Engaging Multistakeholder Perspectives to Identify Patient-Centered Research Priorities Regarding Vaccine Uptake Among Adults With Autoimmune Conditions.

OBJECTIVE: The study objective was to prioritize topics for future patient-centered research to increase uptake of common vaccines, such as for pneumococcal pneumonia, influenza, herpes zoster, human papillomavirus, and severe acute respiratory syndrome coronavirus 2, among adults living with autoimmune conditions. METHODS: A steering committee (SC) was formed that included clinicians, patients, patient advocates, and researchers associated with rheumatic diseases (psoriatic arthritis, rheumatoid arthritis, vasculitis), inflammatory bowel disease, and multiple sclerosis. Through a scoping review and discussions, SC members identified research topics regarding vaccine uptake and/or hesitancy for prioritization. A larger multistakeholder alliance that included patients and patient advocates, clinicians, researchers, policy makers, regulators, and vaccine manufacturers conducted a modified Delphi exercise online with three rating rounds and one ranking round. Frequency analysis and comparisons across stakeholder groups were conducted. A weighted ranking score was generated for each item in the ranking round for final prioritization. RESULTS: Through the Delphi process, 33 research topics were identified, of which 13 topics were rated as critical by more than 70% of all stakeholders (n = 31). The two highest ranked critical topics per the full stakeholder group were "How well a vaccine works for adults with autoimmune conditions" and "How beliefs about vaccine safety affect vaccine uptake." CONCLUSION: A multistakeholder group identified key topics as critically important priorities for future research to decrease vaccine hesitancy and improve uptake of vaccines for adults with autoimmune conditions.

Web-Based Mindfulness-Based Interventions for Well-being: Randomized Comparative Effectiveness Trial.

BACKGROUND: Mindfulness can improve overall well-being by training individuals to focus on the present moment without judging their thoughts. However, it is unknown how much mindfulness practice and training are necessary to improve well-being. OBJECTIVE: The primary aim of this study was to determine whether a standard 8-session web-based mindfulness-based cognitive therapy (MBCT) program, compared with a brief 3-session mindfulness intervention, improved overall participant well-being. In addition, we sought to explore whether the treatment effects differed based on the baseline characteristics of the participants (ie, moderators). METHODS: Participants were recruited from 17 patient-powered research networks, web-based communities of stakeholders interested in a common research area. Participants were randomized to either a standard 8-session MBCT or a brief 3-session mindfulness training intervention accessed on the web. The participants were followed for 12 weeks. The primary outcome of the study was well-being, as measured by the World Health Organization-Five Well-Being Index. We hypothesized that MBCT would be superior to a brief mindfulness training. RESULTS: We randomized 4411 participants, 3873 (87.80%) of whom were White and 3547 (80.41%) of female sex assigned at birth. The mean baseline World Health Organization-Five Well-Being Index score was 50.3 (SD 20.7). The average self-reported well-being in each group increased over the intervention period (baseline to 8 weeks; model-based slope for the MBCT group: 0.78, 95% CI 0.63-0.93, and brief mindfulness group: 0.76, 95% CI 0.60-0.91) as well as the full study period (ie, intervention plus follow-up; baseline to 20 weeks; model-based slope for MBCT group: 0.41, 95% CI 0.34-0.48; and brief mindfulness group: 0.33, 95% CI 0.26-0.40). Changes in self-reported well-being were not significantly different between MBCT and brief mindfulness during the intervention period (model-based difference in slopes: -0.02, 95% CI -0.24 to 0.19; P=.80) or during the intervention period plus 12-week follow-up (-0.08, 95% CI -0.18 to 0.02; P=.10). During the intervention period, younger participants (P=.05) and participants who completed a higher percentage of intervention sessions (P=.005) experienced greater improvements in well-being across both interventions, with effects that were stronger for participants in the MBCT condition. Attrition was high (ie, 2142/4411, 48.56%), which is an important limitation of this study. CONCLUSIONS: Standard MBCT improved well-being but was not superior to a brief mindfulness intervention. This finding suggests that shorter mindfulness programs could yield important benefits across the general population of individuals with various medical conditions. Younger people and participants who completed more intervention sessions reported greater improvements in well-being, an effect that was more pronounced for participants in the MBCT condition. This finding suggests that standard MBCT may be a better choice for younger people as well as treatment-adherent individuals. TRIAL REGISTRATION: ClinicalTrials.gov NCT03844321; https://clinicaltrials.gov/ct2/show/NCT03844321.

Patient-Powered Research Networks of the Autoimmune Research Collaborative: Rationale, Capacity, and Future Directions.

Patient-Powered Research Networks (PPRNs) are US-based registry infrastructures co-created by advocacy groups, patient research partners, academic investigators, and other healthcare stakeholders. Patient-Powered Research Networks collect information directly from patients to conduct and disseminate the results of patient-centered/powered research that helps patients make more informed decisions about their healthcare. Patient-Powered Research Networks gather and utilize real-world data and patient-reported outcomes to conduct comparative effectiveness, safety, and other research, and leverage the Internet to accomplish this effectively and efficiently. Four PPRNs focused on autoimmune and immune-mediated conditions formed the Autoimmune Research Collaborative: ArthritisPower (rheumatoid arthritis, spondyloarthritis, and other rheumatic and musculoskeletal diseases), IBD Partners (inflammatory bowel disease), iConquerMS (multiple sclerosis), and the Vasculitis PPRN (vasculitis). The Autoimmune Research Collaborative aims to inform the healthcare decision making of patients, care partners, and other stakeholders, such as clinicians, regulators, and payers. Illustrated by practical applications from the Autoimmune Research Collaborative and its constituent PPRNs, this article discusses the shared capacities and challenges of the PPRN model, and the opportunities presented by collaborating across autoimmune conditions to design, conduct, and disseminate patient-centered outcomes research.

The liver toxicity biomarker study phase I: markers for the effects of tolcapone or entacapone.

The Liver Toxicity Biomarker Study is a systems toxicology approach to discover biomarkers that are indicative of a drug's potential to cause human idiosyncratic drug-induced liver injury. In phase I, the molecular effects in rat liver and blood plasma induced by tolcapone (a "toxic" drug) were compared with the molecular effects in the same tissues by dosing with entacapone (a "clean" drug, similar to tolcapone in chemical structure and primary pharmacological mechanism). Two durations of drug exposure, 3 and 28 days, were employed. Comprehensive molecular analysis of rat liver and plasma samples yielded marker analytes for various drug-vehicle or drug-drug comparisons. An important finding was that the marker analytes associated with tolcapone only partially overlapped with marker analytes associated with entacapone, despite the fact that both drugs have similar chemical structures and the same primary pharmacological mechanism of action. This result indicates that the molecular analyses employed in the study are detecting substantial "off-target" markers for the two drugs. An additional interesting finding was the modest overlap of the marker data sets for 3-day exposure and 28-day exposure, indicating that the molecular changes in liver and plasma caused by short- and long-term drug treatments do not share common characteristics.

Sample size and statistical power considerations in high-dimensionality data settings: a comparative study of classification algorithms.

BACKGROUND: data generated using 'omics' technologies are characterized by high dimensionality, where the number of features measured per subject vastly exceeds the number of subjects in the study. In this paper, we consider issues relevant in the design of biomedical studies in which the goal is the discovery of a subset of features and an associated algorithm that can predict a binary outcome, such as disease status. We compare the performance of four commonly used classifiers (K-Nearest Neighbors, Prediction Analysis for Microarrays, Random Forests and Support Vector Machines) in high-dimensionality data settings. We evaluate the effects of varying levels of signal-to-noise ratio in the dataset, imbalance in class distribution and choice of metric for quantifying performance of the classifier. To guide study design, we present a summary of the key characteristics of 'omics' data profiled in several human or animal model experiments utilizing high-content mass spectrometry and multiplexed immunoassay based techniques. RESULTS: the analysis of data from seven 'omics' studies revealed that the average magnitude of effect size observed in human studies was markedly lower when compared to that in animal studies. The data measured in human studies were characterized by higher biological variation and the presence of outliers. The results from simulation studies indicated that the classifier Prediction Analysis for Microarrays (PAM) had the highest power when the class conditional feature distributions were Gaussian and outcome distributions were balanced. Random Forests was optimal when feature distributions were skewed and when class distributions were unbalanced. We provide a free open-source R statistical software library (MVpower) that implements the simulation strategy proposed in this paper. CONCLUSION: no single classifier had optimal performance under all settings. Simulation studies provide useful guidance for the design of biomedical studies involving high-dimensionality data.

The liver toxicity biomarker study: phase I design and preliminary results.

Drug-induced liver injury (DILI) is the primary adverse event that results in withdrawal of drugs from the market and a frequent reason for the failure of drug candidates in development. The Liver Toxicity Biomarker Study (LTBS) is an innovative approach to investigate DILI because it compares molecular events produced in vivo by compound pairs that (a) are similar in structure and mechanism of action, (b) are associated with few or no signs of liver toxicity in preclinical studies, and (c) show marked differences in hepatotoxic potential. The LTBS is a collaborative preclinical research effort in molecular systems toxicology between the National Center for Toxicological Research and BG Medicine, Inc., and is supported by seven pharmaceutical companies and three technology providers. In phase I of the LTBS, entacapone and tolcapone were studied in rats to provide results and information that will form the foundation for the design and implementation of phase II. Molecular analysis of the rat liver and plasma samples combined with statistical analyses of the resulting datasets yielded marker analytes, illustrating the value of the broad-spectrum, molecular systems analysis approach to studying pharmacological or toxicological effects.

Metabolomics-based systems biology and personalized medicine: moving towards n = 1 clinical trials?

Personalized medicine - defined as customized medical care for each patient's unique condition - in the broader context of personalized health, will make significant strides forward when a systems approach is implemented to achieve the ultimate in disease phenotyping and to create novel therapeutics that address system-wide molecular perturbations caused by disease processes. Combination drug therapies with individualized optimization are likely to become a major focus. Metabolomics incorporates the most advanced approaches to molecular phenotype system readout and provides the ideal theranostic technology platform for the discovery of biomarker patterns associated with healthy and diseased states, for use in personalized health monitoring programs and for the design of individualized interventions.

Lost in translation? Role of metabolomics in solving translational problems in drug discovery and development.

Too few drug discovery projects generate a marketed drug product, often because preclinical studies fail to predict the clinical experience with a drug candidate. Improving the success of preclinical-to-clinical translation is of paramount importance in optimizing the pharmaceutical value chain. Here, we advance the case for a molecular systems approach to crossing the preclinical-to-clinical translational chasm and for metabolomic analysis of readily accessible bodyfluids as a key technology in translational activities.:

Innovation: Rescuing drug discovery: in vivo systems pathology and systems pharmacology.

The pharmaceutical industry is currently beleaguered by close scrutiny from the financial community, regulators and the general public. Productivity, in terms of new drug approvals, has generally been falling for almost a decade and the safety of a number of highly successful drugs has recently been brought into question. Here, we discuss whether taking an in vivo systems approach to drug discovery and development could be the paradigm shift that rescues the industry.