The hardest problem in biopharma is picking the right target.

Despite rapid biotechnological progress, up to 90% of drug candidates fail in the clinic. Poor choice of targets drives these failures through toxicity and lack of translational efficacy.

This is not for lack of information: we have a wealth of genetic and genomic evidence about targets that can mitigate these risks. Genomic evidence is associated with considerable increases in program success (~3x). Notable cases include PCSK9 (cholesterol), TYK2 (autoimmune disease), and TL1A (GI disorders). Yet this information is rarely used. Datasets are isolated, of uneven quality, and not easily accessible. A recent review has shown that despite demonstrable successes, only ⅓ of active drug programs use genomic evidence, relying instead on “gut feel” and “expert opinion.”

We are building a “Bloomberg for Biology”

It’s an intuitive software product enabling founders, executives, scientists, and investors to understand the genomic association of a target with disease, its biological context, the feasibility of drugging it, its potential safety risks, and the competitive landscape of clinical trials related to it.

By making these data easily accessible with clear provenance and traceable analytics, they can be used confidently by a variety of decision-makers without needing to hire and manage consultants or computational biologists. Fresnel enables assessing not only the causality of the target in disease and the risks it presents, but also how it can be approached and a sense of the commercial opportunity.