Neurological disorders present a high health care burden to society and are becoming of increasing concern with an aging global population. Adding to this burden, numerous medications are associated with adverse drug reactions that influence the nervous system, causing debilitating drug-induced neurotoxicities.
Pharmacogenomics offers the potential to make medications both safer and more effective through the use of genetics to determine optimal therapeutic regimes on an individualized basis.
For example, there are numerous immunomodulatory agents available for the treatment of the neurodegenerative disorder, multiple sclerosis, but each come with their own risk with regards to adverse events.
My research has identified a novel pharmacogenomic biomarker for drug-induced liver injury in multiple sclerosis. The genetic variant increases the risk for this serious adverse reaction from interferon-beta by eight times and is also associated with the expression of an interferon regulatory factor gene, IRF6. This finding was replicated in an independent cohort and was also shown to be associated with peak biochemical liver test results in Vanderbilt’s BioVU repository.
Further, my research on drug-induced neuropathies caused by the chemotherapeutic agent, vincristine, has revealed that there is an overlap between genes influencing drug-induced neuropathies and those that heritable neuropathy conditions.
An additional area of interest is studying genetic modifiers of the adult-onset neurodegenerative disorder, Huntington disease (HD), that is caused by a CAG repeat expansion in the HTT gene. Unfortunately, repeat length only explains approximately 70% of the variability of age of onset observed between patients, reflecting the need for additional modifiers to be identified.
I have recently been studying genomic modifiers of HD, both at the HTT locus, and throughout the genome, through studying the flanking repeat structure at the disease locus, as well as performing a transcription-wide association study using data from over 4,000 HD patients. The results of these two studies offer the potential to improve age of onset prediction models and prioritize novel therapeutic targets.