Head of the Chair, Vice Dean of the Faculty Jagiellonian University - Medical College Kraków, Poland
Orodispersible tablets are rising to the level of commonly used oral dosage forms whenever immediate release of API is required. Therefore, there is a need for optimization of their formulation to satisfy the primary requirement of their unique property of extra short disintegration time followed by defined dissolution profile. This talk shows how machine learning models can both predict and explain complex dissolution and disintegration behaviors, offering practical tools for decision-making.
Learning Objectives:
Understand the application of AI and Machine Learning in drug development.
Learn how AI and machine learning models can predict and explain complex dissolution and disintegration behaviors in pharmaceutical formulations.
Explore practical tools for predicting disintegration time
Gain insights into how predictive models can streamline the formulation process by accurately forecasting orodispersible tablet disintegration times based on real-world data.
Implement AI-based decision-making in formulation development.
Discover how to integrate AI-driven predictions into formulation strategies to improve decision-making, reduce trial-and-error, and optimize drug development workflows.
