AI is transforming how the pharma industry approaches drug development. On average, the industry invests $2.6 billion to bring a new treatment to market, taking 12 to 15 years with a 10% success rate. But pharma pioneers are leveraging AI to rethink traditional models. Sanofi, for example, is developing a “lab-in-a-loop” that uses AI agents to potentially compress elements of discovery work from years to weeks.
One implication of this progress is that pharma companies are using AI to unlock potential treatments for smaller and smaller patient populations, as in for rarer diseases and conditions that traditionally would not be a priority. Indeed, over the past decade or so, the percentage of FDA drug approvals that address rare conditions has risen from below 30% to above 50%.
This represents amazing progress and tremendous hope for the millions of Americans who suffer with rare conditions.
But this progress also raises a unique challenge for pharma companies. The smaller the patient population for a new treatment, the harder it becomes to reach that audience.
Traditional marketing has been designed, for the most part, to reach the largest audience for the lowest-possible cost, which, in many cases, makes economic sense. The logic works if you’re selling pizza or toothpaste. But the model breaks down if a drug treatment is relevant to an audience of just thousands rather than millions.
