Neuroimaging as a tool for pain diagnosis and analgesic development

Lessons learned from using fMRI in the early clinical development of a mu-opioid receptor antagonist for disorders of compulsive consumption

Functional magnetic resonance imaging (fMRI) has been widely used to gain a greater understanding of brain circuitry abnormalities in CNS disorders. fMRI has also been used to examine pharmacological modulation of brain circuity and is increasingly being used in early clinical drug development as functional pharmacodynamic index of target engagement, and to provide early indication of clinical efficacy. In this short review, we summarize data from experimental medicine and early clinical development studies of a mu-opioid receptor antagonist, GSK1521498 developed for disorders of compulsive consumption including binge eating in obesity. We demonstrate how fMRI can be used to answer important questions of early clinical drug development relating to; (1) target engagement, (2) dose response relationships, (3) differential efficacy and (4) prediction of behavioural and clinically relevant outcomes. We also highlight important methodological factors that need to be considered when conducting fMRI studies in drug development given the challenges faced with small sample sizes in Phase 1 and early proof of mechanism studies. While these data highlight the value of fMRI as a biomarker in drug development, its use for making Go/No-go decisions is still faced with challenges given the variability of responses, interpretation of brain activation changes and the limited data linking drug induced changes in brain activity to clinical or behavioural outcome. These challenges need to be addressed to fulfil the promise of fMRI as a tool in clinical drug development.

The Prediction of Acute Postoperative Pain Based on Neural Oscillations Measured before the Surgery

Even with an improved understanding of pain mechanisms and advances in perioperative pain management, inadequately controlled postoperative pain remains. Predicting acute postoperative pain based on presurgery physiological measures could provide valuable insights into individualized, effective analgesic strategies, thus helping improve the analgesic efficacy. Considering the strong correlation between pain perception and neural oscillations, we hypothesize that acute postoperative pain could be predicted by neural oscillations measured shortly before the surgery. Here, we explored the relationship between neural oscillations 2 hours before the thoracoscopic surgery and the subjective intensity of acute postoperative pain. The spectral power density of resting-state beta and gamma band oscillations at the frontocentral region was significantly different between patients with different levels of acute postoperative pain (i.e., low pain vs. moderate/high pain). A positive correlation was also observed between the spectral power density of resting-state beta and gamma band oscillations and subjective reports of postoperative pain. Then, we predicted the level of acute postoperative pain based on features of neural oscillations using machine learning techniques, which achieved a prediction accuracy of 92.54% and a correlation coefficient between the real pain intensities and the predicted pain intensities of 0.84. Altogether, the prediction of acute postoperative pain based on neural oscillations measured before the surgery is feasible and could meet the clinical needs in the future for better control of postoperative pain and other unwanted negative effects. The study was registered on the Clinical Trial Registry (https://clinicaltrials.gov/ct2/show/NCT03761576?term=NCT03761576&draw=2&rank=1) with the registration number NCT03761576.

The search for pain biomarkers in the human brain

Non-invasive functional brain imaging is used more than ever to investigate pain in health and disease, with the prospect of finding new means to alleviate pain and improve patient wellbeing. The observation that several brain areas are activated by transient painful stimuli, and that the magnitude of this activity is often graded with pain intensity, has prompted researchers to extract features of brain activity that could serve as biomarkers to measure pain objectively. However, most of the brain responses observed when pain is present can also be observed when pain is absent. For example, similar brain responses can be elicited by salient but non-painful auditory, tactile and visual stimuli, and such responses can even be recorded in patients with congenital analgesia. Thus, as argued in this review, there is still disagreement on the degree to which current measures of brain activity exactly relate to pain. Furthermore, whether more recent analysis techniques can be used to identify distributed patterns of brain activity specific for pain can be only warranted using carefully designed control conditions. On a more general level, the clinical utility of current pain biomarkers derived from human functional neuroimaging appears to be overstated, and evidence for their efficacy in real-life clinical conditions is scarce. Rather than searching for biomarkers of pain perception, several researchers are developing biomarkers to achieve mechanism-based stratification of pain conditions, predict response to medication and offer personalized treatments. Initial results with promising clinical perspectives need to be further tested for replicability and generalizability.

Electroencephalographic Patterns in Chronic Pain: A Systematic Review of the Literature

The main objective of this study is to review and summarize recent findings on electroencephalographic patterns in individuals with chronic pain. We also discuss recent advances in the use of quantitative Electroencephalography (qEEG) for the assessment of pathophysiology and biopsychosocial factors involved in its maintenance over time. Data collection took place from February 2014 to July 2015 in PubMed, SciELO and PEDro databases. Data from cross-sectional studies and longitudinal studies, as well as clinical trials involving chronic pain participants were incorporated into the final analysis. Our primary findings related to chronic pain were an increase of theta and alpha EEG power at rest, and a decrease in the amplitude of evoked potentials after sensory stimulation and cognitive tasks. This review suggests that qEEG could be considered as a simple and objective tool for the study of brain mechanisms involved in chronic pain, as well as for identifying the specific characteristics of chronic pain condition. In addition, results show that qEEG probably is a relevant outcome measure for assessing changes in therapeutic studies.

Effective connectivity predicts future placebo analgesic response: A dynamic causal modeling study of pain processing in healthy controls

A better understanding of the neural mechanisms underlying pain processing and analgesia may aid in the development and personalization of effective treatments for chronic pain. Clarification of the neural predictors of individual variability in placebo analgesia (PA) could aid in this process. The present study examined whether the strength of effective connectivity (EC) among pain-related brain regions could predict future placebo analgesic response in healthy individuals. In Visit 1, fMRI data were collected from 24 healthy subjects (13 females, mean age=22.56, SD=2.94) while experiencing painful thermal stimuli. During Visit 2, subjects were conditioned to expect less pain via a surreptitiously lowered temperature applied at two of the four sites on their feet. They were subsequently scanned again using the Visit 1 (painful) temperature. Subjects used an electronic VAS to rate their pain following each stimulus. Differences in ratings at conditioned and unconditioned sites were used to measure placebo response (PA scores). Dynamic causal modeling was used to estimate the EC among a set of brain regions related to pain processing at Visit 1 (periaqueductal gray, thalamus, rostral anterior cingulate cortex, dorsolateral prefrontal cortex). Individual PA scores from Visit 2 were regressed on salient EC parameter estimates from Visit 1. Results indicate that both greater left hemisphere modulatory DLPFC➔PAG connectivity and right hemisphere, endogenous thalamus➔DLPFC connectivity were significantly predictive of future placebo response (R(2)=0.82). To our knowledge, this is the first study to identify the value of EC in understanding individual differences in PA, and may suggest the potential modifiability of endogenous pain modulation.

Translational pain biomarkers in the early development of new neurotherapeutics for pain management

Translation of the analgesic efficacy of investigational neurotherapeutics from pre-clinical pain models into clinical trial phases is associated with a high risk of failure. Application of human pain biomarkers in early stages of clinical trials can potentially enhance the rate of successful translation, which would eventually reduce both length and costs of drug development after the pre-clinical stage. Human pain biomarkers are based on the standardized activation of pain pathways followed by the assessment of ongoing and paroxysmal pain, plus evoked responses which can be applied to healthy individuals and patients prior to and after pharmacological interventions. This review discusses the rationality and feasibility of advanced human pain biomarkers in early phases of drug development for pain management which is still an unmet medical need.

TLR 2 and 4 responsiveness from isolated peripheral blood mononuclear cells from rats and humans as potential chronic pain biomarkers

Background

Chronic pain patients have increased peripheral blood mononuclear cell Interkeukin-1β production following TLR2 and TLR4 simulation. Here we have used a human-to-rat and rat-to-human approach to further investigate whether peripheral blood immune responses to TLR agonists might be suitable for development as possible systems biomarkers of chronic pain in humans.

Methods and Results

Study 1: using a graded model of chronic constriction injury in rats, behavioral allodynia was assessed followed by in vitro quantification of TLR2 and TLR4 agonist-induced stimulation of IL-1β release by PBMCs and spinal cord tissues (n = 42; 6 rats per group). Statistical models were subsequently developed using the IL-1β responses, which distinguished the pain/no pain states and predicted the degree of allodynia. Study 2: the rat-derived statistical models were tested to assess their predictive utility in determining the pain status of a published human cohort that consists of a heterogeneous clinical pain population (n = 19) and a pain-free population (n = 11). The predictive ability of one of the rat models was able to distinguish pain patients from controls with a ROC AUC of 0.94. The rat model was used to predict the presence of pain in a new chronic pain cohort and was able to accurately predict the presence of pain in 28 out of the 34 chronic pain participants.

Conclusions

These clinical findings confirm our previous discoveries of the involvement of the peripheral immune system in chronic pain. Given that these findings are reflected in the prospective graded rat data, it suggests that the TLR response from peripheral blood and spinal cord were related to pain and these clinical findings do indeed act as system biomarkers for the chronic pain state. Hence, they provide additional impetus to the neuroimmune interaction to be a drug target for chronic pain.

Imaging pain in arthritis: advances in structural and functional neuroimaging

Arthritis is a heterogeneous disease characterized by joint stiffness, swelling, and pain. Although primarily considered a peripheral joint disease, the severity of pain reported by arthritis patients does not always reflect the extent of joint pathology detectable by conventional means. Using structural and functional brain imaging techniques, a growing number of evolving neuroimaging methods are providing insight into these observed discrepancies at different time-scales. Of these methods, functional magnetic resonance imaging is exploited for short-term evoked pain examination and treatment evaluation; 'resting-state' approaches provide insight into fluctuations in pain; perfusion imaging captures elements of on-going clinical pain; and morphological brain assessment provides evidence for long-term structural changes in the brain associated with chronic pain. Further insight into arthritic pain processing at the brain-systems level could in the future be provided by combined neuroimaging approaches, specifically investigating the interactions between functional and structural alterations.

Decision-making using fMRI in clinical drug development: revisiting NK-1 receptor antagonists for pain

Substance P (SP) and neurokinin-1 receptors (NK-1R) are localized within central and peripheral sensory pain pathways. The roles of SP and NK-1R in pain processing, the anatomical distribution of NK-1R and efficacy observed in preclinical pain studies involving pain and sensory sensitization models, suggested that NK-1R antagonists (NK-1RAs) would relieve pain in patient populations. Despite positive data available in preclinical tests for a role of NK-1RAs in pain, clinical studies across several pain conditions have been negative. In this review, we discuss how functional imaging-derived information on activity in pain-processing brain regions could have predicted that NK-1RAs would have a low probability of success in this therapeutic domain.

The brain in chronic pain: clinical implications

This article examines the present, and potential future, impact of brain imaging on chronic pain. It is argued that novel theories of chronic pain are coming to the fore, specifically through brain imaging of the human brain in chronic pain. Such studies show that the brain reorganizes in relation to chronic pain, in a pattern specific to the type of clinical pain, and that brain networks and receptor targets are being identified and reverse translated to animal studies of their efficacy and mechanisms. Future studies need to integrate across human brain imaging techniques, as well as more intensive reverse translational methods.

Using NMR approaches to drive the search for new CNS therapeutics

The use of MRI-based imaging in drug development has received increased interest recently because of the difficulties associated with the development of CNS pharmacotherapies. While not yet routine, there have been significant advances in imaging that allow this technology to be used for evaluating disease state and drug effects. For disease states, both single and longitudinal studies of non-invasive measures may be obtained to provide a read-out of disease processes and, potentially, to predict the disease state and its evolution. In addition, imaging has enabled the development of improved preclinical disease models based on changes in brain circuitry. Pharmacological MRI, the imaging-based evaluation of drug effects, includes measures of direct effects on the brain, as well as the effects of chronic dosing on brain changes and neurochemical changes associated with these brain effects using magnetic resonance spectroscopy. Thus, imaging may become an integrated process in drug development, during both the preclinical and clinical stages. However, validation, the implementation of good clinical practices and regulatory acceptance are hurdles that remain to be overcome.