A combined physiologically‐based pharmacokinetic and quantitative systems pharmacology model for modeling amyloid aggregation in Alzheimer's disease

Abstract

Antibody-mediated removal of aggregated βamyloid (Aβ) is the current, most clinically advanced potential disease-modifying treatment approach for Alzheimer's disease. We describe a quantitative systems pharmacology (QSP) approach of the dynamics of Aβ monomers, oligomers, protofibrils, and plaque using a detailed microscopic model of Aβ 40 and Aβ 42 aggregation and clearance of aggregated Aβ by activated microglia cells, which is enhanced by the interaction of antibody-bound Aβ. The model allows for the prediction of Aβ positron emission tomography (PET) imaging load as measured by a standardized uptake value ratio. A physiology-based pharmacokinetic model is seamlessly integrated to describe target exposure of monoclonal antibodies and simulate dynamics of cerebrospinal fluid (CSF) and plasma biomarkers, including CSF Aβ 42 and plasma Aβ 42 /Aβ 40 ratio biomarkers. Apolipoprotein E genotype is implemented as a difference in microglia clearance. By incorporating antibody-bound, plaque-mediated macrophage activation in the perivascular compartment, the model also predicts the incidence of amyloid-related imaging abnormalities with edema (ARIA-E). The QSP platform is calibrated with pharmacological and clinical information on aducanumab, bapineuzumab, crenezumab, gantenerumab, lecanemab, and solanezumab, predicting adequately the change in PET imaging measured amyloid load and the changes in the plasma Aβ 42 /Aβ 40 ratio while slightly overestimating the change in CSF Aβ 42 . ARIA-E is well predicted for all antibodies except bapineuzumab. This QSP model could support the clinical trial design of different amyloid-modulating interventions, define optimal titration and maintenance schedules, and provide a first step to understand the variability of biomarker response in clinical practice.

Quantitative systems pharmacology‐based exploration of relevant anti‐amyloid therapy challenges in clinical practice

“…Previously we have demonstrated the relationship between changes in these fluid biomarkers and central amyloid load in observational natural history studies. 6 We next investigated changes in these easily accessible fluid biomarkers pre‐ and posttreatment with amyloid antibodies.…”

“…Using a clinically validated QSP model, 6 this paper explores a number of challenges in clinical practice for treatment with the two approved amyloid antibody medications, aducanumab and lecanemab, as well as donanemab—which has filed for approval. This biophysically realistic computer model combines aggregation dynamics and microglia‐dependent clearance together with PBPK modeling of amyloid antibody target exposure and engagement with readouts for SUVR amyloid PET imaging and ARIA‐E liability, both important drivers of clinical response.…”

“…The QSP model used for simulations of various scenarios on amyloid readouts has been published before. 6 Basically, the model consists of a physiologically‐based pharmacokinetic (PBPK) model coupled to a mechanistic QSP model of amyloid aggregation. The PBPK model 11 lumps peripheral tissues together but models explicitly lymph, plasma, four different CSF and the interstitial fluid (ISF) compartments including the blood‐brain barrier (BBB) and the blood‐CSF‐barrier (BCSFB).…”

“…A possible solution is to use a predictive Quantitative Systems Pharmacology (QSP) approach that describes the biomarker changes and ARIA‐E liability at the group level from published clinical trials and applies the results to individual patient situations. This mechanistic QSP model 6 is based on detailed biological and biophysical insights about the aggregation and clearance processes of amyloid beta (Aβ) species and is therefore generalizable beyond the clinical trials and the antibodies used for model calibration. The model outcome in terms of amyloid imaging and fluid biomarkers as well as ARIA‐E liability is fully calibrated using clinical data on longitudinal observational studies as well as interventional studies with the six antibodies bapineuzumab, solanezumab, crenezumab, gantenerumab, aducanumab, and lecanemab.…”

Advancing precision medicine therapeutics for Parkinson’s utilizing a shared quantitative systems pharmacology model and framework

“…Examples of QSP models and their applications include models for cardiovascular diseases that have included building mechanistic and multi-scale dynamical models to predict impact of many novel agents such as sGC activators, neprilysin inhibition, and angiotensinII blockade, PCSK9 inhibition, on cardiac function, systemic circulation and vascular function, blood pressure and lipid metabolism (Ming et al, 2017;Knox et al, 2016;Hallow and Gebremichael, 2017;Peskin and McQueen 1988;Moss et al, (Geerts et al, 2023). Square: Gaucher Disease: Gaucher Disease QSP model used to simulate and optimize the impact of enzyme replacement and substrate reduction treatment protocols (Abrams et al, 2020).…”