Use of a Novel Artificial Intelligence Platform on Mobile Devices to Assess Dosing Compliance in a Phase 2 Clinical Trial in Subjects With Schizophrenia

Earle E Bain, Laura Shafner, David P Walling, Ahmed A Othman, Christy Chuang-Stein, John Hinkle, Adam Hanina, Earle E Bain, Laura Shafner, David P Walling, Ahmed A Othman, Christy Chuang-Stein, John Hinkle, Adam Hanina

Abstract

Background: Accurately monitoring and collecting drug adherence data can allow for better understanding and interpretation of the outcomes of clinical trials. Most clinical trials use a combination of pill counts and self-reported data to measure drug adherence, despite the drawbacks of relying on these types of indirect measures. It is assumed that doses are taken, but the exact timing of these events is often incomplete and imprecise.

Objective: The objective of this pilot study was to evaluate the use of a novel artificial intelligence (AI) platform (AiCure) on mobile devices for measuring medication adherence, compared with modified directly observed therapy (mDOT) in a substudy of a Phase 2 trial of the α7 nicotinic receptor agonist (ABT-126) in subjects with schizophrenia.

Methods: AI platform generated adherence measures were compared with adherence inferred from drug concentration measurements.

Results: The mean cumulative pharmacokinetic adherence over 24 weeks was 89.7% (standard deviation [SD] 24.92) for subjects receiving ABT-126 who were monitored using the AI platform, compared with 71.9% (SD 39.81) for subjects receiving ABT-126 who were monitored by mDOT. The difference was 17.9% (95% CI -2 to 37.7; P=.08).

Conclusions: Using drug levels, this substudy demonstrates the potential of AI platforms to increase adherence, rapidly detect nonadherence, and predict future nonadherence. Subjects monitored using the AI platform demonstrated a percentage change in adherence of 25% over the mDOT group. Subjects were able to use the technology successfully for up to 6 months in an ambulatory setting with early termination rates that are comparable to subjects outside of the substudy.

Trial registration: ClinicalTrials.gov NCT01655680 https://ichgcp.net/clinical-trials-registry/NCT01655680?term=NCT01655680.

Keywords: artificial intelligence; clinical trials as topic; medication adherence.

Conflict of interest statement

Conflicts of Interest: Laura Shafner and Adam Hanina are employees of AiCure (New York, NY, USA) and consultants to AbbVie. Adam Hanina is a shareholder of AiCure (New York, NY, USA). David Walling is a consultant to Otsuka, Eli Lilly, Janssen, and Acadia. John E. Hinkle, PhD, has active consulting agreements with AiCure and several drug discovery, pharmaceutical, medical food, and contract research organizations. Christy Chuang-Stein, PhD, reports consulting agreements with AiCure, AstraZeneca, AbbVie, TEVA, Amgen, and Merck Serono. Earle Bain, MD, and Ahmed A. Othman, PhD are employees and shareholders of AbbVie.

©Earle E Bain, Laura Shafner, David P Walling, Ahmed A Othman, Christy Chuang-Stein, John Hinkle, Adam Hanina. Originally published in JMIR Mhealth and Uhealth (http://mhealth.jmir.org), 21.02.2017.

Figures

Figure 1
Figure 1
Artificial intelligence platform.
Figure 2
Figure 2
Geometric mean ABT-126 plasma concentrations, normalized to the 50 milligram dose, for subjects who participated in the adherence substudy stratified by artificial intelligence platform versus modified directly observed therapy use. Error bars indicate mean with standard errors. ng/mL: nanograms/milliliter; mg: milligram.
Figure 3
Figure 3
Cumulative adherence based on study drug (ABT-126) concentration.

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