Oleg Ursu

A comprehensive map of molecular drug targets

The success of mechanism-based drug discovery depends on the definition of the drug target. This definition becomes even more important as we try to link drug response to genetic variation, understand stratified clinical efficacy and safety, rationalize the differences between drugs in the same therapeutic class and predict drug utility in patient subgroups. However, drug targets are often poorly defined in the literature, both for launched drugs and for potential therapeutic agents in discovery and development. Here, we present an updated comprehensive map of molecular targets of approved drugs. We curate a total of 893 human and pathogen-derived biomolecules through which 1,578 US FDA-approved drugs act. These biomolecules include 667 human-genome-derived proteins targeted by drugs for human disease. Analysis of these drug targets indicates the continued dominance of privileged target families across disease areas, but also the growth of novel first-in-class mechanisms, particularly in oncology. We explore the relationships between bioactivity class and clinical success, as well as the presence of orthologues between human and animal models and between pathogen and human genomes. Through the collaboration of three independent teams, we highlight some of the ongoing challenges in accurately defining the targets of molecular therapeutics and present conventions for deconvoluting the complexities of molecular pharmacology and drug efficacy.

A crowdsourcing evaluation of the NIH chemical probes.

Between 2004 and 2008, the US National Institutes of Health Molecular Libraries and Imaging initiative pilot phase funded 10 high-throughput screening centers, resulting in the deposition of 691 assays into PubChem and the nomination of 64 chemical probes. We crowdsourced the Molecular Libraries and Imaging initiative output to 11 experts, who expressed medium or high levels of confidence in 48 of these 64 probes.

Associating Drugs, Targets and Clinical Outcomes into an Integrated Network Affords a New Platform for Computer-Aided Drug Repurposing

Finding new uses for old drugs is a strategy embraced by the pharmaceutical industry, with increasing participation from the academic sector. Drug repurposing efforts focus on identifying novel modes of action, but not in a systematic manner. With intensive data mining and curation, we aim to apply bio‐ and cheminformatics tools using the DRUGS database, containing 3837 unique small molecules annotated on 1750 proteins. These are likely to serve as drug targets and antitargets (i.e., associated with side effects, SE). The academic community, the pharmaceutical sector and clinicians alike could benefit from an integrated, semantic‐web compliant computer‐aided drug repurposing (CADR) effort, one that would enable deep data mining of associations between approved drugs (D), targets (T), clinical outcomes (CO) and SE. We report preliminary results from text mining and multivariate statistics, based on 7684 approved drug labels, ADL (Dailymed) via text mining. From the ADL corresponding to 988 unique drugs, the “adverse reactions” section was mapped onto 174 SE, then clustered via principal component analysis into a 5×5 self‐organizing map that was integrated into a Cytoscape network of SE‐D‐T‐CO. This type of data can be used to streamline drug repurposing and may result in novel insights that can lead to the identification of novel drug actions.

Characterization of a Cdc42 Protein Inhibitor and Its Use as a Molecular Probe

Background: By integrating extracellular signals with actin cytoskeletal changes, Cdc42 plays important roles in cell physiology and has been implicated in human diseases. Results: A small molecule was found to selectively inhibit Cdc42 in biochemical and cellular assays. Conclusion: The identified compound is a highly Cdc42-selective inhibitor. Significance: The described first-in-class Cdc42 GTPase-selective inhibitor will have applications in drug discovery and fundamental research. Cdc42 plays important roles in cytoskeleton organization, cell cycle progression, signal transduction, and vesicle trafficking. Overactive Cdc42 has been implicated in the pathology of cancers, immune diseases, and neuronal disorders. Therefore, Cdc42 inhibitors would be useful in probing molecular pathways and could have therapeutic potential. Previous inhibitors have lacked selectivity and trended toward toxicity. We report here the characterization of a Cdc42-selective guanine nucleotide binding lead inhibitor that was identified by high throughput screening. A second active analog was identified via structure-activity relationship studies. The compounds demonstrated excellent selectivity with no inhibition toward Rho and Rac in the same GTPase family. Biochemical characterization showed that the compounds act as noncompetitive allosteric inhibitors. When tested in cellular assays, the lead compound inhibited Cdc42-related filopodia formation and cell migration. The lead compound was also used to clarify the involvement of Cdc42 in the Sin Nombre virus internalization and the signaling pathway of integrin VLA-4. Together, these data present the characterization of a novel Cdc42-selective allosteric inhibitor and a related analog, the use of which will facilitate drug development targeting Cdc42-related diseases and molecular pathway studies that involve GTPases.

Understanding drug‐likeness

‘Drug‐likeness’, a qualitative property of chemicals assigned by experts committee vote, is widely integrated into the early stages of lead and drug discovery. Its conceptual evolution paralleled work related to Pfizers ‘rule of five’ and lead‐likeness, and is placed within this framework. The discrimination between ‘drugs’ (represented by a collection of pharmaceutically relevant small molecules, some of which are marketed drugs) and ‘nondrugs’ (typically, chemical reagents) is possible using a wide variety of statistical tools and chemical descriptor systems. Here we summarize 18 papers focused on drug‐likeness, and provide a comprehensive overview of progress in the field. Tools that estimate drug‐likeness are valuable in the early stages of lead discovery, and can be used to filter out compounds with undesirable properties from screening libraries and to prioritize hits from primary screens. As the goal is, most often, to develop orally available drugs, it is also useful to optimize drug‐like pharmacokinetic properties. We examine tools that evaluate drug‐likeness and some of their shortcomings, challenges facing these tools, and address the following issues: What is the definition of drug‐likeness and how can it be utilized to reduce attrition rate in drug discovery? How difficult is it to distinguish drugs from nondrugs? Are nondrug datasets reliable? Can we estimate oral drug‐likeness? We discuss a drug‐like filter and recent advances in the prediction of oral drug‐likeness. The heuristic aspect of drug‐likeness is also addressed.

A competitive nucleotide binding inhibitor: in vitro characterization of Rab7 GTPase inhibition.

Mapping the functionality of GTPases through small molecule inhibitors represents an underexplored area in large part due to the lack of suitable compounds. Here we report on the small chemical molecule 2-(benzoylcarbamothioylamino)-5,5-dimethyl-4,7-dihydrothieno[2,3-c]pyran-3-carboxylic acid (PubChem CID 1067700) as an inhibitor of nucleotide binding by Ras-related GTPases. The mechanism of action of this pan-GTPase inhibitor was characterized in the context of the Rab7 GTPase as there are no known inhibitors of Rab GTPases. Bead-based flow cytometry established that CID 1067700 has significant inhibitory potency on Rab7 nucleotide binding with nanomolar inhibitor (K(i)) values and an inhibitory response of ≥97% for BODIPY-GTP and BODIPY-GDP binding. Other tested GTPases exhibited significantly lower responses. The compound behaves as a competitive inhibitor of Rab7 nucleotide binding based on both equilibrium binding and dissociation assays. Molecular docking analyses are compatible with CID 1067700 fitting into the nucleotide binding pocket of the GTP-conformer of Rab7. On the GDP-conformer, the molecule has greater solvent exposure and significantly less protein interaction relative to GDP, offering a molecular rationale for the experimental results. Structural features pertinent to CID 1067700 inhibitory activity have been identified through initial structure-activity analyses and identified a molecular scaffold that may serve in the generation of more selective probes for Rab7 and other GTPases. Taken together, our study has identified the first competitive GTPase inhibitor and demonstrated the potential utility of the compound for dissecting the enzymology of the Rab7 GTPase, as well as serving as a model for other small molecular weight GTPase inhibitors.

DrugCentral: online drug compendium

DrugCentral (http://drugcentral.org) is an open-access online drug compendium. DrugCentral integrates structure, bioactivity, regulatory, pharmacologic actions and indications for active pharmaceutical ingredients approved by FDA and other regulatory agencies. Monitoring of regulatory agencies for new drugs approvals ensures the resource is up-to-date. DrugCentral integrates content for active ingredients with pharmaceutical formulations, indexing drugs and drug label annotations, complementing similar resources available online. Its complementarity with other online resources is facilitated by cross referencing to external resources. At the molecular level, DrugCentral bridges drug-target interactions with pharmacological action and indications. The integration with FDA drug labels enables text mining applications for drug adverse events and clinical trial information. Chemical structure overlap between DrugCentral and five online drug resources, and the overlap between DrugCentral FDA-approved drugs and their presence in four different chemical collections, are discussed. DrugCentral can be accessed via the web application or downloaded in relational database format.

Emerging trends in the discovery of natural product antibacterials

This article highlights current trends and advances in exploiting natural sources for the deployment of novel and potent anti-infective countermeasures. The key challenge is to therapeutically target bacterial pathogens that exhibit a variety of puzzling and evolutionarily complex resistance mechanisms. Special emphasis is given to the strengths, weaknesses, and opportunities in the natural product antibacterial drug discovery arena, and to emerging applications driven by advances in bioinformatics, chemical biology, and synthetic biology in concert with exploiting bacterial phenotypes. These efforts have identified a critical mass of natural product antibacterial lead compounds and discovery technologies with high probability of successful implementation against emerging bacterial pathogens.

Fluorescent substrates for flow cytometric evaluation of efflux inhibition in ABCB1, ABCC1, and ABCG2 transporters

ATP binding cassette (ABC) transmembrane efflux pumps such as P-glycoprotein (ABCB1), multidrug resistance protein 1 (ABCC1), and breast cancer resistance protein (ABCG2) play an important role in anticancer drug resistance. A large number of structurally and functionally diverse compounds act as substrates or modulators of these pumps. In vitro assessment of the affinity of drug candidates for multidrug resistance proteins is central to predict in vivo pharmacokinetics and drug-drug interactions. The objective of this study was to identify and characterize new substrates for these transporters. As part of a collaborative project with Life Technologies, 102 fluorescent probes were investigated in a flow cytometric screen of ABC transporters. The primary screen compared substrate efflux activity in parental cell lines with their corresponding highly expressing resistant counterparts. The fluorescent compound library included a range of excitation/emission profiles and required dual laser excitation as well as multiple fluorescence detection channels. A total of 31 substrates with active efflux in one or more pumps and practical fluorescence response ranges were identified and tested for interaction with eight known inhibitors. This screening approach provides an efficient tool for identification and characterization of new fluorescent substrates for ABCB1, ABCC1, and ABCG2.

Badapple: promiscuity patterns from noisy evidence

BackgroundBioassay data analysis continues to be an essential, routine, yet challenging task in modern drug discovery and chemical biology research. The challenge is to infer reliable knowledge from big and noisy data. Some aspects of this problem are general with solutions informed by existing and emerging data science best practices. Some aspects are domain specific, and rely on expertise in bioassay methodology and chemical biology. Testing compounds for biological activity requires complex and innovative methodology, producing results varying widely in accuracy, precision, and information content. Hit selection criteria involve optimizing such that the overall probability of success in a project is maximized, and resource-wasteful “false trails” are avoided. This “fail-early” approach is embraced both in pharmaceutical and academic drug discovery, since follow-up capacity is resource-limited. Thus, early identification of likely promiscuous compounds has practical value.ResultsHere we describe an algorithm for identifying likely promiscuous compounds via associated scaffolds which combines general and domain-specific features to assist and accelerate drug discovery informatics, called Badapple: bioassay-data associative promiscuity pattern learning engine. Results are described from an analysis using data from MLP assays via the BioAssay Research Database (BARD) http://bard.nih.gov. Specific examples are analyzed in the context of medicinal chemistry, to illustrate associations with mechanisms of promiscuity. Badapple has been developed at UNM, released and deployed for public use two ways: (1) BARD plugin, integrated into the public BARD REST API and BARD web client; and (2) public web app hosted at UNM.ConclusionsBadapple is a method for rapidly identifying likely promiscuous compounds via associated scaffolds. Badapple generates a score associated with a pragmatic, empirical definition of promiscuity, with the overall goal to identify “false trails” and streamline workflows. Unlike methods reliant on expert curation of chemical substructure patterns, Badapple is fully evidence-driven, automated, self-improving via integration of additional data, and focused on scaffolds. Badapple is robust with respect to noise and errors, and skeptical of scanty evidence.