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 2024.
The course series introduces interdisciplinary research in drug discovery with mathematics as the language and computation as the tool. We welcome bachelor, master, and PhD students of diverse backgrounds including (but not limited to) mathematics, computer science, physics, chemistry, (computational) biology, pharmacy, and other fields such as epidemiology and medicine.
The course is in-person only. Remote or virtual attendance is unfortunately not feasible. We have a diverse and lively class room that work with and learn from each other interactively, which is challenging in a virtual or hybrid setting.
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
- Assessment
- Syllabus
- Lecture 1: Introduction to drug discovery (20.09.2024)
- Lecture 2: The What, the Who, and the How of drug discovery (27.09.2024)
- Lecture 3: Key questions in drug discovery (04.10.2024)
- Lecture 4: Proteins as drug targets (11.10.2024)
- Lecture 5: Protein-ligand interaction (18.10.2024)
- No lecture on 25.10.2024
- Lecture 6: Statistical model and machine learning (01.11.2024)
- Lecture 7: Causal inference (08.11.2024)
- Lecture 8: Lead identification and optimization (15.11.2024)
- Lecture 9: Mechanism and mode of action of drugs (22.11.2024. The lecture takes place exceptionally at Biozentrum, Hörsaal U1.141)
- Dies academicus (29.11.2024, no lecture)
- Lectuer 10: DMPK and PKPD modelling (06.12.2024)
- Lecture 11: Guest lecture (13.12.2024)
- Lecture 12: A collaboration challenge (20.12.2024)
- Further questions or suggestions?
- Offline activities
- 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. In-person attendance is required.
Course material and licensing
Course material, including lecture notes, slides, and reading material, are shared on this web site, http://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 pre-course survey. Your reply helps me to shape the course to meet your needs.
Assessment
The final note is given by participation including in-class quizzes (30%), offline activities (40%), and a collaboration challenge in the final session (30%).
The list of students from whom I received replies to in-class quizzes, i.e. the colour papers, as well as offline activity submissions, is available here. Please make a comment if you find mistakes.
Syllabus
Lecture 1: Introduction to drug discovery
The first lecture introduces drugs and drug discovery.
- Preparatory reading/watching
- If you need a refresher of the central dogma of biology, please watch this YouTube video.
- If you are not familiar with the process of drug discovery and development, you may benefit from watching this YouTube video made by Novartis.
- Slides of lecture 1
- Coincidentally, just the day before the lecture, the news broke that three scientists - Joel Habener, Lotte Bjerre Knudsen, and Svetlana Mojsov - were awarded the prestigious Lasker~Debakey clinical medical research award for the discovery and development of GLP-1 based drugs that have revolutionized the treatment of obesity. Website of the Lasker Foundation. I thank Urs Saner who shared the news with me.
- There is no offline activity this week. However, please fill out the anonymous post lecture survey.
Lecture 2: The What, the Who, and the How of drug discovery
- Slides of lecture 2
- Offline activities of lecture 2:
- Fill out the anonymous post-lecture survey of lecture 2.
- Classify the top-selling drugs in 2023 by their modality, and interpret the patterns of indications. The drugs are listed in the slide (including the link to the original poster in PDF format). Please submit your replies here via Google Form. Deadline of assignment: before lecture 3.
- Continue the discussion of the role-play game. A random member of each team will be selected next week to give an update of the team.
Lecture 3: Key questions in drug discovery
- Slides of lecture 3
- Mandatory offline activities of lecture 3:
- Please read the history of GLP-1-based therapy for obesity made available by the Lasker Foundation, including the
Read Moresection section in this page, and this YouTube video. - Please watch the Nobel Lecture by Katalin Karikó on developing mRNA for therapy
- What commonalities and differences did you perceive in the process of scientific discoveries that led to semaglutide and SARS-Cov-2 mRNA vaccines? Submit your reply via this Google Form. Deadline: before lecture 4.
- You are welcome to provide feedback and/or ask questions by filling out the anonymous post-lecture survey for the third lecture.
- Please read the history of GLP-1-based therapy for obesity made available by the Lasker Foundation, including the
- Optional offline activity: Please watch the Nobel Lecture by Drew Weissmann on Nucleoside Modified mRNA-LNP Therapeutics
Lecture 4: Proteins as drug targets
- Slides of lecture 4
- Mandatory offline activities of lecture 4:
- Please kindly provide feedback and/or ask questions anonymously by filling out the anonymous post-lecture survey for the fourth lecture.
- Please read the advanced scientific background information for Nobel Prize in Chemistry 2024.
- The blog In the Pipepine, created and maintained by Derek Lowe, is a popular blog among drug discovers. Read his summary of the Chemistry Nobel Prize 2024.
- Having read the material, what is your take on the value of being able to predict protein structure for drug discovery? What is the most surprising learning for you? And do you have questions? Submit your replies via this Google Form. Deadline: latest Saturday following lecture 5.
- Optional offline activity: Read the scientific background information for Nobel Prize in Physics 2024.
Lecture 5: Protein-ligand interaction
- Slides of lecture 5
- Mandatory offline activities of lecture 5:
- Please kindly provide feedback and/or questions anonymously by filling out the anonymous post-lecture survey for the fifth lecture.
- Choose one task below to perform. In each case, consider using the Feynman technique.
- If you have a strong background in mathematics/statistics and particularly in machine learning, please review the material shared in the slides about proteins so that you gain more knowledge about drug targets.
- If you have a strong background in biology but yet to develop more skills in machine learning, please read An Introduction to Machine Learning.
- If you have strong background in both fields, please read Accurate structure prediction of biomolecular interactions with AlphaFold3, by Abramson et al.
- Confirm that you have performed one of the tasks above by filling out this Google Form. Deadline: Friday of lecture 6.
Lecture 6: Statistical model and machine learning
- Slides of lecture 6
- Mandatory offline activities of lecture 6:
- Mandatory: Keep thinking about the last question that we discussed in the course (see slide page 23). Find your own examples and explanations to answer the questions; which outcomes (red stars or blue crosses) would support which models, and why. Submit your answers to this Google Form. Deadline: before lecture 7.
- Optional: you are welcome to provide feedback and/or raise questions anonymously by filling out the anonymous post-lecture survey for the sixth lecture.
- Optional: read the review article Causal inference in drug discovery and development.
Lecture 7: Causal inference
- Slides of lecture 7
- Offline activities of lecture 7:
- Mandatory: read the article The environment and disease: Association and Causation, a talk given by Austin Bradford Hill in 1965, introducing the Hill’s criterion for causality. And review today’s material. Fill this Google Form before the eighth lecture.
- Optional: you are welcome to provide feedback, and/or raise questions, anonymously by filling out the anonymous post-lecture survey for the seventh lecture.
- Optional: read Applying the Bradford Hill criteria in the 21st century: how data integration has changed causal inference in molecular epidemiology by Fedak et al. (2015). This extends the material that we covered in the lecture and the mandatory reading, and introduces the current understanding of causal inference based on data integration.
Lecture 8: Lead identification and optimization
- Slides of lecture 8
- Offline activities of lecture 8:
- You are welcome to provide feedback, and/or raise questions, anonymously by filling out the anonymous post-lecture survey for the eighth lecture.
- Mandatory: Read 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. Use this and other resources, for instance Wikipedia and large language models (LLM) such as ChatGPT, to answer following questions in your own words. Submit the answers here via Google Form until November 29th.
- What is surface plasmon resonance (SPR), and how it is used to study the kinetics of target-ligand interactions?
- What do $K_d$, $k_{on}$, and $k_{off}$ mean in the context of kinetics of target-ligand interaction?
- What does microsomal clearance mean? Why it is important to measure it in drug discovery?
- Why is plasma protein binding an important parameter for drug discovery?
- What does GSH adduct mean? Why GSH assay is required for drug candidates?
- What is hERG, and what does hERG assay measure?
- What is Ames test, and what does it measure?
- What is the micronucleus test, and why it is important to perform it?
- What does phototoxicity mean, and why it is important to predict or measure it?
- Optional: In the lecture, we dissected the example of the discovery of a novel, reversible, and specific MAGL inhibitor. If you are interested in exploring the topic further, read the original publication Structure-Guided Discovery of cis-Hexahydro-pyrido-oxazinones as Reversible, Drug-like Monoacylglycerol Lipase Inhibitors by Bernd Kuhn, et al. (J Med Chem, 2024). An unsolicited advice: don’t get frustrated if you meet details that you do not understand at the first sight. That is normal when reading interdisciplinary papers. It may help to focus on the big lecture.
Lecture 9: Mechanism and mode of action of drug candidates
- Slides of lecture 9
- Offline activities of lecture 9:
- You are welcome to provide feedback, and/or raise questions, anonymously by filling out the anonymous post-lecture survey for the nineth lecture on network analysis and omics.
- Mandatory: Read Drug discovery effectiveness from the standpoint of therapeutic mechanisms and indications by Shih et al., Nature Reviews Drug Discovery (2018). Submit your learnings here via Google Form until December the 6th.
Lecture 10: DMPK and PKPD modeling
- Slides of lecture 10
- Offline activities of lecture 10: Pick one publication to read depending on your interest. Answer the questions by filling out this Google Form before December 15th.
- [Introduction to PBPK modelling] 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.
- [Application of machine learning for PK prediction] Stoyanova, R. et al. Computational Predictions of Nonclinical Pharmacokinetics at the Drug Design Stage. J. Chem. Inf. Model. 63, 442–458 (2023).
Lecture 11: Guest lecture
We will have two guest speakers sharing their experience working in drug discovery and development:
- Audrey Yeo. Audrey is a Biostatistician and Statistical Software Engineer with three plus years of Pharma experience. At F. Hoffmann La-Roche, she leads the development of a state-of-art engineering tool “phase1b” to enhance decision making for drug discovery. Audrey’s background in Pharma spans oncology, hematology and neuroscience rare disease areas and enjoys creating statistical software and contributing to conversations about the development of early phase trials. Audrey has a MSc Biostatistics (Uni of Zürich) and M Nursing (Uni of Sydney) and is also a Registered Nurse who worked in the COVID ICU during the lockdown in her home city of Melbourne, Australia. Personal website: https://audreyyeoch.github.io/
- Nicole Schaefer. Nicole is a seasoned Scientific Researcher and certified apprentice supervisor with approximately 25 years of experience in research and development. Currently, she supports Roche’s Biomolecules in clinical trials, managing clinical samples such as cerebronspinal fluid (CSF), Aqueous Humor, Blood, and Serum from patients to scientists for further analysis. Nicole has a robust background in Reprotoxicology, cell-based in vitro assays, and 3D-Organoid methods. She holds a Swiss federal diploma in Biology and is also a graduated cosmetician. In her spare time, Nicole volunteers for the foundation “Lookgoodfeelbetter”: https://lgfb.ch/
Each talk will be about 20 minutes, followed by Q&A.
There is no offline activity this week. We welcome your feedback and/or questions to the guest speakers: please fill this anonymous feedback form.
Lecture 12: A collaboration challenge
In the collaboration challenge, students will be divided randomly into teams. The challenge will be tackled by both inter-team and intra-team collaboration. No specific preparation is necessary. More details will be announced in the course.
Offline activities
Further questions or suggestions?
Please contact the lecturer, Jitao David Zhang, at jitao-david.zhang@unibas.ch.