Time-dependent AI-Modeling of the anticancer efficacy of synthesized gallic acid analogues

Computational Biology and Chemistry
Lubna SherinShahida Shujaat

Abstract

Main objective of this study is mapping of the anticancer efficacy of synthesized gallic acid analogues using modeling and artificial intelligence (AI) over a large range of concentrations and exposure times to explore the underline mechanisms of drug action and draw careful inferences regarding drug response heterogeneity. Two series of gallic acid derivatives i.e. esters and amides have been synthesized and characterized by FTIR, NMR and mass spectrometry. The compounds have been tested in vitro for their anticancer activity against wild type human ovarian cancer cell line A2780, prostate cancer cell line PC3 and normal human fibroblast cells 3T3. To completely characterize optimal anticancer activity, a comprehensive model using piecewise recursive Hill model is used to quantitatively assess the in vitro anticancer effect of the tested compounds as a function of concentration and exposure time for periods ranging from 24 to 72 h. A robust artificial intelligence approach i.e. the "Support Vector Machine (SVM) Learning Algorithm" is adopted to utilize the data obtained at different temporal values, to identify compounds that trail forecasting algorithm. All the synthesized analogues were found biocompatible. Significantly low...Continue Reading

Citations

Jul 18, 2019·Wiley Interdisciplinary Reviews. Systems Biology and Medicine·Ibrahim M Chamseddine, Katarzyna A Rejniak
Dec 29, 2020·Cancer Management and Research·Jie ZhouLi Li
Apr 30, 2021·Seminars in Cancer Biology·Jamal Elkhader, Olivier Elemento
Sep 28, 2021·Food and Chemical Toxicology : an International Journal Published for the British Industrial Biological Research Association·Milad AshrafizadehRajender S Varma

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