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Preclinical Models of Pain

A preclinical partner focused on understanding the neural pathways of pain and the mechanisms involved.

 

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Pain Expertise

MD Biosciences is dedicated to the study of pain and neurodegenerative diseases. The understanding of pain and novel mechanisms requires a deep understanding of the science as well as the resources in terms of expertise and infrastructure to effectively study therapeutic candidates in early and translational phases. 

 

As with any model, the right selection of species, stimuli, assessments, and end-point analysis leads to a quality study that brings insight into the condition and potential therapeutic. MD Biosciences expertise in study design contributes to predictive data from preclinical studies. 

behavior

Behavior assessments

Evaluating pain from stimuli and resulting emotional responses in non-clinical animals. 

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Electrophysiology

Adds to the understanding of pain, particularly when there is limited mobility as a result of disease. 

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Biomarkers

Clinically relevant biomarkers such as nerve fiber analysis adds insight into neuropathic conditions.

Understanding behavior as a result of pain.

Measuring pain is often times straight forward using stimuli and measuring the resulting response. But the clinical situation not only evaluates response to the stimuli but the emotional component that patients report. In animal studies, we must rely on vocalization assessments and tests such as the open field to see how animals exhibit anxiety and behave emotionally as a result of the painful condition. 

open field_POP publication
 

Preclinical Pain Models

MD Biosciences works extensively in various pain conditions. Please review the categories below to see the various models within the different types of pain. 

 

READY TO DISCUSS YOUR PROJECT?

If you are ready to discuss how a partnership can support 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 when studies are designed right.

 

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