Leiden Early Drug Discovery & Development

Pursuing new anti-cancer therapy as a team

Cancer is the leading cause of death in the Netherlands, and, with over 100 different types of cancer, it’s not a simple disease. Today, skin, breast, lung, prostate and colon cancer are the most diagnosed forms. Therefore, the discovery and development of new drugs has the ability to significantly improve the life span and quality of life for many cancer patients and their families.

At LED3, we have many internal and external collaborations to pursue these new treatments. LED3 members Profs. Heitman and Van der Stelt are also member of Oncode Institute, and they work together with biotech companies such as NTRC and Pivot Park Screening Center. These collaborations are aimed at identifying and optimizing novel molecules and probes to visualize and modulate kinases, GPCRs and lipid signaling in cancer cells and tumor associated immune cells.

Furthermore, we have performed several high throughput screenings campaigns and executed multiple hit- and lead optimization programs for acute myeloid leukemia and triple negative breast cancer in collaboration with the Leiden University Medical Center, Netherlands Cancer Institute and Hubrecht Institute. Finally, within LED3 we are developing and applying novel machine learning and chemical proteomic methods for the profiling of on-target and off-target activities of leads in cancer cells [1-3].

References

1. Koenders STA, Wijaya LS, Erkelens MN, Bakker AT, van der Noord VE, van Rooden EJ, Burggraaff L, Putter PC, Botter E, Wals K, van den Elst H, den Dulk H, Florea BI, van de Water B, van Westen GJP, Mebius RE, Overkleeft HS, Le Dévédec SE, van der Stelt M. Development of a Retinal-Based Probe for the Profiling of Retinaldehyde Dehydrogenases in Cancer Cells. ACS Cent Sci. 2019 Dec 26;5(12):1965-1974. doi: 10.1021/acscentsci.9b01022.
2. Janssen APA, Grimm SH, Wijdeven RHM, Lenselink EB, Neefjes J, van Boeckel CAA, van Westen GJP, van der Stelt M. Drug Discovery Maps, a Machine Learning Model That Visualizes and Predicts Kinome-Inhibitor Interaction Landscapes. J Chem Inf Model. 2019 Mar 25;59(3):1221-1229. doi: 10.1021/acs.jcim.8b00640.
3. van der Wel T, Hilhorst R, den Dulk H, van den Hooven T, Prins NM, Wijnakker JAPM, Florea BI, Lenselink EB, van Westen GJP, Ruijtenbeek R, Overkleeft HS, Kaptein A, Barf T, van der Stelt M. Chemical genetics strategy to profile kinase target engagement reveals role of FES in neutrophil phagocytosis. Nat Commun. 2020 Jun 25;11(1):3216. doi: 10.1038/s41467-020-17027-5.