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How can modelling and simulation optimize the clinical development of biosimilars?

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The size of the biologic drug market make entry attractive. However, due to the complex manufacturing process and the high variability for biologics, the failure rate for biosimilars is high. In Phase III, the failure rate is expected to be around 20-50%. Considering the associated cost, in the range of 100M $, the development of a biosimilar is risky (Blackstone et al, 2013).

Modeling and simulation (M&S), has been in use 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 its decision making. The use of M&S for evaluating pharmacokinetic/pharmacodynamic (PK/PD) relationships can support the biosimilar program, with high regulatory impact. In principle, regulators have accepted that PK/PD, dose-response or longitudinal analyses are more sensitive methods than clinical outcome analysis at a single fixed time-point to detect differences between originator and the biosimilar (EMA, 2014). Although traditional statistical methods commonly are used for the primary evaluation of pivotal clinical trial data, 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 versus the originator (Dodds et al, 2013). Also the FDA acknowledges that M&S can be useful when designing studies, e.g. for dose selection and defining acceptable limits for PD similarity (FDA, 2014). 

Through efficient use of available public domain data and information on the new product, SGS Exprimo (http://www.exprimo.com/) has tools in place that can facilitate decision making and increase the probability of a successful study in order to cut costs and save time. By integration and pooling of 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. The models allow for estimating variability within and between subjects. Moreover, it’s possible to simultaneously account for multiple factors explaining varying exposures and responses across individuals, including the formation of anti-therapeutic antibodies. Using the models for subsequent clinical trial simulation, various study designs can rapidly be explored in silico (doses, sample size, study duration, reduced sampling schedules, inclusion/exclusion criteria, and choice of statistical evaluation method). By simulating multiple virtual clinical studies and calculating the outcome for each study in accordance with regulatory guidelines, the probability of concluding PK/clinical similarity can be explored under various scenarios. The influence of an expected difference between the originator and new product (e.g. 0, 1, 3, 5 or 10%) on the required sample size can easily be calculated. The most cost-effective design with a sufficient probability of a successful outcome can then be chosen. The methods also apply for bridging of results across study populations/therapeutic indications.

As a full service provider, SGS’ experience of biosimilars range from physico-chemical characterization and non-clinical testing to clinical studies and regulatory consulting. SGS Exprimo complements the SGS clinical research offering, having methods in place to optimize the design and evaluation of clinical biosimilar studies and programs. 

SGS Exprimo’s dedicated biosimilar team have experience of attending health authority meetings and have received positive feedback from regulators on their methodology. Thanks to Simulo (www.simulo.eu), a validated clinical trial simulation software developed by SGS Exprimo, the impact of multiple influential factors on any study design can rapidly be evaluated. After finalization of a project, the client is provided with a free version of the software enabling ad hoc exploration of additional ideas, retaining the know-how in house. Recent SGS Exprimo experiences include PK and PK/PD projects for adalimumab, bevacizumab, filgrastim, trastuzumab and rituximab. For adalimumab, simulations showed that the probability of both PK and clinical similarity was high at a reduced sample size compared to the original design proposed by the sponsor. Other projects have shown that the sample size to some extent rather should be increased to avoid a negative study.

References
1.    Blackstone E.A, Fuhr J.P, The Economics of Biosimilars. Am Health Drug Benefits. 2013;6(8):469-478
2.    2013 activity report of the Modelling and Simulation Working Group (MSWG)   EMA/303848/2014
3.    Dodds M, Chow V, Marcus R, Perez-Ruixo JJ, Shen D, Gibbs M. The Use of Pharmacometrics to Optimize Biosimilar Development. J Pharm Sci 2013: 3908-14
4.    FDA Guidance for Industry, Clinical Pharmacology Data to Support a Demonstration of Biosimilarity to a Reference Product, May 2014

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