Research output per year
Research output per year
DR.
Research activity per year
*** a.westerman@amsterdamumc.nl ***
The brain tumor glioblastoma is currently a non-curable disease. Because mono-therapies have a limited efficacy, combinations of therapies might be more effective. However, the selection of effective drug combinations is not a trivial task since hundred thousand combinations can be made with clinically approved drugs. By using different bioinformatics strategies, we have been able to predict and validate drug-target interactions (i.e. synergy). By using synergistic drug combinations and/or multi-target drugs, we have a powerful methodology to enable multi-drug therapy and this enables identification of biomarkers that predict synergy-sensitivity.
Ongoing projects
AI-IMPACT (Health Holland funded): Drug discovery of multi-target (polypharmacological) kinase inhibitors Kinase inhibitors as being used in the clinic commonly target multiple kinase proteins. We have shown that high efficacies of kinase inhibitors in laboratory experiments can be explained by their ability to inhibit multiple target at once (also called poly-pharmacology). To interface multi-target drugs to tumor vulnerabilities, we have generated a target predictor for 100,000 kinase inhibitors. Based on the availability of 3000 drug-kinase structures present in the KLIFS database (www.klifs.vu-compmedchem.nl), we developed a convolutional neural network prediction model to predict the target fingerprint of these kinase inhibitors using 270,000 compound ligand measurements. This prediction model will enable us to match bioactivity (target) fingerprints to personalized (vulnerability) fingerprints and design optimal compounds that can reach the brain.
THE TOXICITY ATLAS (Health Holland funded): Balancing therapy efficacy and adverse events of multitarget therapies Therapy combinations with desirable efficacies might not be easily translated for clinical use given the potential toxic effect of drug combinations. Using a bioinformatic approach, we provide a rationale for selecting therapy combinations aimed to provide an optimal balance between efficacy and side effects. This is expected to enable further implementation of personalized combination therapies in the clinic. Our approach, called the toxicology atlas, forms a global representation of different responses of the human body to FDA approved drugs. This representation will guide us to which vulnerabilities such as additive toxicity have to be avoided.
Attacking Glioblastoma Heterogeneity using Macrophage Metabolic Rewiring and Targeted Therapy (NWO funded Open Competition Domain Science – XL call, consortia). We propose to attack glioblastoma heterogeneity by rewiring the metabolism of macrophages in the tumor micro-environment as well as the tumor cells directly. The team will target metabolic vulnerabilities using novel nanoparticle tools to cross the blood-brain barrier. This Facilitated drug delivery is expected to render tumor cells more susceptible to specific combination therapies, tailored to each glioblastoma patient specifically. The teams will use complementary model systems (zebrafish, mouse, and human) and innovative chemistry to uncover precise mechanistic insights of glioblastoma malignancy with an outlook to create a translational path to patients.
Team
Other Scientific roles
Alumni PhD
Alumni Science
In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This person’s work contributes towards the following SDG(s):
VU/UvA Lecturer Bioinformatics, Amsterdam University College
1 Jan 2020 → …
Research output: Contribution to journal › Article › Academic › peer-review
Research output: Contribution to journal › Article › Academic › peer-review
Research output: Contribution to journal › Article › Academic › peer-review
Research output: Contribution to journal › Article › Academic › peer-review
Research output: Contribution to journal › Article › Academic › peer-review