Systematic in silico analysis of clinically tested drugs for reducing amyloid-beta plaque accumulation in Alzheimer's disease



Recent clinical trials of Aβ antibodies have established a causative relationship between plaque reduction and positive clinical and functional outcomes. Therefore, Applied Biomath undertook an exercise to quantitatively assess the antibody characteristics that predict Aβ plaque clearance by evaluating the effect of various classes of anti-Aβ therapeutic approaches to better predict potential clinical benefit. We developed a quantitative systems pharmacology (QSP) model using eight different Aβ targeting approaches (aducanumab, lecanemab, crenezumab, solanezumab, bapineuzumab, elenbecestat, verubecestat, and semagacestat).


  • To provide guidance for clinical development of AD therapies, we developed a single QSP model to analyze treatment effects of anti-Aβ antibodies, BACE, and γ-secretase inhibitors on Aβ monomer, oligomer, and plaque.
  • Model calibration to clinical data for eight investigational drugs with a range of mechanisms provides rigorous constraints on model parameters and model structure, and hence a high degree of confidence in model predictions.
  • The model provides insights into which drug design properties impact plaque changes in AD. For example, due to the very slow rate of turnover of endogenous plaque, inhibitors of plaque formation are predicted to lead to slow plaque removal and hence have a minimal effect on plaque levels in the brain within the duration of a clinical trial. This may partly explain the lack of clinical efficacy for secretase inhibitors and non-plaque-clearing mAbs (crenezumab and solanezumab).
  • The calibrated model can be used to predict biomarker changes for novel therapeutic candidates using preclinical or early clinical data.

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