Physical pain is a common health problem with vast individual, economic, and social consequences.1 The percentage of people in pain around the world rose from 26.3% in 2009 to 32.1% in 2021, an increase of approximately 500 million people.1 Persistent pain in the U.S. is estimated to affect over 100 million adults at any given time, and carries direct and indirect costs of over $600 billion annually.2
Though analgesic medications are among the most common treatments, long-term administration of common therapies such as nonsteroidal anti-inflammatory drugs (NSAIDs) and opioids involves risks of organ damage, overdose, and in some cases drug dependence and misuse syndromes.2 Such findings have stimulated intensive efforts to direct specific treatments to those patients who are most likely to experience meaningful pain relief and functional improvements, and least likely to experience serious side effects.2
Interpatient variability in pain therapy prompts calls for precision medicine
However, interpatient variability in analgesic outcomes is impressively broad and can be the source of significant frustration in clinical trials.2 Multiple pain mechanisms and outcome-relevant patient characteristics may be active to varying degrees in different patients, meaning that successful treatment is likely to be based at the level of the individual rather than at the level of the disease.2 Collectively, this state of affairs has led to calls for personalized or tailored pain therapeutics.2
To deliver on the promise of personalized care, researchers have attempted to address the challenge of non-adherence to analgesic medication, which is very common in chronic pain populations.3 In a review of 25 studies, non-adherence rates to pain prescriptions ranged from 8% to 62%, with a weighted mean of 40%.3
Algorithms used to personalize pain therapeutic interventions
Some researchers have attempted to personalize pain therapeutics by using empirically based algorithms that determine the optimal treatments and introducing supportive technology to ensure treatment compliance.
In one case, the sponsor of a phase II neuropathic pain trial implemented AiCure drug adherence and electronic diary (eDiary) technology with pain scales to streamline randomization. During screening, information captured in the eDiary was used as evidence participants met baseline pain variability criteria. Please read the case study to learn more.
The use of electronic tools to perform real-time and more frequent assessment of pain can help researchers meet a recommendation from the Initiative on Methods, Measurement, and Pain Assessment in Clinical Trials (IMMPACT) to consider an index of temporal variability in pain intensity as part of the baseline phenotyping of trial participants.3 When pain assessments based on eDiary responses are combined with medication adherence outcomes using AiCure technology, researchers get the additional benefit of understanding individual links between dosing behaviors and patient-reported outcomes.
The IMMPACT recommendations call for further research on electronic patient reported outcomes (ePRO) and other real-time data capture methods for potential use as outcome measures in analgesic trials.
The recommendations also emphasize the need for strategies to educate participants about the importance of adherence at initiation of treatment and during follow-up. In addition to promoting adherence, data from adherence monitoring systems such as AiCure Patient Connect can be used to explain potential efficacy failures in clinical trials and help direct decisions regarding future research.
Overall, the development of personalized pain management strategies requires a better understanding of the complex interplay between various pain mechanisms and patient characteristics. By tailoring treatments to individual needs, personalized pain management has the potential to improve treatment outcomes and patient satisfaction.
1 Pain trends and pain growth disparities, 2009–2021
2 Patient phenotyping in clinical trials of chronic pain treatments: IMMPACT recommendations - PMC
3 Prevalence and determinants of medication non‐adherence in chronic pain patients: a systematic review