Recent Publications:

Nature Biotechnology	Comprehensive characterization of cytochrome P450 isozyme selectivity across chemical libraries [ PDF ] Veith H, Southall N, Huang R, James T, Fayne D, Artemenko N, Shen M, Inglese J, Austin CP, Lloyd DG, Auld DS The cytochrome P450 (CYP) gene family catalyzes drug metabolism and bioactivation and is therefore relevant to drug development. We determined potency values for 17,143 compounds against five recombinant CYP isozymes (1A2, 2C9, 2C19, 2D6 and 3A4) using an in vitro bioluminescent assay. The compounds included libraries of US Food and Drug Administration (FDA)-approved drugs and screening libraries. We observed cross-library isozyme inhibition (30–78%) with important differences between libraries. Whereas only 7% of the typical screening library was inactive against all five isozymes, 33% of FDA-approved drugs were inactive, reflecting the optimized pharmacological properties of the latter. Our results suggest that low CYP 2C isozyme activity is a common property of drugs, whereas other isozymes, such as CYP 2D6, show little discrimination between drugs and unoptimized compounds found in screening libraries. We also identified chemical substructures that differentiated between the five isozymes. The pharmacological compendium described here should further the understanding of CYP isozymes.
Journal of Medicinal Chemistry	Structure Mechanism Insights and the Role of Nitric Oxide Donation Guide the Development of Oxadiazole-2-Oxides as Therapeutic Agents against Schistosomiasis [ PDF ] Rai G, Sayed AA, Lea WA, Luecke HF, Chakrapani H, Prast-Nielsen S, Jadhav A, Leister W, Shen M, Inglese J, Austin CP, Keefer L, Arner ES, Simeonov A, Maloney DJ, Williams DL, Thomas CJ Schistosomiasis is a chronic parasitic disease affecting hundreds of millions of individuals worldwide. Current treatment depends on a single agent, praziquantel, raising concerns of emergence of resistant parasites. Here, we continue our explorations of an oxadiazole-2-oxide class of compounds we recently identified as inhibitors of thioredoxin glutathione reductase (TGR), a selenocysteine-containing flavoenzyme required by the parasite to maintain proper cellular redox balance. Through systematic evaluation of the core molecular structure of this chemotype, we define the essential pharmacophore, establish a link between the nitric oxide donation and TGR inhibition, determine the selectivity for this chemotype versus related reductase enzymes, and present evidence that these agents can be modified to possess appropriate drug metabolism and pharmacokinetic properties. The mechanistic link between exogenous NO donation and parasite injury is expanded and better defined. The results of these studies verify the utility of oxadiazole-2-oxides as novel inhibitors of TGR and as efficacious antischistosomal agents.
PLoS ONE	Identification and Characterization of Inhibitors of Human Apurinic/apyrimidinic Endonuclease APE1 Simeonov AS, Kulkarni A, Dorjsuren D, Jadhav A, Shen M, McNeill DR, Austin CP, Wilson DM APE1 is the major nuclease for excising abasic (AP) sites and particular 3'-obstructive termini from DNA, and is an integral participant in the base excision repair (BER) pathway. BER capacity plays a prominent role in dictating responsiveness to agents that generate oxidative or alkylation DNA damage, as well as certain chain-terminating nucleoside analogs and 5-fluorouracil. We describe within the development of a robust, 1536-well automated screening assay that employs a deoxyoligonucleotide substrate operating in the red-shifted fluorescence spectral region to identify APE1 endonuclease inhibitors. This AP site incision assay was used in a titration-based high-throughput screen of the Library of Pharmacologically Active Compounds (LOPAC1280), a collection of well-characterized, drug-like molecules representing all major target classes. Prioritized hits were authenticated and characterized via two high-throughput screening assays – a Thiazole Orange fluorophore-DNA displacement test and an E. coli endonuclease IV counterscreen – and a conventional, gel-based radiotracer incision assay. The top, validated compounds, i.e. 6-hydroxy-DL-DOPA, Reactive Blue 2 and myricetin, were shown to inhibit AP site cleavage activity of whole cell protein extracts from HEK 293T and HeLa cell lines, and to enhance the cytotoxic and genotoxic potency of the alkylating agent methylmethane sulfonate. The studies herein report on the identification of novel, small molecule APE1-targeted bioactive inhibitor probes, which represent initial chemotypes towards the development of potential pharmaceuticals.
Journal of Medicinal Chemistry	Application of predictive QSAR models to database mining: identification and experimental validation of novel anticonvulsant compounds. Shen M, Béguin C, Golbraikh A, Stables JP, Kohn H, Tropsha A. We have developed a drug discovery strategy that employs variable selection quantitative structure-activity relationship (QSAR) models for chemical database mining. The approach starts with the development of rigorously validated QSAR models obtained with the variable selection k nearest neighbor (kNN) method (or, in principle, with any other robust model-building technique). Model validation is based on several statistical criteria, including the randomization of the target property (Y-randomization), independent assessment of the training set model's predictive power using external test sets, and the establishment of the model's applicability domain. All successful models are employed in database mining concurrently; in each case, only variables selected as a result of model building (termed descriptor pharmacophore) are used in chemical similarity searches comparing active compounds of the training set (queries) with those in chemical databases. Specific biological activity (characteristic of the training set compounds) of external database entries found to be within a predefined similarity threshold of the training set molecules is predicted on the basis of the validated QSAR models using the applicability domain criteria. Compounds judged to have high predicted activities by all or the majority of all models are considered as consensus hits. We report on the application of this computational strategy for the first time for the discovery of anticonvulsant agents in the Maybridge and National Cancer Institute (NCI) databases containing ca. 250 000 compounds combined. Forty-eight anticonvulsant agents of the functionalized amino acid (FAA) series were used to build kNN variable selection QSAR models. The 10 best models were applied to mining chemical databases, and 22 compounds were selected as consensus hits. Nine compounds were synthesized and tested at the NIH Epilepsy Branch, Rockville, MD using the same biological test that was employed to assess the anticonvulsant activity of the training set compounds; of these nine, four were exact database hits and five were derived from the hits by minor chemical modifications. Seven of these nine compounds were confirmed to be active, indicating an exceptionally high hit rate. The approach described in this report can be used as a general rational drug discovery tool.
Journal of Computer-Aided Molecular Design	Rational selection of training and test sets for the development of validated QSAR models. Golbraikh A, Shen M, Xiao Z, Xiao YD, Lee KH, Tropsha A. Quantitative Structure-Activity Relationship (QSAR) models are used increasingly to screen chemical databases and/or virtual chemical libraries for potentially bioactive molecules. These developments emphasize the importance of rigorous model validation to ensure that the models have acceptable predictive power. Using k nearest neighbors (kNN) variable selection QSAR method for the analysis of several datasets, we have demonstrated recently that the widely accepted leave-one-out (LOO) cross-validated R2 (q2) is an inadequate characteristic to assess the predictive ability of the models [Golbraikh, A., Tropsha, A. Beware of q2! J. Mol. Graphics Mod. 20, 269-276, (2002)]. Herein, we provide additional evidence that there exists no correlation between the values of q2 for the training set and accuracy of prediction (R2) for the test set and argue that this observation is a general property of any QSAR model developed with LOO cross-validation. We suggest that external validation using rationally selected training and test sets provides a means to establish a reliable QSAR model. We propose several approaches to the division of experimental datasets into training and test sets and apply them in QSAR studies of 48 functionalized amino acid anticonvulsants and a series of 157 epipodophyllotoxin derivatives with antitumor activity. We formulate a set of general criteria for the evaluation of predictive power of QSAR models.
Journal of Medicinal Chemistry	Development and validation of k-nearest-neighbor QSPR models of metabolic stability of drug candidates. Shen M, Xiao Y, Golbraikh A, Gombar VK, Tropsha A. Computational ADME (absorption, distribution, metabolism, and excretion) models may be used early in the drug discovery process in order to flag drug candidates with potentially problematic ADME profiles. We report the development, validation, and application of quantitative structure-property relationship (QSPR) models of metabolic turnover rate for compounds in human S9 homogenate. Biological data were obtained from uniform bioassays of 631 diverse chemicals proprietary to GlaxoSmithKline (GSK). The models were built with topological molecular descriptors such as molecular connectivity indices or atom pairs using the k-nearest neighbor variable selection optimization method developed at the University of North Carolina (Zheng, W.; Tropsha, A. A novel variable selection QSAR approach based on the k-nearest neighbor principle. J. Chem. Inf. Comput. Sci., 2000, 40, 185-194.). For the purpose of validation, the whole data set was divided into training and test sets. The training set QSPR models were characterized by high internal accuracy with leave-one-out cross-validated R2 (q2) values ranging between 0.5 and 0.6. The test set compounds were correctly classified as stable or unstable in S9 assay with an accuracy above 85%. These models were additionally validated by in silico metabolic stability screening of 107 new chemicals under development in several drug discovery programs at GSK. One representative model generated with MolConnZ descriptors predicted 40 compounds to be metabolically stable (turnover rate less than 25%), and 33 of them were indeed found to be stable experimentally. This success (83% concordance) in correctly picking chemicals that are metabolically stable in the human S9 homogenate spells a rapid, computational screen for generating components of the ADME profile in a drug discovery process.
Journal of Medicinal Chemistry	Quantitative structure-activity relationship analysis of functionalized amino acid anticonvulsant agents using k nearest neighbor and simulated annealing PLS methods. Shen M, LeTiran A, Xiao Y, Golbraikh A, Kohn H, Tropsha A. We report the development of rigorously validated quantitative structure-activity relationship (QSAR) models for 48 chemically diverse functionalized amino acids with anticonvulsant activity. Two variable selection approaches, simulated annealing partial least squares (SA-PLS) and k nearest neighbor (kNN), were employed. Both methods utilize multiple descriptors such as molecular connectivity indices or atom pair descriptors, which are derived from two-dimensional molecular topology. QSAR models with high internal accuracy were generated, with leave-one-out cross-validated R2 (q2) values ranging between 0.6 and 0.8. The q2 values for the actual dataset were significantly higher than those obtained for the same dataset with randomly shuffled activity values, indicating that models were statistically significant. The original dataset was further divided into several training and test sets, with highly predictive models providing q2 values greater than 0.5 for the training sets and R2 values greater than 0.6 for the test sets. These models were capable of predicting with reasonable accuracy the activity of 13 novel compounds not included in the original dataset. The successful development of highly predictive QSAR models affords further design and discovery of novel anticonvulsant agents.