When Zika virus emerged as a public health emergency there were no drugs or vaccines approved for its prevention or treatment. We used a high-throughput screen for Zika virus protease inhibitors to identify several inhibitors of Zika virus infection. We expressed the NS2B-NS3 Zika virus protease and conducted a biochemical screen for small-molecule inhibitors. A quantitative structure-activity relationship model was employed to virtually screen ∼138,000 compounds, which increased the identification of active compounds, while decreasing screening time and resources. Candidate inhibitors were validated in several viral infection assays. Small molecules with favorable clinical profiles, especially the five-lipoxygenase-activating protein inhibitor, MK-591, inhibited the Zika virus protease and infection in neural stem cells. Members of the tetracycline family of antibiotics were more potent inhibitors of Zika virus infection than the protease, suggesting they may have multiple mechanisms of action. The most potent tetracycline, methacycline, reduced the amount of Zika virus present in the brain and the severity of Zika virus-induced motor deficits in an immunocompetent mouse model. As Food and Drug Administration-approved drugs, the tetracyclines could be quickly translated to the clinic. The compounds identified through our screening paradigm have the potential to be used as prophylactics for patients traveling to endemic regions or for the treatment of the neurological complications of Zika virus infection.

Since 2009, the Tox21 project has screened ∼8500 chemicals in more than 70 high-throughput assays, generating upward of 100 million data points, with all data publicly available through partner websites at the United States Environmental Protection Agency (EPA), National Center for Advancing Translational Sciences (NCATS), and National Toxicology Program (NTP). Underpinning this public effort is the largest compound library ever constructed specifically for improving understanding of the chemical basis of toxicity across research and regulatory domains. Each Tox21 federal partner brought specialized resources and capabilities to the partnership, including three approximately equal-sized compound libraries. All Tox21 data generated to date have resulted from a confluence of ideas, technologies, and expertise used to design, screen, and analyze the Tox21 10K library. The different programmatic objectives of the partners led to three distinct, overlapping compound libraries that, when combined, not only covered a diversity of chemical structures, use-categories, and properties but also incorporated many types of compound replicates. The history of development of the Tox21 "10K" chemical library and data workflows implemented to ensure quality chemical annotations and allow for various reproducibility assessments are described. Cheminformatics profiling demonstrates how the three partner libraries complement one another to expand the reach of each individual library, as reflected in coverage of regulatory lists, predicted toxicity end points, and physicochemical properties. ToxPrint chemotypes (CTs) and enrichment approaches further demonstrate how the combined partner libraries amplify structure-activity patterns that would otherwise not be detected. Finally, CT enrichments are used to probe global patterns of activity in combined ToxCast and Tox21 activity data sets relative to test-set size and chemical versus biological end point diversity, illustrating the power of CT approaches to discern patterns in chemical-activity data sets. These results support a central premise of the Tox21 program: A collaborative merging of programmatically distinct compound libraries would yield greater rewards than could be achieved separately.

Due to their role in many important signaling pathways, phosphatidylinositol 5-phosphate 4-kinases (PI5P4Ks) are attractive targets for the development of experimental therapeutics for cancer, metabolic, and immunological disorders. Recent efforts to develop small molecule inhibitors for these lipid kinases resulted in compounds with low- to sub-micromolar potencies. Here, we report the identification of CVM-05-002 using a high-throughput screen of PI5P4Kα against our in-house kinase inhibitor library. CVM-05-002 is a potent and selective inhibitor of PI5P4Ks, and a 1.7 Å X-ray structure reveals its binding interactions in the ATP-binding pocket. Further investigation of the structure-activity relationship led to the development of compound 13, replacing the rhodanine-like moiety present in CVM-05-002 with an indole, a potent pan-PI5P4K inhibitor with excellent kinome-wide selectivity. Finally, we employed isothermal cellular thermal shift assays (CETSAs) to demonstrate the effective cellular target engagement of PI5P4Kα and -β by the inhibitors in HEK 293T cells.

Phosphatidylinositol 5-phosphate 4-kinases (PI5P4Ks) are important molecular players in a variety of diseases, such as cancer. Currently available PI5P4K inhibitors are reversible small molecules, which may lack selectivity and sufficient cellular on-target activity. In this study, we present a new class of covalent pan-PI5P4K inhibitors with potent biochemical and cellular activity. Our designs are based on THZ-P1-2, a covalent PI5P4K inhibitor previously developed in our lab. Here, we report further structure-guided optimization and structure-activity relationship (SAR) study of this scaffold, resulting in compound 30, which retained biochemical and cellular potency, while demonstrating a significantly improved selectivity profile. Furthermore, we confirm that the inhibitors show efficient binding affinity in the context of HEK 293T cells using isothermal CETSA methods. Taken together, compound 30 represents a highly selective pan-PI5P4K covalent lead molecule.

Differential scanning fluorometry (DSF) is an efficient and high-throughput method to analyze protein stability, as well as detect ligand interactions through perturbations of the protein's melting temperature. The method monitors protein unfolding by observing the fluorescence changes of a sample, whether through an environmentally sensitive fluorophore or by intrinsic protein fluorescence, while a temperature gradient is applied. Here, we describe in detail how to develop and optimize DSF assays to identify protein-ligand interactions while exploring different buffer and additive conditions. Analysis of the data and further applications of the method are also discussed.

Advances in the development of high-throughput screening and automated chemistry have rapidly accelerated the production of chemical and biological data, much of them freely accessible through literature aggregator services such as ChEMBL and PubChem. Here, we explore how to use this comprehensive mapping of chemical biology space to support the development of large-scale quantitative structure-activity relationship (QSAR) models. We propose a new deep learning consensus architecture (DLCA) that combines consensus and multitask deep learning approaches together to generate large-scale QSAR models. This method improves knowledge transfer across different target/assays while also integrating contributions from models based on different descriptors. The proposed approach was validated and compared with proteochemometrics, multitask deep learning, and Random Forest methods paired with various descriptors types. DLCA models demonstrated improved prediction accuracy for both regression and classification tasks. The best models together with their modeling sets are provided through publicly available web services at https://predictor.ncats.io .

Natural products and their derivatives continue to be wellsprings of nascent therapeutic potential. However, many laboratories have limited resources for biological evaluation, leaving their previously isolated or synthesized compounds largely or completely untested. To address this issue, the Canvass library of natural products was assembled, in collaboration with academic and industry researchers, for quantitative high-throughput screening (qHTS) across a diverse set of cell-based and biochemical assays. Characterization of the library in terms of physicochemical properties, structural diversity, and similarity to compounds in publicly available libraries indicates that the Canvass library contains many structural elements in common with approved drugs. The assay data generated were analyzed using a variety of quality control metrics, and the resultant assay profiles were explored using statistical methods, such as clustering and compound promiscuity analyses. Individual compounds were then sorted by structural class and activity profiles. Differential behavior based on these classifications, as well as noteworthy activities, are outlined herein. One such highlight is the activity of (-)-2(S)-cathafoline, which was found to stabilize calcium levels in the endoplasmic reticulum. The workflow described here illustrates a pilot effort to broadly survey the biological potential of natural products by utilizing the power of automation and high-throughput screening.

INTRODUCTION: Drug plasma protein binding remains highly relevant to research and drug development, making the assessment and profiling of compound affinity to plasma proteins essential to drug discovery efforts. Although there are a number of fully-characterized methods, they lack the throughput to handle large numbers of compounds. As the evaluation of adsorption, distribution, metabolism, and excretion is addressed earlier in the drug development timeline, the need for higher-throughput methods has grown. Areas Covered: This review will highlight recent developments on methods for profiling drug plasma binding, with an emphasis on fluorescent probes and emerging high-throughput methodologies. Expert Opinion: There have been a number of high-throughput assays developed in recent years to meet the scaled up demands for compound profiling. Ultimately, the selection of assay technology relies on a number of factors, such as capabilities of the laboratory and the breadth and amount of data required. Fluorescent probe displacement assays are highly flexible and amenable to high-throughput screening, easily scaling up to handle large compound libraries. Recent developments in fluorescence technologies, such as homogenous time-resolved fluorescence and probes utilizing the aggregation-induced emission effect, have improved the sensitivity of these assays. Other technologies, such as microscale thermophoresis and quantitative structure-activity relationship modeling, are gaining popularity as alternative techniques for drug plasma protein binding characterization.

Aldehyde dehydrogenases (ALDHs) are responsible for the metabolism of aldehydes (exogenous and endogenous) and possess vital physiological and toxicological functions in areas such as CNS, inflammation, metabolic disorders, and cancers. Overexpression of certain ALDHs (e.g., ALDH1A1) is an important biomarker in cancers and cancer stem cells (CSCs) indicating the potential need for the identification and development of small molecule ALDH inhibitors. Herein, a newly designed series of quinoline-based analogs of ALDH1A1 inhibitors is described. Extensive medicinal chemistry optimization and biological characterization led to the identification of analogs with significantly improved enzymatic and cellular ALDH inhibition. Selected analogs, e.g., 86 (NCT-505) and 91 (NCT-506), demonstrated target engagement in a cellular thermal shift assay (CETSA), inhibited the formation of 3D spheroid cultures of OV-90 cancer cells, and potentiated the cytotoxicity of paclitaxel in SKOV-3-TR, a paclitaxel resistant ovarian cancer cell line. Lead compounds also exhibit high specificity over other ALDH isozymes and unrelated dehydrogenases. The in vitro ADME profiles and pharmacokinetic evaluation of selected analogs are also highlighted.

Proliferating cells, including cancer cells, obtain serine both exogenously and via the metabolism of glucose. By catalyzing the first, rate-limiting step in the synthesis of serine from glucose, phosphoglycerate dehydrogenase (PHGDH) controls flux through the biosynthetic pathway for this important amino acid and represents a putative target in oncology. To discover inhibitors of PHGDH, a coupled biochemical assay was developed and optimized to enable high-throughput screening for inhibitors of human PHGDH. Feedback inhibition was minimized by coupling PHGDH activity to two downstream enzymes (PSAT1 and PSPH), providing a marked improvement in enzymatic turnover. Further coupling of NADH to a diaphorase/resazurin system enabled a red-shifted detection readout, minimizing interference due to compound autofluorescence. With this protocol, over 400,000 small molecules were screened for PHGDH inhibition, and following hit validation and triage work, a piperazine-1-thiourea was identified. Following rounds of medicinal chemistry and SAR exploration, two probes (NCT-502 and NCT-503) were identified. These molecules demonstrated improved target activity and encouraging ADME properties, enabling in vitro assessment of the biological importance of PHGDH, and its role in the fate of serine in PHGDH-dependent cancer cells. This manuscript reports the assay development and medicinal chemistry leading to the development of NCT-502 and -503 reported in Pacold et al. (2016).