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Daan Geerke
(VU Amsterdam)

Daan Geerke obtained his PhD degree at ETH Zürich in 2007 and afterwards joined IBM Almaden Research Center in San Jose, California, for a postdoctoral fellowship. In 2009 he was appointed Assistant-Professor at Vrije Universiteit Amsterdam, within the Molecular and Computational Toxicology group headed by Prof. Dr. Nico P.E. Vermeulen (until 2017) and (since 2017) by Prof. Dr. Paul Jennings. Currently, he is (as Associate-Professor) supervising a senior scientist and three PhD students, and his research focuses on in silico rationalization and prediction of drug metabolism and action upon binding to flexible enzymes such as human and bacterial Cytochrome P450s, proteases or methyl transferases, in direct collaboration with 'wet-chemistry' colleagues within MCT, VU, medical centers and industry. In addition he has since many years worked on development of biomolecular (polarizable) force fields. Amongst other he has been awarded a Dutch NWO-Vidi grant (in 2013), a NWO / NLeSC eScience grant (in 2015) and he has been coordinator of scientific activities at Vrije Universiteit in the context of the IMI-JU eTOX project on in silico prediction of toxicities and, more recently, within the EU-H2020 OpenRiskNet project.

OpenTox Euro 2019 talk: Site-of-metabolism prediction in OpenRiskNet

Metabolites can play an important role in adverse effects of parent drug (or other xenobiotic) compounds. During the EU-H2020 OpenRiskNet project, several partners (VU Amsterdam, HHU/HITeC Hamburg, Uppsala University, JGU Mainz) have worked together on making methods and tools available within the OpenRiskNet platform for metabolite and site-of-metabolism (SOM) prediction. For that purpose we have integrated ligand-based metabolite predictors (e.g., MetPred, FAME 3, SMARTCyp) and protein-structure and -dynamics based models to predict SOMs of Cytochrome P450 (CYP450) substrates. CYP450s metabolize ~75% of the currently marketed drugs and their active-site shape and plasticity often play an important role in determining the substrate's SOM. To facilitate the combined use of the metabolite prediction approaches and their outcomes, we made Jupyter notebooks available that gather and visualize results from the integrated services. Here we illustrate the possible added value of their combined use in the context of a pilot study on SOM prediction for compounds with known metabolite-associated toxicity. Finally we shortly discuss related work from our laboratory, on predicting Cytochrome P450 binding affinity prediction.