Translational research occupies a unique stage in the drug discovery and development process. This is where hypothesis meets reality, where a new molecule developed in the lab can either be translated into a lifesaving medicine, or become another failure in this notoriously high-attrition game of drug development.
Because of the pivotal role of translational research in the success of the drug and the high stakes of failure in late stage clinical development, technological innovations that can improve the success rate of the translation between the bench and the bedside can play an outsized role in pharma R&D productivity. AI, being the most transformative technology in our society today, is expected to be a game changer in translational research; but where are the opportunities and how will they happen?
To limit the scope of our discussion, I will focus only on machine learning, the branch of AI that enjoyed the most breakthroughs in recent years, and the development of biomarkers, a key success factor in translational success and precision medicine. I would like to discuss three types of biomarkers in particular: molecular biomarkers, digital biomarkers, and imaging biomarkers.
The classic example of a molecular biomarker is HER2 overexpression in breast cancer which is routinely measured in a companion diagnostic for Herceptin, the first precision medicine approved by FDA, to treat HER2-positive breast cancer patients. Since then many molecular biomarkers have been identified and successfully used in targeted therapies (e.g., BRAF V60E mutation status for Zalboraf to treat metastatic melanoma). Despite the successes, there are only a small number of clinically validated biomarkers so far. For many new drugs, especially those with more complex mechanisms of action such as in immunotherapy, a data-driven approach is often necessary. Typically a large pool of data from genomic or transcriptomic profiling experiments, protein assays, flow cytometry assays, will be combined with lab data and other clinical data, which will then be interrogated for potential biomarkers that can predict clinical response. This is where machine learning can be particularly helpful.
Indeed, several biotech companies have employed this approach in their R&D models. For example, Anavex used a computational platform to identify genetic variant biomarkers to stratify a sub-population of Alzheimer’s disease based on whole genome sequencing data of patients in a Phase 2a study. Improved clinical outcomes was observed in this subpopulation treated with their experimental drug. Berg also positions itself as an AI-driven biotech company that operates at the intersection of biology, technology and artificial intelligence analytics. It has identified novel biomarkers in multiple disease areas with drugs that have reached phase II clinical trials.
At larger pharmaceutical companies, new AI and digital transformation strategies are being implemented to improve the drug development process. At Roche, a recent company-wide machine learning challenge attracted more than a hundred teams of data scientists in building predictive models to find the best treatment options (e.g., immunotherapy, chemo, or combination therapy) for patients with a variety of cancers, using both clinical and transcriptomics data from a dozen past clinical trials.