In Silico and in Vitro Assessment of OATP1B1 Inhibition in Drug Discovery.

The organic anion-transporting polypeptide 1B1 transporter belongs to the solute carrier superfamily and is highly expressed at the basolateral membrane of hepatocytes. Several clinical studies show drug-drug interactions involving OATP1B1, thereby prompting the International Transporter Consortium to label OATP1B1 as a critical transporter that can influence a compound's disposition. To examine OATP1B1 inhibition early in the drug discovery process, we established a medium-throughput concentration-dependent OATP1B1 assay. To create an in silico OATP1B1 inhibition model, deliberate in vitro assay enrichment was performed with publically known OATP1B1 inhibitors, noninhibitors, and compounds from our own internal chemistry. To date, approximately 1200 compounds have been tested in the assay with 60:40 distribution between noninhibitors and inhibitors. Bagging, random forest, and support vector machine fingerprint (SVM-FP) quantitative structure-activity relationship classification models were created, and each method showed positive and negative predictive values >90%, sensitivity >80%, specificity >95%, and Matthews correlation coefficient >0.8 on a prospective test set indicating the ability to distinguish inhibitors from noninhibitors. A SVMF-FP regression model was also created that showed an R2 of 0.39, Spearman's rho equal to 0.76, and was capable of predicting 69% of the prospective test set within the experimental variability of the assay (3-fold). In addition to the in silico quantitative structure-activity relationship (QSAR) models, physicochemical trends were examined to provide structure activity relationship guidance to early discovery teams. A JMP partition tree analysis showed that among the compounds with calculated logP >3.5 and ≥1 negatively charged atom, 94% were identified as OATP1B1 inhibitors. The combination of the physicochemical trends along with an in silico QSAR model provides discovery project teams a valuable tool to identify and address drug-drug interaction liability due to OATP1B1 inhibition.

Predicting flexible loop regions that interact with ligands: the challenge of accurate scoring.

Flexible loop regions play a critical role in the biological function of many proteins and have been shown to be involved in ligand binding. In the context of structure-based drug design, using or predicting an incorrect loop configuration can be detrimental to the study if the loop is capable of interacting with the ligand. Three protein systems, each with at least one flexible loop region in close proximity to the known binding site, were selected for loop prediction using the CorLps program; a six residue loop region from phosphoribosylglycinamide formyltransferase (GART), two nine residue loop regions from cytochrome P450 (CYP) 119, and an 11 residue loop region from enolase were selected for loop prediction. The results of this study indicate that the statistically based DFIRE scoring function implemented in the CorLps program did not accurately rank native-like predicted loop configurations in any protein system. In an attempt to improve the ranking of the native-like predicted loop configurations, the MM/GBSA and the optimized MM/GBSA-dsr scoring functions were used to re-rank the predicted loops with and without bound ligand. In general, single snapshot MM/GBSA scoring provided the best ranking of native-like loop configurations. Based on the scoring function analyses presented, the optimal ranking of native-like loop configurations is still a difficult challenge and the choice of the "best" scoring function appears to be system dependent.

Computer-aided drug design platform using PyMOL.

The understanding and optimization of protein-ligand interactions are instrumental to medicinal chemists investigating potential drug candidates. Over the past couple of decades, many powerful standalone tools for computer-aided drug discovery have been developed in academia providing insight into protein-ligand interactions. As programs are developed by various research groups, a consistent user-friendly graphical working environment combining computational techniques such as docking, scoring, molecular dynamics simulations, and free energy calculations is needed. Utilizing PyMOL we have developed such a graphical user interface incorporating individual academic packages designed for protein preparation (AMBER package and Reduce), molecular mechanics applications (AMBER package), and docking and scoring (AutoDock Vina and SLIDE). In addition to amassing several computational tools under one interface, the computational platform also provides a user-friendly combination of different programs. For example, utilizing a molecular dynamics (MD) simulation performed with AMBER as input for ensemble docking with AutoDock Vina. The overarching goal of this work was to provide a computational platform that facilitates medicinal chemists, many who are not experts in computational methodologies, to utilize several common computational techniques germane to drug discovery. Furthermore, our software is open source and is aimed to initiate collaborative efforts among computational researchers to combine other open source computational methods under a single, easily understandable graphical user interface.

Long-chain carboxychromanols, metabolites of vitamin E, are potent inhibitors of cyclooxygenases.

Cyclooxygenase (COX-1/COX-2)-catalyzed eicosanoid formation plays a key role in inflammation-associated diseases. Natural forms of vitamin E are recently shown to be metabolized to long-chain carboxychromanols and their sulfated counterparts. Here we find that vitamin E forms differentially inhibit COX-2-catalyzed prostaglandin E(2) in IL-1beta-stimulated A549 cells without affecting COX-2 expression, showing the relative potency of gamma-tocotrienol approximately delta-tocopherol > gamma-tocopherol >> alpha- or beta-tocopherol. The cellular inhibition is partially diminished by sesamin, which blocks the metabolism of vitamin E, suggesting that their metabolites may be inhibitory. Consistently, conditioned media enriched with long-chain carboxychromanols, but not their sulfated counterparts or vitamin E, reduce COX-2 activity in COX-preinduced cells with 5 microM arachidonic acid as substrate. Under this condition, 9'- or 13'-carboxychromanol, the vitamin E metabolites that contain a chromanol linked with a 9- or 13-carbon-length carboxylated side chain, inhibits COX-2 with an IC(50) of 6 or 4 microM, respectively. But 13'-carboxychromanol inhibits purified COX-1 and COX-2 much more potently than shorter side-chain analogs or vitamin E forms by competitively inhibiting their cyclooxygenase activity with K(i) of 3.9 and 10.7 microM, respectively, without affecting the peroxidase activity. Computer simulation consistently indicates that 13'-carboxychromanol binds more strongly than 9'-carboxychromanol to the substrate-binding site of COX-1. Therefore, long-chain carboxychromanols, including 13'-carboxychromanol, are novel cyclooxygenase inhibitors, may serve as anti-inflammation and anticancer agents, and may contribute to the beneficial effects of certain forms of vitamin E.

New computational method for prediction of interacting protein loop regions.

Flexible loop regions of proteins play a crucial role in many biological functions such as protein-ligand recognition, enzymatic catalysis, and protein-protein association. To date, most computational methods that predict the conformational states of loops only focus on individual loop regions. However, loop regions are often spatially in close proximity to one another and their mutual interactions stabilize their conformations. We have developed a new method, titled CorLps, capable of simultaneously predicting such interacting loop regions. First, an ensemble of individual loop conformations is generated for each loop region. The members of the individual ensembles are combined and are accepted or rejected based on a steric clash filter. After a subsequent side-chain optimization step, the resulting conformations of the interacting loops are ranked by the statistical scoring function DFIRE that originated from protein structure prediction. Our results show that predicting interacting loops with CorLps is superior to sequential prediction of the two interacting loop regions, and our method is comparable in accuracy to single loop predictions. Furthermore, improved predictive accuracy of the top-ranked solution is achieved for 12-residue length loop regions by diversifying the initial pool of individual loop conformations using a quality threshold clustering algorithm.

Potentially increasing the metabolic stability of drug candidates via computational site of metabolism prediction by CYP2C9: The utility of incorporating protein flexibility via an ensemble of structures.

Cytochrome P450 enzymes are responsible for metabolizing many endogenous and xenobiotic molecules encountered by the human body. It has been estimated that 75% of all drugs are metabolized by cytochrome P450 enzymes. Thus, predicting a compound's potential sites of metabolism (SOM) is highly advantageous early in the drug development process. We have combined molecular dynamics, AutoDock Vina docking, the neighboring atom type (NAT) reactivity model, and a solvent-accessible surface-area term to form a reactivity-accessibility model capable of predicting SOM for cytochrome P450 2C9 substrates. To investigate the importance of protein flexibility during the ligand-binding process, the results of SOM prediction using a static protein structure for docking were compared to SOM prediction using multiple protein structures in ensemble docking. The results reported here indicate that ensemble docking increases the number of ligands that can be docked in a bioactive conformation (ensemble: 96%, static: 85%) but only leads to a slight improvement (49% vs. 44%) in predicting an experimentally known SOM in the top-1 position for a ligand library of 75 CYP2C9 substrates. Using ensemble docking, the reactivity-accessibility model accurately predicts SOM in the top-1 ranked position for 49% of the ligand library and considering the top-3 predicted sites increases the prediction success rate to approximately 70% of the ligand library. Further classifying the substrate library according to K(m) values leads to an improvement in SOM prediction for substrates with low K(m) values (57% at top-1). While the current predictive power of the reactivity-accessibility model still leaves significant room for improvement, the results illustrate the usefulness of this method to identify key protein-ligand interactions and guide structural modifications of the ligand to increase its metabolic stability.