Neuropathic pain (NP) is a major clinical problem and current therapies do not provide adequate analgesia for many patients. Subjecting animals to lesions modelling NP, or to tests designed to assess responses to painful stimuli, is inevitably associated with distress and discomfort.
We plan to systematically identify all animal experiments modelling NP. Using meta-regression we will provide evidence as to whether less noxious tests are as predictive as more severe alternatives to refine their use to minimise pain and suffering. We will also investigate the effect of experimental duration and post-operative analgesia regimens to reduce the period of suffering of the animals. We will provide precise estimates of the observed variance of different tests. This will identify which tests require fewer animals and allow robust sample size calculations to be performed and reported to reduce the number of animals sacrificed in experiments which are too small for the effect sought or those which use more animals than required. Limitations in the validity of experiments reduce their reliability and may compromise their utility. Preclinical studies rarely report measures to reduce potential sources of bias.
We will use meta-analysis to quantify the impact of quality on estimates of treatment effects in the NP literature. We will quantify the impact of publication bias in the NP literature; this will highlight the importance of the issue and encourage the dissemination of all data derived from the use of animals. We will also make the database publicly available; this will enable colleagues to identify whether experiments have already been conducted and reduce the unnecessary replication of experiments. This project will generate robust empirical data without the use of animals. It will contribute towards a reduction in the number of animals used and the refinement of experimental design, tests used and has the potential to reform and improve animal welfare in this field.
Currie GL et al. (2019). Animal models of chemotherapy-induced peripheral neuropathy: A machine-assisted systematic review and meta-analysis. PLoS Biol 17(5):e3000243. doi: 10.1371/journal.pbio.3000243
Rice ASC et al. (2018). Sensory profiling in animal models of neuropathic pain: a call for back-translation. Pain 159(5):819-824. doi: 10.1097/j.pain.0000000000001138
Zwetsloot PP et al. (2017). Standardized mean differences cause funnel plot distortion in publication bias assessments. eLife 6:e24260. doi: 10.7554/eLife.24260
Andrews NA et al. (2015). Ensuring transparency and minimization of methodologic bias in preclinical pain research: PPRECISE considerations. Pain 17(4):901-9. doi: 10.1097/j.pain.0000000000000458
Finnerup NB et al. (2015). Pharmacotherapy for neuropathic pain in adults: a systematic review and meta-analysis. Lancet Neurology 14(2):162-73. doi: 10.1016/S1474-4422(14)70251-0
Knopp KL et al. (2015). Experimental design and reporting standards for improving the internal validity of pre-clinical studies in the field of pain: Consensus of the IMI-Europain consortium. Scandinavian Journal of Pain 7:58-70. doi: 10.1016/j.sjpain.2015.01.006
Currie GL et al. (2014). Using animal models to understand cancer pain in humans. Current pain and headache reports 18(6):423. doi: 10.1007/s11916-014-0423-6
Principal investigatorDr Emily Sena
InstitutionUniversity of Edinburgh
Co-InvestigatorDr Malcolm Macleod
Dr Gillian Currie
Dr Lesley Colvin
Dr Andrew Rice