Majority of drug approvals in the past several years have leveraged one or more technologies in the modeling and simulation toolbox. These methods are used by companies to inform key drug development decisions and by regulators as a key component of clinical pharmacology and medical reviews.
For companies, these tools support dose justification for general populations, specific covariates, and special populations; amplify efficacy trends; explain rare toxicities; and provide comparative efficacy information. From the regulators seat, these tools provide key answers to development gap questions, allow for a better understanding of risk: benefit, identify issues for further characterization (including the potential minimization of post-marketing requirements), and inform the drug label.
Example: Using Modeling and Simulation to Optimize the Timing of Maternal Influenza Vaccination
While model-informed drug development is used extensively in anti-infectious drug development, its use to support vaccine development is not as widespread. Currently, vaccination timing for infants six months and older is scheduled around routine checkups. Until 6 months of age, infants rely on transferred maternal antibodies, typically IgGs, from the placenta. This transfer depends on maternal antibody levels and fetal gestational age. The U.S. maternal vaccination policy set by the Advisory Committee on Immunization Practices (ACIP), a branch of the CDC, recommends that pregnant women receive a variety of vaccinations. Vaccination for influenza can be administered at any time during pregnancy. However, both maternal antibody levels and gestational age are time-dependent which suggests that “any time during pregnancy” may not be optimal. Certara, in collaboration with the Bill & Melinda Gates Foundation and leveraging data from the Maternal Immunization Working Group in the Centers for Disease Control (CDC), developed a model that would better assess the timing and efficacy of maternal vaccinations. This model could then be further employed to predict infant antibody (Ab) levels at birth. To learn more, read this blog post.
Example: Model-informed Translational Medicine Strategies for Influenza for Infants
The challenge was to establish a safe and effective dosing of the anti-viral therapy oseltamivir for infants. Data on children older than one year indicated that infants would have a similar response to the therapy, but toxicology information indicated that dosing for infants needed careful analysis. A quantitative clinical-pharmacology adaptive trial design was developed, informed by PK modelling from the first study. Using modeling and simulation, scientists analyzed the pharmacokinetic and pharmacodynamic data to determine the appropriate dosing for infants. Within six months of submission, oseltamivir was the first therapy approved by the FDA, and then later by the EMA, for the treatment of influenza in infants as young as two weeks old.