Background: Anti-amyloid-β (Aβ) monoclonal antibodies (mAbs) are currently in development
for treating Alzheimer’s disease.
Objectives: To address the complexity of Aβ target engagement profiles, improve the understanding of
crenezumab Pharmacokinetics (PK) and Aβ Pharmacodynamics (PD) in the brain, and facilitate comparison
of anti-Aβ therapies with different binding characteristics.
Methods: A mechanistic mathematical model was developed describing the distribution, elimination,
and binding kinetics of anti-Aβ mAbs and Aβ (monomeric and oligomeric forms of Aβ1-40 and
Aβ1-42) in the brain, Cerebrospinal Fluid (CSF), and plasma. Physiologically meaningful values were
assigned to the model parameters based on the previous data, with remaining parameters fitted to clinical
measurements of Aβ concentrations in CSF and plasma, and PK/PD data of patients undergoing anti-Aβ
therapy. Aβ target engagement profiles were simulated using a Monte Carlo approach to explore the impact
of biological uncertainty in the model parameters.
Results: Model-based estimates of in vivo affinity of the antibody to monomeric Aβ were qualitatively
consistent with the previous data. Simulations of Aβ target engagement profiles captured observed mean
and variance of clinical PK/PD data.
Conclusion: This model is useful for comparing target engagement profiles of different anti-Aβ therapies
and demonstrates that 60 mg/kg crenezumab yields a significant increase in Aβ engagement compared
with lower doses of solanezumab, supporting the selection of 60 mg/kg crenezumab for phase 3
studies. The model also provides evidence that the delivery of sufficient quantities of mAb to brain interstitial
fluid is a limiting step with respect to the magnitude of soluble Aβ oligomer neutralization.