ADME properties evaluation in drug discovery: in silico prediction of blood–brain partitioning

Abstract

The absorption, distribution, metabolism and excretion properties are important for drugs, and prediction of these properties in advance will save the cost of drug discovery substantially. The ability to penetrate the blood-brain barrier is critical for drugs targeting central nervous system, which is represented by the ratio of its concentration in brain and in blood. Herein, a quantitative structure-property relationship study was carried out to predict blood-brain partitioning coefficient (logBB) of a data set consisting of 287 compounds. Four different methods including support vector machine, multivariate linear regression, multivariate adaptive regression splines and random forest were employed to build prediction models with 116 molecular descriptors selected by Boruta algorithm. The RF model had best performance in training set ([Formula: see text] = 0.938), test set ([Formula: see text] = 0.840) and tenfold cross-validation ([Formula: see text] = 0.788). Finally, we found that the polar surface area and octanol-water partition coefficient have the greatest influence on blood-brain partitioning. Results suggest that the proposed model is a useful and practical tool to predict the logBB values of drug candidates.

Identification of novel inhibitor targeting Fyn kinase using molecular docking analysis

“…ADME properties evaluation of identified chief compound, A Plogpo scored highest range (Table 2). It was observed that identified compounds with a group of hydrogen bonding donor and acceptor pair as found This present study results were matching the findings of Zhu 10. The biological membrane access ranges were found to be range from 293.11 to 2288.96 and the range of QPPMDCK was 96.8 to 100 11.…”

Towards Deep Neural Network Models for the Prediction of the Blood–Brain Barrier Permeability for Diverse Organic Compounds

“…External estimates of probability that a compound will undergo active efflux mediated by P-glycoprotein (P-gp) can also be included [21,24,37]. In other approaches, large pools of various 1D, 2D (including molecular fingerprints), and 3D molecular descriptors calculated by different methods [26,[38][39][40][41][42][43][44][45][46][47][48][49][50][51][52] are analyzed by various statistical learning techniques, e.g., multiple linear regression, linear discriminant analysis, partial least squares regression, support vector machines, artificial neural networks, random forests, etc., often in combination with some descriptor selection protocols [23,24,26,[43][44][45][46][47][48][49][50][51][52]. However, the limited size of the training sets, use of unverified data, and too-small modeling errors for such an inherently noisy endpoint often give rise to the concerns of possible model overfitting [16].…”

Análise in silico do perfil farmacocinético e toxicológico de fármacos em pesquisa para o tratamento da COVID-19

“…O BBB é classificado como a mais significativa barreira para limitar e restringir a passagem de substâncias da corrente sanguínea para o cérebro e isto se dá devido a sua alta impenetrabilidade e seletividade. No que se refere à classificação da passagem de moléculas pela barreira hematoencefálica (BHE), tem-se os seguintes valores: >2,0,entre 0,1 e 2,0 e <0,1, sendo estes respectivamente denominados como: atravessa livremente, atravessa de forma moderada e atravessa de forma reduzida ou não atravessa (Sharma, Lakkadwala, Modgil, & Singh, 2016;Dolabela et al, 2018 (Felice, et al, 2020;Zhu, et al, 2018).…”