Pharmacokinetic-pharmacodynamic modeling of the effectiveness and safety of buprenorphine and fentanyl in rats

Abstract

Objective: Respiratory depression is a serious and potentially life-threatening side-effect of opioid therapy. The objective of this investigation was to characterize the relationship between buprenorphine or fentanyl exposure and the effectiveness and safety outcome in rats.

Methods: Data on the time course of the antinociceptive and respiratory depressant effect were analyzed on the basis of population logistic regression PK-PD models using non-linear mixed effects modeling software (NONMEM). The pharmacokinetics of buprenorphine and fentanyl were described by a three- and two-compartment model, respectively. A logistic regression model (linear logit model) was used to characterize the relationship between drug exposure and the binary effectiveness and safety outcome.

Results: For buprenorphine, the odds ratios (OR) were 28.5 (95% CI, 6.9-50.1) and 2.10 (95% CI, 0.71-3.49) for the antinociceptive and respiratory depressant effect, respectively. For fentanyl these odds ratios were 3.03 (95% CI, 1.87-4.21) and 2.54 (95% CI, 1.26-3.82), respectively.

Conclusion: The calculated safety index (OR(antinociception)/OR(respiratory depression)) for fentanyl of 1.20 suggests that fentanyl has a low safety margin, implicating that fentanyl needs to be titrated with caution. For buprenorphine the safety index is 13.54 suggesting that buprenorphine is a relatively safe opioid.

Fig. 1

Goodness-of-fit plots for the final population pharmacokinetic model of buprenorphine.

Fig. 2

Goodness-of-fit plots for the final population pharmacokinetic model of fentanyl.

Fig. 3

Goodness-of-fit plots of the logistic regression model for buprenorphine. Observed (left panels) and simulated (right panels) values of the buprenorphine’s effectiveness (upper panels) and safety (lower panels) outcome for the different concentration categories. Simulations were repeated in total a 100 times. The concentrations were divided in the following categories (scores at C_e = 0, between 0 and 25th quantiles, 25th–50th quantiles, 50th–75th quantiles, >75th quantiles; ■ score = 0 and ⧄ score = 1).

Fig. 4

Goodness-o-fit plots of the logistic regression model for fentanyl. Observed (left panels) and simulated (right panels) values of the buprenorphine’s effectiveness (upper panels) and safety (lower panels) outcome for the different concentration categories. Simulations were repeated in total a 100 times. The concentrations were divided in the following categories (scores at C_e = 0, between 0 and 25th quantiles, 25th–50th quantiles, 50th–75th quantiles, >75th quantiles; ■ score = 0 and ⧄ score = 1).

Fig. 5

Estimated therapeutic utility function of buprenorphine (dashed line) or fentanyl (solid line) treatment as a function of its respective biophase exposure. The therapeutic utility function is defined as probability of antinociception—probability of respiratory depression.