title: A framework for in-silico formulation design using multivariate latent variable regression methods.
abstract: A comprehensive Quality by Design development paradigm should consider the impact of raw materials and formulation on the final drug product. This work proposes a quantitative approach to simultaneously predict particle, powder, and compact mechanical properties of a pharmaceutical blend, based on that of the raw materials. A new, two-step, multivariate modeling method, referred to as the weighted scores PLS, was developed to address the challenge of predicting the properties of a powder blend while enabling process understanding. The model validation exercise is shown along with selected practical applications. It is shown how the proposed in-silico model exhibits sufficient predictive power to be an important tool in the pharmaceutical development decision making process while requiring minimal experimentation and material usage.
authors.affiliation: Pfizer Global Research and Development, Eastern Point Road, Groton, CT 06340, USA. Mark.Polizzi@Pfizer.com
authors.forename: Mark A
authors.initials: MA
authors.lastname: Polizzi
authors.forename: Salvador
authors.initials: S
authors.lastname: García-Muñoz
mesh_terms: D003198:Computer Simulation; D016208:Databases, Factual; D004339:Drug Compounding / Q000379:methods*; D015195:Drug Design; D015999:Multivariate Analysis; D004364:Pharmaceutical Preparations / Q000737:chemistry*; D015203:Reproducibility of Results
chemical_list: D004364:Pharmaceutical Preparations