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Quantitative systems pharmacology modelling of Parkinson’s disease to compare anti-α-synuclein therapeutics

S. Bakshi, PH. Vander Graaf (Breda, Netherlands)

Meeting: MDS Virtual Congress 2021

Abstract Number: 459

Keywords: Alpha-synuclein

Category: Parkinson’s Disease: Pharmacology and Therapy

Objective: We previously constructed a quantitative systems pharmacology (QSP) model of Parkinson’s disease (PD) that combines pathogenesis and spatial propagation of misfolded α-synuclein (Asyn) [1]. The aim of this work was to apply our model to interpret recent data of the first clinical trials of anti-Asyn therapeutics.

Background: PD is a multifactorial neurodegenerative disease with no cure yet. Mathematical modelling can help understand complex neurological diseases and several models to date address PD pathogenesis mechanisms [1, 2].

Method: Our model consists of Asyn aggregation and its feedback with oxidative stress. In addition, Asyn species can exist in the interstitial fluid (ISF) and are in dynamic flux with intracellular space. Asyn species in the ISF can diffuse and lead to spatial PD propagation in the model. We combine this model with typical pharmacokinetics of monoclonal antibodies (mABs) with ~0.1-0.3% brain penetration. Next, we incorporate the target binding profiles of two leading anti-Asyn drugs in Phase II clinical development [3, 4].

Results: Previously, we showed that the naïve PD model (i.e. in absence of drugs) displays existence of two steady states – a healthy state with low Asyn misfolding and low oxidative stress, and a diseased state with high Asyn misfolding and high oxidative stress. We now demonstrate we are able to incorporate quantitative knowledge of the affinities of clinical compounds with respect to various Asyn targets.

Conclusion: We have applied our mechanistic model of the effect of anti-Asyn therapeutics on the speed of propagation of PD pathology in the brain to interpret outcomes of recent clinical trials in PD. The model allowed us to identify targets that are most profitable and to study the role of non-efficacious targets in reducing drug availability.

References: 1. Bakshi et al. (2019). https://uploads.strikinglycdn.com/files/5aeb9f90-cc03-4736-820e-b9614e5f2577/5.%20ABS-64927083_4714414_FIGON_Abstract_SB.pdf 2. Bakshi S, Chelliah V, Chen C, van der Graaf PH. Mathematical Biology Models of Parkinson’s Disease. CPT Pharmacometrics Syst Pharmacol. 2018 Nov 2 [cited 2018 Dec 10]; Available from: http://doi.wiley.com/10.1002/psp4.12362 3. Roche and Prothena will Advance Prasinezumab into Late-Stage Clinical Development Study in Parkinson’s Disease. https://www.globenewswire.com/news-release/2020/10/20/2111378/0/en/Roche-and-Prothena-will-Advance-Prasinezumab-into-Late-Stage-Clinical-Development-Study-in-Parkinson-s-Disease.html 4. Phase 2 study of BIIB054 in Parkinson’s disease did not achieve proof-of-concept. https://investors.biogen.com/static-files/6286c4fc-fc15-4b5b-9e78-11739f96a21c

To cite this abstract in AMA style:

S. Bakshi, PH. Vander Graaf. Quantitative systems pharmacology modelling of Parkinson’s disease to compare anti-α-synuclein therapeutics [abstract]. Mov Disord. 2021; 36 (suppl 1). https://www.mdsabstracts.org/abstract/quantitative-systems-pharmacology-modelling-of-parkinsons-disease-to-compare-anti-%ce%b1-synuclein-therapeutics/. Accessed May 15, 2025.
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