Sponsored by Certara
Model-Informed Drug Development (MIDD) has revolutionized the pharmaceutical industry by turning complex data into actionable insights. From in silico simulations of pharmacokinetics (PK) and pharmacodynamics (PD) to AI-driven risk assessments for liver injury, MIDD is guiding critical decisions such as dosing recommendations, “go/no-go” calls, and pipeline planning. This approach brings enormous value by enhancing cost efficiency, speeding up time to market, and delivering better treatments to patients.
The integration of Artificial Intelligence (AI) into MIDD doesn’t replace traditional methods but enhances them. AI accelerates model development by enriching data and providing more accurate validation. During this webinar, Certara experts will demonstrate how AI, including machine learning, is transforming MIDD into a faster and more powerful tool for decision-making in drug development.
Key topics include the application of deep learning in drug discovery, GPTs for validating QSP models that establish therapeutic potential, the use of machine learning in efficacy and safety analysis across diverse populations, and the evolving regulatory perspectives on AI’s role in evidence generation. Join us to explore how AI is streamlining the development of clinically relevant medicines, breaking down barriers, and enhancing our ability to bring new treatments to patients.
Learning Objectives:
• Understand the key benefits of Model-Informed Drug Development (MIDD): Learn how MIDD, through in silico simulations of PK, PD, and machine-learning risk assessments, supports critical decision-making in drug development, including "go/no-go" decisions, dosing strategies, and pipeline planning.
• Explore the role of AI in accelerating and enriching MIDD: Gain insights into how Artificial Intelligence, particularly deep learning and machine learning, extracts greater value from data, validates models, and speeds up the drug development process.
• Examine practical applications of AI in drug development: Discover how deep learning is used in drug discovery to train property prediction models and extract bioactivity data, how GPTs contribute to the creation of PBPK and QSP models, and how machine learning demonstrates drug efficacy and safety across diverse populations.
• Learn about regulatory perspectives on AI: Understand the evolving role of AI in generating evidence for regulatory decision-making and its impact on the approval and development of new medicines.
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Her past appointments include leadership roles within large Pharma as well as in small biotech organizations including head of clinical pharmacology, clinical leader, project development leader, head of clinical operations and due diligence asset assessment. She has over 10 years of experience in providing consulting advice to the pharmaceutical industry and non-profit Global Health Organizations ranging from individual project support to strategic TA strategy and development planning, portfolio management and corporate transformation. She joined Certara in 2017 and is currently the SVP of Drug Development Science within Integrated Drug Development.
