Universiteit Leiden

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Research project

Prediction of human (CNS) target site concentrations in health and disease

Prediction of human (CNS) target site concentrations in health and disease In the vision of Prof. de Lange we will only be able to predict human (central nervous system, CNS) target site concentrations and effects if we perform systematic, condition-dependent, integrative, and strictly quantitative research, providing smart data, combined with advanced mathematical modelling (“Mastermind Research Approach”).

Elizabeth (Liesbeth) de Lange
  • De Lange ECM, van der Brink W, Yamamoto Y, de Witte W, Wong YC. Novel CNS drug discovery and development approach: model-based integration to predict neuro-pharmacokinetics and pharmacodynamics. Expert Opin Drug Discov. 2017 Dec;12(12):1207-1218. doi: 10.1080/17460441.2017.1380623. Epub 2017 Sep 21.
  •  De Lange ECM, Hammarlund-Udenaes M. Translational aspects of blood-brain barrier transport and central nervous system effects of drugs: From discovery to patients. Clin Pharmacol Ther. 2015 Apr;97(4):380-94. doi: 10.1002/cpt.76. Epub 2015 Feb 20
  • De Lange ECM*. The mastermind approach to CNS drug therapy: translational prediction of human brain distribution, target site kinetics, and therapeutic effects. Fluids and Barriers of the CNS 2013, 10:12.

With this profound and systematic approach, Prof de Lange and her team have been able to unravel the individual contributions and mutual interdependencies of pharmacokinetic (PK) processes that play a role in CNS drug distribution, and has proven be the only portal to the development of the detailed physiologically-based pharmacokinetic (PBPK) CNS drug distribution model (LeiCNSPK-v1.0) that is able to adequately predict rat and human PK profiles in multiple physiological compartments of the CNS. 

With this model, as first in the world, it is now possible to predict PK profiles of compounds at different CNS locations in human, solely based on compound properties that can be obtained in vitro/in silico. In other words, the mathematical model can replace the use of many experimental animals. This will have a great impact on the success in CNS drug discovery and development, and the diminishing of the use of experimental animals.

  • Yamamoto Y, Danhof M, de Lange EC. Microdialysis: the Key to Physiologically Based Model Prediction of Human CNS Target Site Concentrations. AAPS J. 2017 Mar 9. doi: 10.1208/s12248-017-0050-3
  • Yamamoto Y, Välitalo PA, Huntjens DR, Proost JH, Vermeulen A, Krauwinkel W, Beukers MW, van den Berg DJ, Hartman RH, Wong YC, Danhof M, van Hasselt JG, de Lange EC*. Predicting drug concentration-time profiles in multiple CNS compartments using a comprehensive physiologically-based pharmacokinetic model. CPT Pharmacometrics Syst Pharmacol. 2017 Sep 11. doi: 10.1002/psp4.12250. [Epub ahead of print]
  • Yamamoto Y, Välitalo PA, van den Berg DJ, Hartman R, van den Brink W, Wong YC, Huntjens DR, Proost JH, Vermeulen A, Krauwinkel W, Bakshi S, Aranzana-Climent V, Marchand S, Dahyot-Fizelier C, Couet W, Danhof M, van Hasselt JG, de Lange EC*. A Generic Multi-Compartmental CNS Distribution Model Structure for 9 Drugs Allows Prediction of Human Brain Target Site Concentrations. Pharm Res. 2017 Feb;34(2):333-351. doi: 10.1007/s11095-016-2065-3
  • Yamamoto Y, Välitalo PA, Wong YC, Huntjens DR, Proost JH, Vermeulen A, Krauwinkel W, Beukers MW, van den Berg DJ, Hartman RH, Wong YC, Danhof M, Kokkif H, Kokkif M, van Hasselt JGC, de Lange ECM*. Prediction of human CNS pharmacokinetics using a physiologically-based pharmacokinetic modeling approach. Eur J Pharm Sci. 2018 Jan 15;112:168-179.

The CNS PBPK model is currently:  further optimized – adding more physiological details (LeiCNSPK-v3.0; being adapted for specific CNS diseases with emphasis on neurodegenerative diseases like Alzheimer’s disease, traumatic brain Injury, and brain tumors; and linked to target interaction for further unravelling of factors in CNS drug effects as described below.

Furthermore, with the recently started (01 Jan 2020) a consortium on “Effective combinational treatment of chronic pain in individual patients by an innovative quantitative systems pharmacology (QSP) pain relief approach”(QSPainRelief) consortium we aim to develop a Quantitative Systems Pharmacology (QSP) model platform that integrates the LEICNS3 and drug-target binding kinetic models and the neural circuit QSP models of InSilicoBioSciences to predict combinations of existing drugs to better relief pain in chronic pain patients while reducing side effects. The consortium consists of 10 partners from 6 different countries (https://www.qspainrelief.eu/)

QSPainrelief model

Current PhD students:

  • Mohammed Saleh (LEICNSPKv3.0 +  CNS drug distribution in Alzheimer’s disease)
  • Berfin Gülave (ÇNS drug distribution in Chronic pain),
  • Divakar Budda (Target binding kinetics in Chronic pain)
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