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Related Concept Videos

Parkinson Disease l: Introduction01:24

Parkinson Disease l: Introduction

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Parkinson’s disease is a chronic, progressive neurodegenerative disorder that primarily affects movement. It is characterized by motor symptoms such as resting tremors, muscle rigidity, bradykinesia (slowness of movement), and postural instability. Patients may notice hand tremors at rest, stiffness during movement, or a shuffling gait. In addition to motor features, non-motor symptoms include sleep disturbances, mood and behavioral changes, constipation, and cognitive impairment, all of...
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Parkinson Disease ll: Pathophysiology01:24

Parkinson Disease ll: Pathophysiology

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Parkinson disease (PD) is a progressive neurodegenerative disorder primarily affecting movement, with additional non-motor features. Its pathophysiology involves complex interactions among genetic susceptibility, environmental exposures, and cellular dysfunction, including dopaminergic neuron loss, protein aggregation, and mitochondrial impairment.Selective NeurodegenerationA key feature is the degeneration of dopaminergic neurons in the substantia nigra pars compacta, leading to reduced...
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Neurodegenerative disorders, such as Parkinson's Disease (PD), involve the gradual and irreversible destruction of neurons in particular brain areas. These disorders exhibit standard features like proteinopathies, selective vulnerability of some neurons, and an interaction of intrinsic properties, genetics, and environmental influences in neural injury.
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Neurodegenerative disorders are progressive diseases that cause irreversible damage and loss to neurons in specific brain areas. Examples of these disorders include Parkinson's disease, Alzheimer's disease, Multiple Sclerosis (MS), and Amyotrophic Lateral Sclerosis (ALS). These disorders share characteristics such as proteinopathies, selective neuronal vulnerability, and a complex interplay between genetic and environmental factors. The primary therapeutic goal for these conditions is...
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Digestion begins with a cephalic phase that prepares the digestive system to receive food. When our brain processes visual or olfactory information about food, it triggers impulses in the cranial nerves innervating the salivary glands and stomach to prepare for food.
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Embryonic and induced pluripotent stem cells are excellent models for disease research because of their ability to self-renew and differentiate into most cell types. Somatic cells from a patient are isolated and reprogrammed into induced pluripotent stem cells or iPSCs. These iPSCs are later differentiated into the desired cell type, which mirrors the diseased cell of the patient. In this way, disease models have been created for investigating diseases such as Down syndrome, type I diabetes,...
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Related Experiment Video

Updated: Apr 30, 2026

Gene-environment Interaction Models to Unmask Susceptibility Mechanisms in Parkinson's Disease
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A Structure-Based Platform for Predicting Chemical-Induced Parkinson's Disease.

Linde Schoenmaker1, Emmelie E M van der Veer1, Daan A Jiskoot1,2

  • 1Division of Medicinal Chemistry, Leiden Academic Centre for Drug Research, Leiden University, Einsteinweg 55, Leiden 2333 CC, Netherlands.

Chemical Research in Toxicology
|April 29, 2026
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Summary

This study introduces a new in silico method using interaction fingerprints (IFPs) to predict neurotoxicity of pesticides by screening metabotropic glutamate receptors (mGluRs). The approach successfully identified potential chemical binders, aiding in the development of safer agrochemicals and Parkinson

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Area of Science:

  • Computational toxicology
  • Pharmacology
  • Neuroscience

Background:

  • Current methods for assessing agrochemical neurotoxicity are insufficient, evidenced by links between pesticide exposure and Parkinson's disease (PD).
  • Mechanism-based in silico screening offers a predictive approach for molecular initiating events, crucial precursors to adverse outcomes.
  • Limited protein-binding data for pesticides necessitates extrapolation methods for broad chemical screening.

Purpose of the Study:

  • To develop and validate a mechanism-based in silico screening approach for predicting neurotoxic potential.
  • To utilize group I metabotropic glutamate receptors (mGluRs) as a case study due to their involvement in chemical-induced PD.
  • To create a user-friendly platform for implementing the developed screening models.

Main Methods:

  • Docking of known active compounds into the allosteric binding site of mGluRs.
  • Computation of interaction fingerprints (IFPs) and training of classification models.
  • Evaluation of model enrichment (ROC AUC), feature importance, and applicability domain analysis.

Main Results:

  • IFP-based mGluR models showed good predictive enrichment (ROC AUC 0.78 and 0.66).
  • Key interactions identified included hydrogen bonds with Asn760 (mGluR1) and aromatic interactions with Trp785 (mGluR5).
  • Virtual screening identified 132 potential mGluR binders, including bifenthrin, validating the model's efficacy.

Conclusions:

  • Interaction fingerprints combined with machine learning provide a powerful tool for mechanism-based in silico toxicology.
  • The developed screening technique can predict potential neurotoxicants and aids in the development of safer agrochemicals.
  • This approach contributes to a paradigm shift towards predictive toxicology, reducing reliance on traditional testing methods.