Modeling and simulation are transforming how drugs are developed and how they are regulated. This is the conclusion from a recent FDA Grand Rounds presentation entitled “How Simulation can Transform Regulatory Pathways.”
The presentation was given on August 9, 2018 by Dr. Tina Morrison. Dr. Morrison is the Chair of FDA’s Agency-wide Modeling and Simulation Working Group and Regulatory Advisor of Computational Modeling for FDA’s Office of Device Evaluation. In her presentation, Dr. Morrison provided an overview of some current modeling and simulation methodologies, highlighted a number of success stories, and discussed the potential of in silico clinical trials to advance new therapeutics.
FDA and Other Regulatory Bodies Are Focused on Modeling and Simulation
To put this into context, in 2011, the FDA released its strategic plan on Advancing Regulatory Science at FDA. Of the 8 science priority areas listed in this plan, 4 of these specifically call out modeling and simulation as important aspects of FDA’s strategy.
More recently, in January 2018, FDA Commissioner Dr. Scott Gottlieb introduced FDA’s 2018 Strategic Policy Roadmap, which identifies the four top priority areas for Agency policymaking in 2018. One of these priorities is to “leverage innovation to improve healthcare, broaden access, and advance public health goals.” Dr. Gottlieb has been a strong advocate that innovations in modeling and simulation and high-performance computing are critical to achieving these goals.
In an effort to further these priorities, FDA has created a working group to raise awareness of modeling and simulation and to advance related policies for the advancement of public health. More than 200 scientists at the FDA are participating in this working group, with Agency-wide engagement across the various FDA centers, divisions, and offices.
Importantly, the push for including modeling and simulation as a central part of drug development is not just taking hold in the U.S. The European Medicines Agency has created its own Modelling and Simulation Working Group. Beyond this, the FDA working group has begun a collaborative partnership with the not-for-profit Avicenna Alliance Association for Predictive Medicine, which is based in the EU. The Avicenna Alliance engages with governments, regulatory authorities, industry, and researchers to promote modeling and simulation and to identify policies and regulatory obstacles to help advance in silico medicine. Under their collaboration agreement, FDA and the Avicenna Alliance will provide each other with technical assistance as well as scientific and policy perspectives on issues relating to in silico medicine and will participate in collaborative research and training opportunities.
Modeling and Simulation Are Already Being Successfully Implemented Across FDA
Modeling and simulation are fast becoming indispensable across the FDA. These strategies are helping to inform regulatory decisions, predict clinical and population-level outcomes, and protect the public health. Here are just a few samples of the success stories:
1. Simulation for Food Ingredient Safety Assessment
In the area of food safety, there was a need to understand the uptake in children of bisphenol-A (BPA), a synthetic organic compound found in some plastics. BPA has been implicated as an endocrine disruptor and there has been ongoing concern that the chemical could leach from consumer plastics like water bottles and even baby bottles.
While our understanding of BPA absorption in adults was supported by a number of clinical investigations, not much was known about BPA in infants and children. Modeling was used to provide this vital information without the need to expose children to the chemical itself.
To do this, FDA’s Office of Food Additive Safety created a mechanistic, physiologically-based pharmacokinetic (PBPK) model to predict BPA exposure in infants. What they learned from their model was that concentrations of BPA were very low in both adults and infants. This information is now being used to support new research around the world in both developed and developing nations.
2. Exposure Assessment for Vaccines Containing Aluminum in Children
The next success story comes from FDA’s Center for Biologics Evaluation and Research where modeling was used to help ensure the safety of vaccines that contain aluminum. PK models were used to calculate the body burden of aluminum for children during their first year of life. The PK model was able to account for variability in body size, growth, recommended vaccine schedule, and the vaccine’s route of administration. It is important to account for these variabilities because they can impact systemic exposure to vaccine components.
When the body burden of aluminum calculated by the model was compared to minimum risk levels obtained from data provided by the Centers for Disease Control and Prevention and its Agency for Toxic Substances and Disease Registry, CBER determined that there is still a wide safety margin when giving children vaccines that contain aluminum, including in the first year of life. Modeling and simulation were used to support the finding that the benefits of aluminum-containing vaccines far outweigh any risk due to aluminum exposure.
3. Modeling & Simulation to Predict Public Health Outcomes
Modeling and simulation has also been used by the FDA to understand the effect of new policies on public health. In one example, regulators were considering a new policy to set a maximum level of nicotine in cigarettes in the U.S. Modeling was used to project the potential population-level effects of this change on public health.
Based upon the models, regulators found that lowering nicotine content in cigarettes could dramatically reduce the number of tobacco-related deaths in the U.S. over the coming decades. Moving forward, these types of models and the information collected from them will be important tools to predict the practical implications of policy decisions on public health.
A second example involves assessing risk from therapies used to treat pain. This topic has gained significant attention in healthcare in light of the ongoing struggle against opioid abuse in many parts of the country. The challenge here is to assess an opioid’s potential risk to public health even when no clinical data are available.
To do this, scientists developed a model-based approach to calculate the structural similarity of a new drug with all controlled substances, to predict biological targets, and to estimate binding affinities at the identified targets. Based upon this key information, experts are now able to predict potential risks and inform regulatory decision-making even before these drugs have made their way into the clinic.
Increased Usage of In Silico Clinical Trials is on the Horizon
The examples above provide just a glimpse into the current successes and future potential of modeling and simulation in drug development. They also presage a not too distant future where more and more clinical trials may be performed in silico; that is, via computer simulation rather than in human subjects. While there will always be a need for human subjects and conventional clinical trials, it is almost certain that in silico trials will end up replacing human clinical trials in some cases. One prime target for this would be trials intended to evaluate drug interaction risks.
The field of in silico medicine is rapidly developing and its practical utility for drug development is undeniable. Given this, and the FDA’s wide-ranging initiatives to promote in silico methodologies both internally and more broadly within the healthcare community, modeling and simulation are more vital than ever to a successful drug development program.
Nuventra’s senior consultants are industry veterans with a wealth of expertise and experience in modeling and simulation as well as overall drug development strategy. Contact us today to learn how the Nuventra team can help you achieve your program goals.