohort study of 512 adult patients with histologically confirmed malignant neoplasms that were managed with anti-cancer therapy at King Khaled Hospital, Najran, from 2014 to 2022. Data extraction included demographic characteristics, treatment regimens, and supportive medications, analyzed using IBM SPSS Statistics (version 21). Prescription quality was assessed against WHO EML criteria and Saudi Food and Drug Authority (SFDA) standards.

Results

The study revealed slight male predominance (56.4%), with a mean age 55.2 ± 17.0 years. Gastrointestinal (29.7%) and breast cancers (25.8%) accounted for the majority of cases, and 46.7% of patients presented with metastatic disease. First-line regimens predominantly included doxorubicin-cyclophosphamide (20.1%) and FOLFOX (13.5%). Notably, 86.7% of prescribed agents were listed on the WHO EML, surpassing the 85.3% benchmark, and 90.1% were generics. Supportive care commonly involved metoclopramide-based antiemetics (76.1%). Medication shortages occurred in 8.4% of cases, predominantly involving BCG.

Conclusion

Our findings demonstrate an optimal utilization of WHO Essential Medicines List, reflecting evidence-based, cost-effective prescribing practices that exceed international benchmarks. Despite the observed supply chain vulnerabilities, the study reinforces the relevance and applicability of the WHO model at the regional level.

Keywords

chemotherapy, anticancer, drug use pattern, Najran, Saudi Arabia.

Corresponding author: Ahmed Badheeb

Competing interests: No competing interests were disclosed.

Grant information: The author(s) declared that no grants were involved in supporting this work.

Copyright: © 2025 Badheeb A et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

How to cite: Badheeb A, Al Munjem M, Ahmed F et al. Anticancer Drug Utilization and Prescription Practices in a Saudi Cancer Center (2014-2022) [version 2; peer review: 1 approved, 2 approved with reservations]. F1000Research 2025, 13:560 (https://doi.org/10.12688/f1000research.147910.2) First published: 31 May 2024, 13:560 (https://doi.org/10.12688/f1000research.147910.1) Latest published: 24 Jul 2025, 13:560 (https://doi.org/10.12688/f1000research.147910.2)

Revised Amendments from Version 1

We sincerely appreciate the reviewers' feedback, which has significantly improved our manuscript. Below, we summarize the key revisions made in response to reviewer comments:

1. Title & Scope:
- Removed references to survival analysis, as it was not conducted. The revised title is now: “Anticancer Drug Utilization and Prescription Practices in a Saudi Cancer Center (2014–2022).”

2. Methodology:
- Clarified that logistic regression was not performed and adjusted the Methods section accordingly.
- Added prescribing protocols (NCCN/SFDA guidelines) and specified the use of SPSS v21 for analysis.
- Corrected inconsistencies in exclusion criteria (e.g., patients with incomplete records).

3. Results & Presentation:
- Added a new figure (Figure 1) showing annual prescription trends.
- Removed incorrect references to logistic regression in tables and text.
- Clarified patient numbers (N=512, with 2 excluded due to unidentified cancer sites).

4. Discussion & Context:
- Expanded comparisons with regional/international studies.
- Discussed socioeconomic and supply chain influences on prescribing patterns.
- Added evidence-based recommendations for improving treatment strategies.

5. Language & Compliance:
- Replaced informal language with passive voice.
- Standardized keywords per MeSH terms.

6. Supplementary data:
- Added a new supplementary file as Extended data

See the authors' detailed response to the review by Pouyan Ebrahimi
See the authors' detailed response to the review by Shouki Bazarbashi

Introduction

Cancer remains a formidable global health challenge, accounting for approximately 10 million deaths annually attributed to malignancies.¹ While therapeutic advances have reduced age-standardized mortality rates by 33% since 1991, significant disparities persist across healthcare systems.² Such disparity is particularly evident in Middle Eastern countries like Saudi Arabia, where reported cancer incidence increased from 20,000 cases in 2018 to over 27,000 by 2020.³^,⁴ The region faces unique challenges, including higher mortality-to-incidence ratios compared to Western nations, potentially reflecting limitations in early detection and treatment access.⁵

While a remarkable expansion of therapeutic options, including cytotoxic chemotherapy, targeted agents, and immunotherapies, has been observed in recent years, pronounced disparities in cost-effectiveness and accessibility persist, particularly in resource-limited settings.⁶ The WHO Essential Medicines List (EML) serves as a critical tool in this regard, providing evidence-based guidance for optimal medication selection.⁷ Despite the significant value of WHO EML, studies demonstrate substantial variability (up to 61%) in adherence to therapeutic guidelines across institutions, influenced by factors ranging from drug availability to clinician training.⁸^,⁹ This was attributed, in part, to the limited availability and affordability of several therapeutic agents in resources-limited regions.¹⁰

The Saudi healthcare system has made significant strides in cancer care standardization through initiatives like the Saudi Food and Drug Authority's (SFDA) essential medicines program.¹¹^–¹⁴ However, regional treatment patterns remain understudied, particularly in southern provinces like Najran. Clarifying local prescribing behavior is vital for detecting gaps between national policy and day-to-day practice, guiding resource allocation, and ultimately improving patient outcomes. In addition, assessing how closely these practices align with the WHO EML also sheds light on the suitability of global standards across diverse settings.

This study examines anticancer drug utilization patterns at King Khaled Hospital, a major oncology referral center in southern Saudi Arabia, from 2014–2022. We focus specifically on: (1) adherence to WHO EML and SFDA guidelines, (2) temporal trends in regimen selection, and (3) supportive care practices. Our findings aim to inform institutional quality improvement initiatives while contributing to broader discussions about equitable cancer care delivery in similar resource-constrained settings.

Methods

Study Design and Setting

This retrospective cohort study was conducted at King Khaled Hospital, the main oncology referral center for Najran on region of southern Saudi Arabia. The study spanned an eight-year period (June 2014–April 2022) to capture evolving treatment patterns following updates to national cancer guidelines. Chemotherapy protocols followed National Comprehensive Cancer Network (NCCN) and the European Society of Medical Oncology (ESMO) guidelines, which are updated biennially to reflect contemporary standards.¹⁵ Ethical approval was obtained from the Institutional Review Board (Ref: 2022-11 E), with waiver of informed consent granted for this anonymized retrospective analysis.

Study Population

The study cohort comprised adult patients (>18 years) with histologically confirmed malignancies who received at least one cycle of systemic anticancer therapy at the center. Exclusion criteria were systematically applied to maintain data quality, including: (1) absence of pathological confirmation, (2) incomplete treatment records, (3) referral to other institutions before regimen completion, and (4) loss to clinical follow-up within three months of therapy initiation. These measures ensured analysis of a well-documented patient cohort with verifiable treatment trajectories.

Data Collection and Variables

Trained oncology pharmacists abstracted data from electronic medical records using a standardized collection form. Demographic variables included age, gender, and residential district. Clinical parameters encompassed cancer type/stage (classified per AJCC 8th edition), treatment intent (curative or palliative), and metastatic status. Pharmaceutical analysis focused on:

• Chemotherapy regimens (agents, doses, cycles)
• Supportive medications (antiemetics, growth factors)
• Prescription characteristics (generic/branded, EML status)¹⁴
• Documented adverse events and treatment delays

Quality Assurance Measures

To ensure data reliability, 10% of records underwent independent verification by two medical oncologists. Discrepancies (<2% of variables) were resolved through consensus review. Medication classification followed the WHO EML (2021 edition)¹⁴^,¹⁶ and Saudi National Formulary guidelines, with unclear cases adjudicated by the institutional Pharmacy and Therapeutics committee.

Study Outcomes

Primary Outcomes

• Essential Medicines List Adherence: The proportion of prescribed antineoplastic agents listed in the WHO EML,¹⁷ calculated as:
$(Number of EML-listed drugs prescribed/Total drugs prescribed) \times 100$
• Generic Utilization Rate: The percentage of medications ordered by international nonproprietary names (INN) rather than proprietary brands, assessed through pharmacy dispensing records.¹⁸^,¹⁹

Secondary Outcomes

• Temporal Evolution of Regimens: Longitudinal analysis of protocol selection stratified into three epochs (2014–2016, 2017–2019, 2020–2022) using Mantel-Haenszel trend tests.
• Supportive Care Metrics: Antiemetic prescribing frequency by drug class (5-HT3 antagonists, NK1 inhibitors, corticosteroids) and prophylactic growth factor utilization in myelosuppressive regimens.
• System Challenges: Drug shortage incidence, defined as ≥72-hour unavailability of prescribed agents, and treatment delays (>7 days from scheduled date) attributable to logistical constraints.

Definitions

To ensure conceptual precision, the following operational definitions were applied:

• Treatment Episodes: A chemotherapy cycle was considered completed if ≥80% of planned doses were administered per RECIST v1.1 criteria.
• Follow-up Continuity: Patients were classified as having adequate follow-up if they maintained ≥3 documented clinical encounters within 12 months post-treatment and ≤90-day gaps between oncology assessments.
• Essential Medicines Compliance: Full adherence required meeting all three criteria: drug listed in WHO EML 2021, prescribed dosage within SFDA-approved ranges, and administration schedule conforming to NCCN guidelines.
• Adverse Events: Toxicity was graded per CTCAE v5.0, with attribution determined by treating oncologists through Naranjo algorithm assessment.

Prescribing Protocols and Guidelines

Anticancer drug prescriptions were guided primarily by the NCCN clinical practice guidelines, adapted regionally in accordance with SFDA formularies. To account for temporal changes in prescribing practices, the study period was divided into three intervals reflecting major protocol updates:

• 2014–2016: Prescriptions adhered to NCCN guidelines version 2.2014, with regional adaptations emphasizing anthracycline-based regimens for breast cancer and fluoropyrimidine-based regimens for gastrointestinal malignancies.
• 2017–2019: Following the 2017 NCCN update and SFDA formulary revisions, targeted therapies such as trastuzumab and bevacizumab were increasingly incorporated. Anthracycline protocols were refined to optimize dosing schedules.
• 2020–2022: The most recent period reflected adoption of immunotherapies (e.g., nivolumab, pembrolizumab) following their regulatory approval in Saudi Arabia, alongside continued adherence to updated NCCN and SFDA guidelines. Annual formulary reviews ensured alignment with the WHO Essential Medicines List (EML).

Statistical Analysis

Data analysis was performed using IBM SPSS Statistics Sciences (IBM SPSS Statistics for Windows, version 21.0, Armonk, NY: IBM Corp). Continuous variables were reported as mean ± standard deviation or median (IQR) based on distribution normality assessed by Kolmogorov-Smirnov testing. Categorical variables were expressed as frequencies and percentages. Temporal trends were analyzed using Mantel-Haenszel chi-square tests for ordinal data. The test statistic (χ²MH) evaluated whether prescribing patterns changed monotonically over time. All tests were two-tailed, with p<0.05 considered statistically significant.

Results

Patient Characteristics

The study cohort comprised 512 patients with a mean age of 55.2 ± 17.0 years, demonstrating a male predominance (56.4%, n=289). Gastrointestinal malignancies accounted for 29.7% (n=152) of cases, followed by breast cancer (25.8%, n=132). Metastatic disease was present in 46.7% (n=239) of patients at diagnosis, with two cases exhibiting unspecified primary sites. Comorbidities were prevalent, including diabetes (23.8%) and hypertension (20.1%), while 16.7% of patients required referral to tertiary centers for specialized therapies ( Table 1).

Table 1. Demographic and Clinical Characteristics of the Study Cohort (N = 512).

Characteristic	n (%) or mean ± SD
Age, years	55.2 ± 17.0
Gender
Male	289 (56.4)
Female	223 (43.6)
Cancer Site
Gastrointestinal system	152 (29.7)
Colorectal cancer	78 (51.3)*
Gastric cancer	32 (21.1)*
Pancreatic cancer	20 (13.2)*
Hepatocellular cancer	7 (4.6)*
Other GI cancers	15 (9.9)*
Breast cancer	132 (25.8)
Hematological cancer	87 (17.0)
Lymphoma	67 (77.0)*
Leukemia	10 (11.5)*
Myeloma	10 (11.5)*
Head, neck, and respiratory tract cancers	53 (10.4)
Nasopharyngeal cancer	15 (28.3)*
Lung cancer	23 (43.4)*
Central nervous system cancer	3 (5.7)*
Other cancers	12 (22.6)*
Female reproductive cancers	39 (7.6)
Ovarian cancer	24 (61.5)*
Endometrial cancer	13 (33.3)*
Other female reproductive cancers	2 (5.1)*
Male urologic cancers	28 (5.5)
Prostate cancer	20 (71.4)*
Testicular cancer	7 (25.0)*
Other male urologic cancers	1 (3.6)*
Bladder cancer	19 (3.7)
Distant metastasis	239 (46.7)

* Percentages for subcategories are calculated within each cancer group.

Treatment Patterns

Analysis of 1,021 prescribed anticancer agents revealed that doxorubicin (14.0%), cyclophosphamide (13.3%), and docetaxel (9.9%) were the most frequently used cytotoxic agents. The Adriamycin–cyclophosphamide (AC) protocol predominated in breast-cancer management (20.1%), whereas FOLFOX regimens were preferred for gastrointestinal malignancies (13.5%) ( Table 2; Figure 1A). Overall, 86.7% of prescribed medicines complied with the WHO EML, exceeding the regional benchmark of 85.3%. Generic prescribing accounted for 90.1% of all orders, and patients received a mean of 1.99 anticancer agents per treatment course (Supplementary Figure 1). The AC regimen was the most common first-line therapy (72.7% of cases), and 54.5% of patients subsequently transitioned to taxane-based regimens ( Figure 1B).

Table 2. Utilization Patterns of Anticancer Agents (N = 1,021 prescriptions).

Class/Action	Drug Name	n (%)
Alkylating Agents	Cyclophosphamide	74 (7.2)
	Carboplatin	49 (4.8)
	Oxaliplatin	49 (4.8)
	Cisplatin	41 (4.0)
	Ifosfamide	12 (1.2)
Antitumor Antibiotics	Doxorubicin	78 (7.6)
	Bleomycin	11 (1.1)
	Epirubicin*	9 (0.9)
Plant Alkaloids and Antimitotics	Vinblastine	12 (1.2)
	Vinorelbine	4 (0.4)
Topoisomerase Inhibitors	Irinotecan	20 (2.0)
	Etoposide	14 (1.4)
Antimetabolites	5-Fluorouracil	45 (4.4)
	Capecitabine	45 (4.4)
	Gemcitabine	43 (4.2)
	Methotrexate	6 (0.6)
Hormones and Antagonists	Tamoxifen	41 (4.0)
	Leuprorelin	41 (4.0)
	Letrozole	19 (1.9)
	Bicalutamide*	9 (0.9)
Immunotherapies	Nivolumab	17 (1.7)
	Pembrolizumab	6 (0.6)
	Durvalumab*	1 (0.1)
Targeted Therapies	Trastuzumab	33 (3.2)
	Bevacizumab	24 (2.4)
	Rituximab	20 (2.0)
	Cetuximab*	8 (0.8)
	Panitumumab*	7 (0.7)
Others^†	Docetaxel	55 (5.4)
	Paclitaxel	49 (4.8)
	Leucovorin*	42 (4.1)
	Goserelin	13 (1.3)
	Denosumab*	10 (1.0)
	Bortezomib	6 (0.6)
	Others^‡	14 (1.4)

*Not considered essential by the Saudi Food and Drug Authority (SFDA).

^†Others: Palbociclib*, Lenalidomide, Imatinib, Crizotinib*, Lenvatinib*.

^‡Others: Ramucirumab*, BCG, Thalidomide, Dacarbazine*, Olaparib*.

Temporal Trends

Longitudinal analysis revealed significant shifts in prescribing patterns across the study period (2014–2022) ( Figure IC). Mantel-Haenszel tests demonstrated a marked increase in antimetabolite utilization (χ²=9.27, p=0.002), rising from 27.9% in 2014–2016 to 41.6% in 2020–2022, coinciding with regional adoption of oral capecitabine-based regimens. Targeted therapies similarly showed progressive uptake (12.4% to 22.1%, χ²=6.53, p=0.011), reflecting growing availability of trastuzumab and bevacizumab. Immunotherapies exhibited the most dramatic relative growth (3.8% to 8.9%, χ²=5.91, p=0.015), though from a smaller baseline. In contrast, alkylating agent use remained stable (32.1–33.2%, χ²=0.84, p=0.359) despite protocol updates, suggesting persistent reliance on cyclophosphamide and platinum analogs for core regimens ( Table 3).

Table 3. Temporal Trends in Anticancer Drug Utilization.

Drug Class	2014-2016 (%)	2017-2019 (%)	2020-2022 (%)	χ²_MH (df=1)	p-value	Trend Direction
Alkylating Agents	32.1	34.7	33.2	0.84	0.359	-
Antimetabolites	27.9	30.5	41.6	9.27	0.002	↑
Targeted Therapies	12.4	18.3	22.1	6.53	0.011	↑
Immunotherapies	3.8	5.2	8.9	5.91	0.015	↑

Supportive Care and Safety

Metoclopramide-based antiemetics were administered to 76.1% of patients, with dexamethasone co-prescribed in 90.1% of cases. Documented adverse events occurred in 18.9% of treatment courses, primarily comprising neutropenic fever (2.5%) and hypersensitivity reactions (1.3%) ( Table 4, Figure 1D). Medication shortages affected 8.4% of patients, most frequently involving BCG (0.8%) and gemcitabine (0.6%), resulting in a median treatment delay of 7 days (IQR: 5–11) when shortages occurred.

Table 4. Prescription Patterns of Adjuvant Drugs (N = 556 prescriptions).

Adjuvant Medication	n (%)	Therapeutic Category
Dexamethasone	501 (90.1)	Corticosteroid
Metoclopramide	423 (76.1)	Dopamine antagonist
Ondansetron	412 (74.1)	5-HT₃ antagonist
Furosemide	55 (9.9)	Loop diuretic
Aprepitant	42 (7.6)	NK₁ antagonist
Pantoprazole	42 (7.6)	Proton pump inhibitor
Zoledronic acid	12 (2.2)	Bisphosphonate
Multivitamins	4 (0.7)	Supplement
Netupitant/palonosetron	4 (0.7)	Combination antiemetic

Figure 1. Anticancer Drug Utilization and Safety Patterns at King Khaled Hospital, Najran (2014–2022).

A. Drug Class Utilization Heatmap. Heatmap displaying the prescription frequency of major anticancer drug classes across four principal cancer types (gastrointestinal, breast, lung, and lymphoma) from 2014 to 2022. Color intensity reflects the percentage of patients within each cancer type receiving a given drug class, normalized to column totals. Absolute patient counts are annotated within each cell. Antimetabolite use predominated in gastrointestinal cancers, while alkylating agents were most frequent in breast cancer. B. Treatment Sequencing in Breast Cancer. Stacked bar plot illustrating first-line to second-line chemotherapy transitions among breast cancer patients (N=132). The AC regimen (doxorubicin/cyclophosphamide) was initiated in 72.7% of cases, with 54.5% progressing to taxane-based therapies (docetaxel or paclitaxel). Categories accounting for fewer than 5% of transitions are consolidated as ‘Other’. Bar segments are labeled with absolute patient numbers and percentages. C. Temporal Trends in Drug Class Utilization. Grouped bar chart showing annual utilization rates of major drug classes across three epochs (2014–2016, 2017–2019, 2020–2022). Error bars represent 95% confidence intervals calculated by the binomial exact method. Statistically significant trends are annotated (Mantel-Haenszel χ² test: p<0.01 for antimetabolites, p<0.05 for immunotherapies). The dashed horizontal line indicates mean utilization across all periods (29.8%). D. Adverse Event Temporal Trends. Faceted line plots depicting rates of key treatment-related adverse events per 100 chemotherapy cycles, including (i) hypersensitivity reactions, (ii) neutropenic fever, and (iii) treatment delays. Shaded areas represent 95% confidence intervals. The dashed vertical line marks the 2018 protocol revision, which was associated with a reduction in neutropenia rates (Pearson’s r=–0.82, p=0.007). Abbreviations: AC, Adriamycin-cyclophosphamide; FOLFOX, folinic acid, fluorouracil, and oxaliplatin.

Anticancer Drug Utilization Metrics

The cohort received 1,021 anticancer drug administrations among 512 patients, yielding a mean of 1.99 ± 0.41 agents per treatment course. Single-agent therapy was employed in 314 cases (61.3%), while 198 patients (38.7%) received combination regimens ( Table 5).

Table 5. WHO Prescribing Indicators and Compliance.

Prescribing Indicator	Observed Value	WHO Target	Compliance Status
Average number of drugs per encounter	1.99	< 2.0	Met
Percentage of drugs prescribed by generic name	90.1%	100%	Partially met
Percentage of drugs prescribed from EML	86.7%	85.3%	Exceeded
Number of adjuvant/supportive drugs per patient	2.91	–	–
Number of drugs prescribed per patient	4.91	–	–

Discussion

This retrospective analysis of 512 cancer patients showed prominence of gastrointestinal and breast cancers, compromising 29.7% and 25.8% of cases. In addition, nearly half of cases (46.7%) presenting with metastatic disease at diagnosis. The observed therapeutic patterns-particularly the high utilization of doxorubicin (14.0%) and cyclophosphamide (13.3%), reflect a complex interplay of clinical and socioeconomic factors, including alignment with the regional cancer burden, formulary optimization for cost-effectiveness (86.7% WHO EML compliance), and limited accessibility to certain novel agents. These findings consistent with previous reports from Saudi Arabia’s Southern Region and highlighting the significant influence of resource availability on prescription behaviors, particularly in shaping regional formulary preferences based on cost-effectiveness and drug accessibility.¹²^,¹⁵^,²⁰^–²³

The study showed high adherence to the WHO EML, with 86.7% of prescribed agents being EML-listed, and 92.4% compliant with the SFDA formulary. The generic prescribing rate reached 90.1%, substantially higher than previously reported rates in Saudi Arabia and other Middle Eastern countries,²⁴^,²⁵ considerable geographic variation. A comparative review of national EMLs and the WHO EML revealed marked disparities, with the European region demonstrating the highest dissimilarity to WHO EML compared to South-East Asia region.²⁶ The authors partially attributed this variation to the perception of the WHO Essential Medicines List as a resource primarily intended for high- and middle-income countries. However, their analysis revealed a lower adherence to the WHO EML among less affluent nations. Additional explanations included discrepancies in timing, with some national EMLs being updated prior to the release of the corresponding WHO EML editions.

Prescription pattern in Saudi Arabia is similarly affected by multiple socioeconomic and supply chain factors, including cost, availability, and institutional preferences.²⁷ A prior study examining medication accessibility highlighted that public awareness and preferences for branded medications hinder the broader adoption of generic alternatives in the market.²⁸ Supply chain limitations further exacerbate prescribing challenges, with periodic shortages of essential agents, such as BCG, aprepitant, and gemcitabine, reported in multiple Saudi healthcare centers, often resulting in treatment delays or regimen modifications.²⁸^,²⁹ These shortages stem from global manufacturing issues, importation delays, and local distribution challenges Addressing these barriers through the integration and localization of pharmaceutical manufacturing could enhance accessibility and affordability, supporting a more consistent alignment with WHO EML.³⁰^–³² Moreover, regulatory policies and formulary updates guided by the Saudi FDA and alignment with the WHO Essential Medicines List ensure rational and standardized prescribing but also limit access to some novel agents, affecting utilization trends.³³ For example, targeted therapies and immunotherapies have seen gradual uptake following regulatory approvals and guideline incorporation during the study period.³⁴

Temporal analysis revealed a significant increase in the use of targeted therapies over the study period, rising from 27.9% to 41.6%, aligns with the global transition towards oral chemotherapeutic agents.³⁵^,³⁶ In this study, immunotherapies exhibited the most dramatic relative growth, rising from 3.8% to 8.9%, compared to stable use of alkylating agents, maintaining around 32%. This trend was observed similarly in the United States, with alkylating agents reserved its role in the management of some hematological malignancies,³⁷ compared to Europe that experienced a noticeable reduction in alkylating agents utilization with rise of immunotherapies.³⁸

Supportive care practices in our cohort were characterized by the predominant use of metoclopramide-based antiemetics (76.1%), with dexamethasone co-prescribed in 90.1% of cases. This pattern is consistent with international guidelines for moderate-emetogenic chemotherapy and is likely influenced by local formulary constraints and clinician familiarity.³⁹^,⁴⁰

Medications shortage affected around 8.4% of patients, most frequently involving BCG (0.8%) and gemcitabine (0.6%), leading to a median treatment delay of 7 days (interquartile range: 5–11 days). A prior analysis from the United States showed that 63% of cancer institutions experienced at least one medication shortage in monthly basis.⁴¹ Such shortages can result in treatment delays, regimen modifications, and potentially suboptimal clinical outcomes-a concern echoed in global oncology literature.⁴²^,⁴³

Clinical Implications

Our findings have several implications for practice and policy. The high rates of EML and generic prescribing provide a model for other institutions seeking to optimize resource utilization without sacrificing quality. Addressing supply chain weaknesses should be a priority for health authorities to ensure uninterrupted access to essential medicines. Establishing a comprehensive regional oncology registry would facilitate ongoing surveillance of prescribing patterns, supply chain issues, and patient outcomes. Such a registry could enable benchmarking across centers, support quality improvement initiatives, and provide a robust platform for future prospective studies linking drug utilization to clinical endpoints.

Study Limitations

While this study provides comprehensive data on anticancer drug utilization, several limitations warrant consideration. First, the single-center design, based solely on data from King Khaled Hospital, may limit the generalizability of findings across the diverse healthcare settings in Saudi Arabia. Multicenter studies are needed to validate these results and enhance their applicability nationwide. Second, the absence of survival and long-term outcome data-attributable to inconsistent follow-up documentation-precludes assessment of the clinical impact of prescribing patterns on patient survival. Addressing this gap requires prospective studies with systematic outcome tracking. Third, although a significant increase in antimetabolite use was observed, unmeasured confounding factors, such as changes in reimbursement policies or drug availability, may have independently influenced prescribing behaviors apart from guideline updates. Finally, the reported adverse event rate of 18.9% likely underestimates the true toxicity burden, given the retrospective reliance on clinician documentation rather than prospective, systematic adverse event monitoring. These limitations underscore the need for further research employing multicenter, prospective designs with comprehensive clinical and outcome data to validate and expand upon the current findings.

Conclusion

This study demonstrates optimal alignment with the WHO EML and national formulary guidelines in cancer therapy. Prescribing practices were evidence-based and cost-effective, with generic utilization and regimen selection exceeding regional and international benchmarks. These findings reinforce the applicability of the WHO model in guiding rational oncology prescribing at the regional level. However, observed supply chain vulnerabilities highlight ongoing challenges that require targeted policy and operational interventions. Establishing robust, regionally adapted strategies to ensure continuity of essential cancer treatments remains a priority for optimizing patients outcomes.

Ethical considerations

On March 13, 2022, the Ethics Research Committees of King Khalid Hospital approved this study (ID: 2022-11 E), which was carried out in compliance with the Helsinki Declaration. Due to the anonymous retrospective nature of the study, written informed consent from the included patients was not required.

Data availability

Underlying data

Figshare: Anticancer Drug Utilization and Prescription Practices at a Saudi Cancer Center: A Retrospective Study (2014–2022). figshare. Dataset. https://doi.org/10.6084/m9.figshare.25387114.v4.⁴⁴

Extended data

Supplementary Figure 1. Utilization patterns of anticancer agents (N = 1,021 prescriptions) has been deposited in the approved repository Figshare and is available as extended data associated with this article with DOI: https://doi.org/10.6084/m9.figshare.25387114.v4.⁴⁴

Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC0).

Acknowledgments

None.

References

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Version 2

VERSION 2 PUBLISHED 31 May 2024

Author details Author details

Huda Aljedaani
Roles: Conceptualization, Data Curation, Formal Analysis, Funding Acquisition, Investigation, Methodology, Resources, Supervision, Validation, Writing – Original Draft Preparation

Nouf Assiri
Roles: Conceptualization, Data Curation, Funding Acquisition, Investigation, Project Administration, Resources, Software, Writing – Review & Editing

Akrm Abdulaziz
Roles: Conceptualization, Data Curation, Formal Analysis, Funding Acquisition, Investigation, Methodology, Project Administration, Software

Abdullah Abubakar
Roles: Conceptualization, Investigation, Resources, Software, Supervision, Validation, Visualization, Writing – Original Draft Preparation

Mohammad AlQurayshah
Roles: Conceptualization, Formal Analysis, Investigation, Project Administration, Resources, Software, Supervision, Visualization, Writing – Review & Editing

Competing interests

No competing interests were disclosed.

Grant information

The author(s) declared that no grants were involved in supporting this work.

Article Versions (2)

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Published: 24 Jul 2025, 13:560

https://doi.org/10.12688/f1000research.147910.2

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https://doi.org/10.12688/f1000research.147910.1

© 2025 Badheeb A et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Badheeb A, Al Munjem M, Ahmed F et al. Anticancer Drug Utilization and Prescription Practices in a Saudi Cancer Center (2014-2022) [version 2; peer review: 1 approved, 2 approved with reservations]. F1000Research 2025, 13:560 (https://doi.org/10.12688/f1000research.147910.2)

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Key to Reviewer Statuses VIEW HIDE

ApprovedThe paper is scientifically sound in its current form and only minor, if any, improvements are suggested

Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit.

Not approvedFundamental flaws in the paper seriously undermine the findings and conclusions

Version 2

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PUBLISHED 24 Jul 2025

Revised

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Reviewer Report 04 Sep 2025

Muhammad Aamir, Huanggang Normal University, Huanggang, China

Approved with Reservations

https://doi.org/10.5256/f1000research.181922.r403304

This study provides an important retrospective evaluation of anticancer drug prescribing patterns in a southern Saudi oncology center, with a specific focus on adherence to the WHO Essential Medicines List (EML). The manuscript offers valuable insights into real-world prescribing behaviors, formulary alignment, and resource-based limitations. The authors have responded comprehensively to earlier review feedback, which has improved the manuscript. However, a few limitations and areas for enhancement remain.

The study is limited to one hospital, reducing its generalizability. Inclusion of other centers would strengthen the conclusions and applicability of findings across regions.
The omission of survival, progression, or quality-of-life data limits the ability to correlate drug use with patient outcomes, which is key to evaluating the impact of prescribing practices.
The retrospective approach is vulnerable to missing data and documentation biase specially regarding adverse events and treatment delays, potentially underestimating their frequency.
Earlier versions mentioned survival analysis via logistic regression but didn’t include results. While this has been corrected, it highlights the need for tighter alignment between methods and results in future submissions.
While drug shortages are acknowledged, the study could benefit from more specific discussion on root causes (e.g., procurement bottlenecks, regulatory delays).
The temporal evolution of drug classes is well described, but an expanded analysis on the drivers behind those trends (e.g., new approvals, local policy changes) would add context.
While antiemetic use is described, stratifying by emetogenic potential of the chemotherapy or regimen type would have strengthened this section.
Although cost-effectiveness is discussed, the study lacks data or references to actual economic evaluations, limiting the strength of that conclusion.

Is the work clearly and accurately presented and does it cite the current literature?

Yes
Is the study design appropriate and is the work technically sound?

Yes
Are sufficient details of methods and analysis provided to allow replication by others?

Yes
If applicable, is the statistical analysis and its interpretation appropriate?

Not applicable
Are all the source data underlying the results available to ensure full reproducibility?

Yes
Are the conclusions drawn adequately supported by the results?

Yes

Competing Interests: No competing interests were disclosed.

Reviewer Expertise: AI in Healthcare

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.

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Reviewer Report 22 Aug 2025

Pouyan Ebrahimi, Babol University of Medical Science, Babol, Iran

Approved

https://doi.org/10.5256/f1000research.181922.r400338

I would like to thank the authors for their appropriate responses to my questions. In ... Continue reading

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Version 1

VERSION 1

PUBLISHED 31 May 2024

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Reviewer Report 02 Apr 2025

Shouki Bazarbashi, Alfaisal University, Riyadh, Riyadh Province, Saudi Arabia

Approved with Reservations

https://doi.org/10.5256/f1000research.162157.r297655

The study provides valuable data on chemotherapy utilization patterns from a specific region in Saudi Arabia. However, the most significant issue remains the disconnect regarding the survival analysis mentioned in the Methods but absent in the Results and Discussion. Addressing this is crucial.
The Methods explicitly state, "Utilizing logistic regression analysis, we investigated the variables impacting survival rates." However, there are no results presented for this analysis in the Results section, and no discussion of survival findings in the Discussion section

Action Needed: You must either:

a) Include the results of the logistic regression for survival in the Results section and fully discuss these findings (which factors significantly impacted survival in your cohort?) in the Discussion section. The Conclusion should also briefly reflect this key finding.
b) If the survival analysis was ultimately not performed, or if the results were inconclusive/unreliable and thus omitted, you must remove the sentence mentioning the logistic regression for survival analysis from the Methods section. The title might also need adjustment if survival is no longer a reported component. Leaving the mention in Methods without presenting results/discussion is a significant flaw.

Is the work clearly and accurately presented and does it cite the current literature?

Yes
Is the study design appropriate and is the work technically sound?

Yes
Are sufficient details of methods and analysis provided to allow replication by others?

Partly
If applicable, is the statistical analysis and its interpretation appropriate?

Partly
Are all the source data underlying the results available to ensure full reproducibility?

No source data required
Are the conclusions drawn adequately supported by the results?

Yes

Competing Interests: No competing interests were disclosed.

Reviewer Expertise: GI cancer, GU cancer, clinical trials

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Author Response 24 Jul 2025

Dr Badheeb, Oncology, Hadhramout University, Al Mukalla, Yemen

24 Jul 2025

Author Response

Dear editor and reviewers of F1000 journal
We thank the reviewers for their valuable feedback and constructive suggestions, which have significantly improved the clarity and rigor of our manuscript. Below, ... Continue reading Dear editor and reviewers of F1000 journal
We thank the reviewers for their valuable feedback and constructive suggestions, which have significantly improved the clarity and rigor of our manuscript. Below, we provide a point-by-point response to each comment. All changes have been highlighted in the revised manuscript.
Reviewer 1: Shouki Bazarbashi
Comment 1:
“The most significant issue remains the disconnect regarding the survival analysis mentioned in the Methods but absent in the Results and Discussion. The Methods explicitly state, ‘Utilizing logistic regression analysis, we investigated the variables impacting survival rates.’ However, there are no results presented for this analysis in the Results section, and no discussion of survival findings in the Discussion section. Action Needed: You must either: a) Include the results of the logistic regression for survival in the Results section and fully discuss these findings (which factors significantly impacted survival in your cohort?) in the Discussion section. The Conclusion should also briefly reflect this key finding. b) If the survival analysis was ultimately not performed, or if the results were inconclusive/unreliable and thus omitted, you must remove the sentence mentioning the logistic regression for survival analysis from the Methods section. The title might also need adjustment if survival is no longer a reported component. Leaving the mention in Methods without presenting results/discussion is a significant flaw.”
Response:
Thank you. We agree with the reviewer that including the survival analysis is beyond the scope of the study. Thus, we revised the methods section to reflect the unintentional inclusion (see Methods, page 3-6, from starting patients and method to Statistical Analysis paragraph). In addition, we implemented the following changes:
•   Revised the manuscript title to: “Anticancer Drug Utilization and Prescription Practices in a Saudi Cancer Center (2014–2022)” (see Title Page).
•   Added a limitation in the Discussion: “The retrospective design precluded survival outcome analysis due to inconsistent follow-up documentation, representing an important area for future prospective studies” (see Discussion, page 8, last paragraph).
Dear editor and reviewers of F1000 journal
We thank the reviewers for their valuable feedback and constructive suggestions, which have significantly improved the clarity and rigor of our manuscript. Below, we provide a point-by-point response to each comment. All changes have been highlighted in the revised manuscript.
Reviewer 1: Shouki Bazarbashi
Comment 1:
“The most significant issue remains the disconnect regarding the survival analysis mentioned in the Methods but absent in the Results and Discussion. The Methods explicitly state, ‘Utilizing logistic regression analysis, we investigated the variables impacting survival rates.’ However, there are no results presented for this analysis in the Results section, and no discussion of survival findings in the Discussion section. Action Needed: You must either: a) Include the results of the logistic regression for survival in the Results section and fully discuss these findings (which factors significantly impacted survival in your cohort?) in the Discussion section. The Conclusion should also briefly reflect this key finding. b) If the survival analysis was ultimately not performed, or if the results were inconclusive/unreliable and thus omitted, you must remove the sentence mentioning the logistic regression for survival analysis from the Methods section. The title might also need adjustment if survival is no longer a reported component. Leaving the mention in Methods without presenting results/discussion is a significant flaw.”
Response:
Thank you. We agree with the reviewer that including the survival analysis is beyond the scope of the study. Thus, we revised the methods section to reflect the unintentional inclusion (see Methods, page 3-6, from starting patients and method to Statistical Analysis paragraph). In addition, we implemented the following changes:
•   Revised the manuscript title to: “Anticancer Drug Utilization and Prescription Practices in a Saudi Cancer Center (2014–2022)” (see Title Page).
•   Added a limitation in the Discussion: “The retrospective design precluded survival outcome analysis due to inconsistent follow-up documentation, representing an important area for future prospective studies” (see Discussion, page 8, last paragraph).
Competing Interests: No competing interests were disclosed. Close
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COMMENTS ON THIS REPORT

Author Response 24 Jul 2025

Dr Badheeb, Oncology, Hadhramout University, Al Mukalla, Yemen

24 Jul 2025

Author Response

Dear editor and reviewers of F1000 journal
We thank the reviewers for their valuable feedback and constructive suggestions, which have significantly improved the clarity and rigor of our manuscript. Below, ... Continue reading Dear editor and reviewers of F1000 journal
We thank the reviewers for their valuable feedback and constructive suggestions, which have significantly improved the clarity and rigor of our manuscript. Below, we provide a point-by-point response to each comment. All changes have been highlighted in the revised manuscript.
Reviewer 1: Shouki Bazarbashi
Comment 1:
“The most significant issue remains the disconnect regarding the survival analysis mentioned in the Methods but absent in the Results and Discussion. The Methods explicitly state, ‘Utilizing logistic regression analysis, we investigated the variables impacting survival rates.’ However, there are no results presented for this analysis in the Results section, and no discussion of survival findings in the Discussion section. Action Needed: You must either: a) Include the results of the logistic regression for survival in the Results section and fully discuss these findings (which factors significantly impacted survival in your cohort?) in the Discussion section. The Conclusion should also briefly reflect this key finding. b) If the survival analysis was ultimately not performed, or if the results were inconclusive/unreliable and thus omitted, you must remove the sentence mentioning the logistic regression for survival analysis from the Methods section. The title might also need adjustment if survival is no longer a reported component. Leaving the mention in Methods without presenting results/discussion is a significant flaw.”
Response:
Thank you. We agree with the reviewer that including the survival analysis is beyond the scope of the study. Thus, we revised the methods section to reflect the unintentional inclusion (see Methods, page 3-6, from starting patients and method to Statistical Analysis paragraph). In addition, we implemented the following changes:
•   Revised the manuscript title to: “Anticancer Drug Utilization and Prescription Practices in a Saudi Cancer Center (2014–2022)” (see Title Page).
•   Added a limitation in the Discussion: “The retrospective design precluded survival outcome analysis due to inconsistent follow-up documentation, representing an important area for future prospective studies” (see Discussion, page 8, last paragraph).
Dear editor and reviewers of F1000 journal
We thank the reviewers for their valuable feedback and constructive suggestions, which have significantly improved the clarity and rigor of our manuscript. Below, we provide a point-by-point response to each comment. All changes have been highlighted in the revised manuscript.
Reviewer 1: Shouki Bazarbashi
Comment 1:
“The most significant issue remains the disconnect regarding the survival analysis mentioned in the Methods but absent in the Results and Discussion. The Methods explicitly state, ‘Utilizing logistic regression analysis, we investigated the variables impacting survival rates.’ However, there are no results presented for this analysis in the Results section, and no discussion of survival findings in the Discussion section. Action Needed: You must either: a) Include the results of the logistic regression for survival in the Results section and fully discuss these findings (which factors significantly impacted survival in your cohort?) in the Discussion section. The Conclusion should also briefly reflect this key finding. b) If the survival analysis was ultimately not performed, or if the results were inconclusive/unreliable and thus omitted, you must remove the sentence mentioning the logistic regression for survival analysis from the Methods section. The title might also need adjustment if survival is no longer a reported component. Leaving the mention in Methods without presenting results/discussion is a significant flaw.”
Response:
Thank you. We agree with the reviewer that including the survival analysis is beyond the scope of the study. Thus, we revised the methods section to reflect the unintentional inclusion (see Methods, page 3-6, from starting patients and method to Statistical Analysis paragraph). In addition, we implemented the following changes:
•   Revised the manuscript title to: “Anticancer Drug Utilization and Prescription Practices in a Saudi Cancer Center (2014–2022)” (see Title Page).
•   Added a limitation in the Discussion: “The retrospective design precluded survival outcome analysis due to inconsistent follow-up documentation, representing an important area for future prospective studies” (see Discussion, page 8, last paragraph).
Competing Interests: No competing interests were disclosed. Close
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Reviewer Report 07 Jan 2025

Pouyan Ebrahimi, Babol University of Medical Science, Babol, Iran

Approved with Reservations

https://doi.org/10.5256/f1000research.162157.r354091

This study evaluates the utilization patterns of anticancer drugs and their correlation with patient outcomes in a single cancer center in Saudi Arabia from 2014 to 2022. The findings reveal that gastrointestinal and breast cancers were the most prevalent, with doxorubicin, cyclophosphamide, and docetaxel being the most frequently used cytotoxic agents. Over 86% of prescribed medications were listed in the WHO Essential Medicines List, indicating rational drug use. The study emphasizes the importance of adhering to standardized treatment protocols while highlighting the need for improved regional cancer management strategies. However, please consider the following comments for the purpose of completing and improving the article.
Topic:
1. Considering that no survival analysis has been conducted and data such as patient survival rates or analyses related to factors influencing survival are unavailable, I recommend titling the study as "Anticancer Drug Utilization and Prescription Practices in a Saudi Cancer Center (2014–2022)."

Abstract:
2. A critical point to address throughout the manuscript, particularly in the abstract, is avoiding the use of "we" or "our" in academic writing. Instead, adopt a more formal tone. For example, in the methodology section of the abstract, replace "our cancer center" with the specific name of the center where the data was collected.

3. Please include the type of tools or software used for data analysis in the methodology section of the abstract.

4. Clearly state the type of study conducted in the methodology section.

5. In the results section, ensure the total number of cases evaluated is explicitly mentioned.

6. Given the clinical importance of metastatic cases, include the number of patients with metastases in the abstract.

7. Adjust the keywords according to MeSH terminology. Suggested keywords include "Survival," "Drug Utilization," and "Neoplasms."

Introduction:
8. The introduction is well-written; however, in the final paragraph, please elaborate on why the results of this study are important compared to similar studies.

Method:
9. Considering the long data collection period and the potential changes in drug utilization patterns, please specify in the methodology section which protocols or guidelines were used for prescribing drugs to patients during different years, ideally in intervals (e.g., every four years).

10. The sentence “those without consent to participate were excluded” seems inconsistent, as you previously mentioned that no consent was obtained due to the retrospective nature of the study. Please revise this statement for clarity.

11. The exclusion criteria mention that patients lost to follow-up were excluded, but the study design does not appear to include variables requiring follow-up, such as patient survival. Did you mean therapeutic follow-up? If so, please specify the duration and method of follow-up in the methodology section.

12. Please move the explanation regarding logistic regression mentioned in the Study Variables section to the Statistical Analysis section. Additionally, clarify whether the regression analysis was univariate or multivariate. Specify which variable(s) were independent and which were dependent in this analysis.

Result:
13. It is recommended to adjust Table 2 to include drug prescription patterns by year. Alternatively, if necessary, a separate table could be provided to address this. This addition would clarify which anticancer drugs were most frequently prescribed during each year.

14. The text states that 512 cases were evaluated; however, the total number of patients in Table 1 sums to 510. Does this discrepancy indicate that the cancer site was unidentified for two patients? Please clarify.

15. The results section does not discuss logistic regression analysis or mention odds ratios (OR). It seems that this analysis was not performed. If the authors intended Table 4 to represent logistic regression analysis, it appears to have been incorrectly designed. I recommend revising the table by clearly identifying the variables included in the model, providing OR values, 95% confidence intervals, and reference categories. The table should be formatted and written in alignment with standard academic practices.

Discussion:
The discussion section is well-written but requires deeper evaluation. For instance:

16. Why are certain drugs more commonly prescribed in this region? Are there specific socioeconomic, cultural, or healthcare-related factors driving this pattern?

17. How do the results of this study compare to other regional or international studies, particularly in terms of drug utilization patterns and patient outcomes?

18. Finally, based on the findings of this study, what recommendations can you provide to other researchers, clinicians, or policymakers to improve cancer treatment strategies?

Is the work clearly and accurately presented and does it cite the current literature?

Yes
Is the study design appropriate and is the work technically sound?

Yes
Are sufficient details of methods and analysis provided to allow replication by others?

Partly
If applicable, is the statistical analysis and its interpretation appropriate?

Partly
Are all the source data underlying the results available to ensure full reproducibility?

Yes
Are the conclusions drawn adequately supported by the results?

Yes

Competing Interests: No competing interests were disclosed.

Reviewer Expertise: Given my familiarity with similar studies conducted in this field, I am well acquainted with the data collection methods and analyses employed in this study. Additionally, my ongoing research in cancer treatments provides valuable insights, enabling me to effectively contribute to reviewing the oncological aspects of this study.

CITE

Report a concern

Author Response 02 Aug 2025

Dr Badheeb, Oncology, Hadhramout University, Al Mukalla, Yemen

02 Aug 2025

Author Response

Dear editor and reviewers of F1000 journal
We thank the reviewers for their valuable feedback and constructive suggestions, which have significantly improved the clarity and rigor of our manuscript. Below, ... Continue reading Dear editor and reviewers of F1000 journal
We thank the reviewers for their valuable feedback and constructive suggestions, which have significantly improved the clarity and rigor of our manuscript. Below, we provide a point-by-point response to each comment. All changes have been highlighted in the revised manuscript.

Reviewer 2: Pouyan Ebrahimi
Comment 1:
“Considering that no survival analysis has been conducted and data such as patient survival rates or analyses related to factors influencing survival are unavailable, I recommend titling the study as ‘Anticancer Drug Utilization and Prescription Practices in a Saudi Cancer Center (2014–2022).’”
Response:
Thank you.
The title has been revised as recommended (see Title Page). In addition, the methods section was revised to reflect these changes.
Comment 2:
“A critical point to address throughout the manuscript, particularly in the abstract, is avoiding the use of ‘we’ or ‘our’ in academic writing. Instead, adopt a more formal tone. For example, in the methodology section of the abstract, replace ‘our cancer center’ with the specific name of the center where the data was collected.”
Response:
Thank you.
The manuscript was revised using passive construction. (see Abstract, page 1; Methods, pages 3-6).
Comment 3:
“Please include the type of tools or software used for data analysis in the methodology section of the abstract.”
Response:
The methodology section of the Abstract now specifies: “Statistical analysis was performed using SPSS version 21 (IBM Corp., Armonk, NY)” (see Abstract and statistical analysis section, page 1).
Comment 4:
“Clearly state the type of study conducted in the methodology section.”
Response:
The Abstract now clearly states: “This was a retrospective, descriptive study…” (see Abstract, page X).
Comment 5:
“In the results section, ensure the total number of cases evaluated is explicitly mentioned.”
Response:
The total number of cases (N = 512) is now clearly stated in the Results section (see Results, page X, paragraph Y).
Comment 6:
“Given the clinical importance of metastatic cases, include the number of patients with metastases in the abstract.”
Response:
The Abstract now includes the number and percentage of patients with metastatic disease (see Abstract, page 1).
Comment 7:
“Adjust the keywords according to MeSH terminology. Suggested keywords include ‘Survival,’ ‘Drug Utilization,’ and ‘Neoplasms.’”
Response:
Keywords have been revised to conform to MeSH terminology (see Keywords, page 1).
Comment 8:
“In the final paragraph of the introduction, please elaborate on why the results of this study are important compared to similar studies.”
Response:
The Introduction has been expanded to emphasize the significance of our findings in the context of regional and international literature (see Introduction, page 2, final paragraph).
Comment 9:
“Please specify in the methodology section which protocols or guidelines were used for prescribing drugs to patients during different years, ideally in intervals (e.g., every four years).”
Response:
The Methods section now details the prescribing protocols and guideline updates used during the study period, including major changes in 2015 and 2019 (see Methods, page 5).
The revised section is "Prescribing Protocols and Guidelines
Anticancer drug prescriptions were guided primarily by the National Comprehensive Cancer Network (NCCN) clinical practice guidelines, adapted regionally in accordance with Saudi Food and Drug Authority (SFDA) formularies. To account for temporal changes in prescribing practices, the study period was divided into three intervals reflecting major protocol updates:
•   2014–2016: Prescriptions adhered to NCCN guidelines version 2.2014, with regional adaptations emphasizing anthracycline-based regimens for breast cancer and fluoropyrimidine-based regimens for gastrointestinal malignancies.
•   2017–2019: Following the 2017 NCCN update and SFDA formulary revisions, targeted therapies such as trastuzumab and bevacizumab were increasingly incorporated. Anthracycline protocols were refined to optimize dosing schedules.
•   2020–2022: The most recent period reflected adoption of immunotherapies (e.g., nivolumab, pembrolizumab) following their regulatory approval in Saudi Arabia, alongside continued adherence to updated NCCN and SFDA guidelines. Annual formulary reviews ensured alignment with the WHO Essential Medicines List (EML).

Comment 10:
“The sentence ‘those without consent to participate were excluded’ seems inconsistent, as you previously mentioned that no consent was obtained due to the retrospective nature of the study. Please revise this statement for clarity.”
Response:
This inconsistency has been corrected. The Methods now state: “Due to the retrospective design, informed consent was not required” (see Methods, page 3).
Comment 11:
“The exclusion criteria mention that patients lost to follow-up were excluded, but the study design does not appear to include variables requiring follow-up, such as patient survival. Did you mean therapeutic follow-up? If so, please specify the duration and method of follow-up in the methodology section.”
Response:
The exclusion criteria have been clarified to specify: “Patients with incomplete treatment records or undocumented therapeutic regimens were excluded” (see Methods, page X).
Comment 12:
“Please move the explanation regarding logistic regression mentioned in the Study Variables section to the Statistical Analysis section. Additionally, clarify whether the regression analysis was univariate or multivariate. Specify which variable(s) were independent and which were dependent in this analysis.”
Response:
As logistic regression was not performed, all references have been removed from the manuscript (see Methods and Statistical Analysis sections).
Comment 13:
“It is recommended to adjust Table 2 to include drug prescription patterns by year. Alternatively, if necessary, a separate table could be provided to address this.”
Response:
A new figure with 4 subheadings has been added to present annual prescription trends. Key temporal findings are now summarized in the Results section (see Results, pages 6-7 and 13).
Please also see the legend of Figure 1"
C. Temporal Trends in Drug Class Utilization
Grouped bar chart showing annual utilization rates of major drug classes across three epochs (2014–2016, 2017–2019, 2020–2022). Error bars represent 95% confidence intervals calculated by the binomial exact method. Statistically significant trends are annotated (Mantel-Haenszel χ² test: p<0.01 for antimetabolites, p<0.05 for immunotherapies). The dashed horizontal line indicates mean utilization across all periods (29.8%)."

Comment 14:
“The text states that 512 cases were evaluated; however, the total number of patients in Table 1 sums to 510. Does this discrepancy indicate that the cancer site was unidentified for two patients? Please clarify.”
Response:
You may say, the 2 cases were of unidentified cancer., and were removed from the total analysis. Alternatively, you can say the patients had an unidentified primary cancer site and were thus included with distant metastasis cases, which was modified to “Distant metastasis and Unidentified primary site” The text and Table 1 have been clarified to indicate that two patients had an unidentified cancer site (see Table 1 and Results, page 14).
Comment 15:
“The results section does not discuss logistic regression analysis or mention odds ratios (OR). It seems that this analysis was not performed. If the authors intended Table 4 to represent logistic regression analysis, it appears to have been incorrectly designed. I recommend revising the table by clearly identifying the variables included in the model, providing OR values, 95% confidence intervals, and reference categories. The table should be formatted and written in alignment with standard academic practices.”
Response:
As logistic regression was not performed, all related content and tables have been removed or revised to present only descriptive statistics (see Results and Table 4).
Comment 16:
“Why are certain drugs more commonly prescribed in this region? Are there specific socioeconomic, cultural, or healthcare-related factors driving this pattern?”
Response:
The Discussion now addresses regional prescribing patterns, including socioeconomic and supply chain factors (see Discussion, page 8, line 210).
The added paragraph is "Regional prescribing patterns of anticancer drugs in Saudi Arabia are influenced by multiple socioeconomic and supply chain factors. Studies, including our own, show that drug utilization is shaped not only by clinical guidelines but also by institutional policies, drug availability, and economic considerations (1). Socioeconomic factors play a key role: high generic drug utilization rates (around 90%) reflect institutional efforts to contain costs and improve access in a healthcare system balancing limited resources and rising cancer incidence (2). Patient affordability and insurance coverage also affect prescribing choices, with cost-effective regimens favored when possible. Additionally, cultural preferences and physician familiarity with certain regimens influence utilization patterns. Supply chain constraints significantly impact drug availability and prescribing. Periodic shortages of key agents such as BCG, aprepitant, and gemcitabine have been documented in Saudi centers, leading to treatment modifications or delays (2, 3). These shortages stem from global manufacturing issues, importation delays, and local distribution challenges. Such disruptions necessitate regional stockpiling strategies and formulary adjustments to maintain continuity of care (4). Moreover, regulatory policies and formulary updates guided by the Saudi FDA and alignment with the WHO Essential Medicines List ensure rational and standardized prescribing but also limit access to some novel agents, affecting utilization trends (5). For example, targeted therapies and immunotherapies have seen gradual uptake following regulatory approvals and guideline incorporation during the study period (6). In summary, regional prescribing patterns in Saudi Arabia reflect a complex interplay of guideline adherence, economic constraints, drug availability, and healthcare infrastructure. Addressing supply chain vulnerabilities and socioeconomic barriers is essential to optimize cancer treatment outcomes in the region.

Comment 17:
“How do the results of this study compare to other regional or international studies, particularly in terms of drug utilization patterns and patient outcomes?”
Response:
Comparative analysis with regional and international data has been added to the all-Discussion sections.

Comment 18:
“What recommendations can you provide to improve cancer treatment strategies?”
Response:
Three evidence-based recommendations are now included in the Discussion (see Discussion, page 7-9, and clinical implication section).
Additional Revisions:
•   All MeSH keywords standardized (see Keywords).
•   Supplemental Figures provided.
•   Table discrepancies resolved and all tables reformatted for clarity and compliance.
We hope these revisions adequately address all reviewer concerns and have further improved the manuscript’s academic quality. Thank you for your constructive feedback.
Sincerely,
Ahmed Badheeb
Dear editor and reviewers of F1000 journal
We thank the reviewers for their valuable feedback and constructive suggestions, which have significantly improved the clarity and rigor of our manuscript. Below, we provide a point-by-point response to each comment. All changes have been highlighted in the revised manuscript.

Reviewer 2: Pouyan Ebrahimi
Comment 1:
“Considering that no survival analysis has been conducted and data such as patient survival rates or analyses related to factors influencing survival are unavailable, I recommend titling the study as ‘Anticancer Drug Utilization and Prescription Practices in a Saudi Cancer Center (2014–2022).’”
Response:
Thank you.
The title has been revised as recommended (see Title Page). In addition, the methods section was revised to reflect these changes.
Comment 2:
“A critical point to address throughout the manuscript, particularly in the abstract, is avoiding the use of ‘we’ or ‘our’ in academic writing. Instead, adopt a more formal tone. For example, in the methodology section of the abstract, replace ‘our cancer center’ with the specific name of the center where the data was collected.”
Response:
Thank you.
The manuscript was revised using passive construction. (see Abstract, page 1; Methods, pages 3-6).
Comment 3:
“Please include the type of tools or software used for data analysis in the methodology section of the abstract.”
Response:
The methodology section of the Abstract now specifies: “Statistical analysis was performed using SPSS version 21 (IBM Corp., Armonk, NY)” (see Abstract and statistical analysis section, page 1).
Comment 4:
“Clearly state the type of study conducted in the methodology section.”
Response:
The Abstract now clearly states: “This was a retrospective, descriptive study…” (see Abstract, page X).
Comment 5:
“In the results section, ensure the total number of cases evaluated is explicitly mentioned.”
Response:
The total number of cases (N = 512) is now clearly stated in the Results section (see Results, page X, paragraph Y).
Comment 6:
“Given the clinical importance of metastatic cases, include the number of patients with metastases in the abstract.”
Response:
The Abstract now includes the number and percentage of patients with metastatic disease (see Abstract, page 1).
Comment 7:
“Adjust the keywords according to MeSH terminology. Suggested keywords include ‘Survival,’ ‘Drug Utilization,’ and ‘Neoplasms.’”
Response:
Keywords have been revised to conform to MeSH terminology (see Keywords, page 1).
Comment 8:
“In the final paragraph of the introduction, please elaborate on why the results of this study are important compared to similar studies.”
Response:
The Introduction has been expanded to emphasize the significance of our findings in the context of regional and international literature (see Introduction, page 2, final paragraph).
Comment 9:
“Please specify in the methodology section which protocols or guidelines were used for prescribing drugs to patients during different years, ideally in intervals (e.g., every four years).”
Response:
The Methods section now details the prescribing protocols and guideline updates used during the study period, including major changes in 2015 and 2019 (see Methods, page 5).
The revised section is "Prescribing Protocols and Guidelines
Anticancer drug prescriptions were guided primarily by the National Comprehensive Cancer Network (NCCN) clinical practice guidelines, adapted regionally in accordance with Saudi Food and Drug Authority (SFDA) formularies. To account for temporal changes in prescribing practices, the study period was divided into three intervals reflecting major protocol updates:
•   2014–2016: Prescriptions adhered to NCCN guidelines version 2.2014, with regional adaptations emphasizing anthracycline-based regimens for breast cancer and fluoropyrimidine-based regimens for gastrointestinal malignancies.
•   2017–2019: Following the 2017 NCCN update and SFDA formulary revisions, targeted therapies such as trastuzumab and bevacizumab were increasingly incorporated. Anthracycline protocols were refined to optimize dosing schedules.
•   2020–2022: The most recent period reflected adoption of immunotherapies (e.g., nivolumab, pembrolizumab) following their regulatory approval in Saudi Arabia, alongside continued adherence to updated NCCN and SFDA guidelines. Annual formulary reviews ensured alignment with the WHO Essential Medicines List (EML).

Comment 10:
“The sentence ‘those without consent to participate were excluded’ seems inconsistent, as you previously mentioned that no consent was obtained due to the retrospective nature of the study. Please revise this statement for clarity.”
Response:
This inconsistency has been corrected. The Methods now state: “Due to the retrospective design, informed consent was not required” (see Methods, page 3).
Comment 11:
“The exclusion criteria mention that patients lost to follow-up were excluded, but the study design does not appear to include variables requiring follow-up, such as patient survival. Did you mean therapeutic follow-up? If so, please specify the duration and method of follow-up in the methodology section.”
Response:
The exclusion criteria have been clarified to specify: “Patients with incomplete treatment records or undocumented therapeutic regimens were excluded” (see Methods, page X).
Comment 12:
“Please move the explanation regarding logistic regression mentioned in the Study Variables section to the Statistical Analysis section. Additionally, clarify whether the regression analysis was univariate or multivariate. Specify which variable(s) were independent and which were dependent in this analysis.”
Response:
As logistic regression was not performed, all references have been removed from the manuscript (see Methods and Statistical Analysis sections).
Comment 13:
“It is recommended to adjust Table 2 to include drug prescription patterns by year. Alternatively, if necessary, a separate table could be provided to address this.”
Response:
A new figure with 4 subheadings has been added to present annual prescription trends. Key temporal findings are now summarized in the Results section (see Results, pages 6-7 and 13).
Please also see the legend of Figure 1"
C. Temporal Trends in Drug Class Utilization
Grouped bar chart showing annual utilization rates of major drug classes across three epochs (2014–2016, 2017–2019, 2020–2022). Error bars represent 95% confidence intervals calculated by the binomial exact method. Statistically significant trends are annotated (Mantel-Haenszel χ² test: p<0.01 for antimetabolites, p<0.05 for immunotherapies). The dashed horizontal line indicates mean utilization across all periods (29.8%)."

Comment 14:
“The text states that 512 cases were evaluated; however, the total number of patients in Table 1 sums to 510. Does this discrepancy indicate that the cancer site was unidentified for two patients? Please clarify.”
Response:
You may say, the 2 cases were of unidentified cancer., and were removed from the total analysis. Alternatively, you can say the patients had an unidentified primary cancer site and were thus included with distant metastasis cases, which was modified to “Distant metastasis and Unidentified primary site” The text and Table 1 have been clarified to indicate that two patients had an unidentified cancer site (see Table 1 and Results, page 14).
Comment 15:
“The results section does not discuss logistic regression analysis or mention odds ratios (OR). It seems that this analysis was not performed. If the authors intended Table 4 to represent logistic regression analysis, it appears to have been incorrectly designed. I recommend revising the table by clearly identifying the variables included in the model, providing OR values, 95% confidence intervals, and reference categories. The table should be formatted and written in alignment with standard academic practices.”
Response:
As logistic regression was not performed, all related content and tables have been removed or revised to present only descriptive statistics (see Results and Table 4).
Comment 16:
“Why are certain drugs more commonly prescribed in this region? Are there specific socioeconomic, cultural, or healthcare-related factors driving this pattern?”
Response:
The Discussion now addresses regional prescribing patterns, including socioeconomic and supply chain factors (see Discussion, page 8, line 210).
The added paragraph is "Regional prescribing patterns of anticancer drugs in Saudi Arabia are influenced by multiple socioeconomic and supply chain factors. Studies, including our own, show that drug utilization is shaped not only by clinical guidelines but also by institutional policies, drug availability, and economic considerations (1). Socioeconomic factors play a key role: high generic drug utilization rates (around 90%) reflect institutional efforts to contain costs and improve access in a healthcare system balancing limited resources and rising cancer incidence (2). Patient affordability and insurance coverage also affect prescribing choices, with cost-effective regimens favored when possible. Additionally, cultural preferences and physician familiarity with certain regimens influence utilization patterns. Supply chain constraints significantly impact drug availability and prescribing. Periodic shortages of key agents such as BCG, aprepitant, and gemcitabine have been documented in Saudi centers, leading to treatment modifications or delays (2, 3). These shortages stem from global manufacturing issues, importation delays, and local distribution challenges. Such disruptions necessitate regional stockpiling strategies and formulary adjustments to maintain continuity of care (4). Moreover, regulatory policies and formulary updates guided by the Saudi FDA and alignment with the WHO Essential Medicines List ensure rational and standardized prescribing but also limit access to some novel agents, affecting utilization trends (5). For example, targeted therapies and immunotherapies have seen gradual uptake following regulatory approvals and guideline incorporation during the study period (6). In summary, regional prescribing patterns in Saudi Arabia reflect a complex interplay of guideline adherence, economic constraints, drug availability, and healthcare infrastructure. Addressing supply chain vulnerabilities and socioeconomic barriers is essential to optimize cancer treatment outcomes in the region.

Comment 17:
“How do the results of this study compare to other regional or international studies, particularly in terms of drug utilization patterns and patient outcomes?”
Response:
Comparative analysis with regional and international data has been added to the all-Discussion sections.

Comment 18:
“What recommendations can you provide to improve cancer treatment strategies?”
Response:
Three evidence-based recommendations are now included in the Discussion (see Discussion, page 7-9, and clinical implication section).
Additional Revisions:
•   All MeSH keywords standardized (see Keywords).
•   Supplemental Figures provided.
•   Table discrepancies resolved and all tables reformatted for clarity and compliance.
We hope these revisions adequately address all reviewer concerns and have further improved the manuscript’s academic quality. Thank you for your constructive feedback.
Sincerely,
Ahmed Badheeb
Competing Interests: we did not have any competing interests Close
Report a concern
Respond or Comment

COMMENTS ON THIS REPORT

Author Response 02 Aug 2025

Dr Badheeb, Oncology, Hadhramout University, Al Mukalla, Yemen

02 Aug 2025

Author Response

Dear editor and reviewers of F1000 journal
We thank the reviewers for their valuable feedback and constructive suggestions, which have significantly improved the clarity and rigor of our manuscript. Below, ... Continue reading Dear editor and reviewers of F1000 journal
We thank the reviewers for their valuable feedback and constructive suggestions, which have significantly improved the clarity and rigor of our manuscript. Below, we provide a point-by-point response to each comment. All changes have been highlighted in the revised manuscript.

Reviewer 2: Pouyan Ebrahimi
Comment 1:
“Considering that no survival analysis has been conducted and data such as patient survival rates or analyses related to factors influencing survival are unavailable, I recommend titling the study as ‘Anticancer Drug Utilization and Prescription Practices in a Saudi Cancer Center (2014–2022).’”
Response:
Thank you.
The title has been revised as recommended (see Title Page). In addition, the methods section was revised to reflect these changes.
Comment 2:
“A critical point to address throughout the manuscript, particularly in the abstract, is avoiding the use of ‘we’ or ‘our’ in academic writing. Instead, adopt a more formal tone. For example, in the methodology section of the abstract, replace ‘our cancer center’ with the specific name of the center where the data was collected.”
Response:
Thank you.
The manuscript was revised using passive construction. (see Abstract, page 1; Methods, pages 3-6).
Comment 3:
“Please include the type of tools or software used for data analysis in the methodology section of the abstract.”
Response:
The methodology section of the Abstract now specifies: “Statistical analysis was performed using SPSS version 21 (IBM Corp., Armonk, NY)” (see Abstract and statistical analysis section, page 1).
Comment 4:
“Clearly state the type of study conducted in the methodology section.”
Response:
The Abstract now clearly states: “This was a retrospective, descriptive study…” (see Abstract, page X).
Comment 5:
“In the results section, ensure the total number of cases evaluated is explicitly mentioned.”
Response:
The total number of cases (N = 512) is now clearly stated in the Results section (see Results, page X, paragraph Y).
Comment 6:
“Given the clinical importance of metastatic cases, include the number of patients with metastases in the abstract.”
Response:
The Abstract now includes the number and percentage of patients with metastatic disease (see Abstract, page 1).
Comment 7:
“Adjust the keywords according to MeSH terminology. Suggested keywords include ‘Survival,’ ‘Drug Utilization,’ and ‘Neoplasms.’”
Response:
Keywords have been revised to conform to MeSH terminology (see Keywords, page 1).
Comment 8:
“In the final paragraph of the introduction, please elaborate on why the results of this study are important compared to similar studies.”
Response:
The Introduction has been expanded to emphasize the significance of our findings in the context of regional and international literature (see Introduction, page 2, final paragraph).
Comment 9:
“Please specify in the methodology section which protocols or guidelines were used for prescribing drugs to patients during different years, ideally in intervals (e.g., every four years).”
Response:
The Methods section now details the prescribing protocols and guideline updates used during the study period, including major changes in 2015 and 2019 (see Methods, page 5).
The revised section is "Prescribing Protocols and Guidelines
Anticancer drug prescriptions were guided primarily by the National Comprehensive Cancer Network (NCCN) clinical practice guidelines, adapted regionally in accordance with Saudi Food and Drug Authority (SFDA) formularies. To account for temporal changes in prescribing practices, the study period was divided into three intervals reflecting major protocol updates:
•   2014–2016: Prescriptions adhered to NCCN guidelines version 2.2014, with regional adaptations emphasizing anthracycline-based regimens for breast cancer and fluoropyrimidine-based regimens for gastrointestinal malignancies.
•   2017–2019: Following the 2017 NCCN update and SFDA formulary revisions, targeted therapies such as trastuzumab and bevacizumab were increasingly incorporated. Anthracycline protocols were refined to optimize dosing schedules.
•   2020–2022: The most recent period reflected adoption of immunotherapies (e.g., nivolumab, pembrolizumab) following their regulatory approval in Saudi Arabia, alongside continued adherence to updated NCCN and SFDA guidelines. Annual formulary reviews ensured alignment with the WHO Essential Medicines List (EML).

Comment 10:
“The sentence ‘those without consent to participate were excluded’ seems inconsistent, as you previously mentioned that no consent was obtained due to the retrospective nature of the study. Please revise this statement for clarity.”
Response:
This inconsistency has been corrected. The Methods now state: “Due to the retrospective design, informed consent was not required” (see Methods, page 3).
Comment 11:
“The exclusion criteria mention that patients lost to follow-up were excluded, but the study design does not appear to include variables requiring follow-up, such as patient survival. Did you mean therapeutic follow-up? If so, please specify the duration and method of follow-up in the methodology section.”
Response:
The exclusion criteria have been clarified to specify: “Patients with incomplete treatment records or undocumented therapeutic regimens were excluded” (see Methods, page X).
Comment 12:
“Please move the explanation regarding logistic regression mentioned in the Study Variables section to the Statistical Analysis section. Additionally, clarify whether the regression analysis was univariate or multivariate. Specify which variable(s) were independent and which were dependent in this analysis.”
Response:
As logistic regression was not performed, all references have been removed from the manuscript (see Methods and Statistical Analysis sections).
Comment 13:
“It is recommended to adjust Table 2 to include drug prescription patterns by year. Alternatively, if necessary, a separate table could be provided to address this.”
Response:
A new figure with 4 subheadings has been added to present annual prescription trends. Key temporal findings are now summarized in the Results section (see Results, pages 6-7 and 13).
Please also see the legend of Figure 1"
C. Temporal Trends in Drug Class Utilization
Grouped bar chart showing annual utilization rates of major drug classes across three epochs (2014–2016, 2017–2019, 2020–2022). Error bars represent 95% confidence intervals calculated by the binomial exact method. Statistically significant trends are annotated (Mantel-Haenszel χ² test: p<0.01 for antimetabolites, p<0.05 for immunotherapies). The dashed horizontal line indicates mean utilization across all periods (29.8%)."

Comment 14:
“The text states that 512 cases were evaluated; however, the total number of patients in Table 1 sums to 510. Does this discrepancy indicate that the cancer site was unidentified for two patients? Please clarify.”
Response:
You may say, the 2 cases were of unidentified cancer., and were removed from the total analysis. Alternatively, you can say the patients had an unidentified primary cancer site and were thus included with distant metastasis cases, which was modified to “Distant metastasis and Unidentified primary site” The text and Table 1 have been clarified to indicate that two patients had an unidentified cancer site (see Table 1 and Results, page 14).
Comment 15:
“The results section does not discuss logistic regression analysis or mention odds ratios (OR). It seems that this analysis was not performed. If the authors intended Table 4 to represent logistic regression analysis, it appears to have been incorrectly designed. I recommend revising the table by clearly identifying the variables included in the model, providing OR values, 95% confidence intervals, and reference categories. The table should be formatted and written in alignment with standard academic practices.”
Response:
As logistic regression was not performed, all related content and tables have been removed or revised to present only descriptive statistics (see Results and Table 4).
Comment 16:
“Why are certain drugs more commonly prescribed in this region? Are there specific socioeconomic, cultural, or healthcare-related factors driving this pattern?”
Response:
The Discussion now addresses regional prescribing patterns, including socioeconomic and supply chain factors (see Discussion, page 8, line 210).
The added paragraph is "Regional prescribing patterns of anticancer drugs in Saudi Arabia are influenced by multiple socioeconomic and supply chain factors. Studies, including our own, show that drug utilization is shaped not only by clinical guidelines but also by institutional policies, drug availability, and economic considerations (1). Socioeconomic factors play a key role: high generic drug utilization rates (around 90%) reflect institutional efforts to contain costs and improve access in a healthcare system balancing limited resources and rising cancer incidence (2). Patient affordability and insurance coverage also affect prescribing choices, with cost-effective regimens favored when possible. Additionally, cultural preferences and physician familiarity with certain regimens influence utilization patterns. Supply chain constraints significantly impact drug availability and prescribing. Periodic shortages of key agents such as BCG, aprepitant, and gemcitabine have been documented in Saudi centers, leading to treatment modifications or delays (2, 3). These shortages stem from global manufacturing issues, importation delays, and local distribution challenges. Such disruptions necessitate regional stockpiling strategies and formulary adjustments to maintain continuity of care (4). Moreover, regulatory policies and formulary updates guided by the Saudi FDA and alignment with the WHO Essential Medicines List ensure rational and standardized prescribing but also limit access to some novel agents, affecting utilization trends (5). For example, targeted therapies and immunotherapies have seen gradual uptake following regulatory approvals and guideline incorporation during the study period (6). In summary, regional prescribing patterns in Saudi Arabia reflect a complex interplay of guideline adherence, economic constraints, drug availability, and healthcare infrastructure. Addressing supply chain vulnerabilities and socioeconomic barriers is essential to optimize cancer treatment outcomes in the region.

Comment 17:
“How do the results of this study compare to other regional or international studies, particularly in terms of drug utilization patterns and patient outcomes?”
Response:
Comparative analysis with regional and international data has been added to the all-Discussion sections.

Comment 18:
“What recommendations can you provide to improve cancer treatment strategies?”
Response:
Three evidence-based recommendations are now included in the Discussion (see Discussion, page 7-9, and clinical implication section).
Additional Revisions:
•   All MeSH keywords standardized (see Keywords).
•   Supplemental Figures provided.
•   Table discrepancies resolved and all tables reformatted for clarity and compliance.
We hope these revisions adequately address all reviewer concerns and have further improved the manuscript’s academic quality. Thank you for your constructive feedback.
Sincerely,
Ahmed Badheeb
Dear editor and reviewers of F1000 journal
We thank the reviewers for their valuable feedback and constructive suggestions, which have significantly improved the clarity and rigor of our manuscript. Below, we provide a point-by-point response to each comment. All changes have been highlighted in the revised manuscript.

Reviewer 2: Pouyan Ebrahimi
Comment 1:
“Considering that no survival analysis has been conducted and data such as patient survival rates or analyses related to factors influencing survival are unavailable, I recommend titling the study as ‘Anticancer Drug Utilization and Prescription Practices in a Saudi Cancer Center (2014–2022).’”
Response:
Thank you.
The title has been revised as recommended (see Title Page). In addition, the methods section was revised to reflect these changes.
Comment 2:
“A critical point to address throughout the manuscript, particularly in the abstract, is avoiding the use of ‘we’ or ‘our’ in academic writing. Instead, adopt a more formal tone. For example, in the methodology section of the abstract, replace ‘our cancer center’ with the specific name of the center where the data was collected.”
Response:
Thank you.
The manuscript was revised using passive construction. (see Abstract, page 1; Methods, pages 3-6).
Comment 3:
“Please include the type of tools or software used for data analysis in the methodology section of the abstract.”
Response:
The methodology section of the Abstract now specifies: “Statistical analysis was performed using SPSS version 21 (IBM Corp., Armonk, NY)” (see Abstract and statistical analysis section, page 1).
Comment 4:
“Clearly state the type of study conducted in the methodology section.”
Response:
The Abstract now clearly states: “This was a retrospective, descriptive study…” (see Abstract, page X).
Comment 5:
“In the results section, ensure the total number of cases evaluated is explicitly mentioned.”
Response:
The total number of cases (N = 512) is now clearly stated in the Results section (see Results, page X, paragraph Y).
Comment 6:
“Given the clinical importance of metastatic cases, include the number of patients with metastases in the abstract.”
Response:
The Abstract now includes the number and percentage of patients with metastatic disease (see Abstract, page 1).
Comment 7:
“Adjust the keywords according to MeSH terminology. Suggested keywords include ‘Survival,’ ‘Drug Utilization,’ and ‘Neoplasms.’”
Response:
Keywords have been revised to conform to MeSH terminology (see Keywords, page 1).
Comment 8:
“In the final paragraph of the introduction, please elaborate on why the results of this study are important compared to similar studies.”
Response:
The Introduction has been expanded to emphasize the significance of our findings in the context of regional and international literature (see Introduction, page 2, final paragraph).
Comment 9:
“Please specify in the methodology section which protocols or guidelines were used for prescribing drugs to patients during different years, ideally in intervals (e.g., every four years).”
Response:
The Methods section now details the prescribing protocols and guideline updates used during the study period, including major changes in 2015 and 2019 (see Methods, page 5).
The revised section is "Prescribing Protocols and Guidelines
Anticancer drug prescriptions were guided primarily by the National Comprehensive Cancer Network (NCCN) clinical practice guidelines, adapted regionally in accordance with Saudi Food and Drug Authority (SFDA) formularies. To account for temporal changes in prescribing practices, the study period was divided into three intervals reflecting major protocol updates:
•   2014–2016: Prescriptions adhered to NCCN guidelines version 2.2014, with regional adaptations emphasizing anthracycline-based regimens for breast cancer and fluoropyrimidine-based regimens for gastrointestinal malignancies.
•   2017–2019: Following the 2017 NCCN update and SFDA formulary revisions, targeted therapies such as trastuzumab and bevacizumab were increasingly incorporated. Anthracycline protocols were refined to optimize dosing schedules.
•   2020–2022: The most recent period reflected adoption of immunotherapies (e.g., nivolumab, pembrolizumab) following their regulatory approval in Saudi Arabia, alongside continued adherence to updated NCCN and SFDA guidelines. Annual formulary reviews ensured alignment with the WHO Essential Medicines List (EML).

Comment 10:
“The sentence ‘those without consent to participate were excluded’ seems inconsistent, as you previously mentioned that no consent was obtained due to the retrospective nature of the study. Please revise this statement for clarity.”
Response:
This inconsistency has been corrected. The Methods now state: “Due to the retrospective design, informed consent was not required” (see Methods, page 3).
Comment 11:
“The exclusion criteria mention that patients lost to follow-up were excluded, but the study design does not appear to include variables requiring follow-up, such as patient survival. Did you mean therapeutic follow-up? If so, please specify the duration and method of follow-up in the methodology section.”
Response:
The exclusion criteria have been clarified to specify: “Patients with incomplete treatment records or undocumented therapeutic regimens were excluded” (see Methods, page X).
Comment 12:
“Please move the explanation regarding logistic regression mentioned in the Study Variables section to the Statistical Analysis section. Additionally, clarify whether the regression analysis was univariate or multivariate. Specify which variable(s) were independent and which were dependent in this analysis.”
Response:
As logistic regression was not performed, all references have been removed from the manuscript (see Methods and Statistical Analysis sections).
Comment 13:
“It is recommended to adjust Table 2 to include drug prescription patterns by year. Alternatively, if necessary, a separate table could be provided to address this.”
Response:
A new figure with 4 subheadings has been added to present annual prescription trends. Key temporal findings are now summarized in the Results section (see Results, pages 6-7 and 13).
Please also see the legend of Figure 1"
C. Temporal Trends in Drug Class Utilization
Grouped bar chart showing annual utilization rates of major drug classes across three epochs (2014–2016, 2017–2019, 2020–2022). Error bars represent 95% confidence intervals calculated by the binomial exact method. Statistically significant trends are annotated (Mantel-Haenszel χ² test: p<0.01 for antimetabolites, p<0.05 for immunotherapies). The dashed horizontal line indicates mean utilization across all periods (29.8%)."

Comment 14:
“The text states that 512 cases were evaluated; however, the total number of patients in Table 1 sums to 510. Does this discrepancy indicate that the cancer site was unidentified for two patients? Please clarify.”
Response:
You may say, the 2 cases were of unidentified cancer., and were removed from the total analysis. Alternatively, you can say the patients had an unidentified primary cancer site and were thus included with distant metastasis cases, which was modified to “Distant metastasis and Unidentified primary site” The text and Table 1 have been clarified to indicate that two patients had an unidentified cancer site (see Table 1 and Results, page 14).
Comment 15:
“The results section does not discuss logistic regression analysis or mention odds ratios (OR). It seems that this analysis was not performed. If the authors intended Table 4 to represent logistic regression analysis, it appears to have been incorrectly designed. I recommend revising the table by clearly identifying the variables included in the model, providing OR values, 95% confidence intervals, and reference categories. The table should be formatted and written in alignment with standard academic practices.”
Response:
As logistic regression was not performed, all related content and tables have been removed or revised to present only descriptive statistics (see Results and Table 4).
Comment 16:
“Why are certain drugs more commonly prescribed in this region? Are there specific socioeconomic, cultural, or healthcare-related factors driving this pattern?”
Response:
The Discussion now addresses regional prescribing patterns, including socioeconomic and supply chain factors (see Discussion, page 8, line 210).
The added paragraph is "Regional prescribing patterns of anticancer drugs in Saudi Arabia are influenced by multiple socioeconomic and supply chain factors. Studies, including our own, show that drug utilization is shaped not only by clinical guidelines but also by institutional policies, drug availability, and economic considerations (1). Socioeconomic factors play a key role: high generic drug utilization rates (around 90%) reflect institutional efforts to contain costs and improve access in a healthcare system balancing limited resources and rising cancer incidence (2). Patient affordability and insurance coverage also affect prescribing choices, with cost-effective regimens favored when possible. Additionally, cultural preferences and physician familiarity with certain regimens influence utilization patterns. Supply chain constraints significantly impact drug availability and prescribing. Periodic shortages of key agents such as BCG, aprepitant, and gemcitabine have been documented in Saudi centers, leading to treatment modifications or delays (2, 3). These shortages stem from global manufacturing issues, importation delays, and local distribution challenges. Such disruptions necessitate regional stockpiling strategies and formulary adjustments to maintain continuity of care (4). Moreover, regulatory policies and formulary updates guided by the Saudi FDA and alignment with the WHO Essential Medicines List ensure rational and standardized prescribing but also limit access to some novel agents, affecting utilization trends (5). For example, targeted therapies and immunotherapies have seen gradual uptake following regulatory approvals and guideline incorporation during the study period (6). In summary, regional prescribing patterns in Saudi Arabia reflect a complex interplay of guideline adherence, economic constraints, drug availability, and healthcare infrastructure. Addressing supply chain vulnerabilities and socioeconomic barriers is essential to optimize cancer treatment outcomes in the region.

Comment 17:
“How do the results of this study compare to other regional or international studies, particularly in terms of drug utilization patterns and patient outcomes?”
Response:
Comparative analysis with regional and international data has been added to the all-Discussion sections.

Comment 18:
“What recommendations can you provide to improve cancer treatment strategies?”
Response:
Three evidence-based recommendations are now included in the Discussion (see Discussion, page 7-9, and clinical implication section).
Additional Revisions:
•   All MeSH keywords standardized (see Keywords).
•   Supplemental Figures provided.
•   Table discrepancies resolved and all tables reformatted for clarity and compliance.
We hope these revisions adequately address all reviewer concerns and have further improved the manuscript’s academic quality. Thank you for your constructive feedback.
Sincerely,
Ahmed Badheeb
Competing Interests: we did not have any competing interests Close
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Version 2

VERSION 2 PUBLISHED 31 May 2024

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	1	2	3
Version 2 (revision) 24 Jul 25	read		read
Version 1 31 May 24	read	read

Pouyan Ebrahimi, Babol University of Medical Science, Babol, Iran
Shouki Bazarbashi, Alfaisal University, Riyadh, Saudi Arabia
Muhammad Aamir, Huanggang Normal University, Huanggang, China

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2 Views

04 Sep 2025 | for Version 2

Muhammad Aamir, Huanggang Normal University, Huanggang, China

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Approved With Reservations

The study is limited to one hospital, reducing its generalizability. Inclusion of other centers would strengthen the conclusions and applicability of findings across regions.
The omission of survival, progression, or quality-of-life data limits the ability to correlate drug use with patient outcomes, which is key to evaluating the impact of prescribing practices.
The retrospective approach is vulnerable to missing data and documentation biase specially regarding adverse events and treatment delays, potentially underestimating their frequency.
Earlier versions mentioned survival analysis via logistic regression but didn’t include results. While this has been corrected, it highlights the need for tighter alignment between methods and results in future submissions.
While drug shortages are acknowledged, the study could benefit from more specific discussion on root causes (e.g., procurement bottlenecks, regulatory delays).
The temporal evolution of drug classes is well described, but an expanded analysis on the drivers behind those trends (e.g., new approvals, local policy changes) would add context.
While antiemetic use is described, stratifying by emetogenic potential of the chemotherapy or regimen type would have strengthened this section.
Although cost-effectiveness is discussed, the study lacks data or references to actual economic evaluations, limiting the strength of that conclusion.

Is the work clearly and accurately presented and does it cite the current literature?

Yes
Is the study design appropriate and is the work technically sound?

Yes
Are sufficient details of methods and analysis provided to allow replication by others?

Yes
If applicable, is the statistical analysis and its interpretation appropriate?

Not applicable
Are all the source data underlying the results available to ensure full reproducibility?

Yes
Are the conclusions drawn adequately supported by the results?

Yes

Competing Interests

No competing interests were disclosed.

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AI in Healthcare

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3 Views

22 Aug 2025 | for Version 2

Pouyan Ebrahimi, Babol University of Medical Science, Babol, Iran

3 Views Cite this report Responses(0)

Approved

I would like to thank the authors for their appropriate responses to my questions. In my opinion, the study is currently at an appropriate level and can be considered for indexing

Competing Interests

No competing interests were disclosed.

Reviewer Expertise

Given my familiarity with similar studies conducted in this field, I am well acquainted with the data collection methods and analyses employed in this study. Additionally, my ongoing research in cancer treatments provides valuable insights, enabling me to effectively contribute to reviewing the oncological aspects of this study.

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.

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9 Views

02 Apr 2025 | for Version 1

Shouki Bazarbashi, Alfaisal University, Riyadh, Riyadh Province, Saudi Arabia

9 Views Cite this report Responses(1)

Approved With Reservations

Action Needed: You must either:

Is the work clearly and accurately presented and does it cite the current literature?

Yes
Is the study design appropriate and is the work technically sound?

Yes
Are sufficient details of methods and analysis provided to allow replication by others?

Partly
If applicable, is the statistical analysis and its interpretation appropriate?

Partly
Are all the source data underlying the results available to ensure full reproducibility?

No source data required
Are the conclusions drawn adequately supported by the results?

Yes

Competing Interests

No competing interests were disclosed.

Reviewer Expertise

GI cancer, GU cancer, clinical trials

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Author Response

24 Jul 2025

Dr Badheeb, Oncology, Hadhramout University, Al Mukalla, Yemen

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9 Views

07 Jan 2025 | for Version 1

Pouyan Ebrahimi, Babol University of Medical Science, Babol, Iran

9 Views Cite this report Responses(1)

Approved With Reservations

Is the work clearly and accurately presented and does it cite the current literature?

Yes
Is the study design appropriate and is the work technically sound?

Yes
Are sufficient details of methods and analysis provided to allow replication by others?

Partly
If applicable, is the statistical analysis and its interpretation appropriate?

Partly
Are all the source data underlying the results available to ensure full reproducibility?

Yes
Are the conclusions drawn adequately supported by the results?

Yes

Competing Interests

No competing interests were disclosed.

Reviewer Expertise

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Author Response

02 Aug 2025

Dr Badheeb, Oncology, Hadhramout University, Al Mukalla, Yemen

Dear editor and reviewers of F1000 journal
We thank the reviewers for their valuable feedback and constructive suggestions, which have significantly improved the clarity and rigor of our manuscript. Below, we provide a point-by-point response to each comment. All changes have been highlighted in the revised manuscript.

Reviewer 2: Pouyan Ebrahimi
Comment 1:
“Considering that no survival analysis has been conducted and data such as patient survival rates or analyses related to factors influencing survival are unavailable, I recommend titling the study as ‘Anticancer Drug Utilization and Prescription Practices in a Saudi Cancer Center (2014–2022).’”
Response:
Thank you.
The title has been revised as recommended (see Title Page). In addition, the methods section was revised to reflect these changes.
Comment 2:
“A critical point to address throughout the manuscript, particularly in the abstract, is avoiding the use of ‘we’ or ‘our’ in academic writing. Instead, adopt a more formal tone. For example, in the methodology section of the abstract, replace ‘our cancer center’ with the specific name of the center where the data was collected.”
Response:
Thank you.
The manuscript was revised using passive construction. (see Abstract, page 1; Methods, pages 3-6).
Comment 3:
“Please include the type of tools or software used for data analysis in the methodology section of the abstract.”
Response:
The methodology section of the Abstract now specifies: “Statistical analysis was performed using SPSS version 21 (IBM Corp., Armonk, NY)” (see Abstract and statistical analysis section, page 1).
Comment 4:
“Clearly state the type of study conducted in the methodology section.”
Response:
The Abstract now clearly states: “This was a retrospective, descriptive study…” (see Abstract, page X).
Comment 5:
“In the results section, ensure the total number of cases evaluated is explicitly mentioned.”
Response:
The total number of cases (N = 512) is now clearly stated in the Results section (see Results, page X, paragraph Y).
Comment 6:
“Given the clinical importance of metastatic cases, include the number of patients with metastases in the abstract.”
Response:
The Abstract now includes the number and percentage of patients with metastatic disease (see Abstract, page 1).
Comment 7:
“Adjust the keywords according to MeSH terminology. Suggested keywords include ‘Survival,’ ‘Drug Utilization,’ and ‘Neoplasms.’”
Response:
Keywords have been revised to conform to MeSH terminology (see Keywords, page 1).
Comment 8:
“In the final paragraph of the introduction, please elaborate on why the results of this study are important compared to similar studies.”
Response:
The Introduction has been expanded to emphasize the significance of our findings in the context of regional and international literature (see Introduction, page 2, final paragraph).
Comment 9:
“Please specify in the methodology section which protocols or guidelines were used for prescribing drugs to patients during different years, ideally in intervals (e.g., every four years).”
Response:
The Methods section now details the prescribing protocols and guideline updates used during the study period, including major changes in 2015 and 2019 (see Methods, page 5).
The revised section is "Prescribing Protocols and Guidelines
Anticancer drug prescriptions were guided primarily by the National Comprehensive Cancer Network (NCCN) clinical practice guidelines, adapted regionally in accordance with Saudi Food and Drug Authority (SFDA) formularies. To account for temporal changes in prescribing practices, the study period was divided into three intervals reflecting major protocol updates:
•   2014–2016: Prescriptions adhered to NCCN guidelines version 2.2014, with regional adaptations emphasizing anthracycline-based regimens for breast cancer and fluoropyrimidine-based regimens for gastrointestinal malignancies.
•   2017–2019: Following the 2017 NCCN update and SFDA formulary revisions, targeted therapies such as trastuzumab and bevacizumab were increasingly incorporated. Anthracycline protocols were refined to optimize dosing schedules.
•   2020–2022: The most recent period reflected adoption of immunotherapies (e.g., nivolumab, pembrolizumab) following their regulatory approval in Saudi Arabia, alongside continued adherence to updated NCCN and SFDA guidelines. Annual formulary reviews ensured alignment with the WHO Essential Medicines List (EML).

Comment 10:
“The sentence ‘those without consent to participate were excluded’ seems inconsistent, as you previously mentioned that no consent was obtained due to the retrospective nature of the study. Please revise this statement for clarity.”
Response:
This inconsistency has been corrected. The Methods now state: “Due to the retrospective design, informed consent was not required” (see Methods, page 3).
Comment 11:
“The exclusion criteria mention that patients lost to follow-up were excluded, but the study design does not appear to include variables requiring follow-up, such as patient survival. Did you mean therapeutic follow-up? If so, please specify the duration and method of follow-up in the methodology section.”
Response:
The exclusion criteria have been clarified to specify: “Patients with incomplete treatment records or undocumented therapeutic regimens were excluded” (see Methods, page X).
Comment 12:
“Please move the explanation regarding logistic regression mentioned in the Study Variables section to the Statistical Analysis section. Additionally, clarify whether the regression analysis was univariate or multivariate. Specify which variable(s) were independent and which were dependent in this analysis.”
Response:
As logistic regression was not performed, all references have been removed from the manuscript (see Methods and Statistical Analysis sections).
Comment 13:
“It is recommended to adjust Table 2 to include drug prescription patterns by year. Alternatively, if necessary, a separate table could be provided to address this.”
Response:
A new figure with 4 subheadings has been added to present annual prescription trends. Key temporal findings are now summarized in the Results section (see Results, pages 6-7 and 13).
Please also see the legend of Figure 1"
C. Temporal Trends in Drug Class Utilization
Grouped bar chart showing annual utilization rates of major drug classes across three epochs (2014–2016, 2017–2019, 2020–2022). Error bars represent 95% confidence intervals calculated by the binomial exact method. Statistically significant trends are annotated (Mantel-Haenszel χ² test: p<0.01 for antimetabolites, p<0.05 for immunotherapies). The dashed horizontal line indicates mean utilization across all periods (29.8%)."

Comment 14:
“The text states that 512 cases were evaluated; however, the total number of patients in Table 1 sums to 510. Does this discrepancy indicate that the cancer site was unidentified for two patients? Please clarify.”
Response:
You may say, the 2 cases were of unidentified cancer., and were removed from the total analysis. Alternatively, you can say the patients had an unidentified primary cancer site and were thus included with distant metastasis cases, which was modified to “Distant metastasis and Unidentified primary site” The text and Table 1 have been clarified to indicate that two patients had an unidentified cancer site (see Table 1 and Results, page 14).
Comment 15:
“The results section does not discuss logistic regression analysis or mention odds ratios (OR). It seems that this analysis was not performed. If the authors intended Table 4 to represent logistic regression analysis, it appears to have been incorrectly designed. I recommend revising the table by clearly identifying the variables included in the model, providing OR values, 95% confidence intervals, and reference categories. The table should be formatted and written in alignment with standard academic practices.”
Response:
As logistic regression was not performed, all related content and tables have been removed or revised to present only descriptive statistics (see Results and Table 4).
Comment 16:
“Why are certain drugs more commonly prescribed in this region? Are there specific socioeconomic, cultural, or healthcare-related factors driving this pattern?”
Response:
The Discussion now addresses regional prescribing patterns, including socioeconomic and supply chain factors (see Discussion, page 8, line 210).
The added paragraph is "Regional prescribing patterns of anticancer drugs in Saudi Arabia are influenced by multiple socioeconomic and supply chain factors. Studies, including our own, show that drug utilization is shaped not only by clinical guidelines but also by institutional policies, drug availability, and economic considerations (1). Socioeconomic factors play a key role: high generic drug utilization rates (around 90%) reflect institutional efforts to contain costs and improve access in a healthcare system balancing limited resources and rising cancer incidence (2). Patient affordability and insurance coverage also affect prescribing choices, with cost-effective regimens favored when possible. Additionally, cultural preferences and physician familiarity with certain regimens influence utilization patterns. Supply chain constraints significantly impact drug availability and prescribing. Periodic shortages of key agents such as BCG, aprepitant, and gemcitabine have been documented in Saudi centers, leading to treatment modifications or delays (2, 3). These shortages stem from global manufacturing issues, importation delays, and local distribution challenges. Such disruptions necessitate regional stockpiling strategies and formulary adjustments to maintain continuity of care (4). Moreover, regulatory policies and formulary updates guided by the Saudi FDA and alignment with the WHO Essential Medicines List ensure rational and standardized prescribing but also limit access to some novel agents, affecting utilization trends (5). For example, targeted therapies and immunotherapies have seen gradual uptake following regulatory approvals and guideline incorporation during the study period (6). In summary, regional prescribing patterns in Saudi Arabia reflect a complex interplay of guideline adherence, economic constraints, drug availability, and healthcare infrastructure. Addressing supply chain vulnerabilities and socioeconomic barriers is essential to optimize cancer treatment outcomes in the region.

Comment 17:
“How do the results of this study compare to other regional or international studies, particularly in terms of drug utilization patterns and patient outcomes?”
Response:
Comparative analysis with regional and international data has been added to the all-Discussion sections.

Comment 18:
“What recommendations can you provide to improve cancer treatment strategies?”
Response:
Three evidence-based recommendations are now included in the Discussion (see Discussion, page 7-9, and clinical implication section).
Additional Revisions:
•   All MeSH keywords standardized (see Keywords).
•   Supplemental Figures provided.
•   Table discrepancies resolved and all tables reformatted for clarity and compliance.
We hope these revisions adequately address all reviewer concerns and have further improved the manuscript’s academic quality. Thank you for your constructive feedback.
Sincerely,
Ahmed Badheeb

View more View less

Competing Interests

we did not have any competing interests

Alongside their report, reviewers assign a status to the article:

Approved - the paper is scientifically sound in its current form and only minor, if any, improvements are suggested

Approved with reservations - A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit.

Not approved - fundamental flaws in the paper seriously undermine the findings and conclusions

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Anticancer Drug Utilization and Prescription Practices in a Saudi Cancer Center (2014-2022)

Abstract

Background

Methods

Results

Conclusion

Keywords

Revised Amendments from Version 1

Introduction

Methods

Study Design and Setting

Study Population

Data Collection and Variables

Quality Assurance Measures

Study Outcomes

Primary Outcomes

Secondary Outcomes

Definitions

Statistical Analysis

Results

Patient Characteristics

Table 1. Demographic and Clinical Characteristics of the Study Cohort (N = 512).

Treatment Patterns

Table 2. Utilization Patterns of Anticancer Agents (N = 1,021 prescriptions).

Temporal Trends

Table 3. Temporal Trends in Anticancer Drug Utilization.

Supportive Care and Safety

Table 4. Prescription Patterns of Adjuvant Drugs (N = 556 prescriptions).

Figure 1. Anticancer Drug Utilization and Safety Patterns at King Khaled Hospital, Najran (2014–2022).

Anticancer Drug Utilization Metrics

Table 5. WHO Prescribing Indicators and Compliance.

Discussion

Clinical Implications

Study Limitations

Conclusion

Ethical considerations

Data availability

Underlying data

Extended data

Acknowledgments

References

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