Ghostwriter BL5611 DrugDiscovery2024 Term Paper

BL5611 Term Paper

Topic 1: Engineered Cell Therapies [2 pages maximum]

Question 1A [70 marks]

Explain the process of CAR T-cell manufacturing. Include and explain both autologous and allogenic manufacturing process.

Question 1B [30 marks]

If you were a company looking to start a CAR T therapy and do clinical trials, which country would you pick? Choose a country of your choice, and elaborate on the pros and cons of doing CAR T therapy there.

Topic 2: GPCR structure-based drug design [2 pages maximum]

Frizzled are GPCRs the function in the Wnt other signaling pathways. When activated, Frizzled leads to activation of Dishevelled in the cytoplasm.

Question 2A [20 marks]

Go to GPCR database (https://gpcrdb.org/structure/) and search for experimental Frizzled GPCR structures and fill in the following table. An example has been done for you. (Note: Do not include Smoothened).

Question 2B [80 marks]

Based on the table in question 2A, and other literature, suggestion a Frizzled receptor to start a structure-based drug design programme on, and what strategies you would use. Strategies that are tailored for the Frizzled GPCR of your choice will get more marks.

Topic 3: RNA Therapeutics [2 pages maximum]

Question 3A [30 marks]

Pompe disease is an autosomal recessive lysosomal disease due to the deficient activity of alpha-glucosidase (GAA) enzyme. How would you design a RNA therapeutics as a treatment for Pompe disease and why?

Question 3B [30 marks]

Discuss the challenges for using mRNA-based therapeutics beyond infectious disease and liver indications.

Question 3C [40 marks]

Four ASO are approved/under investigation for duchenne muscular dystropy - eteplirsen, viltolarsen, golodirsen, and casimersen. What are the differences in these four ASOs and what mutations do they target?

Topic 4: AI data mining and drug screening [2 pages maximum]

Question 4A [10 marks]

Explain the difference between supervised and unsupervised learning.

Question 4B [90 marks]

Read the paper (“A small-molecule TNIK inhibitor targets fibrosis in preclinical and clinical models”). How was AI used in the drug discovery process? Which parts of the pipeline benefited from AI, which did not?

Topic 5: Peptide-based drug discovery and mapping of drug binding sites [2 pages maximum]

Reference for lectures:

Markus Muttenthaler, Glenn F. King, David J. Adams, and Paul F. Alewood. Trends in peptide drug discovery. Nature Reviews Drug Discovery. 20(4), 309-325 (2021)

Final reading assignment:

Jennifer Couzin-Frankel. Obesity meets its match. Science. 382(6676), 1227 (2023)

Katie Kingwell. Macrocycle drugs serve up new opportunity. Nature Reviews Drug Discovery. 22(10), 771-773 (2023)

Supplemental reading (History of Science):

Jennifer Couzin-Frankel. Sidelined. Science. 381(6664), 1274-1277 (2023)

Question 5A [50 marks]

After reading the news feature on GLP-1 drugs: Which new drug product should be added to Table 1 from Muttenthaler et al?  Can you identify some of the other GLP-1 analogues in Table 1, and what differentiates this new addition?

Question 5B [50 marks]

After reading the news feature on Merck’s PCSK9 inhibitor: Which peptide macrocycle became the first molecule derived from mRNA display to receive FDA approval?  (Hint: It was also developed by Ra Pharma).