Herein we provide a living summary of the data generated during the COVID Moonshot project focused on the development of SARS-CoV-2 main protease (Mpro) inhibitors. Our approach uniquely combines crowdsourced medicinal chemistry insights with high throughput crystallography, exascale computational chemistry infrastructure for simulations, and machine learning in triaging designs and predicting synthetic routes. This manuscript describes our methodologies leading to both covalent and non-covalent inhibitors displaying protease IC50 values under 150 nM and viral inhibition under 5 uM in multiple different viral replication assays. Furthermore, we provide over 200 crystal structures of fragment-like and lead-like molecules in complex with the main protease. Over 1000 synthesized and ordered compounds are also reported with the corresponding activity in Mpro enzymatic assays using two different experimental setups. The data referenced in this document will be continually updated to reflect the current experimental progress of the COVID Moonshot project, and serves as a citable reference for ensuing publications. All of the generated data is open to other researchers who may find it of use.

The in vitro metabolism and in vivo pharmacokinetic (PK) properties of DNDI-VL-2098, a potential oral agent for Visceral Leishmaniasis (VL) were studied and used to predict its human pharmacokinetics. DNDI-VL-2098 showed a low solubility (10μM) and was highly permeable (>200nm/s) in the Caco-2 model. It was stable in vitro in liver microsomes and hepatocytes and no metabolite was detectable in circulating plasma from dosed animals suggesting very slow, if any, metabolism of the compound. DNDI-VL-2098 was moderate to highly bound to plasma proteins across the species tested (94-98%). DNDI-VL-2098 showed satisfactory PK properties in mouse, hamster, rat and dog with a low blood clearance (<15% of hepatic blood flow except hamster), a volume of distribution of about 3 times total body water, acceptable half-life (1-6h across the species) and good oral bioavailability (37-100%). Allometric scaling of the preclinical PK data to human gave a blood half-life of approximately 20h suggesting that the compound could be a once-a-day drug. Based on the above assumptions, the minimum efficacious dose predicted for a 50kg human was 150mg and 300mg, using efficacy results in the mouse and hamster, respectively.

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Discovering a new drug is a complex but sequential process from discovery to preclinical development, followed with clinical drug development. It has been estimated that ∼ 87% of the phase III failures are accounted for either due to lack of efficacy (66%) or due to safety issues (21%). Majority of these failures are for compounds targeted for novel mechanisms of actions with unmet medical need, in particular, oncology and neurodegenerative disorders. Some of the reasons for these failures can be attributed to lack of appropriate preclinical animal models, biomarkers/surrogate markers, and effective pharmacokinetic (PK)–pharmacodynamic (PD) evaluation during early drug discovery. Translational research that integrates computer‐aided drug design (CADD), PK, PD, drug metabolism (DM), and drug transport along with biomarkers and humanized animal models are instrumental in making informed decisions from early drug discovery through clinical development. The ability to correlate drug effect through modeling and simulations starts from early drug discovery and preclinical evaluation, including use of novel biomarkers. Such models validate the PK and PD relationships and provide a basis for their applications and guide the Phase I through Phase III clinical trials more effectively, minimizing the late stage failures. Thus, PK–PD evaluation has become an integral part of drug discovery and provides valuable insights to aid in optimizing the next steps for drug development. This chapter is focused on translational drug discovery research with particular emphasis on selective utilization of CADD; absorption, distribution, metabolism, and excretion; toxicology; PK; and PD evaluations, which identify potential liabilities early so as to minimize late‐stage failures during drug development. This chapter also provides a brief overview on means and measures that can be adopted to integrate early drug discovery research along with efficacy and safety biomarkers for meaningful transition to drug development.