In the context of Pharma 4.0, the design tools that support the pharmaceutical Quality by Design(QbD) are iterating fast toward intelligent or smart design. The conventional development methods for traditional Chinese medicine(TCM) preparations have the limitations such as over dependence on experience, low dimensions for the designed experiment parameters, poor compatibility between the process and equipment, and high trial-and-error cost during process scale-up. Therefore, this paper innovatively proposed the intelligent co-design involving material, process, and equipment for manufacturing high-quality TCM preparations, and introduced the design philosophy, targets, tools, and applications with TCM oral solid dosage(OSD) as an example. In terms of design philosophy, the pharmaceutical design tetrahedron composed of critical material attributes, critical process parameters, critical equipment attributes, and critical quality attributes was developed. The design targets were put forward based on the product performance classification system. The design tools involve a design platform that contains several modules, such a as the iTCM material database, the processing route classification system, the system modeling and simulation, and reliability-based optimization. The roles of different modules in obtaining essential and universal design knowledge of the key common manufacturing units were introduced. At last, the applications of the co-design methodology involving material, process, and equipment in the high shear wet granulation process development and the improvement of the dissolving or dispersion capability of TCM formula granules are illustrated. The research on advanced pharmaceutical design theory and methodology will help enhance the efficiency and reliability of drug development, improve the product quality, and promote the innovation of high-end TCM products across the industry.
Keywords: manufacturing classification system; material database; pharmaceutical design tetrahedron; quality by intelligent design(QbID); reliability-based optimization; system modeling and simulation.