Martina Chiesa 1,2, Elena Righetti 2, Enrico Domenici 1,2, Federico Reali 2
1 Università di Trento (Trento, Italy), 2 Fondazione The Microsoft Research - University of Trento Centre for Computational and Systems Biology (COSBI) (Rovereto, Italy)
Objectives: Parkinson’s disease (PD) – the second most prevalent neurodegenerative disorder worldwide [1] – is characterized by the progressive accumulation of misfolded and aggregated alpha-synuclein (aSyn). This presynaptic neuronal protein is involved in synaptic function and neurotransmitter release, and its toxic aggregates, including oligomeric and fibrillar species, are closely associated with disease onset and progression [1]. PD involves multiple interconnected molecular mechanisms, and this biological complexity still limits the development of treatments able to halt or slow neurodegeneration. While the microscopic processes underlying aSyn aggregation – such as primary nucleation, fibril elongation, and secondary nucleation – have been quantitatively studied in vitro, their behavior in cellular systems remains insufficiently understood [2].
In vitro studies have clarified aggregation kinetics under controlled experimental conditions, providing important mechanistic insights into processes such as nucleation and fibril growth. However, the cellular environment is more complex. Cells synthesize aSyn and regulate its levels through proteasomal and lysosomal degradation pathways. Interactions with lipids and other cellular components, together with homeostatic thresholds, influence whether misfolded species accumulate or are degraded. These additional layers modify the overall aggregation dynamics compared to test-tube conditions [2]. Translating aggregation mechanisms to a physiologically relevant cellular context requires an explicit representation of intracellular protein turnover. Quantitative modeling approaches offer a useful framework to bridge the gap between experimental settings and cellular behavior [1].
In this work, we extend a previously developed in vitro mechanistic model of aSyn aggregation [5] to the cellular level by incorporating protein synthesis and degradation pathways and investigate how core aggregation mechanisms interact with intracellular proteostasis under physiological and PD-relevant conditions. This work establishes a cell-level mechanistic module compatible with a Quantitative Systems Pharmacology (QSP) framework, enabling quantitative evaluation of aggregation-targeting strategies and integration of key cellular processes into a coherent mathematical structure. By embedding aggregation kinetics within a QSP-compatible structure, the model provides in silico mechanistic insight into the cellular drivers of disrupted aSyn homeostasis over extended timescales, supports preclinical experimental research, and contributes to the rational design of therapeutic strategies targeting aggregation and degradation pathways [3, 4].
Methods: We extend the in vitro aSyn aggregation model presented in [5] to incorporate protein synthesis, misfolding and degradation pathways. The model is formulated as a nonlinear system of ordinary differential equations, including mass-action and catalytic reactions. The equations were implemented in MATLAB R2024b and numerically integrated using the ode23s solver. Model calibration was carried out using integrated preclinical datasets, including cellular measurements from the SH-SY5Y neuroblastoma cell line [6]. The new parameters introduced in the model were estimated using a multi-start least squares optimization procedure. In addition, uncertainty quantification was performed to evaluate the robustness and reliability of the model predictions. Confidence intervals (95%) for the estimated parameters were computed using a local linear approximation based on the Jacobian of the residuals obtained by finite differences and the corresponding covariance matrix, assuming a t-distribution of the estimation error.
Results: The extended model translates the in vitro aggregation model presented in [5] to a cellular-level framework. The extended model describes aSyn synthesis, misfolding, aggregation, and, with particular focus, degradation pathways within cells. The model is consistent with the experimental datasets used for calibration and reproduces aSyn dynamics reported in the literature, including SH-SY5Y cellular data [6]. Simulations confirm that impaired degradation leads to progressive accumulation of misfolded and aggregated aSyn species. Compared with laboratory experiments, the model allows simulations over longer time periods, enabling the exploration of disease-like progression beyond typical experimental windows. Sensitivity analysis shows that parameters related to degradation have a strong impact on aSyn levels, highlighting potential targets for pharmacological strategies against aSyn aggregation.
Conclusions: The proposed cell-level model of aSyn synthesis, misfolding, aggregation, and degradation highlights the central role of impaired protein degradation mechanisms in aSyn accumulation and PD onset and progression. By providing an in silico representation of aSyn aggregation and degradation processes grounded in both in vitro and cellular data, the model offers a mechanistic framework to support therapeutic development targeting aSyn homeostasis. Moreover, this cellular module can be integrated into broader neuronal models, including pathways related to dopamine regulation or mitochondrial dysfunction, or embedded within larger QSP frameworks for quantitative investigation of disease progression.
References:
[1] Bloomingdale, P., et al. Hallmarks of neurodegenerative disease: a systems pharmacology perspective. CPT: Pharmacometrics & Systems Pharmacology (2022).
[2] Righetti, E., et al. Mechanistic models of α-synuclein homeostasis for Parkinson’s disease: a blueprint for therapeutic intervention. Frontiers in Applied Mathematics and Statistics, 8 (2022).
[3] Abrams, R., et al. A quantitative systems pharmacology model of Gaucher disease type 1 provides mechanistic insight into the response to substrate reduction therapy with eliglustat. CPT: Pharmacometrics & Systems Pharmacology, 9, 374–383 (2020).
[4] Ivanova, O., et al. Quantitative systems pharmacology model of α-synuclein pathology in a Parkinson’s disease-like mouse for investigation of passive immunotherapy mechanisms. CPT: Pharmacometrics & Systems Pharmacology, 13(10), 1798–1809 (2024).
[5] Righetti, E., et al. A mechanistic model of pure and lipidic α-synuclein aggregation for advancing Parkinson’s therapies. Communications Chemistry, 8(1), 186 (2025).
[6] Sang, J., et al. Super-resolution imaging reveals α-synuclein seeded aggregation in SH-SY5Y cells. Communications Biology, 4, 1–12 (2021).
Reference: PAGE 34 (2026) Abstr 12010 [www.page-meeting.org/?abstract=12010]
Poster: Drug/Disease Modelling - CNS