Establishing the predictive validity of preclinical pain models across human pain indications. 

Despite multiple approved analgesics, effective and well-tolerated treatments for many pain conditions remain limited. Most provide only partial relief and are often constrained by dose-limiting side effects, underscoring the need for improved therapies. Translating preclinical efficacy into consistent clinical benefit has also proven challenging, highlighting the importance of models that better predict clinical outcomes. Building on the Praxis Analysis of Concordance (PAC) framework, originally developed to evaluate the predictive validity of preclinical seizure models in epilepsy, PAC-PAIN systematically assesses the relationship between preclinical pain model responses and clinical efficacy across diverse analgesic classes. 

Approved analgesics, including opioids, NSAIDs, antiseizure medications and antidepressants were evaluated across established pain models using reported TD₅₀ and ED₅₀ values. Protective index values were calculated and a weighted scale representing relative analgesic efficacy was used to grade preclinical response for each therapy within each model. Clinical use and perceived efficacy were similarly evaluated across pain indications. Predictive validity was assessed using the PAC scoring matrix, with concordance scores assigned ranging from complete discordance (-1) to complete concordance (1) between preclinical and clinical responses. Scores were summed and normalized to generate a global translational concordance score for each model. 

Findings from the PAC-PAIN framework revealed variable concordance between preclinical models and clinical outcomes. Preliminary trends indicate that certain models show greater translational concordance for specific pain indications, while others demonstrate limited predictive value. 

PAC-PAIN provides a systematic evaluation of translational concordance between preclinical and clinical outcomes across analgesic classes, with emerging patterns highlighting models with greater translation to human efficacy. Continued expansion of the PAC-PAIN framework is anticipated to strengthen its utility for optimizing preclinical model selection and advancing pain therapeutic discovery.