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Review

The IC-50-time evolution is a new model to improve drug responses consistency of large scale studies

[version 1; peer review: awaiting peer review]
ABDELKRIM ALILECHE
https://orcid.org/0000-0002-0463-3636
ABDELKRIM ALILECHE
https://orcid.org/0000-0002-0463-3636
PUBLISHED 07 Mar 2022
Author details Author details
OPEN PEER REVIEW
REVIEWER STATUS AWAITING PEER REVIEW

Abstract

Abstract: Large-scale studies combining hundreds of cancer cell lines and many cancer drugs, with their promises and challenges, represent a new development in the in vitro screening of cancer drugs. However, drugs sensitivity results of the same cancer cell lines exposed to the same cancer drugs generated different IC50s by these studies as noticed by Haibe-Kains B et al (1). These inconsistencies are due to many factors: the experimental conditions and the use of the Four Parameter Logistic (4PL) regression model to analyze drugs sensitivity results. A new model based on the Levasseur LM et al model, the Gompertzian growth model of in vitro monolayer culture, and the IC-50 time course evolution is more appropriate to improve the accuracy of these large scale studies.

Keywords

CANCER, DRUGS, IC-50, GOMPERTZ, TIME POINT EVOLUTION, MONOLAYER

Corresponding author: ABDELKRIM ALILECHE
Competing interests: No competing interests were disclosed.
Grant information: The author(s) declared that no grants were involved in supporting this work.
Copyright:  © 2022 ALILECHE A. 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: ALILECHE A. The IC-50-time evolution is a new model to improve drug responses consistency of large scale studies [version 1; peer review: awaiting peer review]. F1000Research 2022, 11:284 (https://doi.org/10.12688/f1000research.108673.1) First published: 07 Mar 2022, 11:284 (https://doi.org/10.12688/f1000research.108673.1) Latest published: 07 Mar 2022, 11:284 (https://doi.org/10.12688/f1000research.108673.1)

List of abbreviations

μM: micro Molar

2D: Two Dimension

3D: Three Dimension

CCLE: Cancer Cell Line Encyclopedia

CGP: Cancer Genome Project

DT: Doubling time

DTP: Developmental Therapeutics Program

DNA: Deoxyribonucleic acid

h: hour

IC-50: Inhibition Concentration 50

miRNA: micro Ribonucleic acid

mRNA: messenger Ribonucleic acid

NCI60: National Cancer Institute 60

USA: United States of America

I. Introduction

In a recent study Haibe-Kaines B et al1 noticed inconsistency in viability estimates and IC-50s between CCLE2 and CGP3 results. The same observations were made about the NCI60 screen by Baggerly KA et al4 and Reinhold WC et al.5 In addition, the examination of the previous studies2–4 and other large-scale studies,6–12 especially their experimental protocols validate Haibe-Kains B et al concerns and predicts coming ones. The whole high throughput screening idea of exposing large panels of cancer cell lines to anticancer drugs and the viability results (symbolized by the classical IC-50s), when combined with the availability of many data bases (DNA, mRNA, proteomics, miRNA etc.) can identify novel biomarkers suitable for diagnosis and treatment. This endeavor has many challenges to overcome to be successful. This type of studies is only possible in vitro. In order to investigate the reasons of inconsistency let’s examine the specifics: parameters of in vitro cell culture, drug exposure timing, the IC-50 as an essential factor of drugs potency and usefulness.

II. Experimental in vitro cell culture conditions

1. Cell densities

A quick survey of the cell densities used in these studies2,3,6–12 shows three figures. First, a fixed cell seeding number going from 2502 to 50011 cells per well. Second, a range variation between low and high cell densities depending on cell lines doubling time: 5000-40,000 for the NCI60/DTP screen,12 300-3600,7 and 1,000-15,000.9 Third, cell density expressed as cellular confluence degree: 70%3 and 80%.8 In addition, the microplates ‘size used in these studies have between 96, 384 and 1536 wells with a reaction volume (which contain cells, media, serum and drugs) respectively 100μl, 20μl and 5μl. The combination of these experimental conditions cannot guarantee for one cancer cell line to grow and respond to the same drug the same way in different studies. The growth inhibitory effects of anticancer drugs depend on cell density used as shown in multiple studies,13,136–142 this being a main cause of inconsistency in viability results between the mentioned large-scale studies.

2. Duration of cell exposure to drugs

It has been known since the early days of cancer chemotherapy that cytotoxicity of anticancer drugs depends on drugs concentration and exposure time.14,15 For the large scale studies the drug exposure time is variable: 48h for JFCR screen6 and NCI60/DT screen,12 72h for CGP,3 72-84h for CCLE,2 and 72h-168h until cell reach 80% confluent.8 For the four-other large-scale studies the exposure is 72h but cell densities are not the same. If the cell doubling time is included, which just for the NCI60/DT screen is between 17.4h (colon HCT-116) and 79.4h (lung HOP-92) cancer cell lines,16 some cell lines have some growth while others didn’t grow at all in the drug exposure time allowed. The same cell line used in the previous studies cannot exhibit the same viability and the IC-50 for every drug. Up to this point the basic parameters of in vitro cancer cell culture (cell density whether expressed as cell seeding number or degree of confluence, cell doubling time and drug exposure time) are not harmonized at all between the different large-scale studies.17

III. The limitations of the Hill model

1. Time factor

The viability results in the large-scale studies are processed with the four-parameter logistic (4PL) regression derived from the Hill function,18,19 so are determined the IC-50s and Hill coefficient. The 4PL is practical in fitting the dose-response curves and deliver the IC-50 that characterized every drug and determine its future as an anticancer drug. However, the Hill model from its inception in 1910, does not include the factor time in anticancer drugs cytotoxicity. It was Fritz Haber143 and others,144–148 being out of cancer research field, to link a toxicant concentration and exposure time of an organism to evaluate the resulting toxicity. The Haber’s law is expressed as C x t = k143 where C is the lethal concentration of the toxicant; t, the exposure time and k, a constant. The Haber’s law did generate many variants as reviewed by Connell et al.145 In the cancer research field, it was Osawa et al20 who showed that anticancer drug cytotoxicity is (C x T) dependent, C being the concentration and T the time. Then Adams et al21 extended it to Cn x T = k, where n is the concentration coefficient and k is the drug exposure constant. All this body of research brings the concept of “dose-time response curves”,148 totally different from the concept “dose response curves” mentioned by a lot of cancer research papers and especially the large-scale studies.2,3,6–12 Levasseur LM et al15 combined cytotoxicity with the Hill model and established a modified Hill model, ICnxxT= k. in which ICx is the amount of inhibition, the equivalent of the IC-50. This is a new “paradigm to facilitate the quantitative assessment of the growth-inhibitory effect of anticancer agents as a function of concentration and exposure time”.15 In addition, the Levasseur LM et al model linked drug exposure time to the IC-50 by this equation IC50 = (k/T)1/n.22 This shows clearly that in the large-scale studies2,3,6–12 there is no connection between the IC50 and the exposure time to drugs, thus the inconsistency noticed by Haibe-Kains B et al1 and Reinhold et al.5 In these large-scale studies, there is a kind of tacit assumption the IC-50 is constant over the time exposure of cancer cells to cancer drugs. I will prove in this review such statement is incorrect.

2. Inflection point

The S shaped dose response curve fitted with the Hill model has only one inflection point and therefore a unique IC-50 taken at one-time point. Prinz et al23 inspecting the NCI60/DT results analyzed with the Hill model, noticed that some results do not fit in it because of the complexity of their dose-response curves. Levasseur LM et al15 noticed that the “double or triple Hill roller coaster concentration-effect curve” can be explained by the coexistence of two populations of cells with different sensitivities (IC-50a and IC-50b) to drugs, by the target’s multiplicity for the same drug,22 and the allosteric nature of the drug-target interaction.23 DiVeroli et al24 point to the multiphasic dose-response curves also referred to as hormesis. Hormesis is a non-monotonic/biphasic dose response, with specific dose response patterns coming in many shapes25,26: U, inverted U, J and bell shapes.152,153 This has been reported with 138 cancer cell lines treated by over 120 drugs.27,28 As a solution to this problem Di Veroli et al developped an algorithm referred to as Dr Fit.

3. Cellular heterogeneity

It is another hurdle to the Hill model used to determine drugs IC-50s and can explain the inconsistency between the IC50s noticed by Haibe-Kains B et al,1 Baggerly KA et al,4 Reinhold WC et al,5 Levasseur LM et al,15 DiVeroli et al,24 Calabrese et al28 and Rashkov et al.29 The issue is how to explain the heterogeneity of cancer cell lines used in vitro and considered homogenous cell lines and checked thoroughly as such?2,3

IV. The Gompertzian growth of cancer cells in vitro

1. The Gompertzian model

Since the Norton et al 1976 landmark paper30 tumor growth has two phases, an initial avascular exponential phase followed by the retardation or decremented exponential phase due to feedback inhibition. It fits well with the Gompertzian model.31 The growth type of cancer cells cultured in vitro as a monolayer or spheroids was not addressed by Haibe-Kains et al1 and also by all the commentaries32–38 related to Haibe-Kains et al concerns. It should be considered one of the hallmarks of cancer whether in in vitro or in vivo clinical studies39 since it will have a huge impact in the selection of future cancer drugs. According to results obtained by three research groups, Drasdo et al,40 Demicheli et al41,42 and Poplawski et al43 cancer cells cultured in vitro, as monolayers or spheroids, or in vivo (injected into mice to induce tumors) have the same Gompertzian growth type. For in vitro spheroids and tumors induced in mice there is always a central necrotic zone (due the difficulty of internal cancer cells to have access to oxygen and other nutrients) surrounded by a growing outer layer of cancer cells. Cancer cell growth in two dimensional (2D) monolayers have similar situation in spite of equal accessibility of all cell in 2D to oxygen and nutrients. In both cases, 2D and 3D, the growth is limited to the outer layer as shown by Bru et al.44 In monolayers, internal cancer cells, squeezed by other surrounding cells, survive by two mechanisms: size reduction divisions45 and quiescence.46 Therefore in vitro monolayers of cancer cells although derived from the same cell line are heterogeneous in their behavior and respond differently to anticancer drugs. The Gompertzian growth type of in vitro cancer cells monolayers are well explained by the “two compartment of cell population growth”.47,48 This cellular heterogeneity had been already mentioned previously by Levasseur LM et al15 and Rashkov et al.29

2. One time point IC-50

The Gompertzian growth of cancer cells in vitro had been neglected by the all the large-scale studies and that has serious consequences on the sampling of IC50s at only one time point from 48h to 156h.2,3,6–12 The dual effect of doubling times diversity and the Gompertzian growth type of these cells applied to large number of cancer cells (60 for the NCI-60 to a thousand and even more), is the main reason of inconsistency of the IC-50s between the different large-scale studies. The same cell line won’t have the same growth level since the sampling of the IC-50 at different times points in these different large-scale studies.

3. Dose dense chemotherapy

The Gompertzian growth type of human tumors has led to the introduction of the dose dense chemotherapy protocols.49,50 Tumor growth is faster for small size tumors than for large size ones. Cancer cells cultured in vitro exhibit the same phenomenon, in the beginning the growth is exponential and after it slows down. Therefore, the IC-50 should be evaluated at different time points especially at an early time point.

4. In vitro self-seeding

Human tumors are characterized by metastasis due to self-seeding.51 There is no metastasis in vitro, but a similar phenomenon is operating since cancer cells are heterogeneous in their growth (a growing population and a quiescent population) and their response to drugs. Once some cancer cells are killed the quiescent cells start growing because there is more space and nutrient available.

5. 2D vs 3D debate

In vitro 2D monolayers of cancer cells does not reproduce the complexity of in vivo mice or human 3D tumors. The stromal reaction, vascular networks, the immune system are missing in vitro.52,53 In addition, the failure to reproduce in vivo the in vitro results obtained with 2D cultures, the 3D cultures became the solution to bridge the gap in this 2D vs 3D debate. However, the Gompertzian growth of cancer cells cultured in 2D or 3D formats, in both cases there is a heterogeneous population of cancer cells, thus in both cases cellular dynamics are similar. Unfortunately, many studies using 3D cell culture systems in vitro, time exposure of cancer cells to drugs is variable: 24h,54 48h,55 72h56 and 168h.57,58 This fact limits the capacity of the 3D spheroids model to improve the accuracy of the 2D monolayers in vitro screening of cancer drugs.

V. The IC-50 time course evolution model

After analysis of the multiple sources of inconsistencies of IC-50s between large scale studies, I would like to propose the following model.

1. The evaluation of drugs IC50 at multiple time points

As above mentioned the large-scale studies the IC-50s were evaluated at only one time point between 48h and 168h.2,3,6–12 The drugs IC-50s were supposed to be constant over time regardless of the chosen time point. This is not always true.

2. At least three times points are necessary

Early time points (2-3h, 24h) are necessary for drugs high doses supposed to kill all cells. This will show how much time is necessary for high doses need to kill all cells, and that depend on cancer cell line (it depends on the doubling time and the genetic makeup). Some drugs have a toxic effect in just 2-3 hours.54 Late time points are necessary for medium and low doses. In addition, drugs ‘killing mechanisms, whether cell cycle dependent like paclitaxel or independent like carboplatin, whether by apoptosis or necrosis, the influence of all these factors cannot be explored by one time point drugs IC50s.

3. New experimental protocol

Current experimental protocols in the large-scale studies and a lot of small-scale studies use one cell set and expose cancer cells to increasing drug doses for a unique period of time going from 48h to 168h, and after determine the IC-50. The new protocol recommends multiple sets, every set specific for a drug exposure time: from 2-3h, 24h, 48h, 72h and even further if the doubling time is long. For every time point, there is an IC-50, thus an IC-50-time course because drugs IC-50s are variable over time. The inconsistency of the drugs IC-50s noticed by Haibe-Kains et al1 is due to the difference in cell drugs exposure times.2,3

4. The IC50 time evolution model

It reflects the cellular phenomenology which is Gompertzian for in vitro monolayers and spheroids. The current large-scale studies using in vitro monolayers are completely disconnected from the reality of cellular dynamic evolution. The same problem exists with the in vitro spheroids. Thus, the 2D vs 3D debate aimed at replacing in vitro monolayers with spheroids should include the new model exposed here for a better accuracy of drugs IC-50s measurement over time.

VI. The IC-50 time course has five different shapes

As presented in Tables 1–6 and Figure 1, a data base collated from www.pubmed.gov and google search, some eighty publications in which 109 cell lines treated with 124 drugs and their IC-50 were evaluated at different time points shows for the first time the IC-50 variation over time. This new model is more appropriate to explore the interaction complexities of cancer drugs and their cellular targets, complexities ignored by the one-time point IC-50 practiced nowadays according to the 4PL model. So instead of one single dot in the S shaped curve inspired by the Hill equation, the new model provides curves with five different shapes as shown in the theoretical arbitrary Figure 1. In total there are 291 cases of IC-50 variations over time: Type 1 (80.76%), Type 2 (4.81%), Type 3 (10.31%), Type 4 (3.78%) and Type 5 (0.34%).

Table 1. IC-50 Time Evolution IC-50 Type 1.

CaseTypeIC-50 24hIC-50 48hIC-50 72hCell lineDrugRef
118μM1.8μM1.2μMMCF-7Arsenic trioxide72
2117μM7μM4.8μMMDA-MB-231Arsenic trioxide72
3128.1μM0.0986μM0.0043μMA-375SLN Docetaxel71
4151.1μM0.231μM0.004μMA-375Taxotere71
510.769μM0.125μM0.0856μMC-26SLN Docetaxel71
612.083μM0.456μM0.0846μMC-26Taxotere71
7113.45μg/ml13.00μg/m12.50μg/mMCF-7TAM70
8113.18μg/ml12.50μg/ml11.78μg/mlMCF-7TAM-SLN70
9117.21μg/ml16.87μg/ml15.97μg/mlMDA-MB-231TAM70
10116.93μg/ml16.00μg/ml15.80μg/mlMDA-MB-231TAM-SLN70
11196μM90μM65μMHepG2Mycotoxin AOH69
1218.1μM5.3μM5.2μMHepG2Mycotoxin 15-ADON69
13115.01μg/ml6.19μg/ml0.94μg/mlBEL7402CNP68
141182.8μM55.4μM17.2μMU-266Justicidin B59
15186.2μM68.4μM27.4μMU-266Etoposide59
161>160μM19.9μM5μMDOHH-2Justicidin B59
171>160μM100.7μM9.5μMDOHH-2Etoposide59
18125.3μM10.3μM8μMREHJusticidin B59
1910.027μM0.014μM0.015μMREHEtoposide59
20188.8μM19μM16.2μMHHJusticidin B59
211104.7μM48.6μM14.7μMHHEtoposide59
22146μM18.1μM6.1μMHUT78Justicidin B59
2319.3μM4.3μM4.2μMHUT78Etoposide59
24114.1μM2.4μM1.5μMOPM-2Justicidin B59
25124.1μM4μM1.3μMOPM-2Etoposide59
26119.3μM0.41μM0.17μMRPMI-8226Justicidin B59
271106.6μM91.1μM14.9μMRPMI-8226Etoposide59
2819.20μM8.30μM4.63μMHepG2Goniothalamin62
29179.10μM63.75μM35.01μMChangGoniothalamin62
301>3mg/ml2.6mg/ml0.5mg/mlHeLaHyd. F Eth. Extract61
3112.20mg/ml1.72mg/ml0.3mg/mlHeLaHyd. F Ph. Extract61
3212.35mg/ml2.04mg/ml0.9mg/mlHeLaSinapinic acid61
3312.63mM2.22mM1.2mMHeLaSodium butyrate61
3412.97mg/ml2.2mg/ml1.6mg/mlHT-29Sinapinic acid61
351>3mM2.2mM2.1mMHCT-116Sinapinic acid61
361>3mM2.2mM2.0mMHCT-116Sodium butyrate61
371>3mM2.36mM1.5mMJURKATSodium butyrate61
381>3mM>3mM0.28mMJURKATSinapinic acid61
3916.1μM4.5μM1.6μMA549Capillin60
4012.8μM0.8μM0.6μMHep-2Capillin60
4111.5μM1.3μM0.9μMA431Hypocretenolide 163
4211.5μM1.3μM1.1μMHep-2Hypocretenolide 163
4312.8μM2.6μM1.2μMSK28Hypocretenolide 163
4413.2μM2.4μM1.8μMSK37Hypocretenolide 163
4510.9μM0.9μM0.8μMA431Helenalin63
4610.9μM0.9μM0.8μMHep-2Helenalin63
4711.3μM0.9μM0.5μMSK28Helenalin63
4811.3μM1.2μM0.7μMSK37Helenalin63
4911.4μM1.2μM1.2μMSW872Helenalin63
501463.3μM280.8μM149.3μMUACC-903JS-21 (3a)64
511150.8μM126.5μM118.5μMUACC-903JS-23 (3c)64
521193.5μM145.7μM108.2μMUACC-903JS-25 (4)64
531614.3μM266.8μM112.7μMUACC-903JS-20 (3)64
5410.51μg/ml0.31μg/ml0.27μg/mlMCF-7DOX-Sol66
5510.61μg/ml0.51μg/ml0.37μg/mlMCF-7/AdrDOX-GNMs66
561>40μM7μM1.25μMIshikawaPerifosine74
571>40μM25μM6μMIshikawaPerifosine74
58115.01μg/ml6.19μg/ml0.94μg/mlBEL7402Chitosan NP75
5910.51mM/l0.33mM/l0.25mM/lCOLO829Lomefloxacin76
6012.5ng/ml2ng/ml1.5ng/mlHBL-2Bortezomib77
61138μM10μM10μMHeLaApigenin87
62189μM72μM68μMSiHaApigenin87
63119μM9.2μM4.1μMEC109Jesridonin88
64161.0μM38.2μM38.9μMEC109Oridonin88
65141.7μM14.4μM4μMEC9706Jesridonin88
66137.5μM28.0μM23.9μMEC9706Oridonin88
671˃100μM11.4μM2.0μMKYSE450Jesridonin88
68130.5μM28.2μM17.1μMKYSE450Oridonin88
691˃100μM61.4μM16.2μMKYSE750Jersidonin88
70135.3μM23.4μM14.3μMKYSE750Oridonin88
71145.8μM21.4μM9.4μMTE-1Jersidonin88
72125.2μM18.0μM8.4μMTE-1Oridonin88
73186.6μM49.8μM28.2μMGES-1Jersidonin88
741˃100μM35.4μM25.2μMHL7702Jersidonin88
7515μg/ml0.6μg/ml0.06μg/mlPrimary HepatocytesAFB189
76118μg/ml9μg/ml4μg/mlHCT15Zerumbone90
77125μg/ml16μg/ml8μg/mlHCT15Cisplatin90
7811954μg/ml1700μg/ml1540μg/mlMCF-7MCRE91
79186.34mM17.83mM8.64mMA549Doxorubicin92
80193.86mM43.28mM37.12mMH1299Doxorubicin92
8117.45μM5.13μM3.98μMJURKATPJ-3493
82120.301μM9.785μM7.008μMHL60PJ-3493
831131mM89mM38mMJURKATDoxorubicin93
84183mM23mM10mMHL60Doxorubicin93
85131.25μM5.1μM3μMA549Cisplatine94
86124.75μM15μM13.5μMA549Silver Nitrate94
87120μM13μM8μMMDA-MB-231EPC-395
88110.58μg/ml8.81μg/ml6.59μg/mlA549TQ96
89119.39μg/ml17.51μg/ml15.62μg/mlA549TQG96
90115.63μg/ml14.97μg/ml12.40μg/mlA549TQ-Fe3O496
91127.31μg/ml18.68μg/ml11.88μg/mlA549TQG-Fe3O496
92116.10μg/ml12.71μg/ml7.04μg/mlA549TQ-Fe3O4 (MF)96
93123.45μg/ml10.78μg/ml9.579μg/mlA549TQ-G-Fe3O4 (MF)96
94113.8μM6.888μM4.362μMA2780Salinomycin97
95112.7μM9.869μM5.022μMSK-OV-3Salinomycin97
96156.6μM51.14μM32.86μMHT-29Apatinib98
97148.76μM44.11μM29.25μMHCT116Apatinib98
9810.59μM0.36μM˂0.03125μMNB1 AmpCrizotinib99
9912.21μM0.77μM˂0.5μMNB3 R1275QCrizotinib99
10011.6μM1.34μM1.1μMSH-SY5Y F1174LCrizotinib99
10112.19μM0.71μM0.64μMIMR32 WTCrizotinib99
10210.31μM0.035μM0.03μMNB1 AmpEntrectinib99
10314.34μM3.32μM2.42μMSH-SY5Y F1174LEntrectinib99
10413.68μM3.29μM3.06μMIMR32 WTEntrectinib99
10515.13μM3.51μM2.13μMMCF-7Mitoxantrone103
10612.58μM1.64μM1.25μMMCF-7Mitoxantrone SLN103
107192.64μM67.34μM52.48μMMCF-7Paclitaxel103
108198.70μM62.31μM46.70μMMCF-7Paclitaxel SLN103
1091267.84μM195.16μM153.16μMMCF-7Methotrexate103
1101154.76μM98.48μM93.80μMMCF-7Mehtotrexate SLN103
111188.89μM13.20μM9.553μMA2780Cisplatin104
1121350.5μM50.96μM25.39μMA2780/DDPCisplatin104
1131105.1μM51.73μM16.13μMSKOV3Cisplatin104
1141446.7μM135.0μM66.70μMSKOV3/DDPCisplatin104
115110.66μM2.51μM2.08μMHS578TCediranib105
116130.77μM15.57μM2.52μMMDA-MB-231Cediranib105
117138.69μM26.54μM18.85μMT47DCediranib105
118115.27μM8.13μM3.69μMMCF-7Arsenic Disulfide106
119125.5μM9.18μM5.37μMMDA-MB-231Arsenic Disulfide106
120149.15μg/ml47.18g/m45.80g/mlPC-3Boswellic Acid107
121149.27g/ml48.58g/ml46.77g/mlPC-3Montelukast Sodium107
122116μM11.5μM9.75μMHL-60As2O3108
123112.27μM7.57μM0.45μMHT-295-FU109
124114.56μM11.20μM1.324μMCACO-25-FU109
1251107μM73μM47μMT47DSilibinin110
12611.71mM0.99mM0.06mMHeLaSafranal111
12712.30mM1.28mM0.5mMMCF-7Safranal111
12812.12mM1.18mM0.29mML929Safranal111
12910.093mM0.063mM0.039mMHeLaSafranal Loaded111
13010.39mM0.24mM0.13mMMCF-7Safranal Loaded111
13110.14mM0.075mM0.063mML929Safranal Loaded111
13211207μM720μM298μMU251β-Asarone116
13311150μM900μM195μMC6β-Asarone116
13417.5μM5.0μM3.0μMJurkatBeauvericin117
13510.74mM0.17mM0.10mMCOLO827Ciprofloxacin118
13610.75μM/ml0.57μM/ml0.53μM/mlU87MGCiprofloxacin119
13710.48μM/ml0.22μM/ml0.15μM/mlU87MGMoxifloxacin119
13810.83μM/ml0.14μM/ml0.03μM/mlMDA-MB-231Ciprofloxacin120
139122.5μM19μM17μMT47DCurcumin121
140110.5μM9.5μM9μMT47DPAMAM Curcumin121
14111.734mM0.742mM0.500mMHCT-116DHCA123
14212.595mM1.188mM0.704mMHCT-15DHCA123
14318.148mM3.018mM1.66mMHeLaDHCA123
14416.942mM4.511mM3.223mMSiHaDHCA123
145118μM15μM13μMHL-60EA-137124
146176.72nM/l34.05nM/l16.7nM/lSW620Bufalin125
14718.89μM3.58μM1.86μMHep-G2OTA126
148155.79μM39.88μM29.48μMHep-G2ZEA126
149134.25μM10.08μM7.36μMHep-G2OTA+ZEA126
150135.64μM4.99μM4.05μMHep-G2OTA+α-ZOL126
151127.67μM11.05μM3.42μMHep-G2OTA+ZEA+α-ZOL126
15211954μg/ml1700μg/ml1560μg/mlMCF-7Mat. Chamomilla127
15310.42μM0.25μM0.04μMRLABT-737128
15415.65μM3.66μM2.92μMH9ABT-737128
155112.72μM14.19μM9.54μMJJN-3ABT-737128
15610.28μM0.12μM0.10μMSKIABT-737128
157176μg/ml58μg/ml39μg/mlMCF-7EADs129
158147μM44μM43μMA549Diosgenin130
15917.14μM5.05μM4.23μMMCF-7BBSKE131
160110.54μM10.13μM7.29μMMCF-7PM131
16114.14μM3.99μM3.43μMMCF-7FA+PM131
16217.84μM6.88μM6.30μMMCF-7FA+PM+free FA131
163140nM/l27nM/l17nM/lDU-145Triptolide133
16412.17ng/ml1.31ng/ml1.16ng/mlA2780Triptolide135
165192ng/ml10.2ng/ml7.34ng/mlOVCAR-3Triptolide135
1661102ng/ml85ng/ml81ng/mlHIO-180Triptolide135
1671142ng/ml111ng/ml99ng/mlCCD-19LnTriptolide135
1681584ng/ml217ng/ml207ng/mlJ774A.1Triptolide135
16910.276mM0.244mM0.213mMLnCapCiprofloxacin154
1701168.8μg/ml22.15μg/ml8.04μg/mlU14Paclitaxel155
171115.0μg/ml1.27μg/ml0.62μg/mlA549Goniothalamin157
172114.43μg/ml0.27μg/ml0.24μg/mlA549Doxorubicin157
173126.93μg/ml10.27μg/ml1.64μg/mlHT29Goniothalamin157
174111.6μg/ml8.57μg/ml6.23μg/mlHMSCGoniothalamin157
175130.98μg/ml23.63μg/ml18.08μg/mlHCT16SGC158
1761129.67μg/ml116.30μg/ml82.27μg/mlHCT16SGE158
1771175.70μg/ml105.8μg/ml61.9μg/mlSiHaSGC158
1781255.03μg/ml113.03μg/ml66.08μg/mlSiHaSGEA158
1791460.4μg/ml291.7μg/ml149.7μg/mlSiHaSGW158
1801185.66μg/ml109.7μg/ml66.7μg/mlHeLaSGC158
1811260.46μg/ml116.5μg/ml68.48μg/mlHeLaSGEA158
1821360.56μg/ml275.9μg/ml146.43μg/mlHeLaSGE158
1831472.6μg/ml291.26μg/ml149.46μg/mlHeLaSGW158
1841301.83μg/ml267.23μg/ml113.7μg/mlMDA-MB-231SGC158
1851408.37μg/ml351.43μg/ml175.90μg/mlMDA-MB-231SGEA158
CaseTypeIC-50 3hIC-50 24hIC-50 120hCell lineDrugRef
1861>32μM0.29μM0.0099μMNCI-H23Paclitaxel65
1871>32μM0.93μM0.078μMNCI-H460Paclitaxel65
1881>32μM24μM0.03μMNCI-H322Paclitaxel65
1891>32μM14μM0.0091μMNCI-H522Paclitaxel65
1901>32μM27μM7.5μMNCI-H727Paclitaxel65
CaseTypeIC-50 2hIC-50 24hIC-50 48hCell lineDrugRef
191126μM9μM8μMLnCap9S1R54
192139μM29μM16μMMDA-MB-2319R54
193118μM12μM10μMMDA-MB-2319S1R54
194193μM39μM37μMHUT-1029R54
CaseTypeIC-50 48hIC-50 72hIC-50 120hCell lineDrugRef
1951129.8μM42.5μM31.0μMHCT-116 WTResveratrol100
196184.1μM7.0μM0.6μMHCT-116 WTIRA-5100
197188.7μM20.2μM9.2μMA-431Resveratrol100
1981133.4μM39.5μM15.4μMA-431IRA-5100
1991186.0μM52.4μM16.1μMCaco-2Resveratrol100
2001348.7μM46.1μM13.4μMCaco-2IRA-5100
2011741.3μM149.1μM33.8μMHCA-7Resveratrol100
2021288.6μM206.7μM51.6μMHCA-7IRA-5100
2031149.1μM71.8μM28.6μMHCT-116 p53-/-Resveratrol100
2041134.2μM57.6μM16.1μMHCT-116 p53-/-IRA-5100
2051263.8μM161.2μM29.6μMLnCapResveratrol100
2061342.3μM166.3μM24.9μMLnCapIRA-5100
CaseTypeIC-50 24hIC-50 48hIC-50 72hIC-50 96hCell lineDrugRef
207186.29μM/ml75.34μM/ml72.42μM/ml69.82μM/mlU-251Temozolomide101
208166.25μM64.00μM57.99μM37.36μMPC-3Flutamide113
209140.4μM30.8μM12.7μM7.9μM22Rv1Cisplatin114
210161.5μM44.0μM7.9μM3.7μMPNT1ACisplatin114
21110.048μM0.036μM0.030μM0.029μMA549Digoxin149
21210.104μM0.107μM0.070μM0.057μMH3255Digoxin149
21310.767mM0.238mM0.212mM0.193mMPC-3Ciprofloxacin154
21413.937μM0.290μM0.250μM0.173μMPC-3Doxorubicin154
215126.25nM7.655nM3.951nM3.194nMPC-3Docetaxel154
CaseTypeIC-50 24hIC-50 48hIC-50 72hIC-50 120hCell lineDrugRef
21618.40μg/ml7.60μg/ml7.40μg/ml6.84μg/mlMRC5TTHL67
21711.36μg/ml0.73μg/ml0.63μg/ml0.30μg/mlMCF-7TTHL67
21816.50μg/ml6.10μg/ml5.45μg/ml0.88μg/mlHepG2TTHL67
21915.55μg/ml5.20μg/ml1.09μg/ml0.39μg/mlT24TTHL67
22017.05μg/ml5.87μg/ml5.20μg/ml4.50μg/mlHCT116TTHL67
22118.00μg/ml7.00μg/ml6.15μg/ml5.30μg/mlHT-29TTHL67
22218.55μg/ml7.90μg/ml6.35μg/ml5.00μg/mlCACO-2TTHL67
CaseTypeIC-50 24hIC-50 48hCell lineDrugRef
22314.0μM2.7μMMCF-7Doxorubicin102
22414.0μM1.4μMMDA-MB-231Doxorubicin102
225177.5μM72μMHT-29Valdecoxib115
226115.1μM4.8μMHUT78BKM10122
227112.4μM3.9μMGRANT A519BKM10122
228114.8μM4.1μMWSU-NHLBKM10122
229141.6μM21.1μMHUT78BEZ235122
230145.1μM25.3μMGRANT A519BEZ235122
231139.2μM18.5μMWSU-NHLBEZ235122
232192.4nM16.1nMMVA4-11Triptolide132
233176.1nM6.9nMOCM-AML3Triptolide132
CaseTypeIC-50 48hIC-50 72hCell lineDrugRef
23411147.91μg/ml921.1μg/mlMCF-7Capecitabine112
235156.14nM/L15.57nM/LOCM-1Triptolide134

Table 2. IC-50 Time evolution IC-50 Type 2.

CaseTypeIC-50 24hIC-50 48hIC-50 72hCell lineDrugRef
23620.6μM0.9μM1.0μMZR75-1Hypocretenolide 163
23720.7μM0.8μM1.1μMZR75-1Helenalin63
23821.4μM1.6μM1.7μMOVCAR3Helenalin63
23920.184μM0.919μM1.652μMAGSClofarabine78
24025.33μg/ml5.34μg/ml7.56μg/mlK562Para-nitro acetophenon151
24127.118μg/ml8.62μg/ml9.75μg/mlPBMCPara-nitro acetophenon151
242210μM23μM30μMHL60EA-136124
243216μM20μM90μMHL60EA-4124
244212.6μg/ml82.8μg/ml188.4μg/mlN2a3-FOC156
24529.25μg/ml37.5μg/ml83.6μg/mlN2a6-FOC156
CaseTypeIC-50 2hIC-50 24hIC-50 48hCell lineDrugRef
246238μM43μM43μMHUT-1029S1R54
CaseTypeIC-50 48hIC-50 72hCell lineDrugRef
247259.22μg/ml92.30μg/mlBCSCDandelion Eth. Extr.42
248214.88μg/ml69.40μg/mlBCSCDandelion Met. Txtr.42
CaseTypeIC-50 24hIC-50 48hCell lineDrugRef
249271μM74μMSW620Valdecoxib115

Table 3. IC-50 Time evolution Type 3.

CaseTypeIC-50 24hIC-50 48hIC-50 72hCell lineDrugRef
25036.0μM0.8μM6.0μMHT-29Capillin60
25133.4μM0.8μM1.4μMMIA Pa Ca-2Capillin60
25233.1μM2.2μM2.8μMSW872Hypocretenolide 163
25330.8μM0.7μM1μMMCF-7Hypocretenolide 163
25432.03μg/ml0.85μg/ml0.86μg/mlMCF-7/AdrDOX-Sol66
25536.2μM3.6μM5.2μMHepG2Mycotoxin 3-ADON69
25632.65μM2.24μM3.27μMNB3 R1275QEntrectinib99
257350μg/ml25μg/ml40μg/mlHCT-116Bark CO AE150
258365μg/ml30μg/ml45μg/mlHCT-116Bark CO ME150
2593˃200μg/ml112μg/ml160μg/mlHCT-116Bark CO AqE150
260311.56μg/ml10.705μg/m11.5μg/mK562Acetanilide151
261313.93μg/m13.16μg/m13.53μg/mPBMCAcetanilide151
262358μM50μM55μMHL60all-trans-RA72
2633362.3μM234.4μM270.5μMA375MJS-22(3b)64
26430.8μM0.5μM0.7μMMCF-7Helenalin63
265352.30μM10.91μM21.98μMHepG2α-ZOL126
266355μM21.12μM29.77μMHepG2ZEA+Αzol126
26730.03μM0.025μM0.03μMHBL-2ABT-737128
268368.9μg/ml25μg/ml95.6μg/mlN2aGOC156
CaseTypeIC-50 4hIC-50 24hIC-50 48hCell lineDrugRef
26931.55μM0.31μM1.68μMA459Osmium arene 173
27030.85μM0.17μM0.32μMA459Osmium arene 273
271333.95μM3.64μM35.73μMA459Osmuim arene 373
27231.92μM1.78μM1.79μMA459Cisplatin73
CaseTypeIC-50 2hIC-50 24hIC-50 48hCell lineDrugRef
273344μM23μM28μMLnCap9R54
CaseTypeIC-50 24hIC-50 48hIC-50 72hIC-50 96hCell lineDrugRef
27439.31μM1.69μM0.42μM0.71μMPC-3Doxorubicin113
275310.53μM1.11μM0.57μM0.68μMPC-3Epirubicin113
2763127.08μM15.31μM18.35μM18.77μMPC-3Cisplatin113
CaseTypeIC-50 24hIC-50 72hIC-50 120hCell lineDrugRef
277348mM6.6mM120mMSW13Ouabain41
CaseTypeIC-50 3hIC-50 48hIC-50 72hCell lineDrugRef
2783>32μM22μM31μMNCI-H676Paclitaxel65
27930.31μM0.0092μM0.017μMNCI-H1155Paclitaxel65

Table 4. IC-50 Time Evolution Type 4.

CaseTypeIC-50 24hIC-50 48hIC-50 72hCell lineDrugRef
2804144.1μM200μM109.3μMUACC-903JS-22(3b)64
2814219.0μM605.4μM100.9μMA375MJS-28(4c)64
282498.92μM107.8μM44.95μMUACC-903JS-26(4a)64
2834180.8μM191.9μM58.1μMUACC-903JS-20(3)64
28440.67μg/ml1.05μg/ml0.69μg/mlMCF-7DOX-GNMs66
285473μM77μM74μMT47DSilibinin Loaded110
28646.1μg/ml7.2μg/ml4.8μg/mlHeLaBerberine61
28742.7μg/m3.5μg/ml1μg/mlL1210Berberine61
28841.9μM2.1μM1.8μMOVCAR3Hypocretenolide 163
CaseTypeIC-50 3hIC-50 24hIC-50 120hCell lineDrugRef
28940.28μM7.5μM0.68μMNCI-H1299Paclitaxel65
CaseTypeIC-50 12hIC-50 24hIC-50 48hIC-50 72hCell lineRefDrug
290418.3μM74.9μM10.6μM1.0μMPC-3114Cisplatin

Table 5. IC-50 Time Evolution Type 5.

CaseTypeIC-50 24hIC-50 48hIC-50 72hCell lineDrugRef
29155ng/ml5ng/ml5ng/mlNCEBBortezomib77

Table 6. Drugs do not have always the same IC-50 Time Evolution Type with different cancer cell lines.

DrugCell lineIC-50 TETRef
CisplatinHCT15190
CisplatinA549194
CisplatinA27801104
CisplatinSKOV31104
Cisplatin22Rv11114
CisplatinPNT1A1114
CisplatinPC-34114
CisplatinPC-33113
Hypocretenolide 1A431163
Hypocretenolide 1Hep-2163
Hypocretenolide 1SK28163
Hypocretenolide 1SK37163
Hypocretenolide 1ZR75-1263
Hypocretenolide 1SW872363
Hypocretenolide 1MCF-7363
Hypocretenolide 1OVCAR3463
BortezomibHBL-2177
BortezomibNCEB577
ResveratrolHCT-1161100
ResveratrolA4311100
ResveratrolCaCO-21100
ResveratrolHCA-71100
ResveratrolHCT-116 553-/-1100
ResveratrolLnCap1100
CediranibHS578T1105
CediranibMDA-MB-2311105
CediranibT47D1105
EtoposideU-266159
EtoposideDOHH-2159
EtoposideREH159
EtoposideHH159
EtoposideHuT78159
EtoposideOPM-2159
EtoposideRPMI-8226159
SafranalHeLA1111
SafranalMCF-71111
SafranalL9291111
CapillinA549160
CapillinHep-2160
CapillinHT-29360
CapillinMIA Pa Ca-2360
PaclitaxelMCF-71103
PaclitaxelNCI-H23165
PaclitaxelNCI-H460165
PaclitaxelNCI-H322165
PaclitaxelNCI-H522165
PaclitaxelNCI-H727165
PaclitaxelNCI-H676365
PaclitaxelNCI-H1155365
PaclitaxelNCI-H1299465
HelnalinA431163
HelnalinSK28163
HelnalinHep-2163
HelnalinSK37163
HelnalinSW872163
HelnalinZR75-1263
HelnalinOVCAR3263
HelnalinMCF-7363
5-FUHT-291109
5-FUCaCO-21109
Sinapinic acidHeLa161
Sinapinic acidHT-29161
Sinapinic acidHCT-116161
Sinapinic acidJURKAT161
BerberineHeLa461
BerberineL1210461
SalinomycinA2780197
SalinomycinSKOV3197
ApatinibHT-29198
ApatinibHCT-116198

Table 7. Cancer cell lines and Drugs and their IC-50 TET.

Cell lineDrugIC-50 TETRef
K562Para-nitro acetophenon255
K562Acetanilide355
MCF-7Arsenic trioxide172
MCF-7TAM170
MCF-7Dox-Sol166
MCF-7MCRE191
MCF-7Mitoxantrone1103
MCF-7Paclitaxel1103
MCF-7Methotrexate1103
MCF-7Arsenic disulfide1106
MCF-7Safranal1111
MCF-7Doxorubicin1102
MCF-7Capecitabin1112
MCF-7TTHL167
MCF-7Hypocretenolide 1367
MCF-7Helenalin367
MCF-7DOX-GNMs466
UACC-903JS-21(3a)164
UACC-903JS-23(3c)164
UACC-903JS-25(4)164
UACC-903JS-20(3)464
UACC-903JS-22(3b)464
UACC-903JS-26(4a)464
A549Capillin160
A549Doxorubicin192
A549Cisplatin194
A549Silver nitrate194
A549TQ196
A549TGG196
A549TQ-Fe3O4196
A549TQG-Fe3O4196
A549TQ-Fe3O4 (MF)196
A549TQ-G-Fe3O4 (MF)196
HL-60PJ-34193
HL-60Doxorubicin193
HL-60Arsenic trioxide1108
HL-60EA-1371124
HL-60EA-1362124
HL-60EA-42124
HL-60all-trans-RA372
HCT-116Sinapinic acid161
HCT-116Sodium butyrate161
HCT-116Apatinib198
HCT-116DHCA1123
HCT-116Resveratrol1100
HCT-116IRA-51100
HCT-116TTHL167
HCT-116Bark CO AE340
LnCap9S1R154
LnCapResveratrol1100
LnCapIRA-51100
LnCap9R354
HeLaSinapinic acid161
HeLaSodium butyrate161
HeLaApigenin187
HeLaSafranal1111
HeLaDHCA1123
HeLaBerberine461
T47DCediranib1105
T47DCurcumin1121
T47DSilibinin1110
T47DSilibilin Loaded.4110
A431Hypocretenolide 1163
A431Helenalin163
A-375JS-20(3)164
A-375SLN Docetaxel171
A-375Taxotere171
A-375JS-22(3b)364
A-375JS-28(4c)464
HT-29Sinapinic acid161
HT-29Apatinib198
HT-295-FU1109
HT-29Valdecoxib1115
HT-29TTHL167
HT-29Capillin360
SW872Helenalin163
SW872Hypoctretenolide 1363
HepG2Mycotxin AOH169
HepG2Gpniothalamin162
HepG2TTHL167
HepG2Mycotoxin 3-ADON369
MDA-MB-231Arsenic trioxide172
MDA-MB-231TAM170
MDA-MB-231EPC-3195
MDA-MB-231Cediranib1105
MDA-MB-231Arsenic disulfide1106
MDA-MB-231Ciprofloxacin1120
MDA-MB-2319R154
MDA-MB-2319S1R154
MDA-MB-231Doxorubicin1102
PC-3Boswellic acid1107
PC-3Flutamide1113
PC-3Doxorubicin3113
PC-3Epirubicin3113
PC-3Cisplatin3113
PC-3Cisplatin4114
PC-3Montelukast Sodium1107
863aee81-3425-4e06-ae1c-e57d341324eb_figure1.gif

Figure 1. Arbitrary values for IC-50 and Standard time points: 24h, 48 and 72h.

1. Type 1

(Cases 1-235) is characterized by an IC-50 decrease over time (Table 1 and Figure 1a). There are several choices of time points: [24h, 48h and 72h for Cases 1-185], [3h, 24h and 120h for Cases 186-190], [2h, 24h, and 48h for Cases 191-194], [48h, 72h, and 120h for Cases 195-206], [24h, 48h, 72h and 96h for Cases 207-215], [24h, 48h,72h and 120h for Cases 216-222], [24h, and 48h for Cases 223-233], and [48h and 72h for Cases 234-235]. The IC-50 decrease is dramatic in many Cases (3, 4, 13, 14, 21, 24-27, 65, 67, 75, 79, 111, 112, 114, 116, 123, 151, 163, 165, 170, 171, 173, 197, 199, 200, 201, 201, 206 and 210). The IC-50 decrease over time depends on the cell lines and drugs. This shows that one IC-50 taken at one time point is misleading in its value and can explain the inconsistency noticed by Haibe-Kains et al,1 Baggerly et al4 and Reinhold et al.5 The increase of sensitivity of cancer cells to drugs, missing with the 4PL model based on one time point IC-50, is consistent with Haber’s law of increase of drug toxicity with time.

2. Type 2

(Cases 236-249) is characterized by an IC-50 increase over time (Table 2 and Figure 1b). There are several choices of time points: [24h, 48h and 72h for Cases 236-245], [2h, 24h and 48h for Case 246], [48h and 72h for Cases 247-248], and [24h, 48h and 72h for Case 249]. This IC-50 increase can be dramatic as in Cases 243-245 and 248. This IC-50 increase over time is not predicted by the Haber’s law and the 4PL model. It is only described by the multiple time points IC-50 introduced by this paper. It shows how cancer cells drug resistance is evidenced in vitro over a short period of time and shows the usefulness of the multiple IC-50-time points model.

3. Type 3

(Cases 250-279) is a V shaped curve characterized by two phases in the interaction between cancer cells and drugs, a decrease phase of the IC-50 followed by an increase of the IC-50 over time (Table 3 and Figure 1c). There are several choices of time points: [24h, 48h and 72h for Cases 250-268], [4h, 24h and 48h for Cases 269-272], [2h, 24h and 48h for Case 273], [24h, 48h and 72h for Cases 274-276], [24h, 72h and 120h for Case 277] and [3h, 48h and 72h for Cases 278-279]. Type 3 is not predicted by the Haber’s law and the 4PL model. This type shows how complex the interaction between cancer cells and drugs can be. In this type we are in vitro out of reach of the immune system and whatever a living organism can do to stop the growth of cancer cells. There are two possible interpretations. The first is based on what have been said before, that not all cancer cells in vitro are growing according the Gompertzian model. The IC-50 decrease phase is the killing of growing cells in vitro, and the IC-50 increase phase shows the resistance of non-growing quiescent cancer cells. After all the majority of cancer drugs are targeting growing cells. The second can be explained by the killing of the bulk of cancer cells in the first phase and the takeover by a resistant clone like cancer stem cells in the second phase. It is an in vitro self-seeding mechanism.51 Type 3 shows the advantage of the multiple IC-50 time points and its far-reaching capacity to explore the complex behavior of cancer cells. This a clear demonstration that cancer cells monolayer is heterogeneous and respond differently to cancer drugs. In addition, the IC-50 taken in different time points between the large-scale studies will lead to dramatic inconsistency.

4. Type 4

Is an Arabic eight-digit Λ or the Greek lambda Λ letter shaped curve also characterized by two phases in the interaction between cancer cells and drugs64–66 (Table 4 and Figure 1d). There are several choices of time points: [24h, 48h and 72h for Cases 280-288], [3h, 24h and 120h for Case 289], [12h, 24h, 48h and 72h for Case 290]. This Type in addition of showing the heterogeneity of cancer in vitro (growing cells vs quiescent cells), demonstrates in IC-50 increasing phase cancer cells resistance, then suddenly in the IC-50 decreasing phase the resistance collapse. So, any IC-50 taken at the time point corresponding to the apex of the Λ is seriously misleading. This type of situation is not predicted by the Haber’s law or the 4PL model.

5. Type 5

Is characterized by a constant IC50 over time (Table 4 and Figure 1e). I found only one case [24h, 48h and 72h for Case 291]. The proteasome inhibitor Bortezomib killing mechanism is not cell cycle dependent.

6. Is there a relationship between cancer drugs molecular targets and their IC-50 Time Evolution Type (TET)?

As Table 5 shows, it is difficult to find a clear pattern for cancer drugs TET. Etoposide which targets DNA Topoisomerase II kills seven cancer cell lines with IC-50 TET 1. Cisplatin kills six cell lines with TET 1 and kills PC-3 cell line with different TET in two different papers: TET3113 and TET4.114 Resveratrol’s molecular target still unknown, it kills six cancer cell lines with TET1. Paclitaxel which targets microtubules kills cancer cells with TET1, 3 and 4. Bortezomib which targets the proteasome system kills cancer cells with TET 1 and 5. Further studies are necessary to explore this relationship, if there is any, between cancer drugs and their IC-50 TET.

7. Is there a relationship between cancer cell lines and cancer drugs IC-50 Time Evolution Type (TET)?

As shown in Table 6 it is hard to find a general pattern. Cancer cell line A549 exposed to 10 drugs respond with the same IC-50 TET1 as reported by different papers.60,92,94,96 Cancer cell line MDA-MB-231 exposed to 9 different drugs respond with the same TET1 as reported by 8 different papers.54,70,72,95,102,105,106 and 120 Cancer cell line MCF-7 has a mixed response to drugs but responds with IC-50 TET1 to 12 drugs as reported by 9 papers.54,70,72,95,102,105,106 and 120 Cancer cell line HCT-116 has a mixed response to drugs but responds with IC-50 TET1 to 7 drugs as reported by 5 papers.61,67,98,100 and 112 Cancer cell line HeLa has a mixed response to drugs but responds with IC-50 TET1 to 5 drugs as reported by 4 papers.61,87,111 and 123 Cancer cell line HT-29 has a mixed response to drugs but responds with IC-50 TET1 to 5 drugs as reported by 5 papers.61,67,98,109 and 115 As shown in Table 6 a variety of cancer cells (K-562, MCF-7, UACC-903, HL-60, HCT-116, LnCap, HeLa, T47D, A-375, HT-29, SW872 and PC-3) have a mixed response of IC-50 TET1,2,3,4 and 5 to many cancer drugs. The good thing is that the response of each cancer cell line is reported by several research groups in the world. That is proof of validity, the strength and the usefulness of the IC-50-time evolution model compared to the one time point IC-50, aka 4PL model based on the Hill equation.

VII. Conclusions

The in vitro testing of cancer drugs remains a necessary step in their evaluation. To solve the inconsistencies of the drugs IC-50s between large scale studies several attempts24,37,79–83 failed. It is my opinion that the in vitro assessment of drugs is still a necessary step before going to in vivo mice studies and human clinical trials. Considering the failure of many drugs at the end, and the billions of dollars to support that, it is necessary to strengthen the prediction power of in vitro studies by considering a better understanding of cancer cells behavior in microplates. It is tempting to use high capacity microplates in which the reaction volume can be as small as 5μl and the number of cells is in the hundreds, making any statistical analysis futile. The automation of the process imposing an arbitrary one time point IC-50 regardless of the diversity of hundreds cancer cells doubling times provides this technology euphoria but does not advance cancer research field nor it improves patient’s life. The growth of cancer cells in vitro as it is in vivo is not a continuous growth. Jacques Monod used to say “the dream of a bacteria is to become two bacteria”. Cancer cells have another dream referred to as the “Gompertzian model”. This model applied in vivo has dramatically improved cancer treatment, the same model governs cancer cells growth in microplates whether in 2D or 3D formats. As I explained the meaning of different IC-50-time evolution Types 1-5, the effects of cancer drugs on cancer cells is time dependent. It was Fritz Haber who noticed that a low dose applied at long time has the same effect as a high dose applied at a short time. The Hill model short of the time factor is the main source of our problems with the in vitro screening of cancer drugs.84 We need to go beyond the Hill model and embrace the IC-50-time course evolution already predicted by Levasseur LM et al modified Hill model,15 the Gompertzian growth type of in vitro,30,40–43 the heterogeneous nature of in vitro monolayers47 and microspheres, and the hormesis phenomenon.25–29 This new model, still a work in progress, connects the IC50 time evolution to in vitro cellular monolayer dynamics: cancer cells exposed to killing drugs do not respond as individual cells but as group of cells governed by quorum sensing.85,86 In addition, the results gathered in 80 papers validate the new model I am presenting.

Declarations

Data availability: No data are associated with this article.

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ALILECHE A. The IC-50-time evolution is a new model to improve drug responses consistency of large scale studies [version 1; peer review: awaiting peer review]. F1000Research 2022, 11:284 (https://doi.org/10.12688/f1000research.108673.1)
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