The normal metabolism of drugs can generate metabolites that have intrinsic chemical reactivity towards cellular molecules, and therefore have the potential to alter biological function and initiate serious adverse drug reactions. Here, we present an assessment of the current approaches used for the evaluation of chemically reactive metabolites. We also describe how these approaches are being used within the pharmaceutical industry to assess and minimize the potential of drug candidates to cause toxicity. At early stages of drug discovery, iteration between medicinal chemistry and drug metabolism can eliminate perceived reactive metabolite-mediated chemical liabilities without compromising pharmacological activity or the need for extensive safety evaluation beyond standard practices. In the future, reactive metabolite evaluation may also be useful during clinical development for improving clinical risk assessment and risk management. Currently, there remains a huge gap in our understanding of the basic mechanisms that underlie chemical stress-mediated adverse reactions in humans. This review summarizes our views on this complex topic, and includes insights into practices considered by the pharmaceutical industry.

Reported predictions of human in vivo hepatic clearance from in vitro data have used a variety of values for the scaling factors human microsomal protein (MPPGL) and hepatocellularity (HPGL) per gram of liver, generally with no consideration of the extent of their inter-individual variability. We have collated and analysed data from a number of sources, to provide weighted meangeo values of human MPPGL and HPGL of 32 mg g-1 (95% Confidence Interval (CI); 29-34 mg.g-1) and 99x10(6) cells.g-1 (95% CI; 74-131 mg.g-1), respectively. Although inter-individual variability in values of MPPGL and HPGL was statistically significant, gender, smoking or alcohol consumption could not be detected as significant covariates by multiple linear regression. However, there was a weak but statistically significant inverse relationship between age and both MPPGL and HPGL. These findings indicate the importance of considering differences between study populations when forecasting in vivo pharmacokinetic behaviour. Typical clinical pharmacology studies, particularly in early drug development, use young, fit, healthy male subjects of around 30 years of age. In contrast, the average age of patients for many diseases is about 60 years of age. The relationship between age and MPPGL observed in this study estimates values of 40 mg.g-1 for a 30 year old individual and 31 mg.g-1 for a 60 year old individual. Investigators may wish to consider the reported covariates in the selection of scaling factors appropriate for the population in which estimates of clearance are being predicted. Further studies are required to clarify the influence of age (especially in paediatric subjects), donor source and ethnicity on values of MPPGL and HPGL. In the meantime, we recommend that the estimates (and their variances) from the current meta-analysis be used when predicting in vivo kinetic parameters from in vitro data.

The 7th amendment to the EU Cosmetics Directive prohibits to put animal-tested cosmetics on the market in Europe after 2013. In that context, the European Commission invited stakeholder bodies (industry, non-governmental organisations, EU Member States, and the Commission's Scientific Committee on Consumer Safety) to identify scientific experts in five toxicological areas, i.e. toxicokinetics, repeated dose toxicity, carcinogenicity, skin sensitisation, and reproductive toxicity for which the Directive foresees that the 2013 deadline could be further extended in case alternative and validated methods would not be available in time. The selected experts were asked to analyse the status and prospects of alternative methods and to provide a scientifically sound estimate of the time necessary to achieve full replacement of animal testing. In summary, the experts confirmed that it will take at least another 7-9 years for the replacement of the current in vivo animal tests used for the safety assessment of cosmetic ingredients for skin sensitisation. However, the experts were also of the opinion that alternative methods may be able to give hazard information, i.e. to differentiate between sensitisers and non-sensitisers, ahead of 2017. This would, however, not provide the complete picture of what is a safe exposure because the relative potency of a sensitiser would not be known. For toxicokinetics, the timeframe was 5-7 years to develop the models still lacking to predict lung absorption and renal/biliary excretion, and even longer to integrate the methods to fully replace the animal toxicokinetic models. For the systemic toxicological endpoints of repeated dose toxicity, carcinogenicity and reproductive toxicity, the time horizon for full replacement could not be estimated.

The use of structured frameworks can be invaluable in promoting harmonization in the assessment of chemical risk. IPCS has therefore updated and extended its mode of action (MOA) framework for cancer to address the issue of human relevance of a carcinogenic response observed in an experimental study. The first stage is to determine whether it is possible to establish an MOA. This comprises a series of key events along the causal pathway to cancer, identified using a weight-of-evidence approach based on the Bradford Hill criteria. The key events are then compared first qualitatively and then quantitatively between the experimental animals and humans. Finally, a clear statement of confidence, analysis, and implications is produced. The IPCS human relevance framework for cancer provides an analytical tool to enable the transparent evaluation of the data, identification of key data gaps, and structured presentation of information that would be of value in the further risk assessment of the compound, even if relevancy cannot be excluded. This might include data on the shape of the dose-response curve, identification of any thresholds and recognition of potentially susceptible subgroups, for example, the basis of genetic or life-stage differences.