AI is reshaping how genomic data is interpreted, how patient outcomes are predicted, and how clinical programs are designed. In precision medicine, where decisions depend on complex molecular and phenotypic signals, these capabilities are particularly consequential.
In the latest episode of The Genetics Podcast, Sano CEO Patrick Short sat down with Daniel O’Connor, an expert in regulatory policy for innovative medicines, particularly those focused on rare diseases. Daniel, who spent nearly 20 years at the MHRA (Medicines and Healthcare products Regulatory Agency) and recently joined the ABPI (Association of the British Pharmaceutical Industry), shared his extensive experience and insights into the regulatory landscape of rare disease drug development.
At Sano, we are constantly exploring new ways to use our technology expertise to make a meaningful impact on healthcare. That’s why we’re excited about our partnership with the Lupus Research Alliance (LRA) on a profoundly important project: the Lupus Nexus initiative. This collaboration represents a significant step forward in our efforts to contribute to critical advancements in the field of lupus research.
Genomic data analysis combines computational biology, statistical modeling, and computer science to extract meaningful insight from genetic information. In precision medicine, this analysis is what connects raw sequencing output to actionable decisions: identifying genetic variants associated with disease, determining their functional significance, and informing the design of targeted therapies.
In the most recent episode of The Genetics Podcast, Sano CEO Dr. Patrick Short explored the latest discoveries in genetics and precision medicine with returning guest Dr. Veera Rajagopal. Dr. Veera, a scientist at Regeneron and quarterly guest on our podcast, provided insights into recent influential studies reshaping our understanding of genetic disorders and their implications for medical treatment.
Predicting how a patient will react to medication or treatment involves understanding many factors, including their genetic makeup. Each patient's genome can hold clues about how they might respond to certain drugs, their risk of adverse drug reactions, or their susceptibility to particular diseases. However, the sheer volume and complexity of genomic data make it challenging for traditional analysis methods to efficiently process and interpret this information.
In the latest episode of The Genetics Podcast, Sano CEO Patrick Short sat down with Jakob Steinfeldt, co-founder and Chief Scientific Officer at Pheiron. Jakob shared his journey from academia to entrepreneurship and the innovative work Pheiron is doing in disease prediction and drug development.
In a recent webinar, Hayley Holt, Senior Programme Manager at Sano Genetics, provided an insightful discussion on Sano's innovative approach to patient finding, starting with the development of a patient finding protocol.
AI is reshaping how genomic data is analyzed, interpreted, and applied across research and clinical settings. The complexity and sheer volume of genomic data poses significant challenges to traditional analytical methods, which were not designed to process datasets at this scale or density. AI addresses this directly by offering computational tools capable of extracting meaningful patterns from vast and complex datasets, enabling advances in disease diagnosis, variant interpretation, and personalized treatment strategies.
As genetic testing becomes central to clinical trial eligibility, patient stratification, and therapeutic development, the ethical dimensions of these workflows demand careful attention. Each new test raises questions about the circumstances under which it should be used, how it is implemented, and what uses are made of its results.