In-silico product formulation design through latent variable model inversion

Abstract

Two of the first important decisions to take in the development of a solid oral drug product are the selection of excipients that are to be mixed with the active pharmaceutical ingredient (API) in a commercial formulation, and the manufacturing route. This work proposes to use a latent variable model methodology presented in a previous work (Polizzi and García-Muñoz, Int. J. Pharm., 2011, 418, 235–242) to enable the in-silico design of new product formulations. A constrained optimization framework is used to invert the underlying model in order to select the best excipients and concentrations for a given API to ensure the achievement of a pharmaceutical blend with a desired profile of particle, powder and compact mechanical properties. The approach is verified by designing a new pharmaceutical formulation for direct compression, using an API that was previously formulated via a wet granulated process. The experimental results confirm the effectiveness of the method. The proposed methodology can act as an important tool to guide and accelerate the decision making process in pharmaceutical product development, while minimizing the required experimentation as well as the raw materials consumption. The approach can be extended to consider other constraints (or targets) such as stability, as long as there is a mathematical way to relate the targets (e.g., degradation extent) to the incoming formulation.