Doctors have at their disposal a battery of tests to measure heartrate, blood pressure and temperature—objective, measurable indicators of disease. But no such indicators, called biomarkers, are available yet for chronic pain.
Without biomarkers, assessing pain and the response to existing treatments must rely on patient self-report. This is often distilled to choosing a number on a pain scale of one to ten or an emoticon ranging from smiling to frowning to crying.
The lack of pain biomarkers also impedes the development of brand new treatments for the estimated twenty-five million Americans who live with daily chronic pain. Right now, only a tiny fraction of candidate therapies for pain are ultimately approved by the US Food and Drug Administration (FDA).
In November 2018, the National Institutes of Health (NIH) held a two-day workshop in Washington, D.C. to discuss the current state and future outlook for the development of biomarkers for pain and for the response to pain treatments. (See videocast of Day 1 here, and of Day 2 here).
Researchers presented data and discussed a number of different types of biomarkers, ranging from brain imaging to genetics, from how patients metabolize drugs to alterations in sleep patterns. The attendees agreed that developing new biomarkers of pain will be key to developing novel therapies and generally improving the outlook for patients with chronic pain.
The meeting was co-chaired by Mary Ann Pelleymounter, a program director at the National Institute of Neurological Disorders and Stroke (NINDS), and Simon Tate, founding partner of Bridge Valley Ventures, Cambridge, UK, and formerly of Biogen and Convergence Pharmaceuticals.
Moving beyond opioids
NINDS director Walter Koroshetz described the purpose of the meeting and of pain research in general as “the scientific quest to free humanity from our dependence on the poppy plant. Can we displace the poppy from the [pain treatment] armamentarium?” Perhaps, Koroshetz said, “but without a biomarker to see what’s working, we will be in trouble.”
“Good science depends on good metrics,” said Dave Thomas, a program officer at the National Institute on Drug Abuse (NIDA) and a member of the NIH Pain Consortium. Of current metrics for pain, he said, “smiley faces, self-reporting—we can do better.”
That’s not to say that patient self-report is not valid and even critical to understanding chronic pain conditions.
“People are concerned that will we ignore patients, or [wonder], ‘will [biomarkers] be used in court?’ That’s not what this is about,” Thomas said. “These [biomarkers] are [there] not to measure the totality of pain, but to advance better treatments, enhance translation from animals to humans, and get better decision-making support for early treatments. Pain is real, and by getting better metrics, we hope to advance the field.”
The effort to develop new biomarkers of pain is a major aim of the Helping End Addiction Long-Term (HEAL) Initiative announced by the NIH last year (see RELIEF related news). HEAL will provide over a billion dollars over two years to fund research on pain and addiction, mainly through grants awarded by NINDS, NIDA, and the National Center for Complementary and Integrative Health (NCCIH).
What is a biomarker?
A biomarker, a mash-up of “biological marker,” was defined in a document from the World Health Organization (WHO) in 2001 as “any substance, structure, or process that can be measured in the body or its products and influence or predict the incidence or outcome of disease.” That definition has expanded to include markers of the response to treatments and other interventions.
More recently, an online resource co-created by the FDA and the NIH called BEST: Biomarkers, EndpointS and other Tools defined a biomarker as “a defined characteristic that is measured as an indicator of normal biological processes, pathogenic [disease] processes, or responses to an exposure or intervention, including therapeutic interventions.”
So what biomarker could best address the needs in chronic pain treatment and research? That depends on the context—whether the aim is to diagnose a pain condition, to predict a patient’s likelihood to develop that condition, or to predict how someone will respond to a particular drug.
How to get there from here
When it comes to developing and implementing new biomarkers, Tate said, “Oncology is really leading the way; we could learn a lot from that field,” which has used genetic, immune and other biomarkers to create more personalized cancer treatments.
One type of biomarker could be used to improve pain treatment immediately: pharmacokinetic and pharmacodynamic markers, which indicate how patients metabolize or otherwise react to certain medications. Some genetic markers can also predict how a patient will respond to certain drugs. Such tests are currently being used to improve treatment for people with depression, and they could help patients optimize their regimen of pain medications more quickly, which can often take months or years. They might also be used to predict who might become addicted to opioids and who could more safely take long-term opioids.
Some data sets are so big that they defy analysis by traditional processing software, and new computer algorithms must be employed to make sense of the information. So-called “big data,” including data describing patient characteristics, for instance, can offer insights about the course of chronic pain conditions and how to intervene.
Along these lines, in partnership with the NIH, Sean Mackey and colleagues at Stanford University have developed an open-source platform called the Collaborative Health Outcomes Information Registry (CHOIR) to collect detailed information that goes beyond what is available in an electronic medical record. For example, questions meant to assess the extent to which a patient catastrophizes over their pain—that is, the degree to which they think about their pain and worry about when it will end and how much worse it will get—could serve as a marker of patients most likely to suffer from chronic pain. Though not strictly a “biomarker,” objective data collected from patients in this way could be used to chart the best course of treatment for a person, or who is most vulnerable to worsening chronic pain.
“From [CHOIR], we get a deep, comprehensive phenotype of each patient,” Mackey said, referring to the observable characteristics of the patient. “It has changed the notion of how we care for patients and conduct research; we need to directly integrate this to patient care.” Mackey and colleagues are exploring how such data could help make predictions and improve treatment.
Brain imaging as a biomarker
Brain imaging also holds potential as a biomarker of pain. Artificial intelligence (AI) is helping researchers to make sense of those images, and to identify markers of brain activity that might represent some part of the painful experience (see related RELIEF news story).
But Karen Davis, a researcher at the University of Toronto who uses brain imaging to understand how pain affects the brain’s structure and function, says it’s important to consider the individual patient and not to over-interpret data collected from large groups of people.
People with chronic pain seem to differ in their brain connectivity, Davis said, referring to the degree to which different parts of the brain are linked together. “We look at the balance between the health and strength of the pain-sensing versus the modulation pathways,” the latter referring to the brain’s capacity to actually dampen pain signals. “If the sensing circuitry is dominant,” Davis continued, “we refer to that as pro-nociceptive,” meaning that the brain might be more attuned to pain signals, whereas if the modulation circuitry is dominant, the brain might be more resilient against developing chronic pain.
Researchers have already discovered a number of different indicators using brain imaging that might be used to predict who will develop chronic pain. A study from A. Vania Apkarian, a pain brain imager at Northwestern University in Chicago, found that patients who would go on to develop chronic pain following an acute incident of low-back pain could be identified by a pattern of magnetic resonance imaging (MRI) activity in the brain’s limbic system, which is involved with emotional and behavioral responses (see Pain Research Forum related news stories here and here).
Similarly, Tor Wager, a pain researcher at the University of Colorado Boulder who will be joining the faculty of Dartmouth College in July, has found evidence for a “neural pain signature,” that is, a complex pattern of brain activation that can detected by a computer algorithm (see related Pain Research Forum news story). While the signature is not affected by some pain treatments, it does shift following cognitive-behavioral therapy (CBT) and some other interventions, Wager said. “This could be an interesting treatment target for [drug] discovery,” referring to how researchers could use knowledge about the pain signature to develop new medications.
Practically speaking, MRI will likely remain too expensive to be used as a pain biomarker for patients. But cheaper techniques that record brain activity such as electroencephalography (EEG) and magnetoencephalography (MEG) could be put to use, Davis said. Ultimately, brain imaging biomarkers might be best used to discover brain processes underlying the development of chronic pain.
In any case, Davis said, imaging “should be used as a predictive tool, rather than as a detector.” Use of brain imaging or other biomarkers to “prove” the existence of pain in a patient would be ethically questionable and could leave people with pain even more vulnerable to denial of care or financial hardship.
The search for pain biomarkers based on brain imaging sparked a bioethics controversy in 2015 when a type of MRI called functional MRI (fMRI) was used in a court of law as evidence of a pain condition. That raised concerns among many pain researchers, including workshop attendees Davis and Mackey.
Mackey testified in the court case against the use of fMRI as evidence of pain, and together with Davis and other researchers subsequently published a consensus statement of recommendations in the journal Nature Reviews Neurology regarding the use of brain imaging as a pain biomarker (see related RELIEF news story).
Other potential biomarkers
Researchers at the meeting also discussed other possible pain biomarkers such as quantitative sensory testing (a method to assess how well nerve fibers are functioning), genetics, brain biochemistry, and even sleep patterns.
But, the attendees agreed, these various biomarkers of pain should not be in competition with one another. Instead, a suite of biomarkers will be needed to understand the molecular and cellular processes driving chronic pain, to better predict who will develop pain or respond to certain drugs, and to track the effects of pain treatments.
A sub-group of workshop attendees plans to publish a white paper detailing the findings of the meeting later this year and to meet regularly to follow up on progress in pain biomarker development. Stay tuned…
Stephani Sutherland, PhD, is a neuroscientist and freelance journalist in Southern California. Follow her on Twitter @SutherlandPhD.
Editor’s note: This story first appeared on the Pain Research Forum and has been adapted for RELIEF.
Image credit: Novi Elysa/123RF Stock Photo.