Literature DB >> 35382067

Using Artificial Intelligence-based Methods to Address the Placebo Response in Clinical Trials.

Erica A Smith1,2,3,4,5,6,7,8,9,10,11, William P Horan1,2,3,4,5,6,7,8,9,10,11, Dominique Demolle1,2,3,4,5,6,7,8,9,10,11, Peter Schueler1,2,3,4,5,6,7,8,9,10,11, Dong-Jing Fu1,2,3,4,5,6,7,8,9,10,11, Ariana E Anderson1,2,3,4,5,6,7,8,9,10,11, Joseph Geraci1,2,3,4,5,6,7,8,9,10,11, Florence Butlen-Ducuing1,2,3,4,5,6,7,8,9,10,11, Jasmine Link1,2,3,4,5,6,7,8,9,10,11, Ni A Khin1,2,3,4,5,6,7,8,9,10,11, Robert Morlock1,2,3,4,5,6,7,8,9,10,11, Larry D Alphs1,2,3,4,5,6,7,8,9,10,11.   

Abstract

The placebo response is a highly complex psychosocial-biological phenomenon that has challenged drug development for decades, particularly in neurological and psychiatric disease. While decades of research have aimed to understand clinical trial factors that contribute to the placebo response, a comprehensive solution to manage the placebo response in drug development has yet to emerge. Advanced data analytic techniques, such as artificial intelligence (AI), might be needed to take the next leap forward in mitigating the negative consequences of high placebo-response rates. The objective of this review was to explore the use of techniques such as AI and the sub-discipline of machine learning (ML) to address placebo response in practical ways that can positively impact drug development. This examination focused on the critical factors that should be considered in applying AI and ML to the placebo response issue, examples of how these techniques can be used, and the regulatory considerations for integrating these approaches into clinical trials.
Copyright © 2022. Matrix Medical Communications. All rights reserved.

Entities:  

Keywords:  artificial intelligence; clinical trials; machine intelligence; machine learning; placebo effect; placebo response

Year:  2022        PMID: 35382067      PMCID: PMC8970233     

Source DB:  PubMed          Journal:  Innov Clin Neurosci        ISSN: 2158-8333


  42 in total

Review 1.  Increasing placebo responses over time in U.S. clinical trials of neuropathic pain.

Authors:  Alexander H Tuttle; Sarasa Tohyama; Tim Ramsay; Jonathan Kimmelman; Petra Schweinhardt; Gary J Bennett; Jeffrey S Mogil
Journal:  Pain       Date:  2015-12       Impact factor: 6.961

Review 2.  The neuroscience of placebo effects: connecting context, learning and health.

Authors:  Tor D Wager; Lauren Y Atlas
Journal:  Nat Rev Neurosci       Date:  2015-07       Impact factor: 34.870

3.  Does elimination of placebo responders in a placebo run-in increase the treatment effect in randomized clinical trials? A meta-analytic evaluation.

Authors:  Sandra Lee; John R Walker; Laura Jakul; Kathryn Sexton
Journal:  Depress Anxiety       Date:  2004       Impact factor: 6.505

Review 4.  A model of placebo response in antidepressant clinical trials.

Authors:  Bret R Rutherford; Steven P Roose
Journal:  Am J Psychiatry       Date:  2013-07       Impact factor: 18.112

5.  A placebo prognostic index (PI) as a moderator of outcomes in the treatment of adolescent depression: Could it inform risk-stratification in treatment with cognitive-behavioral therapy, fluoxetine, or their combination?

Authors:  Lorenzo Lorenzo-Luaces; Natalie Rodriguez-Quintana; Tennisha N Riley; John R Weisz
Journal:  Psychother Res       Date:  2020-03-29

6.  Meta-analysis of the placebo response in antidepressant trials.

Authors:  Winfried Rief; Yvonne Nestoriuc; Sarah Weiss; Eva Welzel; Arthur J Barsky; Stefan G Hofmann
Journal:  J Affect Disord       Date:  2009-02-26       Impact factor: 4.839

7.  A Novel Strategy to Identify Placebo Responders: Prediction Index of Clinical and Biological Markers in the EMBARC Trial.

Authors:  Madhukar H Trivedi; Charles South; Manish K Jha; A John Rush; Jing Cao; Benji Kurian; Mary Phillips; Diego A Pizzagalli; Joseph M Trombello; Maria A Oquendo; Crystal Cooper; Daniel G Dillon; Christian Webb; Bruce D Grannemann; Gerard Bruder; Patrick J McGrath; Ramin Parsey; Myrna Weissman; Maurizio Fava
Journal:  Psychother Psychosom       Date:  2018-08-15       Impact factor: 25.617

8.  Multivariate resting-state functional connectivity predicts responses to real and sham acupuncture treatment in chronic low back pain.

Authors:  Yiheng Tu; Ana Ortiz; Randy L Gollub; Jin Cao; Jessica Gerber; Courtney Lang; Joel Park; Georgia Wilson; Wei Shen; Suk-Tak Chan; Ajay D Wasan; Robert R Edwards; Vitaly Napadow; Ted J Kaptchuk; Bruce Rosen; Jian Kong
Journal:  Neuroimage Clin       Date:  2019-05-28       Impact factor: 4.881

9.  Brain and psychological determinants of placebo pill response in chronic pain patients.

Authors:  Etienne Vachon-Presseau; Sara E Berger; Taha B Abdullah; Lejian Huang; Guillermo A Cecchi; James W Griffith; Thomas J Schnitzer; A Vania Apkarian
Journal:  Nat Commun       Date:  2018-09-12       Impact factor: 14.919

10.  European Headache Federation recommendations for placebo and nocebo terminology.

Authors:  Dimos D Mitsikostas; Charlotte Blease; Elisa Carlino; Luana Colloca; Andrew L Geers; Jeremy Howick; Andrea W M Evers; Magne A Flaten; John M Kelley; Irving Kirsch; Regine Klinger; Antoinette MaassenVanDenBrink; Daniel E Moerman; Petros P Sfikakis; Lene Vase; Tor D Wager; Fabrizio Benedetti
Journal:  J Headache Pain       Date:  2020-09-25       Impact factor: 7.277

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