Through integrated ‘omics projects, researchers at the LSI are implementing or developing technologies aimed at changing clinical practice to enable precise prescribing for safer and more effective therapies.
Impact on Family Practice Prescribing
Each year in Canada, there are approximately 200,000 severe adverse drug events, claiming 10,000 to 22,000 lives, and costing $13.7 to $17.7 billion. Physicians cannot predict whether a patient will gain the desired benefit from a prescribed medication or whether they will experience harmful side effects. This is particularly relevant in primary care, where Family Physicians (FPs) deliver 85% of healthcare, and write the majority of prescriptions (≈20,000/year). Genetic tests may reduce this potential harm for many medications; however there is currently no way of incorporating genetic information into the routine FP prescribing processes. We have developed a project with Dr Martin Dawes, Head, Family Practice Department to develop and test a decision support tool, TreatGx, which creates a drug/dose recommendation, using genetic data and information in the family physician’s electronic medical record. The anticipated project outcomes are a set of evidence-informed genetically based algorithms that will have been tested in the context of a community-based primary care clinical encounter.
Impact on Cancer Therapy
Therapeutic efficacy in cancer is one of the lowest of all indications. This is not surprising when we consider that each patient is different, each cancer is unique and cancers are clonally diverse populations of cells that obey evolutionary principles. Today there are about 90 known mutations for which a treatment is available or is in late stage clinical trial. To impact cancer therapy and deliver safer more effective therapy, Contextual Genomics working together with the LSI aims to a state-of-the-art cancer diagnostic test that surveys these 90 “actionable” mutations. Such a test will enable clinicians to prescribe available drug therapies based on the presence of the mutations and match the specific cancer and patient with the treatment that has the highest likelihood of success in the shortest possible time, improving outcomes in a cost and time efficient manner.
Impact on Disease Detection
Identifying disease early before it manifests is a key tenet of Personalized Medicine. Integrated Translational Teams are establishing the means to classify newborns along the autism spectrum so that correct early behavioral intervention can be enacted to the greatest benefit of the child. Instead of waiting for sudden death as the first evidence of an inherited cardiac arrhythmia we are developing tests to identify risk at the time of birth. Using microbiomic approaches we have established the means to determine in the first three months after birth who will develop asthma.