Plasma microRNAs to Select Optimal Patients for Antibody Production from Anti-Addiction Vaccines

Background: Cocaine and illicit amphetamines (disguised as "Adderall") are being laced with fentanyl and producing accidental and intentional fatal overdoses. Vaccines can prevent these overdoses, but 33% of humans generate insufficient anti-drug antibody (AB) levels. Plasma microRNAs (miRs) can be used to predict non-responders. We have plasma stored from 152 cocaine vaccine trial participants following three vaccinations over 9 weeks and examined miRs as potential response biomarkers. Methods: We compared 2517 miRs before anti-cocaine vaccination in participants with the highest (n = 25) to the lowest (n = 23) antibody levels. False Discovery Rates (FDRs) were applied to identify differentially expressed (DE) miRs. We used miR target prediction pipelines to identify the miR-regulated genes. Results: Using a DE-FDR < 0.05 and a >3-fold difference between high- and low-AB responders yielded 12 miRs down and 3 miRs up compared to low-AB patients. Furthermore, 11 among 1673 genes were targeted by 3 or more of the 12 down DE-miRs. Conclusions: A significant DE-miR for identifying optimal antibody responders replicated previous vaccine study predictors (miR-150), and several more miRs appear to be strong candidates for future consideration in replications based upon significance of individual DE-miRs and upon multiple miRs converging on individual genes.