Pharmacy stakeholder reports on ethical and logistical considerations in anti-opioid vaccine development

Abstract

Background: As opioid use disorder (OUD) incidence and its associated deaths continue to persist at elevated rates, the development of novel treatment modalities is warranted. Recent strides in this therapeutic area include novel anti-opioid vaccine approaches. This work compares logistical and ethical considerations surrounding currently available interventions for opioid use disorder with an anti-opioid vaccine approach.

Methods: The opinions of student pharmacists and practicing pharmacists assessing knowledge, perceptions, and attitudes toward current and future OUD management strategies were characterized using a staged, multi-modal research approach incorporating a focus group, pilot survey development and refinement, and final survey deployment. Survey responses were assessed using one- and two-way parametric and non-parametric analyses where appropriate, and multi-dimensional matrix profiles were compared using z-tests following an exhaustive combinatorial sum of differences calculation between items within each compared matrix.

Results: Focus group content analysis revealed a high level of agreeableness among participants regarding anti-opioid vaccine technology and a sense of shared ownership regarding solutions to the opioid epidemic at large. Pilot survey results demonstrated subject ability to consider both pragmatic and ethical considerations related to current therapeutics and novel interventions in a single instrument, with high endurance amongst engaged subjects. Access inequality was the most concerning ethical consideration identified for anti-opioid vaccines. Support for anti-opioid vaccine implementation across various clinical scenarios was strongest for voluntary use amongst individuals in recovery, and lowest for mandatory use in at-risk individuals.

Conclusions: Ethical and logistical concerns surrounding anti-opioid vaccines were largely similar to those for current OUD therapeutics overall. Anti-opioid vaccines were endorsed as helpful potential additions to current OUD therapeutic approaches, particularly for voluntary use in the later stages of clinical progression.

Keywords: Drug development; Ethics; Logistics; Opioid; Stakeholder; Vaccine.

Fig. 1

Flowchart describing basic phases of the research project and retention for each phase

Fig. 2

Pharmacist-reported (a, b) and student-reported (c, d) rankings for importance of outcomes (a, c) and approaches (b, d) with regard to OUD treatment and management. The dotted line on the figures indicates the midpoint of the scale. ANOVA w Tukey’s Correction: **p < 0.01 vs. overdose prevention; ***p < 0.001 vs. overdose prevention; ****p < 0.0001 vs. overdose prevention; ^#p < 0.05 vs. decreased reward; ^{^}p < 0.05 vs. treatment access; ^{^^^}p < 0.001 vs. treatment access; ⁺⁺⁺p < 0.001 vs. new treatments; ⁺⁺⁺⁺p < 0.0001 vs. new treatments

Fig. 3

Pharmacist-reported (a, b) and student-reported (c, d) familiarity with currently available OUD treatments (a, c) and anti-opioid vaccines (b, d). Pharmacist-reported (e, f) and student-reported (g, h) availability (e, g) and utilization (f, h) of resources for managing OUD. The dotted line indicates the scale midpoint. Data plotted as counts (a–d), percentages (e, g) or mean ± SEM (f, h). Kruskal–Wallis with Dunn’s: **p < 0.01 vs. PDMP; ****p < 0.0001 vs. PDMP; ^#p < 0.05 vs. dropbox

Fig. 4

Pharmacist-reported availability (a, c) and utilization (b, d) of injectable (a, b) and non-injectable (c, d) medication formulations for managing OUD. Student-reported availability (e) and utilization (f) of medications used to manage OUD. The dotted line (a, c, e) indicates the scale midpoint. Data plotted as percentages (a, c, e) or mean ± SEM (b, d, f). Kruskal–Wallis with Dunn’s: ⁺⁺⁺p < 0.001 vs. SL/buccal buprenorphine; *p < 0.05 vs. Suboxone®; ****p < 0.0001 vs. Suboxone®; ^#p < 0.05 vs. Naloxone

Fig. 5

Pharmacist-reported perceptions of current OUD treatment efficacy as compared to other medication-managed therapeutic areas (a) and perceived impact of anti-opioid vaccines for OUD treatment (b). Student-reported perceptions of current interventions’ effectiveness in promoting individual outcomes (c) and perceived impact of anti-opioid vaccines for OUD treatment (d). The dotted lines indicate a neutral response. Data plotted as counts (a, b, d) or mean ± SEM (c). Kruskal–Wallis with Dunn’s: *p < 0.05 vs. overdose prevention; **p < 0.005 vs. overdose prevention

Fig. 6

Pharmacist-reported perceptions regarding logistical barriers (a, b) and ethical concerns (c, d) for current treatments (a, c) and anti-opioid vaccines (b, d). All data plotted as mean ± SEM. Friedman with Dunn’s: *p < 0.05 vs. time; **p < 0.01 vs. time; ^##p < 0.005 vs. time; ^###p < 0.001 vs. time; ^####p < 0.0001 vs. time; ^%p < 0.05 vs. provider availability; ^$$p < 0.01 vs. affordability of current OUD Treatments ^{^}p < 0.05 vs. access inequality; ^{^^^}p < 0.001 vs. access inequality; ^{^^^^}p < 0.0001 vs. access inequality; ⁺p < 0.05 vs. access inequality; ⁺⁺⁺p < 0.0005 vs. access inequality

Fig. 7

Average rating differences between pharmacist-reported logistical barriers (a ● = insufficient time, ■ = drug availability, ▲ = drug affordability, ▼ = provider access, ◆ = patient refusal) and ethical concerns (b ● = confidentiality breach, ■ = healthcare inequality, ▲ = moral hazard, ▼ = reduced autonomy, ◆ = value inefficiency). Filled shapes (●) correspond to current therapeutic responses while open shapes (○) correspond to anti-opioid vaccine responses. Colored shapes denote the five differences (●-○) in each matrix that were used to define the ‘reference equivalency’ model. Distribution of possible sums of differences in observed data and simulated random distribution for logistical barriers (c) and ethical concerns (d). Position of the sum of differences for the ‘reference equivalency’ model is denoted by the dashed lines, with subpanel zoomed in on the portion of the observed distribution where this value is found. Data plotted as average difference across all subjects (a, b) and counts of values falling within bins with a size of 0.1 (c, d)

Fig. 8

Pharmacist-reported support for the use of vaccine-based therapy across various clinical scenarios (a, b) and populations (c, d), under voluntary-use (a, c) and mandatory-use (b, d) scenarios. The dotted line on the figures indicates a true neutral response. All data plotted as mean ± SEM. ****p < 0.0001 vs. voluntary use in populations at-risk for OUD; ^###p < 0.001 vs. voluntary use in populations with an OUD diagnosis with ongoing use;^{^^}p < 0.01 vs. voluntary use in populations post-opioid overdose;⁺⁺p < 0.01 vs. voluntary use in populations in drug court for opioid-related offences; ^$$$$p < 0.0001 vs. voluntary in-recovery; ^%%%p < 0.001 vs. voluntary use in children; ^>>>p < 0.001 vs. voluntary use in pregnant individuals; ^<<<p < 0.001 vs. voluntary use in prisoners