A mathematical model of insulin resistance in Parkinson’s disease

Braatz, Elise M.; Coleman, Randolph A.

doi:10.1016/j.compbiolchem.2015.04.003

Cited by 16 publications

(13 citation statements)

References 51 publications

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“…Despite the promising early data, there may be some caveats. Computational models combining PD and insulin resistance indicate that early correction or restoration of insulin resistance would seem to offer the most maximal effects (Braatz and Coleman, 2015), and in parallel, it appears that the timing of when to instigate these drugs appears crucial. Studies in AD indicate that neurons become increasingly insulin resistant during the disease process, and also reveal that PPARy is downregulated in ageing mice compared to young mice (Escribano et al, 2009).…”

Section: Resultsmentioning

confidence: 99%

Insulin resistance and Parkinson’s disease: A new target for disease modification?

Athauda

Foltynie

2016

Progress in Neurobiology

261

209

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There is growing evidence that patients with Type 2 diabetes have an increased risk of developing Parkinson's disease and share similar dysregulated pathways suggesting common underlying pathological mechanisms. Historically insulin was thought solely to be a peripherally acting hormone responsible for glucose homeostasis and energy metabolism. However accumulating evidence indicates insulin can cross the blood-brain-barrier and influence a multitude of processes in the brain including regulating neuronal survival and growth, dopaminergic transmission, maintenance of synapses and pathways involved in cognition. In conjunction, there is growing evidence that a process analogous to peripheral insulin resistance occurs in the brains of Parkinson's disease patients, even in those without diabetes. This raises the possibility that defective insulin signalling pathways may contribute to the development of the pathological features of Parkinson's disease, and thereby suggests that the insulin signalling pathway may potentially be a novel target for disease modification. Given these growing links between PD and Type 2 diabetes it is perhaps not unsurprising that drugs used the treatment of T2DM are amongst the most promising treatments currently being prioritised for repositioning as possible novel treatments for PD and several clinical trials are under way. In this review, we will examine the underlying cellular links between insulin resistance and the pathogenesis of PD and then we will assess current and future pharmacological strategies being developed to restore neuronal insulin signalling as a potential strategy for slowing neurodegeneration in Parkinson's disease.

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Section: Resultsmentioning

confidence: 99%

Insulin resistance and Parkinson’s disease: A new target for disease modification?

Athauda

Foltynie

2016

Progress in Neurobiology

261

209

View full text Add to dashboard Cite

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“…Like Büchel et al ., the authors showed qualitatively that disruption of modeled cellular pathways results in PD. A curious connection among insulin resistance, inflammation, DA, and Asyn aggregation was modeled in a theoretical work by Braatz and Coleman …”

Section: Mechanistic Models In Pdmentioning

confidence: 99%

“…A subset of those models, which do not include Asyn aggregation kinetics, do include some kind of the Asyn aggregates (such as oligomers or fibrils) as a model species. In these cases, the feedback between misfolded Asyn and ROS or proteasomes is modeled . A few other models focus on non‐Asyn mediated pathogenesis mechanism (in particular the role of DA homeostasis).…”

Section: Mechanistic Models In Pdmentioning

confidence: 99%

“…A curious connection among insulin resistance, inflammation, DA, and Asyn aggregation was modeled in a theoretical work by Braatz and Coleman. 49 All the models utilizing the BST or FBA approach contain several variables and processes. Lack of kinetic information regarding these processes has led the authors to use relative species concentrations and parameter values in these models.…”

Section: Da Metabolismmentioning

confidence: 99%

“…In these cases, the feedback between misfolded Asyn and ROS or proteasomes is modeled. 37,39,49 A few other models focus on non-Asyn mediated pathogenesis mechanism (in particular the role of DA homeostasis). We feel there is a clear possibility to enrich the pathogenesis models with realistic Asyn aggregation kinetics and parameters derived from aggregation models.…”

Section: Da Metabolismmentioning

confidence: 99%

See 2 more Smart Citations

Mathematical Biology Models of Parkinson's Disease

Bakshi

Chelliah²,

Chen

et al. 2018

CPT Pharmacom & Syst Pharma

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Parkinsons disease ( PD ) is a progressive neurodegenerative disease with substantial and growing socio‐economic burden. In this multifactorial disease, aging, environmental, and genetic factors contribute to neurodegeneration and dopamine ( DA ) deficiency in the brain. Treatments aimed at DA restoration provide symptomatic relief, however, no disease modifying treatments are available, and PD remains incurable to date. Mathematical modeling can help understand such complex multifactorial neurological diseases. We review mathematical modeling efforts in PD with a focus on mechanistic models of pathogenic processes. We consider models of α‐synuclein (Asyn) aggregation, feedbacks among Asyn, DA , and mitochondria and proteolytic systems, as well as pathology propagation through the brain. We hope that critical understanding of existing literature will pave the way to the development of quantitative systems pharmacology models to aid PD drug discovery and development.

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Hallmarks of neurodegenerative disease: A systems pharmacology perspective

Bloomingdale

Karelina

Ramakrishnan

et al. 2022

CPT Pharmacom & Syst Pharma

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Age‐related central neurodegenerative diseases, such as Alzheimer's and Parkinson's disease, are a rising public health concern and have been plagued by repeated drug development failures. The complex nature and poor mechanistic understanding of the etiology of neurodegenerative diseases has hindered the discovery and development of effective disease‐modifying therapeutics. Quantitative systems pharmacology models of neurodegeneration diseases may be useful tools to enhance the understanding of pharmacological intervention strategies and to reduce drug attrition rates. Due to the similarities in pathophysiological mechanisms across neurodegenerative diseases, especially at the cellular and molecular levels, we envision the possibility of structural components that are conserved across models of neurodegenerative diseases. Conserved structural submodels can be viewed as building blocks that are pieced together alongside unique disease components to construct quantitative systems pharmacology (QSP) models of neurodegenerative diseases. Model parameterization would likely be different between the different types of neurodegenerative diseases as well as individual patients. Formulating our mechanistic understanding of neurodegenerative pathophysiology as a mathematical model could aid in the identification and prioritization of drug targets and combinatorial treatment strategies, evaluate the role of patient characteristics on disease progression and therapeutic response, and serve as a central repository of knowledge. Here, we provide a background on neurodegenerative diseases, highlight hallmarks of neurodegeneration, and summarize previous QSP models of neurodegenerative diseases.

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A mathematical model of insulin resistance in Parkinson’s disease

Cited by 16 publications

References 51 publications

Insulin resistance and Parkinson’s disease: A new target for disease modification?

Insulin resistance and Parkinson’s disease: A new target for disease modification?

Mathematical Biology Models of Parkinson's Disease

Hallmarks of neurodegenerative disease: A systems pharmacology perspective

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