Alexey V. Zakharov

Collaborative development of predictive toxicology applications

OpenTox provides an interoperable, standards-based Framework for the support of predictive toxicology data management, algorithms, modelling, validation and reporting. It is relevant to satisfying the chemical safety assessment requirements of the REACH legislation as it supports access to experimental data, (Quantitative) Structure-Activity Relationship models, and toxicological information through an integrating platform that adheres to regulatory requirements and OECD validation principles. Initial research defined the essential components of the Framework including the approach to data access, schema and management, use of controlled vocabularies and ontologies, architecture, web service and communications protocols, and selection and integration of algorithms for predictive modelling. OpenTox provides end-user oriented tools to non-computational specialists, risk assessors, and toxicological experts in addition to Application Programming Interfaces (APIs) for developers of new applications. OpenTox actively supports public standards for data representation, interfaces, vocabularies and ontologies, Open Source approaches to core platform components, and community-based collaboration approaches, so as to progress system interoperability goals.The OpenTox Framework includes APIs and services for compounds, datasets, features, algorithms, models, ontologies, tasks, validation, and reporting which may be combined into multiple applications satisfying a variety of different user needs. OpenTox applications are based on a set of distributed, interoperable OpenTox API-compliant REST web services. The OpenTox approach to ontology allows for efficient mapping of complementary data coming from different datasets into a unifying structure having a shared terminology and representation.Two initial OpenTox applications are presented as an illustration of the potential impact of OpenTox for high-quality and consistent structure-activity relationship modelling of REACH-relevant endpoints: ToxPredict which predicts and reports on toxicities for endpoints for an input chemical structure, and ToxCreate which builds and validates a predictive toxicity model based on an input toxicology dataset. Because of the extensible nature of the standardised Framework design, barriers of interoperability between applications and content are removed, as the user may combine data, models and validation from multiple sources in a dependable and time-effective way.

CERAPP : Collaborative Estrogen Receptor Activity Prediction Project

Background: Humans are exposed to thousands of man-made chemicals in the environment. Some chemicals mimic natural endocrine hormones and, thus, have the potential to be endocrine disruptors. Most of these chemicals have never been tested for their ability to interact with the estrogen receptor (ER). Risk assessors need tools to prioritize chemicals for evaluation in costly in vivo tests, for instance, within the U.S. EPA Endocrine Disruptor Screening Program. Objectives: We describe a large-scale modeling project called CERAPP (Collaborative Estrogen Receptor Activity Prediction Project) and demonstrate the efficacy of using predictive computational models trained on high-throughput screening data to evaluate thousands of chemicals for ER-related activity and prioritize them for further testing. Methods: CERAPP combined multiple models developed in collaboration with 17 groups in the United States and Europe to predict ER activity of a common set of 32,464 chemical structures. Quantitative structure–activity relationship models and docking approaches were employed, mostly using a common training set of 1,677 chemical structures provided by the U.S. EPA, to build a total of 40 categorical and 8 continuous models for binding, agonist, and antagonist ER activity. All predictions were evaluated on a set of 7,522 chemicals curated from the literature. To overcome the limitations of single models, a consensus was built by weighting models on scores based on their evaluated accuracies. Results: Individual model scores ranged from 0.69 to 0.85, showing high prediction reliabilities. Out of the 32,464 chemicals, the consensus model predicted 4,001 chemicals (12.3%) as high priority actives and 6,742 potential actives (20.8%) to be considered for further testing. Conclusion: This project demonstrated the possibility to screen large libraries of chemicals using a consensus of different in silico approaches. This concept will be applied in future projects related to other end points. Citation: Mansouri K, Abdelaziz A, Rybacka A, Roncaglioni A, Tropsha A, Varnek A, Zakharov A, Worth A, Richard AM, Grulke CM, Trisciuzzi D, Fourches D, Horvath D, Benfenati E, Muratov E, Wedebye EB, Grisoni F, Mangiatordi GF, Incisivo GM, Hong H, Ng HW, Tetko IV, Balabin I, Kancherla J, Shen J, Burton J, Nicklaus M, Cassotti M, Nikolov NG, Nicolotti O, Andersson PL, Zang Q, Politi R, Beger RD, Todeschini R, Huang R, Farag S, Rosenberg SA, Slavov S, Hu X, Judson RS. 2016. CERAPP: Collaborative Estrogen Receptor Activity Prediction Project. Environ Health Perspect 124:1023–1033; http://dx.doi.org/10.1289/ehp.1510267

Quantitative prediction of antitarget interaction profiles for chemical compounds.

The evaluation of possible interactions between chemical compounds and antitarget proteins is an important task of the research and development process. Here, we describe the development and validation of QSAR models for the prediction of antitarget end-points, created on the basis of multilevel and quantitative neighborhoods of atom descriptors and self-consistent regression. Data on 4000 chemical compounds interacting with 18 antitarget proteins (13 receptors, 2 enzymes, and 3 transporters) were used to model 32 sets of end-points (IC(50), K(i), and K(act)). Each set was randomly divided into training and test sets in a ratio of 80% to 20%, respectively. The test sets were used for external validation of QSAR models created on the basis of the training sets. The coverage of prediction for all test sets exceeded 95%, and for half of the test sets, it was 100%. The accuracy of prediction for 29 of the end-points, based on the external test sets, was typically in the range of R(2)(test) = 0.6-0.9; three tests sets had lower R(2)(test) values, specifically 0.55-0.6. The proposed approach showed a reasonable accuracy of prediction for 91% of the antitarget end-points and high coverage for all external test sets. On the basis of the created models, we have developed a freely available online service for in silico prediction of 32 antitarget end-points: http://www.pharmaexpert.ru/GUSAR/antitargets.html.

QSAR modeling of imbalanced high-throughput screening data in PubChem.

Many of the structures in PubChem are annotated with activities determined in high-throughput screening (HTS) assays. Because of the nature of these assays, the activity data are typically strongly imbalanced, with a small number of active compounds contrasting with a very large number of inactive compounds. We have used several such imbalanced PubChem HTS assays to test and develop strategies to efficiently build robust QSAR models from imbalanced data sets. Different descriptor types [Quantitative Neighborhoods of Atoms (QNA) and “biological” descriptors] were used to generate a variety of QSAR models in the program GUSAR. The models obtained were compared using external test and validation sets. We also report on our efforts to incorporate the most predictive of our models in the publicly available NCI/CADD Group Web services (http://cactus.nci.nih.gov/chemical/apps/cap).

General Photoinduced Sequential Electrocyclization/[1,9]-Sigmatropic Rearrangement/Ring-Opening Reaction of Diarylethenes.

A novel and efficient photochemical transformation of diarylethenes comprising a five-membered heterocyclic ring and phenyl moiety is described. This reaction provides a simple method for the preparation of functionalized naphthalene derivatives via photorearrangement reaction of diarylethenes, and the process is characterized by high efficiency that was determined by NMR monitoring. Some mechanistic aspects of this process have been also explored. It was found that the reaction includes tandem transformation of three basic processes: the photocyclization of the hexatriene system, [1,9]-sigmatropic rearrangement, and heterocyclic ring opening. Diarylethenes with different heterocycle moieties (thiophene, benzo[b]thiophene, furan, indole, imidazole, thiazole, oxazole, pyrazole) have been involved into this process, and the target naphthalenes with good yields have been obtained. The opportunity for use in the transformation of diarylethenes with different heterocyclic residues permits synthesis of naphthalenes with desired functional groups. The general character and high efficiency of the reaction promise that the transformation can be an effective synthetic route for the annulation of benzene rings to various aromatic systems, including heterocycles.

Computational tools and resources for metabolism-related property predictions. 1. Overview of publicly available (free and commercial) databases and software

Metabolism has been identified as a defining factor in drug development success or failure because of its impact on many aspects of drug pharmacology, including bioavailability, half-life and toxicity. In this article, we provide an outline and descriptions of the resources for metabolism-related property predictions that are currently either freely or commercially available to the public. These resources include databases with data on, and software for prediction of, several end points: metabolite formation, sites of metabolic transformation, binding to metabolizing enzymes and metabolic stability. We attempt to place each tool in historical context and describe, wherever possible, the data it was based on. For predictions of interactions with metabolizing enzymes, we show a typical set of results for a small test set of compounds. Our aim is to give a clear overview of the areas and aspects of metabolism prediction in which the currently available resources are useful and accurate, and the areas in which they are inadequate or missing entirely.

QSAR Modeling and Prediction of Drug–Drug Interactions

Severe adverse drug reactions (ADRs) are the fourth leading cause of fatality in the U.S. with more than 100,000 deaths per year. As up to 30% of all ADRs are believed to be caused by drug-drug interactions (DDIs), typically mediated by cytochrome P450s, possibilities to predict DDIs from existing knowledge are important. We collected data from public sources on 1485, 2628, 4371, and 27,966 possible DDIs mediated by four cytochrome P450 isoforms 1A2, 2C9, 2D6, and 3A4 for 55, 73, 94, and 237 drugs, respectively. For each of these data sets, we developed and validated QSAR models for the prediction of DDIs. As a unique feature of our approach, the interacting drug pairs were represented as binary chemical mixtures in a 1:1 ratio. We used two types of chemical descriptors: quantitative neighborhoods of atoms (QNA) and simplex descriptors. Radial basis functions with self-consistent regression (RBF-SCR) and random forest (RF) were utilized to build QSAR models predicting the likelihood of DDIs for any pair of drug molecules. Our models showed balanced accuracy of 72-79% for the external test sets with a coverage of 81.36-100% when a conservative threshold for the models applicability domain was applied. We generated virtually all possible binary combinations of marketed drugs and employed our models to identify drug pairs predicted to be instances of DDI. More than 4500 of these predicted DDIs that were not found in our training sets were confirmed by data from the DrugBank database.

Computational tools and resources for metabolism-related property predictions. 2. Application to prediction of half-life time in human liver microsomes

BACKGROUND The most important factor affecting metabolic excretion of compounds from the body is their half-life time. This provides an indication of compound stability of, for example, drug molecules. We report on our efforts to develop QSAR models for metabolic stability of compounds, based on in vitro half-life assay data measured in human liver microsomes. METHOD A variety of QSAR models generated using different statistical methods and descriptor sets implemented in both open-source and commercial programs (KNIME, GUSAR and StarDrop) were analyzed. The models obtained were compared using four different external validation sets from public and commercial data sources, including two smaller sets of in vivo half-life data in humans. CONCLUSION In many cases, the accuracy of prediction achieved on one external test set did not correspond to the results achieved with another test set. The most predictive models were used for predicting the metabolic stability of compounds from the open NCI database, the results of which are publicly available on the NCI/CADD Group web server ( http://cactus.nci.nih.gov ).

Design, Synthesis and Pharmacological Evaluation of Novel Vanadium-Containing Complexes as Antidiabetic Agents

Based on the data about structure and antidiabetic activity of twenty seven vanadium and zinc coordination complexes collected from literature we developed QSAR models using the GUSAR program. These QSAR models were applied to 10 novel vanadium coordination complexes designed in silico in order to predict their hypoglycemic action. The five most promising substances with predicted potent hypoglycemic action were selected for chemical synthesis and pharmacological evaluation. The selected coordination vanadium complexes were synthesized and tested in vitro and in vivo for their hypoglycemic activities and acute rat toxicity. Estimation of acute rat toxicity of these five vanadium complexes was performed using a freely available web-resource (http://way2drug.com/GUSAR/acutoxpredict.html). It has shown that the selected compounds belong to the class of moderate toxic pharmaceutical agents, according to the scale of Hodge and Sterner. Comparison with the predicted data has demonstrated a reasonable correspondence between the experimental and predicted values of hypoglycemic activity and toxicity. Bis{tert-butyl[amino(imino)methyl]carbamato}oxovanadium (IV) and sodium(2,2′-Bipyridyl)oxo-diperoxovanadate(V) octahydrate were identified as the most potent hypoglycemic agents among the synthesized compounds.

A new approach to radial basis function approximation and its application to QSAR.

We describe a novel approach to RBF approximation, which combines two new elements: (1) linear radial basis functions and (2) weighting the model by each descriptor’s contribution. Linear radial basis functions allow one to achieve more accurate predictions for diverse data sets. Taking into account the contribution of each descriptor produces more accurate similarity values used for model development. The method was validated on 14 public data sets comprising nine physicochemical properties and five toxicity endpoints. We also compared the new method with five different QSAR methods implemented in the EPA T.E.S.T. program. Our approach, implemented in the program GUSAR, showed a reasonable accuracy of prediction and high coverage for all external test sets, providing more accurate prediction results than the comparison methods and even the consensus of these methods. Using our new method, we have created models for physicochemical and toxicity endpoints, which we have made freely available in the form of an online service at http://cactus.nci.nih.gov/chemical/apps/cap.