Objectives: With the high rate of failure for disease modifying treatments for Alzheimer’s Disease (AD; 99.6% - Cummings et al. 2014), there is an urgent need to: (1) better understand the failures, (2) improve clinical trial design, and (3) inform the next generation of therapeutics. In an attempt to address these needs, we developed a quantitative systems pharmacology (QSP) model describing amyloid beta (Ab) dynamics in AD, incorporating mechanisms for multiple anti-Ab drugs, and predicting plaque reduction over time.
Methods: A QSP model was developed describing three different pools of Ab: monomer, soluble oligomer, and plaque for patients with mild to moderate AD. The model includes intercompartment transport of Ab species between: brain interstitial fluid (ISF), cerebrospinal fluid (CSF), and plasma. The baseline model was calibrated with data from stable isotope labeling kinetics (SILK) experiments. Mechanisms for three anti-Ab drugs, Crenezumab, Solanezumab, and Bapineuzumab, were added, and fitting was performed to publicly available pharmacokinetics (PK) and pharmacodynamics (PD) data from clinical studies.
Results: After fitting plasma PK, a single QSP model captured plasma Ab, CSF Ab, and plaque level changes reported from clinical trials of the three anti-Ab drugs. Our analyses predict that with a hypothetical dose of 10 mg/kg, Solanezumab or Bapineuzumab that have a low affinity for plaque induces low plaque clearance over 5 years (5.2% and 1.8% respectively). In contrast, Crenezumab which binds with higher affinity to plaque is predicted to reduce plaque faster (32.6%).
Conclusions: We developed a single QSP model to describe the effects of anti-Ab drugs on different Ab species in AD patients. The model suggests that drugs with lower affinity to plaque will not reduce plaque significantly in 5 years. This model will be expanded to include tau biology, simulate effects of combination therapy, and simulate patient variability.