Mu-opioid receptor binding of morphine and its main metabolites: impact of non-linear BBB transport of morphine.
Divakar Budda (1*), Berfin Gülave (1*), JG Coen van Hasselt (1), Elizabeth CM de Lange (1)
(1) Division of Systems Pharmacology and Pharmacy, Leiden Academic Centre for Drug Research, Leiden University, Leiden, The Netherlands
Introduction: Chronic pain is a difficult disease, due to the complexity of neuronal plasticity [1,2], and many other factors involved. Target sites for drug treatment are multiple, mainly in the CNS, being protected by the blood-brain barrier (BBB). Morphine is still an important drug used for treatment of chronic pain. It has nonlinear transport across BBB, while its main metabolites, morphine-6-glucuronide (M6G) and morphine-3-glucoronide (M3G), do not [3,4]. Morphine and M6G have analgesic effects [5], while M3G antagonizes this effect [6], and all three compounds can compete for same binding site at the mu-opioid receptor (MOR). The effect of nonlinearity of morphine BBB transport on binding of morphine, M3G, M6G at the MOR, especially during long term treatment such as in chronic pain can have clinical applications but has not been investigated yet.
Objectives: The aim of this study was to investigate the impact of nonlinear BBB transport of morphine on the MOR target occupancies (TO) of morphine, M3G, M6G compounds in human, for clinically relevant doses of 5-150 mg morphine across multiple dose intervals, for intravenous, oral immediate and oral extended-release formulations.
Methods: The LeiCNS-PK3.0 (CNS PBPK) model with morphine’s nonlinear BBB transport [7] was used to predict brain target site concentrations. MOR mRNA expression data from Human Protein Atlas (HPA) [8] and their relative regional differences were used to calculate the total mRNA expression as a surrogate for protein expression. MOR mRNA expression was assumed to be constant, without turnover or other molecular level processes such as desensitization, internalization etc. Binding affinity values, derived in the same experiment were used as basis of relative binding capacity and to determine binding kinetic rate constants Kon and Koff, based on the assumption of diffusion limited binding by stokes Einstein’s law [9] at the synaptic cleft for morphine, M3G, and M6G. Simulations were carried out for wide range of doses to understand the effect of nonlinearity for the different dose regimens and administration routes. Simulations were performed using R statistical software [10], with the rxode2 package [11] for ordinary differential equations solving.
Results: Plasma concentrations of morphine, M3G, M6G did not reflect those of pain matrix target sites in the CNS. In IV infusion, nonlinear morphine BBB transport led to a ratio of 4%, 44%, 52% for both 10mg and 50mg in plasma, whereas 26%, 51%, 23% at 10mg while 27%, 62%, 11% in brain extracellular fluid concentrations for M3G, M6G and morphine, respectively. This CNS target site concentration changes influenced the TO leading to more morphine MOR TO at lower doses, whereas more M6G MOR TO at higher doses in IV Infusion. Finally, both the CNS target site concentrations, and the TOs are affected by nonlinearity in different dose regimens and routes of administration.
Conclusions: Nonlinear BBB transport of morphine influences the MOR TO ratios of morphine, M3G, M6G, depending on dose and route of administration, and thereby may have an impact on the clinical effects of morphine treatment for chronic pain relief.
References:
[1] Melzack R. J Dent Edu (2001) 65(12):1378-82.
[2] Larrea LG & Peyron R. Pain (2013) 154 (s29-s43).
[3] Oosten et al. Clin Pharmacokinet (2017) 56(7), 733–746.
[4] Groenendaal, D et al (2007) 151(5), 701–71.
[5] Hanna et al (1990) J Anaesthesia 64(5), 547–550.
[6] Gong et al (1991) Eur J Pharmacol 193(1), 47–56.
[7] Gülave et al (2023) Eur J Pharm Sci submitted.
[8] Sjöstedt et al (2020). Science, 367(6482).
[9] Cruickshank (1924) Proc Royal Soc London 106(740), 724–749.
[10] R Core Team (2022) https://www.r-project.org/.
[11] Matthew M & Fidler L (2022) https://nlmixr2.github.io/rxode2/.