Eisai Inc.
Clinical Pharmacology Science, Modeling & Simulation
Antonio Cabal is currently a Director, Quantitative Systems Pharmacology, at Eisai Inc., Exton, PA. He took his Bachelor of Science degree in Mechanics and Mathematics from Lomonosov Moscow State University, Moscow, Russia. Following his undergraduate work, he worked on ocean circulation models at Havana's Institute of Oceanology, in Havana, Cuba before beginning graduate studies in computational fluid dynamics. He completed a Master of Science at Simon Fraser University in Vancouver, B.C., and his Ph.D. at the University of Western Ontario, in London, Ontario, Canada, both in Applied Mathematics. He then spent seven years with the Eastman Kodak Research Labs in Rochester, New York, applying mathematical models and computational simulations to multi-physic problems associated with micro-electro-mechanical systems (MEMS) design and optimization for inkjet printer. In January 2005 Dr. Cabal took a position as Scientist/Associate Disease Manager at Archimedes, Inc., where he developed mathematical models of human physiology and its interaction with the health care system to guide health care policy. In August 2006 he joined Merck where for 14 years he held various positions of increasing responsibility leading multidisciplinary teams working on the scientific development and implementation of Quantitative System Pharmacology (QSP) models.

Presenter of 1 Presentation

QUANTITATIVE SYSTEMS PHARMACOLOGY (QSP) AMYLOID PLATFORM : MULTISCALE COMPUTATIONAL MODELING OF ABETA BIOLOGY AND ITS INTERACTION WITH LECANEMAB PHARMACOLOGY

Session Type
SYMPOSIUM
Date
Sun, 20.03.2022
Session Time
09:05 AM - 11:05 AM
Room
ONSITE: 113
Lecture Time
09:05 AM - 09:20 AM

Abstract

Aims

A QSP Platform was developed integrating the known features of amyloid beta (Aβ) aggregation and microglia biology with the pharmacology of lecanemab to evaluate the dynamics of the modulation of Aβ aggregation cascade by the unique binding properties of lecanemab targeting aggregated Aβ species for the treatment of Alzheimer’s Disease (AD).

Methods

A reduced physiological based pharmacokinetic (PBPK) model for lecanemab interstitial fluid concentrations was coupled with a mathematical model of the Aβ aggregation based on elongation, primary and secondary nucleation, and fragmentation. Removal of Aβ peptides was modeled by microglia-dependent clearance through phagocytosis of the antibody-Aβ-complexes and modulated by selective binding affinity of lecanemab. Activation of immune cells by antibody-bound plaques in a perivascular compartment was assumed to drive ARIA-E liability.

Results

The QSP Aβ Platform predicts well the observed lifetime trajectory of Aβ species (monomers, oligomers, protofibrils, and plaque), including the effect of APOE genotype. It was qualified against observed clinical PK and biomarker (Plasma Aβ, CSF Aβ, and PET SUVr) data for lecanemab. The model characterized the temporal changes of the clinical biomarkers during lecanemab treatment, its ARIA-E incidence, and the dynamics of SUVr after termination of treatment.

Conclusions

A QSP Aβ Platform was developed and qualified using existing clinical PK and PD data from lecanemab. The model describes the lecanemab PK and the time course of Aβ species in plasma, CSF, and ISF. It is a valuable tool to explore the impact of patient baseline characteristics and treatments on amyloid dynamics.

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