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Quantitative systems pharmacology model of Alzheimer’s disease to study efficacy of combinatorial therapy on multiple pathology components
EP32859
Poster Title: Quantitative systems pharmacology model of Alzheimer’s disease to study efficacy of combinatorial therapy on multiple pathology components
Submitted on 30 Jul 2020
Author(s): Tatiana Karelina
Affiliations: InSysBio, Moscow, Russia
This poster was presented at AAIC Virtual Event
Poster Views: 68
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Poster Information
Abstract: Background: Immunotherapies targeting tau or Aβ remain the key focus for neurologists. But low brain
penetration and limited scope of targeting may restrict efficacy. This restriction may be overcome by
combinations with other treatments targeting protein degradation systems. Quantitative systems
pharmacology model allows for integration of available information from multiple preclinical studies to
simulate clinical studies. The goal was to develop the translational model of key AD markers, verified and
validated on the literature data and to simulate effects of combinations.
Method: Predeveloped Ab and tau pathology submodels have been merged through the model of
intracellular protein degradation systems (proteasome, autophagy), which is known to fail progressively in
AD. Literature data (concentration baselines and dynamics, SUVR) from mice and human have been
used for calibration. Multiple in vitro data for intracellular dynamics perturbations (rapamycin, proteasome
inhibitors, vinblastine) available from the literature were used to estimate the intracellular regulation
parameters. Immunotherapy mechanism is described through 1) binding of antibodies and prevention of
seeding and growth of aggregates, 2) activation of microglia for Ab degradation. Data from clinical studies
for immunotherapy (BAN2401) and BACE inhibitor (verubecestat) have been used for retrospective
model validation, together with data for treatment by rapamycin, calpain inhibitors, proteasome activation
in mouse AD models.
Result: Model correctly describes the Aβ and tau accumulation and mutual exacerbation in mice and
humans. It correctly predicts effect of rapamycin, calpain inhibitors, proteasome activation on tau tangles
in tau-transgenic mouse and absence of effect of Aβ targeting on tau pathology markers in humans.
Simulation of Aβ immunotherapy starting from 65 years (Braak stage 3-4), predicts 80% Aβ plaque
difference vs placebo within 2 years, without effect on tau markers. Rapamycin is predicted to clear tau
pathology, with modest efficacy (up to 30-40%) of Aβ clearing. An example of combination, anti-Aβ
immunotherapy with rapamycin is most effective in retarding both pathologies (up to 80-90%) in
hippocampus.
Conclusion: QSP modeling allows for choice of combinations of newly emerging biologics combined with
targeting cell metabolism, to lead optimization and increase of treatment efficacy.
Summary: QSP modeling allows for choice of combinations of newly emerging biologics combined with
targeting cell metabolism to lead optimization and increase of treatment efficacy.
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