The new article "Quantitative systems pharmacology model of α-synuclein pathology in Parkinson's disease-like mouse for investigation of passive immunotherapy mechanisms" by Olga Ivanova and Tatiana Karelina was published in PSPJournal at the end of August 2024.
Olga Ivanova comments on the article, "Our model of PD-like pathology in mice captures the complex dynamics of αSyn metabolism throughout the brain and the influence of neuroinflammation on PD pathogenesis and dopaminergic neurodegeneration. The model simulations predict how different proposed mechanisms of anti-αSyn immunotherapy may contribute to pathology reduction and how anti-inflammatory therapy may enhance the effect of immunotherapy."
About InSysBio
InSysBio is a group of Quantitative Systems Pharmacology (QSP) companies located in Limassol, Cyprus (INSYSBIO CY Ltd) and Edinburgh, UK (INSYSBIO UK LIMITED). InSysBio was founded in 2004 and has an extensive track record of helping pharmaceutical companies to make right decisions on the critical stages of drug research and development by application of QSP modeling. InSysBio’s innovative QSP approach has already become a part of the drug development process implemented by our strategic partners: there are more than 100 completed projects in collaboration with leaders of pharmaceutical industry. For more information about InSysBio, its solutions and services, visit www.insysbio.com
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14 Jan 2021 14:28
InSysBio to publish the new article on identifiability analysis in PLOS Computational Biology
[Moscow – 11.01.2021] InSysBio, one of the world’s pioneers of Quantitative Systems Pharmacology (QSP) modeling, has published its recent scientific research on identifiability in PLOS Computational Biology.
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26 Jan 2021 13:14
InSysBio to announce the upgrade of its SbmlViewer application to version 0.2.7
[Moscow – 26.01.2021] InSysBio, one of the world’s pioneers of Quantitative Systems Pharmacology (QSP) modeling, launces the new version of its SbmlViewer open project. Generally, SbmlViewer represents a tool for fast and easy reading of biological models written in SBML format.
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