Independent Tailoring of Dose and Drug Release via a Modularized Product Design Concept for Mass Customization

Abstract

Independent individualization of multiple product attributes, such as dose and drug release, is a crucial overarching requirement of pharmaceutical products for individualized therapy as is the unified integration of individualized product design with the processes and production that drive patient access to such therapy. Individualization intrinsically demands a marked increase in the number of product variants to suit smaller, more stratified patient populations. One established design strategy to provide enhanced product variety is product modularization. Despite existing customized and/or modular product design concepts, multifunctional individualization in an integrated manner is still strikingly absent in pharma. Consequently, this study aims to demonstrate multifunctional individualization through a modular product design capable of providing an increased variety of release profiles independent of dose and dosage form size. To further exhibit that increased product variety is attainable even with a low degree of product modularity, the modular design was based upon a fixed target dosage form size of approximately 200 mm3 comprising two modules, approximately 100 mm3 each. Each module contained a melt-extruded and molded formulation of 40% w/w metoprolol succinate in a PEG1500 and Kollidon® VA64 erodible hydrophilic matrix surrounded by polylactic acid and/or polyvinyl acetate as additional release rate-controlling polymers. Drug release testing confirmed the generation of predictable, combined drug release kinetics for dosage forms, independent of dose, based on a product’s constituent modules and enhanced product variety through a minimum of six dosage form release profiles from only three module variants. Based on these initial results, the potential of the reconfigurable modular product design concept is discussed for unified integration into a pharmaceutical mass customization/mass personalization context.