There is extensive genetic variation among the human population in the amino acid sequence of a drug receptor protein. Some variants may lead to altered drug response or adverse reactions (red).
The individual phenotypic and molecular response to a given therapeutic treatment is a convoluted trail, shaped by inherited and environmental factors. The genetic variation across individuals leads to personal differences in the metabolism, therapeutic effects and adverse reactions of drugs. Pharmacogenomics aims to target treatments by considering each individual’s genetic makeup. The main goal of personalised medicine is to tailor drug prescriptions to individuals to achieve the most effective and least harmful treatment. This is important to quicker find the optimal of multiple drugs and to avoid unnecessarily high dosages.
Key to realising the potential of personalised medicine is integrating systems biology, statistics and bioinformatics to associate genotypes to phenotypes. Our work bridges the fields of genetics and genomics, structural biology, pharmacology, drug development and medicine, as a case study for GPCRs. This is very timely considering the novel big datasets which combined with high-throughput pharmacology and the unique Danish registries provide an innovative new approach to advance personalised medicine.
On this foundation, we have started a new project titled Advancing personalised medicine for psychiatric diseases through integrative GPCR Pharmacogenomics, funded by the Lundbeck Foundation (~10 million DKK). The project is headed by Hans Bräuner-Osborne and the computational data science analyses are driven by Alexander Hauser within the Gloriam group. The project is run in collaboration with iPSYCH collaborators Preben Bo Mortensen (National Centre for Register-based Research) and Thomas Werge (Institute of Biological Psychiatry).
The project spans four specific aims:
1. Predict GPCR mutations affecting CNS drug response (in silico)
2. Determine how psychiatric drug responses are affected by genetic variations (in vitro)
3. Delineate genetic variants associated with disease or a change of clinical therapy (in human)
4. Create a public platform to phenotype GPCR variants for personalised medicine (in field)
Our linking of missense variants to patient disease cohorts will be very valuable for: (i) more accurate patient sub-diagnoses for genotype-based personalised treatment, (ii) patient stratification upon entering clinical trials, and (iii) increased precision in delineation of changes in clinical therapy, (iv) personalise medicine prescriptions based on GPCR genotypes, (v) prioritise drugs for pharmacovigilance investigations, and (vi) design post-market follow-up studies e.g. drug repurposing.