The global prevalence of lung cancer as the primary cause of cancer-induced mortality is expected to continue in the foreseeable future. According to the GLOBACON report 2020, lung cancer is the leading cause of new cancer cases worldwide, comprising 11.4% of all cancers and resulting in 18% of cancer-related fatalities. ¹ Approximately 85% of lung tumors are non-small cells and out of them, 60% to 70% of non-small cell lung cancer (NSCLC) patients exhibit stage III or stage IV disease. ² The approval of multiple tyrosine kinase inhibitors (TKI) in the last decade has significantly transformed the management of NSCLC. More than 22 TKIs are currently being utilized in clinical settings or are undergoing advanced trials to target oncogenic drivers of NSCLC, including EGFR, BRAF, MET, ALK fusion rearrangement, and ROS 1-fusion rearrangement. ^{3, 4} Currently, several FDA-approved first-line therapies exist for patients with metastatic NSCLC with epidermal growth factor receptor (EGFR) exon 19 deletions or exon 21 L858R mutations, which work by competitively blocking ATP binding and subsequent phosphorylation of the EGFR tyrosine kinase domain. ^{5– 7}

The objective of targeted therapies for NSCLC is to enhance anticancer efficacy while minimizing adverse effects compared to traditional anticancer medications. This approach has yielded promising results, leading to a significant increase in the number of targeted therapeutic agents developed over the last decade. ⁸ Nevertheless, extensive utilization of this approach has raised concerns regarding its potential cardiotoxicity and off-target effects. Pathways that give rise to pathological survival and uncontrolled proliferation of cancer cells often affect the survival of normal cells, including cardiomyocytes. Inhibition of prosurvival kinases in regular cardiomyocytes may lead to cardiomyopathy, which is a clear manifestation of on-target cardiotoxicity when targeting these pathways in cancer cells. ⁹ The widespread use of TKIs has raised concerns regarding their potential cardiotoxicity and associated symptoms, including, but not limited to, heart failure, arrhythmia/QT prolongation, hypertension, and acute coronary syndrome/myocardial ischemia. Individuals with preexisting cardiac illnesses are at a heightened risk of cardiac damage. It has been observed that 23% of patients diagnosed with lung cancer also have underlying cardiovascular (CV) conditions. The risk of cardiovascular disease (CVD) is elevated in such individuals with lung cancer. The observed association could potentially be attributed to the presence of chronic inflammation in CVD. ^{10, 11}

In response to this issue, various oncology and cardiology governing bodies in the United States and Europe have released guidelines regarding the monitoring of patients undergoing cancer treatments that may have cardiotoxic effects. These guidelines cover the management of CV toxicities and monitoring of CVD in patients with cancer. ¹² Left ventricular dysfunction (LVD) and heart failure (HF) are prevalent markers of cardiotoxicity associated to anti-cancer therapies. The long-term use of crizotinib has been linked to bradycardia and QT prolongation. In a phase III clinical trial, it was observed that 69% of patients experienced at least one episode of asymptomatic sinus bradycardia, which is characterized by a heart rate of less than 60 beats per minute. Additionally, 1.4% of patients exhibited QTc prolongation. ^{13, 14} Reports have indicated that TKIs that interfere with the vascular endothelial growth factor (VEGF) signalling pathway can result in hypertension. The frequency of adverse effects in patients receiving VEGFR inhibitors ranges from 11% to 45%. The prevalence of hypertension has been reported to vary between 17% and 42% in patients treated with sorafenib and between 15% and 47% in those treated with sunitinib. ^{9, 10} Anthracyclines, mitotic inhibitors, alkylating agents, proteasome inhibitors, and TKIs have all been linked to cardiotoxicity and symptoms, such as ischemia, arrhythmias, hypertension, LVD, and the development of clinical heart HF. ¹¹ The European Society for Medical Oncology (ESMO) favors the use of angiotensin-converting enzyme (ACE) II inhibitors in asymptomatic patients with LVD who exhibit an ejection fraction of less than 40%. A proactive pharmacological approach is recommended for the management of hypertension associated with cancer treatment to reduce the risk of CV complications. In this particular situation, the most favored pharmacological agents for managing hypertension are ACE inhibitors and dihydropyridine calcium channel blockers. This assertion holds particularly true in cases where VEGF therapy is utilized. ^{15, 16}

The treatment of associated cardiovascular diseases in patients with NSCLC necessitates the use of multiple medications, leading to polypharmacy. Consequently, the potential for clinically significant DDIs is a crucial factor to be considered, particularly for patients who are prescribed multiple medications to manage comorbidities, as this may result in heightened toxicity or reduced therapeutic efficacy of the anticancer drugs. Empirical research conducted on cancer patients has recorded the interactions of TKIs with antiepileptic drugs, proton pump inhibitors, antiretroviral medications, antibiotics, and grapefruit juice, with an average rate of drug-drug interactions ranging from 4-40%. Furthermore, pharmacological interventions intended to manage cardiovascular toxicities can also potentially result in DDIs. It is noteworthy that inquiries concerning such interactions are frequently omitted from preliminary clinical trials conducted during the initial phases of pharmaceutical development. Advancements in improving cancer survival rates are also sometimes overshadowed by pharmacokinetic/pharmacodynamic interactions associated with TKIs and medications used to treat concurrent CVD.

Therefore, the primary objective of this investigation was to identify plausible pharmacokinetic and pharmacodynamic DDIs in individuals diagnosed with NSCLC and are prescribed TKIs and concomitant CVDs through a comprehensive methodology that combines drug interaction checkers, IBM Micromedex ^®, and Drugs.com ^®, as well as an in-silico modelling technique for molecular docking. The integration of data obtained from the DDI checker softwares and in silico computational technique is anticipated to yield a more all-encompassing depiction and projection of probable DDIs linked to TKIs and CV medications. This will aid oncologists in adjusting dosages, selecting an optimal standard care protocol, and delivering optimal supportive care.

According to the reports, a significant proportion of individuals diagnosed with lung cancer are of advanced age, with approximately 60% of patients falling within this demographic. Furthermore, up to 80% of this elderly cohort exhibited comorbidities, including but not limited to CVD, neuropsychiatric disorders, respiratory disorders, digestive disorders, and arthritis. CVD is commonly linked to chronic inflammation, which explains the observed correlation between these two medical conditions, and a report has also indicated a statistically significant association between the incidence of CVD and lung cancer. ^{31, 32} The observed trend can be primarily attributed to the significant and rapid increase in the number of oral anticancer therapies , specifically molecularly targeted treatments such as TKIs. According to the World Health Organization Drug Monitoring Centre’s collection of spontaneous adverse reaction reports, TKIs and 10 CV drugs were identified in 348 reports and were found to be linked to QT prolongation. ³³ According to Waters et.al (2015), the prevalence of CYP3A-mediated interaction potential with TKIs and concurrently administered medications was 47%, 22%, and 11% for substrates, inhibitors, and inducers, respectively. ³⁴ A retrospective cross-sectional analysis of adult patients with lung cancer in the United States Veterans Affairs (VA) healthcare system was conducted by Sawsan Rashdan et al. in 2021 and it was observed that 5.3% of patients who were administered concomitant medications had prescriptions for drugs linked to major DDIs, while 55.9% of patients had potential DDIs. ³⁵

Thus, chemotherapy for NSCLC is a multidimensional regimen designed to address comorbidities and toxicity-related symptoms. This may lead to complications owing to the potential occurrence of pharmacokinetic and pharmacodynamic DDIs. Therefore, assessment of potential DDIs occurring with NSCLC medications and CV drugs can provide preliminary knowledge for screening, identification, and management of DDIs in these comorbid conditions. The study utilized drug interaction checkers and published literature to anticipate potential pharmacokinetic interactions between TKIs and concurrently administered drugs. No studies on pharmacokinetic DDIs of TKI with CV medications have been reported to assess the severity of the interaction with NSCLC therapy. Hence, in this study, we thoroughly evaluated the prevalence and risk of pharmacokinetic and pharmacodynamic interactions and their severity using computational tools and softwares such as IBM Micromedex and Schrödinger. Our study results revealed that the majority of pharmacokinetic/pharmacodynamic DDIs identified by IBM Micromedex were of major severity, whereas Drugs.com had moderate severity. Synergistic inhibition/interference in the pathways of human PXR and hERG can give rise to both pharmacokinetic and pharmacodynamic interactions. Therefore, molecular docking studies assessed the docking scores and binding energy calculations in the binding pockets of both proteins for TKIs and CV drugs.

The study revealed that the interaction between hERG protein and various TKIs was prominent in second- and third-generation TKIs, such as mobocertinib, dabrafenib, tepotinib, selpercatinib, brigatinib, cabozantinib, osimertinib, entrectinib, crizotinib, and afatinib. Ceretinib, crizotinib, and moboceritinib induce QT prolongation in patients. In contrast, crizotinib induced bradycardic symptoms and loralatinib prolonged the PR interval. In addition, gefitinib directly reported QT prolongation along with the involvement of hERG, validating the results of the molecular docking studies. hERG is also involved in a number of cellular processes, including apoptosis, angiogenesis, and cell proliferation Wan. et. al. (2020) also studied its involvement in cardiotoxicity and possible role as a therapeutic target in cancer physiology. ³⁶ Occhipinti M et al. reported that statin use was negatively associated with progression free survival in Stage IV advanced NSCLC patients treated with EGFR-TKIs ( p = 0.02; HR 0.281, 95% CI, 0.096–0.825), suggesting a negative statin drug interaction with TKIs. ³⁷

ACE inhibitors such as lisinopril and angiotensin II blockers, including telmisartan and olmesartan, as well as calcium channel blockers and β-blockers, are the primary CV drugs that interact with the hERG protein. These interactions are based on hydrophobic interactions and the net binding energy. In addition to the aforementioned drugs, dabigatran, dronedarone, and quinidine exhibited a significant affinity for the hERG protein. In 2005, Farid et al. conducted a study on the homo-tetrameric pore domain of hERG. This study aimed to investigate potential energy mapping and binding possibilities. The results showed that compounds containing hydrophobic groups, such as terfenadine, cisapride, sertindole, and ibutilide, exhibited strong binding properties. These findings suggested that the tetrameric ring may act as a strong binding pore. ³⁸ Zhang et. al . (2016) established a study validating the interaction between amiodarone and hERG potassium channel blocker pore with mutagenesis and its in vitro IC ₅₀ for the F656A mutation. ³⁹ Additionally, Munawar et.al. (2019) validated the involvement of potassium ions and water molecules in the hERG channel in the binding of the drug to the pore, which might be the possible reason for the difference in selectivity between drugs belonging to the same class. ⁴⁰ Co-docking studies of TKIs revealed that ceritinib and cabozantinib were particularly susceptible to interactions with CV drugs including diltiazem, dronedarone, propranolol, apixaban, amiodarone, dabigatran, and ticagrelor. Concurrent use of TKIs in combination with calcium channel blockers, dual antiplatelet agents, and ACE II inhibitors may result in cardiac arrhythmia and increase the likelihood of QT interval prolongation. Studies have reported that certain drugs, including amiodarone, quinidine, sotalol, and dofetilide, are associated with an increased risk of QT interval prolongation.

Several TKIs and CV drugs have been reported to increase QTc interval in patients. ^{33, 41, 42} There is an increased risk of developing DDIs among cancer patients receiving TKI drugs with the concomitant use of CV drugs. ^{43, 44} Most pharmacodynamic DDIs identified in our study from IBM Micromedex were of major severity, whereas pharmacodynamic DDIs identified from Drugs.com had a significant proportion of both major and moderate severity. It was evident from the results obtained from IBM Micromedex and drug.com interactions checker that CV drugs such as amiodarone, dronedarone, quinidine, propranolol, and sotalol have shown potential for pharmacodynamic interactions ( Figures 1 and 2). The majority of drugs cause QTc interval prolongation by inhibition of the hERG subunit of the channel that conducts major ventricular repolarizing potassium current (IKr) during phases 2–3 of the action potential. ^{45, 46} In TKI-initiated cancer patients with concomitant CV drug intake, regular ECG monitoring is strongly recommended. Prospective studies evaluating QTc prolongation among cancer patients on the concomitant use of TKIs and CV drugs are needed to establish conclusive evidence.

In 2014, Van Leeuwen et al. suggested that to enhance the secure administration of TKIs in clinical oncology, it is necessary to assess co-prescribed medications, herbal supplements, and dietary elements such as grapefruit juice, as well as cardiac risk factors and physical examination. ⁴⁷ Several clinical studies, such as those conducted by Akbulut et al. (2022) and Amina Haouala et al. (2010), have also reported DDIs between CV and anticancer agents. ^{43, 48} Owing to their extended usage and status as substrates of CYP450 and efflux transporters P-gp and BCRP, patients receiving TKIs are at an elevated risk for pharmacokinetic DDIs. TKIs used in the treatment of NSCLC, including ceritinib, afatinib, cabozantinib, and crizotinib, have also been identified as inhibitors of CYP3A4 and P-gp. ⁴⁹ The results of the drug interaction checkers and molecular docking analyses indicated that these drugs have a high binding affinity to the ligand-binding domain of the PXR protein, evidenced by their high binding energies, as shown in Figure 2 and Table 2. Similarly, it was evident from the results of the drug interaction checker softwares that CV drugs such as calcium channel blockers, β-blockers, anticoagulants, and angiotensin II blockers have the potential to cause major pharmacokinetic interactions and the potential to inhibit the PXR protein. The NB energies of these inhibitors ranged from -1500 to -2400 Kcal/mol. Calcium channel blockers and β-blockers are known for their ability to inhibit CYP3A4. ^{50, 51} Concurrent use of these drugs may substantially increase the plasma concentration of TKIs. In a crossover study involving healthy volunteers, Teng et al. (2013) investigated the pharmacokinetic interactions between diltiazem and CYP3A4 substrates. The results of this study indicate that diltiazem significantly inhibits CYP3A4, resulting in pharmacokinetic DDIs. ⁵² Hukkanen et. al. (2015) also reported the inhibitory activity of atorvastatin on PXR protein, indicating the potential of statins as drugs for the occurrence of DDIs. ⁵³

Information derived from both IBM Micromedex and Drugs.com revealed that several pharmacokinetic DDIs between statins and TKIs of major and moderate severity were also identified in both the databases. The results of the binding energy calculations were also in the same line, where simvastatin (BE = -33.39), lovastatin (BE = -33.36), and pravastatin (BE = -41.66) showed prominent interactions with dabrafenib, ceritinib, crizotinib, larotrectinib, and lorlatinib. DDIs between these statins and TKIs were identified using both drug information databases. The drug information databases have identified several other antihypertensive drugs, including telmisartan, nifedipine, carvedilol, amlodipine, dabigatran, amiodarone, and felodipine, as drugs having major and moderate severity DDIs with TKIs. Computational assessments indicated that the drugs exhibited robust interactions with PXR both independently and when combined, as revealed through docking. Further research is necessary to determine the clinical significance of these DDIs. This research will help to establish conclusive evidence and develop guidelines for the identification, monitoring, and management of DDIs in clinics.

The use of TKIs in the treatment of NSCLC, in conjunction with CV drugs, may result in various potential DDIs. These predictions were made using drug-drug interaction checkers and in silico molecular docking technique. The significance of a thorough treatment plan is highlighted, especially for patients undergoing treatment for NSCLC with other illnesses, such as CVD or cardiac toxicity caused by chemotherapy. Multidisciplinary treatment is administered to the patients diagnosed with NSCLC. The co-administration of TKIs that have the potential for QT prolongation or drugs that are inducers or inhibitors of CYP3A4 and P-gp with CV drugs, such as antiarrhythmic and antihypertensive drugs, may result in significant DDIs and adverse effects. The present study highlights the necessity for further well-planned investigations to validate the existing guidelines for the safe prescription and development of ideal chemotherapy regimens for patients. The utilization of medical tools, software, and computational methods is imperative to document all therapy-related interactions. Collaborative efforts between clinical oncologists, cardiologists, and pharmacists are also necessary to effectively identify drug interactions in patients with cancer. The resulting integrated dataset can then be utilized to develop a comprehensive surveillance strategy for monitoring, identifying, and managing DDIs during TKI therapy. This approach will lead to improved treatment outcomes, reduced healthcare costs, lower morbidity rates, and an overall improved quality of life for patients.

Prajakta Harish Patil: Conceptualization of the study, collection of data, and writing of the manuscript. Mrunal Pradeep Desai: Collection and interpretation of data. Gayathri Baburaj: Collection and interpretation of data. Levin Thomas: Collection and interpretation of data. Viswam Subeesh: Collection and interpretation of data and review of the manuscript. Sumit Birangal: Collection and interpretation of data. Mahadev Rao: Data review and manuscript review. Gurupur Gautham Shenoy: Interpretation of data, review of data, and review of manuscript. Jagadish P. C.: Conceptualization of study, Review of data, Review of manuscript.