Modeling and simulation (M&S) in the drug development process is an industry-proven scientific approach used to inform crucial drug development decisions such as dosing, drug-drug interaction (DDI), and other critical safety and efficacy questions. The FDA has shown a strong commitment to utilizing M&S in the drug development process.
The FDA is responsible for assuring the safety, efficacy, and security of human drugs, for advancing the public health by helping to speed innovations to make medicines more effective and safer, and for helping the public get accurate, science-based information they need to use medicines. To support this mission, the FDA has a long history of supporting new approaches and technologies. The Critical Path Initiative (CPI), launched in March 2004, developed the FDA’s initial strategy to drive innovation in the scientific processes through which medical products are developed, evaluated, and manufactured. It listed 76 tangible examples of applying new scientific discoveries to drug development to better predict the safety and efficacy of drugs and medical devices. The CPI was followed by the 21st Century Cures Act (Cures Act), signed into law in December 2016, which provides the FDA with additional capabilities and tools aimed at modernizing clinical trial designs and clinical outcome assessments to further enhance innovation.
FDA Commissioner Dr. Scott Gottlieb is continuing the push to make drug development more efficient and effective and is committing the agency to aggressively pursuing and implementing these new approaches and technologies. In a blog early in Gottlieb’s tenure in 2017 he stated: “FDA’s Center for Drug Evaluation and Research (CDER) is currently using modeling and simulation to predict clinical outcomes, inform clinical trial designs, support evidence of effectiveness, optimize dosing, predict product safety, and evaluate potential adverse event mechanisms. We’ll be putting out additional, updated guidance on how aspects of these in silico tools can be advanced and incorporated into different aspects of drug development.”
In a September 2017 speech to the Regulatory Affairs Professional Society, Gottlieb went further and specifically emphasized the value of modeling and simulation (also referred to as model-informed drug development) in drug development, stating that “almost 100 percent of all new drug applications for new molecular entities have components of modeling and simulation.” Gottlieb also discussed the importance of having the FDA’s policies and regulatory procedures keep pace with the increasing sophistication and complexity of these methodologies.
This push by the FDA (and other regulatory bodies around the world, including the European Medicines Agency and the Pharmaceuticals and Medical Devices Agency) to accept and even promote the increased use of M&S to speed drug approvals comes at an opportune time for the global biopharma industry. In the U.S., R&D productivity (as measured by number of successful FDA approvals per dollar spent) has been declining since the late 1990s, the combination of fewer new molecular entities (NMEs) making it through the regulatory process and higher costs of drug discovery and development.
The cost of launching a new drug is approaching $2.6B (though “only” $1.4B of that is in out-of-pocket dollars) and takes 10 to 12 years to go from lab to market, so it is more important than ever that biopharmaceutical business, scientific, and regulatory leaders use all the available tools to get more products through the regulatory process and to the market, more quickly and more efficiently.
Late stage, pivotal trials (usually Phases 2 and 3 trials) typically represent 40 to 50 percent of development time and 60 to 65 percent of development cost, so this is an area that has been getting a great deal of attention from drug development organizations. Development leaders have been using the old tried-and-true methods of process improvement, better sourcing to reduce cost and shrink the timeline, such as earlier and more frequent stage-gate decision making, striving to “fail faster” so as to focus more dollars on potential winners, increasing outsourcing of clinical development to CROs, and conducting trials and data analysis in low-cost environments such as Eastern Europe. However, to achieve even greater benefits, a bigger commitment to rethinking the whole clinical development approach is needed, which will likely lead to an even greater reliance on M&S across development.
M&S is used to inform many crucial drug development decisions. Models integrate biochemical knowledge, human biology in patients and patient populations, disease development, and drug characteristics with specific outcome objectives, often biomarkers, and link all these to clinical trial protocols. M&S has become more accessible and more widely utilized in the last 15 years as we gain a better understanding of human biology, the role of DNA in the key biochemical systems and processes, and disease progression, along with increased availability of large amounts of clinical data. The availability of greater computing power has enabled the efficient solving of the key mathematical equations underlying the models to make M&S much more usable and cost-effective.
Models have a profound impact across many therapeutic areas for both small molecule (traditional chemical-based) and large molecule (biologicals) drug development. In the therapeutic areas of most interest for the industry today — oncology and rare/orphan diseases (often for pediatric patients), increasing speed to launch and improving the efficiency of development literally means the difference between life and death for patients. In many instances, M&S is the only way to answer key scientific and regulatory questions about a drug’s safety and efficacy, as the standard, placebo-double blind clinical trials are either unethical or highly impractical.
M&S can affect every phase of the drug development process, from early, preclinical studies to the design and execution of pivotal late-stage development studies for regulatory filings, as well as enabling more informed commercial decisions around the benefits of a specific drug for specific population segments or indications. In this regard, M&S is a vital part of the emerging field of precision medicine.
M&S usage can reduce development costs; minimize compound scientific, regulatory, and clinical uncertainty; increase likelihood of regulatory success; and reduce time-to-launch, thereby increasing the return on R&D investment (increase productivity). M&S can enable more targeted and precise dosing, reducing the need to run multi-arm dosing studies. As more data is made publicly available from clinical trials to use for M&S, the models and answers to “what-if” questions become better representations of reality.
The tangible business value for M&S results from:
- Optimizing trial design (the number of patients, length, desired outcomes, protocols) to provide needed information on dosing and drug interactions, etc.
- Eliminating the need for clinical trials in selected situations and populations (e.g., pediatrics or rare/orphan diseases, oncology, special populations)
- Predicting what will happen under certain conditions, e.g., dosing, interactions with other drugs (DDI), in different patient populations
Recent studies using comparable compounds with high and low M&S usage show the reduction in number of patients for specific trials and trial completion time (first and last patient visits). This allows us to quantify the value of M&S. For a new drug targeting schizophrenia, there was a 95 percent reduction in the number of subjects needed for Phase 3, and even though the time saved in Phase 3 was only 12 percent (about four months in this case), the drug developer was able to avoid certain intermediate trials, bringing the total time savings to almost two years when compared with comparable drugs going through the same process without using M&S. For a recent drug targeting multiple cancers, the benefits are even more dramatic: 90 percent reduction in the number of patients for Phase 3 trials and a 75 percent reduction in trial completion time, a savings of over three years.1
M&S can deliver significant business, scientific, and clinical value to biopharma companies that are able to fully integrate it in their drug development and regulatory strategy to reduce costs, accelerate time to filing, and improve the likelihood of regulatory approval. Using models is demonstrably more efficient and less costly and time-consuming than traditional clinical and regulatory studies. While scientists working in drug development understand the pharmacologic benefits of M&S, senior leaders in the biopharma industry are often unaware of its potentially broader strategic value to inform key scientific, regulatory, and commercial decisions. Until now, the full financial benefits of integrating M&S have not been well articulated and have not become a key driver of development strategies. However, the use of M&S is becoming more pervasive throughout the development process; for example, 90 percent of the 2015 new drugs and biologics approved by the FDA leveraged M&S for dose selection, safety determinations, trial design, bridging studies, and other drug label information. The use of these tools will only continue to increase over time as the value proposition becomes clearer and biopharma leaders more fully appreciate its strategic value.
- Glass, et al. Pharmaceutical Organizational Size and Phase 3 Clinical Trial Completion Times. DIA Therapeutic Innovation & Regulatory Science 1-7, 2016.
About The Author:
Michael K. Eckstut is the founder and managing principal of MKE Bioservices, LLC, a specialty consulting firm focusing on improving the effectiveness and reliability of drug development. Previously, Eckstut was head of Certara Strategic Consulting, a leading provider of software and consulting services for drug development modeling and simulation. He also headed the life science practice and the west region for Archstone Consulting and has been a senior partner at Booz-Allen and A.T. Kearney. He holds a B.S. and M.S. in chemical engineering from Rensselaer Polytechnic Institute and an MBA (as a Baker Scholar) from Harvard Business School. You can reach him at email@example.com or connect with him on LinkedIn.