Welcome to the website for Applied Mathematics and Informatics In Drug Discovery (AMIDD), the course series running at the Department of Mathematics and Informatics, University of Basel in the fall semester 2022.

The course series introduces interdisciplinary research in drug discovery with mathematics as the language and informatics as the tool. We have a diverse and lively class room that learn together and from each other: every year about two third students of the class study mathematics or computer science, while other students study physics, chemistry, (computational) biology, pharmacy, and other fields such as epidemiology and medicine.

More information on the course can be found at the course directory of the University Basel.

Table of content

• Time and place
• Course material and licensing
• Pre-course survey
• Syllabus
  • 1. Drug discovery: an overview (23.09.2022)
  • 2. The central dogma and drug discovery (30.09.2022)
  • 3. Biological sequence analysis (07.10.2022)
  • 4. From sequences to structures (14.10.2022)
  • 5. Proteins and ligands (21.10.2022)
  • 6. Structure- and ligand-based drug design (28.10.2022)
  • 7. From individial interactions to networks (04.11.2022)
  • 8. Biological networks (11.11.2022)
  • 9. Omics and cellular modelling (18.11.2022, exceptionally at Kollegienhaus, Hörsaal 120)
  • 10.Dies academicus (25.11.2022, no lecture)
  • 11. PK/PD modelling (02.12.2022)
  • 12. Guest-speaker session (09.12.2022)
• Student presentation topics and reference papers
  • 13. Student presentation (I) (16.12.2022)
  • 14. Student presentation (II) (23.12.2022)
• End-term project
• Assessment
• Further questions or suggestions?
• Archives of past courses

Time and place

The lecture takes place on Fridays between 12:15 and 14:00 at Spiegelgasse 5, Seminarraum 05.002.

Course material and licensing

Course material, including lecture notes, slides, and reading material, are shared on the course’s web site, AMIDD.ch, unless otherwise specified in the course.

All course material, unless otherwise stated, is shared under the Creative Commons (CC-BY-SA 4.0) license.

Pre-course survey

Prior to attending the first session, please fill out the voluntary pre-course survey. Your reply helps me to shape the course to meet your needs.

Syllabus

1. Drug discovery: an overview

• Slides
• Anonymous Post-lecture Survey of Lecture 1
• Material for offline activities (see slides 31-33)
  • Watch a video on the discovery and development of the drug Herceptin, presented by Susan Desmond-Hellmann, and answer questions. See the questions and submit your answers here via a Google Form.
  • Required reading: Principles of early drug discovery by Hughes et al.

2. The central dogma and drug discovery

• Slides
• Material for offline activities (see slides 22-24)
  • Required reading: Clinical efficacy of a RAF inhibitor needs broad target blockade in BRAF-mutant melanoma by Bollag et al., Nature 2010.
  • Optional reading: A comprehensive map of molecular drug targets by Santos et al., Nature Reviews Drug Discovery, 2017
• Submit your answers for Offline Activities here via Google Form

3. Biological sequence analysis

• Slides
• Anonymous Post-lecture Survey, #3
• Material for offline activities
  • Please go through the slides #22 and #23 to try out calculating the Levenshtein distance with dynamic programming.
  • Required reading: Discovery of a selective inhibitor of oncogenic B-Raf kinase with potent antimelanoma activity by Tsai et al., PNAS 2008.
  • Optional reading: Richard Bell, the inventor of Dynamic Programming, on the origin of the name. See slide #29.
  • Handout for lecture 3 and 4, which contains genetic codes, information on amino acids, and offline exercises for both lecture 3 and 4. Not all questions need to be answered now. Please read the instructions in the Google Form below to finish this week’s task.
  • Submit your answers for offline activities here via the Google Form AMIDD-2022-OfflineActivity-Lecture3.

4. From sequences to structures

• Slides
• Material for offline activities
  • Required reading: What does AlphaFold mean for drug discovery, Asher Mullard, Nature Reviews Drug Discovery. See the PDF version here.
  • Optional readings:
    • AlphaFold2 is here: what’s behind the structure prediction miracle, by Oxford Protein Informatics Group (OPIG). Recommended if you are interested in protein structure prediction and how AlphaFold2 achieved good performance.
  • A question about Markov chains: Given the Markov chain model represented on slide #15 in the slides, what is the ratio between $p(ACGTGGT|M)$ and $p(ACCTGGT|M)$?
  • Handout for lecture 3 and 4, please finish the tasks titled A subset of BLOSUM 50 values per aligned residue pair and What does Fomivirsen target?.
  • Submit your answers for offline activities here via the Google Form AMIDD-2022-OA-Lecture4.

5. Proteins and ligands

• Slides
• Material for offline activities:
  • Watch the YouTube video about the Ramachandran Principal by Prof. Eric Martz, or read the notes (including slides) on Proteopedia, and finish a Practice Quiz.
  • Required reading: Evaluation of the Biological Activity of Compounds: Techniques and Mechanism of Action Studies by Iain G. Dougall and John Unitt, chapter two of the book The Practice of Medicinal Chemistry. To answer offline-activity questions, it is required to read pages 15-22 (1-8 of the 29 pages in total, before section ‘4. Types of Enzyme Inhibition and Their Analysis’), page 27 (section 6A), and pages 34-37 (Assay Biostatistics). The rest is optional reading.
  • Submit your results here via Google Form
  • Optional reading: Mathematics techniques in structural biology by John R. Quine. Recommended booklet for students interested in applications of mathematics in determining structures of DNA and proteins without or with drugs.

6. Structure- and ligand-based drug design

• Slides
• Anonymous post-lecture survey for lecture 6
• Material for offline activities:
  • Required reading: An introduction to machine learning by Badillo et al..
  • Required reading for this and next week: Computational methods in drug discovery by Gregory Sliwoski et al.. Please submit your replies to questions via this Google Form. The deadline of submitting the replies is November 11th.

7. From individual interactions to networks

• Slides. This week we discussed QSAR, machine learning models, and causal inference. And we introduced compartment models to model ligand-receptor interaction and enzyme kinetics. Next week we will continue with the compartment model and biological network analysis.
• This week’s offline activity is to finish the readings of the last week.
  • Required reading: An introduction to machine learning by Badillo et al..
  • Required reading for this and next week: Computational methods in drug discovery by Gregory Sliwoski et al.. Please submit your replies to questions via this Google Form. The deadline of submitting the replies is November 11th.
• Optional reading: Causal inference in drug discovery and development, pre-print manuscript on arxiv.

8. Biological networks

• Slides
• Offline activities: three optional readings for fun:
  1. Bennett, Craig M., Michael B. Miller, and George L. Wolford. “Neural Correlates of Interspecies Perspective Taking in the Post-Mortem Atlantic Salmon: An Argument for Multiple Comparisons Correction.” Neuroimage 47, no. Suppl 1 (2009): S125.
  2. Lazebnik, Yuri. “Can a Biologist Fix a Radio?—Or, What I Learned While Studying Apoptosis.” Cancer Cell 2, no. 3 (September 1, 2002): 179–82. https://doi.org/10.1016/S1535-6108(02)00133-2
  3. Jonas, Eric, and Konrad Paul Kording. “Could a Neuroscientist Understand a Microprocessor?” PLOS Computational Biology 13, no. 1 (January 12, 2017): e1005268. https://doi.org/10.1371/journal.pcbi.1005268.

9. Omics and cellular modelling

• Slides
• Offline activities are about image analysis. Biological image analysis is another important way to characterize MoA of compounds besides omics methods.
  • Required readings (1): Biological Image Analysis Primer by Erik Meijering and Gert van Cappellen (2006).
  • Required readings (2): Rudin, Markus, and Ralph Weissleder. 2003. “Molecular Imaging in Drug Discovery and Development.” Nature Reviews Drug Discovery 2 (2): 123–31. (PDF version)
  • Submit your offline activities with regard to the required reading here via the Google Form. Deadline: Friday, December 2nd, 2022.

10. Dies academicus

No lectures. We invite registered students to visit Roche. Details are announced via email.

11. PK/PD modelling

• Slides
• Offline activities
  • Required reading: the backup slides of lecture 10 to learn about the principles of population modelling, especially non-linear mixed-effect models (NLMEs), and about clinical trials.
  • Optional reading:
    • Davies, Michael, et al.. 2020. “Improving the Accuracy of Predicted Human Pharmacokinetics: Lessons Learned from the AstraZeneca Drug Pipeline Over Two Decades.” Trends in Pharmacological Sciences 41 (6): 390–408. A good introduction to prediction of PK profiles in industry.
    • Jones, H. M., and K. Rowland‐Yeo. 2013. “Basic Concepts in Physiologically Based Pharmacokinetic Modeling in Drug Discovery and Development.” CPT: Pharmacometrics & Systems Pharmacology 2 (8): 63. A good introduction to PBPK modelling.

12. Guest-speaker session

To be announced

Student presentation topics and reference papers

Each team is expected to deliver a talk of 30 minutes, which is followed by 10 minutes of Q&A.

You can vote for your presentation topics via the Google Form of voting for presentation topics by November 14th.

In HS 2022, our class will present on the following four topics:

1. Phenotypic drug discovery: how phenotypic drug discovery complements target-based drug discovery.
2. Productivity in drug discovery: return of investment, cost, and research productivity in drug discovery.
3. Mathematical modeling in drug discovery: mathematical modeling techniques used in preclinical and clinical studies.
4. Reverse translation: how we can leverage data from clinical trials and observational studies to empower preclinical studies.

13. Student presentation (I)

Team 1: Phenotypic drug discovery (December 16th)

1. Vincent, Fabien, Arsenio Nueda, Jonathan Lee, Monica Schenone, Marco Prunotto, and Mark Mercola. “Phenotypic Drug Discovery: Recent Successes, Lessons Learned and New Directions.” Nature Reviews Drug Discovery, May 30, 2022, 1–16. https://doi.org/10.1038/s41573-022-00472-w.
2. Moffat, John G., Fabien Vincent, Jonathan A. Lee, Jörg Eder, and Marco Prunotto. 2017. “Opportunities and Challenges in Phenotypic Drug Discovery: An Industry Perspective.” Nature Reviews Drug Discovery 16 (8): 531–43. https://doi.org/10.1038/nrd.2017.111. (PDF)
3. Vincent, Fabien, Paula M. Loria, Andrea D. Weston, Claire M. Steppan, Regis Doyonnas, Yue-Ming Wang, Kristin L. Rockwell, and Marie-Claire Peakman. “Hit Triage and Validation in Phenotypic Screening: Considerations and Strategies.” Cell Chemical Biology 27, no. 11 (November 19, 2020): 1332–46. https://doi.org/10.1016/j.chembiol.2020.08.009.

Team 2: Productivity in drug discovery and development (December 16th)

1. Smietana, Katarzyna, Leeland Ekstrom, Barbara Jeffery, and Martin Møller. “Improving R&D Productivity.” Nature Reviews Drug Discovery 14, no. 7 (July 2015): 455–56. https://doi.org/10.1038/nrd4650.
2. Ringel, Michael S., Jack W. Scannell, Mathias Baedeker, and Ulrik Schulze. “Breaking Eroom’s Law.” Nature Reviews Drug Discovery 19, no. 12 (April 16, 2020): 833–34. https://doi.org/10.1038/d41573-020-00059-3.
3. Fernando, Kathy, Sandeep Menon, Kathrin Jansen, Prakash Naik, Gianluca Nucci, John Roberts, Shuang Sarah Wu, and Mikael Dolsten. “Achieving End-to-End Success in the Clinic: Pfizer’s Learnings on R&D Productivity.” Drug Discovery Today, December 2021, S1359644621005444. https://doi.org/10.1016/j.drudis.2021.12.010.
4. Scannell, Jack W., James Bosley, John A. Hickman, Gerard R. Dawson, Hubert Truebel, Guilherme S. Ferreira, Duncan Richards, and J. Mark Treherne. “Predictive Validity in Drug Discovery: What It Is, Why It Matters and How to Improve It.” Nature Reviews Drug Discovery, October 4, 2022, 1–17. https://doi.org/10.1038/s41573-022-00552-x.

14. Student presentation (II)

Team 3: Mathematical modelling in drug discovery (December 23rd)

1. Handel, Andreas, Nicole L. La Gruta, and Paul G. Thomas. “Simulation Modelling for Immunologists.” Nature Reviews Immunology 20, no. 3 (March 2020): 186–95. https://doi.org/10.1038/s41577-019-0235-3.
2. Gieschke, R., and J. L. Steimer. “Pharmacometrics: Modelling and Simulation Tools to Improve Decision Making in Clinical Drug Development.” European Journal of Drug Metabolism and Pharmacokinetics 25, no. 1 (March 2000): 49–58. https://doi.org/10.1007/BF03190058.
3. Elmokadem, Ahmed, Matthew M. Riggs, and Kyle T. Baron. “Quantitative Systems Pharmacology and Physiologically-Based Pharmacokinetic Modeling With Mrgsolve: A Hands-On Tutorial.” CPT: Pharmacometrics & Systems Pharmacology 8, no. 12 (2019): 883–93. https://doi.org/10.1002/psp4.12467.

Team 4: Reverse translation (December 23rd)

1. Kasichayanula, Sreeneeranj, and Karthik Venkatakrishnan. “Reverse Translation: The Art of Cyclical Learning.” Clinical Pharmacology & Therapeutics 103, no. 2 (February 1, 2018): 152–59. https://doi.org/10.1002/cpt.952.
2. Maciejewski, Mateusz, Eugen Lounkine, Steven Whitebread, Pierre Farmer, William DuMouchel, Brian K Shoichet, and Laszlo Urban. “Reverse Translation of Adverse Event Reports Paves the Way for De-Risking Preclinical off-Targets.” Edited by Fiona M Watt. ELife 6 (August 8, 2017): e25818. https://doi.org/10.7554/eLife.25818.
3. Michoel, Tom and Zhang, Jitao David. “Causal inference in drug discovery and development”. https://arxiv.org/abs/2209.14664.

End-term project

The choice of topic and the team composition is to be submitted via this Google Form of voting for the project topic by Thursday, December the 1st. Two people working together need to vote just once. The deadline for submitting the essay by email is January 13th, 2022. No extension is possible.

All participants are expected to finish the end-term project in a team of two people, which is preferred, or individually. She or he or the team shall choose one concept from a list of candidate topics, upon which they will write a short essay introducing the concept to non-experts, with examples and ideally applications in drug discovery. The topics will be announced during the semester.

List of candidate topics of year 2022:

• Simple to intermediate
  • Principal component analysis (PCA)
  • The Lotka-Volterra predator-prey model
  • Response surface method designs
• Intermediate to demanding
  • Exploratory factor analysis
  • Regression trees and random forests
  • Mann-Whitney-Wilcoxon tests
  • Regularization (e.g. Ridge, LASSO, and elastic net)
• Advanced
  • Instrumental variable and Mendelian randomization
  • Graph neural network
  • Shapley values

The essay should have an abstract (less than 200 words), a list of references, and a main text that does not exceed 3,000 words. Visual elements like table and figures that help readers understand the concept are welcome but not obligatory. In case of a team work, the contribution of the two people should be specified.

Essays in literature-programming styles, combining texts and source code with Jupyter notebook or Rmarkdown, are encouraged. For such essays the source code does not count to the total words.

Submitted essays will be shared with the whole class for open review and joint learning.

Assessment

The final note is given by participation (40%), presentation (30%), and project work (30%).

Further questions or suggestions?

Please contact the lecturer, Jitao David Zhang, at jitao-david.zhang@unibas.ch.

Archives of past courses

• AMIDD 2021
• AMIDD 2020
• AMIDD 2019