CAD Seminar Series: Mechanistic Modeling of Spatiotemporal Drug Penetration and Exposure in the Huma
When:
September 25, 2024
2:30 p.m. to 3:30 p.m.
2:30 p.m. to 3:30 p.m.
Where:
Event category:
Seminar
Hybrid
Speaker: Jing Li, Professor of Oncology, Wayne State University School of Medicine; Director, Pharmacology and Metabolomics Core
Karmanos Cancer Institute (https://oncology.med.wayne.edu/profile/bb8374)
Time: Wednesday, September 25, from 2:30 to 3:30 pm
Location for in-person participants: 1146 FAB
Zoom link for online audience: https://wayne-edu.zoom.us/j/92845590121?pwd=CpRA5Wa5gzSMn2xiVkR2abD83O5nrH.1
Title: Mechanistic Modeling of Spatiotemporal Drug Penetration and Exposure in the Human Central Nervous System and Brain Tumors
Abstract : The pharmacokinetics of many new and existing drugs in the human central nervous system (CNS) and brain tumors remain poorly understood or misunderstood because direct measurement of spatiotemporal drug penetration and exposure within the human brain and brain tumors is difficult or infeasible given the challenge of sampling and limitation of currently available imaging and analytical technologies. The lack of quantitative knowledge on CNS and tumor pharmacokinetics of systemically administered drugs makes development of new drugs and use of existing drugs for treating brain cancer a challenging and often unsuccessful task. Innovative approaches are needed to resolve the gap of our knowledge. Physiologically based pharmacokinetic (PBPK) modeling offers a unique mechanism-based computational modeling approach for quantitative prediction of CNS pharmacokinetics, given its capability of incorporating biological system-specific data and drug-specific data into a pharmacokinetic model and predicting in vivo kinetic processes based on mechanistic scaling of in vitro data. This presentation introduces the basic pharmacology principles, PBPK modeling methodology, and our experience in the development of mechanistic PBPK models for prediction of spatiotemporal drug penetration and exposure in the human CNS and brain tumors. The developed mechanistic modeling platform provides a valuable tool to assist the design of efficient clinical trials, selection of right drug candidates, and optimization of dosing regimens for rational drug development of new drugs and optimal use of current drugs for brain cancer treatment.
—————————————————————
CAD Seminar Series: Advancing Knowledge, Innovation, and Collaboration in Computation, AI, and Data Science (CAD)
The CAD Seminar Series is a dedicated platform for advancing knowledge, fostering innovation, and promoting collaboration across the fields of Computation, Artificial Intelligence, and Data Science. This series brings together leading experts, researchers, and professionals to explore the latest developments, tackle emerging challenges, and drive forward-thinking solutions at the convergence of these critical disciplines.
Objectives:
• Advance Knowledge: Share cutting-edge research and insights that push the boundaries of what is known in CAD.
• Foster Innovation: Encourage the development of novel ideas and solutions through interdisciplinary dialogue and creative thinking.
• Promote Collaboration: Unite expertise across disciplines and build bridges between academia, industry, and government to address complex problems and create opportunities for joint ventures.
Schedule: The seminars will be held on a weekly basis (unless otherwise noted) throughout the academic year. For Fall 2024, the seminars are tentatively scheduled for Wednesdays from 2:30 PM to 3:30 PM, and will be available both in-person at 1146 FAB and online. Each seminar will spotlight a particular topic, providing an in-depth exploration and fostering lively discussions.
Target Audience: The CAD Seminar Series is designed for a diverse audience, including faculty, researchers, students, and professionals in Computation, AI, Data Science, and related fields. It serves as a forum for exchanging ideas, networking, and contributing to the growth of these rapidly evolving areas. We highly recommend in-person attendance to enhance engagement and networking opportunities with speakers and fellow participants.
Call for Participation: We welcome contributions from researchers, practitioners, and students. Whether presenting your work, participating in discussions, or attending as a learner, your involvement is crucial to the success of this collaborative initiative.
—————————————————————————