AA1 – Life Sciences

Project

AA1-1

The Spatio-Temporal Modelling of Mechanisms Underlying Pain Relief via the µ-Opioid Receptor

Project Heads

Martin Lohse, Christof Schütte, Christoph Stein, Marcus Weber, Stefanie Winkelmann (since January 2021)

Project Members

Vikram Sunkara (till January 2021, FU), Noureldin Saleh (till August 2019, ZIB), Sourav Ray (from January 2020, ZIB)

Project Duration

First funding period: 01.01.2019 – 31.12.2020; Second funding period: 01.01.21 – 31.12.2022

Located at

FU Berlin / ZIB

Description

This project aims at understanding the effects of drugs inducing pain relief (painkillers) via activation of opioid receptors on the molecular and cellular level. Our previous mathematical results helped to uncover that changes in pH encountered in painfully injured tissues cannot fully explain the increased activation rates of mu-opioid receptors (MOR) in the injured/inflamed environment. Other inflammatory components (e.g. radicals such as H2O2) need to be included. We have now extended our models by integrating stochastic components describing localized chemical reactions at the receptor and altered signaling processes (e.g. G-protein activation, cAMP production, membrane ion currents).

Project Webpages

A German documentary about pain relief can be accessed here. M. Weber and C. Stein have contributed to this ARTE TV documentary in 02/2020.

M. Weber and C. Stein have presented a common outreach talk at URANIA in 03/2020. The link is here.

There is a ZIB webpage about this project here.

A website showing the clinical relevance and the experimental anesthesiology approach at Charité can be found here.

Selected Publications

  • J. Möller, A. Isbilir, T. Sungkaworn, B. Osberg, C. Karathanasis, V. Sunkara, E.O. Grushevskyi, A. Bock, P. Annibale, M. Heilemann, C. Schuette, and M.J. Lohse
    Single molecule mu-opioid receptor membrane-dynamics reveal agonist-specific dimer formation with super-resolved precision.
    Nature-Chemical Biology (2020), DOI: 10.1038/s41589-020-0566-1
  • A. Bittracher, C. Schuette
    A probabilistic algorithm for aggregating vastly undersampled large Markov chains.
    submitted to Physica D (2019)
  • S. Winkelmann, C. Schütte
    Stochastic Dynamics in Computational Biology.
    To appear in Springer’s Frontiers in Applied Dynamical Systems (2019)
  • Ray S., Sunkara V., Schütte C., Weber M.
    How to calculate pH-dependent binding rates for receptor-ligand systems based on thermodynamic simulations with different binding motifs. Molecular Simulation (Taylor & Francis): DOI 10.1080/08927022.2020.1839660, 2020.
  • R. J. Rabben, S. Ray, M. Weber: ISOKANN: Invariant subspaces of Koopman Operators learned by a Neural Network. The J. of Chem. Phys., 153(11):114109, 2020
  • Meyer J, Del Vecchio G, Seitz V, Massaly N, Stein C:
    Modulation of mu-opioid receptor activation by acidic pH is dependent on ligand structure and an ionizable amino acid residue.
    Br J Pharmacol 2019; 176:4510-20; DOI: 10.1111/bph.14810
  • Del Vecchio G, Labuz D, Temp J, Seitz V, Kloner M, Negrete R, Rodriguez-Gaztelumendi A, Weber M, Machelska H, Stein C:
    pKa of opioid ligands as a discriminating factor for side effects.
    Sci Rep 2019; 9:19344. doi.org/10.1038/s41598-019-55886-1
    correction: Sci Rep 2020; 10:4366. doi.org/10.1038/s41598-020-61224-7
  • Lešnik S, Hodošček M, Bren U, Stein C, Bondar AN: Potential energy function for fentanyl-based opioid pain killers. J Chem Inf Model 2020; 60:3566−76; dx.doi.org/10.1021/acs.jcim.0c00185
  • Massaly N, Temp J, Machelska H, Stein C: Uncovering the analgesic effects of a pH-dependent mu-opioid receptor agonist using a model of non-evoked ongoing pain. Pain 2020; 161:2798–804. doi: 10.1097/j.pain.0000000000001968
  • Baamonde A, Menéndez L, González-Rodríguez S, Lastra A, Seitz V, Stein C, Machelska H: A low pKa ligand inhibits cancer-associated pain in mice by activating peripheral mu-opioid receptors. Sci Rep 2020; 10:18599; doi.org/10.1038/s41598-020-75509-4

Review Articles:

  • Stein C:
    Schmerzinhibition durch Opioide – neue Konzepte.
    Der Schmerz 2019;33:295-302
    DOI: 10.1007/s00482-019-0386-y
  • Stein C, Kopf A:
    Pain therapy – are there new options on the horizon?
    Best Practice & Research Clinical Rheumatology 2019;33:101420
    doi.org/10.1016/j.berh.2019.06.002
  • Stein C:
    Opioid analgesia: recent developments
    Curr Opin Supportive & Palliative Care 2020;14:112-117
    DOI:10.1097/SPC.0000000000000495

Book Chapters:

  • Stein C, Gaveriaux-Ruff C:
    Opioids and Pain.
    In: The Oxford Handbook of the Neurobiology of Pain. ed. by Wood J. Oxford University Press, New York 2020: 729-769.
    DOI: 10.1093/oxfordhb/9780190860509.013.9
  • Stein C:
    Analgesics.
    In: Encyclopedia of Molecular Pharmacology 3rd Edition, ed. by Offermanns S, Rosenthal W. Springer, Berlin Heidelberg New York 2020: 1-6. doi.org/10.1007/978-3-030-21573-6_6-1
  • Stein C, Kopf A:
    Management of the patient with chronic pain.
    In: Miller’s Anesthesia 9th Edition. ed. by Gropper MA. Elsevier, Philadelphia 2020: 1604-21
  • Rittner HL, Oehler B, Stein C:
    Immune system, pain and analgesia.
    In: The Senses: A Comprehensive Reference; Vol. 6. Pain, ed. by Schaible HG, Pogatzki-Zahn E. Elsevier, Philadelphia 2020

Selected Pictures

Different binding modes at only one pH value

binding mode
Binding Mode of Fentanyl at pH7
The binding modes of opioids depend on the pH value of the environment. With changing the pH-value, the protonation state of the opioid and of the receptor changes, too. From a modelling point of view, each protonation event leads to a new mathematical model to describe the dynamics of the molecular system. I.e. for every protonation state of the system we end up with a different modelling and, thus, with a different resulting kinetics. The mathematical problem is given by the fact, that changes in pH-value do not simply change the protonation state, they change the probabilities for each of the states. Hence, the “correct” physical modelling of an opiod-receptor binding process is not given by one mathematical model, it is given by a set of different models combined with different statistical weights. One mathematical goal of this project was to find a way to simulate molecular processes, if the mathematical descripition of the process being used is a statistical mixture of different physical models.

Simulation of protonation states

Simulation of the opioid receptor and opioids
Simulation of the opioid receptor and opioids
In this project we created all possible different physical models (different protonation states) of the opoid receptor and its binding opioid molecule. Then we simulated the underlying Markov process for each of them separately. For the mixture of the different models, we clustered all Markov states of the different physical models together and created a transition rate matrix (a dicretized Fokker-Planck operator) for the entire process by regarding the probabilities for the single models. The last step of combining the single simulation results was possible by using a certain type of discretization of Fokker-Planck operators.
pH dependent binding rate of opioids
The outcome of our procedure revealed that Fentanyl compared to NFEPP  (two different opioids) have very different binding rates in inflamed compared to healthy tissue. For the first time in opioid research, we were able to plot a continuous curve which shows the pH-dependence of activation rates of opiod-receptors.

We were able to explain the outcome of clinical tests on the basis of this dependence.

Network_Illustration
There is a transnational challenge in going from in vitro to in situ. We bridging this gap by developing mathematical models to capture the spatio-temporal dynamics of the opioid in situ.

Our recent in vitro studies on MOR expressed in transfected cells have yielded the following results: H2O2 did not interfere with binding of the standard MOR ligand DAMGO. Higher H2O2 concentrations decreased G-protein coupling (measured by binding of [35S]-GTPγS) induced by the standard MOR agonist fentanyl. At acidic pH, the pH-dependent ligand NFEPP (but not fentanyl) more potently activated MOR-dependent G-protein coupling. H2O2 did not influence fentanyl-induced inhibition of forskolin-stimulated cAMP production. These results will be complemented by measurements of membrane ion currents.

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