Division of Systems Biomedicine and Pharmacology
The Quantitative Pharmacology group headed by Coen van Hasselt focusses on the application of state-of-the-art quantitative pharmacological mathematical and statistical (pharmacometrics) modeling combined with systems biology modeling approaches, to characterize biomarkers for of disease and treatment response.
Bacterial infections & antimicrobial pharmacology
Our research is primarily focused to optimize treatment of bacterial infections using antibiotics, aiming to integrate optimal drug exposure in different patient populations, personalized immune response biomarkers, and bacterial growth kill dynamics. Through this integration we believe antimicrobial dose regimens can be further optimized leading to optimal efficacy and minimal resistance development. To these aims, we apply a tight integration between computational modeling and experimental work.
Adverse drug reactions and oncology
We also interested in quantitative pharmacological modeling approaches and state-of-the-art clinical and experimental data to better understand the pharmacology of adverse drug reactions to antimicrobial drugs and anticancer drugs. Finally we are interested in the translational and clinical modeling approaches to optimize treatment and trial designs of anti-cancer agents.
Expertise & facilities
In our group we have expertise in the areas of modeling in pharmacometrics & systems pharmacology, bioinformatics analysis of omics data, basic molecular biology approaches, and antibiotics PK/PD experiments. We have a fully equipped experimental laboratory and a dedicated high performance computing cluster. All our work is done according to FAIR principles.
Internships and positions
We have projects available for motivated 6-9 month internship students with an interest in computational modeling in the area of pharmacology (antibiotics, oncology) or with interest in experimental approaches to characterize antibiotic PK-PD and resistance. Drop us an email if you are interested. Positions for PhD students and postdocs are always posted at the Leiden University vacancies page. However, if you can secure your own funding through scholarships or grants please let us know.
2017 - 2021: ZonMW ABR grant (PI): Metabolomic biomarkers of bacterial infection
2017 - 2018: ZonMW ETH grant (PI): Whole genome sequencing of antimicrobial resistance
2017 - 2021: ZonMW MKMD grant (co-PI): Microvasculature on a chip (WP lead systems pharmacology)
2015 - 2018: H2020 Marie Curie Individual Fellowship (PI): Systems pharmacology
- Aulin L.B.S., Valitalo P.A., Rizk M.L., Visser S.A.G., Rao G., van der Graaf P.H., van Hasselt J.G.C., Validation of a Model Predicting Anti-infective Lung Penetration in the Epithelial Lining Fluid of Humans. Pharm Res. 2018; 35(2): 26.
- De Cock P.A., Mulla H., Desmet S., De Somer F., McWhinney B.C., Ungerer J.P., Moerman A., Commeyne S., Vande Walle J., Francois K., van Hasselt J.G., De Paepe P., Population pharmacokinetics of cefazolin before, during and after cardiopulmonary bypass to optimize dosing regimens for children undergoing cardiac surgery. J Antimicrob Chemother. 2017; 72(3): 791-800.
- Välitalo P.A., Griffioen K., Rizk M.L., Visser S.A., Danhof M., Rao G., van der Graaf P.H., van Hasselt J.G., Structure-Based Prediction of Anti-infective Drug Concentrations in the Human Lung Epithelial Lining Fluid. Pharm Res. 2016; 33(4): 856-67.
- Yamamoto Y., Välitalo P.A., Wong Y.C., Huntjens D.R., Proost J.H., Vermeulen A., Krauwinkel W., Beukers M.W., Kokki H., Kokki M., Danhof M., van Hasselt J.G.C., de Lange E.C.M., Prediction of human CNS pharmacokinetics using a physiologically-based pharmacokinetic modeling approach. Eur J Pharm Sci. 2018; 112: 168-179.
- Kohler I., Hankemeier T., van der Graaf P.H., Knibbe C.A.J., van Hasselt J.G.C., Integrating clinical metabolomics-based biomarker discovery and clinical pharmacology to enable precision medicine. Eur J Pharm Sci. 2017; 109S: S15-S21.
- van Hasselt J.G.C., Iyengar R.. Systems pharmacology-based identification of pharmacogenomic determinants of adverse drug reactions using human iPSC-derived cell lines. Curr Opin Sys Bio 2017; 4 9-15.
- Shim J.V., Chun B., van Hasselt J.G.C., Birtwistle M.R., Saucerman J.J., Sobie E.A., Mechanistic Systems Modeling to Improve Understanding and Prediction of Cardiotoxicity Caused by Targeted Cancer Therapeutics. Front Physiol. 2017; 8: 651.
- van Hasselt J.G., Gupta A., Hussein Z., Beijnen J.H., Schellens J.H., Huitema A.D., Disease Progression/Clinical Outcome Model for Castration-Resistant Prostate Cancer in Patients Treated With Eribulin. CPT Pharmacometrics Syst Pharmacol. 2015; 4(7): 386-95.
- van Hasselt J.G., van der Graaf P.H., Towards integrative systems pharmacology models in oncology drug development. Drug Discov Today Technol. 2015; 1-8.