Genetic testing has expanded significantly, both in clinical contexts and beyond. Understanding the distinctions between testing modalities—genotyping, targeted panels, exome sequencing, and whole genome sequencing—matters for decisions about study design, eligibility criteria, and participant qualification. The available testing modalities differ meaningfully in scope, cost, and the nature of information they generate.
Virtual and decentralized trial models have moved from emergency workaround to strategic default for many precision medicine programs. This article examines when a virtual approach creates real operational and scientific value, and where sponsors should focus to make it work.
- Sano Genetics supports personalised medicine research by increasing participation in clinical trials and guiding patients through the process
- Half of clinical trials are delayed due to recruitment issues and 85% fail as they can’t retain enough participants
- Seed round will fund free at-home DNA testing kits for 3,000 people affected by Long Covid, further development of its tech platform and team expansion
If you missed our webinar, you can catch up on an exciting discussion about the future of precision medicine research in multiple sclerosis here on our blog.
Advances in large-scale genome sequencing, data storage, and analysis are accelerating the development of precision medicine across a growing number of therapeutic areas. These advancements are already helping to improve outcomes for patients with chronic diseases such as coronary artery disease and breast cancer. Depression, however, presents a distinct challenge.
With the exponential growth of genomic data and analysis techniques we are seeing huge breakthroughs in use of polygenic risk scores to predict genetic risk of many common diseases, such as cancer, heart disease and diabetes. The study of genetic risk for common diseases is complex, as many genetic and environmental variants affect the disease risk. But following the success of genome-wide association studies (GWAS) in identifying the causal variants associated with the disease, polygenic risk scores (PRS) provide a way of aggregating all the variants carried by an individual into a single risk score.
Clinical trials increasingly depend on genomic data to identify the right patients, reduce variability in treatment response, and accelerate the path to approval. As sequencing costs continue to fall and the infrastructure for storing, processing, and analyzing large-scale genetic data matures, incorporating genomics into trial design is no longer aspirational. It is becoming a structural requirement. For sponsors and clinical teams working in precision medicine, the question is not whether to use genomics, but how to integrate it effectively across the trial lifecycle.