Pharmaceutical Modelling

The course focuses on mathematical models and computer programming for a quantitative understanding of diverse pharmaceutically relevant problems. This includes models at different scales, both for molecular and particle level properties, interactions between molecules/particles and their interactions with the organism. The course will via 'real-life' practical examples provide the students with knowledge about the theory behind methods used for pharmaceutical modelling and simulation of system behavior. The students will be provided with input data for the different systems studied.

In the lectures, the students are introduced to the fundamental principles behind methods in pharmaceutical modelling. In the exercises, the students get hands-on experience with methods used in academia and industry and get an opportunity to apply these methods on 'real-life' problems.

The course begins with a introduction and brush-up on fundamental mathematical tools, building on the knowledge obtained during the bachelor courses, e.g. physical chemistry. We then apply and modify computer scripts that model the pharmaceutical systems, and discuss these models in relation to the literature.

The topics covered in the lectures and exercises are:

• Introduction to basic multivariate calculus and linear algebra
• Introduction to differential equations
• Model optimization
• Machine learning, deep learning and artificial intelligence.
• Multivariate data analysis
• Molecular dynamics
• Image analysis

Visualization of data is an important aspect of the course

Examples on areas covered in the lectures and exercises are:

• Interatomic forces in biological and crystalline drug systems – molecular dynamics
• Image analysis of digital images from e.g. electron microscopy studies.
• Least squares optimization of models against experimental data.
• Multivariate methods for process analytical technology, e.g. powder diffraction, Raman and NIR spectroscopies.
• Training of deep neural networks for classification of data.

Objective

The course is relevant for pharmaceutical research within both drug discovery and drug development where it is important to:

• Understand the theory behind models on various levels of the drug discovery and development process
• Get hands-on experience with modern programming tools in pharmaceutical modelling
• Know the accuracy and applicability of mathematical models