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

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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." } { "@context": "http://schema.org", "@type": "BreadcrumbList", "itemListElement": [ { "@type": "ListItem", "position": "1", "item": { "@id": "https://f1000research.com/", "name": "Home" } }, { "@type": "ListItem", "position": "2", "item": { "@id": "https://f1000research.com/browse/articles", "name": "Browse" } }, { "@type": "ListItem", "position": "3", "item": { "@id": "https://f1000research.com/articles/11-284", "name": "The IC-50-time evolution is a new model to improve drug responses..." } } ] } Home Browse The IC-50-time evolution is a new model to improve drug responses... ALL Metrics - Views Downloads Get PDF Get XML Cite How to cite this article 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 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. Close Copy Citation Details Export Export Citation Sciwheel EndNote Ref. Manager Bibtex ProCite Sente EXPORT Select a format first Track Share ▬ ✚ 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 Biology, Boise State University, Boise, ID, 83725, USA ABDELKRIM ALILECHE 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 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. READ ALL READ LESS Keywords CANCER, DRUGS, IC-50, GOMPERTZ, TIME POINT EVOLUTION, MONOLAYER Corresponding Author(s) ABDELKRIM ALILECHE ( [email protected] ) Close 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 al 1 noticed inconsistency in viability estimates and IC-50s between CCLE 2 and CGP 3 results. The same observations were made about the NCI60 screen by Baggerly KA et al 4 and Reinhold WC et al. 5 In addition, the examination of the previous studies 2 – 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 studies 2 , 3 , 6 – 12 shows three figures. First, a fixed cell seeding number going from 250 2 to 500 11 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 screen 6 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 Haber 143 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 = k 143 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 al 20 who showed that anticancer drug cytotoxicity is ( C x T ) dependent, C being the concentration and T the time. Then Adams et al 21 extended it to C n 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 al 15 combined cytotoxicity with the Hill model and established a modified Hill model, IC n x xT= k . in which IC x 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 IC 50 = (k/T) 1/n . 22 This shows clearly that in the large-scale studies 2 , 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 al 1 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 al 23 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 al 15 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 al 24 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 shapes 25 , 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 al 28 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 paper 30 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 al 1 and also by all the commentaries 32 – 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 studies 39 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 al 41 , 42 and Poplawski et al 43 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 divisions 45 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 al 15 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 72h 56 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 al 1 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. Case Type IC-50 24h IC-50 48h IC-50 72h Cell line Drug Ref 1 1 8μM 1.8μM 1.2μM MCF-7 Arsenic trioxide 72 2 1 17μM 7μM 4.8μM MDA-MB-231 Arsenic trioxide 72 3 1 28.1μM 0.0986μM 0.0043μM A-375 SLN Docetaxel 71 4 1 51.1μM 0.231μM 0.004μM A-375 Taxotere 71 5 1 0.769μM 0.125μM 0.0856μM C-26 SLN Docetaxel 71 6 1 2.083μM 0.456μM 0.0846μM C-26 Taxotere 71 7 1 13.45μg/ml 13.00μg/m 12.50μg/m MCF-7 TAM 70 8 1 13.18μg/ml 12.50μg/ml 11.78μg/ml MCF-7 TAM-SLN 70 9 1 17.21μg/ml 16.87μg/ml 15.97μg/ml MDA-MB-231 TAM 70 10 1 16.93μg/ml 16.00μg/ml 15.80μg/ml MDA-MB-231 TAM-SLN 70 11 1 96μM 90μM 65μM HepG2 Mycotoxin AOH 69 12 1 8.1μM 5.3μM 5.2μM HepG2 Mycotoxin 15-ADON 69 13 1 15.01μg/ml 6.19μg/ml 0.94μg/ml BEL7402 CNP 68 14 1 182.8μM 55.4μM 17.2μM U-266 Justicidin B 59 15 1 86.2μM 68.4μM 27.4μM U-266 Etoposide 59 16 1 >160μM 19.9μM 5μM DOHH-2 Justicidin B 59 17 1 >160μM 100.7μM 9.5μM DOHH-2 Etoposide 59 18 1 25.3μM 10.3μM 8μM REH Justicidin B 59 19 1 0.027μM 0.014μM 0.015μM REH Etoposide 59 20 1 88.8μM 19μM 16.2μM HH Justicidin B 59 21 1 104.7μM 48.6μM 14.7μM HH Etoposide 59 22 1 46μM 18.1μM 6.1μM HUT78 Justicidin B 59 23 1 9.3μM 4.3μM 4.2μM HUT78 Etoposide 59 24 1 14.1μM 2.4μM 1.5μM OPM-2 Justicidin B 59 25 1 24.1μM 4μM 1.3μM OPM-2 Etoposide 59 26 1 19.3μM 0.41μM 0.17μM RPMI-8226 Justicidin B 59 27 1 106.6μM 91.1μM 14.9μM RPMI-8226 Etoposide 59 28 1 9.20μM 8.30μM 4.63μM HepG2 Goniothalamin 62 29 1 79.10μM 63.75μM 35.01μM Chang Goniothalamin 62 30 1 >3mg/ml 2.6mg/ml 0.5mg/ml HeLa Hyd. F Eth. Extract 61 31 1 2.20mg/ml 1.72mg/ml 0.3mg/ml HeLa Hyd. F Ph. Extract 61 32 1 2.35mg/ml 2.04mg/ml 0.9mg/ml HeLa Sinapinic acid 61 33 1 2.63mM 2.22mM 1.2mM HeLa Sodium butyrate 61 34 1 2.97mg/ml 2.2mg/ml 1.6mg/ml HT-29 Sinapinic acid 61 35 1 >3mM 2.2mM 2.1mM HCT-116 Sinapinic acid 61 36 1 >3mM 2.2mM 2.0mM HCT-116 Sodium butyrate 61 37 1 >3mM 2.36mM 1.5mM JURKAT Sodium butyrate 61 38 1 >3mM >3mM 0.28mM JURKAT Sinapinic acid 61 39 1 6.1μM 4.5μM 1.6μM A549 Capillin 60 40 1 2.8μM 0.8μM 0.6μM Hep-2 Capillin 60 41 1 1.5μM 1.3μM 0.9μM A431 Hypocretenolide 1 63 42 1 1.5μM 1.3μM 1.1μM Hep-2 Hypocretenolide 1 63 43 1 2.8μM 2.6μM 1.2μM SK28 Hypocretenolide 1 63 44 1 3.2μM 2.4μM 1.8μM SK37 Hypocretenolide 1 63 45 1 0.9μM 0.9μM 0.8μM A431 Helenalin 63 46 1 0.9μM 0.9μM 0.8μM Hep-2 Helenalin 63 47 1 1.3μM 0.9μM 0.5μM SK28 Helenalin 63 48 1 1.3μM 1.2μM 0.7μM SK37 Helenalin 63 49 1 1.4μM 1.2μM 1.2μM SW872 Helenalin 63 50 1 463.3μM 280.8μM 149.3μM UACC-903 JS-21 (3a) 64 51 1 150.8μM 126.5μM 118.5μM UACC-903 JS-23 (3c) 64 52 1 193.5μM 145.7μM 108.2μM UACC-903 JS-25 (4) 64 53 1 614.3μM 266.8μM 112.7μM UACC-903 JS-20 (3) 64 54 1 0.51μg/ml 0.31μg/ml 0.27μg/ml MCF-7 DOX-Sol 66 55 1 0.61μg/ml 0.51μg/ml 0.37μg/ml MCF-7/Adr DOX-GNMs 66 56 1 >40μM 7μM 1.25μM Ishikawa Perifosine 74 57 1 >40μM 25μM 6μM Ishikawa Perifosine 74 58 1 15.01μg/ml 6.19μg/ml 0.94μg/ml BEL7402 Chitosan NP 75 59 1 0.51mM/l 0.33mM/l 0.25mM/l COLO829 Lomefloxacin 76 60 1 2.5ng/ml 2ng/ml 1.5ng/ml HBL-2 Bortezomib 77 61 1 38μM 10μM 10μM HeLa Apigenin 87 62 1 89μM 72μM 68μM SiHa Apigenin 87 63 1 19μM 9.2μM 4.1μM EC109 Jesridonin 88 64 1 61.0μM 38.2μM 38.9μM EC109 Oridonin 88 65 1 41.7μM 14.4μM 4μM EC9706 Jesridonin 88 66 1 37.5μM 28.0μM 23.9μM EC9706 Oridonin 88 67 1 ˃100μM 11.4μM 2.0μM KYSE450 Jesridonin 88 68 1 30.5μM 28.2μM 17.1μM KYSE450 Oridonin 88 69 1 ˃100μM 61.4μM 16.2μM KYSE750 Jersidonin 88 70 1 35.3μM 23.4μM 14.3μM KYSE750 Oridonin 88 71 1 45.8μM 21.4μM 9.4μM TE-1 Jersidonin 88 72 1 25.2μM 18.0μM 8.4μM TE-1 Oridonin 88 73 1 86.6μM 49.8μM 28.2μM GES-1 Jersidonin 88 74 1 ˃100μM 35.4μM 25.2μM HL7702 Jersidonin 88 75 1 5μg/ml 0.6μg/ml 0.06μg/ml Primary Hepatocytes AFB1 89 76 1 18μg/ml 9μg/ml 4μg/ml HCT15 Zerumbone 90 77 1 25μg/ml 16μg/ml 8μg/ml HCT15 Cisplatin 90 78 1 1954μg/ml 1700μg/ml 1540μg/ml MCF-7 MCRE 91 79 1 86.34mM 17.83mM 8.64mM A549 Doxorubicin 92 80 1 93.86mM 43.28mM 37.12mM H1299 Doxorubicin 92 81 1 7.45μM 5.13μM 3.98μM JURKAT PJ-34 93 82 1 20.301μM 9.785μM 7.008μM HL60 PJ-34 93 83 1 131mM 89mM 38mM JURKAT Doxorubicin 93 84 1 83mM 23mM 10mM HL60 Doxorubicin 93 85 1 31.25μM 5.1μM 3μM A549 Cisplatine 94 86 1 24.75μM 15μM 13.5μM A549 Silver Nitrate 94 87 1 20μM 13μM 8μM MDA-MB-231 EPC-3 95 88 1 10.58μg/ml 8.81μg/ml 6.59μg/ml A549 TQ 96 89 1 19.39μg/ml 17.51μg/ml 15.62μg/ml A549 TQG 96 90 1 15.63μg/ml 14.97μg/ml 12.40μg/ml A549 TQ-Fe 3 O 4 96 91 1 27.31μg/ml 18.68μg/ml 11.88μg/ml A549 TQG-Fe 3 O 4 96 92 1 16.10μg/ml 12.71μg/ml 7.04μg/ml A549 TQ-Fe 3 O 4 (MF) 96 93 1 23.45μg/ml 10.78μg/ml 9.579μg/ml A549 TQ-G-Fe 3 O 4 (MF) 96 94 1 13.8μM 6.888μM 4.362μM A2780 Salinomycin 97 95 1 12.7μM 9.869μM 5.022μM SK-OV-3 Salinomycin 97 96 1 56.6μM 51.14μM 32.86μM HT-29 Apatinib 98 97 1 48.76μM 44.11μM 29.25μM HCT116 Apatinib 98 98 1 0.59μM 0.36μM ˂0.03125μM NB1 Amp Crizotinib 99 99 1 2.21μM 0.77μM ˂0.5μM NB3 R1275Q Crizotinib 99 100 1 1.6μM 1.34μM 1.1μM SH-SY5Y F1174L Crizotinib 99 101 1 2.19μM 0.71μM 0.64μM IMR32 WT Crizotinib 99 102 1 0.31μM 0.035μM 0.03μM NB1 Amp Entrectinib 99 103 1 4.34μM 3.32μM 2.42μM SH-SY5Y F1174L Entrectinib 99 104 1 3.68μM 3.29μM 3.06μM IMR32 WT Entrectinib 99 105 1 5.13μM 3.51μM 2.13μM MCF-7 Mitoxantrone 103 106 1 2.58μM 1.64μM 1.25μM MCF-7 Mitoxantrone SLN 103 107 1 92.64μM 67.34μM 52.48μM MCF-7 Paclitaxel 103 108 1 98.70μM 62.31μM 46.70μM MCF-7 Paclitaxel SLN 103 109 1 267.84μM 195.16μM 153.16μM MCF-7 Methotrexate 103 110 1 154.76μM 98.48μM 93.80μM MCF-7 Mehtotrexate SLN 103 111 1 88.89μM 13.20μM 9.553μM A2780 Cisplatin 104 112 1 350.5μM 50.96μM 25.39μM A2780/DDP Cisplatin 104 113 1 105.1μM 51.73μM 16.13μM SKOV3 Cisplatin 104 114 1 446.7μM 135.0μM 66.70μM SKOV3/DDP Cisplatin 104 115 1 10.66μM 2.51μM 2.08μM HS578T Cediranib 105 116 1 30.77μM 15.57μM 2.52μM MDA-MB-231 Cediranib 105 117 1 38.69μM 26.54μM 18.85μM T47D Cediranib 105 118 1 15.27μM 8.13μM 3.69μM MCF-7 Arsenic Disulfide 106 119 1 25.5μM 9.18μM 5.37μM MDA-MB-231 Arsenic Disulfide 106 120 1 49.15μg/ml 47.18g/m 45.80g/ml PC-3 Boswellic Acid 107 121 1 49.27g/ml 48.58g/ml 46.77g/ml PC-3 Montelukast Sodium 107 122 1 16μM 11.5μM 9.75μM HL-60 As2O3 108 123 1 12.27μM 7.57μM 0.45μM HT-29 5-FU 109 124 1 14.56μM 11.20μM 1.324μM CACO-2 5-FU 109 125 1 107μM 73μM 47μM T47D Silibinin 110 126 1 1.71mM 0.99mM 0.06mM HeLa Safranal 111 127 1 2.30mM 1.28mM 0.5mM MCF-7 Safranal 111 128 1 2.12mM 1.18mM 0.29mM L929 Safranal 111 129 1 0.093mM 0.063mM 0.039mM HeLa Safranal Loaded 111 130 1 0.39mM 0.24mM 0.13mM MCF-7 Safranal Loaded 111 131 1 0.14mM 0.075mM 0.063mM L929 Safranal Loaded 111 132 1 1207μM 720μM 298μM U251 β-Asarone 116 133 1 1150μM 900μM 195μM C6 β-Asarone 116 134 1 7.5μM 5.0μM 3.0μM Jurkat Beauvericin 117 135 1 0.74mM 0.17mM 0.10mM COLO827 Ciprofloxacin 118 136 1 0.75μM/ml 0.57μM/ml 0.53μM/ml U87MG Ciprofloxacin 119 137 1 0.48μM/ml 0.22μM/ml 0.15μM/ml U87MG Moxifloxacin 119 138 1 0.83μM/ml 0.14μM/ml 0.03μM/ml MDA-MB-231 Ciprofloxacin 120 139 1 22.5μM 19μM 17μM T47D Curcumin 121 140 1 10.5μM 9.5μM 9μM T47D PAMAM Curcumin 121 141 1 1.734mM 0.742mM 0.500mM HCT-116 DHCA 123 142 1 2.595mM 1.188mM 0.704mM HCT-15 DHCA 123 143 1 8.148mM 3.018mM 1.66mM HeLa DHCA 123 144 1 6.942mM 4.511mM 3.223mM SiHa DHCA 123 145 1 18μM 15μM 13μM HL-60 EA-137 124 146 1 76.72nM/l 34.05nM/l 16.7nM/l SW620 Bufalin 125 147 1 8.89μM 3.58μM 1.86μM Hep-G2 OTA 126 148 1 55.79μM 39.88μM 29.48μM Hep-G2 ZEA 126 149 1 34.25μM 10.08μM 7.36μM Hep-G2 OTA+ZEA 126 150 1 35.64μM 4.99μM 4.05μM Hep-G2 OTA+α-ZOL 126 151 1 27.67μM 11.05μM 3.42μM Hep-G2 OTA+ZEA+α-ZOL 126 152 1 1954μg/ml 1700μg/ml 1560μg/ml MCF-7 Mat. Chamomilla 127 153 1 0.42μM 0.25μM 0.04μM RL ABT-737 128 154 1 5.65μM 3.66μM 2.92μM H9 ABT-737 128 155 1 12.72μM 14.19μM 9.54μM JJN-3 ABT-737 128 156 1 0.28μM 0.12μM 0.10μM SKI ABT-737 128 157 1 76μg/ml 58μg/ml 39μg/ml MCF-7 EADs 129 158 1 47μM 44μM 43μM A549 Diosgenin 130 159 1 7.14μM 5.05μM 4.23μM MCF-7 BBSKE 131 160 1 10.54μM 10.13μM 7.29μM MCF-7 PM 131 161 1 4.14μM 3.99μM 3.43μM MCF-7 FA+PM 131 162 1 7.84μM 6.88μM 6.30μM MCF-7 FA+PM+free FA 131 163 1 40nM/l 27nM/l 17nM/l DU-145 Triptolide 133 164 1 2.17ng/ml 1.31ng/ml 1.16ng/ml A2780 Triptolide 135 165 1 92ng/ml 10.2ng/ml 7.34ng/ml OVCAR-3 Triptolide 135 166 1 102ng/ml 85ng/ml 81ng/ml HIO-180 Triptolide 135 167 1 142ng/ml 111ng/ml 99ng/ml CCD-19Ln Triptolide 135 168 1 584ng/ml 217ng/ml 207ng/ml J774A.1 Triptolide 135 169 1 0.276mM 0.244mM 0.213mM LnCap Ciprofloxacin 154 170 1 168.8μg/ml 22.15μg/ml 8.04μg/ml U14 Paclitaxel 155 171 1 15.0μg/ml 1.27μg/ml 0.62μg/ml A549 Goniothalamin 157 172 1 14.43μg/ml 0.27μg/ml 0.24μg/ml A549 Doxorubicin 157 173 1 26.93μg/ml 10.27μg/ml 1.64μg/ml HT29 Goniothalamin 157 174 1 11.6μg/ml 8.57μg/ml 6.23μg/ml HMSC Goniothalamin 157 175 1 30.98μg/ml 23.63μg/ml 18.08μg/ml HCT16 SGC 158 176 1 129.67μg/ml 116.30μg/ml 82.27μg/ml HCT16 SGE 158 177 1 175.70μg/ml 105.8μg/ml 61.9μg/ml SiHa SGC 158 178 1 255.03μg/ml 113.03μg/ml 66.08μg/ml SiHa SGEA 158 179 1 460.4μg/ml 291.7μg/ml 149.7μg/ml SiHa SGW 158 180 1 185.66μg/ml 109.7μg/ml 66.7μg/ml HeLa SGC 158 181 1 260.46μg/ml 116.5μg/ml 68.48μg/ml HeLa SGEA 158 182 1 360.56μg/ml 275.9μg/ml 146.43μg/ml HeLa SGE 158 183 1 472.6μg/ml 291.26μg/ml 149.46μg/ml HeLa SGW 158 184 1 301.83μg/ml 267.23μg/ml 113.7μg/ml MDA-MB-231 SGC 158 185 1 408.37μg/ml 351.43μg/ml 175.90μg/ml MDA-MB-231 SGEA 158 Case Type IC-50 3h IC-50 24h IC-50 120h Cell line Drug Ref 186 1 >32μM 0.29μM 0.0099μM NCI-H23 Paclitaxel 65 187 1 >32μM 0.93μM 0.078μM NCI-H460 Paclitaxel 65 188 1 >32μM 24μM 0.03μM NCI-H322 Paclitaxel 65 189 1 >32μM 14μM 0.0091μM NCI-H522 Paclitaxel 65 190 1 >32μM 27μM 7.5μM NCI-H727 Paclitaxel 65 Case Type IC-50 2h IC-50 24h IC-50 48h Cell line Drug Ref 191 1 26μM 9μM 8μM LnCap 9S1R 54 192 1 39μM 29μM 16μM MDA-MB-231 9R 54 193 1 18μM 12μM 10μM MDA-MB-231 9S1R 54 194 1 93μM 39μM 37μM HUT-102 9R 54 Case Type IC-50 48h IC-50 72h IC-50 120h Cell line Drug Ref 195 1 129.8μM 42.5μM 31.0μM HCT-116 WT Resveratrol 100 196 1 84.1μM 7.0μM 0.6μM HCT-116 WT IRA-5 100 197 1 88.7μM 20.2μM 9.2μM A-431 Resveratrol 100 198 1 133.4μM 39.5μM 15.4μM A-431 IRA-5 100 199 1 186.0μM 52.4μM 16.1μM Caco-2 Resveratrol 100 200 1 348.7μM 46.1μM 13.4μM Caco-2 IRA-5 100 201 1 741.3μM 149.1μM 33.8μM HCA-7 Resveratrol 100 202 1 288.6μM 206.7μM 51.6μM HCA-7 IRA-5 100 203 1 149.1μM 71.8μM 28.6μM HCT-116 p53-/- Resveratrol 100 204 1 134.2μM 57.6μM 16.1μM HCT-116 p53-/- IRA-5 100 205 1 263.8μM 161.2μM 29.6μM LnCap Resveratrol 100 206 1 342.3μM 166.3μM 24.9μM LnCap IRA-5 100 Case Type IC-50 24h IC-50 48h IC-50 72h IC-50 96h Cell line Drug Ref 207 1 86.29μM/ml 75.34μM/ml 72.42μM/ml 69.82μM/ml U-251 Temozolomide 101 208 1 66.25μM 64.00μM 57.99μM 37.36μM PC-3 Flutamide 113 209 1 40.4μM 30.8μM 12.7μM 7.9μM 22Rv1 Cisplatin 114 210 1 61.5μM 44.0μM 7.9μM 3.7μM PNT1A Cisplatin 114 211 1 0.048μM 0.036μM 0.030μM 0.029μM A549 Digoxin 149 212 1 0.104μM 0.107μM 0.070μM 0.057μM H3255 Digoxin 149 213 1 0.767mM 0.238mM 0.212mM 0.193mM PC-3 Ciprofloxacin 154 214 1 3.937μM 0.290μM 0.250μM 0.173μM PC-3 Doxorubicin 154 215 1 26.25nM 7.655nM 3.951nM 3.194nM PC-3 Docetaxel 154 Case Type IC-50 24h IC-50 48h IC-50 72h IC-50 120h Cell line Drug Ref 216 1 8.40μg/ml 7.60μg/ml 7.40μg/ml 6.84μg/ml MRC5 TTHL 67 217 1 1.36μg/ml 0.73μg/ml 0.63μg/ml 0.30μg/ml MCF-7 TTHL 67 218 1 6.50μg/ml 6.10μg/ml 5.45μg/ml 0.88μg/ml HepG2 TTHL 67 219 1 5.55μg/ml 5.20μg/ml 1.09μg/ml 0.39μg/ml T24 TTHL 67 220 1 7.05μg/ml 5.87μg/ml 5.20μg/ml 4.50μg/ml HCT116 TTHL 67 221 1 8.00μg/ml 7.00μg/ml 6.15μg/ml 5.30μg/ml HT-29 TTHL 67 222 1 8.55μg/ml 7.90μg/ml 6.35μg/ml 5.00μg/ml CACO-2 TTHL 67 Case Type IC-50 24h IC-50 48h Cell line Drug Ref 223 1 4.0μM 2.7μM MCF-7 Doxorubicin 102 224 1 4.0μM 1.4μM MDA-MB-231 Doxorubicin 102 225 1 77.5μM 72μM HT-29 Valdecoxib 115 226 1 15.1μM 4.8μM HUT78 BKM10 122 227 1 12.4μM 3.9μM GRANT A519 BKM10 122 228 1 14.8μM 4.1μM WSU-NHL BKM10 122 229 1 41.6μM 21.1μM HUT78 BEZ235 122 230 1 45.1μM 25.3μM GRANT A519 BEZ235 122 231 1 39.2μM 18.5μM WSU-NHL BEZ235 122 232 1 92.4nM 16.1nM MVA4-11 Triptolide 132 233 1 76.1nM 6.9nM OCM-AML3 Triptolide 132 Case Type IC-50 48h IC-50 72h Cell line Drug Ref 234 1 1147.91μg/ml 921.1μg/ml MCF-7 Capecitabine 112 235 1 56.14nM/L 15.57nM/L OCM-1 Triptolide 134 Table 2. IC-50 Time evolution IC-50 Type 2. Case Type IC-50 24h IC-50 48h IC-50 72h Cell line Drug Ref 236 2 0.6μM 0.9μM 1.0μM ZR75-1 Hypocretenolide 1 63 237 2 0.7μM 0.8μM 1.1μM ZR75-1 Helenalin 63 238 2 1.4μM 1.6μM 1.7μM OVCAR3 Helenalin 63 239 2 0.184μM 0.919μM 1.652μM AGS Clofarabine 78 240 2 5.33μg/ml 5.34μg/ml 7.56μg/ml K562 Para-nitro acetophenon 151 241 2 7.118μg/ml 8.62μg/ml 9.75μg/ml PBMC Para-nitro acetophenon 151 242 2 10μM 23μM 30μM HL60 EA-136 124 243 2 16μM 20μM 90μM HL60 EA-4 124 244 2 12.6μg/ml 82.8μg/ml 188.4μg/ml N2a 3-FOC 156 245 2 9.25μg/ml 37.5μg/ml 83.6μg/ml N2a 6-FOC 156 Case Type IC-50 2h IC-50 24h IC-50 48h Cell line Drug Ref 246 2 38μM 43μM 43μM HUT-102 9S1R 54 Case Type IC-50 48h IC-50 72h Cell line Drug Ref 247 2 59.22μg/ml 92.30μg/ml BCSC Dandelion Eth. Extr. 42 248 2 14.88μg/ml 69.40μg/ml BCSC Dandelion Met. Txtr. 42 Case Type IC-50 24h IC-50 48h Cell line Drug Ref 249 2 71μM 74μM SW620 Valdecoxib 115 Table 3. IC-50 Time evolution Type 3. Case Type IC-50 24h IC-50 48h IC-50 72h Cell line Drug Ref 250 3 6.0μM 0.8μM 6.0μM HT-29 Capillin 60 251 3 3.4μM 0.8μM 1.4μM MIA Pa Ca-2 Capillin 60 252 3 3.1μM 2.2μM 2.8μM SW872 Hypocretenolide 1 63 253 3 0.8μM 0.7μM 1μM MCF-7 Hypocretenolide 1 63 254 3 2.03μg/ml 0.85μg/ml 0.86μg/ml MCF-7/Adr DOX-Sol 66 255 3 6.2μM 3.6μM 5.2μM HepG2 Mycotoxin 3-ADON 69 256 3 2.65μM 2.24μM 3.27μM NB3 R1275Q Entrectinib 99 257 3 50μg/ml 25μg/ml 40μg/ml HCT-116 Bark CO AE 150 258 3 65μg/ml 30μg/ml 45μg/ml HCT-116 Bark CO ME 150 259 3 ˃200μg/ml 112μg/ml 160μg/ml HCT-116 Bark CO AqE 150 260 3 11.56μg/ml 10.705μg/m 11.5μg/m K562 Acetanilide 151 261 3 13.93μg/m 13.16μg/m 13.53μg/m PBMC Acetanilide 151 262 3 58μM 50μM 55μM HL60 all-trans-RA 72 263 3 362.3μM 234.4μM 270.5μM A375M JS-22(3b) 64 264 3 0.8μM 0.5μM 0.7μM MCF-7 Helenalin 63 265 3 52.30μM 10.91μM 21.98μM HepG2 α-ZOL 126 266 3 55μM 21.12μM 29.77μM HepG2 ZEA+Αzol 126 267 3 0.03μM 0.025μM 0.03μM HBL-2 ABT-737 128 268 3 68.9μg/ml 25μg/ml 95.6μg/ml N2a GOC 156 Case Type IC-50 4h IC-50 24h IC-50 48h Cell line Drug Ref 269 3 1.55μM 0.31μM 1.68μM A459 Osmium arene 1 73 270 3 0.85μM 0.17μM 0.32μM A459 Osmium arene 2 73 271 3 33.95μM 3.64μM 35.73μM A459 Osmuim arene 3 73 272 3 1.92μM 1.78μM 1.79μM A459 Cisplatin 73 Case Type IC-50 2h IC-50 24h IC-50 48h Cell line Drug Ref 273 3 44μM 23μM 28μM LnCap 9R 54 Case Type IC-50 24h IC-50 48h IC-50 72h IC-50 96h Cell line Drug Ref 274 3 9.31μM 1.69μM 0.42μM 0.71μM PC-3 Doxorubicin 113 275 3 10.53μM 1.11μM 0.57μM 0.68μM PC-3 Epirubicin 113 276 3 127.08μM 15.31μM 18.35μM 18.77μM PC-3 Cisplatin 113 Case Type IC-50 24h IC-50 72h IC-50 120h Cell line Drug Ref 277 3 48mM 6.6mM 120mM SW13 Ouabain 41 Case Type IC-50 3h IC-50 48h IC-50 72h Cell line Drug Ref 278 3 >32μM 22μM 31μM NCI-H676 Paclitaxel 65 279 3 0.31μM 0.0092μM 0.017μM NCI-H1155 Paclitaxel 65 Table 4. IC-50 Time Evolution Type 4. Case Type IC-50 24h IC-50 48h IC-50 72h Cell line Drug Ref 280 4 144.1μM 200μM 109.3μM UACC-903 JS-22(3b) 64 281 4 219.0μM 605.4μM 100.9μM A375M JS-28(4c) 64 282 4 98.92μM 107.8μM 44.95μM UACC-903 JS-26(4a) 64 283 4 180.8μM 191.9μM 58.1μM UACC-903 JS-20(3) 64 284 4 0.67μg/ml 1.05μg/ml 0.69μg/ml MCF-7 DOX-GNMs 66 285 4 73μM 77μM 74μM T47D Silibinin Loaded 110 286 4 6.1μg/ml 7.2μg/ml 4.8μg/ml HeLa Berberine 61 287 4 2.7μg/m 3.5μg/ml 1μg/ml L1210 Berberine 61 288 4 1.9μM 2.1μM 1.8μM OVCAR3 Hypocretenolide 1 63 Case Type IC-50 3h IC-50 24h IC-50 120h Cell line Drug Ref 289 4 0.28μM 7.5μM 0.68μM NCI-H1299 Paclitaxel 65 Case Type IC-50 12h IC-50 24h IC-50 48h IC-50 72h Cell line Ref Drug 290 4 18.3μM 74.9μM 10.6μM 1.0μM PC-3 114 Cisplatin Table 5. IC-50 Time Evolution Type 5. Case Type IC-50 24h IC-50 48h IC-50 72h Cell line Drug Ref 291 5 5ng/ml 5ng/ml 5ng/ml NCEB Bortezomib 77 Table 6. Drugs do not have always the same IC-50 Time Evolution Type with different cancer cell lines. Drug Cell line IC-50 TET Ref Cisplatin HCT15 1 90 Cisplatin A549 1 94 Cisplatin A2780 1 104 Cisplatin SKOV3 1 104 Cisplatin 22Rv1 1 114 Cisplatin PNT1A 1 114 Cisplatin PC-3 4 114 Cisplatin PC-3 3 113 Hypocretenolide 1 A431 1 63 Hypocretenolide 1 Hep-2 1 63 Hypocretenolide 1 SK28 1 63 Hypocretenolide 1 SK37 1 63 Hypocretenolide 1 ZR75-1 2 63 Hypocretenolide 1 SW872 3 63 Hypocretenolide 1 MCF-7 3 63 Hypocretenolide 1 OVCAR3 4 63 Bortezomib HBL-2 1 77 Bortezomib NCEB 5 77 Resveratrol HCT-116 1 100 Resveratrol A431 1 100 Resveratrol CaCO-2 1 100 Resveratrol HCA-7 1 100 Resveratrol HCT-116 553-/- 1 100 Resveratrol LnCap 1 100 Cediranib HS578T 1 105 Cediranib MDA-MB-231 1 105 Cediranib T47D 1 105 Etoposide U-266 1 59 Etoposide DOHH-2 1 59 Etoposide REH 1 59 Etoposide HH 1 59 Etoposide HuT78 1 59 Etoposide OPM-2 1 59 Etoposide RPMI-8226 1 59 Safranal HeLA 1 111 Safranal MCF-7 1 111 Safranal L929 1 111 Capillin A549 1 60 Capillin Hep-2 1 60 Capillin HT-29 3 60 Capillin MIA Pa Ca-2 3 60 Paclitaxel MCF-7 1 103 Paclitaxel NCI-H23 1 65 Paclitaxel NCI-H460 1 65 Paclitaxel NCI-H322 1 65 Paclitaxel NCI-H522 1 65 Paclitaxel NCI-H727 1 65 Paclitaxel NCI-H676 3 65 Paclitaxel NCI-H1155 3 65 Paclitaxel NCI-H1299 4 65 Helnalin A431 1 63 Helnalin SK28 1 63 Helnalin Hep-2 1 63 Helnalin SK37 1 63 Helnalin SW872 1 63 Helnalin ZR75-1 2 63 Helnalin OVCAR3 2 63 Helnalin MCF-7 3 63 5-FU HT-29 1 109 5-FU CaCO-2 1 109 Sinapinic acid HeLa 1 61 Sinapinic acid HT-29 1 61 Sinapinic acid HCT-116 1 61 Sinapinic acid JURKAT 1 61 Berberine HeLa 4 61 Berberine L1210 4 61 Salinomycin A2780 1 97 Salinomycin SKOV3 1 97 Apatinib HT-29 1 98 Apatinib HCT-116 1 98 Table 7. Cancer cell lines and Drugs and their IC-50 TET. Cell line Drug IC-50 TET Ref K562 Para-nitro acetophenon 2 55 K562 Acetanilide 3 55 MCF-7 Arsenic trioxide 1 72 MCF-7 TAM 1 70 MCF-7 Dox-Sol 1 66 MCF-7 MCRE 1 91 MCF-7 Mitoxantrone 1 103 MCF-7 Paclitaxel 1 103 MCF-7 Methotrexate 1 103 MCF-7 Arsenic disulfide 1 106 MCF-7 Safranal 1 111 MCF-7 Doxorubicin 1 102 MCF-7 Capecitabin 1 112 MCF-7 TTHL 1 67 MCF-7 Hypocretenolide 1 3 67 MCF-7 Helenalin 3 67 MCF-7 DOX-GNMs 4 66 UACC-903 JS-21(3a) 1 64 UACC-903 JS-23(3c) 1 64 UACC-903 JS-25(4) 1 64 UACC-903 JS-20(3) 4 64 UACC-903 JS-22(3b) 4 64 UACC-903 JS-26(4a) 4 64 A549 Capillin 1 60 A549 Doxorubicin 1 92 A549 Cisplatin 1 94 A549 Silver nitrate 1 94 A549 TQ 1 96 A549 TGG 1 96 A549 TQ-Fe 3 O 4 1 96 A549 TQG-Fe 3 O 4 1 96 A549 TQ-Fe 3 O 4 (MF) 1 96 A549 TQ-G-Fe 3 O 4 (MF) 1 96 HL-60 PJ-34 1 93 HL-60 Doxorubicin 1 93 HL-60 Arsenic trioxide 1 108 HL-60 EA-137 1 124 HL-60 EA-136 2 124 HL-60 EA-4 2 124 HL-60 all-trans-RA 3 72 HCT-116 Sinapinic acid 1 61 HCT-116 Sodium butyrate 1 61 HCT-116 Apatinib 1 98 HCT-116 DHCA 1 123 HCT-116 Resveratrol 1 100 HCT-116 IRA-5 1 100 HCT-116 TTHL 1 67 HCT-116 Bark CO AE 3 40 LnCap 9S1R 1 54 LnCap Resveratrol 1 100 LnCap IRA-5 1 100 LnCap 9R 3 54 HeLa Sinapinic acid 1 61 HeLa Sodium butyrate 1 61 HeLa Apigenin 1 87 HeLa Safranal 1 111 HeLa DHCA 1 123 HeLa Berberine 4 61 T47D Cediranib 1 105 T47D Curcumin 1 121 T47D Silibinin 1 110 T47D Silibilin Loaded. 4 110 A431 Hypocretenolide 1 1 63 A431 Helenalin 1 63 A-375 JS-20(3) 1 64 A-375 SLN Docetaxel 1 71 A-375 Taxotere 1 71 A-375 JS-22(3b) 3 64 A-375 JS-28(4c) 4 64 HT-29 Sinapinic acid 1 61 HT-29 Apatinib 1 98 HT-29 5-FU 1 109 HT-29 Valdecoxib 1 115 HT-29 TTHL 1 67 HT-29 Capillin 3 60 SW872 Helenalin 1 63 SW872 Hypoctretenolide 1 3 63 HepG2 Mycotxin AOH 1 69 HepG2 Gpniothalamin 1 62 HepG2 TTHL 1 67 HepG2 Mycotoxin 3-ADON 3 69 MDA-MB-231 Arsenic trioxide 1 72 MDA-MB-231 TAM 1 70 MDA-MB-231 EPC-3 1 95 MDA-MB-231 Cediranib 1 105 MDA-MB-231 Arsenic disulfide 1 106 MDA-MB-231 Ciprofloxacin 1 120 MDA-MB-231 9R 1 54 MDA-MB-231 9S1R 1 54 MDA-MB-231 Doxorubicin 1 102 PC-3 Boswellic acid 1 107 PC-3 Flutamide 1 113 PC-3 Doxorubicin 3 113 PC-3 Epirubicin 3 113 PC-3 Cisplatin 3 113 PC-3 Cisplatin 4 114 PC-3 Montelukast Sodium 1 107 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 al 4 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 drugs 64 – 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: TET3 113 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 TET 1 , 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 attempts 24 , 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 monolayers 47 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. References 1. 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Publisher Full Text Comments on this article Comments (0) Version 1 VERSION 1 PUBLISHED 07 Mar 2022 ADD YOUR COMMENT Comment Author details Author details Biology, Boise State University, Boise, ID, 83725, USA ABDELKRIM ALILECHE Roles: Conceptualization, Data Curation, Formal Analysis, Funding Acquisition, Investigation, Methodology, Project Administration, Resources, Software, Supervision, Validation, Visualization, Writing – Original Draft Preparation, 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 (1) version 1 Published: 07 Mar 2022, 11:284 https://doi.org/10.12688/f1000research.108673.1 Copyright © 2022 ALILECHE A. 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