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CELSA - Active Learning - Active learning and interpretation of computational model on tissue specific permeability for stock and virtual drug discovery library

From 01-10-2021 to 30-09-2023

Description

One of the critical points in the discovery phase of drug research is the selection of a relatively narrow collection of samples from a large number of available and virtual compounds that provide sufficient chemical information to identify effective chemical structures at the given biological target after highthroughput biological screening (HTS). In this process, the increasing costs with numbers of measurement and the inherent need of medicinal chemists for structural diversity seem to contradict each other. In addition, the hit compounds provided by HTS must also consider drug-likeness and compliance with ADME processes. The aim of our research is to find a solution to these two problems in the form of cheminformatics process development and in vitro experimental results.

To characterize the molecular bank compounds, a general physicochemical model was chosen that provides information on the tissue-specific distribution, and expected toxicity of drug candidates. Our studies are based on the robust, in vitro HTS permeability model, the PAMPA (Parallel Artificial Membrane Permeability Assay) system. Commercially available natural lipid extracts, such as bloodbrain barrier (BBB), liver, heart, and gastrointestinal (GI) specific lipids used in the PAMPA system provide determination of drug permeability (Pe) and membrane retention (MR) at different biological barriers, but in addition through the different medium pH set on both sides of PAMPA system, it also provides the study of the proton-dissociation properties of the HTS hits. Thus, in addition to specific absorption (Pe) and accumulation (MR) properties, we can also provide a solution for the characterization of toxicity phenomena such as phospholipidosis resulting from cytosolic-lysosomal (pH7.4 - pH 4.0) drug transport.