Drug Therapy Optimization System Based on a Hybrid Approach Combining Clinical Data and In Silico Modeling - Perspective View and Concept Description
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Personalized pharmacotherapy has become a new paradigm of safe and efficient treatment and its role is specifically seen nowadays in the COVID-19 era. The therapies explored for SARS-CoV-2 are based on repurposed marketed antiviral drugs that have known cardiac safety issues. It gives a perfect example of the need to develop a tool that helps to optimize dosing strategies. The below-described project aims to propose a general concept and to develop an advanced prototype of the pharmacotherapy optimization system based on mathematical models and clinical data. A hybrid approach is proposed, merging various sources of data and techniques. The system is planned to be algorithm naive and clinicians are actively included in its development and verification. A combination of the PBPK and pharmacodynamic models, including artificial intelligence and Internet-based data-sharing technologies, has been used. Antazoline has been chosen as an exemplary drug due to the challenges connected with the optimal dosing for individual patients during the atrial fibrillation conversion. The developed PBPK model allows for precise exposure assessment for individual patients, while the QSP model predicts the ECG modification triggered by the drug. Further, plans cover thorough verification of the system and expansion towards mobile application.
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