Author Correction: Exploring use of unsupervised clustering to associate signaling profiles of GPCR ligands to clinical response.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Exploring use of unsupervised clustering to associate signaling profiles of GPCR ligands to clinical response.

Signaling diversity of G protein-coupled (GPCR) ligands provides novel opportunities to develop more effective, better-tolerated therapeutics. Taking advantage of these opportunities requires identifying which effectors should be specifically activated or avoided so as to promote desired clinical responses and avoid side effects. However, identifying signaling profiles that support desired clinical outcomes remains challenging. This study describes signaling diversity of mu opioid receptor (MOR) ligands in terms of logistic and operational parameters for ten different in vitro readouts. It then uses unsupervised clustering of curve parameters to: classify MOR ligands according to similarities in type and magnitude of response, associate resulting ligand categories with frequency of undesired events reported to the pharmacovigilance program of the Food and Drug Administration and associate signals to side effects. The ability of the classification method to associate specific in vitro signaling profiles to clinically relevant responses was corroborated using ß2-adrenergic receptor ligands.

Developing structure-activity relationships for the prediction of hepatotoxicity.

Drug-induced liver injury is a major issue of concern and has led to the withdrawal of a significant number of marketed drugs. An understanding of structure-activity relationships (SARs) of chemicals can make a significant contribution to the identification of potential toxic effects early in the drug development process and aid in avoiding such problems. This process can be supported by the use of existing toxicity data and mechanistic understanding of the biological processes for related compounds. In the published literature, this information is often spread across diverse sources and can be varied and unstructured in quality and content. The current work has explored whether it is feasible to collect and use such data for the development of new SARs for the hepatotoxicity endpoint and expand upon the limited information currently available in this area. Reviews of hepatotoxicity data were used to build a structure-searchable database, which was analyzed to identify chemical classes associated with an adverse effect on the liver. Searches of the published literature were then undertaken to identify additional supporting evidence, and the resulting information was incorporated into the database. This collated information was evaluated and used to determine the scope of the SARs for each class identified. Data for over 1266 chemicals were collected, and SARs for 38 classes were developed. The SARs have been implemented as structural alerts using Derek for Windows (DfW), a knowledge-based expert system, to allow clearly supported and transparent predictions. An evaluation exercise performed using a customized DfW version 10 knowledge base demonstrated an overall concordance of 56% and specificity and sensitivity values of 73% and 46%, respectively. The approach taken demonstrates that SARs for complex endpoints can be derived from the published data for use in the in silico toxicity assessment of new compounds.

Evaluation of a published in silico model and construction of a novel Bayesian model for predicting phospholipidosis inducing potential.

The identification of phospholipidosis (PPL) during preclinical testing in animals is a recognized problem in the pharmaceutical industry. Depending on the intended indication and dosing regimen, PPL can delay or stop development of a compound in the drug discovery process. Therefore, for programs and projects where a PPL finding would have adverse impact on the success of the project, it would be desirable to be able to rapidly identify and screen out those compounds with the potential to induce PPL as early as possible. Currently, electron microscopy is the gold standard method for identifying phospholipidosis, but it is low-throughput and resource-demanding. Therefore, a low-cost, high-throughput screening strategy is required to overcome these limitations and be applicable in the drug discovery cycle. A recent publication by Ploemen et al. (Exp. Toxicol. Pathol. 2004, 55, 347-55) describes a method using the computed physicochemical properties pKa and ClogP as part of a simple calculation to determine a compound's potential to induce PPL. We have evaluated this method using a set of 201 compounds, both public and proprietary, with known in vivo PPL-inducing ability and have found the overall concordance to be 75%. We have proposed simple modifications to the model rules, which improve the model's concordance to 80%. Finally, we describe the development of a Bayesian model using the same compound set and found its overall concordance to be 83%.