Drug development is a resource and time-intensive process resulting in attrition rates of up to 90%. As a result, repurposing existing drugs with established safety and pharmacokinetic profiles is gaining traction as a way of accelerating therapeutics development. Here we have developed unique machine learning-driven Natural Language Processing and biomedical semantic technologies that mine over 53 million biomedical documents to automate the generation of a 911M edge knowledge graph. We then applied subgraph queries that relate drugs to diseases using genetic evidence to identify potential drug repurposing candidates for a broad range of diseases. We use Carney Complex, a disease with no known treatment, to illustrate our approach. This analysis revealed Ruxolitinib (Incyte, trade name Jakafi), a JAK1/2 inhibitor with an established safety and efficacy profile approved to treat myelofibrosis, as a potential candidate for the treatment of Carney Complex through off-target drug activity.

Tumor necrosis factor α (TNFα) is a soluble cytokine that is directly involved in systemic inflammation through the regulation of the intracellular NF-κB and MAPK signaling pathways. The development of biologic drugs that inhibit TNFα has led to improved clinical outcomes for patients with rheumatoid arthritis and other chronic autoimmune diseases; however, TNFα has proven to be difficult to drug with small molecules. Herein, we present a two-phase, fragment-based drug discovery (FBDD) effort in which we first identified isoquinoline fragments that disrupt TNFα ligand-receptor binding through an allosteric desymmetrization mechanism as observed in high-resolution crystal structures. The second phase of discovery focused on the de novo design and optimization of fragments with improved binding efficiency and drug-like properties. The 3-indolinone-based lead presented here displays oral, in vivo efficacy in a mouse glucose-6-phosphate isomerase (GPI)-induced paw swelling model comparable to that seen with a TNFα antibody.

A high-throughput screen against human DGAT-1 led to the identification of a core structure that was subsequently optimized to afford the potent, selective, and orally bioavailable compound 14. Oral administration at doses ≥0.03 mg/kg significantly reduced postprandial triglycerides in mice following an oral lipid challenge. Further assessment in both acute and chronic safety pharmacology and toxicology studies demonstrated a clean profile up to high plasma levels, thus culminating in the nomination of 14 as clinical candidate ABT-046.

Efficient elucidation of the biological mechanism of action of novel compounds remains a major bottleneck in the drug discovery process. To address this need in the area of oncology, we report the development of a multiparametric high-content screening assay panel at the level of single cells to dramatically accelerate understanding the mechanism of action of cell growth-inhibiting compounds on a large scale. Our approach is based on measuring 10 established end points associated with mitochondrial apoptosis, cell cycle disruption, DNA damage, and cellular morphological changes in the same experiment, across three multiparametric assays. The data from all of the measurements taken together are expected to help increase our current understanding of target protein functions, constrain the list of possible targets for compounds identified using phenotypic screens, and identify off-target effects. We have also developed novel data visualization and phenotypic classification approaches for detailed interpretation of individual compound effects and navigation of large collections of multiparametric cellular responses. We expect this general approach to be valuable for drug discovery across multiple therapeutic areas.

Fragment-based lead discovery has undergone remarkable changes over the last 15 years. During this time, the pharmaceutical industry has changed dramatically as well, and continued evolution of the industry is assured. These changes present many challenges but also several opportunities for executing fragment-based drug design. This article will explore some of the more significant changes in the industry and how they may affect future discovery efforts related to fragment-based initiatives.

Two approaches have emerged for creating libraries of compounds for use in biological screening assays for drug discovery — fragment-based ligand design and diversity-oriented synthesis. Advocates of each approach discuss their favoured strategy.

We present a probabilistic framework for interpreting structure-based virtual screening that returns a quantitative likelihood of observing bioactivity and can be quantitatively combined with ligand-based screening methods to yield a cumulative prediction that consistently outperforms any single screening metric. The approach has been developed and validated on more than 30 different protein targets. Transforming structure-based in silico screening results into robust probabilities of activity enables the general fusion of multiple structure- and ligand-based approaches and returns a quantitative expectation of success that can be used to prioritize (or deprioritize) further discovery activities. This unified probabilistic framework offers a paradigm shift in how docking and scoring results are interpreted, which can enhance early lead-finding efforts by maximizing the value of in silico computational tools.

Inhibition of protein kinases has validated therapeutic utility for cancer, with at least seven kinase inhibitor drugs on the market. Protein kinase inhibition also has significant potential for a variety of other diseases, including diabetes, pain, cognition, and chronic inflammatory and immunologic diseases. However, as the vast majority of current approaches to kinase inhibition target the highly conserved ATP-binding site, the use of kinase inhibitors in treating nononcology diseases may require great selectivity for the target kinase. As protein kinases are signal transducers that are involved in binding to a variety of other proteins, targeting alternative, less conserved sites on the protein may provide an avenue for greater selectivity. Here we report an affinity-based, high-throughput screening technique that allows nonbiased interrogation of small molecule libraries for binding to all exposed sites on a protein surface. This approach was used to screen both the c-Jun N-terminal protein kinase Jnk-1 (involved in insulin signaling) and p38α (involved in the formation of TNFα and other cytokines). In addition to canonical ATP-site ligands, compounds were identified that bind to novel allosteric sites. The nature, biological relevance, and mode of binding of these ligands were extensively characterized using two-dimensional (1)H/(13)C NMR spectroscopy, protein X-ray crystallography, surface plasmon resonance, and direct enzymatic activity and activation cascade assays. Jnk-1 and p38α both belong to the MAP kinase family, and the allosteric ligands for both targets bind similarly on a ledge of the protein surface exposed by the MAP insertion present in the CMGC family of protein kinases and distant from the active site. Medicinal chemistry studies resulted in an improved Jnk-1 ligand able to increase adiponectin secretion in human adipocytes and increase insulin-induced protein kinase PKB phosphorylation in human hepatocytes, in similar fashion to Jnk-1 siRNA and to rosiglitazone treatment. Together, the data suggest that these new ligand series bind to a novel, allosteric, and physiologically relevant site and therefore represent a unique approach to identify kinase inhibitors.

When targeting G-protein coupled receptors (GPCRs) in early stage drug discovery, or for novel targets, the type of ligand most likely to produce the desired therapeutic effect may be unknown. Therefore, it can be desirable to identify potential lead compounds from multiple categories: agonists, antagonists, and allosteric modulators. In this study, we developed a triple addition calcium flux assay using FLIPR Tetra to identify multiple ligand classes for the metabotropic glutamate receptor 3 (mGlu3), using a cell line stably co-expressing the human G-protein-coupled mGlu3 receptor, a promiscuous G-protein (G(alpha 16)), and rat Glast, a glutamate transporter. Compounds were added to the cells followed by stimulation with EC(10) and then EC(80) concentration of glutamate, the physiological agonist for mGlu receptors. This format produced a robust assay, facilitating the identification of agonists, positive allosteric modulators and antagonists/negative allosteric modulators. Follow up experiments were conducted to exclude false positives. Using this approach, we screened a library of approximately 800,000 compounds using FLIPR Tetra and identified viable leads for all three ligand classes. Further characterization revealed the selectivity of individual ligands.

Although it is increasingly being recognized that drug-target interaction networks can be powerful tools for the interrogation of systems biology and the rational design of multitargeted drugs, there is no generalized, statistically validated approach to harmonizing sequence-dependent and pharmacology-dependent networks. Here we demonstrate the creation of a comprehensive kinome interaction network based not only on sequence comparisons but also on multiple pharmacology parameters derived from activity profiling data. The framework described for statistical interpretation of these network connections also enables rigorous investigation of chemotype-specific interaction networks, which is critical for multitargeted drug design.

ADVERTISEMENT RETURN TO ISSUEPREVPerspectiveNEXTCheminformatic Tools for Medicinal ChemistsSteven W. Muchmore, Jeremy J. Edmunds, Kent D. Stewart, and Philip J. Hajduk*View Author Information Pharmaceutical Discovery Division, Abbott Laboratories, Abbott Park, Illinois 60064*To whom correspondence should be addressed. Phone: (847) 937-0368. Fax: (847) 938-2478. E-mail: [email protected]Cite this: J. Med. Chem. 2010, 53, 13, 4830–4841Publication Date (Web):March 24, 2010Publication History Received5 February 2010Published online24 March 2010Published inissue 8 July 2010https://doi.org/10.1021/jm100164zCopyright © 2010 American Chemical SocietyRIGHTS & PERMISSIONSArticle Views9531Altmetric-Citations61LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InReddit Read OnlinePDF (1 MB) Get e-AlertsSUBJECTS:Chemical calculations,Chemoinformatics,Molecules,Partition coefficient,Solubility Get e-Alerts

NMR spectroscopy has enjoyed widespread success as a method for screening protein targets, especially in the area of fragment-based drug discovery. However, current methods for NMR-based screening all suffer certain limitations. Two-dimensional methods like "SAR by NMR" require isotopically labeled protein and are limited to proteins less than about 50 kDa. For one-dimensional, ligand-based methods, results can be confounded by nonspecific compound binding, resonance overlap, or the need for a special NMR probe. We present here a ligand-based method that relies on the exchange broadening observed for a (13)C-labeled molecule upon binding to a protein target (labeled ligand displacement). This method can be used to screen both individual compounds and mixtures and is free of the artifacts inherent in other ligand-based methods.

Herein we describe the identification and characterization of a class of molecules that are believed to extend into a region of p38 known as the 'switch pocket'. Although these molecules lack a canonical hinge binding motif, they show K(i) values as low as 100 nM against p38. We show that molecules that interact with this region of the protein demonstrate different binding kinetics than a canonical ATP mimetic, as well as a wide range of kinome profiles. Thus, the switch pocket presents new opportunities for kinome selectivity which could result in unique biochemical responses and offer new opportunities in the field of kinase drug discovery.

The Bcl-2 family of proteins plays a major role in the regulation of apoptosis, or programmed cell death. Overexpression of the anti-apoptotic members of this family (Bcl-2, Bcl-xL, and Mcl-1) can render cancer cells resistant to chemotherapeutic agents and therefore these proteins are important targets for the development of new anti-cancer agents. Here we describe the discovery of a potent, highly selective, Bcl-2 inhibitor using SAR by NMR and structure-based drug design which could serve as a starting point for the development of a Bcl-2 selective anti-cancer agent. Such an agent would potentially overcome the Bcl-xL mediated thrombocytopenia observed with ABT-263.

Many successful drugs bind to and modulate multiple targets in vivo . Successfully navigating protein–ligand polypharmacology will be a crucial and increasingly utilized component of pharmaceutical research. As publicly available databases of ligand activity values continue to grow in size and quality, infrastructure is needed to enable scientists to create and interact with these networks to fuel hypothesis-driven science. While most of the individual tools for creating this infrastructure exist, effectively connecting the data to the network to the scientist is very much a work in progress. Standards for publishing network data are also important to facilitate the analysis and comparison of networks from different research groups using different methods.