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Translational model of Neuropathic Pain

A large animal model of peripheral neuritis that results in painful neuropathy mimicking the clinical situation. 


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Neuropathic Pain model in Pigs

Preclinical rodent models largely contribute to our understanding of chronic neuropathic pain, however these animal models are limiting due to poor clinical translation. Since pigs share similarities with humans, such as skin innervation and neurological resemblance, MD Biosciences developed a pig model for chronic pain caused by surgically-induced peripheral neuritis (PNT) (Castel et al., 2016). In the model, 75% of animals exhibit mechanical and tactile allodynia by day 28 post surgery, demonstrating painful neuropathy. We also showed that morphine and gabapentin inhibited hypersensitivity to von Frey filaments and feather stimuli, reversing spontaneous pain-related behavior in a dose-related manner. 


Pig PNT Study Design

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MD Biosciences incorporates a range of in vivo measures and endpoint assessments that provide a robust data package that enable researchers to make critical decisions. 

Pain behavioral anaysis


Pain behavior tests include dynamic weightbearing and Hargreaves to assess the distribution on each paw and sensitivity to heat.  



Evaluate pro-inflammatory and anti-inflammatory biomarkers from CSF samples. 



IHC and histological staining to evaluate inflammatory markers in the paw skin. 

Scientific Data

In a study, we aimed to evaluate the effect of Gabapentin, Aprepitant and Morphine in the pig PNT model. The graph shows the behavior score as a result of the nerve injury before and post treatment. 

behavior score pig PNT model
Review the complete dataset.

Other data obtained in this study were Von Frey, Open Field, Walking Distance and Skin Biomarkers for nerve analysis. 

PNT datasheet


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If you are ready to discuss how translational studies can fit into your development program, our scientists are eager to explore the possibilities with you. Like many other pharmaceutical and medical device developers, you can rely on predictive preclinical data.  


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