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European Biopharmaceutical Review
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To move away from ‘all-comers’ trials, the early identification of genetic factors influencing effectiveness in patient subpopulations is key. A recent increase in the use of predictive human tissue models led to the idea for a nationwide multidisciplinary collaboration that combined assays in fresh, diseased human tissues studied ex vivo, with whole exome sequencing. This allowed genomic markers to be recognised, distinguishing patients identified by the human tissue assays as ‘good’ or ‘poor’ responders.
This Scottish pharmacogenomics project has made great strides in the understanding of chronic obstructive pulmonary disease (COPD) and inflammatory bowel diseases (IBD) by using whole exome sequencing, bioinformatics and fresh human tissue assays to relate drug response to genotype. The study findings underpin a new framework for patient stratification in clinical trials that combines in vitro and molecular informatics to improve pharma success rates and accelerate the availability of effective medications.
The most common cause of drug failure is poor efficacy at Phase 1 or 3, which is, in part, attributed to clinical trials of entire patient populations, including both ‘responders’ and ‘nonresponders’ (1,2). Precision medicine can improve the prediction of clinical efficacy by selecting only those patient subpopulations likely to gain clear benefit for trials, but such forecasts require methods to identify patient subpopulations at an early stage, ideally during preclinical testing (2-4). Human tissue data generated in early discovery via a wide range of models – including stem cells, fresh human tissues and pathological specimens – is increasingly being used in preclinical testing (5-7). Tissue samples collected from the target patient population allow researchers to directly test the effect of new drugs in systems that closely reflect the in vivo situation, providing an early prediction of efficacy. The responses of tissues from different patients when studied in vitro can vary considerably, reflecting the clinical reality of responders and non-responders.
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