Fuelling the development of biosimilars

Bernardo de Moguel Lillo and Daniel Röshammar, SGS discuss how through the efficient use of modelling and simulation decisions can be made increasing the probability of a successful outcome for biosimilar studies by integrating public domain information and in-house data

The relatively low cost to enter the “generic” market and the size of the biologic drug market make entry attractive.

However, the failure rate for biosimilars is deemed high, due to the complex manufacturing process and the high variability expected for biologics.

Considering that the associated cost for developing a biosimilar is estimated at US$100 million, there is a high risk-cost relationship in the establishment of clinical biosimilarity.1

It is therefore of great interest to investigate the possibility to optimise the design of clinical trials of biosimilars to increase the studies’ efficiency (e.g., robust results, shorter duration, fewer patients, reduced cost). Because these studies have a great regulatory impact, they must be executed in accordance with regulatory guidelines for the evaluation of biosimilarity.2,3

M&S has been used in the pharmaceutical industry for more than two decades, and can be of competitive advantage for drug sponsors seeking to improve their drug development process and decision making. The use of M&S for evaluating pharmacokinetic/ pharmacodynamic (PK/PD) relationships can support a biosimilar programme, and offers high regulatory impact. In principle, regulators have accepted that PK/PD, dose-response and longitudinal analyses are more sensitive methods than clinical outcome analysis at a single fixed time-point to detect differences between the originator and biosimilar.4

Although traditional statistical methods are commonly used for the primary evaluation of pivotal clinical trial data, model-based simulations are increasingly used to optimise the design of clinical PK, PK/PD and outcome studies for biosimilars, by leveraging quantitative knowledge of the new product against the originator.5

Additionally, the FDA acknowledges that M&S can be useful when designing studies, for example, when determining dose selection and defining the acceptable limits for PD similarity.

References

  1. Blackstone E.A, Fuhr J.P. "The Economics of Biosimilars." Am Health Drug Benefits. 6(8):469-478 (2013)
  2. EMA. Guideline on Similar Biological Medicinal Products
  3. FDA draft guidance on Clinical Pharmacology Data to Support a Demonstration of Biosimilarity to a Reference Product. (2014)
  4. 2013 activity report of the Modelling and Simulation Working Group
  5. Dodds M et al., "The Use of Pharmacometrics to Optimize Biosimilar Development." J Pharm Sci 3908-14, (2013)

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