The Impact of Mathematical Modeling in Understanding the Mechanisms Underlying Neurodegeneration: Evolving Dimensions and Future Directions

Lloret-Villas, Audald; Varusai, Thawfeek; Juty, Nick; Laibe, Camille; Novère, Nicolas Le; Hermjakob, Henning; Chelliah, Vijayalakshmi

doi:10.1002/psp4.12155

Cited by 40 publications

(23 citation statements)

References 96 publications

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“…Mathematical models of PD have developed concomitantly with accumulation of experimental insight and address several of the mechanistic aspects of PD pathogenesis. A systematic review of modeling efforts in various NDs has recently been published . In this review, we focus on various approaches in PD modeling.…”

Section: List Of Abbreviationsmentioning

confidence: 99%

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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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Section: List Of Abbreviationsmentioning

confidence: 99%

“…A systematic review of modeling efforts in various NDs has recently been published. 7 In this review, we focus on various approaches in PD modeling. PD models may be broadly categorized into two classes: (i) mechanistic models and (ii) phenotypic models.…”

mentioning

confidence: 99%

Mathematical Biology Models of Parkinson's Disease

Bakshi

Chelliah²,

Chen

et al. 2018

CPT Pharmacom & Syst Pharma

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“…From their modelling efforts it was evident that developing such a comprehensive model of SNc neuron would be a significant leap in understanding the subcellular mechanisms underlying neurodegeneration in PD. A comprehensive literature survey on modelling efforts related to PD pathogenesis was recently published (Bakshi et al, 2019;Lloret-Villas et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Influence of Energy Deficiency on the Molecular Processes ofSubstantia Nigra Pars CompactaCell for Understanding Parkinsonian Neurodegeneration: A Comprehensive Biophysical Computational Model

Muddapu

Chakravarthy

2020

Preprint

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Parkinson's disease (PD) is the second most prominent neurodegenerative disease around the world. Although it is known that PD is caused by the loss of dopaminergic cells in substantia nigra pars compacta (SNc), the decisive cause of this inexorable cell loss is not clearly elucidated. We hypothesize that "Energy deficiency at a sub-cellular/cellular/systems level can be a common underlying cause for SNc cell loss in PD." Here, we propose a comprehensive computational model of SNc cell which helps us to understand the pathophysiology of neurodegeneration at subcellular level in PD. The proposed model incorporates a rich vein of molecular dynamics related to SNc neurons such as ion channels, active pumps, ion exchangers, dopamine turnover processes, energy metabolism pathways, calcium buffering mechanisms, alpha-synuclein aggregation, Lewy body formation, reactive oxygen species (ROS) production, levodopa uptake, and apoptotic pathways. The proposed model was developed and calibrated based on experimental data. The influx of glucose and oxygen into the model was controlled, and the consequential ATP variations were observed.Apart from this, the dynamics of other molecular players (alpha-synuclein, ROS, calcium, and dopamine) known to play an important role in PD pathogenesis are also studied. The aim of the study was to see how deficits in supply of energy substrates (glucose and oxygen) lead to a deficit in ATP, and furthermore, deficits in ATP are the common factor underlying the pathological molecular-level changes including alpha-synuclein aggregation, ROS formation, calcium elevation, and dopamine dysfunction. The model suggests that hypoglycemia plays a more crucial role in leading to ATP deficits than hypoxia. We believe that the proposed model provides an integrated modelling framework to understand the neurodegenerative processes underlying PD.(1) ATP production by aerobic glucose metabolism, (2) Ca 2+ efflux by ATP-dependent calcium pump, (3) DAcyt packing into vesicles by VMAT using H + -ATPase-induced concentration gradient, (4) ATP-dependent protein degradation by UPS and autophagy, (5) ROS scavenging mechanism by glutathione, (6) α-syn* aggregation due to ROS-induced UPS impairment, (7) ROS formation due to α-syn* induced mitochondrial dysfunction, (8) ROS formation due to DAcyt autoxidation, (9) DAcyt accumulation due to α-syn* induced vesicle recycling impairment, (10) α-syn* aggregation due to DAcyt induced CMA impairment, (11) Reduced ATP production due to α-syn* induced mitochondrial dysfunction, (12) Reduced ATP production due to ROS-induced mitochondrial dysfunction, (13) Reduced ATP production due to Ca 2+ induced mitochondrial dysfunction, (14) DAcyt accumulation due to Ca 2+ induced DA synthesis, (15) Ca 2+ accumulation due to α-syn* induced dysregulation of Ca 2+ homoestasis, (16) α-syn* aggregation due to Ca 2+ induced calpain activation, (17) Reduced ATP production due to mitochondrial DNA deletions, (18) Reduced ATP production due to ROS formation induced by complex anatomical structure...

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“…Mechanistically oriented QSP models also prove useful in placing biomarkers of efficacy, safety, or disease pathophysiology and phenotype in the appropriate quantitative and dynamic context for a therapeutic treatment of choice . In the course of QSP model development and testing, QSP modeling may help reconcile (or not) what, at a first glance, may appear as discrepancies in data, e.g., as obtained from different animal models or trials or discrepancies between in vitro and in vivo (nonhuman) findings or in vivo and clinical findings .…”

mentioning

confidence: 99%

“…1,2,[4][5][6] QSP may further be used in optimizing doses and dosing regimens 4,7,8 or in support of dose-sequencing decisions for drug combinations 9 given that a QSP model typically contains multiple effectors and at least one pharmacodynamic marker of interest-often the pharmacodynamic endpoint in a given study-downstream of the drug or compound target.Mechanistically oriented QSP models also prove useful in placing biomarkers of efficacy, safety, or disease pathophysiology and phenotype in the appropriate quantitative and dynamic context for a therapeutic treatment of choice. 5,[10][11][12][13] In the course of QSP model development and testing, QSP modeling may help reconcile (or not) what, at a first glance, may appear as discrepancies in data, e.g., as obtained from different animal models or trials or discrepancies between in vitro and in vivo (nonhuman) findings or in vivo and clinical findings. 14,15 Broadly, QSP models may also be used to derive translational significance and to make inferences for compounds within a dynamic pathophysiological context captured in the model, e.g., from in vitro to in vivo (nonhuman) and from in vivo to human.…”

mentioning

confidence: 99%

Quantitative Systems Pharmacology: An Exemplar Model‐Building Workflow With Applications in Cardiovascular, Metabolic, and Oncology Drug Development

Helmlinger

Соколов²,

Peskov

et al. 2019

CPT Pharmacom & Syst Pharma

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Quantitative systems pharmacology (QSP), a mechanistically oriented form of drug and disease modeling, seeks to address a diverse set of problems in the discovery and development of therapies. These problems bring a considerable amount of variability and uncertainty inherent in the nonclinical and clinical data. Likewise, the available modeling techniques and related software tools are manifold. Appropriately, the development, qualification, application, and impact of QSP models have been similarly varied. In this review, we describe the progressive maturation of a QSP modeling workflow: a necessary step for the efficient, reproducible development and qualification of QSP models, which themselves are highly iterative and evolutive. Furthermore, we describe three applications of QSP to impact drug development; one supporting new indications for an approved antidiabetic clinical asset through mechanistic hypothesis generation, one highlighting efficacy and safety differentiation within the sodium‐glucose cotransporter‐2 inhibitor drug class, and one enabling rational selection of immuno‐oncology drug combinations.

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The Impact of Mathematical Modeling in Understanding the Mechanisms Underlying Neurodegeneration: Evolving Dimensions and Future Directions

Cited by 40 publications

References 96 publications

Mathematical Biology Models of Parkinson's Disease

Mathematical Biology Models of Parkinson's Disease

Influence of Energy Deficiency on the Molecular Processes ofSubstantia Nigra Pars CompactaCell for Understanding Parkinsonian Neurodegeneration: A Comprehensive Biophysical Computational Model

Quantitative Systems Pharmacology: An Exemplar Model‐Building Workflow With Applications in Cardiovascular, Metabolic, and Oncology Drug Development

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