Cardio-vascular safety beyond hERG: in silico modelling of a guinea pig right atrium assay.

As chemists can easily produce large numbers of new potential drug candidates, there is growing demand for high capacity models that can help in driving the chemistry towards efficacious and safe candidates before progressing towards more complex models. Traditionally, the cardiovascular (CV) safety domain plays an important role in this process, as many preclinical CV biomarkers seem to have high prognostic value for the clinical outcome. Throughout the industry, traditional ion channel binding data are generated to drive the early selection process. Although this assay can generate data at high capacity, it has the disadvantage of producing high numbers of false negatives. Therefore, our company applies the isolated guinea pig right atrium (GPRA) assay early-on in discovery. This functional multi-channel/multi-receptor model seems much more predictive in identifying potential CV liabilities. Unfortunately however, its capacity is limited, and there is no room for full automation. We assessed the correlation between ion channel binding and the GPRA's Rate of Contraction (RC), Contractile Force (CF), and effective refractory frequency (ERF) measures assay using over six thousand different data points. Furthermore, the existing experimental knowledge base was used to develop a set of in silico classification models attempting to mimic the GPRA inhibitory activity. The Naïve Bayesian classifier was used to built several models, using the ion channel binding data or in silico computed properties and structural fingerprints as descriptors. The models were validated on an independent and diverse test set of 200 reference compounds. Performances were assessed on the bases of their overall accuracy, sensitivity and specificity in detecting both active and inactive molecules. Our data show that all in silico models are highly predictive of actual GPRA data, at a level equivalent or superior to the ion channel binding assays. Furthermore, the models were interpreted in terms of the descriptors used to highlight the undesirable areas in the explored chemical space, specifically regions of low polarity, high lipophilicity and high molecular weight. In conclusion, we developed a predictive in silico model of a complex physiological assay based on a large and high quality set of experimental data. This model allows high throughput in silico safety screening based on chemical structure within a given chemical space.

Permeability of small molecules through a lipid bilayer: a multiscale simulation study.

The transmembrane permeation of eight small (molecular weight <100) organic molecules across a phospholipid bilayer is investigated by multiscale molecular dynamics simulation. The bilayer and hydrating water are represented by simplified, efficient coarse-grain models, whereas the permeating molecules are described by a standard atomic-level force-field. Permeability properties are obtained through a refined version of the z-constraint algorithm. By constraining each permeant at selected depths inside the bilayer, we have sampled free energy differences and diffusion coefficients across the membrane. These data have been combined, according to the inhomogeneous solubility-diffusion model, to yield the permeability coefficients. The results are generally consistent with previous atomic-level calculations and available experimental data. Computationally, our multiscale approach proves 2 orders of magnitude faster than traditional atomic-level methods.

A quantitative coarse-grain model for lipid bilayers.

A simplified particle-based computer model for hydrated phospholipid bilayers has been developed and applied to quantitatively predict the major physical features of fluid-phase biomembranes. Compared with available coarse-grain methods, three novel aspects are introduced. First, the main electrostatic features of the system are incorporated explicitly via charges and dipoles. Second, water is accurately (yet efficiently) described, on an individual level, by the soft sticky dipole model. Third, hydrocarbon tails are modeled using the anisotropic Gay-Berne potential. Simulations are conducted by rigid-body molecular dynamics. Our technique proves 2 orders of magnitude less demanding of computational resources than traditional atomic-level methodology. Self-assembled bilayers quantitatively reproduce experimental observables such as electron density, compressibility moduli, dipole potential, lipid diffusion, and water permeability. The lateral pressure profile has been calculated, along with the elastic curvature constants of the Helfrich expression for the membrane bending energy; results are consistent with experimental estimates and atomic-level simulation data. Several of the results presented have been obtained for the first time using a coarse-grain method. Our model is also directly compatible with atomic-level force fields, allowing mixed systems to be simulated in a multiscale fashion.

Structural studies of Salmonella typhimurium ArnB (PmrH) aminotransferase: a 4-amino-4-deoxy-L-arabinose lipopolysaccharide-modifying enzyme.

Lipid A modification with 4-amino-4-deoxy-L-arabinose confers on certain pathogenic bacteria, such as Salmonella, resistance to cationic antimicrobial peptides, including those derived from the innate immune system. ArnB catalysis of amino group transfer from glutamic acid to the 4"-position of a UDP-linked ketopyranose molecule to form UDP-4-amino-4-deoxy-L-arabinose represents a key step in the lipid A modification pathway. Structural and functional studies of the ArnB aminotransferase were undertaken by combining X-ray crystallography with biochemical analyses. High-resolution crystal structures were solved for two native forms and one covalently inhibited form of S. typhimurium ArnB. These structures permitted identification of key residues involved in substrate binding and catalysis, including a rarely observed nonprolyl cis peptide bond in the active site.