Improving the predictive power of xenograft and syngeneic anti-tumour studies using mice humanised for pathways of drug metabolism.

Drug development is an expensive and time-consuming process, with only a small fraction of drugs gaining regulatory approval from the often many thousands of candidates identified during target validation. Once a lead compound has been identified and optimised, they are subject to intensive pre-clinical research to determine their pharmacodynamic, pharmacokinetic and toxicological properties, procedures which inevitably involve significant numbers of animals - mainly mice and rats, but also dogs and monkeys in much smaller numbers and for specific types of drug candidates. Many compounds that emerge from this process, having been shown to be safe and efficacious in pre-clinical studies, subsequently fail to replicate this outcome in clinical trials, therefore wasting time, money and, most importantly, animals. The poor predictive power of animal models in pre-clinical studies is predominantly due to lack of efficacy or safety reasons, which in turn can be attributed mainly to the significant species differences in drug metabolism between humans and animals. To circumvent this, we have developed a complex transgenic mouse model - 8HUM - which faithfully replicates human Phase I drug metabolism (and its regulation), and which will generate more human-relevant data [ REFINEMENT ] from fewer animals [ REDUCTION ] in a pre-clinical setting and reduce attrition in the clinic. One key area for the pre-clinical application of animals in an oncology setting - almost exclusively mice - is their use in anti-tumour studies. We now further demonstrate the utility of the 8HUM mouse using a murine melanoma cell line as a syngeneic tumour and also present an immunodeficient version 8HUM_Rag2 -/- - for use in xenograft studies. These models will be of significant benefit not only to Pharma for pre-clinical drug development work, but also throughout the drug efficacy, toxicology, pharmacology, and drug metabolism communities, where fewer animals will be needed to generate more human-relevant data.