Abstract: Drug discovery is a complex multistep process. A central question in drug discovery is the potency and efficacy of the compound being tested. For cancer, which is at its core a proliferative disorder, inhibiting cell proliferation is an effective therapeutic strategy. Recent studies of plant derived compounds have shown that they are selective inhibitors of key components of cell cycle network (cyclin-dependent kinases and Cdc25). Our goal is to use dynamical modelling to determine which compounds are the best candidates for future chemical modifications to make them into potential drugs. We use the ordinary differential equation (ODE) models in MATLAB to determine the ranking of hypothetical compounds as potential drugs by testing the effect of different concentrations on inhibition of the eukaryotic cell cycle (minimal CDK network 2015), assuming that greater inhibition of the cell cycle at lower concentration of the drug will mean a better drug. Our results rank the hypothetical drugs in order of their efficacy, based on ATP competitive inhibition, and give hints about effectiveness of big sets of compounds with potential anti-cancer activity. Hence, we present a preliminary In Silico drug-discovery method which strengthens the mathematical modelling as a cost-effective first step and powerful approach for investigating complex cell signalling networks.
Keywords: Drug-discovery, Cancer Prevention and Treatment, Cell Cycle Model, Physical Activity, Anticancer.
Title: In Silico Drug Discovery for Cancer Therapeutics Using Dynamical Modeling
Author: Diego Bonilla, Yurany Moreno, Jorge Petro, Eduardo Ramírez, Jose Molina, Adis Ayala
International Journal of Healthcare Sciences
ISSN 2348-5728 (Online)
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