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. HepG2 cells are more sensitive for basic compounds, whereas THLE cells have a relatively higher sensitivity for the acidic and neutral compounds. These in vitro cytotoxicity assays when combined with physicochemical properties (cLogP >3 and topological polar surface area (TPSA) <75Å(2)), are the most effective means to prioritize compounds having a lower probability of causing adverse events in vivo.

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. Their application to the modern drug discovery paradigm is also discussed. WHAT THE READER WILL GAIN: Key highlights of a review of the recent scientific literature for the prediction of Ames mutagenicity and chromosome damage and an appreciation of the factors that limit the predictive performance of in silico systems. TAKE HOME MESSAGE: Current in silico systems perform well in the mutagenicity prediction of the publicly-derived data on which they are based, but their performance outside the applicability domain is considerably lower. We conclude that it is the lack of mechanistic structure-activity relationships and limited access to high quality proprietary data which are holding back computational genotoxicity from reaching higher predictive levels.

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. This work provides a 3D pharmacophore for primase ligands, SAR trends, and leads that can be further optimized.

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. Using a model system consisting of two sequence-related self-complementary ribonucleotide octamers for which NMR data was available, 0.3 ns particle mesh Ewald MD simulations were performed in the AMBER force field in the presence of explicit water and counterions. Agreement of the averaged properties of the molecular dynamics ensembles with NMR data such as homonuclear proton nuclear Overhauser effect (NOE)-based distance constraints, homonuclear proton and heteronuclear (1)H-(31)P coupling constant (J) data, and qualitative NMR information on hydrogen bond occupancy, was systematically assessed. Despite the short length of the simulation, the ensemble generated from it agreed with the NMR experimental constraints more completely than the single optimized NMR structure. This suggests that short unrestrained MD simulations may be of utility in interpreting NMR results. As expected, a 0.5 ns simulation utilizing a distance dependent dielectric did not improve agreement with the NMR data, consistent with its inferior exploration of conformational space as assessed by 2-D RMSD plots. Thus, ability to rapidly improve agreement with NMR constraints may be a sensitive diagnostic of the MD methods themselves.

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.