Utility of Physiologically Based Absorption Modeling in Implementing Quality by Design in Drug Development

• Published in: The AAPS journal Vol. 13; no. 1; pp. 59 - 71
• Main Authors: Zhang, Xinyuan, Lionberger, Robert A., Davit, Barbara M., Yu, Lawrence X.
• Format: Journal Article
• Language: English
• Published: Boston Springer US 01.03.2011
• Subjects: Absorption - physiology; Algorithms; Anticonvulsants - chemistry; Anticonvulsants - pharmacokinetics; Biochemistry; Biomedical and Life Sciences; Biomedicine; Biotechnology; Capsules; Carbamazepine - chemistry; Carbamazepine - pharmacokinetics; Chemistry, Pharmaceutical; Computer Simulation; Dosage Forms; Drug Design; Fasting - metabolism; Food-Drug Interactions; Humans; Intestinal Absorption - physiology; Models, Biological; Models, Statistical; Pharmacokinetics; Pharmacology/Toxicology; Pharmacy; Quality Improvement; Research Article; Solubility; Suspensions; Tablets; Therapeutic Equivalency; quality by design (QbD); modified release (MR); advanced compartmental absorption and transit (ACAT) model; gastroplus
• ISSN: 1550-7416; 1550-7416
• DOI: 10.1208/s12248-010-9250-9

To implement Quality by Design (QbD) in drug development, scientists need tools that link drug products properties to in vivo performance. Physiologically based absorption models are potentially useful tools; yet, their utility of QbD implementation has not been discussed or explored much in the literature. We simulated pharmacokinetics (PK) of carbamazepine (CBZ) after administration of four oral formulations, immediate-release (IR) suspension, IR tablet, extended-release (XR) tablet and capsule, under fasted and fed conditions and presented a general diagram of a modeling and simulation strategy integrated with pharmaceutical development. We obtained PK parameters and absorption scale factors (ASFs) by deconvolution of the PK data for IR suspension under fasted condition. The model was validated for other PK profiles of IR formulations and used to predict PK for XR formulations. We explored three key areas where a modeling and simulation approach impacts QbD. First, the model was used to help identify optimal in vitro dissolution conditions for XR formulations. Second, identification of critical formulations variables was illustrated by a parameter sensitivity analysis of mean particle radius for the IR tablet that showed a PK shift with decreased particle radius, C max was increased and T max was decreased. Finally, virtual trial simulations allowed incorporation of inter-subject variability in the model. Virtual bioequivalence studies performed for two test formulations suggested that an in vitro dissolution test may be a more sensitive discriminative method than in vivo PK studies. In summary, a well-validated predictive model is a potentially useful tool for QbD implementation in drug development.