Statistical models for identifying frequent hitters in high throughput screening

Tackling assay interference associated with small molecules

Biochemical and cell-based assays are essential to discovering and optimizing efficacious and safe drugs, agrochemicals and cosmetics. However, false assay readouts stemming from colloidal aggregation, chemical reactivity, chelation, light signal attenuation and emission, membrane disruption, and other interference mechanisms remain a considerable challenge in screening synthetic compounds and natural products. To address assay interference, a range of powerful experimental approaches are available and in silico methods are now gaining traction. This Review begins with an overview of the scope and limitations of experimental approaches for tackling assay interference. It then focuses on theoretical methods, discusses strategies for their integration with experimental approaches, and provides recommendations for best practices. The Review closes with a summary of the critical facts and an outlook on potential future developments.

Identification of Chemical Scaffolds Targeting Drug-Resistant and Latent Mycobacterium tuberculosis through High-Throughput Whole-Cell Screening

Identification of structurally unique chemical entities targeting unexplored bacterial targets is a prerequisite to combat increasing drug resistance against Mycobacterium tuberculosis. This study employed a whole-cell screening approach as an initial filter to scrutinize a 10,000-compound chemical library, resulting in the discovery of seven potent compounds with MIC values ranging from 1.56 to 25 μM. These compounds were categorized into four distinct chemical groups. Remarkably, they demonstrated efficacy against drug-resistant and nonreplicating tuberculosis strains, highlighting their effectiveness across different infection states. With a favorable selectivity index (>10), these compounds showed a safe therapeutic range and exhibited potency in an intracellular model of Mtb infection, mimicking the in vivo setup. Combining these identified hits with established anti-TB drugs revealed additive effects with rifampicin, isoniazid, and bedaquiline. Notably, IIIM-IDD-01 exhibited synergy with isoniazid and bedaquiline, likely due to their complementary mechanisms of targeting Mtb. Most potent hits, IIIM-IDD-01 and IIIM-IDD-02, displayed time- and concentration-dependent killing of Mtb. Mechanistic insights were sought through SEM and docking studies, although comprehensive evaluation is ongoing to unravel the hits' specific targets and modes of action. The hits demonstrated favorable pharmacokinetic properties (ADME-Tox) and showed a low risk of adverse effects, along with a predicted high level of oral bioavailability. These promising hits can serve as an initial basis for subsequent medicinal chemistry endeavors aimed at developing a new series of anti-TB agents. Moreover, the study affirms the significance of high-throughput in vitro assays for the TB drug discovery. It also emphasizes the necessity of targeting diverse TB strains to address the heterogeneity of tuberculosis bacteria.

AI-Driven Discovery of SARS-CoV-2 Main Protease Fragment-like Inhibitors with Antiviral Activity In Vitro

SARS-CoV-2 is the causative agent of COVID-19 and is responsible for the current global pandemic. The viral genome contains 5 major open reading frames of which the largest ORF1ab codes for two polyproteins, pp1ab and pp1a, which are subsequently cleaved into 16 nonstructural proteins (nsp) by two viral cysteine proteases encoded within the polyproteins. The main protease (Mpro, nsp5) cleaves the majority of the nsp's, making it essential for viral replication and has been successfully targeted for the development of antivirals. The first oral Mpro inhibitor, nirmatrelvir, was approved for treatment of COVID-19 in late December 2021 in combination with ritonavir as Paxlovid. Increasing the arsenal of antivirals and development of protease inhibitors and other antivirals with a varied mode of action remains a priority to reduce the likelihood for resistance emerging. Here, we report results from an artificial intelligence-driven approach followed by in vitro validation, allowing the identification of five fragment-like Mpro inhibitors with IC50 values ranging from 1.5 to 241 μM. The three most potent molecules (compounds 818, 737, and 183) were tested against SARS-CoV-2 by in vitro replication in Vero E6 and Calu-3 cells. Compound 818 was active in both cell models with an EC50 value comparable to its measured IC50 value. On the other hand, compounds 737 and 183 were only active in Calu-3, a preclinical model of respiratory cells, showing selective indexes twice as high as those for compound 818. We also show that our in silico methodology was successful in identifying both reversible and covalent inhibitors. For instance, compound 818 is a reversible chloromethylamide analogue of 8-methyl-γ-carboline, while compound 737 is an N-pyridyl-isatin that covalently inhibits Mpro. Given the small molecular weights of these fragments, their high binding efficiency in vitro and efficacy in blocking viral replication, these compounds represent good starting points for the development of potent lead molecules targeting the Mpro of SARS-CoV-2.

Pharmacological Chaperones and Protein Conformational Diseases: Approaches of Computational Structural Biology

Whenever a protein fails to fold into its native structure, a profound detrimental effect is likely to occur, and a disease is often developed. Protein conformational disorders arise when proteins adopt abnormal conformations due to a pathological gene variant that turns into gain/loss of function or improper localization/degradation. Pharmacological chaperones are small molecules restoring the correct folding of a protein suitable for treating conformational diseases. Small molecules like these bind poorly folded proteins similarly to physiological chaperones, bridging non-covalent interactions (hydrogen bonds, electrostatic interactions, and van der Waals contacts) loosened or lost due to mutations. Pharmacological chaperone development involves, among other things, structural biology investigation of the target protein and its misfolding and refolding. Such research can take advantage of computational methods at many stages. Here, we present an up-to-date review of the computational structural biology tools and approaches regarding protein stability evaluation, binding pocket discovery and druggability, drug repurposing, and virtual ligand screening. The tools are presented as organized in an ideal workflow oriented at pharmacological chaperones' rational design, also with the treatment of rare diseases in mind.

Isolation, culture, and use of primary murine myoblasts in small-molecule screens

Small-molecule screens (SMS) are often performed using transformed cell lines that have limited physiological relevance to the biological system being investigated, resulting in poor translational outcomes. To circumvent this limitation, we present a protocol to perform SMS in primary murine myoblasts. We describe steps for isolating primary skeletal muscle myoblasts with greater than 95% purity, then describe techniques to establish a robust dynamic range, and conclude with steps to initiate a successful SMS. For complete details on the use and execution of this protocol, please refer to Richler and Yaffe (1970),¹ Rando and Blau (1994),² and Earle et al. (2020).³.

Computational methods to assist in the discovery of pharmacological chaperones for rare diseases

Pharmacological chaperones are chemical compounds able to bind proteins and stabilize them against denaturation and following degradation. Some pharmacological chaperones have been approved, or are under investigation, for the treatment of rare inborn errors of metabolism, caused by genetic mutations that often can destabilize the structure of the wild-type proteins expressed by that gene. Given that, for rare diseases, there is a general lack of pharmacological treatments, many expectations are poured out on this type of compounds. However, their discovery is not straightforward. In this review, we would like to focus on the computational methods that can assist and accelerate the search for these compounds, showing also examples in which these methods were successfully applied for the discovery of promising molecules belonging to this new category of pharmacologically active compounds.