Integrated data-driven and experimental approaches to accelerate lead optimization targeting SARS-CoV-2 main protease.

Identification of potential therapeutic candidates can be expedited by integrating computational modeling with domain aware machine learning (ML) models followed by experimental validation in an iterative manner. Generative deep learning models can generate thousands of new candidates, however, their physiochemical and biochemical properties are typically not fully optimized. Using our recently developed deep learning models and a scaffold as a starting point, we generated tens of thousands of compounds for SARS-CoV-2 Mpro that preserve the core scaffold. We utilized and implemented several computational tools such as structural alert and toxicity analysis, high throughput virtual screening, ML-based 3D quantitative structure-activity relationships, multi-parameter optimization, and graph neural networks on generated candidates to predict biological activity and binding affinity in advance. As a result of these combined computational endeavors, eight promising candidates were singled out and put through experimental testing using Native Mass Spectrometry and FRET-based functional assays. Two of the tested compounds with quinazoline-2-thiol and acetylpiperidine core moieties showed IC[Formula: see text] values in the low micromolar range: [Formula: see text] [Formula: see text]M and 3.41±0.0015 [Formula: see text]M, respectively. Molecular dynamics simulations further highlight that binding of these compounds results in allosteric modulations within the chain B and the interface domains of the Mpro. Our integrated approach provides a platform for data driven lead optimization with rapid characterization and experimental validation in a closed loop that could be applied to other potential protein targets.

Lead optimization, pharmacophore development and scaffold design of protein kinase CK2 inhibitors as potential COVID-19 therapeutics.

Therapeutic agents being designed against COVID-19 have targeted either the virus directly or the host cellular machinery. A particularly attractive host target is the ubiquitous and constitutively active serine-threonine kinase, Protein kinase CK2 (CK2). CK2 enhances viral protein synthesis by inhibiting the sequestration of host translational machinery as stress granules and assists in viral egression via association with the N-protein at filopodial protrusions of the infected cell. CK2 inhibitors such as Silmitasertib have been proposed as possible therapeutic candidates in COVID-19 infections. The present study aims to optimize Silmitasertib, develop pharmacophore models and design unique scaffolds to modulate CK2. The lead optimization phase involved the generation of compounds structurally similar to Silmitasertib via bioisostere replacement followed by a multi-stage docking approach to identify drug-like candidates. Molecular dynamics (MD) simulations were performed for two promising candidates (ZINC-43206125 and PC-57664175) to estimate their binding stability and interaction. Top scoring candidates from the lead optimization phase were utilized to build ligand-based pharmacophore models. These models were then merged with structure-based pharmacophores (e-pharmacophores) to build a hybrid hypothesis. This hybrid hypothesis was validated against a decoy set and used to screen a diverse kinase inhibitors library to identify favored chemical features in the retrieved actives. These chemical features include; an anion, an aromatic ring and an H-bond acceptor. Based on the knowledge of these features; de-novo scaffold design was carried out which identified phenindiones, carboxylated steroids, macrocycles and peptides as novel scaffolds with the potential to modulate CK2.Communicated by Ramaswamy H. Sarma.

Forces Driving a Magic Bullet to Its Target: Revisiting the Role of Thermodynamics in Drug Design, Development, and Optimization.

Drug discovery strategies have advanced significantly towards prioritizing target selectivity to achieve the longstanding goal of identifying "magic bullets" amongst thousands of chemical molecules screened for therapeutic efficacy. A myriad of emerging and existing health threats, including the SARS-CoV-2 pandemic, alarming increase in bacterial resistance, and potentially fatal chronic ailments, such as cancer, cardiovascular disease, and neurodegeneration, have incentivized the discovery of novel therapeutics in treatment regimens. The design, development, and optimization of lead compounds represent an arduous and time-consuming process that necessitates the assessment of specific criteria and metrics derived via multidisciplinary approaches incorporating functional, structural, and energetic properties. The present review focuses on specific methodologies and technologies aimed at advancing drug development with particular emphasis on the role of thermodynamics in elucidating the underlying forces governing ligand-target interaction selectivity and specificity. In the pursuit of novel therapeutics, isothermal titration calorimetry (ITC) has been utilized extensively over the past two decades to bolster drug discovery efforts, yielding information-rich thermodynamic binding signatures. A wealth of studies recognizes the need for mining thermodynamic databases to critically examine and evaluate prospective drug candidates on the basis of available metrics. The ultimate power and utility of thermodynamics within drug discovery strategies reside in the characterization and comparison of intrinsic binding signatures that facilitate the elucidation of structural-energetic correlations which assist in lead compound identification and optimization to improve overall therapeutic efficacy.

Estimating thermodynamic equilibrium solubility and solute-solvent interactions of niclosamide in eight mono-solvents at different temperatures.

Niclosamide is an FDA-approved oral anthelmintic drug currently being repurposed for COVID-19 infection. Its interesting applicability in multiple therapeutic indications has sparked interest in this drug/ scaffold. Despite its therapeutic use for several years, its detailed solubility information from Chemistry Manufacturing & Controls perspective is unavailable. Thus, the present study is intended to determine the mole fraction solubility of niclosamide in commonly used solvents and cosolvents at a temperature range of 298.15-323.15 K. The polymorphic changes from crystalline to monohydrate forms post-equilibration in various solvents were observed. The maximum mole fraction solubility of niclosamide at 323.15 K is 1.103 × 10-3 in PEG400, followed by PEG200 (5.272 × 10-4), 1-butanol (3.047 × 10-4), 2-propanol (2.42 × 10-4), ethanol (1.66 × 10-4), DMSO (1.52 × 10-4), methanol (7.78 × 10-5) and water (3.27 × 10-7). The molecular electrostatic potential showed a linear correlation with the solubility. PEG400 has higher electrostatic potential, and H-bond acceptor count, which forms a hydrogen bond with phenolic -OH of niclosamide and thus enhances its solubility. This data is valuable for the drug discovery and development teams working on the medicinal chemistry and process chemistry of this scaffold.

Development of potent and selective inhibitors targeting the papain-like protease of SARS-CoV-2.

The COVID-19 pandemic has been disastrous to society and effective drugs are urgently needed. The papain-like protease domain (PLpro) of SARS-CoV-2 (SCoV2) is indispensable for viral replication and represents a putative target for pharmacological intervention. In this work, we describe the development of a potent and selective SCoV2 PLpro inhibitor, 19. The inhibitor not only effectively blocks substrate cleavage and immunosuppressive function imparted by PLpro, but also markedly mitigates SCoV2 replication in human cells, with a submicromolar IC50. We further present a convenient and sensitive activity probe, 7, and complementary assays to readily evaluate SCoV2 PLpro inhibitors in vitro or in cells. In addition, we disclose the co-crystal structure of SCoV2 PLpro in complex with a prototype inhibitor, which illuminates their detailed binding mode. Overall, these findings provide promising leads and important tools for drug discovery aiming to target SCoV2 PLpro.