• Robert R Edwards, Robert H Dworkin, Dennis C Turk, Martin S Angst, Raymond Dionne, Roy Freeman, Per Hansson, Simon Haroutounian, Lars Arendt-Nielsen, Nadine Attal, Ralf Baron, Joanna Brell, Shay Bujanover, Laurie B Burke, Daniel Carr, Amy S Chappell, Penney Cowan, Mila Etropolski, Roger B Fillingim, Jennifer S Gewandter, Nathaniel P Katz, Ernest A Kopecky, John D Markman, George Nomikos, Linda Porter, Bob A Rappaport, RiceAndrew S CASCImperial College, London, United Kingdom., Joseph M Scavone, Joachim Scholz, Lee S Simon, Shannon M Smith, Jeffrey Tobias, Tina Tockarshewsky, Christine Veasley, Mark Versavel, Ajay D Wasan, Warren Wen, and David Yarnitsky.
• Harvard Medical School, Boston, MA, USA.
• Pain. 2016 Sep 1; 157 (9): 1851-1871.

AbstractThere is tremendous interpatient 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 (ie, 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.

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