Evaluation and application analysis of animal models of PIPNP based on data mining.

Abstract

The aim of this study is to systematically retrieve, synthesize, and assess methodologies employed in the development of paclitaxel-induced peripheral neuropathic pain (PIPNP) animal models, with the objective of optimizing protocols for model construction and evaluation. A structured search strategy was implemented using the terms "Paclitaxel-induced peripheral neuropathic pain" OR "Chemotherapy-induced peripheral neuropathic pain" AND "animal models" OR "rats" OR "mice" OR "experimental animals" across the China National Knowledge Infrastructure, Wanfang, and VIP databases. Identical search criteria were applied to the PubMed and Web of Science platforms. The literature search covered publications from database inception through December 31, 2024. A total of 128 articles were reviewed, including 18 in Chinese and 110 in English. All studies employed rodent models, including 16 strains and including both sexes, with ages ranging from 5 weeks to 10 months. In rats, 28 distinct modeling protocols were identified: 4 involving continuous paclitaxel injections, 23 employing alternate-day injections, and 1 using a single administration. For mice, 21 methods were recorded, comprising 6 continuous, 13 alternate-day, and 2 single-injection protocols. Upon successful model induction, six behavioral pain alterations were consistently documented, evaluated using 16 different assessment techniques. Current PIPNP models predominantly utilize 6- to 8-week-old male Sprague-Dawley (SD) rats, with paclitaxel administered at 2 mg/kg on either days 0, 2, 4, and 6 or days 1, 3, 5, and 7. Based on data mining and comparative evaluation of modeling approaches, the use of 6- to 8-week-old male SD rats or 8- to 10-week-old male C57BL/6J mice with paclitaxel administered at 2 mg/kg on days 1, 3, 5, and 7 is recommended for establishing PIPNP models. The translational alignment between PIPNP models and clinical phenotypes remains insufficient and requires further refinement.