Opioid tolerance development: a pharmacokinetic/pharmacodynamic perspective

Abstract

The opioids are commonly used to treat acute and severe pain. Long-term opioid administration eventually reaches a dose ceiling that is attributable to the rapid onset of analgesic tolerance coupled with the slow development of tolerance to the untoward side effects of respiratory depression, nausea and decreased gastrointestinal motility. The need for effective-long term analgesia remains. In order to develop new therapeutics and novel strategies for use of current analgesics, the processes that mediate tolerance must be understood. This review highlights potential pharmacokinetic (changes in metabolite production, metabolizing enzyme expression, and transporter function) and pharmacodynamic (receptor type, location and functionality; alterations in signaling pathways and cross-tolerance) aspects of opioid tolerance development, and presents several pharmacodynamic modeling strategies that have been used to characterize time-dependent attenuation of opioid analgesia.

Figures

Fig. 1

Downstream mediators of antinociception following opioid agonist binding to the MOR. a Binding of the agonist changes ion channel conductance and alters the cAMP pathway. b Activation of the NMDA receptor/NO pathway. Chronic opioid exposure alters synaptic cleft ion conductance and glutamate expression, leading to the displacement of the Mg2+ block of the NMDA receptor, an influx of Ca2+ and conversion of l-Arg to NO, a mediator of tolerance. Administering an NMDA receptor antagonist attenuates and delays the onset of tolerance by blocking Ca2+ influx and associated alterations

Fig. 2

Scheme depicting the PK-PD model of tolerance following multiple morphine i.v. bolus doses. The time course of morphine concentrations following multiple i.v. bolus doses was described by a two-compartment model with a central volume of distribution Vc, distribution between the central and peripheral compartment according to the rate constants k12 and k21, and elimination from the central compartment by the first-order rate constant k10. The time course of antinociception was described using an approach derived by Porchet et al. (108) where the first-order rate constants of effect onset, k1e and k1t, link the central compartment to the effect (E) and tolerance (T) compartments of volumes Ve and Vt and, effect offset is governed by the first-order rate constants ke0 and kt0 [Adapted from Ouellet and Pollack (118)]

Fig. 3

Relationship between antinociceptive response and morphine concentrations in blood during continuous infusion at the time of peak response (circles) or at 12 h into the infusion (triangles). Data are presented as mean for clarity; lines indicate the fit of the Hill equation to the data with the following parameter values: peak effect versus concentration data (Emax = 100%, EC50 = 324 ng/ml, γ = 1.92); effect at 12 h versus concentration (Emax = 34.8%, EC50 = 262 ng/ml, γ = 1.92) [data were obtained from Ouellet and Pollack (118)]

Fig. 4

Time course of antinociceptive response during morphine infusion in rats. Infusion rates were selected to produce steady-state concentrations of 200–299 ng/ml (squares), 300–399 ng/ml (inverted triangles), 400–499 ng/ml (diamonds), 500–599 ng/ml (circles), or >600 ng/ml (triangles). Closed circles indicate animals that did not receive morphine. Data are represented as mean for clarity; lines indicate the fit of an integrated PK - PD model (Fig. 2) with the assumption that tolerance is driven by accumulation of a hypothetical partial agonist (Eq. 4) [Adapted from Ouellet and Pollack (118)]

Fig. 5

Recovery of antinociceptive response to a 2-mg/kg bolus dose of morphine following termination of a 12-h morphine infusion (2 mg kg−1 h−1). Data are presented as mean ± SE; lines indicate model simulations based on an assumption that tolerance is driven by accumulation of a hypothetical reverse agonist (dashed line),competitive antagonist (dotted line), non-competitive antiagonist (dot-dashed line), or partial agonist (solid line) [Adapted from Ouellet and Pollack (118)]

Fig. 6

PK-PD model for l-arginine-associated stimulation of nitric oxide production in rats [From Heinzen and Pollack (115)]

Fig. 7

Concentration-time profiles for brain NO during (a) saline or administration of l-arginine (b 250 mg kg−1 h−1, c 500 mg kg−1 h−1, or d 1,000 mg kg−1 h−1). Data are mean ± SE. Lines indicate the fit of the PK-PD model (Fig. 6) to the data [From Heinzen and Pollack (115)]

Fig. 8

Scheme depicting the PK-PD model of morphine disposition, NO production and antinociceptive effect. The disposition of morphine administered as a zero-order infusion (k0) into the blood of volume VBL was described by the first-order rate constants of transfer between the blood and brain, k12 and k21, and by the first-order rate constant of elimination k10 from the blood. Antinociception was mediated by morphine in the brain of volume VBR by acting as an agonist at the MOR and by indirectly stimulating the production of NO, which indirectly inhibited antinociception. The actions of morphine on NO production and antinociception was described using sigmoidal Emax models, where Emax, E or S is maximum effect or stimulation; EC50, E or S is the concentration that elicits 50% effect or stimulation; γE or S is the shape factor dictating the relationship between concentration and effect or stimulation of NO production (kON); kOFF is the first-order rate constant of NO degradation; NOBR* is the concentration of NO in the hypothetical compartment that indirectly inhibits effect; Imax,E is the maximum inhibitory effect of NO; IC50,E is the concentration of NO that inhibits 50% of the effect; and γI is the shape factor of the inhibitory effect of NO on antinociception [Adapted from Heinzen and Pollack (116)]

Fig. 9

Blood morphine concentrations (top), brain morphine concentrations (middle), and antinociceptive effect (bottom) during and following an 8-h morphine infusion at 0.3- (circles), 1- (triangles), 2- (squares), or 3- (diamonds) mg kg−1 h−1. Lines indicate the fit of the PK/PD model to data [Adapted from Heinzen and Pollack (116)]