Pharmaceutical R&D Efficiency Plummets Hundredfold Since 1950, Experts Diagnose Causes

London, UK – The pharmaceutical industry has experienced a drastic decline in research and development (R&D) efficiency, with the number of new drugs approved per billion dollars of inflation-adjusted R&D investment falling roughly a hundredfold between 1950 and 2010. This phenomenon, dubbed "Eroom's Law" (Moore's Law spelled backward), highlights the increasing cost and complexity of bringing new medications to market despite technological advancements. The term was coined by Jack Scannell and colleagues in a seminal 2012 paper, "Diagnosing the decline in pharmaceutical R&D efficiency," published in Nature Reviews Drug Discovery.

Jack Scannell's work, which has garnered significant attention, identifies several key factors contributing to this decline. One major issue is the "better than the Beatles" problem, where new drugs must demonstrate significant improvement over existing, often inexpensive, generic treatments. This raises the bar for efficacy and necessitates larger, more costly clinical trials.

Another contributing factor is the "cautious regulator" problem, reflecting a progressive lowering of risk tolerance by regulatory agencies. This makes the R&D process both costlier and more protracted due to increasingly stringent safety requirements. The tendency to "throw money at it," by adding more resources to R&D projects, can also lead to inefficiencies and project overruns.

Furthermore, a "basic research–brute force" bias has been noted, where there's an overestimation of the ability of basic research and high-throughput screening methods to translate into successful clinical outcomes. Experts also point to a decline in the predictive validity of preclinical models, meaning laboratory tests often fail to accurately predict a drug's performance in human trials, leading to high failure rates later in development.

While Eroom's Law describes a long-standing trend, some analyses suggest a modest uptick in drug approvals since 2010. This partial reversal has been attributed by some to macroeconomic factors, such as a lower cost of capital, making R&D investments more attractive. There is also an increased focus on rare diseases, where drug development pathways can sometimes be more streamlined.

The role of artificial intelligence (AI) and advanced computational methods is increasingly seen as a potential avenue to counteract Eroom's Law. Companies like Exscientia and Schrödinger are leveraging AI to accelerate drug discovery, from identifying novel targets to designing and optimizing drug candidates. However, the overall impact of AI on industry-wide R&D productivity remains a subject of ongoing discussion among experts, with some emphasizing that improvements in predictive validity of models, rather than just speed, will be crucial for sustained progress.