Chapter 14. In Vitro Dissolution and In Vitro-In Vivo Correlation
Synopsis
Author
Mrs. Mansi Sinha
Assistant Professor, Dept. of Pharmacology, IIMT College of Pharmacy, Greater Noida, Uttar Pradesh, India
Abstract
The integration of computational systems in dissolution testing and in vitro-in vivo correlation (IVIVC) has revolutionized pharmaceutical development and quality control processes. Advanced software platforms enable automated data collection, analysis, and interpretation of dissolution profiles while facilitating real-time monitoring of drug release kinetics. Machine learning applications enhance the accuracy of dissolution profile comparisons and improve the prediction of in vivo drug behavior based on in vitro data. Automated systems handle large datasets from multiple dissolution tests, performing complex mathematical calculations for various dissolution models. The implementation of artificial intelligence has improved the establishment of meaningful correlations between in vitro dissolution data and in vivo drug performance. Real-time analysis systems provide immediate feedback on formulation performance, enabling rapid optimization of drug delivery systems. Integration with laboratory information management systems (LIMS) ensures data integrity and compliance with regulatory requirements. Computational tools assess factors affecting dissolution variability and generate standardized reports for regulatory submissions. Advanced modeling techniques incorporate physiological parameters to better predict in vivo drug behavior from dissolution data.
Keywords: Dissolution Testing; In Vitro-In Vivo Correlation; Computational Analysis; Drug Release Kinetics; Data Analysis; Machine Learning
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