bioRxivNeurosciencePreprint
A Computational Model of Levodopa-Induced Toxicity in Substantia Nigra Pars Compacta in Parkinson's Disease
Vignayanandam Ravindernath MuddapuiD, Karthik VijayakumariD, Keerthiga Ramakrishnan, V. Srinivasa ChakravarthyiD
Abstract
BackgroundParkinsons disease (PD) is caused by the progressive loss of dopaminergic cells in substantia nigra pars compacta (SNc). The root cause of this cell loss in PD is still not decisively elucidated. A recent line of thinking traces the cause of PD neurodegeneration to metabolic deficiency. Due to exceptionally high energy demand, SNc neurons exhibit a higher basal metabolic rate and higher oxygen consumption rate, which results in oxidative stress. Recently, we have suggested that the excitotoxic loss of SNc cells might be due to energy deficiency occurring at different levels of neural hierarchy. Levodopa (LDOPA), a precursor of dopamine, which is used as a symptom-relieving treatment for PD, leads to outcomes that are both positive and negative. Several researchers suggested that LDOPA might be harmful to SNc cells due to oxidative stress. The role of LDOPA in the course of PD pathogenesis is still debatable. New MethodWe hypothesize that energy deficiency can lead to LDOPA-induced toxicity (LIT) in two ways: by promoting dopamine-induced oxidative stress and by exacerbating excitotoxicity in SNc. We present a multiscale computational model of SNc-striatum system, which will help us in understanding the mechanism behind neurodegeneration postulated above and provides insights for developing disease-modifying therapeutics. ResultsIt was observed that SNc terminals are more vulnerable to energy deficiency than SNc somas. During LDOPA therapy, it was observed that higher LDOPA dosage results in increased loss of somas and terminals in SNc. It was also observed that co-administration of LDOPA and glutathione (antioxidant) evades LDOPA-induced toxicity in SNc neurons. Comparison with Existing MethodsOur proposed multiscale model of SNc-striatum system is first of its kind, where SNc neuron was modelled at biophysical level, and striatal neurons were modelled at spiking level. ConclusionsWe show that our proposed model was able to capture LDOPA-induced toxicity in SNc, caused by energy deficiency.
- organism: human
- research focus: benchmarking
- research focus: software / tool
- research focus: cell type annotation
- approach: statistical model
- approach: simulation
- Citations
- 5
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- A computational model of loss of dopaminergic cells in Parkinson’s disease due to glutamate-induced excitotoxicity
Vignayanandam Ravindernath Muddapu, Alekhya Mandali, … Srikanth Ramaswamy·Frontiers in Neural CircuitsPeer-reviewed·Feb 24, 2019
- A Multi-Scale Computational Model of Excitotoxic Loss of Dopaminergic Cells in Parkinson's Disease
Vignayanandam Ravindernath Muddapu, V. Srinivasa Chakravarthy·Frontiers in NeuroinformaticsPeer-reviewed·Sep 30, 2020
- INFLUENCE OF ENERGY DEFICIENCY ON THE MOLECULAR PROCESSES OF SUBSTANTIA NIGRA PARS COMPACTA CELL FOR UNDERSTANDING PARKINSONIAN NEURODEGENERATION: A COMPREHENSIVE BIOPHYSICAL COMPUTATIONAL MODEL
Vignayanandam Ravindernath Muddapu, V. Srinivasa Chakravarthy·Scientific ReportsPeer-reviewed·Jan 18, 2021
- Levodopa increases substantia nigra iron: implications for Parkinson's disease
Guangwei Du, Luke Bransom, … Richard B. Mailman·bioRxiv·Jun 1, 2026
- Neonatal 6-OHDA lesion of the SNc induces striatal compensatory sprouting from surviving SNc dopaminergic neurons without VTA contribution
William Tanguay, Charles Ducrot, … Louis‐Éric Trudeau·European Journal of NeurosciencePeer-reviewed·Sep 1, 2021
- Preservation of dopamine neurotransmission during nigrostriatal neuron loss in rat Parkinson model: evidence for increased dopamine signaling in substantia nigra
Ashley Galfano, Robert McManus, … Michael F. Salvatore·bioRxiv·Sep 12, 2025