Angelica Quartino, United States of America
Genentech Inc. Clinical PharmacologyAuthor Of 1 Presentation
BENCHMARKING DISEASE MODIFYING THERAPIES FOR ALZHEIMER’S DISEASE USING A QUANTITATIVE SYSTEMS PHARMACOLOGY MODEL
Abstract
Aims
Evaluating the impact of disease-modifying investigational Aβ and tau targeting monoclonal antibodies using a quantitative systems pharmacology model representing the disease pathology of Alzheimer’s Disease (AD).
Methods
The model includes the mechanisms of Aβ and tau pathologies in AD. The model tracks the production, aggregation, transport, and clearance of biomarkers associated with Aβ and tau pathways in the brain, CSF, and plasma, as well as the pharmacokinetics–pharmacodynamics of investigational treatments (solanezumab, crenezumab, aducanumab, gantenerumab, and semorinemab) on these processes. SimBiology, a MATLAB® based application was used for the implementation of the model.
Results
The model scope enables scientific hypothesis assessments of: biomarker target engagement in brain/CSF, biomarker transport dynamics, and therapy-benchmarking. It is calibrated to pharmacokinetic and pharmacodynamic/biomarker data in plasma and CSF from published and in-house clinical studies. Simulations allow quantitative assessment of target engagement in brain and CSF enabling a comparison between therapies intervening the Aβ and tau pathways and study effect of different doses, durations, and dosing regimens (flat/titration doses). The model is able to predict available imaging-based Aβ PET SUVR data of plaque targeting antibodies directly assessing disease burden. The model is positioned for simulation-based analysis of tau targeting antibodies and their effect on biomarker engagement and dynamics in brain, CSF, and plasma.
Conclusions
The model serves as a tool for simulation-based quantitative assessment of target engagement and biomarker dynamics by disease-modifying therapies in the brain and CSF. The model provides a comprehensive platform to test scientific hypothesis related to target and biomarker dynamics.