Chemotypes sensitivity and predictivity of in vivo outcomes for cytotoxic assays in THLE and HepG2 cell lines.

In the present study, we demonstrate the utility of in vitro ATP depletion assays in both THLE and HepG2 cells for predicting the toxicological outcome in Exploratory Toxicology Studies across 446 Pfizer proprietary compounds. Our results suggest a higher likelihood of selecting suitable compounds for in vivo safety studies by using cytotoxicity assays in multiple cell-lines over a single cell line. In addition, we demonstrate that different cell-lines have different sensitivities to compounds depending on their ionization state, that is, acid, base or neutral.

Using an in vitro cytotoxicity assay to aid in compound selection for in vivo safety studies.

Recent publications have demonstrated that using calculated physiochemical properties can help in the design of compounds that have a decreased risk of significant findings in rodent toxicology studies. In this Letter, we extend this concept and incorporate results from a high throughput cytotoxicity assay to help the drug discovery community select compounds for progression into in vivo studies. The results are presented in an easily interpretable odds ratio so that teams can readily compare compounds and progress potential clinical candidates to the necessary rodent in vivo studies.

The computational prediction of genotoxicity.

Importance Of The Field: The computational prediction of genotoxicity is important to the early identification of those chemical entities that have the potential to cause carcinogenicity in humans.

Areas Covered In This Review: The review discusses key scientific developments in the prediction of Ames mutagenicity and in vitro chromosome damage over the past 4 - 5 years. The performance and limitations of computational approaches are discussed in relation to published and internal validation exercises.

Discovery of novel dialkyl substituted thiophene inhibitors of HCV by in silico screening of the NS5B RdRp.

A novel 5,4-dialkyl substituted thiophene was discovered by in silico screening of the 3D polymerase crystal structure (1GX6) that demonstrated single digit micromolar HCV inhibition activity in the replicon assay and dose-dependent inhibition in the replicase complex assay. Subsequently, SAR was explored with a small set of dialkyl and tetrahydro-benzo thiophenes. Since these thiophenes inhibit synthesis of both, single- and double-stranded RNAs, their mechanism of action is distinct from other known HCV inhibitors.

Small molecule inhibitors of E. coli primase, a novel bacterial target.

Bacterial primase is essential for DNA replication in Gram-positive and Gram-negative bacteria. It is also structurally distinct from eukaryotic primases, and therefore an attractive, but under-explored, target for therapeutic intervention. We applied virtual screening to discover primase inhibitors, and subsequently several commercially available analogs of these initial hits showed potent primase inhibition and in vitro antibacterial activity.

RNA unrestrained molecular dynamics ensemble improves agreement with experimental NMR data compared to single static structure: a test case.

Nuclear magnetic resonance (NMR) provides structural and dynamic information reflecting an average, often non-linear, of multiple solution-state conformations. Therefore, a single optimized structure derived from NMR refinement may be misleading if the NMR data actually result from averaging of distinct conformers. It is hypothesized that a conformational ensemble generated by a valid molecular dynamics (MD) simulation should be able to improve agreement with the NMR data set compared with the single optimized starting structure.

The impact of informatics and computational chemistry on synthesis and screening.

High-throughput synthesis and screening technologies have enhanced the impact of computational chemistry on the drug discovery process. From the design of targeted, drug-like libraries to 'virtual' optimization of potency, selectivity and ADME/Tox properties, computational chemists are able to efficiently manage costly resources and dramatically shorten drug discovery cycle times. This review will describe some of the successful strategies and applications of state-of-the-art algorithms to enhance drug discovery, as well as key points in the drug discovery process where computational methods can have, and have had, greatest impact.