SMIMM1131U Drug Discovery
Master's Programme in Industrial Drug Development -
compulsory
The course is intended for drug discovery and development
professionals, who would like to advance their knowledge in modern
technologies and approaches used in target
identification/validation, drug discovery and lead
optimisation.
The following topics will be covered in lectures during the course:
- Target classes: receptors, channels, transporters and enzymes
- Target identification and evaluation
- Target validation using genetic technologies
- Drug discovery aspects related to drug development aspects
- Drug design aspects related to biopharmaceuticals and small molecules
- Translational aspects ranging from a molecular target to whole animals
- Case stories
Target prioritization, potential pitfalls and literature search will be covered in workshops.
The overall objective of the course is to provide the student with a general comprehension of the elements involved in modern drug discovery. The course will therefore cover a mix of general topics, as well as specific example cases.
The majority of drug discovery programs are directed towards design of either biopharmaceuticals or small molecules acting selectively at specific targets. As an initial step, the target involved in a given disease has to be identified and validated. Subsequently, ligands designed and optimized to act selectively at the target are identified. During this lead optimization process a number of considerations, such as ADME, toxicology and production, have to be taken into consideration. Finally, the lead candidates have to show efficacy in relevant animal disease models before the final candidate can enter pre-clinical studies. In the this course, we will focus on the target identification and validation as well as drug discovery and lead optimization processes.
Upon completion of the course, students are expected to be able to:
Knowledge
- Demonstrate comprehension of target identification and validation methods and processes
- Demonstrate insight into lead optimization methods and processes
Skills
- Ability to select important parameters for lead optimization and lead candidate progression
- Orally present a subject in the drug discovery area (however, this is not part of the assessment)
Competencies
- Conduct a valid scientific literature study in the area of drug discovery
- Write a scientifically-based report on drug discovery aspects using scientific literature
Recommended reading for the lectures:
“Drug Discovery and Development” by Humphrey P. Rang and Duncan Richards (Elsevier, 3rd Ed, 2022, ISBN 978-0-7020-7804-0)
The below chapters are ‘need to know’ before the lectures as they form the basis of the course, including lectures and workshops:
Section 1: Introduction and background:
Chapter 2: The nature of disease and the purpose of therapy
Chapter 3: Therapeutic modalities
Section 2: Drug Discovery:
Chapter 4: Target selection
Chapter 5: From drug target to drug discovery
The following chapters are ‘nice to know’ before the lectures. They will increase your basic knowledge of the subject and thus increase your outcome of the lecture:
Section 2: Drug Discovery:
Chapters 6 to 13
Workshops
All workshop notes and materials are ‘need to know’.
Examination
The examination report will be based on a literature search performed by the participants and there is therefore no common syllabus for the course. Selected reviews may be provided by the organizers prior to the course as an introduction to the examination topic.
Note: The exam will require students to set aside time after the course dates.
- Category
- Hours
- Lectures
- 24
- Preparation
- 18
- Theory exercises
- 18
- Exam
- 22
- Total
- 82
Registered students register via the self-service on
KUnet.
New students apply
via this link:
Drug Discovery – University of Copenhagen
- Credit
- 3 ECTS
- Type of assessment
- Home assignment
- Type of assessment details
- The individual set written assignment is based on an example case. The assignment must be based on original peer-reviewed scientific articles found by literature search performed by the participants and there is therefore no common syllabus for the course. The participants will be introduced to literature searches during the course and the examination assignment is typically based on 20-40 articles.
- Aid
- All aids allowed
Read more about the GAI-rules: Generative AI and good academic practice in UCPH's education programmes – University of Copenhagen
- Marking scale
- 7-point grading scale
- Censorship form
- External censorship
- Exam period
Deadline for submission of written report: See exam schedule.
- Re-exam
Same as ordinary.
Criteria for exam assesment
To achieve a grade of 12, the student must be able to:
Knowledge
- Demonstrate comprehension of target identification and validation methods and processes
- Demonstrate insight into lead optimization methods and processes
Skills
- Apply basic computational methods in the area of bioinformatics and literature serach
- Ability to select important parameters for lead optimization and lead candidate progression
Competencies
- Conduct a valid scientific literature study in the area of drug discovery
- Write a scientifically based report on drug discovery aspects using scientific literature
Course information
- Language
- English
- Course code
- SMIMM1131U
- Credit
- 3 ECTS
- Level
- Part Time Master
- Duration
- 5 days. Note: The exam will require students to set aside time after the course dates.
- Placement
- Spring
- Schedule
- See course calendar
- Course capacity
- 25 participants
Price
Fees are published on the course webpage at Drug Discovery – University of Copenhagen.
Textbooks must be purchased by the participants.
Study board
- Study Board for the Professionel Master´s Degree Programmes at The Faculty og Health and Medical Science
Contracting department
- Department of Drug Design and Pharmacology
Contracting faculty
- Faculty of Health and Medical Sciences
Course Coordinators
- Stephan Pless (13-78796a756d66733375716a787845787a736933707a336970)
Lecturers
Lecturers will be leading experts from both industry and
academia (the names below are a list of previous lecturers on this
course). Note that lecturers will be selected based on course
composition, attendee numbers and availability.
Philipp Jaeger, Boehringer Ingelheim
Lotte Bjerre Knudsen, Novo Nordisk
Cord Brakebusch, BRIC/University of Copenhagen
David Hackos, Genentech
Sebastian Meyer, Numab
Benny Bang-Andersen, Lundbeck A/S
Torben Hansen, University of Copenhagen
Kristian Strømgaard, University of Copenhagen
Morten Grunnet, Lundbeck A/S
Petrine Wellendorph, Ceremedy
Thomas Høeg-Jensen, Novo Nordisk