For centrally acting drugs, understanding abuse potential is crucial for public health and regulatory decision-making. The U.S. Food and Drug Administration (FDA) provides detailed guidelines on assessing abuse potential through its guidance, Assessment of Abuse Potential of Drugs (2017). This guidance helps researchers design human abuse potential (HAP) clinical studies that can determine if a new drug may be prone to abuse. Statistical analysis plays a central role in these studies, providing the rigor and objectivity necessary for evaluating abuse risk.
The Purpose of Human Abuse Potential Studies
HAP studies are conducted as single-dose, randomized, double-blind crossover studies, where participants receive the test drug, positive control(s), and placebo. The analytical approach to HAP studies focuses on key comparisons between the test drug and positive control, a drug with known abuse potential, as well as placebo. These comparisons provide benchmarks for determining subsequent regulatory actions regarding the drug, such as scheduling or labeling.
Statistical Approaches in Human Abuse Potential Studies
Subjective outcome measures form the basis for evaluation of abuse potential in HAP studies. Because of potential variability in subjective responses, comparisons between test drug, positive control(s), and placebo are within-subject. These measures are also administered at multiple timepoints during a treatment period.
- Descriptive Statistics: Assessing the time course of drug effects is important, accomplished through descriptive statistics and graphical outputs. However, derived endpoints, such as peak effect or “Emax,” form the basis of HAP study analysis.
- Inferential Statistics: In HAP studies, primary and secondary endpoints typically undergo different types of analyses.
- Primary Hypothesis Testing: “Drug Liking” Emax is typically used as the primary endpoint for hypothesis testing. This testing serves to confirm both the validity of the study and to make relevant comparisons required for regulatory decision-making:
- Positive Control vs. Placebo: This comparison determines whether the study can be considered sensitive and valid for assessing the abuse potential of the test drug. A typical margin (δ) for the expected difference between positive control and placebo would be 15 points on a 100-point visual analog scale (VAS).
- Test Drug vs. Positive Control: This comparison determines the relative abuse potential of the test drug compared to the positive control. Each dose is evaluated in a stepwise manner relative to the dose or doses of positive control. The expected δ for this difference is typically 0 or ≤5.
- Test Drug vs. Placebo: This comparison determines the absolute abuse potential of the test drug compared to placebo, using a typical margin of 11.
- Secondary and Supportive Analyses: While key endpoints (Emax) and comparisons are similar to those of the primary analyses, key secondary and secondary endpoints are typically evaluated using one- or two-sided testing with margins of 0 for all comparisons. Supportive endpoints may be analyzed using only descriptive statistics.
- Primary Hypothesis Testing: “Drug Liking” Emax is typically used as the primary endpoint for hypothesis testing. This testing serves to confirm both the validity of the study and to make relevant comparisons required for regulatory decision-making:
Importance of Statistical Rigor in Abuse Potential Studies
The role of statistics in HAP studies goes beyond hypothesis testing, ensuring that results are reproducible, accurate, and meaningful. The statistical techniques used help minimize bias and provide a framework for understanding the likelihood of abuse of the drug once approved. The analyses are complicated, nuanced, and constantly evolving; therefore, it’s critical for sponsors to consult with experts in HAP study analyses and interpretation. For these same reasons, it’s also important that sponsors consult with FDA on statistical analysis plans for these studies.
Conclusion
Statistical analysis plays a vital role in HAP studies, providing the necessary tools to make accurate, evidence-based decisions. By employing a variety of statistical methods, researchers can effectively assess the abuse potential of new drugs and provide important data for scheduling and labeling decisions.
