A precision medicine framework using artificial intelligence for the identification and confirmation of genomic biomarkers of response to an Alzheimer's disease therapy: Analysis of the blarcamesine (ANAVEX2‐73) Phase 2a clinical study

Abstract

Abstract Introduction The search for drugs to treat Alzheimer's disease (AD) has failed to yield effective therapies. Here we report the first genome‐wide search for biomarkers associated with therapeutic response in AD. Blarcamesine (ANAVEX2‐73), a selective sigma‐1 receptor (SIGMAR1) agonist, was studied in a 57‐week Phase 2a trial (NCT02244541). The study was extended for a further 208 weeks (NCT02756858) after meeting its primary safety endpoint. Methods Safety, clinical features, pharmacokinetic, and efficacy, measured by changes in the Mini‐Mental State Examination (MMSE) and the Alzheimer's Disease Cooperative Study‐Activities of Daily Living scale (ADCS‐ADL), were recorded. Whole exome and transcriptome sequences were obtained for 21 patients. The relationship between all available patient data and efficacy outcome measures was analyzed with unsupervised formal concept analysis (FCA), integrated in the Knowledge Extraction and Management (KEM) environment. Results Biomarkers with a significant impact on clinical outcomes were identified at week 57: mean plasma concentration of blarcamesine (slope MMSE:P < .041), genomic variants SIGMAR1 p.Gln2Pro (ΔMMSE:P < .039; ΔADCS‐ADL:P < .063) and COMT p.Leu146fs (ΔMMSE:P < .039; ΔADCS‐ADL:P < .063), and baseline MMSE score (slope MMSE:P < .015). Their combined impact on drug response was confirmed at week 148 with linear mixed effect models. Discussion Confirmatory Phase 2b/3 clinical studies of these patient selection markers are ongoing. This FCA/KEM analysis is a template for the identification of patient selection markers in early therapeutic development for neurologic disorders.

Keywords

• Alzheimer's disease
• association rules
• biomarker
• genomic analysis
• knowledge extraction management
• machine learning