Literature DB >> 32069833

Biphasic Mathematical Model of Cell-Drug Interaction That Separates Target-Specific and Off-Target Inhibition and Suggests Potent Targeted Drug Combinations for Multi-Driver Colorectal Cancer Cells.

Jinyan Shen1,2, Li Li1,3, Tao Yang1,2, Paul S Cohen1, Gongqin Sun1.   

Abstract

Quantifying the response of cancer cells to a drug, and understanding the mechanistic basis of the response, are the cornerstones for anti-cancer drug discovery. Classical single target-based IC50 measurements are inadequate at describing cancer cell responses to targeted drugs. In this study, based on an analysis of targeted inhibition of colorectal cancer cell lines, we develop a new biphasic mathematical model that accurately describes the cell-drug response. The model describes the drug response using three kinetic parameters: ratio of target-specific inhibition, F1, potency of target-specific inhibition, Kd1, and potency of off-target toxicity, Kd2. Determination of these kinetic parameters also provides a mechanistic basis for predicting effective combination targeted therapy for multi-driver cancer cells. The experiments confirmed that a combination of inhibitors, each blocking a driver pathway and having a distinct target-specific effect, resulted in a potent and synergistic blockade of cell viability, improving potency over mono-agent treatment by one to two orders of magnitude. We further demonstrate that mono-driver cancer cells represent a special scenario in which F1 becomes nearly 100%, and the drug response becomes monophasic. Application of this model to the responses of >400 cell lines to kinase inhibitor dasatinib revealed that the ratio of biphasic versus monophasic responses is about 4:1. This study develops a new mathematical model of quantifying cancer cell response to targeted therapy, and suggests a new framework for developing rational combination targeted therapy for colorectal and other multi-driver cancers.

Entities:  

Keywords:  biphasic analysis; colorectal cancer; combination targeted therapy; dose reduction index; protein kinase inhibitors

Year:  2020        PMID: 32069833     DOI: 10.3390/cancers12020436

Source DB:  PubMed          Journal:  Cancers (Basel)        ISSN: 2072-6694            Impact factor:   6.639


  4 in total

1.  Application of a Biphasic Mathematical Model of Cancer Cell Drug Response for Formulating Potent and Synergistic Targeted Drug Combinations to Triple Negative Breast Cancer Cells.

Authors:  Jinyan Shen; Li Li; Niall G Howlett; Paul S Cohen; Gongqin Sun
Journal:  Cancers (Basel)       Date:  2020-04-27       Impact factor: 6.639

2.  Understanding the effect of measurement time on drug characterization.

Authors:  Hope Murphy; Gabriel McCarthy; Hana M Dobrovolny
Journal:  PLoS One       Date:  2020-05-14       Impact factor: 3.240

3.  Identification of Lethal Inhibitors and Inhibitor Combinations for Mono-Driver versus Multi-Driver Triple-Negative Breast Cancer Cells.

Authors:  Geng Chia Ku; Abygail G Chapdelaine; Marina K Ayrapetov; Gongqin Sun
Journal:  Cancers (Basel)       Date:  2022-08-20       Impact factor: 6.575

4.  Comparative kinase and cancer cell panel profiling of kinase inhibitors approved for clinical use from 2018 to 2020.

Authors:  Jeffrey J Kooijman; Wilhelmina E van Riel; Jelle Dylus; Martine B W Prinsen; Yvonne Grobben; Tessa J J de Bitter; Antoon M van Doornmalen; Janneke J T M Melis; Joost C M Uitdehaag; Yugo Narumi; Yusuke Kawase; Jeroen A D M de Roos; Nicole Willemsen-Seegers; Guido J R Zaman
Journal:  Front Oncol       Date:  2022-09-14       Impact factor: 5.738

  4 in total

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