A data science roadmap for open science organizations engaged in early-stage drug discovery.

The Structural Genomics Consortium is an international open science research organization with a focus on accelerating early-stage drug discovery, namely hit discovery and optimization. We, as many others, believe that artificial intelligence (AI) is poised to be a main accelerator in the field. The question is then how to best benefit from recent advances in AI and how to generate, format and disseminate data to enable future breakthroughs in AI-guided drug discovery. We present here the recommendations of a working group composed of experts from both the public and private sectors. Robust data management requires precise ontologies and standardized vocabulary while a centralized database architecture across laboratories facilitates data integration into high-value datasets. Lab automation and opening electronic lab notebooks to data mining push the boundaries of data sharing and data modeling. Important considerations for building robust machine-learning models include transparent and reproducible data processing, choosing the most relevant data representation, defining the right training and test sets, and estimating prediction uncertainty. Beyond data-sharing, cloud-based computing can be harnessed to build and disseminate machine-learning models. Important vectors of acceleration for hit and chemical probe discovery will be (1) the real-time integration of experimental data generation and modeling workflows within design-make-test-analyze (DMTA) cycles openly, and at scale and (2) the adoption of a mindset where data scientists and experimentalists work as a unified team, and where data science is incorporated into the experimental design.

Chemogenomics for NR1 nuclear hormone receptors.

Nuclear receptors (NRs) regulate transcription in response to ligand binding and NR modulation allows pharmacological control of gene expression. Although some NRs are relevant as drug targets, the NR1 family, which comprises 19 NRs binding to hormones, vitamins, and lipid metabolites, has only been partially explored from a translational perspective. To enable systematic target identification and validation for this protein family in phenotypic settings, we present an NR1 chemogenomic (CG) compound set optimized for complementary activity/selectivity profiles and chemical diversity. Based on broad profiling of candidates for specificity, toxicity, and off-target liabilities, sixty-nine comprehensively annotated NR1 agonists, antagonists and inverse agonists covering all members of the NR1 family and meeting potency and selectivity standards are included in the final NR1 CG set. Proof-of-concept application of this set reveals effects of NR1 members in autophagy, neuroinflammation and cancer cell death, and confirms the suitability of the set for target identification and validation.

Discovery of a Small Molecule Activator of Slack (Kcnt1) Potassium Channels That Significantly Reduces Scratching in Mouse Models of Histamine-Independent and Chronic Itch.

Various disorders are accompanied by histamine-independent itching, which is often resistant to the currently available therapies. Here, it is reported that the pharmacological activation of Slack (Kcnt1, KNa1.1), a potassium channel highly expressed in itch-sensitive sensory neurons, has therapeutic potential for the treatment of itching. Based on the Slack-activating antipsychotic drug, loxapine, a series of new derivatives with improved pharmacodynamic and pharmacokinetic profiles is designed that enables to validate Slack as a pharmacological target in vivo. One of these new Slack activators, compound 6, exhibits negligible dopamine D2 and D3 receptor binding, unlike loxapine. Notably, compound 6 displays potent on-target antipruritic activity in multiple mouse models of acute histamine-independent and chronic itch without motor side effects. These properties make compound 6 a lead molecule for the development of new antipruritic therapies targeting Slack.

Development of Selective Pyrido[2,3-d]pyrimidin-7(8H)-one-Based Mammalian STE20-Like (MST3/4) Kinase Inhibitors.

Mammalian STE20-like (MST) kinases 1-4 play key roles in regulating the Hippo and autophagy pathways, and their dysregulation has been implicated in cancer development. In contrast to the well-studied MST1/2, the roles of MST3/4 are less clear, in part due to the lack of potent and selective inhibitors. Here, we re-evaluated literature compounds, and used structure-guided design to optimize the p21-activated kinase (PAK) inhibitor G-5555 (8) to selectively target MST3/4. These efforts resulted in the development of MR24 (24) and MR30 (27) with good kinome-wide selectivity and high cellular potency. The distinct cellular functions of closely related MST kinases can now be elucidated with subfamily-selective chemical tool compounds using a combination of the MST1/2 inhibitor PF-06447475 (2) and the two MST3/4 inhibitors developed. We found that MST3/4-selective inhibition caused a cell-cycle arrest in the G1 phase, whereas MST1/2 inhibition resulted in accumulation of cells in the G2/M phase.

Synthesis of Pyrazole-Based Macrocycles Leads to a Highly Selective Inhibitor for MST3.

MST1, MST2, MST3, MST4, and YSK1 are conserved members of the mammalian sterile 20-like serine/threonine (MST) family that regulate cellular functions such as proliferation and migration. The MST3 isozyme plays a role in regulating cell growth and apoptosis, and its dysregulation has been linked to high-grade tumors. To date, there are no isoform-selective inhibitors that could be used for validating the role of MST3 in tumorigenesis. We designed a series of 3-aminopyrazole-based macrocycles based on the structure of a promiscuous inhibitor. By varying the moieties targeting the solvent-exposed region and optimizing the linker, macrocycle JA310 (21c) was synthesized. JA310 exhibited high cellular potency for MST3 (EC50 = 106 nM) and excellent kinome-wide selectivity. The crystal structure of the MST3-JA310 complex provided intriguing insights into the binding mode, which is associated with large-scale structural rearrangements. In summary, JA310 demonstrates the utility of macrocyclization for the design of highly selective inhibitors and presents the first chemical probe for MST3.