Understanding quantitative effects of anti-amyloid therapies on tau biomarkers and functional outcome. Insights from...

Anti-amyloid antibodies have the potential to become the standard of care in Alzheimer's Disease (AD) and large datasets from clinical trials allow the testing of predictive models on fluid biomarkers and functional outcomes. However, identifying an easily accessible biomarker to determine the time to switch to maintenance therapy, and identifying patient profiles with optimal cognitive benefit, are still unresolved issues in clinical practice.

Predicted changes in monomers, oligomers, protofibrils and plaques were simulated using a well-validated Quantitative Systems Pharmacology model based on biophysical and biological assumptions of amyloid synthesis, aggregation and clearance. This model was combined with a previously calibrated computational neuronal network model of cognitive outcome in AD patients by introducing the effect of amyloid and tau oligomers on specific voltage- and ligand-gated ion channels, informed by preclinical studies.

The model accounted for 70% and 50% of the variance of clinically observed changes in plasma p-tau181 and Clinical Dementia Rating-Sum Of Boxes (CDR-SOB) respectively, in clinical trials of seven amyloid antibodies. We derived an antibody specific normalized decrease of plasma p-tau181 (-15% for donanemab, -45% for aducanumab and -75% for lecanemab) to determine trial duration for achieving central amyloid negativity. Using the concept of information processing bandwidth, the model suggests that anti-amyloid antibodies slow the cognitive worsening compared to placebo while at the same time lowering plasma p-tau181 levels by reducing neuronal firing. Finally, the model suggests that independently from the degree of amyloid reduction, the beneficial cognitive effect of treatment decreases with more advanced neuronal pathology and higher baseline tau-load. This provides a hypothesis for the impact of disease pathology and gender effect on functional outcomes with lecanemab and gantenerumab.

With further validation, this model has the capability to support optimization of clinical trial design for amyloid-tau combination therapy.