Can Modeling and Simulation Drive Clinical Development of Biosimilars?

Jackie Syrop

The failure rate of biosimilars in clinical trials is considered high because of the complex manufacturing process and the high variability expected for biologics. With associated development costs for a biosimilar estimated to be $100 million, there is a high risk-cost relationship in the establishing clinical biosimilarity.

The failure rate of biosimilars in clinical trials is considered high because of the complex manufacturing process and the high variability expected for biologics. With associated development costs for a biosimilar estimated to be $100 million, there is a high risk-cost relationship in the establishing clinical biosimilarity.

A recent essay in the November 30, 2017 issue of Clinical Research News suggests that the efficient use of modeling and simulation (M&S) allows decision making that will increase the likelihood of successful outcomes in studies of biosimilars. The authors present case studies using M&S in biosimilar studies of adalimumab, rituximab, and pegfilgrastim, and show how M&S could optimize biosimilar trial success rates.

In use for more than 2 decades, M&S in clinical trials of biosimilars could increase study efficiency by allowing for robust results, a shorter duration, involvement of fewer patients, and reduced cost, and could provide a competitive advantage for drug sponsors seeking to improve their drug development processes and decision making.

Using M&S to evaluate pharmacokinetic/pharmacodynamic (PK/PD) relationships can support a biosimilar program, the authors note. Model-based simulations are increasingly used to optimize the design of clinical PK, PK/PD, and outcome studies for biosimilars by leveraging quantitative knowledge of the new product against the originator. The FDA has acknowledged that M&S can be useful when designing studies, for example, to determine dose selection and define the acceptable limits for PD similarity.

M&S can make use of available data in the public domain and in-house information on the proposed biosimilar product to make efficient decisions on study design that will increase the probability of a successful outcome, the authors state. “By integrating information across dose levels, using longitudinal PK/PD and disease progression models, uncertainty can be reduced in the estimated PK, PD, efficacy and safety endpoints,” they write. “The models allow variability within, and between, subjects to be estimated, and it is also possible to simultaneously account for multiple factors to explain variation in exposure and response across individuals, including the formation of anti-therapeutic antibodies.”

Using M&S for clinical trial simulation, different study designs can quickly be explored in computer simulations as to dose, sample size, study duration, reduced sampling schedules, inclusion and exclusion criteria, and the choice of a statistical evaluation method. The influence of an expected difference between the originator and new product on the required sample size can easily be calculated with M&S, the authors note, and the most cost-effective design with a sufficient probability of a successful outcome can be chosen. These methods can also be used to apply results across study populations and therapeutic indications, furthering the clinical development of biosimilars.