Placebo effects in health and disease : how expectations shape treatment outcomes

The context in which a medical treatment is administrated influences treatment outcomes. As of today, the health care system has little knowledge about the non-specific components that contribute to the positive effect of a given therapy, often referred to as the placebo component, and how this may be harnessed in order to maximize treatment effects. The overreaching aim of this doctoral thesis is to investigate the impact of non-specific treatment components on clinical outcomes, in particular the role of expectations. More specifically, this thesis focuses on two clinically relevant, yet poorly investigated, topics: 1) Are placebo effects dependent on higher order cognitions? This was investigated among patients with intellectual disability (ID) and in an experimental setting in healthy individuals. 2) Are placebo effects affected by the duration of a chronic disease? In order to study this, outcomes from a randomized controlled trial (RCT) among fibromyalgia (FM) patients were analyzed. Study I investigated the influence of non-conscious expectations on placebo analgesia, using an implicit priming task called Scrambled Sentence Test (SST). Healthy participants were randomized to receive positive or neutral expectations via the SST, followed by a placebo manipulation with a sham analgesic device. Results demonstrated no effect of implicit priming on placebo analgesia, yet the study indicates that placebo analgesia is largely explained by prior experience of pain relief, and that the social interaction with a trustworthy clinician may have competed with the possible effect of implicit priming. Study II examined the relationship between placebo analgesia and the time (months, years) a person has been exposed to chronic disease, by assessing placebo responses in a pharmacological trial in patients with FM. Results revealed that FM duration was associated with baseline pain levels as well as placebo analgesia. These results point to the importance of early FM interventions, as the chance to harness endogenous pain regulation and to avoid chronification may be higher early in the disease course. Study III investigated how treatment expectations may shape outcomes in pharmacological clinical trials among patients with ID. The placebo component in ID clinical trials was examined by performing a meta-analysis comparing drug responses in open-label trials (with 100% certainty of getting the real drug) with drug responses in placebo-controlled trials (with 50% chance of getting the real drug). The results demonstrated placebo effects among patients with ID, as the effect of the real drug in open-label context was associated with better treatment outcomes than the same drug in a placebo-controlled context. Our study validates the notion that patients with ID are influenced by contextual factors in clinical trials in spite of severe cognitive deficits

Exploring the pharmacodynamics of multidrug combinations and using the advances in technology to individualise anaesthetic drug titration

In current practice, pharmacokinetic-dynamic (PK/PD) models are frequently used to describe the combined relationship between the time course of drug plasma concentrations (PK) and the time independent relationship between the drug concentration at the receptor site and the clinical effect (PD). This thesis contributes to the knowledge in anaesthetic pharmacology and explores the dose-response relationships of propofol and sevoflurane (with and without the coadministration of remifentanil) in greater detail using PK/PD models. Our studies show that PK/PD models are useful in clinical practice. The concept of neural inertia could have an influence on these models, but is still controversial in humans and it does not break down the essence and applicability of these PK/PD models. Subsequently, we used these models to compare the pharmacodynamics of propofol and sevoflurane (with and without remifentanil) at both a population level as well as at an individual level. This comparison let us describe potency ratios between both hypnotics which is very helpful for anaesthetist when switching between these drugs for any reason during a case. We applied the same PK/PD models and similar potency ratios in clinical practice using the SmartPilot® View, a drug advisory system, to guide anaesthetic drug titration, and we assessed its clinical utility. Finally, we evaluated a novel method to analyse the cerebral drug effect on the EEG using Artificial Intelligence in order to explore the feasibility of whether a single index can quantify the hypnotic effect in a drug-independent way

Integration of Spectroscopic and Mass Spectrometric Tools for the Analysis of Novel Psychoactive Substances in Forensic and Toxicology Applications

Analytical methods aiming for the detection of novel psychoactive substances are continuously revised due to their utility in the seized drug and toxicology realms. One method frequently employed for the preliminary identification of illicit materials is portable Raman spectroscopy. Even when a substance in possession of an offender is identified, conclusive evidence that it may have been consumed requires additional confirmatory work and further toxicological evaluation of a biological specimen. Many times, the substance consumed may not be detected in the analyzed specimen due to its extensive metabolism. It is therefore challenging to rule out the identity of the drug ingested if metabolic studies have not been performed on a particular substance. This research aims to evaluate portable Raman as a quick, safe, non-destructive method for drug analysis using the instrument’s built-in algorithms and in-house machine and deep learning algorithms. Furthermore, metabolic and toxicologic studies using zebrafish and human liver microsomes are used to elucidate selected opioids. In the first part of this research, a portable Raman instrument—TacticID was validated according to the United Nations Office on Drugs and Crime guidelines using 14 drugs and 15 cutting agents commonly encountered in seized drugs. Analysis was performed through glass and plastic packaging. In-house binary mixtures (n = 64) at the following ratios—1:4, 1:7, 1:10, and 1:20 were evaluated and the results compared to direct analysis in real-time mass spectrometry (DART-MS). Whereas Raman performed better at detecting diluents which consisted of the majority in the mixtures, DART-MS resulted in higher identification for easily ionizable drugs which were present in lower percentages. To compliment the weaknesses in each technique, both methods were combined, resulting in 96% accuracy. However, analysis of 15 authentic adjudicated cases resulted in 83% accuracy using the combined methods, demonstrating the usefulness of these methods as preliminary tests over traditional subjective techniques such as color tests. In instances where a portable Raman instrument is used for drug screening, its accuracy as a single technique is crucial. In this study, the correct identification of the instrument detecting both drug and diluent in binary mixtures was 19%. Therefore, machine learning methods were explored as alternatives to the instrument’s built-in hit quality index algorithm. The findings in this research demonstrated that neural networks and convolutional neural networks were superior to the other algorithms, increasing the correct identification of both compounds to 65 and 64%, respectively. This work demonstrated how the contribution of machine learning can help improve the accuracy of analytical instruments outputs thereby increasing confidence in compounds reported. In the second part of this research, zebrafish which share 70% of gene similarity to humans, were used as a toxicity model to provide information about drug effects on a living system. Fentanyl was selected as a model drug and zebrafish (0 – 96 hours post fertilization) were dosed at 0.01 – 100 µM. Major dose dependent phenotypic effects included pericardial malformations, spine, and yolk extension malformation, all of which inhibited the normal growth and development of the larvae. Additionally, the metabolism of fentanyl and valerylfentanyl were elucidated using zebrafish. Therefore, this work provided insight into the zebrafish model as an alternative to human toxicity and metabolism. The knowledge gained through this research will be used to understand the mechanisms by which these toxic and metabolic effects are observed

Predicting optimal anesthesia level from propofol and remifentanil concentration: analysis of covariate factors for individualization

Treballs Finals de Grau d'Enginyeria Biomèdica. Facultat de Medicina i Ciències de la Salut. Universitat de Barcelona. Curs: 2020-2021. Tutor: Pedro Gambús Cerrillo. Tutor Extern: Sebastián Jaramillo SelmanGeneral anesthesia involves some targeting effects which aim to prevent the patient from suffering against the therapeutic aggression. These effects are hypnosis, analgesia, amnesia and immobility and to achieve them a combination of drugs is delivered into the patient, from which propofol and remifentanil are highlighted. In the operating room, monitoring systems are used to assess the depth of anesthesia in real time. This monitoring includes basic systems such as arterial blood pressure, oxygenation or electrocardiogram and electroencephalogram derived measures, which are more complex; from this last group, BIS index is a good indicator. Being able to predict the anesthetic depth from a set of input variables could be valuable during the surgery, as it would help the anesthesiologists to prevent adverse effects, and it would help the post-operative recovery. Knowing this, the aim of this project is to predict the probability to be in the optimal level of anesthesia, which is related to the BIS index. This probability is obtained from the input concentration of propofol and remifentanil, a hypnotic and an analgesic drug respectively, and from the demographic variables such as age, height or gender. To do so, a Logistic Regression model will be built with data from patients undergoing general anesthesia in Cirurgia Major Ambulatòria (CMA) in Hospital Clínic

How to Modulate Peripheral and Central Nervous System to Treat Acute Postoperative Pain and Prevent Pain Persistence

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The Landscape of Chronic Pain : Broader Perspectives

Chronic pain is a global health concern. This special issue on matters related to chronic pain aims to draw on research and scholarly discourse from an eclectic mix of areas and perspectives. The purpose of this non-systematic topical review is to précis an assortment of contemporary topics related to chronic pain and its management to nurture debate about research, practice and health care policy. The review discusses the phenomenon of pain, the struggle that patients have trying to legitimize their pain to others, the utility of the acute-chronic dichotomy, and the burden of chronic pain on society. The review describes the introduction of chronic primary pain in the World Health Organization's International Classification of Disease, 11th Revision and discusses the importance of biopsychosocial approaches to manage pain, the consequences of overprescribing and shifts in service delivery in primary care settings. The second half of the review explores pain perception as a multisensory perceptual inference discussing how contexts, predictions and expectations contribute to the malleability of somatosensations including pain, and how this knowledge can inform the development of therapies and strategies to alleviate pain. Finally, the review explores chronic pain through an evolutionary lens by comparing modern urban lifestyles with genetic heritage that encodes physiology adapted to live in the Paleolithic era. I speculate that modern urban lifestyles may be painogenic in nature, worsening chronic pain in individuals and burdening society at the population level

From Requirements to Code: Model Based Development of a Medical Cyber Physical System

The advanced use of technology in medical devices has improved the way health care is delivered to patients. Unfortunately, the increased complexity of modern medical devices poses challenges for development, assurance, and regulatory approval. In an e ort to improve the safety of advanced medical devices, organizations such as FDA have supported exploration of techniques to aid in the development and regulatory approval of such systems. In an ongoing research project, our aim is to provide effective development techniques and exemplars of system development artifacts that demonstrate state of the art development techniques. In this paper we present an end-to-end model-based approach to medical device software development along with the artifacts created in the process. While outlining the approach, we also describe our experiences, challenges, and lessons learned in the process of formulating and analyzing the requirements, modeling the system, formally verifying the models, generating code, and executing the generated code in the hardware for generic patient controlled analgesic infusion pump (GPCA). We believe that the development artifacts and techniques presented in this paper could serve as a generic reference to be used by researchers, practitioners, and authorities while developing and evaluating cyber physical medical devices