Quietly, without any political rancor, the FDA Reauthorization Act of 2017 (FDARA) was passed by the US Congress and was signed into law by President Trump in late August. The FDARA reauthorizes the Prescription Drug User Act Fee Amendments (PDUFA) for the fifth time, the Medical Device User Act Fee Amendments (MDUFA) for the third time, and both the Generic Drug User Fee Amendments (GDUFA) and the Biosimilars User Act Fee Amendments (BsUFA). It will go into effect beginning fiscal year 2018 (Sept, 2017) through 2022.
The FDARA is closely aligned with the recently approved 21st Century Cures Act. We recently covered this topic in our blog, which underscores the import of modeling and simulation as highlighted by FDA Commissioner Scott Gottlieb in his July 7 statement:
“I want to highlight one example of these steps, which we’re investing in, and will be expanding on, as part of our broader Innovation Initiative. It’s the use of in silico tools in clinical trials for improving drug development and making regulation more efficient.
In silico clinical trials use computer models and simulations to develop and evaluate devices and drugs. Modeling and simulation play a critical role in organizing diverse data sets and exploring alternate study designs. This enables safe and effective new therapeutics to advance more efficiently through the different stages of clinical trials.
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.”
PDUFA VI: Enhancing regulatory science and regulatory decision tools to support drug development and review
With two new and substantive sections, PDUFA VI identifies how regulatory science and regulatory decision support tools can be leveraged to support the drug development and review process. These sections outline specific areas of innovation the agency will undertake.
Under Regulatory Science, we identified several key actions for sponsors:
- The promotion of innovation through enhanced communication between FDA and sponsors during drug development. FDA will maintain “dedicated drug development communication and training staff in CDER and CBER;”
- Ensure sustained success of breakthrough therapy program, drugs for rare diseases, and drug-device combinations;
- The agency will consider an early consultation on the use of new surrogate endpoints;
- Enhancing Use of Real World Evidence (RWE) for use in regulatory decision-making.
The new section on Regulatory Decision Tools focuses on the use of quantitative methods for drug development:
- To facilitate the development and application of exposure-based, biological, and statistical models derived from preclinical and clinical data sources, herein referred to as ‘model-informed drug development’ (MIDD) approaches, specifically:
- Physiologically-based pharmacokinetic modeling (PBPK);
- Design analysis and inferences from dose-exposure-response studies;
- Disease progression model development, including natural history and trial simulation;
- Immunogenicity and correlates of protection for evaluating biological products;
- Enhancing capacity to review complex innovative designs, the agency will develop the staff capacity to facilitate appropriate use of complex adaptive, Bayesian, and other novel clinical trial designs;
- FDA will develop the capacity to review and provide feedback to sponsors on the readiness of submitted analysis data sets for statistical review and for biomarker qualification review;
- To incorporate the patient’s voice in drug development and decision-making, the FDA will systematize the collection and use of patient and caregiver data;
GDUFA sets its regulatory science priorities
Generic drugs continue to play a pivotal role in providing safe, efficacious and cost-effective therapies to millions of patients. And as these drugs become more complex, so will the regulatory science approaches needed to develop and approve them. To that end, FDA has stated four GDUFA regulatory science priority areas, all of which require computational and analytical methods (MIDD):
- Post-market evaluation of generic drugs
- Equivalence of complex products
- Equivalence of locally-acting products
- Therapeutic equivalence evaluation and standards
The agency has acknowledged that computational and analytical tools are essential to modernizing the ANDA review process and directly impact the four priority areas stated above. Specific modeling and simulation tools that the FDA will investigate include PBPK or absorption models; pharmacodynamic models or clinical trial simulation; systems biology; and quantitative risk modeling. Additional research priorities include investigating the use of modeling and simulation tools to address questions of substitutability outside the range of traditional bioequivalence studies such as pediatric and geriatric populations or patients taking proton-pump inhibitors and generalization of statistical methods for evaluating in vitro equivalence.
MIDD—At the center
The value and benefits of MIDD for regulatory decision-making can no longer be questioned. MIDD has become essential to modern drug development, impacting all phases of the process, used to increase our understanding of benefit/risk, determine go/no go decisions, assess safety and efficacy of new therapies, guide dose selection, address the needs of special populations, identify issues that need further characterization, answer myriad drug development questions, evaluate alternative formulations and drug indications, and inform drug labeling decisions. Beyond the many ways that MIDD informs drug development decisions and strengthens the science, it also reduces time and cost to market via smarter and potentially smaller or avoided studies.
The chart below shows 2016 as the tipping point, and in the US, with the FDARA and 21st Century Cures, 2017 further proves the point.
Timeline of Regulatory Guidance Advocating Use of Model-informed Drug Development
To learn more about how PBPK modeling is being used in regulatory review, product labeling and safety monitoring, please read our white paper.