QSAR studies on P-glycoprotein-regulated multidrug resistance and on its reversal by phenothiazines

Recent Advances on P-Glycoprotein (ABCB1) Transporter Modelling with In Silico Methods

ABC transporters play a critical role in both drug bioavailability and toxicity, and with the discovery of the P-glycoprotein (P-gp), this became even more evident, as it plays an important role in preventing intracellular accumulation of toxic compounds. Over the past 30 years, intensive studies have been conducted to find new therapeutic molecules to reverse the phenomenon of multidrug resistance (MDR) ), that research has found is often associated with overexpression of P-gp, the most extensively studied drug efflux transporter; in MDR, therapeutic drugs are prevented from reaching their targets due to active efflux from the cell. The development of P-gp inhibitors is recognized as a good way to reverse this type of MDR, which has been the subject of extensive studies over the past few decades. Despite the progress made, no effective P-gp inhibitors to reverse multidrug resistance are yet on the market, mainly because of their toxic effects. Computational studies can accelerate this process, and in silico models such as QSAR models that predict the activity of compounds associated with P-gp (or analogous transporters) are of great value in the early stages of drug development, along with molecular modelling methods, which provide a way to explain how these molecules interact with the ABC transporter. This review highlights recent advances in computational P-gp research, spanning the last five years to 2022. Particular attention is given to the use of machine-learning approaches, drug-transporter interactions, and recent discoveries of potential P-gp inhibitors that could act as modulators of multidrug resistance.

Multiclass Classifier for P-Glycoprotein Substrates, Inhibitors, and Non-Active Compounds

P-glycoprotein (P-gp) is a transmembrane protein that actively transports a wide variety of chemically diverse compounds out of the cell. It is highly associated with the ADMET (absorption, distribution, metabolism, excretion and toxicity) properties of drugs/drug candidates and contributes to decreasing toxicity by eliminating compounds from cells, thereby preventing intracellular accumulation. Therefore, in the drug discovery and toxicological assessment process it is advisable to pay attention to whether a compound under development could be transported by P-gp or not. In this study, an in silico multiclass classification model capable of predicting the probability of a compound to interact with P-gp was developed using a counter-propagation artificial neural network (CP ANN) based on a set of 2D molecular descriptors, as well as an extensive dataset of 2512 compounds (1178 P-gp inhibitors, 477 P-gp substrates and 857 P-gp non-active compounds). The model provided a good classification performance, producing non error rate (NER) values of 0.93 for the training set and 0.85 for the test set, while the average precision (AvPr) was 0.93 for the training set and 0.87 for the test set. An external validation set of 385 compounds was used to challenge the model’s performance. On the external validation set the NER and AvPr values were 0.70 for both indices. We believe that this in silico classifier could be effectively used as a reliable virtual screening tool for identifying potential P-gp ligands.

Computational predictive models for P-glycoprotein inhibition of in-house chalcone derivatives and drug-bank compounds

The human P-glycoprotein (P-gp) efflux pump is of great interest for medicinal chemists because of its important role in multidrug resistance (MDR). Because of the high polyspecificity as well as the unavailability of high-resolution X-ray crystal structures of this transmembrane protein, ligand-based, and structure-based approaches which were machine learning, homology modeling, and molecular docking were combined for this study. In ligand-based approach, individual two-dimensional quantitative structure-activity relationship models were developed using different machine learning algorithms and subsequently combined into the Ensemble model which showed good performance on both the diverse training set and the validation sets. The applicability domain and the prediction quality of the developed models were also judged using the state-of-the-art methods and tools. In our structure-based approach, the P-gp structure and its binding region were predicted for a docking study to determine possible interactions between the ligands and the receptor. Based on these in silico tools, hit compounds for reversing MDR were discovered from the in-house and DrugBank databases through virtual screening using prediction models and molecular docking in an attempt to restore cancer cell sensitivity to cytotoxic drugs.

Computational prediction of CNS drug exposure based on a novel in vivo dataset

PURPOSE

To develop a computational model for predicting CNS drug exposure using a novel in vivo dataset.

METHODS

The brain-to-plasma (B:P) ratio of 43 diverse compounds was assessed following intravenous administration to Swiss Outbred mice. B:P ratios were subjected to PLS modeling using calculated molecular descriptors. The obtained results were transferred to a qualitative setting in which compounds predicted to have a B:P ratio > 0.3 were sorted as high CNS exposure compounds and those below this value were sorted as low CNS exposure compounds. The model was challenged with an external test set consisting of 251 compounds for which semi-quantitative values of CNS exposure were available in the literature.

RESULTS

The dataset ranged more than 1700-fold in B:P ratio, with 16 and 27 compounds being sorted as low and high CNS exposure drugs, respectively. The model was a one principal component model based on five descriptors reflecting molecular shape, electronegativity, polarisability and charge transfer, and allowed 74% of the compounds in the training set and 76% of the test set to be predicted correctly.

CONCLUSION

A qualitative computational model has been developed which accurately classifies compounds as being high or low CNS exposure drugs based on rapidly calculated molecular descriptors.

How can we best use structural information on P-glycoprotein to design inhibitors?

This year marks the 30th anniversary of the discovery of the multidrug resistance (MDR) ATP-binding cassette (ABC) transporter P-glycoprotein (P-gp). Since then a considerable research effort has attempted to provide a greater understanding of the biological enigma of "multidrug" efflux. Moreover, the growing correlation between P-gp expression and a negative prognosis or poor outcome for chemotherapy has sparked significant interest in the generation of inhibitors. How close are we to overcoming the unwanted actions of P-gp in resistant cancer following 30 years of research? The initial inhibitors were pre-existing clinically used compounds and exploited the broad specificity of P-gp. Unfortunately, the concentrations required to inhibit P-gp meant that these compounds generated considerable toxicity. Pharmacological investigations progressed to rational design using the 1st generation compounds as a template structure. Inherent toxicity of the drugs was reduced; however, pharmacokinetic interactions with the anticancer drugs were unsustainable. Generation of the most recent of inhibitors employed combinatorial chemistry to produce a handful of potent and selective P-gp inhibitors. Some of these drugs have progressed to clinical trials with poor results or in some cases, undisclosed progress. There remains a clear need for the generation of P-gp inhibitors and this review describes the potential for a structure-based design to facilitate this undertaking. In particular, the plethora of functional data can provide important regions on the protein that could conceivably be exploited as inhibitor targets.

Recent progress in understanding the mechanism of P-glycoprotein-mediated drug efflux

P-glycoprotein (P-gp) is an ATP-dependent drug pump that can transport a broad range of hydrophobic compounds out of the cell. The protein is clinically important because of its contribution to the phenomenon of multidrug resistance during AIDS/HIV and cancer chemotherapy. P-gp is a member of the ATP-binding cassette (ABC) family of proteins. It is a single polypeptide that contains two repeats joined by a linker region. Each repeat has a transmembrane domain consisting of six transmembrane segments followed by a hydrophilic domain containing the nucleotide-binding domain. In this mini-review, we discuss recent progress in determining the structure and mechanism of human P-glycoprotein.

Which in vitro Screens Guide the Prediction of Oral Absorption and Volume of Distribution?

The development of medium to high-throughput in vitro screening of ADME (Absorption, Distribution, Metabolism, Excretion) properties has been the reply to higher demands on drug metabolism scientists to cope with progress in chemistry and biology. Two areas will be discussed here, namely screens for oral absorption and for volume of distribution. The prediction of these human pharmacokinetic parameters can be based on proper combination of simple physicochemical measurements. In the future in vitro screens most likely will be combined with in silico assessments of various ADME properties leading to the concept of in combo screening in drug discovery.