Analytical identification of metabolites for a drug candidate is usually a time consuming and low-throughput task and is performed only at the later phases of drug development. Therefore the possibility to predict possible sites of human liver microsomal (HLM) metabolism using in silico techniques would be a very attractive feature for any medicinal chemist.

Of all the challenges facing medicinal chemists in general, one of the most significant must be transforming an active molecule into a viable drug. Lead optimization efforts are guided by a combination of factors, such as potency, ease of synthesis, patentability concerns, specific synthetic constrains of the interaction with the target, as well as the lead’s toxicity and ADME properties.

This study presents a novel approach to aid this assessment based on probabilistic predictors of mutagenicity in Ames test and binding to Estrogen Receptor, supplemented by a knowledge-based system of structural alerts.

Analytical identification of metabolites for a drug candidate is usually a time consuming and low-throughput task which is performed only in late drug development phases. Therefore the ability to predict possible sites of human liver microsomal metabolism using in silico techniques would be highly beneficial for any medicinal chemist.

Metabolic stability, determined in liver microsomes, is one of the primary assays used in early drug discovery. A key factor limiting compound half-life is the cytochrome P450 mediated metabolism. High clearance by these enzymes implies a higher and more frequent dosing as well as poses a risk for individual variations in exposure.

Characterizing metabolite structures and metabolic pathways is essential to understanding the potential biological implications of compounds in drug discovery.

Characterizing metabolite structures and metabolic pathways is essential to understanding the potential biological implications of compounds in drug discovery.

Quality by Design (QbD) has become popular within the pharmaceutical industry and the FDA has cited a risk-based approach to drug development as a desirable state for the near future.1 In this state, more complete information and transparency relating to risk assessment will be made available for new drug aplications speeding the approval process, and preventing late-stage failures.

Lexicon Pharmaceuticals have demonstrated that a practical automated verification system using HPLC, LC/MS, and 1D and 2D NMR can be implemented in an industrial/pharmaceutical environment. This system has proven to be robust, and provides added value to compound collection integrity and quality.

LIMS are useful in managing certain types of data for quality management and sample tracking. However, these systems are generally limited in the types of information they can process and store. A unique automated quality management system has been developed and implemented at a fragrance manufacturer to evaluate chromatographic and spectral data of samples during quality testing.