Predicting Activation of the Promiscuous Human Pregnane X Receptor by Pharmacophore Ensemble/Support Vector Machine Approach

Natural volatiles preventing mosquito biting: An integrated screening platform for accelerated discovery of ORco antagonists

Insect olfactory receptors are heteromeric ligand-gated cation channels composed of an obligatory receptor subunit, ORco, and one of many variable subunits, ORx, in as yet undefined molar ratios. When expressed alone ex vivo, ORco forms homotetrameric channels gated by ORco-specific ligands acting as channel agonists. Using an insect cell-based system as a functional platform for expressing mosquito odorant receptors ex vivo, we identified small molecules of natural origin acting as specific ORco channel antagonists, orthosteric or allosteric relative to a postulated ORco agonist binding site, which cause severe inhibition of olfactory function in mosquitoes. In the present communication, we have compiled common structural features of such orthosteric antagonists and developed a ligand-based pharmacophore whose properties are deemed necessary for binding to the agonist binding site and causing inhibition of ORco's biological function. In silico screening of an available collection of natural volatile compounds with the pharmacophore resulted in identification of several ORco antagonist hits. Cell-based functional screening of the same compound collection resulted in the identification of several compounds acting as orthosteric and allosteric antagonists of ORco channel function ex vivo and inducing anosmic behaviors to Aedes albopictus mosquitoes in vivo. Comparison of the in silico screening results with those of the functional assays revealed that the pharmacophore predicted correctly seven out of the eight confirmed orthosteric antagonists and none of the allosteric ones. Because the pharmacophore screen produced additional hits that did not cause inhibition of the ORco channel function, we also generated a support vector machine (SVM) model based on two descriptors of all pharmacophore hits. Training of the SVM on the ex vivo validated compound collection resulted in the selection of the confirmed orthosteric antagonists with a very low cross-validation out-of-sample misclassification rate. Employment of the combined pharmacophore-SVM platform for in silico screening of a larger collection of olfaction-relevant volatiles produced several new hits. Functional validation of randomly selected hits and rejected compounds from this screen confirmed the power of this virtual screening platform as a convenient tool for accelerating the pace of discovery of novel vector control agents. To the best of our knowledge, this study is the first one that combines a pharmacophore with a SVM model for identification of AgamORco antagonists and specifically orthosteric ones.

Development and Experimental Validation of Regularized Machine Learning Models Detecting New, Structurally Distinct Activators of PXR

The pregnane X receptor (PXR) regulates the metabolism of many xenobiotic and endobiotic substances. In consequence, PXR decreases the efficacy of many small-molecule drugs and induces drug-drug interactions. The prediction of PXR activators with theoretical approaches such as machine learning (ML) proves challenging due to the ligand promiscuity of PXR, which is related to its large and flexible binding pocket. In this work we demonstrate, by the example of random forest models and support vector machines, that classifiers generated following classical training procedures often fail to predict PXR activity for compounds that are dissimilar from those in the training set. We present a novel regularization technique that penalizes the gap between a model’s training and validation performance. On a challenging test set, this technique led to improvements in Matthew correlation coefficients (MCCs) by up to 0.21. Using these regularized ML models, we selected 31 compounds that are structurally distinct from known PXR ligands for experimental validation. Twelve of them were confirmed as active in the cellular PXR ligand-binding domain assembly assay and more hits were identified during follow-up studies. Comprehensive analysis of key features of PXR biology conducted for three representative hits confirmed their ability to activate the PXR.

Computational prediction of cytochrome P450 inhibition and induction

Cytochrome P450 (CYP) enzymes play an important role in the phase I metabolism of many xenobiotics. Most drug-drug interactions (DDIs) associated with CYP are caused by either CYP inhibition or induction. The early detection of potential DDIs is highly desirable in the pharmaceutical industry because DDIs can cause serious adverse events, which can lead to poor patient health and drug development failures. Recently, many computational studies predicting CYP inhibition and induction have been reported. The current computational modeling approaches for CYP metabolism are classified as ligand- and structure-based; various techniques, such as quantitative structure-activity relationships, machine learning, docking, and molecular dynamic simulation, are involved in both the approaches. Recently, combining these two approaches have resulted in improvements in the prediction accuracy of DDIs. In this review, we present important, recent developments in the computational prediction of the inhibition of four clinically crucial CYP isoforms (CYP1A2, 2C9, 2D6, and 3A4) and three nuclear receptors (aryl hydrocarbon receptor, constitutive androstane receptor, and pregnane X receptor) involved in the induction of CYP1A2, 2B6, and 3A4, respectively.

Insight Analysis of Promiscuous Estrogen Receptor α-Ligand Binding by a Novel Machine Learning Scheme

Estrogen receptor α (ERα) plays a significant role in occurrence of breast cancer and may cause various adverse side-effects when ERα is an off-target protein. A theoretical model was derived to predict the binding affinity of ERα using the pharmacophore ensemble/support vector machine (PhE/SVM) scheme to consider the promiscuous characteristic of ERα. The estimations by PhE/SVM were discovered to be in good agreement with the observed values for those training molecules ( n = 31, r² = 0.80, q_CV² = 0.77, RMSE = 0.57, s = 0.58), test molecules ( n = 179, q² = 0.91-0.96, RMSE = 0.33, s = 0.26) and outliers ( n = 15, q² = 0.80-0.86, RMSE = 0.56, s = 0.49). When subjected to various statistical validations, the PhE/SVM model consistently fulfilled the strictest criteria. A mock test also asserted its predictivity. When compared with crystal structures, the calculated results are consistent with the reported ERα-ligand co-complex structure, and the plasticity nature of ERα is also disclosed. Consequently, this precise, fast, and robust model can be adopted to predict ERα-ligand binding affinities and to design safer non-ERα-targeted pharmaceuticals in the process of drug discovery and development.

Discovery of Beclabuvir: A Potent Allosteric Inhibitor of the Hepatitis C Virus Polymerase

The discovery of beclabuvir occurred through an iterative series of structure-activity relationship studies directed at the optimization of a novel class of indolobenzazepines. Within this research, a strategic decision to abandon a highly potent but physiochemically problematic series in favor of one of lower molecular weight and potency was key in the realization of the program’s objectives. Subsequent cycles of analog design incorporating progressive conformational constraints successfully addressed off-target liabilities and identified compounds with improved physiochemical profiles. Ultimately, a class of alkyl-bridged piperazine carboxamides was found to be of particular interest, and from this series, beclabuvir was identified as having superior antiviral, safety, and pharmacokinetic properties. The clinical evaluation of beclabuvir in combination with both the NS5A replication complex inhibitor daclatasvir and the NS3 protease inhibitor asunaprevir in a single, fixed-dose formulation (Ximency) resulted in the approval by the Japanese Pharmaceutical and Food Safety Bureau for its use in the treatment of patients infected with genotype 1 HCV.

In silico methods for predicting drug-drug interactions with cytochrome P-450s, transporters and beyond

Drug-drug interactions (DDIs) are associated with severe adverse effects that may lead to the patient requiring alternative therapeutics and could ultimately lead to drug withdrawal from the market if they are severe. To prevent the occurrence of DDI in the clinic, experimental systems to evaluate drug interaction have been integrated into the various stages of the drug discovery and development process. A large body of knowledge about DDI has also accumulated through these studies and pharmacovigillence systems. Much of this work to date has focused on the drug metabolizing enzymes such as cytochrome P-450s as well as drug transporters, ion channels and occasionally other proteins. This combined knowledge provides a foundation for a hypothesis-driven in silico approach, using either cheminformatics or physiologically based pharmacokinetics (PK) modeling methods to assess DDI potential. Here we review recent advances in these approaches with emphasis on hypothesis-driven mechanistic models for important protein targets involved in PK-based DDI. Recent efforts with other informatics approaches to detect DDI are highlighted. Besides DDI, we also briefly introduce drug interactions with other substances, such as Traditional Chinese Medicines to illustrate how in silico modeling can be useful in this domain. We also summarize valuable data sources and web-based tools that are available for DDI prediction. We finally explore the challenges we see faced by in silico approaches for predicting DDI and propose future directions to make these computational models more reliable, accurate, and publically accessible.

In silico prediction of inhibition of promiscuous breast cancer resistance protein (BCRP/ABCG2)

BACKGROUND

Breast cancer resistant protein has an essential role in active transport of endogenous substances and xenobiotics across extracellular and intracellular membranes along with P-glycoprotein. It also plays a major role in multiple drug resistance and permeation of blood-brain barrier. Therefore, it is of great importance to derive theoretical models to predict the inhibition of both transporters in the process of drug discovery and development. Hitherto, very limited BCRP inhibition predictive models have been proposed as compared with its P-gp counterpart.

METHODOLOGY/PRINCIPAL FINDINGS

An in silico BCRP inhibition model was developed in this study using the pharmacophore ensemble/support vector machine scheme to take into account the promiscuous nature of BCRP. The predictions by the PhE/SVM model were found to be in good agreement with the observed values for those molecules in the training set (n= 22, r2 =0.82, qCV2=0.73, RMSE= 0.40, s = 0.24), test set (n =97, q2=0.75-0.89, RMSE= 0.31, s= 0.21), and outlier set (n= 16, q2 =0.72-0.91, RMSE= 0.29, s=0.17). When subjected to a variety of statistical validations, the developed PhE/SVM model consistently met the most stringent criteria. A mock test by HIV protease inhibitors also asserted its predictivity.

CONCLUSIONS/SIGNIFICANCE

It was found that this accurate, fast, and robust PhE/SVM model can be employed to predict the BCRP inhibition of structurally diverse molecules that otherwise cannot be carried out by any other methods in a high-throughput fashion to design therapeutic agents with insignificant drug toxicity and unfavorable drug-drug interactions mediated by BCRP to enhance clinical efficacy and/or circumvent drug resistance.

Discovery and preclinical characterization of the cyclopropylindolobenzazepine BMS-791325, a potent allosteric inhibitor of the hepatitis C virus NS5B polymerase

Described herein are structure-activity relationship studies that resulted in the optimization of the activity of members of a class of cyclopropyl-fused indolobenzazepine HCV NS5B polymerase inhibitors. Subsequent iterations of analogue design and syntheses successfully addressed off-target activities, most notably human pregnane X receptor (hPXR) transactivation, and led to significant improvements in the physicochemical properties of lead compounds. Those analogues exhibiting improved solubility and membrane permeability were shown to have notably enhanced pharmacokinetic profiles. Additionally, a series of alkyl bridged piperazine carboxamides was identified as being of particular interest, and from which the compound BMS-791325 (2) was found to have distinguishing antiviral, safety, and pharmacokinetic properties that resulted in its selection for clinical evaluation.

Noncanonical mechanisms to regulate nuclear receptor signaling

Nuclear receptor (NR)-targeted therapies comprise a large class of clinically employed drugs. A number of drugs currently being used against this protein class were designed as structural analogs of the endogenous ligand of these receptors. In recent years, there has been significant interest in developing newer strategies to target NRs, especially those that rely on mechanistic pathways of NR function. Prominent among these are noncanonical means of targeting NRs, which include selective NR modulation, NR coactivator interaction inhibition, inhibition of NR DNA binding, modulation of NR cellular localization, modulation of NR ligand biosynthesis and downregulation of NR levels in target tissues. This article reviews each of these promising emerging strategies for NR drug development and highlights some of most significant successes achieved in using them.

Prediction of promiscuous p-glycoprotein inhibition using a novel machine learning scheme

BACKGROUND

P-glycoprotein (P-gp) is an ATP-dependent membrane transporter that plays a pivotal role in eliminating xenobiotics by active extrusion of xenobiotics from the cell. Multidrug resistance (MDR) is highly associated with the over-expression of P-gp by cells, resulting in increased efflux of chemotherapeutical agents and reduction of intracellular drug accumulation. It is of clinical importance to develop a P-gp inhibition predictive model in the process of drug discovery and development.

METHODOLOGY/PRINCIPAL FINDINGS

An in silico model was derived to predict the inhibition of P-gp using the newly invented pharmacophore ensemble/support vector machine (PhE/SVM) scheme based on the data compiled from the literature. The predictions by the PhE/SVM model were found to be in good agreement with the observed values for those structurally diverse molecules in the training set (n = 31, r(2) = 0.89, q(2) = 0.86, RMSE = 0.40, s = 0.28), the test set (n = 88, r(2) = 0.87, RMSE = 0.39, s = 0.25) and the outlier set (n = 11, r(2) = 0.96, RMSE = 0.10, s = 0.05). The generated PhE/SVM model also showed high accuracy when subjected to those validation criteria generally adopted to gauge the predictivity of a theoretical model.

CONCLUSIONS/SIGNIFICANCE

This accurate, fast and robust PhE/SVM model that can take into account the promiscuous nature of P-gp can be applied to predict the P-gp inhibition of structurally diverse compounds that otherwise cannot be done by any other methods in a high-throughput fashion to facilitate drug discovery and development by designing drug candidates with better metabolism profile.