Learning from amyloid trials in Alzheimer's disease. A virtual patient analysis using a quantitative systems pharmacology approach

Abstract

Background: Many trials of amyloid-modulating agents fail to improve cognitive outcome in Alzheimer's disease despite substantial reduction of amyloid β levels.

Methods: We applied a mechanism-based Quantitative Systems Pharmacology model exploring the pharmacodynamic interactions of apolipoprotein E (APOE), Catechol -O -methyl Transferase (COMTVal158Met), and 5-HT transporter (5-HTTLPR) rs25531 genotypes and aducanumab.

Results: The model predicts large clinical variability. Anticipated placebo differences on Alzheimer's Disease Assessment Scale (ADAS)-COG in the aducanumab ENGAGE and EMERGE ranged from 0.77 worsening to 1.56 points improvement, depending on the genotype-comedication combination. 5-HTTLPR L/L subjects are found to be the most resilient. Virtual patient simulations suggest improvements over placebo between 4% and 20% at the 10 mg/kg dose, depending on the imbalance of the 5-HTTLPR genotype and exposure. In the Phase II PRIME trial, maximal anticipated placebo difference at 10 mg/kg ranges from 0.3 worsening to 5.3 points improvement.

Discussion: These virtual patient simulations, once validated against clinical data, could lead to better informed future clinical trial designs.

Keywords: aducanumab; genotype; medication; pharmacodynamic effect; responder profile.