Use of a linearization approximation facilitating stochastic model building

Buprenorphine exposure levels to optimize treatment outcomes in opioid use disorder

The severity of the ongoing opioid crisis, recently exacerbated by the COVID-19 pandemic, emphasizes the importance for individuals suffering from opioid use disorder (OUD) to have access to and receive efficacious, evidence-based treatments. Optimal treatment of OUD should aim at blocking the effects of illicit opioids while controlling opioid craving and withdrawal to facilitate abstinence from opioid use and promote recovery. The present work analyses the relationship between buprenorphine plasma exposure and clinical efficacy in participants with moderate to severe OUD using data from two clinical studies (39 and 504 participants). Leveraging data from placebo-controlled measures assessing opioid blockade, craving, withdrawal and abstinence, we found that buprenorphine plasma concentrations sustained at 2-3 ng/ml (corresponding to ≥70% brain mu-opioid receptor occupancy) optimized treatment outcomes in the majority of participants, while some individuals (e.g., injecting opioid users) needed higher concentrations. Our work also included non-linear mixed effects modeling and survival analysis, which identified a number of demographic, genetic and social factors modulating treatment response and retention. Altogether, these findings provide key information on buprenorphine plasma levels that optimize clinical outcomes and increase the likelihood of individual treatment success. NLM identifiers: NCT02044094, NCT02357901.

Population Pharmacokinetics of a Monthly Buprenorphine Depot Injection for the Treatment of Opioid Use Disorder: A Combined Analysis of Phase II and Phase III Trials

Background

BUP-XR (a.k.a. RBP-6000 or SUBLOCADE™) is an extended-release subcutaneous buprenorphine formulation for the treatment of opioid use disorder. BUP-XR was designed to provide sustained buprenorphine exposure throughout the monthly dosing interval, at concentrations sufficient to control all aspects of the disease (withdrawal, craving, and blockade of opioid subjective effects).

Objectives

To characterize the population pharmacokinetics of BUP-XR based on phase II and phase III data and to evaluate whether target therapeutic concentrations were reached with the dosing regimens evaluated in the phase III program.

Methods

The population pharmacokinetic analysis included 570 subjects with opioid use disorder who received up to 12 monthly BUP-XR injections following induction with sublingual buprenorphine.

Results

In phase III studies, target therapeutic concentrations of buprenorphine were achieved from the first injection and maintained over the entire treatment duration. Buprenorphine plasma concentration–time profiles were well described by a two-compartment model, with first-order absorption for sublingual buprenorphine and a dual absorption submodel for BUP-XR. A covariate analysis evaluated the effects of subjects’ demographic characteristics, laboratory data, and genetic status regarding buprenorphine-metabolizing enzymes. Only two covariates, body mass index and body weight, were retained in the final model. Overall, their effects were not of sufficient magnitude to justify a dose adjustment. Finally, pharmacokinetic simulations showed that buprenorphine plasma concentrations decreased slowly after discontinuation of treatment and that a 2-week occasional delay in dosing would not impact efficacy, which translated into labeling claims.

Discussion

In conclusion, the present analysis led to the development of a robust population pharmacokinetic model and confirms the ability of BUP-XR to deliver and maintain therapeutic plasma concentrations over the entire treatment duration.

Electronic supplementary material

The online version of this article (10.1007/s40262-020-00957-0) contains supplementary material, which is available to authorized users.

Variability Attribution for Automated Model Building

We investigated the possible advantages of using linearization to evaluate models of residual unexplained variability (RUV) for automated model building in a similar fashion to the recently developed method “residual modeling.” Residual modeling, although fast and easy to automate, cannot identify the impact of implementing the needed RUV model on the imprecision of the rest of model parameters. We used six RUV models to be tested with 12 real data examples. Each example was first linearized; then, we assessed the agreement in improvement of fit between the base model and its extended models for linearization and conventional analysis, in comparison to residual modeling performance. Afterward, we compared the estimates of parameters’ variabilities and their uncertainties obtained by linearization to conventional analysis. Linearization accurately identified and quantified the nature and magnitude of RUV model misspecification similar to residual modeling. In addition, linearization identified the direction of change and quantified the magnitude of this change in variability parameters and their uncertainties. This method is implemented in the software package PsN for automated model building/evaluation with continuous data.

Population pharmacokinetic drug-drug interaction pooled analysis of existing data for rifabutin and HIV PIs

OBJECTIVES

Extensive but fragmented data from existing studies were used to describe the drug-drug interaction between rifabutin and HIV PIs and predict doses achieving recommended therapeutic exposure for rifabutin in patients with HIV-associated TB, with concurrently administered PIs.

METHODS

Individual-level data from 13 published studies were pooled and a population analysis approach was used to develop a pharmacokinetic model for rifabutin, its main active metabolite 25-O-desacetyl rifabutin (des-rifabutin) and drug-drug interaction with PIs in healthy volunteers and patients who had HIV and TB (TB/HIV).

RESULTS

Key parameters of rifabutin affected by drug-drug interaction in TB/HIV were clearance to routes other than des-rifabutin (reduced by 76%-100%), formation of the metabolite (increased by 224% in patients), volume of distribution (increased by 606%) and distribution to the peripheral compartment (reduced by 47%). For des-rifabutin, clearance was reduced by 35%-76% and volume of distribution increased by 67%-240% in TB/HIV. These changes resulted in overall increased exposure to rifabutin in TB/HIV patients by 210% because of the effects of PIs and 280% with ritonavir-boosted PIs.

CONCLUSIONS

Given together with non-boosted or ritonavir-boosted PIs, rifabutin at 150 mg once daily results in similar or higher exposure compared with rifabutin at 300 mg once daily without concomitant PIs and may achieve peak concentrations within an acceptable therapeutic range. Although 300 mg of rifabutin every 3 days with boosted PI achieves an average equivalent exposure, intermittent doses of rifamycins are not supported by current guidelines.