Preclinical and Clinical Data Analysis in Predictive Drug Discovery/Development

7.5 credits

Syllabus, Master's level, 3FG289

A revised version of the syllabus is available.
Education cycle
Second cycle
Main field(s) of study and in-depth level
Drug Discovery and Development A1N, Pharmaceutical Sciences A1N
Grading system
Fail (U), Pass (G), Pass with distinction (VG)
Finalised by
The Educational Board of Pharmacy, 26 May 2016
Responsible department
Department of Pharmacy

General provisions

The course is given in English when necessary.

This course substitutes and corresponds to 3FG890, Preclinical and Clinical Data Analysis in Predictive Drug Discovery/Development.

Entry requirements

For applicants within:

- The programme for Master of Science in Pharmacy, at least 150 credits within the programme is required and have gone through all courses in semester 1-7 within the programme.

- The programme for Master of Science in Chemical Engineering, at least 120 credits within the programme is required, and courses corresponding to Probability and Statistics 5 credits, Medicinal Chemistry 7.5 credits and Pharmacokinetics 7.5 credits.

- The programme for Master of Science in Drug Discovery and Development, admission to the programme and attendance of the course Drug Discovery and Development 7.5 credits is required.

- The programme for Master of Science in Pharmaceutical Modelling, admission to the programme and attendance of the course Drug Discovery and Development 7.5 credits in required.

- Acceptance to a single subject course requires at least 150 credits from scientific of technical education, including knowledge corresponding to at least 60 credits in total within the subjects Pharmacy, Pharmaceutical science, Pharmaceutical Bioscience and/or Pharmaceutical Chemistry, and courses corresponding to Statistics at least 4.5 credits, Medicinal Chemistry at least 6 credits and Phamacokinetics at least 7.5 credits. Other education is tried individually. Knowledge in English equivalent to that required for basic eligibility to Swedish higher education.

Learning outcomes

After having completed the course, the students should be knowledgeable in the general concepts and standard methods in multivariate data analysis, experimental design, parameter estimation, and the evaluation of model fit, and be able to apply these to typical drug discovery/development questions. Specifically, the students should be able to:

- explain and use basic concepts and methods in statistics

- explain and use standard regression methods and methods for estimating model parameters

- describe and use methods for exploratory and predictive multivariate data analysis

- describe and apply basic concepts of model evaluation

- describe and use basic statistical experimental design methods

- apply these methods to typical drug discovery/development scenarios, such as the analysis of genomic, proteomic, chemical and biological data, the modelling of relationships between chemical structures of drug molecules and their physical, biological and pharmacological properties, and the modelling of drug effects using preclinical and clinical data.

- analyse, assess and present the results of statistical data analyses


The course covers the use of statistical methods to analyse and model chemical and biological data in drug discovery and development. The course illustrates general concepts of the differant methodologies discussed, their applicability to specific types of problems encountered in the development of new drugs, and their practical use. Focus lies on applications in drug discovery and development, including: informatics applied to genomic, proteomic, chemical and biological data, structure-property relationship modelling, and model-based preclinical and clinical pharmaceutical research. By applying the methods discussed to relevant cases, the students will gain hands-on experience in several statistical methods and software.

The course contents include, for example:

- basic statistical concepts, such as distributions (continuous/discrete), location/dispersion/shape, hypothesis testing, ANOVA, Bayesian statistics, prior/posterior probabilties

- regression methods and methods for estimating model parameters (e.g., linear/multiple linear/non-linear/robust regression, maximum likelihood estimation)

- multivariate data analysis (e.g., principal components analysis, cluster analysis, partial least squares projection, support vector machines, decision trees)

- model evaluation (e.g., cross-validation, boot-strapping, permutation analysis)

- statistical experimental design (e.g., factorial design, D-optimal design)


Teaching is a mixture of lectures, computer exercises and group exercises. Compulsory sections: course introduction, computer exercises, group exercises, and student presentations.


Examination will comprise oral or written presentation of group exercises. Passing the course requires attendance and approved examination of the compulsory sections. The possibility to supplement failed compulsory parts can be provided at the next course occasion in case of vacancies. Students who have failed the first examination are allowed five re-examinations.