Validating a biosignature-predicting placebo pill response in chronic pain in the settings of a randomized controlled trial

Advancing the prediction and understanding of placebo responses in chronic back pain using large language models

Placebo analgesia in chronic pain is a widely studied clinical phenomenon, where expectations about the effectiveness of a treatment can result in substantial pain relief when using an inert treatment agent. While placebos offer an opportunity for non-pharmacological treatment in chronic pain, not everyone demonstrates an analgesic response. Prior research has identified biopsychosocial factors that determine the likelihood of an individual to respond to a placebo, yet generalizability and ecological validity in those studies have been limited due to the inability to account for dynamic personal and treatment effects-which are well-known to play a role. Here, we assessed the potential of using fine-tuned large language models (LLMs) to predict placebo responders in chronic low-back pain using contextual features extracted from patient interviews, as they speak about their lifestyle, pain, and treatment history. We re-analyzed data from two clinical trials where individuals performed open-ended interviews and used these to develop a predictive model of placebo response. Our findings demonstrate that semantic features extracted with LLMs accurately predicted placebo responders, achieving a classification accuracy of 74% in unseen data, and validating with 70% accuracy in an independent cohort. Further, LLMs eliminated the need for pre-selecting search terms or to use dictionary approaches, enabling a fully data-driven approach. This LLM method further provided interpretable insights into psychosocial factors underlying placebo responses, highlighting nuanced linguistic patterns linked to responder status, which tap into semantic dimensions such as "anxiety," "resignation," and "hope." These findings expand on prior research by integrating state-of-art NLP techniques to address limitations in interpretability and context sensitivity of standard methods like bag-of-words and dictionary-based approaches. This method highlights the role of language models to link language and psychological states, paving the way for a deeper yet quantitative exploration of biopsychosocial phenomena, and to understand how they relate to treatment outcomes, including placebo.

Does Active Inference Provide a Comprehensive Theory of Placebo Analgesia?

Placebo interventions generate mismatches between expected pain and sensory signals from which pain states are inferred. Because we lack direct access to bodily states, we can only infer whether nociceptive activity indicates tissue damage or results from noise in sensory channels. Predictive processing models propose to make optimal inferences using prior knowledge given noisy sensory data. However, these models do not provide a satisfactory explanation of how pain relief expectations are translated into physiological manifestations of placebo responses. Furthermore, they do not account for individual differences in the ability to endogenously regulate nociceptive activity in predicting placebo analgesia. The brain not only passively integrates prior pain expectations with nociceptive activity to infer pain states (perceptual inference) but also initiates various types of actions to ensure that sensory data are consistent with prior pain expectations (active inference). We argue that depending on whether the brain interprets conflicting sensory data (prediction errors) as a signal to learn from or noise to be attenuated, the brain initiates opposing types of action to facilitate learning from sensory data or, conversely, to enhance the biasing influence of prior pain expectations on pain perception. Furthermore, we discuss the role of stress, anxiety, and unpredictability of pain in influencing the weighting of prior pain expectations and sensory data and how they relate to the individual ability to regulate nociceptive activity (endogenous pain modulation). Finally, we provide suggestions for future studies to test the implications of the active inference model of placebo analgesia.

Personalized treatment of neuropathic pain: Where are we now?

BACKGROUND

The treatment of neuropathic pain remains a major unmet need that the development of personalized and refined treatment strategies may contribute to address.

DATABASE

In this narrative review, we summarize the various approaches based on objective biomarkers or clinical markers that could be used.

RESULTS

In principle, the validation of objective biomarkers would be the most robust approach. However, although promising results have been reported demonstrating a potential value of genomics, anatomical or functional markers, the clinical validation of these markers has only just begun. Thus, most of the strategies documented to date have been based on the development of clinical markers. In particular, many studies have suggested that the identification of specific subgroups of patients presenting with specific combinations of symptoms and signs would be a relevant approach. Two main approaches have been used to identify relevant sensory profiles: quantitative sensory testing and specific patients reported outcomes based on description of pain qualities.

CONCLUSION

We discuss here the advantages and limitations of these approaches, which are not mutually exclusive.

SIGNIFICANCE

Recent data indicate that various new treatment strategies based on predictive biological and/or clinical markers could be helpful to better personalized and therefore improve the management of neuropathic pain.

Placebo effects in osteoarthritis: implications for treatment and drug development

Osteoarthritis (OA) is the most common form of arthritis worldwide, affecting ~500 million people, yet there are no effective treatments to halt its progression. Without any structure-modifying agents, management of OA focuses on ameliorating pain and improving function. Treatment approaches typically have modest efficacy, and many patients have contraindications to recommended pharmacological treatments. Drug development for OA is hindered by the gradual and progressive nature of the disease and the targeting of established disease in clinical trials. Additionally, new medications for OA cannot receive regulatory approval without demonstrating improvements in both structure (pathological features of OA) and symptoms (reduced pain and/or improved function). In clinical trials, people with OA show high 'placebo responses', which hamper the ability to identify new effective treatments. Placebo responses refer to the individual variability in response to placebos given in the context of clinical trials and other settings. Placebo effects refer specifically to short-lasting improvements in symptoms that occur because of physiological changes. To mitigate the effects of the placebo phenomenon, we must first understand what it is, how it manifests, how to identify placebo responders in OA trials and how these insights can be used to improve clinical trials in OA. Leveraging placebo responses and effects in clinical practice might provide additional avenues to augment symptom management of OA.

The Biology of Placebo and Nocebo Effects on Experimental and Chronic Pain: State of the Art

(1) Background: In recent years, placebo and nocebo effects have been extensively documented in different medical conditions, including pain. The scientific literature has provided strong evidence of how the psychosocial context accompanying the treatment administration can influence the therapeutic outcome positively (placebo effects) or negatively (nocebo effects). (2) Methods: This state-of-the-art paper aims to provide an updated overview of placebo and nocebo effects on pain. (3) Results: The most common study designs, the psychological mechanisms, and neurobiological/genetic determinants of these phenomena are discussed, focusing on the differences between positive and negative context effects on pain in experimental settings on healthy volunteers and in clinical settings on chronic pain patients. Finally, the last section describes the implications for clinical and research practice to maximize the medical and scientific routine and correctly interpret the results of research studies on placebo and nocebo effects. (4) Conclusions: While studies on healthy participants seem consistent and provide a clear picture of how the brain reacts to the context, there are no unique results of the occurrence and magnitude of placebo and nocebo effects in chronic pain patients, mainly due to the heterogeneity of pain. This opens up the need for future studies on the topic.

Predicting placebo analgesia in patients with chronic pain using natural language processing: a preliminary validation study

ABSTRACT

Patients with chronic pain show large placebo effects in clinical trials, and inert pills can lead to clinically meaningful analgesia that can last from days to weeks. Whether the placebo response can be predicted reliably, and how to best predict it, is still unknown. We have shown previously that placebo responders can be identified through the language content of patients because they speak about their life, and their pain, after a placebo treatment. In this study, we examine whether these language properties are present before placebo treatment and are thus predictive of placebo response and whether a placebo prediction model can also dissociate between placebo and drug responders. We report the fine-tuning of a language model built based on a longitudinal treatment study where patients with chronic back pain received a placebo (study 1) and its validation on an independent study where patients received a placebo or drug (study 2). A model built on language features from an exit interview from study 1 was able to predict, a priori, the placebo response of patients in study 2 (area under the curve = 0.71). Furthermore, the model predicted as placebo responders exhibited an average of 30% pain relief from an inert pill, compared with 3% for those predicted as nonresponders. The model was not able to predict who responded to naproxen nor spontaneous recovery in a no-treatment arm, suggesting specificity of the prediction to placebo. Taken together, our initial findings suggest that placebo response is predictable using ecological and quick measures such as language use.

Chronic pain domains and their relationship to personality, abilities, and brain networks

Chronic pain is a multidimensional pathological state. Recent evidence suggests that specific brain properties and patients' psychological and physical traits are distorted in chronic pain patients. However, the relationship between these alterations and pain dimensions remains poorly understood. Here, we first evaluated multiple dimensions of chronic pain by assessing a broad battery of pain-related questionnaire scores (23 outcomes) of 107 chronic low back pain patients and identified 3 distinct chronic pain domains: magnitude, affect & disability, and quality. Second, we investigated the pain domains relationship with measures of personality, social interaction, psychological traits, and ability traits (77 biopsychosocial & ability [biopsy&ab] outcomes). Pain magnitude (out-of-sample [OOS] ) is associated with emotional control, attention, and working memory, with higher pain scores showing lower capacity to regulate and adapt behaviorally. Pain affect & disability (OOS associated with anxiety, catastrophizing and social relationships dysfunction. Pain quality did not relate significantly to biopsy&ab variables. Third, we mapped these 3 pain domains to brain functional connectivity. Pain magnitude mainly associated with the sensorimotor and the cingulo-opercular networks (OOS ). Pain affect & disability related to frontoparietal and default mode networks (OOS . Pain quality integrated sensorimotor, auditory, and cingulo-opercular networks (OOS ). Mediation analysis could link functional connectivity and biopsy&ab models to respective pain domains. Our results provide a global overview of the complexity of chronic pain, showing how underlying distinct domains of the experience map to different biopsy&ab correlates and underlie unique brain network signatures.

On the Relationship Between Pain Variability and Relief in Randomized Clinical Trials

Previous research reports suggest greater baseline variability is associated with greater pain relief in those who receive a placebo. However, studies that evidence this association do not control for confounding effects from regression to the mean and natural history. In this report, we analyzed data from two randomized clinical trials (Placebo I and Placebo II, total N = 139) while adjusting for the effects of natural history and regression to the mean via a no treatment group. Results agree between the two placebo groups in each study: both placebo groups showed negligible semi-partial correlations between baseline variability and adjusted response [r_sp (CI_95%) = 0.22 (0.03, 0.42) and 0 (−0.07, 0.07) for Placebo I and II, respectively]. The no treatment group in Placebo I showed a negative correlation [−0.22 (−0.43, −0.02)], but the no treatment and drug groups in Placebo II's correlations were negligible [−0.02 (−0.08, 0.02) and 0.00 (−0.10, 0.12) for the no treatment and drug groups, respectively]. When modeled as a linear covariate, baseline pain variability accounted for <1% of the variance in post-intervention pain across both studies. Even after adjusting for baseline pain and natural history, the inability of baseline pain variability to account for substantial variance in pain response highlights that previous results concerning pain variability and treatment response may be inconsistent. Indeed, the relationship appears to be neither consistently specific nor sensitive to improvements in the placebo group. More work is needed to understand and establish the prognostic value of baseline pain variability—especially its placebo specificity and generalizability across patient populations.

What Is the Numerical Nature of Pain Relief?

Pain relief, or a decrease in self-reported pain intensity, is frequently the primary outcome of pain clinical trials. Investigators commonly report pain relief in one of two ways: using raw units (additive) or using percentage units (multiplicative). However, additive and multiplicative scales have different assumptions and are incompatible with one another. In this work, we describe the assumptions and corollaries of additive and multiplicative models of pain relief to illuminate the issue from statistical and clinical perspectives. First, we explain the math underlying each model and illustrate these points using simulations, for which readers are assumed to have an understanding of linear regression. Next, we connect this math to clinical interpretations, stressing the importance of statistical models that accurately represent the underlying data; for example, how using percent pain relief can mislead clinicians if the data are actually additive. These theoretical discussions are supported by empirical data from four longitudinal studies of patients with subacute and chronic pain. Finally, we discuss self-reported pain intensity as a measurement construct, including its philosophical limitations and how clinical pain differs from acute pain measured during psychophysics experiments. This work has broad implications for clinical pain research, ranging from statistical modeling of trial data to the use of minimal clinically important differences and patient-clinician communication.