(T1030-10-63) Predictive Power and Operational Use of SCISSOR to Streamline SC Biologics Drug Development

Purpose: Predicting the bioperformance, specifically bioavailability (BA), of subcutaneously (SC) administered biologics, remains a large knowledge gap in drug development. The underlying causes for the clinical BA spread (40-90%) across different commercial SC monoclonal antibodies (mAbs) are not entirely understood. Furthermore, there is no established reliable translation from preclinical species to human or a reliable in vitro method that predicts clinical bioperformance. This lack of understanding creates uncertainty in formulation development, clinical dose estimation, and supporting IV to SC formulation transition later in development. Thus, there is an urgent need for development of biopharmaceutics bioperformance prediction models based on in vitro, in vivo, and or in silico platforms. In this presentation, we will discuss the predictive power and operational use of one of the in vitro models of interest, SubCutaneous Injection Site Simulator (SCISSOR, Pion Inc), to streamline SC biologics drug development.

Methods: For the investigation of SCISSOR, multiple internal studies were implemented with different SC biologics programs, and all currently available relevant publications on SCISSOR’s predictive power were carefully reviewed. Experimentally, we evaluated different formulation variations of SC biologic drug substances on SCISSOR, keeping the injection volume and formulation concentrations consistent, to determine if SCISSOR was able to differentiate among different formulations. Secondly, the effect of the injection volume and the effect of the formulation concentration were evaluated to determine the impact on biologics release profiles. Finally, we performed comparability studies on SCISSOR for SC biologic drug substance that experienced minor changes (i.e. excipient change) and major changes (i.e. buffer change or process change) in their formulations.

Results: SCISSOR (1) is able to capture the behavior of the formulation at the injection site (i.e. detected any precipitation/aggregation of formulations at the simulated site of injection); (2) has the capacity to differentiate formulations with different release profiles, which is valuable for excipient or formulation screening in formulation development; (3) has the potential to obtain IVIVR based on the release profiles and preclinical and clinical assessment. However, the translatability of the SCISSOR results and its predictive power to the clinic has not been fully demonstrated, as supported by multiple publications in the literature and our internal analysis. Thus, the predictive power of SCISSOR needs to be investigated further with more examples of different types of SC biologic products and their corresponding clinical data. To establish this IVIVR, SCISSOR could be routinely incorporated into SC biologic drug development’s workflows.

Conclusion: See Results Section

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