Abstract

Peripheral neuropathy (PN) is a multifaceted disorder characterised by peripheral nerve damage, manifesting in symptoms like pain, weakness, and autonomic dysfunction. This review assesses preclinical models in PN research, evaluating their relevance to human disease and their role in therapeutic development. The Streptozotocin (STZ)-induced diabetic rat model is widely used to simulate diabetic neuropathy but has limitations in faithfully replicating disease onset and progression. Cisplatin-induced PN models are suitable for studying chemotherapy-induced peripheral neuropathy (CIPN) and closely resemble human pathology. However, they may not fully replicate the spectrum of sensory and motor deficits. Paclitaxel-induced models also contribute to understanding CIPN mechanisms and testing neuroprotective agents. Surgical or trauma-induced models offer insights into nerve regeneration and repair strategies. Medications such as gabapentin, pregabalin, duloxetine, and fluoxetine have demonstrated promise in these models, enhancing our understanding of their therapeutic efficacy. Despite progress, developing models that accurately mirror human PN remains imperative due to its complex nature. Continuous refinement and innovative approaches are critical for effective drug discovery. This review underscores the strengths and limitations of current models and advocates for an integrated approach to address the complexities of PN better and optimise treatment outcomes.

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.