Predictive Pharmacology

Research projects

An overview of research projects at the Predictive Pharmacology group.

Prediction of human (CNS) target site concentrations in health and disease
Predicting early Alzheimer’s disease stage in human
Drug-target binding kinetics in vivo
Spatial (3D) CNS drug distribution model
Prediction of human gut (colon cancer) target site concentrations and PKPD relationships
Pharmacometabolomics; prediction of system-wide multi-biomarker drug response

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”).

_{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)

Predicting early Alzheimer's disease stage in human

A new research line is the development of liquid biopsy fingerprints to predict early Alzheimer’s disease (AD) stage in human in readily accessible body fluids in human (in collaboration with: Dr. Geert-Jan Groeneveld, CHDR; Prof. Elga de Vries, Free University Medical Center; and others).

A literature search on current state-of-the-art-knowledge on fluid-based biomarkers of AD, especially at the earlier stages, indicated gaps in useful (longitudinal and multilevel) data. Currently, such smart data are being obtained from animal experimentation and from humans. The final goal is to develop a systems model on Alzheimer’s disease progression, based on multiple body fluid biomarker fingerprints, which would allow assessment of AD stage, and effects of drugs.

_{Qin T, Prins S, Groeneveld GJ, Van Westen G, de Vries HE, Wong YC, Bischoff LJM, de Lange ECM*. Utility of Animal Models to Understand Human Alzheimer's Disease, Using the Mastermind Research Approach to Avoid Unnecessary Further Sacrifices of Animals. Int J Mol Sci. 2020 Apr 30;21(9). pii: E3158. doi: 10.3390/ijms21093158.}
_{Hurtado MO, Kohler I, de Lange EC. Next-generation biomarker discovery in Alzheimer's disease using metabolomics - from animal to human studies. Bioanalysis. 2018 Sep 1;10(18):1525-1546. doi: 10.4155/bio-2018-0135. Epub 2018 Sep 10.}

Current Postdoc:

Frédérique Kok (Cytokines in Alzheimer’s disease)

Current PhD student:

Tian Qin (extracellular vesicles in Alzheimer’s didease)

Drug-target binding kinetics in vivo

A next, general pharmacological problem to be tackled is how drug-target binding kinetics in vivo, affects target occupancy as an important indicator of the time-course of drug effects. Drug target binding kinetics (BK) typically has been determined in isolation, while in the living body all PK processes in the body occur in parallel. Therefore, there is a need for integration of these processes. This was part of the IMI K4DD project. Several generic mathematical models have been developed that provide substantial insights in the condition-dependent contribution of these different processes.

_{De Witte WE, Wong YC, Nederpelt I, Heitman LH, Danhof M, van der Graaf PH, Gilissen RA, de Lange EC. Mechanistic models enable the rational use of in vitro drug-target binding kinetics for better drug effects in patients. Expert Opin Drug Discov. 2015 Oct 20:1-19. [Epub ahead of print]}
_{De Witte WEA, Danhof M, van der Graaf PH, de Lange EC. The implications of target saturation for the use of drug-target residence time. Nat Rev Drug Discov. 2018 Dec 28;18(1):82-84.}
_{De Witte WEA, Rottschäfer V, Danhof M, van der Graaf PH, Peletier LA, de Lange ECM. Modelling the delay between pharmacokinetics and EEG effects of morphine in rats: binding kinetic versus effect compartment models. J Pharmacokinet Pharmacodyn. 2018 Aug;45(4):621-635. doi: 10.1007/s10928-018-9593-x. Epub 2018 May 18.}
_{De Witte WEA, Rottschäfer V, Danhof M, van der Graaf PH, Peletier LA, de Lange ECM. Correction to: Modelling the delay between pharmacokinetics and EEG effects of morphine in rats: binding kinetic versus effect compartment models. J Pharmacokinet Pharmacodyn. 2018 Oct;45(5):763. doi: 10.1007/s10928-018-9604-y.}
_{De Witte WEA, Vauquelin G, van der Graaf PH, de Lange EC. The influence of drug distribution and drug-target binding on target occupancy: The rate-limiting step approximation. Eur J Pharm Sci. 2017 May 12. pii: S0928-0987(17)30252-X. doi: 10.1016/j.ejps.2017.05.024}
_{de Witte WEA, Versfelt JW, Kuzikov M, Rolland S, Georgi V, Gribbon P, Gul S, Huntjens D, van der Graaf PH, Danhof M, Fernández-Montalván A, Witt G, de Lange ECM. In vitro and in silico analysis of the effects of D2 receptor antagonist target binding kinetics on the cellular response to fluctuating dopamine concentrations. Br J Pharmacol. 2018 Nov;175(21):4121-4136. doi: 10.1111/bph.14456. Epub 2018 Sep 21}
_{De Witte W, Danhof M, Van der Graaf PH, de Lange EC. In vivo Target Residence Time and Kinetic Selectivity: The Association Rate Constant as Determinant. Trends Pharmacol Sci. 2016 Oct;37(10):831-42}
_{Nederpelt I, Kuzikov M, de Witte WEA, Schnider P, Tuijt B, Gul S, IJzerman AP, de Lange ECM, Heitman LH. From receptor binding kinetics to signal transduction; a missing link in predicting in vivo drug-action. Sci Rep. 2017 Oct 26;7(1):14169. doi: 10.1038/s41598-017-14257-4.}
_{Vlot AHC, Witte WEA, Danhof M, van der Graaf PH, van Westen GJP, de Lange ECM*. Target and tissue selectivity prediction by integrated mechanistic pharmacokinetic-target binding and quantitative structure activity modelling AAPSJ. 2017 Dec 4;20(1):11. doi: 10.1208/s12248-017-0172-7.}

This work is now proceeded by PhD student Divakar Budda in QSPainRelief (see above).

Spatial (3-Dimensional) CNS drug distribution model

Another research line is the development of a spatial CNS drug distribution model, by ultimately including the 3-dimensional anatomical organization of the CNS. The mathematical level of this research is very challenging, and therefore is performed in collaboration with the Mathematical Institute of our Faculty of Science (Dr. Vivi Rottschäfer).

_{Vendel E, Rottschäfer V, de Lange EC. Improving the prediction of local drug distribution profiles in the brain with a new 2D mathematical model. Bull Math Biol. 2018 Aug 8. doi: 10.1007/s11538-018-0469-4. [Epub ahead of print]}
_{Vendel E, Rottschäfer V, de Lange ECM. The need for mathematical modelling of spatial drug distribution within the brain. Fluids Barriers CNS. 2019 May 16;16(1):12. doi: 10.1186/s12987-019-0133-x. Review}

Prediction of human gut (colon cancer) target site concentrations and PKPD relationships

The advanced insights obtained for the CNS PBPK model development are currently used to develop advanced mathematical models for drug distribution prediction in other body tissues protected by barriers, such as the gut. The gut PBPK model will be linked to drug effects for treatment of colon cancer. This research is part of the IWT TRAIN project and is a collaboration with Janssen Pharmaceutica (coordinator Dr Jan Snoeys), KU Leuven, University of and the Hubrecht Organoid Technology (HUB).

Current PhD student:

Anthony Gebhart (Development of a Gut PBPKPD model)

Pharmacometabolomics; prediction of system-wide multi-biomarker drug response

The lack of success of new CNS drugs in clinical development is in part due to the complexity of the CNS, unexpected side effects, difficulties for drugs to penetrate the brain, but also by the lack of biomarkers. This research line, called pharmaco-metabolomics integrates PKPD modeling and metabolomics, and used the Mastermind Research Approach to obtain time-course data on drug distribution, and in parallel to metabolomics PD in different body fluids (CNS extracellular fluid, cerebrospinal fluid, plasma). Combining PKPD modeling of the drug and metabolite kinetics with statistical techniques such as Principle Component Analysis, reveal also unexpected drug effects. (Collaboration with Prof. Thomas Hankemeier and Prof. Piet Hein van der Graaf).

_{Van den Brink WJ, van den Berg DJ, Bonsel F, Hartman R, Wong YC, van der Graaf PH, De Lange ECM. Blood-based biomarkers of quinpirole pharmacology: multivariate PK/PD and metabolomics to unravel the underlying dynamics in plasma and brain. CPT Pharmacometrics Syst Pharmacol. 2019 Feb;8(2):107-117. doi: 10.1002/psp4.12370. Epub 2019 Jan 24.}
_{Van den Brink WJ, Elassais-Schaap J, Gonzalez B, Harms A, van der Graaf PH, Hankemeier T, de Lange ECM. Remoxipride causes multiple pharmacokinetic/pharmacodynamic response patterns in pharmacometabolomics in rats. Eur J Pharm Sci. 2017 Nov 15;109:431-440. doi: 10.1016/j.ejps.2017.08.031. Epub 2017 Sep 4}
_{Van den Brink WJ, Palic S, Kohler I, de Lange ECM. Correction to: Access to the CNS: Biomarker Strategies for DopaminergicTreatments.Pharm Res. 2018 Mar 19;35(5):102. doi: 10.1007/s11095-018-2388-3.}
_{Van den Brink WJ, van den Berg DJ, Bonsel FEM, Hartman R, Wong YC, van der Graaf PH, de Lange ECM. Fingerprints of CNS drug effects: a plasma neuroendocrine reflection of D2 receptor activation using multi-biomarker pharmacokinetic/pharmacodynamic modelling. Br J Pharmacol. 2018 Oct;175(19):3832-3843. doi: 10.1111/bph.14452. Epub 2018 Aug 31.}
_{Van den Brink WJ, Wong YC, Gülave B, van der Graaf PH, de Lange EC*. Revealing the Neuroendocrine Response After Remoxipride Treatment Using Multi-Biomarker Discovery and Quantifying It by PK/PD Modeling. AAPS J. 2017 Jan;19(1):274-285. doi: 10.1208/s12248-016-0002-3}
_{Van den Brink WJ, Hankemeier Th, Van der Graaf PH, de Lange ECM. Bundling arrows: improving translational CNS drug development by integrated PK/PD-metabolomics. Expert Opin Drug Discov. 2018 Jun;13(6):539-550. doi: 10.1080/17460441.2018.1446935. Epub 2018 Mar 8.}

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

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.

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

Predicting early Alzheimer's disease stage in human

Hurtado MO, Kohler I, de Lange EC. Next-generation biomarker discovery in Alzheimer's disease using metabolomics - from animal to human studies. Bioanalysis. 2018 Sep 1;10(18):1525-1546. doi: 10.4155/bio-2018-0135. Epub 2018 Sep 10.

Drug-target binding kinetics in vivo

De Witte WE, Wong YC, Nederpelt I, Heitman LH, Danhof M, van der Graaf PH, Gilissen RA, de Lange EC. Mechanistic models enable the rational use of in vitro drug-target binding kinetics for better drug effects in patients. Expert Opin Drug Discov. 2015 Oct 20:1-19. [Epub ahead of print]

De Witte WEA, Danhof M, van der Graaf PH, de Lange EC. The implications of target saturation for the use of drug-target residence time. Nat Rev Drug Discov. 2018 Dec 28;18(1):82-84.

De Witte WEA, Vauquelin G, van der Graaf PH, de Lange EC. The influence of drug distribution and drug-target binding on target occupancy: The rate-limiting step approximation. Eur J Pharm Sci. 2017 May 12. pii: S0928-0987(17)30252-X. doi: 10.1016/j.ejps.2017.05.024

De Witte W, Danhof M, Van der Graaf PH, de Lange EC. In vivo Target Residence Time and Kinetic Selectivity: The Association Rate Constant as Determinant. Trends Pharmacol Sci. 2016 Oct;37(10):831-42

Nederpelt I, Kuzikov M, de Witte WEA, Schnider P, Tuijt B, Gul S, IJzerman AP, de Lange ECM, Heitman LH. From receptor binding kinetics to signal transduction; a missing link in predicting in vivo drug-action. Sci Rep. 2017 Oct 26;7(1):14169. doi: 10.1038/s41598-017-14257-4.

Vlot AHC, Witte WEA, Danhof M, van der Graaf PH, van Westen GJP, de Lange ECM*. Target and tissue selectivity prediction by integrated mechanistic pharmacokinetic-target binding and quantitative structure activity modelling AAPSJ. 2017 Dec 4;20(1):11. doi: 10.1208/s12248-017-0172-7.

Spatial (3-Dimensional) CNS drug distribution model

Vendel E, Rottschäfer V, de Lange EC. Improving the prediction of local drug distribution profiles in the brain with a new 2D mathematical model. Bull Math Biol. 2018 Aug 8. doi: 10.1007/s11538-018-0469-4. [Epub ahead of print]

Vendel E, Rottschäfer V, de Lange ECM. The need for mathematical modelling of spatial drug distribution within the brain. Fluids Barriers CNS. 2019 May 16;16(1):12. doi: 10.1186/s12987-019-0133-x. Review

Prediction of human gut (colon cancer) target site concentrations and PKPD relationships

Pharmacometabolomics; prediction of system-wide multi-biomarker drug response

Van den Brink WJ, Palic S, Kohler I, de Lange ECM. Correction to: Access to the CNS: Biomarker Strategies for DopaminergicTreatments.Pharm Res. 2018 Mar 19;35(5):102. doi: 10.1007/s11095-018-2388-3.

Van den Brink WJ, Wong YC, Gülave B, van der Graaf PH, de Lange EC*. Revealing the Neuroendocrine Response After Remoxipride Treatment Using Multi-Biomarker Discovery and Quantifying It by PK/PD Modeling. AAPS J. 2017 Jan;19(1):274-285. doi: 10.1208/s12248-016-0002-3

Van den Brink WJ, Hankemeier Th, Van der Graaf PH, de Lange ECM. Bundling arrows: improving translational CNS drug development by integrated PK/PD-metabolomics. Expert Opin Drug Discov. 2018 Jun;13(6):539-550. doi: 10.1080/17460441.2018.1446935. Epub 2018 Mar 8.

_{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.}

_{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}

_{Hurtado MO, Kohler I, de Lange EC. Next-generation biomarker discovery in Alzheimer's disease using metabolomics - from animal to human studies. Bioanalysis. 2018 Sep 1;10(18):1525-1546. doi: 10.4155/bio-2018-0135. Epub 2018 Sep 10.}

_{De Witte WE, Wong YC, Nederpelt I, Heitman LH, Danhof M, van der Graaf PH, Gilissen RA, de Lange EC. Mechanistic models enable the rational use of in vitro drug-target binding kinetics for better drug effects in patients. Expert Opin Drug Discov. 2015 Oct 20:1-19. [Epub ahead of print]}

_{De Witte WEA, Danhof M, van der Graaf PH, de Lange EC. The implications of target saturation for the use of drug-target residence time. Nat Rev Drug Discov. 2018 Dec 28;18(1):82-84.}

_{De Witte WEA, Vauquelin G, van der Graaf PH, de Lange EC. The influence of drug distribution and drug-target binding on target occupancy: The rate-limiting step approximation. Eur J Pharm Sci. 2017 May 12. pii: S0928-0987(17)30252-X. doi: 10.1016/j.ejps.2017.05.024}

_{De Witte W, Danhof M, Van der Graaf PH, de Lange EC. In vivo Target Residence Time and Kinetic Selectivity: The Association Rate Constant as Determinant. Trends Pharmacol Sci. 2016 Oct;37(10):831-42}

_{Nederpelt I, Kuzikov M, de Witte WEA, Schnider P, Tuijt B, Gul S, IJzerman AP, de Lange ECM, Heitman LH. From receptor binding kinetics to signal transduction; a missing link in predicting in vivo drug-action. Sci Rep. 2017 Oct 26;7(1):14169. doi: 10.1038/s41598-017-14257-4.}

_{Vlot AHC, Witte WEA, Danhof M, van der Graaf PH, van Westen GJP, de Lange ECM*. Target and tissue selectivity prediction by integrated mechanistic pharmacokinetic-target binding and quantitative structure activity modelling AAPSJ. 2017 Dec 4;20(1):11. doi: 10.1208/s12248-017-0172-7.}

_{Vendel E, Rottschäfer V, de Lange EC. Improving the prediction of local drug distribution profiles in the brain with a new 2D mathematical model. Bull Math Biol. 2018 Aug 8. doi: 10.1007/s11538-018-0469-4. [Epub ahead of print]}

_{Vendel E, Rottschäfer V, de Lange ECM. The need for mathematical modelling of spatial drug distribution within the brain. Fluids Barriers CNS. 2019 May 16;16(1):12. doi: 10.1186/s12987-019-0133-x. Review}

_{Van den Brink WJ, Palic S, Kohler I, de Lange ECM. Correction to: Access to the CNS: Biomarker Strategies for DopaminergicTreatments.Pharm Res. 2018 Mar 19;35(5):102. doi: 10.1007/s11095-018-2388-3.}

_{Van den Brink WJ, Wong YC, Gülave B, van der Graaf PH, de Lange EC*. Revealing the Neuroendocrine Response After Remoxipride Treatment Using Multi-Biomarker Discovery and Quantifying It by PK/PD Modeling. AAPS J. 2017 Jan;19(1):274-285. doi: 10.1208/s12248-016-0002-3}

_{Van den Brink WJ, Hankemeier Th, Van der Graaf PH, de Lange ECM. Bundling arrows: improving translational CNS drug development by integrated PK/PD-metabolomics. Expert Opin Drug Discov. 2018 Jun;13(6):539-550. doi: 10.1080/17460441.2018.1446935. Epub 2018 Mar 8.}