Drug Innovation At Risk By Trump Budget, Says Congressional Budget Office

According to a new U.S. Congressional Budget Office analysis, President Donald Trump’s 2026 budget plan proposes cuts to the National Institutes of Health that could significantly reduce the number of new drugs entering the market.

The agency projected that slashing NIH funding by billions would gradually erode the pipeline of drug innovation over the coming decades.

Reuters noted that the Trump administration’s budget proposal includes an $18 billion reduction in NIH funding, amounting to a 40% cut to the agency’s research budget.

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In a letter, Director Phillip L. Swagel analyzed the potential long-term effects of such cuts, focusing on two hypothetical scenarios:

• A permanent 10% cut to NIH funding and a nine-month increase in the Food and Drug Administration’s (FDA) review timeline for new drug applications (NDAs).
  
  In this first scenario, CBO estimated that a 10% reduction in government funding to NIH, particularly for external preclinical research, would lead to a 4.5% decline in the number of new drugs reaching the market.
  
  That equates to roughly two fewer drugs each year, though the full impact would be felt over 30 years, with the sharpest effect appearing in the third decade.
  
  The agency noted that additional cuts in other areas of NIH’s budget could further depress drug development, but it has not yet quantified those effects.
  
• The second scenario involved a hypothetical nine-month delay in FDA review times.
  
  According to the CBO, this would result in a sharp drop in drug approvals during the first year due to the delayed process. Beyond the immediate effect, the longer review period would also increase development costs, leading to a sustained 2% annual reduction in new drug approvals — about one fewer drug each year — by the second decade after implementation.

CBO was also asked to examine the impact of a more severe funding cut — between 35% and 38% — but has not yet determined whether past trends can reliably predict the consequences of reductions of that magnitude.

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