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• W4205900559 abstract "In recent years mental disorders, such as depression, have become an alarmingly frequent problem in modern society. Therefore, a need for novel anti-depressant agents is ever-growing. With low drug acceptance rate, industry is struggling to introduce new anti-depressant agents, and every single tool helping to change this state of things is of immense value. In this study, a set of potential novel anti-depressant derivatives with confirmed pharmacological activity was subjected to a number of assays. A group of four drug properties, assessed in early stages of drug development were chosen for those assays. Lipophilicity, membrane permeability and drug-protein binding were estimated using biomimetic chromatography methods, while metabolic stability was estimated using in-vitro enzymatic assay followed by Liquid chromatography coupled with mass spectrometry (LC-MS) analysis. Afterwards, 3D models of studied compounds were geometrically optimized using Density Functional Theory (DFT) molecular mechanics protocol in order to resemble real-state energies and structure of those derivatives. After calculating molecular descriptors from optimized 3D models, a matrix combining data from assays and molecular descriptors was created. Using Genetic Algorithm Multiple Linear Regression (GA-MLR) on this matrix allowed to create four mathematical models, one for each of the studied properties, which accurately predicts properties crucial in early drug-development stages. Such models can severely help in reducing costs of drug-development, by allowing exclusion of extremely unfavorable derivatives – even before the stage of synthesis." @default.
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• W4205900559 date "2022-04-01" @default.
• W4205900559 modified "2023-10-12" @default.
• W4205900559 title "Use of biomimetic chromatography and in vitro assay to develop predictive GA-MLR model for use in drug-property prediction among anti-depressant drug candidates" @default.
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• W4205900559 doi "https://doi.org/10.1016/j.microc.2022.107183" @default.
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