Accelerating Lead Identification by High Throughput Virtual Screening: Prospective Case Studies from the Pharmaceutical Industry.

At the onset of a drug discovery program, the goal is to identify novel compounds with appropriate chemical features that can be taken forward as lead series. Here, we describe three prospective case studies, Bruton Tyrosine Kinase (BTK), RAR-Related Orphan Receptor γ t (RORγt), and Human Leukocyte Antigen DR isotype (HLA-DR) to illustrate the positive impact of high throughput virtual screening (HTVS) on the successful identification of novel chemical series. Each case represents a project with a varying degree of difficulty due to the amount of structural and ligand information available internally or in the public domain to utilize in the virtual screens. We show that HTVS can be effectively employed to identify a diverse set of potent hits for each protein system even when the gold standard, high resolution structural data or ligand binding data for benchmarking, is not available.

An Analysis of Different Components of a High-Throughput Screening Library.

Since many projects at pharmaceutical organizations get their start from a high-throughput screening (HTS) campaign, improving the quality of the HTS deck can improve the likelihood of discovering a high-quality lead molecule that can be progressed to a drug candidate. Over the past decade, Janssen has implemented several strategies for external compound acquisition to augment the screening deck beyond the chemical space and number of molecules synthesized for internal projects. In this report, we analyzed the performance of each of those compound collections in the screening campaigns performed internally within Janssen during the last five years. We classified the screening library into two broad categories: Internal and External. The comparison of the performance of these sets of libraries was done by considering the primary, confirmation, and dose response hit rates. Our analysis revealed that Internal compounds (resulting from numerous medicinal chemistry efforts against diverse protein targets) have higher average confirmation hit rates than External ones; however, actives from both categories show similar probabilities of hitting multiple distinct targets. We also investigated the property landscape of both sets of libraries to identify the key elements which make a difference in these categories of compounds. From this analysis, Janssen aims to understand the descriptor landscape of the compounds with the highest hit rates and to use them for improving its future acquisition strategies as well as to inform our plating strategy.

Determination of a Focused Mini Kinase Panel for Early Identification of Selective Kinase Inhibitors.

We analyzed an extensive data set of 3000 Janssen kinase inhibitors (spanning some 40 therapeutic projects) profiled at 414 kinases in the DiscoverX KINOME scan to better understand the necessity of using such a full kinase panel versus simply profiling one's compound at a much smaller number of kinases, or mini kinase panel (MKP), to assess its selectivity. To this end, we generated a series of MKPs over a range of sizes and of varying kinase membership using Monte Carlo simulations. By defining the kinase hit index (KHI), we quantified a compound's selectivity based on the number of kinases it hits. We find that certain combinations (rather than a random selection) of kinases can result in a much lower average error. Indeed, we identified a focused MKP with a 45.1% improvement in the average error (compared to random) that yields an overall correlation of R2 = 0.786-0.826 for the KHI compared to the full kinase panel value. Unlike using a full kinase panel, which is both time and cost restrictive, a focused MKP is amenable to the triaging of all early stage compounds. In this way, promiscuous compounds are filtered out early on, leaving the most selective compounds for lead optimization.