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There was a consensus that chronic pain clinical trials should assess outcomes representing six core domains: (1) pain, (2) physical functioning, (3) emotional functioning, (4) participant ratings of improvement and satisfaction with treatment, (5) symptoms and adverse events, (6) participant disposition (e.g. adherence to the treatment regimen and reasons for premature withdrawal from the trial). Although consideration should be given to the assessment of each of these domains, there may be exceptions to the general recommendation to include all of these domains in chronic pain trials. When this occurs, the rationale for not including domains should be provided. It is not the intention of these recommendations that assessment of the core domains should be considered a requirement for approval of product applications by regulatory agencies or that a treatment must demonstrate statistically significant effects for all of the relevant core domains to establish evidence of its efficacy.

There is tremendous inter-patient variability in the response to analgesic therapy (even for efficacious treatments), which can be the source of great frustration in clinical practice. This has led to calls for “precision medicine”, or personalized pain therapeutics (i.e., empirically-based algorithms that determine the optimal treatments, or treatment combinations, for individual patients) that would presumably improve both the clinical care of patients with pain, and the success rates for putative analgesic drugs in Phase 2 and 3 clinical trials. However, before implementing this approach, the characteristics of individual patients or subgroups of patients that increase or decrease the response to a specific treatment need to be identified. The challenge is to identify the measurable phenotypic characteristics of patients that are most predictive of individual variation in analgesic treatment outcomes, and the measurement tools that are best suited to evaluate these characteristics. In this article, we present evidence on the most promising of these phenotypic characteristics for use in future research, including psychosocial factors, symptom characteristics, sleep patterns, responses to noxious stimulation, endogenous pain-modulatory processes, and response to pharmacologic challenge. We provide evidence-based recommendations for core phenotyping domains and recommend measures of each domain.

While a variety of cultural, psychological and physiological factors contribute to variability in both clinical and experimental contexts, the role of genetic factors in human pain sensitivity is increasingly recognized as an important element. This study was performed to evaluate genetic influences on variability in human pain sensitivity associated with gender, ethnicity and temperament. Pain sensitivity in response to experimental painful thermal and cold stimuli was measured with visual analogue scale ratings and temperament dimensions of personality were evaluated. Loci in the vanilloid receptor subtype 1 gene (TRPV1), delta opioid receptor subtype 1 gene (OPRD1) and catechol O-methyltransferase gene (COMT) were genotyped using 5' nuclease assays. A total of 500 normal participants (306 females and 194 males) were evaluated. The sample composition was 62.0% European American, 17.4% African American, 9.0% Asian American, and 8.6% Hispanic, and 3.0% individuals with mixed racial parentage. Female European Americans with the TRPV1 Val(585) Val allele and males with low harm avoidance showed longer cold withdrawal times based on the classification and regression tree (CART) analysis. CART identified gender, an OPRD1 polymorphism and temperament dimensions of personality as the primary determinants of heat pain sensitivity at 49 degrees C. Our observations demonstrate that gender, ethnicity and temperament contribute to individual variation in thermal and cold pain sensitivity by interactions with TRPV1 and OPRD1 single nucleotide polymorphisms.