A global manufacturer of consumer products.
As part of their overall regulatory requirements, the client was asked to provide information on the known or predicted metabolites of a number of ingredients in their products. This formed part of a broader programme of work that bibra conducted for this client, in which the toxicological hazards of both these same ingredients and those metabolites determined to be of the highest priority for further evaluation were also assessed.
Bibra was asked to provide a combined report on each ingredient of interest, detailing the metabolites identified in the published scientific literature – including those implied by Expert Group reviews of groups of structurally-related substances – and those predicted by reliable (Q)SAR models. The combined list of literature and in silico predicted metabolites would then be ranked in terms of high and low priority compounds for future toxicological assessment.
Approach and outcome
The bibra toxicologists developed a robust protocol to combine the literature and computational aspects of this work. Literature searches were conducted in several sources, including bibra’s in-house database TRACE. Data on relevant stereoisomers were also considered. Any metabolite reported in a peer-reviewed publication, as well as any potential metabolite that could be inferred from descriptions of likely metabolic pathways by Expert Groups, were tabulated in a report, including details on the site and extent of metabolism.
A (Q)SAR approach was applied, in which metabolites were predicted using the OECD (Q)SAR Toolbox metabolic profilers and the Meteor Nexus software package. The results from these two programmes were combined and duplicates removed. These, too, were tabulated in the report.
A final table combined the literature and in silico metabolites. All (potential) metabolites were subjected to a pre-determined prioritisation based on the overall weight-of-evidence (such as whether a metabolite was identified confidently by authors of peer-reviewed publications, or whether it was predicted in multiple (Q)SAR models or with very high confidence), with the high-priority compounds subject to future toxicological assessment.