Integration of artificial neural network and physiologically based biopharmaceutic models in the development of sustained-release formulations

Model-informed drug development is an important area recognized by regulatory authorities and is gaining increasing interest from the generic drug industry. Physiologically based biopharmaceutics modeling (PBBM) is a valuable tool to support drug development and bioequivalence assessments. This study aimed to utilize an artificial neural network (ANN) with a multilayer perceptron (MLP) model to develop a sustained-release matrix tablet of metformin HCl 500 mg, and to test the likelihood of the prototype formulation being bioequivalent to Glucophage & REG; XR, using PBBM modeling and virtual bioequivalence (vBE). The ANN with MLP model was used to simultaneously optimize 735 formulations to determine the optimal formulation for Glucophage & REG; XR release. The optimized formulation was evaluated and compared to Glucophage & REG; XR using PBBM modeling and vBE. The optimized formulation consisted of 228 mg of hydroxypropyl methylcellulose (HPMC) and 151 mg of PVP, and exhibited an observed release rate of 42% at 1 h, 47% at 2 h, 55% at 4 h, and 58% at 8 h. The PBBM modeling was effective in assessing the bioequivalence of two formulations of metformin, and the vBE evaluation demonstrated the utility and relevance of translational modeling for bioequivalence assessments. The study demonstrated the effectiveness of using PBBM modeling and model-informed drug development methodologies, such as ANN and MLP, to optimize drug formulations and evaluate bioequivalence. These tools can be utilized by the generic drug industry to support drug development and biopharmaceutics assessments. This study utilized an ANN to optimize a sustained-release metformin HCl tablet and assessed its bioequivalence using PBBM modeling. The optimized formulation exhibited sustained-release, with release rates of 42% at 1 h, 58% at 8 h. This study highlights the utility of MIDD methodologies to support generic drug development. Integration Of Artificial Neural Network and Physiologically Based Biopharmaceutic Models in the Development of Sustained-Release Formulation.image

Automated summary

This study used an artificial neural network and physiologically based biopharmaceutics modeling to optimize a sustained-release metformin HCl tablet and assess its bioequivalence. The optimized formulation exhibited sustained-release, with release rates of 42% at 1 h and 58% at 8 h. This study highlights the utility of model-informed drug development methodologies in supporting generic drug development.