• Robert R Edwards, Kristin L Schreiber, Robert H Dworkin, Dennis C Turk, Ralf Baron, Roy Freeman, Troels S Jensen, Alban Latremoliere, John D Markman, RiceAndrew S CASCImperial College, London, UK., Michael Rowbotham, Roland Staud, Simon Tate, Clifford J Woolf, Nick A Andrews, Daniel B Carr, Luana Colloca, Doina Cosma-Roman, Penney Cowan, Luda Diatchenko, John Farrar, Jennifer S Gewandter, Ian Gilron, Robert D Kerns, Serge Marchand, Gwendolyn Niebler, Kushang V Patel, Lee S Simon, Tina Tockarshewsky, Geertrui F Vanhove, Daniel Vardeh, Gary A Walco, Ajay D Wasan, and Ursula Wesselmann.
• Harvard Medical School, Boston, Massachusetts. Electronic address: RREdwards@Partners.org.
• J Pain. 2023 Feb 1; 24 (2): 204225204-225.

AbstractLarge variability in the individual response to even the most-efficacious pain treatments is observed clinically, which has led to calls for a more personalized, tailored approach to treating patients with pain (ie, "precision pain medicine"). Precision pain medicine, currently an aspirational goal, would consist of empirically based algorithms that determine the optimal treatments, or treatment combinations, for specific patients (ie, targeting the right treatment, in the right dose, to the right patient, at the right time). Answering this question of "what works for whom" will certainly improve the clinical care of patients with pain. It may also support the success of novel drug development in pain, making it easier to identify novel treatments that work for certain patients and more accurately identify the magnitude of the treatment effect for those subgroups. Significant preliminary work has been done in this area, and analgesic trials are beginning to utilize precision pain medicine approaches such as stratified allocation on the basis of prespecified patient phenotypes using assessment methodologies such as quantitative sensory testing. Current major challenges within the field include: 1) identifying optimal measurement approaches to assessing patient characteristics that are most robustly and consistently predictive of inter-patient variation in specific analgesic treatment outcomes, 2) designing clinical trials that can identify treatment-by-phenotype interactions, and 3) selecting the most promising therapeutics to be tested in this way. This review surveys the current state of precision pain medicine, with a focus on drug treatments (which have been most-studied in a precision pain medicine context). It further presents a set of evidence-based recommendations for accelerating the application of precision pain methods in chronic pain research. PERSPECTIVE: Given the considerable variability in treatment outcomes for chronic pain, progress in precision pain treatment is critical for the field. An array of phenotypes and mechanisms contribute to chronic pain; this review summarizes current knowledge regarding which treatments are most effective for patients with specific biopsychosocial characteristics.Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.

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