Drugs acting on central nervous system (CNS) may take longer duration to reach the market as these compounds have a higher attrition rate in clinical trials due to the complexity of the brain, side effects, and poor blood-brain barrier (BBB) permeability compared to non-CNS-acting compounds. The roles of active efflux transporters with BBB are still unclear. The aim of the present work was to develop a predictive model for BBB permeability that includes the MRP-1 transporter, which is considered as an active efflux transporter. A support vector machine model was developed for the classification of MRP-1 substrates and non-substrates, which was validated with an external data set and Y-randomization method. An artificial neural network model has been developed to evaluate the role of MRP-1 on BBB permeation. A total of nine descriptors were selected, which included molecular weight, topological polar surface area, ClogP, number of hydrogen bond donors, number of hydrogen bond acceptors, number of rotatable bonds, P-gp, BCRP, and MRP-1 substrate probabilities for model development. We identified 5 molecules that fulfilled all criteria required for passive permeation of BBB, but they all have a low logBB value, which suggested that the molecules were effluxed by the MRP-1 transporter.

Nowadays most of the CNS acting therapeutic molecules are failing in clinical trials due to efflux transporters at the blood brain barrier (BBB) which imparts resistance and poor ADMET properties of these molecules. CNS acting drug molecules interact with the BBB prior to their target site, so there is a need to develop predictive models for BBB permeability which can be used in the initial phases of drug discovery process. Most of the drug molecules are transported to the brain via passive diffusion which is explored extensively; on the other hand, the role of active efflux transporters in BBB permeability is unclear. Our aim is to develop predictive models for BBB permeability that include active efflux transporters. An in silico model has been developed to assess the role of BCRP on BBB permeation. Eight descriptors were selected, which also include BCRP substrate probabilities used for model development and show a relationship between BCRP and logBB. From our analysis, it was found that 11 molecules satisfied all criteria required for BBB permeation but have low logBB values. These 11 molecules are predicted as BCRP substrates from the model developed, suggesting that the molecules are effluxed by the BCRP transporter. This predictive ability was further validated by docking of these 11 molecules into BCRP protein. This study provides a new mechanistic insight into correlation of low logBB values and efflux mechanism of BCRP in BBB.

The breast cancer resistant protein (BCRP) is an important transporter and its inhibitors play an important role in cancer treatment by improving the oral bioavailability as well as blood brain barrier (BBB) permeability of anticancer drugs. In this work, a computational model was developed to predict the compounds as BCRP inhibitors or non-inhibitors. Various machine learning approaches like, support vector machine (SVM), k-nearest neighbor (k-NN) and artificial neural network (ANN) were used to develop the models. The Matthews correlation coefficients (MCC) of developed models using ANN, k-NN and SVM are 0.67, 0.71 and 0.77, and prediction accuracies are 85.2%, 88.3% and 90.8% respectively. The developed models were tested with a test set of 99 compounds and further validated with external set of 98 compounds. Distribution plot analysis and various machine learning models were also developed based on druglikeness descriptors. Applicability domain is used to check the prediction reliability of the new molecules.

Therapeutic agents exert their pharmacological and adverse effects by interacting with molecular targets. Even if drug molecules are intended to interact with specific targets in a desirable manner, they are often found to bind to other targets. Nowadays, research is focused on a single molecule that simultaneously targets multiple disease-causing proteins. Therefore, off-target identification of existing chemical space can be a valuable tool to find safe and effective multi-targeted therapeutic agents at a significantly lower cost to patients. Phloroglucinols represent a class of compounds, which exhibits a diverse range of biological activities, such as anti-HIV, antimalarial, antileishmanial, antituberculosis, antibacterial, and antifungal. The aim of the current study is to explore untapped potential of various series of phloroglucinols against HIV reverse transcriptase (HIV-RTase). A series of filtering parameters was applied in search of viable phloroglucinol derivatives against HIV-RTase. A library of phloroglucinol derivatives was screened based on their toxicity potential followed by predicted ADME parameters. The filtered compounds were then carried forward for docking analysis against HIV-RTase. A set of 37 phloroglucinol compounds with diverse pharmacological profile was found to have good binding affinity towards HIV-RTase. These molecules formed hydrogen bonds with Lys101, Lys103, Val106, and Leu234 residues and π–π stacking interaction with Tyr318 residue of the protein. Here, we propose potential phloroglucinol derivatives with different known biological activity that can be repurposed as potential hits against HIV.

The Human Pregnane X Receptor (hPXR) is a regulator of drug metabolising enzymes (DME) and efflux transporters (ET). The prediction of hPXR activators and non-activators has pharmaceutical importance to predict the multiple drug resistance (MDR) and drug-drug interactions (DDI). In this study, we developed and validated the computational prediction models to classify hPXR activators and non-activators. We employed four machine learning methods support vector machine (SVM), k-nearest neighbour (k-NN), random forest (RF) and naïve bayesian (NB). These methods were used to develop molecular and fingerprint based descriptors for the prediction of hPXR activators and non-activators. Total 529 molecules consitsting of 317 activators and 212 non-activators were used for model development. The overall prediction accuracy of models was 69% to 99% to classify hPXR activators and nonactivators using RDkit descriptors. In case of 5 and 10-fold cross validation the prediction accuracy for training set is 74% to 82% and 79% to 83% for hPXR activators respectively and 50% to 62% and 49% to 65% non-activators, respectively. The external test prediction is between 59% to 73% for hPXR activators and 55% to 68% for hPXR non-activators. In addition, consensus models were developed in which the best model shows overall 75% to 83% accuracy for fingerprint and RDkit descriptors, respectively. The best developed model will be utilized for the prediction of hPXR activators and non-activators.