Discovery, characterization, and lead optimization of 7-azaindole non-nucleoside HIV-1 reverse transcriptase inhibitors.

A library of 585 compounds built off a 7-azaindole core was evaluated for anti-HIV-1 activity, and ten hits emerged with submicromolar potency and therapeutic index >100. Of these, three were identified as non-nucleoside reverse transcriptase (RT) inhibitors and were assayed against relevant resistant mutants. Lead compound 8 inhibited RT with submicromolar potency (IC50=0.73µM) and also maintained some activity against the clinically important RT mutants K103N and Y181C (IC50=9.2, 3.5µM) in cell-free assays. Free energy perturbation guided lead optimization resulted in the development of a compound with a two-fold increase in potency against RT (IC50=0.36µM). These data highlight the discovery of a unique scaffold with the potential to move forward as next-generation anti-HIV-1 agents.

Design, synthesis and evaluation of novel anti-HCV molecules that deliver intracellularly three highly potent NS5A inhibitors.

The design and synthesis of new non-symmetrical NS5A inhibitors with sulfur containing amino acids is reported along with their ability to block HCV replication in an HCV 1b replicon system. These compounds display EC50 values in the picomolar range with a large therapeutic index (>10(6)). Moreover, cellular pharmacology studies show that our preferred compounds intracellularly deliver three potent NS5A inhibitors.

Approaches for the development of antiviral compounds: the case of hepatitis C virus.

Traditional methods for general drug discovery typically include evaluating random compound libraries for activity in relevant cell-free or cell-based assays. Success in antiviral development has emerged from the discovery of more focused libraries that provide clues about structure activity relationships. Combining these with more recent approaches including structural biology and computational modeling can work efficiently to hasten discovery of active molecules, but that is not enough. There are issues related to biology, toxicology, pharmacology, and metabolism that have to be addressed before a hit compound becomes nominated for clinical development. The objective of gaining early preclinical knowledge is to reduce the risk of failure in Phases 1, 2, and 3, leading to the goal of approved drugs that benefit the infected individual. This review uses hepatitis C virus (HCV), for which we still do not have an ideal therapeutic modality, as an example of the multidisciplinary efforts needed to discover new antiviral drugs for the benefit of humanity.

"Plate cherry picking": a novel semi-sequential screening paradigm for cheaper, faster, information-rich compound selection.

This work describes a novel semi-sequential technique for in silico enhancement of high-throughput screening (HTS) experiments now employed at Novartis. It is used in situations in which the size of the screen is limited by the readout (e.g., high-content screens) or the amount of reagents or tools (proteins or cells) available. By performing computational chemical diversity selection on a per plate basis (instead of a per compound basis), 25% of the 1,000,000-compound screening was optimized for general initial HTS. Statistical models are then generated from target-specific primary results (percentage inhibition data) to drive the cherry picking and testing from the entire collection. Using retrospective analysis of 11 HTS campaigns, the authors show that this method would have captured on average two thirds of the active compounds (IC(50) < 10 microM) and three fourths of the active Murcko scaffolds while decreasing screening expenditure by nearly 75%. This result is true for a wide variety of targets, including G-protein-coupled receptors, chemokine receptors, kinases, metalloproteinases, pathway screens, and protein-protein interactions. Unlike time-consuming "classic" sequential approaches that require multiple iterations of cherry picking, testing, and building statistical models, here individual compounds are cherry picked just once, based directly on primary screening data. Strikingly, the authors demonstrate that models built from primary data are as robust as models built from IC(50) data. This is true for all HTS campaigns analyzed, which represent a wide variety of target classes and assay types.