Population Pharmacokinetic Model for Tramadol and O-desmethyltramadol in Older Patients

Abstract

Background and objectives: Tramadol is commonly prescribed to manage chronic pain in older patients. However, there is a gap in the literature describing the pharmacokinetic parameters for tramadol and its active metabolite (O-desmethyltramadol [ODT]) in this population. The objective of this study was to develop and evaluate a population pharmacokinetic model for tramadol and ODT in older patients.

Methods: Twenty-one patients who received an extended-release oral tramadol dose (25-100 mg) were recruited. Tramadol and ODT concentrations were determined using a validated liquid chromatography/tandem mass spectrometry method. A population pharmacokinetic model was developed using non-linear mixed-effects modelling. The performance of the model was assessed by visual predictive check.

Results: A two-compartment, first-order absorption model with linear elimination best described the tramadol concentration data. The absorption rate constant was 2.96/h (between-subject variability [BSV] 37.8%), apparent volume of distribution for the central compartment (V₁/F) was 0.373 l (73.8%), apparent volume of distribution for the peripheral compartment (V₂/F) was 0.379 l (97.4%), inter-compartmental clearance (Q) was 0.0426 l/h (2.19%) and apparent clearance (CL/F) was 0.00604 l/h (6.61%). The apparent rate of metabolism of tramadol to ODT (k_t) was 0.0492 l/h (78.5%) and apparent clearance for ODT (CL_m) was 0.143 l/h (21.6%). Identification of Seniors at Risk score (ISAR) and creatinine clearance (CrCL) were the only covariates included in the final model, where a higher value for the ISAR increased the maximum concentration (C_max) of tramadol and reduced the BSV in Q from 4.71 to 2.19%. A higher value of CrCL reduced tramadol C_max and half-life (T_1/2) and reduced the BSV in V₂/F (from 148 to 97.4%) and in CL/F (from 78.9 to 6.61%).

Conclusion: Exposure to tramadol increased with increased frailty and reduced CrCL. Prescribers should consider patients frailty status and CrCL to minimise the risk of tramadol toxicity in such cohort of patients.

Fig. 1

Scheme of the model used to describe the tramadol and O-desmethyltramadol plasma concentration-time profiles. K_a first-order absorption constant, V₁ volume of distribution in centeral compartment, V₂ volume of distribution of peripheral compartment, Q inter-compartmental clearance, CL clearance, K_t rate of biotransforimg parent to metabolite, CL_m clearance of metabolite

Fig. 2

Observed individual concentrations of tramadol and O-desmethyltramadol. Tramadol dose was standardised to 25 mg. Sex: • = female, ▲ = Male. Each colour represents an individual participant’s data

Fig. 3

Relationships between participants’ characteristics and tramadol exposure. Frailty: fit; 0–2 Identification of Senior At Risk (ISAR), frail; ISAR ≥ 3. Mortality risk: moderate; CCI 0–5, high; CCI ≥ 6. Polypharmacy: not present; < 5 medications, present; ≥ 5 medications. Tramadol dose was standardised to 25 mg. CCI Charlson Comorbidity Index

Fig. 4

Relationships between participants’ characteristics and O-desmethyltramadol exposure. Frailty: fit; 0–2 Identification of Senior At Risk (ISAR), frail; ISAR ≥ 3. Mortality risk: moderate; CCI 0–5, high; CCI ≥ 6. Polypharmacy: not present: < 5 medications, present; ≥ 5 medications. Tramadol dose was standardised to 25 mg. CCI Charlson Comorbidity Index

Fig. 5

Correlation between participant characteristics. Ratio ODT/tramadol concentration ratio at steady state, CrCL creatinine clearance, ISAR Identification of Senior at Risk, CCI Charlson Comorbidity Index, NPM Number of prescribed medications, X3A4.inh CYP3A4 inhibitor, X2D6.inh CYP2D6 inhibitor

Fig. 6

Visual predictive check (VPC) for tramadol (upper) and O-desmethyltramadol (lower) concentration versus time based on 1000 Monte Carlo simulations. The solid blue lines represent the 10th, 50th and 90th percentiles of the observed data. The shaded regions represent the 90% confidence intevals around the 10th, 50th and 90th percentiles of the simulated data. The blue circles are observed concentration

Fig. 7

Goodness-of-fit (GOF) plots of observed versus predicted concentrations obtained from the final model for tramadol (top) and O-desmethyltramadol (bottom). (Right) Individual predicted concentrations versus observed concentration. (Left) Population predicted concentration versus observed concentrations. The yellow curve represents the spline line of the plot

Fig. 8

Goodness-of-fit (GOF) plots of the residual obtained from the final model for tramadol. (Top) Residuals versus time with on left population residuals and on right individual residuals; (bottom) residuals versus predicted tramadol concentrations with on left population residuals and on right individual residuals. The yellow curve represents the spline line of the plot

Fig. 9

Goodness-of-fit (GOF) plots of the residual obtained from the final model for O-desmethyltramadol. (Top) Residuals versus time with on left population residuals and on right individual residuals; (bottom) residuals versus predicted O-desmethyltramadol concentrations with on left population residuals and on right individual residuals. The yellow curve represents the spline line of the plot

Fig. 10

Simulated exposure of tramadol with different doses at different range of ISAR (fit = 0, frail = 3 and severe frail = 6). Tramadol doses are presented with line colour; red = 100 mg, black = 50 mg. ISAR Identification of Seniors at Risk

Fig. 11

Simulated exposure of tramadol with different doses at different range of creatinine clearance (CrCL 30, 75 and 120 ml/min). Tramadol doses are presented with line colour; red = 100 mg, black = 50 mg