February 18, 2020

The opioid crisis is a major public health issue, as more than 1.2 million individuals in the US are reported to have opioid use disorder1, while the number of overdose-related deaths reached over 47,000 in 20172.  Overall, the total economic burden of the opioid crisis in the United States in 2019 was projected to be approaching $200 billion3.  This epidemic is in stark contrast to the fact that more than 50 million adults (20.4%) in the US are living with chronic pain, including nearly 20 million individuals dealing with high impact chronic pain4 (persistent pain that reduces life or work activities). As a result of this dichotomy, the FDA has challenged the biopharmaceutical industry to accelerate development of non-opioid analgesics, along with abuse-deterrent opioid formulations and/or solid dosage forms5.

While the opioid crisis may be creating opportunity for development of novel therapies to treat chronic pain, drug development in this area has historically struggled.  According to a BIO (Biotechnology Industry Organization) report released in 20186, only two novel NCEs were approved for pain indications from 2007-2017, while other analgesia approvals during this period were reformulations of existing compounds or drugs with approval history prior to 2007.  Even more alarming, the success rate for analgesics progressing from Phase 1 to Phase 2 is reported to be only 2%, compared to an average of 10% probability of progression in other indications and therapeutic areas6.  

While the need for new, innovative drugs to treat chronic pain is clear, their clinical development has challenged scientists and drug developers for a variety of reasons.  First, targeting novel pharmacologic mechanisms have, at times, led to safety issues.  For example, while anti-NGF agents have demonstrated efficacy in treating low back pain and osteoarthritis, their use has also led to neurological adverse events and potential joint destruction7,8.  In addition, accurate assessment of pain in clinical trials can be difficult, as each patient’s subjective perception of pain intensity and tolerance of painful stimuli varies significantly.  Lastly, the well-documented placebo response9 further complicates the calculation of the true therapeutic effect of the test article.  These issues lead to clinical trials that require large sample sizes to achieve adequate study power, often exceeding 500 patients, which increases both drug development costs and timelines. 

The placebo response has substantially hindered the development of analgesic drugs over the last several decades, and as much as 2/3 of the measured treatment response can be attributed to the placebo response10.  While the placebo response itself is well-documented, recent observations have identified and highlighted the multi-factorial sources of the placebo response, including patient psychological traits and expectation11–14.  Even more confounding, the placebo response intensity seems to be increasing over time15while drug response has remained constant – leading to diminished treatment effect.  This phenomenon is more pronounced in the US when compared to Europe or other areas of the world.

While there is a clear unmet medical need for novel therapeutics for chronic pain as an alternative to opioids, the biopharmaceutical industry and other stakeholders (clinicians, regulators, investors, etc.) continue to struggle with the issues that have plagued analgesic development for decades.  This scenario is a clear call-to-action for the development and implementation of strategies that address these hurdles.

Placebell©™ is a technology that has been recently introduced to the biopharmaceutical industry to reduce the impact of the placebo response in clinical trials.  This approach considers factors that are unique to each patient – such as personality and patient demographics – as inputs to a proprietary machine learning-based algorithm that calculates a unique score, the Placebell©™ Covariate.  This value predicts which patients will be high placebo responders and can ultimately be used in statistical analyses to reduce data variability related to the placebo response.  Initial data in peripheral neuropathic pain suggests that this approach can reduce variability by as much as 30%, which translates into increased trial power and reduced risk of trial failure.  The advent of these types of new, innovative solutions to critical issues is providing novel opportunities to reduce the risk to drug development programs for pain therapeutics.  


1.        Lipari RN, Park-Lee E. Key Substance Use and Mental Health Indicators in the United States: Results from the 2018 National Survey on Drug Use and Health.; 2019. Accessed February 14, 2020.

2.        Overdose Death Rates | National Institute on Drug Abuse (NIDA). Accessed February 14, 2020.

3.        Weaver A, Matt Caverly PEER REVIEWERS Steve Melek M, Melanie Kuester M, Anne Jackson B. Economic Impact of Non-Medical Opioid Use in the United States Economic Impact of Non-Medical Opioid Use in the United States Annual Estimates and Projections For.; 2019.

4.        Dahlhamer J, Lucas J, Zelaya, C, et al. Prevalence of Chronic Pain and High-Impact Chronic Pain Among Adults — United States, 2016. MMWR Morb Mortal Wkly Rep. 2018;67(36):1001-1006. doi:10.15585/mmwr.mm6736a2

5.        Statement from FDA Commissioner Scott Gottlieb, M.D. on the agency’s 2019 policy and regulatory agenda for continued action to forcefully address the tragic epidemic of opioid abuse | FDA. Accessed February 14, 2020.

6.        Thomas D, Wessel C. The State of Innovation in Highly Prevalent Chronic Diseases Volume II: Pain and Addiction Therapeutics.; 2018. Accessed February 14, 2020.

7.        Bannwarth B, Kostine M. Targeting nerve growth factor (NGF) for pain management: What does the future hold for NGF antagonists? Drugs. 2014;74(6):619-626. doi:10.1007/s40265-014-0208-6

8.        Lane NE, Corr M. Osteoarthritis in 2016: Anti-NGF treatments for pain-two steps forward, one step back? Nat Rev Rheumatol. 2017;13(2):76-78. doi:10.1038/nrrheum.2016.224

9.        Colloca L. The Placebo Effect in Pain Therapies. Annu Rev Pharmacol Toxicol. 2019;59(1):191-211. doi:10.1146/annurev-pharmtox-010818-021542

10.      Häuser W, Bartram-Wunn E, Bartram C, Reinecke H, Tölle T. Systematic review: Placebo response in drug trials of fibromyalgia syndrome and painful peripheral diabetic neuropathy – Magnitude and patient-related predictors. Pain. 2011;152(8):1709-1717. doi:10.1016/j.pain.2011.01.050

11.      Corsi N, Colloca L. Placebo and Nocebo Effects: The Advantage of Measuring Expectations and Psychological Factors. Front Psychol. 2017;8(MAR):308. doi:10.3389/fpsyg.2017.00308

12.      Vachon-Presseau E, Berger SE, Abdullah TB, et al. Brain and psychological determinants of placebo pill response in chronic pain patients. Nat Commun. 2018;9(1):1-15. doi:10.1038/s41467-018-05859-1

13.      Reicherts P, Gerdes ABM, Pauli P, Wieser MJ. Psychological placebo and nocebo effects on pain rely on expectation and previous experience. J Pain. 2016;17(2):203-214. doi:10.1016/j.jpain.2015.10.010

14.      Zhou L, Wei H, Zhang H, et al. The Influence of Expectancy Level and Personal Characteristics on Placebo Effects: Psychological Underpinnings. Front Psychiatry. 2019;10(FEB):20. doi:10.3389/fpsyt.2019.00020

15.      Tuttle AH, Tohyama S, Ramsay T, et al. Increasing placebo responses over time in U.S. clinical trials of neuropathic pain. Pain. 2015;156(12):2616-2626. doi:10.1097/j.pain.0000000000000333

Author: Erica Smith


VP, Business Development

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