Mathematical Model-Based Accelerated Development of Extended-release Metformin Hydrochloride Tablet Formulation

Area of Science:

• Pharmaceutical Sciences
• Pharmacokinetics
• Computational Modeling

Background:

• Extended-release metformin formulations require precise control over drug release kinetics.
• Tablet geometry significantly influences drug release from matrix systems.
• Predictive modeling can optimize formulation development and reduce clinical testing burden.

Purpose of the Study:

• To develop and validate a computational fluid dynamic (CFD) model for predicting metformin release from HPMC matrix tablets.
• To utilize a surface area/volume (SA/V) approach for designing high-dose (1000 mg) metformin tablets with equivalent release profiles.
• To predict clinical exposure of the new 1000 mg Met XR tablet using a pharmacokinetic absorption model and validate with bioequivalence data.

Main Methods:

• Development of a CFD model incorporating drug substance properties, formulation composition, and tablet geometry.
• Application of the SA/V approach to design 1000 mg Met XR tablet geometry for equivalent release.
• Construction of a pharmacokinetic absorption model in GastroPlus™ using in vitro dissolution and physicochemical data.
• Comparison of simulated in vitro release and in vivo pharmacokinetic profiles with experimental data.

Main Results:

• CFD simulations successfully predicted metformin release kinetics, maintaining similar dissolution behavior across different tablet geometries via constant SA/V ratio.
• Experimental dissolution profiles of high-strength tablets matched simulated release.
• The pharmacokinetic absorption model accurately predicted equivalent human exposure across all Met XR strengths.
• Clinical bioequivalence study confirmed the predicted equivalent in vivo exposure.

Conclusions:

• CFD modeling is a reliable tool for predicting metformin release from HPMC matrix extended-release tablets.
• The SA/V approach effectively ensures consistent drug release kinetics across varying tablet strengths and geometries.
• Integrated in vitro-in silico modeling accurately predicts in vivo performance, supporting efficient drug development and bioequivalence assessment.