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Systematic review and co-ordinate based meta-analysis to summarize the utilization of functional brain imaging in conjunction with human models of peripheral and central sensitization.
BACKGROUND AND OBJECTIVE: Functional magnetic resonance imaging, in conjunction with models of peripheral and/or central sensitization, has been used to assess analgesic efficacy in healthy humans. This review aims to summarize the use of these techniques to characterize brain mechanisms of hyperalgesia/allodynia and to evaluate the efficacy of analgesics. DATABASES AND DATA TREATMENT: Searches were performed (PubMed-Medline, Cochrane, Web of Science and Clinicaltrials.gov) to identify and review studies. A co-ordinate based meta-analysis (CBMA) was conducted to quantify neural activity that was reported across multiple independent studies in the hyperalgesic condition compared to control, using GingerALE software. RESULTS: Of 217 publications, 30 studies met the inclusion criteria. They studied nine different models of hyperalgesia/allodynia assessed in the primary (14) or secondary hyperalgesia zone (16). Twenty-three studies focused on neural correlates of hyperalgesic conditions and showed consistent changes in the somatosensory cortex, prefrontal cortices, insular cortex, anterior cingulate cortex, thalamus and brainstem. The CBMA on 12 studies that reported activation coordinates for a contrast comparing the hyperalgesic state to control produced six activation clusters (significant at false discovery rate of 0.05) with more peaks for secondary (17.7) than primary zones (7.3). Seven studies showed modulation of brain activity by analgesics in five of the clusters but also in four additional regions. CONCLUSIONS: This meta-analysis revealed substantial but incomplete overlap between brain areas related to neural mechanisms of hyperalgesia and those reflecting the efficacy of analgesic drugs. Studies testing in the secondary zone were more sensitive to evaluate analgesic efficacy on central sensitization at brainstem or thalamocortical levels. SIGNIFICANCE: Experimental pain models that provide a surrogate for features of pathological pain conditions in healthy humans and functional imaging techniques are both highly valuable research tools. This review shows that when used together, they provide a wealth of information about brain activity during pain states and analgesia. These tools are promising candidates to help bridge the gap between animal and human studies, to improve translatability and provide opportunities for identification of new targets for back-translation to animal studies.
IMI2-PainCare-BioPain-RCT2 protocol: a randomized, double-blind, placebo-controlled, crossover, multicenter trial in healthy subjects to investigate the effects of lacosamide, pregabalin, and tapentadol on biomarkers of pain processing observed by non-invasive neurophysiological measurements of human spinal cord and brainstem activity.
BACKGROUND: IMI2-PainCare-BioPain-RCT2 is one of four similarly designed clinical studies aiming at profiling a set of functional biomarkers of drug effects on specific compartments of the nociceptive system that could serve to accelerate the future development of analgesics. IMI2-PainCare-BioPain-RCT2 will focus on human spinal cord and brainstem activity using biomarkers derived from non-invasive neurophysiological measurements. METHODS: This is a multisite, single-dose, double-blind, randomized, placebo-controlled, 4-period, 4-way crossover, pharmacodynamic (PD) and pharmacokinetic (PK) study in healthy subjects. Neurophysiological biomarkers of spinal and brainstem activity (the RIII flexion reflex, the N13 component of somatosensory evoked potentials (SEP) and the R2 component of the blink reflex) will be recorded before and at three distinct time points after administration of three medications known to act on the nociceptive system (lacosamide, pregabalin, tapentadol), and placebo, given as a single oral dose in separate study periods. Medication effects on neurophysiological measures will be assessed in a clinically relevant hyperalgesic condition (high-frequency electrical stimulation of the skin), and in a non-sensitized normal condition. Patient-reported outcome measures (pain ratings and predictive psychological traits) will also be collected; and blood samples will be taken for pharmacokinetic modelling. A sequentially rejective multiple testing approach will be used with overall alpha error of the primary analysis split between the two primary endpoints, namely the percentage amplitude changes of the RIII area and N13 amplitude under tapentadol. Remaining treatment arm effects on RIII, N13 and R2 recovery cycle are key secondary confirmatory analyses. Complex statistical analyses and PK-PD modelling are exploratory. DISCUSSION: The RIII component of the flexion reflex is a pure nociceptive spinal reflex widely used for investigating pain processing at the spinal level. It is sensitive to different experimental pain models and to the antinociceptive activity of drugs. The N13 is mediated by large myelinated non-nociceptive fibers and reflects segmental postsynaptic response of wide dynamic range dorsal horn neurons at the level of cervical spinal cord, and it could be therefore sensitive to the action of drugs specifically targeting the dorsal horn. The R2 reflex is mediated by large myelinated non-nociceptive fibers, its circuit consists of a polysynaptic chain lying in the reticular formation of the pons and medulla. The recovery cycle of R2 is widely used for assessing brainstem excitability. For these reasons, IMI2-PainCare-BioPain-RCT2 hypothesizes that spinal and brainstem neurophysiological measures can serve as biomarkers of target engagement of analgesic drugs for future Phase 1 clinical trials. Phase 2 and 3 clinical trials could also benefit from these tools for patient stratification. TRIAL REGISTRATION: This trial was registered on 02 February 2019 in EudraCT ( 2019-000755-14 ).
IMI2-PainCare-BioPain-RCT1: study protocol for a randomized, double-blind, placebo-controlled, crossover, multi-center trial in healthy subjects to investigate the effects of lacosamide, pregabalin, and tapentadol on biomarkers of pain processing observed by peripheral nerve excitability testing (NET).
BACKGROUND: Few new drugs have been developed for chronic pain. Drug development is challenged by uncertainty about whether the drug engages the human target sufficiently to have a meaningful pharmacodynamic effect. IMI2-PainCare-BioPain-RCT1 is one of four similarly designed studies that aim to link different functional biomarkers of drug effects on the nociceptive system that could serve to accelerate the future development of analgesics. This study focusses on biomarkers derived from nerve excitability testing (NET) using threshold tracking of the peripheral nervous system. METHODS: This is a multisite single-dose, subject and assessor-blind, randomized, placebo-controlled, 4-period, 4-way crossover, pharmacodynamic (PD), and pharmacokinetic (PK) study in healthy subjects. Biomarkers derived from NET of large sensory and motor fibers and small sensory fibers using perception threshold tracking will be obtained before and three times after administration of three medications known to act on the nociceptive system (lacosamide, pregabalin, tapentadol) and placebo, given as a single oral dose with at least 1 week apart. Motor and sensory NET will be assessed on the right wrist in a non-sensitized normal condition while perception threshold tracking will be performed bilaterally on both non-sensitized and sensitized forearm skin. Cutaneous high-frequency electrical stimulation is used to induce hyperalgesia. Blood samples will be taken for pharmacokinetic purposes and pain ratings as well as predictive psychological traits will be collected. A sequentially rejective multiple testing approach will be used with overall alpha error of the primary analysis split across the two primary outcomes: strength-duration time constant (SDTC; a measure of passive membrane properties and nodal persistent Na+ conductance) of large sensory fibers and SDTC of large motor fibers comparing lacosamide and placebo. The key secondary endpoint is the SDTC measured in small sensory fibers. Remaining treatment arm effects on key NET outcomes and PK modelling are other prespecified secondary or exploratory analyses. DISCUSSION: Measurements of NET using threshold tracking protocols are sensitive to membrane potential at the site of stimulation. Sets of useful indices of axonal excitability collectively may provide insights into the mechanisms responsible for membrane polarization, ion channel function, and activity of ionic pumps during the process of impulse conduction. IMI2-PainCare-BioPain-RCT1 hypothesizes that NET can serve as biomarkers of target engagement of analgesic drugs in this compartment of the nociceptive system for future Phase 1 clinical trials. Phase 2 and 3 clinical trials could also benefit from these tools for patient stratification. TRIAL REGISTRATION: This trial was registered 25/06/2019 in EudraCT ( 2019-000942-36 ).
IMI2-PainCare-BioPain-RCT3: a randomized, double-blind, placebo-controlled, crossover, multi-center trial in healthy subjects to investigate the effects of lacosamide, pregabalin, and tapentadol on biomarkers of pain processing observed by electroencephalography (EEG).
BACKGROUND: IMI2-PainCare-BioPain-RCT3 is one of four similarly designed clinical studies aiming at profiling a set of functional biomarkers of drug effects on the nociceptive system that could serve to accelerate the future development of analgesics, by providing a quantitative understanding between drug exposure and effects of the drug on nociceptive signal processing in human volunteers. IMI2-PainCare-BioPain-RCT3 will focus on biomarkers derived from non-invasive electroencephalographic (EEG) measures of brain activity. METHODS: This is a multisite single-dose, double-blind, randomized, placebo-controlled, 4-period, 4-way crossover, pharmacodynamic (PD) and pharmacokinetic (PK) study in healthy subjects. Biomarkers derived from scalp EEG measurements (laser-evoked brain potentials [LEPs], pinprick-evoked brain potentials [PEPs], resting EEG) will be obtained before and three times after administration of three medications known to act on the nociceptive system (lacosamide, pregabalin, tapentadol) and placebo, given as a single oral dose in separate study periods. Medication effects will be assessed concurrently in a non-sensitized normal condition and a clinically relevant hyperalgesic condition (high-frequency electrical stimulation of the skin). Patient-reported outcomes will also be collected. A sequentially rejective multiple testing approach will be used with overall alpha error of the primary analysis split between LEP and PEP under tapentadol. Remaining treatment arm effects on LEP or PEP or effects on EEG are key secondary confirmatory analyses. Complex statistical analyses and PK-PD modeling are exploratory. DISCUSSION: LEPs and PEPs are brain responses related to the selective activation of thermonociceptors and mechanonociceptors. Their amplitudes are dependent on the responsiveness of these nociceptors and the state of the pathways relaying nociceptive input at the level of the spinal cord and brain. The magnitude of resting EEG oscillations is sensitive to changes in brain network function, and some modulations of oscillation magnitude can relate to perceived pain intensity, variations in vigilance, and attentional states. These oscillations can also be affected by analgesic drugs acting on the central nervous system. For these reasons, IMI2-PainCare-BioPain-RCT3 hypothesizes that EEG-derived measures can serve as biomarkers of target engagement of analgesic drugs for future Phase 1 clinical trials. Phase 2 and 3 clinical trials could also benefit from these tools for patient stratification. TRIAL REGISTRATION: This trial was registered 25/06/2019 in EudraCT ( 2019%2D%2D001204-37 ).
Central Sensitization in Knee Osteoarthritis: Relating Presurgical Brainstem Neuroimaging and PainDETECT‐Based Patient Stratification to Arthroplasty Outcome
ObjectiveThe neural mechanisms of pain in knee osteoarthritis (OA) are not fully understood, and some patients have neuropathic‐like pain associated with central sensitization. To address this, we undertook the present study in order to identify central sensitization using neuroimaging and PainDETECT and to relate it to postarthroplasty outcome.MethodsPatients awaiting arthroplasty underwent quantitative sensory testing, psychological assessment, and functional magnetic resonance imaging (fMRI). Neuroimaging (fMRI) was conducted during punctate stimulation (n = 24) and cold stimulation (n = 20) to the affected knee. The postoperative outcome was measured using the Oxford Knee Score, patient‐reported moderate‐to‐severe long‐term pain postarthroplasty, and a range of pain‐related questionnaires.ResultsPatients with neuropathic‐like pain presurgery (identified using PainDETECT; n = 14) reported significantly higher pain in response to punctate stimuli and cold stimuli near the affected joint (P < 0.05). Neural activity in these patients, compared to those without neuropathic‐like pain, was significantly lower in the rostral anterior cingulate cortex (P < 0.05) and higher in the rostral ventromedial medulla (RVM) during punctate stimulation (P < 0.05), with significant functional connectivity between these two areas (r = 0.49, P = 0.018). Preoperative neuropathic‐like pain and higher neural activity in the RVM were associated with moderate‐to‐severe long‐term pain after arthroplasty (P = 0.0356).ConclusionThe psychophysical and neuroimaging data suggest that a subset of OA patients have centrally mediated pain sensitization. This was likely due to supraspinally mediated reductions in inhibition and increases in facilitation of nociceptive signaling, and was associated with a worse outcome following arthroplasty. The neurobiologic confirmation of central sensitization in patients with features of neuropathic pain, identified using PainDETECT, provides further support for the investigation of such bedside measures for patient stratification, to better predict postsurgical outcomes.
Validation of an FMRI Based Classification Pipeline for Detecting Analgesic Efficacy from Neuroimaging Data
Aim: Functional magnetic resonance imaging (FMRI) can provide objective evidence of target engagement and potential efficacy of analgesics in the early clinical phases of drug development. Such evidence-based go/no-go decisions on compound selection has the potential to improve the efficiency of the analgesic drug discovery process. We have previously developed a classification-based protocol for assessing the effects of analgesics on brain responses to pain as measured by FMRI1. It utilises machine learning to derive signatures of analgesic response across many brain regions from existing FMRI studies of analgesics. This protocol can detect evidence of pharmacodynamic effects where a compound shows consistent effects on brain responses across individuals, and efficacy where the compound’s effects correspond to an analgesic signature derived from the classification database. To further validate the protocol for drug development, we set out to test this protocol in a completely independent FMRI data set (n=24) from a double-blind, randomised, placebo-controlled, three-way crossover study in a healthy volunteer model of central sensitization (CS), a crucial mechanism underpinning neuropathic pain states. This study assessed whether brain responses to painful stimuli can differentiate a clinically effective analgesic in neuropathic pain (gabapentin) from an ineffective analgesic (ibuprofen) and both from placebo2. We expected our protocol to detect gabapentin to show evidence of effective analgesia but not ibuprofen. Methods: We separately assessed the gabapentin and ibuprofen arms of the study with the protocol. The pharmacodynamic effects were determined by assessing (using leave-one-out method) whether a trained classifier could discriminate the drug arm of the study from placebo based on brain responses to painful stimuli. The signature of efficacy was derived from five double-blind, randomised, placebo-controlled FMRI studies with a cross over design. These studies compared the brain effects of different classes of various analgesics (remifentanil, tramadol, pregabalin, and delta-9-tetrahydrocannabinol) to placebo and did not include the drugs under assessment. We used independent component analysis for dimensionality reduction, and a support vector machine with a linear kernel for classification. We assessed discrimination accuracy (drug vs placebo) as a measure of the presence of pharmacodynamic and analgesic signature effects, where chance p = 0.5. Results: Gabapentin, but not ibuprofen, showed evidence of a reliable pharmacodynamic effect. Using a classifier based on the brain responses to painful stimuli observed in other individuals, the brain response to painful stimuli with gabapentin could be correctly distinguished from placebo in 79% of individuals (p=0.003). In contrast, responses following ibuprofen could not be distinguished from placebo (45%, p=0.72). In the assessment for analgesic efficacy, the classifier could correctly identify the gabapentin arm in 17 of 24 subjects (p=0.03), indicating that the compound shows brain effects resembling those found in our training set of efficacious compounds. Ibuprofen showed no such evidence, with discrimination below chance (p=0.92). At a study level, the gabapentin arm showed robust evidence for our signature of analgesic efficacy while ibuprofen did not. We also found evidence that specific analgesic compounds had distinct signatures. A classifier trained on a separate study of gabapentin could reliably identify the gabapentin conditions in this study (p=0.03). However, classifiers trained on studies of the opioid remifentanil failed to identify gabapentin in this data (p=0.5), while successfully identifying effects in remifentanil studies. Conclusion: Here we show that a machine learning protocol generated to detect analgesic efficacy is able to detect brain changes related to analgesic efficacy of a known effective analgesic (gabapentin) in a data set independent of the training data set; however, it failed to detect analgesic effects of ibuprofen. This is in keeping with the known clinical efficacy of these compounds in neuropathic pain, as well as our original study that independently showed gabapentin and not ibuprofen or placebo suppressed neural activity evoked by painful stimuli2. Our classifier failed to detect any ibuprofen induced pharmacodynamic effects on the brain. Ibuprofen is not a centrally acting compound. It has predominantly peripherally mediated analgesic effects; therefore, ibuprofen is unlikely to contribute to measurable effects on pain related brain activity across individuals. Additionally, we are able to show that machine learning protocols can identify brain activity that is specific to the drug class. In the future, we aim to apply our protocol to assess compounds with potential yet clinically unproven analgesic efficacy in early drug development. 1. Duff EP, Vennart W, Wise RG, et al. Learning to identify CNS drug action and efficacy using multistudy fMRI data. Sci Transl Med 2015; 7(274): 274ra16. 2. Wanigasekera V, Mezue M, Andersson J, Kong Y, Tracey I. Disambiguating Pharmacodynamic Efficacy from Behavior with Neuroimaging: Implications for Analgesic Drug Development. Anesthesiology 2016; 124(1): 159-68.
What Should Clinicians Tell Patients about Placebo and Nocebo Effects? Practical Considerations Based on Expert Consensus.
INTRODUCTION: Clinical and laboratory studies demonstrate that placebo and nocebo effects influence various symptoms and conditions after the administration of both inert and active treatments. OBJECTIVE: There is an increasing need for up-to-date recommendations on how to inform patients about placebo and nocebo effects in clinical practice and train clinicians how to disclose this information. METHODS: Based on previous clinical recommendations concerning placebo and nocebo effects, a 3-step, invitation-only Delphi study was conducted among an interdisciplinary group of internationally recognized experts. The study consisted of open- and closed-ended survey questions followed by a final expert meeting. The surveys were subdivided into 3 parts: (1) informing patients about placebo effects, (2) informing patients about nocebo effects, and (3) training clinicians how to communicate this information to the patients. RESULTS: There was consensus that communicating general information about placebo and nocebo effects to patients (e.g., explaining their role in treatment) could be beneficial, but that such information needs to be adjusted to match the specific clinical context (e.g., condition and treatment). Experts also agreed that training clinicians to communicate about placebo and nocebo effects should be a regular and integrated part of medical education that makes use of multiple formats, including face-to-face and online modalities. CONCLUSIONS: The current 3-step Delphi study provides consensus-based recommendations and practical considerations for disclosures about placebo and nocebo effects in clinical practice. Future research is needed on how to optimally tailor information to specific clinical conditions and patients' needs, and on developing standardized disclosure training modules for clinicians.
Hippocampus mediates nocebo impairment of opioid analgesia through changes in functional connectivity
AbstractThe neural mechanisms underlying placebo analgesia have attracted considerable attention over the recent years. In contrast, little is known about the neural underpinnings of a nocebo‐induced increase in pain. We previously showed that nocebo‐induced hyperalgesia is accompanied by increased activity in the hippocampus that scaled with the perceived level of anxiety. As a key node of the neural circuitry of perceived threat and fear, the hippocampus has recently been proposed to coordinate defensive behaviour in a context‐dependent manner. Such a role requires close interactions with other regions involved in the detection of and responses to threat. Here, we investigated the functional connectivity of the hippocampus during nocebo‐induced hyperalgesia. Our results show an increase in functional connectivity between hippocampus and brain regions implicated in the processing of sensory‐discriminative aspects of pain (posterior insula and primary somatosensory/motor cortex) as well as the periaqueductal grey. This nocebo‐induced increase in connectivity scaled with an individual's increase in anxiety. Moreover, hippocampus connectivity with the amygdala was negatively correlated with the pain intensity reported during nocebo hyperalgesia relative to the placebo condition. Our findings suggest that the hippocampus links nocebo‐induced anxiety to a heightened responsiveness to nociceptive input through changes in its crosstalk with pain‐modulatory brain areas.
The effects of lacosamide, pregabalin, and tapentadol on peripheral nerve excitability: A randomized, double-blind, placebo-controlled, crossover, multi-center trial in healthy subjects.
BACKGROUND: Chronic pain is a leading cause of disability globally, with limited treatment options and frequent adverse effects. The IMI-PainCare-BioPain project aimed to enhance analgesic drug development by standardizing biomarkers. This study, IMI2-PainCare-BioPain-RCT1, evaluated the effects of lacosamide, pregabalin, and tapentadol on peripheral nerve excitability in healthy subjects through a randomized, double-blind, placebo-controlled crossover trial. METHODS: The study included 43 healthy participants aged 18-45 years. Participants underwent four treatment periods where they received single doses of lacosamide (200 mg), pregabalin (150 mg), tapentadol (100 mg), or placebo. High-frequency stimulation was applied to induce hyperalgesia. The two primary endpoints were changes in Strength Duration Time Constant (SDTC) in large sensory and motor fibers between lacosamide and placebo periods at the first post-dose timepoint compared to baseline (60 min). Other predefined endpoints included recovery cycle, threshold electrotonus (TEd), and S2 accommodation as well as effects of pregabalin and tapentadol. RESULTS: Lacosamide statistically significantly reduced SDTC in large sensory fibers (mean reduction 0.04 (95% CI 0.01-0.08), p = 0.012) and in motor fibers (mean reduction 0.04 (95% CI 0.00-0.07), p = 0.039) but had no effect on small sensory fibers at the first timepoint compared to placebo. There were no effects of pregabalin and tapentadol on SDTC. Of other predefined endpoints, lacosamide produced statistically significant changes in subexcitability, S2 accommodation TEd(peak), and TEd40(Accom) in large sensory fibers. No statistically significant changes were observed in refractoriness, relative refractory period, or accommodation half-time at the first timepoint compared to placebo. CONCLUSIONS: This study demonstrates that nerve excitability testing can detect pharmacodynamic effects on large myelinated fibers in healthy subjects. Lacosamide statistically significantly reduced peripheral nerve excitability, particularly in large sensory fibers.
Imaging opioid analgesia in the human brain and its potential relevance for understanding opioid use in chronic pain
Opioids play an important role for the management of acute pain and in palliative care. The role of long-term opioid therapy in chronic non-malignant pain remains unclear and is the focus of much clinical research. There are concerns regarding analgesic tolerance, paradoxical pain and issues with dependence that can occur with chronic opioid use in the susceptible patient. In this review, we discuss how far human neuroimaging research has come in providing a mechanistic understanding of pain relief provided by opioids, and suggest avenues for further studies that are relevant to the management of chronic pain with opioids. This article is part of the Special Issue Section entitled 'Neuroimaging in Neuropharmacology'. © 2014 The Authors. Published by Elsevier Ltd.
Disambiguating Pharmacodynamic Efficacy from Behavior with Neuroimaging
AbstractAbstract After development of experimental central sensitization, gabapentin reduces the activation of pain-related brain areas as well as functional connectivity between the thalamus and secondary somatosensory cortex, whereas ibuprofen does not when compared with placebo. Functional imaging may be a viable tool for evaluating analgesic efficacy during early stages of drug development. Background Attrition rates of new analgesics during drug development are high; poor assay sensitivity with reliance on subjective outcome measures being a crucial factor. Methods The authors assessed the utility of functional magnetic resonance imaging with capsaicin-induced central sensitization, a mechanism relevant in neuropathic pain, for obtaining mechanism-based objective outcome measures that can differentiate an effective analgesic (gabapentin) from an ineffective analgesic (ibuprofen) and both from placebo. The authors used a double-blind, randomized phase I study design (N = 24) with single oral doses. Results Only gabapentin suppressed the secondary mechanical hyperalgesia–evoked neural response in a region of the brainstem’s descending pain modulatory system (right nucleus cuneiformis) and left (contralateral) posterior insular cortex and secondary somatosensory cortex. Similarly, only gabapentin suppressed the resting-state functional connectivity during central sensitization between the thalamus and secondary somatosensory cortex, which was plasma gabapentin level dependent. A power analysis showed that with 12 data sets, when using neural activity from the left posterior insula and right nucleus cuneiformis, a statistically significant difference between placebo and gabapentin was detected with probability ≥ 0.8. When using subjective pain ratings, this reduced to less than or equal to 0.6. Conclusions Functional imaging with central sensitization can be used as a sensitive mechanism–based assay to guide go/no-go decisions on selecting analgesics effective in neuropathic pain in early human drug development. We also show analgesic modulation of neural activity by using resting-state functional connectivity, a less challenging paradigm that is ideally suited for patient studies because it requires no task or pain provocation.