Association of partial systemic exposure and abuse potential for opioid analgesics with abuse deterrence labeling claims supporting product-specific guidance

Abstract

Background Over the past decade, U.S. FDA has approved 10 opioid analgesics in abuse-deterrent formulations (ADFs). ADFs are intended to reduce abuse of a prescription opioid through manipulation of the product to use one or more routes of abuse. Although it is critically needed for evaluation of the abuse deterrent properties of an opioid product, the relationship between systemic exposure and likelihood of abuse of the opioid has not been fully characterized. To fill the current knowledge gap, we have evaluated the association of subjective measures predictive of abuse potential (e.g., scores of “drug liking,” “take drug again”), which are referred to as ‘pharmacodynamic (PD)’ responses for measuring abuse potential, with systemic exposure of the opioid using the data from all the clinical abuse potential trials submitted to FDA in support of the approval of innovator ADFs. Methods Extensive pharmacokinetic (PK) and subjective response data from 11 clinical abuse potential trials in recreational opioid users following oral and nasal administration of intact and manipulated oxycodone, hydrocodone and morphine products from the FDA internal database were utilized for the present analysis. This retrospective study used data collected from January 11 th , 2010 until March 25 th , 2015. The potential relationship between PK metrics, especially those for early exposure measures, and the subjective measures of drug liking and take drug again as PD metrics of abuse potential were explored using linear and logistic regression analyses. Heterogeneity analysis was conducted to assess study-to-study variation and multi-level logistic regression analysis was used to affirm the identified PK-PD relationship based on pooled data. Findings Following oral and nasal administration of intact and manipulated opioids, the maximum visual analogue scale (VAS) for Drug Liking was generally achieved no later than the time to peak plasma drug concentration. Both heterogeneity analysis and multi-level logistic regression indicated insignificant inter study variability for the evaluated PK-PD relationships. Duration of Drug Liking response (i.e., VAS ≥ 65) lasted for 2 to 4 h after drug administration. The early portion of the systemic area under the plasma concentration-time curve (AUC), e.g., partial AUCs in the first 3 h and 4 h were found to be associated with abuse potential measures including maximum Drug Liking VAS and maximum Taking Drug Again VAS. Neither a formulation factor (e.g., immediate-release vs. extended-release, intact vs. manipulated) nor a route of administration was identified as a significant factor together with early partial AUCs to predict the probability of maximum Drug Liking or maximum Take Drug Again responses being greater than or equal to 65. Interpretation Our assessment indicates that the measure of early systemic drug exposure of opioids is the bes...

Nicotine flux and pharmacokinetics-based considerations for early assessment of nicotine delivery systems

“…However, it must also be noted that all NRTs such as nicotine patch, nicotine gum, Nicorette® inhaler, and Nicorette® nasal sprays with no abuse liability have a < 1. Recent studies have described partial systemic exposure (pAUC, a specific portion of the total AUC) as a PK parameter to evaluate the abuse liability of drugs ( Zhao et al, 2021 ), which could apply to a head-to-head comparison of nicotine delivery systems, but the utility for evaluating real-world ad libitum use across different products and routes needs to be explored. More importantly, a closer look at the nicotine PK from smoking cigarettes ( Feyerabend et al, 1985 ) and different consumption patterns underscores the need to consider advanced metrics such as frequency-dependent exposure-responses during ad libitum use ( Allain et al, 2015 , Schulthess et al, 2018 ).…”

Recent Advances in Machine Learning Assisted Hydrogel Flexible Sensing

“…In addition, LR is more sensitive to multicollinearity data, which may lead to model instability. LR has many applications in financial risk prediction, disease risk assessment, and drug efficacy assessment [73–75] . DT is a heuristic algorithm, the core is to apply information gain criteria to each node of the decision tree to select features, and then recursively construct the decision tree.…”

“…LR has many applications in financial risk prediction, disease risk assessment, and drug efficacy assessment. [73][74][75] DT is a heuristic algorithm, the core is to apply information gain criteria to each node of the decision tree to select features, and then recursively construct the decision tree. It is simple and intuitive, easy to explain and understand, and basically does not need to preprocess the data, its most common disadvantage is easy to overfit.…”

Review: Role of Model-Informed Drug Development Approaches in the Lifecycle of Drug Development and Regulatory Decision-Making

“…MIDD and MIE focused modeling tools serve as a critical toolset to optimize study design with reduced study duration and sample size, to identify the most sensitive BE metrics, and/or establish science driven BE acceptance criteria with appropriate error controls. For instance, partial area under curve (pAUC) for PK can be used to assess the opioid products with abuse deterrence properties (42). For BE study design, one prominent case goes to the publication of Product-Specific Guidance for the levonorgestrel Intrauterine System (IUS) in January 2020.…”