Planning and executing a modern drug launch is one of humanity’s most cognitively complex coordination tasks—not unlike a rocket launch. Biopharma companies must pull together information from thousands of point solutions, share it across hundreds of siloed contributors inside and outside of their organizations, all while keeping everything up to date.
This information extraction, storage, and processing burden is unmanageable, and leads directly to poor decision quality and gross inefficiency: today, it costs upwards of $300 million to develop a new drug, 90% of drugs fail to make it through clinical trials, and ~55% of drugs that do launch fail to recoup their development costs in revenue. Even the most well resourced pharmaceutical firms in the world—$50bn annual revenue behemoths like Pfizer, or Eli Lilly—can only manage to launch a handful of new drugs in a good year.
Industry return on investment is in decline, and the pressure on the bottom lines of biopharma companies means teams are being asked to do more with less—all while using outdated software that isn't tailored to their needs. Exacerbating the problem, because each drug development decision is so multifaceted—requiring input from multiple teams (clinical, medical, regulatory, etc.)—companies are ground down by coordination challenges.
The industry has been slow to adopt new technology tools which could improve the efficiency of their processes. This is not due to conservatism, as some claim, but it is mainly because the value proposition of existing software tools is unclear, and narrow tools lack the flexibility of the Microsoft Office suite.
Reminiscent of C.P. Snow’s “The Two Cultures”, we believe a major barrier to the development of better tools is the lack of people with knowledge and skills that span both biopharma and software development. Tools developed by software engineers without experience working in the industry tend not to fit into existing workflows, have high friction, or do not surface the most meaningful information. Internal tools aren’t built to last, or don’t incorporate modern software design principles. Biotech leaders are promoted for their good judgment in drug development and commercialization, and not for their expertise in software or information technology.
To help relieve the demands on their staff, biopharma executives are increasingly turning to third party ‘information clearinghouses’ (consulting firms, data providers, and market research agencies), hoping they can distill insight from the firehose of information and help clarify their decisions. But these companies are themselves inefficient, reliant on manual work, and tend to exacerbate the problem by throwing more data and analysis on already stretched teams.
Drug developers are drowning in data, but dying of thirst.
The critical bottleneck today is not our ability to discover and design new molecules, but rather, the capability to take those drugs through development and launch them. Our scientists produce an abundance of great ideas for new therapies, many of which are withering on the vine.
Accompanying these industry trends has been a huge growth in the volume and type of data that’s available. Artificial intelligence is more capable than ever at working with the unstructured or text-based data that is common in the life sciences. We believe that there’s an opportunity to build on these advances in data and technology to develop new software tools that unify disparate sources of data, and thereby meaningfully improve the decision quality, personnel leverage, and capital efficiency of the biopharmaceutical industry. If we succeed, we will significantly increase society’s capacity to bring new drugs to market.
The biopharmaceutical industry is too important to continue to use outdated software, and we believe that now is the right time to build something better.
If you believe that too, we’d love to hear from you.
