Physiologically Based Pharmacokinetic Modeling is a sophisticated approach in pharmacodynamics that integrates physiological parameters to predict drug behavior within the body. It considers factors such as organ function, blood flow, and tissue composition to simulate drug concentration changes over time accurately. Unlike traditional pharmacokinetic models, PBPK offers a more comprehensive understanding of drug interactions, absorption, distribution, metabolism, and excretion.
PBPK models enhance drug development by providing insights into variability among individuals and populations, aiding in dose optimization and safety assessments. They play a pivotal role in predicting drug-drug interactions and supporting regulatory decisions. By incorporating physiological realism, PBPK modeling contributes to refining dosage regimens, minimizing adverse effects, and expediting the development of new therapeutic agents. As a powerful tool, PBPK modeling continues to revolutionize the field of pharmacodynamics, fostering more precise and personalized approaches to drug design and administration.
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Paulo Cesar De Morais, Catholic University of Brasilia, Brazil
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Consolato M Sergi, Universities of Alberta and Ottawa, Canada
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Prashant Bhokardankar, Datta Meghe Ayurved College, India
Title : Principles and standards for managing healthcare transformation towards personalized, preventive, predictive, participative precision medicine ecosystems
Bernd Blobel, University of Regensburg, Germany
Title : Personalized and Precision Medicine (PPM) as a unique healthcare model based on design-inspired biotech- & biopharma-driven applications to secure the human healthcare and biosafety
Sergey Suchkov, N.D. Zelinskii Institute for Organic Chemistry of the Russian Academy of Sciences & InMedStar, Russian Federation
Title : Antibody proteases as translational tools of the next step generation to be applied for biopharmacy related and precision medical practice
Sergey Suchkov, N.D. Zelinskii Institute for Organic Chemistry of the Russian Academy of Sciences & InMedStar, Russian Federation
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Hitasha Vithalani , IIT Gandhinagar, India
Title : Cognitivevoice: Novel machine learning model leveraging acoustic features to predict future cognitive decline in Parkinson’s Disease
Aadya Daga, Hamilton High School, United States
Title : Platelet-activating factor-receptor pathway mediates solar radiation-induced extracellular vesicle release in human keratinocytes
Ravi P Sahu, Wright State University, United States