Pharmaceutical Modelling

Course content

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
Education

Bachelor Programme in Pharmacy (Danish programme) - elective

 

The course is co-taught with students from the master's program in pharmacy, Medicinal Chemsistry and Pharmaceutical Sciences.

Learning outcome

At the end of the course, students are expected to be able to:

Knowledge

  • Explain the mathematical principles behind selected methods used
  • Be critical to the quality of the data and developed mathematical models
  • To link modeling results and experimental work

 

Skills

  • To be able to automate data handling and visualization
  • Develop pharmaceutical models
  • Evaluate the accuracy of the models
  • Have hands-on experience with mathematical and statistical software

 

Competences

  • Apply models in pharmaceutical research and development
  • Critically evaluate the usability of diverse computational platforms for pharmaceutical problems
  • Select the appropriate mathematical model to solve problems in pharmaceutical sciences

 

No prior computer programming knowledge is needed.

Lectures: 12 hours
Class room exercises: 10 hours
Computer exercises: 20 hours
Project work: 70 hours
Supervision during project work: Guidelines will be available on the course homepage
Mathematical test and oral presentation of group work.

Literature

Munk and Munro: Maths for chemistry. Latest edition.

Lecture notes.

If you are applying for the course as a credit transfer student, you must have passed SFAB20015U Biopharmaceuticals -bioorganisk kemi or have acquired similar competencies comparable to the math curriculum on the bachelor level of the pharmacy education in another course. Documentation for corresponding competencies in the form of a course description and an exam result must be attached to your application.

Oral
Individual
Continuous feedback during the course of the semester
Feedback by final exam (In addition to the grade)
ECTS
7,5 ECTS
Type of assessment
Oral examination, 30 minutes
The oral examination is individual and without preparation.
The oral examination is based on the learning portfolio that the student has prepared during the course of teaching.
Aid
Without aids

It is permitted to bring the learning portfolio on which the oral examination is based on to the examination.

Marking scale
7-point grading scale
Censorship form
No external censorship
Criteria for exam assessment

To achieve the grade 12 the student must be able to:

 

Knowledge

  • Explain the mathematical principles behind selected methods used
  • Be critical to the quality of the data and developed mathematical models
  • Link modeling results and experimental work

 

Skills

  • Develop pharmaceutical models
  • Evaluate the accuracy of the models
  • Have hands-on experience with mathematical and statistical software

 

Competences

  • Apply models in pharmaceutical research and development
  • Critically evaluate the usability of diverse computational platforms for pharmaceutical problems
  • Select the appropriate mathematical model to solve problems in pharmaceutical sciences
  • Category
  • Hours
  • Lectures
  • 12
  • Preparation
  • 70
  • Theory exercises
  • 28
  • Project work
  • 70
  • Guidance
  • 6
  • Exam
  • 20
  • English
  • 206