Making innovation cheaper and easier is Good
The Future of Drug Development: Faster, Cheaper, Better
As often documented on this Substack, we’re in an era of miraculous medical achievement. From the fundamental capabilities of mRNA vaccines to the series dent being made in the obesity scourge by GLP-1 medication. However, there is still friction and problems to solve in the world of medical discovery. Bringing products to market is borderline torture due to the exorbitant cost of clinical trials, a barrier that can and does slow down development.
Why have these costs skyrocketed? It’s a combination of rigorous regulatory standards, patient recruitment as well as the fact that the studies are more complex making data collection and analysis more substantial.
But fear not! Help is on the way. One avenue to lower costs is being led by Highlander Health, a company founded by former Verily executives Amy Abernethy and Brad Hirsch. They would know! Verily is an Alphabet Company that focuses on financing medical R&D. What Highlander is doing is focusing on the data collection and analysis cost centers, with the hope that by streamlining them the trial process will come down in cost. By leveraging existing medical records, hospital data, and other sources of information, researchers can gather valuable insights more efficiently, potentially accelerating the development of new therapies.
There’s also a public sector effort being led by some of the predominant American Universities and research institutions who are sharing resources, expertise, and data, thereby reducing the costs of individual studies. In theory these consortia can pool resources for patient recruitment, data management, and analysis, leading to more efficient and cost-effective trials. Additionally, consortia can facilitate collaboration between researchers from different institutions, fostering the exchange of ideas and accelerating the pace of discovery.
Finally, and of course, there’s Artificial intelligence! AI is being developed to analyze vast amounts of medical data, identify patterns, and accelerate drug discovery and development. For example, one exploration path is patient selection. The theory being that if AI can predict which patients are most likely to benefit from a particular treatment, the trial time and cost can be reduced by producing more pertinent and vital information. Additionally, AI is being developed to automate tasks such as data entry and analysis.
The potential benefits of these efforts are immense and far reaching, by reducing the time and cost it takes to bring new drugs to market, lives will be saved. Additionally, lower trial costs eliminate a major hurdle that face some of the smaller, up and coming companies in the space.
Medical innovation is great.
Making it cheaper and easier is good.
Yay!