An Elemental Impurities Excipient Database: A Viable Tool for ICH Q3D Drug Product Risk Assessment.

To support the practical implementation of the International Council for Harmonisation (ICH) Q3D guideline, which describes a risk-based approach to the control of elemental impurities in drug products, a consortium of pharmaceutical companies has established a database to collate the results of analytical studies of the levels of elemental impurities within pharmaceutical excipients. This database currently includes the results of 26,723 elemental determinations for 201 excipients and represents the largest known, and still rapidly expanding, collection of data of this type. Analysis of the database indicates good coverage of excipients relevant to real-world drug product formulations and tested element profiles consistent with ICH Q3D recommendations. The database includes the results from multiple analytical studies for an excipient and thus incorporates within it an indication of both excipient supplier and batch-to-batch variability as well as any variability associated with the different testing organizations and methods employed. The data confirm the findings of earlier smaller studies that elemental impurity concentrations in excipients are generally low and when used in typical proportions in formulated drug products are unlikely to pose a significant patient safety risk. The database is now in active use as one line of evidence in ICH Q3D risk assessments.

Identification of drug metabolites in human plasma or serum integrating metabolite prediction, LC-HRMS and untargeted data processing.

BACKGROUND: Comprehensive identification of human drug metabolites in first-in-man studies is crucial to avoid delays in later stages of drug development. We developed an efficient workflow for systematic identification of human metabolites in plasma or serum that combines metabolite prediction, high-resolution accurate mass LC-MS and MS vendor independent data processing. Retrospective evaluation of predictions for 14 (14)C-ADME studies published in the period 2007-January 2012 indicates that on average 90% of the major metabolites in human plasma can be identified by searching for accurate masses of predicted metabolites. Furthermore, the workflow can identify unexpected metabolites in the same processing run, by differential analysis of samples of drug-dosed subjects and (placebo-dosed, pre-dose or otherwise blank) control samples. To demonstrate the utility of the workflow we applied it to identify tamoxifen metabolites in serum of a breast cancer patient treated with tamoxifen. RESULTS & CONCLUSION: Previously published metabolites were confirmed in this study and additional metabolites were identified, two of which are discussed to illustrate the advantages of the workflow.