If you have ever argued, as we have, for the resources and time needed for model-based drug development (MBDD), you have likely encountered that irritating accountant in the room who says, “Sure, this modeling stuff sounds interesting, but how much MONEY will this save us?”
My answer: $97M of savings per New Drug Application (NDA).
Three recent publications make the case for substantial benefits of MBDD—also known as biosimulation—on per NDA costs. In this blog post, I will show how these authors have quantified these savings.
Saving patients, time, and increasing the probability of technical success
Milligan and his colleagues reviewed 9 projects that used various MBDD approaches in their paper “Model-Based Drug Development: A Rational Approach to Efficiently Accelerate Drug Development.”1 They examined drug programs for disease indications ranging from thromboembolism to urinary tract infections (UTIs). The authors discussed savings in three forms: fewer subjects needed for clinical trials, shorter study duration, and increased probability of success. Of the nine projects examined, seven reduced the number of required trial subjects with the average savings of 907 patients, four saved a year of study duration, and three significantly increased the probability of technical success.
The value of a trial patient saved
That’s fine you say, but where are the dollars? MBDD reduces the number of subjects needed to participate in clinical trials, but how much money is saved? Last March, the Pharmaceutical Research and Manufacturers of America (PhRMA) trade group commissioned the Battelle Institute to study this question. The Battelle researchers found that per-patient trial costs ranged from $16,500 (infectious disease patient) to $59,500 (oncology patient). The mean cost for all diseases was $36,500 per patient.2
The costs reported by the authors are direct, out-of-pocket costs. In other blog posts, I have called these “accounting costs.” But what about the savings of development time and the value of increased probability of technical success?
Quantifying the savings from investing in MBDD
In “How to improve R&D productivity: The pharmaceutical industry’s grand challenge”3 Paul and coauthors constructed a microeconomic model of NDA costs, with sensitivity analysis of cost, cycle time, and probability of technical success for 8 stages of drug R&D from target-to-hit to product launch. The authors calculated that each 1% improvement in the probability of Phase 2 technical success saves $25M from the cost of an NDA. Each year saved in Phase 3 trials equals $156M in reduced costs.
Putting it all together
I conducted a financial analysis to find the ultimate savings from leveraging MBDD. The table below combines the claimed savings from the Milligan paper with the per-patient trial costs from the PhRMA/Battelle report and Paul’s value of a 1) year’s cycle time savings in Phase 3 and 2) increased technical success. I assumed an improved probability of technical success of just one percent, which Paul et al calculated to be worth $25M per NDA. The total savings in the 9 cases reported by Milligan et al range from $25M in UTI and urinary incontinence, to $212M in thromboembolism, with an average of $97M savings per NDA.
To put this in perspective, the number of card-carrying pharmacometricians is about 2,000 scientists. If they earn in the range of $175,000 annually, then the entire pharmacometrics industry costs sponsors about $350M. If the $97M savings had been applied to the 45 new drugs approved by the FDA in 2015, the savings would have been $4.4B.
Join the biosimulation revolution
Biosimulation can influence every phase of the drug development process. It has the power to help develop new treatments for deadly diseases. Read our white paper, “The Benefits of Biosimulation in Drug Development,” to learn how biosimulation transforms data into information and information into knowledge.
 Milligan, P A; Brown, M J; Marchant, B; Martin, S W; van der Graaf, P H; Benson, N; Nucci, G; Nichols, D J; Boyd, R A; Mandema, J W; Krishnaswami, S; Zwillich, S; Gruben, D; Anziano, R J; Stock, T C; Lalonde, R L, Model-Based Drug Development: A Rational Approach to Efficiently Accelerate Drug Development, Clinical Pharmacology & Therapeutics, Jun2013, Vol. 93 Issue 6, p502-514. 13p.
 Biopharmaceutical Industry-Sponsored Clinical Trials: Impact on State Economies. Prepared by Battelle Technology Partnership Practice for the Pharmaceutical Research and Manufacturers of America (PhRMA), March 2015.
 Steven M. Paul, Daniel S. Mytelka, Christopher T. Dunwiddie, Charles C. Persinger, Bernard H. Munos, Stacy R. Lindborg & Aaron L. Schacht, How to improve R&D productivity: the pharmaceutical industry’s grand challenge, Nature Reviews Drug Discovery 9, 203-214 (March 2010) | doi:10.1038/nrd3078