PAIN Reports (Feb 2021)
Patient phenotyping in clinical trials of chronic pain treatments: IMMPACT recommendations
- 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,
- Andrew S.C. Rice,
- Joseph M. Scavone,
- Joachim Scholz,
- Lee S. Simon,
- Shannon M. Smith,
- Jeffrey Tobias,
- Tina Tockarshewsky,
- Christine Veasley,
- Mark Versavel,
- Ajay D. Wasan,
- Warren Wen,
- David Yarnitsky
Affiliations
- Robert R. Edwards
- a Harvard Medical School, Boston, MA, USA
- Robert H. Dworkin
- b University of Rochester, Rochester, NY, USA
- Dennis C. Turk
- c University of Washington, Seattle, WA, USA
- Martin S. Angst
- d Stanford University, Palo Alto, CA, USA
- Raymond Dionne
- e East Carolina University, Greenville, NC, USA
- Roy Freeman
- a Harvard Medical School, Boston, MA, USA
- Per Hansson
- f Oslo University Hospital, Oslo, Norway and Karolinska Institute, Stockholm, Sweden
- Simon Haroutounian
- g Washington University, St. Louis, MO, USA
- Lars Arendt-Nielsen
- h Aalborg University, Aalborg, Denmark
- Nadine Attal
- i Hôpital Ambroise Paré, APHP and INSERM U 987, Boulogne-Billancourt, France and University Versailles Saint Quentin, Versailles, France
- Ralf Baron
- j University of Kiel, Kiel, Germany
- Joanna Brell
- k MetroHealth Medical Center, Cleveland, OH, USA
- Shay Bujanover
- l Depomed, Newark, CA, USA
- Laurie B. Burke
- m LORA Group, LLC, Royal Oak, MD, USA
- Daniel Carr
- o Tufts University, Boston, MA, USA
- Amy S. Chappell
- p Eli Lilly, Indianapolis IN, USA
- Penney Cowan
- q American Chronic Pain Association, Rocklin, CA, USA
- Mila Etropolski
- r Johnson and Johnson, Titusville, NJ, USA
- Roger B. Fillingim
- s University of Florida, Gainesville, FL, USA
- Jennifer S. Gewandter
- b University of Rochester, Rochester, NY, USA
- Nathaniel P. Katz
- o Tufts University, Boston, MA, USA
- Ernest A. Kopecky
- u Collegium Pharmaceutical, Inc., Canton, MA, USA
- John D. Markman
- b University of Rochester, Rochester, NY, USA
- George Nomikos
- v Astellas Pharma, Northbrook, IL, USA
- Linda Porter
- w National Institutes of Health, Bethesda, MD, USA
- Bob A. Rappaport
- x Arlington, VA, USA
- Andrew S.C. Rice
- y Imperial College, London, United Kingdom
- Joseph M. Scavone
- z Pfizer, Groton, CT, USA
- Joachim Scholz
- aa Columbia University, New York, NY, USA
- Lee S. Simon
- bb SDG LLC, Cambridge, MA, USA
- Shannon M. Smith
- b University of Rochester, Rochester, NY, USA
- Jeffrey Tobias
- cc Jazz Pharmaceuticals, Palo Alto, CA, USA
- Tina Tockarshewsky
- dd Ceres Consulting, Fort Montgomery, NY, USA
- Christine Veasley
- ee Chronic Pain Research Alliance, North Kingstown, RI, USA
- Mark Versavel
- ff Zalicus, Cambridge, MA, USA
- Ajay D. Wasan
- gg University of Pittsburgh, Pittsburgh, PA, USA
- Warren Wen
- hh Purdue Pharma, Stamford, CT, USA
- David Yarnitsky
- ii Rambam Health Care Campus and Technion Faculty of Medicine, Haifa, Israel
- DOI
- https://doi.org/10.1097/PR9.0000000000000896
- Journal volume & issue
-
Vol. 6,
no. 1
p. e899
Abstract
Abstract. There 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.
