The fear of toxicity pushes many companies to pursue the same few well-validated targets, leading to an average of nine assets per target. This hyper-competition not only crowds the market but, more importantly, leaves vast patient populations without effective options because their diseases lack these "popular" targets.
Related Insights
When prioritizing pipelines, biotechs must consider commercial viability, not just science. With China's ecosystem specializing in fast-follow "Me Too" drugs, such assets are becoming commoditized. To secure funding and premium exits, companies must focus on truly differentiated "first-in-class" or "best-in-class" programs.
Luba Greenwood reframes competition in biotech as a positive force. When multiple companies pursue the same biological target, it validates the target's importance and accelerates discovery. This collaborative mindset benefits the entire field and, ultimately, patients, as the best and safest drug will prevail.
Biotech companies create more value by focusing on de-risking molecules for clinical success, not engineering them from scratch. Specialized platforms can create molecules faster and more reliably, allowing developers to focus their core competency on advancing de-risked assets through the pipeline.
The Orphan Drug Act successfully incentivized R&D for rare diseases. A similar policy framework is needed for common, age-related diseases. Despite their massive potential markets, these indications suffer from extremely high failure rates and costs. A new incentive structure could de-risk development and align commercial goals with the enormous societal need for longevity.
A-muto suggests many drug programs fail due to toxicity from hitting the wrong epitope, not a flawed biological concept. By identifying and targeting a structural epitope unique to the diseased state of the same protein, these previously abandoned but promising therapies could be salvaged.
With over 5,000 oncology drugs in development and a 9-out-of-10 failure rate, the current model of running large, sequential clinical trials is not viable. New diagnostic platforms are essential to select drugs and patient populations more intelligently and much earlier in the process.
Despite billions invested over 20 years in targeted and genome-based therapies, the real-world benefit to cancer patients has been minimal, helping only a small fraction of the population. This highlights a profound gap and the urgent need for new paradigms like functional precision oncology.
A-muto's CEO argues that shaving months off discovery isn't the real prize. The massive cost in drug development comes from late-stage clinical failures. By selecting highly disease-specific targets upfront, their platform aims to reduce the high attrition rate in clinical trials, which is the true driver of cost and delay.
ProPhet's strategy is to focus on 'hard-to-drug' proteins, which are often avoided because they lack the structural data required for traditional discovery. Because ProPhet's AI model needs very little protein information to predict interactions, this data scarcity becomes a competitive advantage.
A key part of Eli Lilly's R&D strategy is tackling large-scale health problems that currently have no treatments and therefore represent a 'zero-dollar market.' This blue-ocean strategy contrasts with competitors who focus on areas with established payment pathways.
