Divakar Budda (1), J. G. Coen van Hasselt (1), Elizabeth C.M. de Lange (1)
(1) Division of Systems Biomedicine and Pharmacology, LACDR, Leiden University, The Netherlands
Introduction: Chronic pain is a prevalent medical problem affecting 20% of Europeans [1] with an annual economic burden of approx. €300 billion [2]. Current analgesics and their combinations have shown inadequate response in ~60% of the patients in terms of efficacy or safety [1,3] The European consortium QSPainRelief (www.qspainrelief.eu) focuses on identifying new combinations of CNS active drugs [4], by the development of an in-silico QSPainRelief model platform to improve chronic pain relief and reduce side-effects.
The drug-target binding kinetics (BK) is important as it governs the magnitude, duration of effects, and side effects [5]. In the case of chronic pain, multiple studies have reported the changes in receptor availability, occupancy, and pain relief owing to their gender, age, physiological conditions, and dependent variables [6,7]. A good correlation between mu-opioid receptor agonists occupancy and analgesia has also been reported recently [8]. We intend to develop a BK model that takes into account these multiple factors and predicts target occupancy, which can be used to inform the selection of optimal dose depending on the patient-specific, disease-specific characters to achieve desired analgesia (individualized therapy).
Objectives: We aim to develop a mathematical BK model for CNS active drugs & their combinations to predict target occupancy (engagement) in chronic pain patients with different characteristics. Here we present the initial simulation results of BK model development.
Methods: A differential equation-based BK model using drug-specific CNS target site pharmacokinetic profiles [6,7], BK parameters association rate constant (Kon), and dissociation rate constant (Koff) was developed using R package RxODE [9]. The simulations were performed under the following assumptions: constant receptor expression (1 mole; arbitrary value for simulations), in healthy conditions, and no role of endogenous ligands, non-specific binding, patient, disease-specific variables. Receptor expression differences between different brain regions were also not implemented in this model yet, assuming homogenous expression for these simulations.
Results: The BK model simulations with Kon (1.78 x 107 M-1min-1(SEM ± 0.08)), Koff (1.39 min-1(SEM ± 0.1)) [10] for morphine revealed that Kon is the major determinant of target occupancy at mu-opioid receptors (1 mole). Upon sensitivity analysis with Koff = Kon/2, target-site concentration was shown to be the determinant for target occupancy.
Conclusion: For morphine at the mu-opioid receptor, the Kon appears to be the major determining factor for mu-opioid receptor occupancy. Data about the disease- and patient-specific variables- including target expression, endogenous ligands, non-specific binding, and target turnover [11-15] is being collected, further simulation results will be presented. Future objectives are to (i) Link the predictions to drug effects via a neural circuit model, and (ii) Validate BK predictions with real-world patient treatment responses.
Funding: This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No 848068 (https://QSPainRelief.eu/).
References:
[1] Van Hecke et al. Br J Anaesth 2013;111(1):13-18
[2] Pain Proposal: A European Consensus report 2016. Accessible at: [3] https://europeanpainfederation.eu/wp-content/uploads/2016/06/pain_proposal.pdf
[3] Elliott et al. Pain 2002;99(1-2): 299-307
[4] Gilron et al. Lancet Neurol 2013;12(11): 1084-95
[5] Yin et al. Mol Biosyst 2013;9(6):1381-89
[6] Yamamoto et al. Eur J Pharm Sci 2018;112:168-179
[7] Saleh, de Lange M, Pharmaceutics 2021;13(1), 95
[8] Takai N et al. Brain Res. 2018; 1680:105-109
[9] Wang W. CPT Pharmacometrics Syst Pharmacol. 2016
[10] Pederson MF et al. Neuropharmacology 2020;166:107718
[11] De Witte et al. Trends Pharmacol Sci 2016;37(10): 831-42
[12] De Witte et al. Eur J Pharm Sci 2017;109S: S83-89
[13] De Witte et al. Br J Pharmacol 2018;175(21): 4121-36
[14] De Witte et al. Nat Rev Drug Discov 2018;18(1): 82-84
[15] Jones et al. Eur J Pain 2004;8(5): 479-85
Reference: PAGE 29 (2021) Abstr 9622 [www.page-meeting.org/?abstract=9622]
Poster: Drug/Disease Modelling - Other Topics