Practical Aspects of Using Large Language Models to Screen Abstracts for Cardiovascular Drug Development: Cross-Sectional Study.

Unlabelled: Cardiovascular drug development requires synthesizing relevant literature about indications, mechanisms, biomarkers, and outcomes. This short study investigates the performance, cost, and prompt engineering trade-offs of 3 large language models accelerating the literature screening process for cardiovascular drug development applications.

Informatics and Artificial Intelligence-Guided Assessment of the Regulatory and Translational Research Landscape of First-in-Class Oncology Drugs in the United States, 2018-2022.

PURPOSE: Cancer drug development remains a critical but challenging process that affects millions of patients and their families. Using biomedical informatics and artificial intelligence (AI) approaches, we assessed the regulatory and translational research landscape defining successful first-in-class drugs for patients with cancer. METHODS: This is a retrospective observational study of all novel first-in-class drugs approved by the US Food and Drug Administration (FDA) from 2018 to 2022, stratified by cancer versus noncancer drugs. A biomedical informatics pipeline leveraging interoperability standards and ChatGPT performed integration and analysis of public databases provided by the FDA, National Institutes of Health, and WHO. RESULTS: Between 2018 and 2022, the FDA approved a total of 247 novel drugs, of which 107 (43.3%) were first-in-class drugs involving a new biologic target. Of these first-in-class drugs, 30 (28%) treatments were indicated for patients with cancer, including 19 (63.3%) for solid tumors and the remaining 11 (36.7%) for hematologic cancers. A median of 68 publications of basic, clinical, and other relevant translational science preceded successful FDA approval of first-in-class cancer drugs, with oncology-related treatments involving fewer median years of target-based research than therapies not related to cancer (33 v 43 years; P < .05). Overall, 94.4% of first-in-class drugs had at least 25 years of target-related research papers, while 85.5% of first-in-class drugs had at least 10 years of translational research publications. CONCLUSION: Novel first-in-class cancer treatments are defined by diverse clinical indications, personalized molecular targets, dependence on expedited regulatory pathways, and translational research metrics reflecting this complex landscape. Biomedical informatics and AI provide scalable, data-driven ways to assess and even address important challenges in the drug development pipeline.

Cross-Sectional Survey of Factors Contributing to COVID-19 Testing Hesitancy Among US Adults at Risk of Severe Outcomes from COVID-19.

INTRODUCTION: The United States Centers for Disease Control and Prevention (CDC) advises testing individuals for COVID-19 after exposure or if they display symptoms. However, a deeper understanding of demographic factors associated with testing hesitancy is necessary. METHODS: A US nationwide cross-sectional survey of adults with risk factors for developing severe COVID-19 ("high-risk" individuals) was conducted from August 18-September 5, 2023. Objectives included characterizing demographics and attitudes associated with COVID-19 testing. Inverse propensity weighting was used to weight the data to accurately reflect the high-risk adult US population as reflected in IQVIA medical claims data. We describe here the weighted results modeled to characterize demographic factors driving hesitancy. RESULTS: In the weighted sample of 5019 respondents at high risk for severe COVID-19, 58.2% were female, 37.8% were ≥ 65 years old, 77.1% were White, and 13.9% had a postgraduate degree. Overall, 67% were Non-testers (who indicated that they were unlikely or unsure of their likelihood of being tested within the next 6 months); these respondents were significantly more likely than Testers (who indicated a higher probability of testing within 6 months) to be female (60.2 vs. 54.1%; odds ratio [OR] [95% confidence interval (CI)], 1.3 [1.1‒1.4]), aged ≥ 65 years old (41.5 vs. 30.3%; OR [95% CI] compared with ages 18‒34 years, 0.6 [0.5‒0.7]), White (82.1 vs. 66.8%; OR [95% CI], 1.4 [1.1‒1.8]), and to identify as politically conservative (40.9 vs. 18.1%; OR [95% CI], 2.6 [2.3‒2.9]). In contrast, Testers were significantly more likely than Non-testers to have previous experience with COVID-19 testing, infection, or vaccination; greater knowledge regarding COVID-19 and testing; greater healthcare engagement; and concerns about COVID-19. CONCLUSIONS: Older, female, White, rural-dwelling, and politically conservative high-risk adults are the most likely individuals to experience COVID-19 testing hesitancy. Understanding these demographic factors will help guide strategies to improve US testing rates.

Patient Acceptability and Preferences for Solid Oral Dosage Form Drug Product Attributes: A Scoping Review.

Background: There is no consistent framework for patient-centric drug product design, despite the common understanding that drug product acceptability and preferences influence adherence and, therefore, drug product effectiveness. The aim of this review was to assess current understanding of patient acceptability and preferences for solid oral dosage form (SODF) drug product attributes, and the potential impact of these attributes on patient behaviors and outcomes. Patients and Methods: A scoping review was conducted. Embase, Ovid MEDLINE®, and PubMed® were searched for full-text articles published between January 2013 and May 2023. Following screening and assessment against predefined inclusion criteria, data were analyzed thematically. Results: Nineteen studies were included. Four overarching domains of drug product attributes were identified and summarized in a framework: appearance, swallowability, palatability, and handling. Each domain was informed by specific drug product attributes: texture, form, size, shape, color, marking, taste, mouthfeel, and smell. The most frequently studied domains were swallowability and appearance, while the most studied attributes were size, shape, and texture. Smell, marking, and mouthfeel were the least studied attributes. Texture intersected all domains, while form, shape, and size intersected appearance, swallowability, and handling. Swallowability and size appeared to be the key domain and attribute, respectively, to consider when designing drug products. Few studies explored the impact of drug product attributes on behaviors and outcomes. Conclusion: While existing studies of drug product attributes have focused on appearance and swallowability, this review highlighted the importance of two less well-understood domains-palatability and handling-in understanding patients' acceptability and preferences for SODF drug products. The framework provides a tool to facilitate patient-centric design of drug products, organizing and categorizing physical drug product attributes into four overarching domains (appearance, swallowability, palatability, and handling), encouraging researchers to comprehensively assess the impact of drug product attributes on patient acceptability, preferences, and outcomes.

Medicines come in a variety of types and forms. These include tablets and capsules. Factors, such as the size and shape of tablets, can affect how people take medicines. However, patients are rarely involved in designing the medicines that they take. In this study, researchers summarized 19 studies published between 2013 and 2023. They wanted to understand how different factors, like size and shape, affect patients' preferences, ability, and willingness to take medicines. Researchers focused on the "physical" aspects of medicines and found 4 common themes: 1) what they look like (appearance), 2) how easy they are to swallow (swallowability), 3) how they taste and feel in the mouth (palatability), and 4) how easy they are to handle (handling). Eight factors were also found: color, markings, shape, size, smell, taste, texture, and how a medicine feels in the mouth (mouthfeel). Most studies focused on what medicines look like and how easy they are to swallow. The factors that researchers mostly looked at were the size, shape, and texture of medicines. The design of medicines can impact patients of different ages, though there may be specific needs for certain groups of patients, including children, older adults, and people with certain diseases. Patient input should become a part of future medicines design to ensure their acceptability.

Using Patient Preferences in Health Technology Assessment: Evaluating Quality-Adjusted Survival Equivalents (QASE) for the Quantification of Non-health Benefits.

Interest in using patient preference (PP) data alongside traditional economic models in health technology assessment (HTA) is growing, including using PP data to quantify non-health benefits. However, this is limited by a lack of standardised methods. In this article, we describe a method for using discrete choice experiment (DCE) data to estimate the value of non-health benefits in terms of quality-adjusted survival equivalence (QASE), which is consistent with the concept of value prevalent among HTA agencies. We describe how PP data can be used to estimate QASE, assess the ability to test the face-validity of QASE estimates of changes in mode of administration calculated from five published DCE oncology studies and review the methodological and normative considerations associated with using QASE to support HTA. We conclude that QASE may have some methodological advantages over alternative methods, but this requires DCEs to estimate second-order effects between length and quality of life. In addition, empirical work has yet to be undertaken to substantiate this advantage and demonstrate the validity of QASE. Further work is also required to align QASE with normative objectives of HTA agencies. Estimating QASE would also have implications for the conduct of DCEs, including standardising and defining more clear attribute definitions.

Priority Outcomes in Sickle Cell Disease Treatment: Co-Creation and Implementation of a Preference Exercise With Patients and Caregivers to Inform Drug Development.

Involving patients as co-leaders and co-creators in research is key to reflecting the patient's voice in decision-making. However, co-creation of patient-centered data to inform decisions is rare, especially in early drug development where patient input is critical to prioritizing patient-relevant outcomes and endpoints for use in clinical trials. Despite the industry's growing commitment to patient centricity, most patients are excluded from sharing their expertise in research; more inclusive methods of engaging patients as research partners are needed. We describe a collaboration between a pharmaceutical company and a patient organization in co-leading and co-creating a program to understand priorities of patients and caregivers for treatment features and outcomes in sickle cell disease to inform endpoint selection in clinical development. The results of this program will be used as a basis for continued interaction between patients and the sponsor and to inform ongoing clinical development and evidence-generation activities. This case study demonstrates an approach to meaningful collaborations between patient organizations and pharmaceutical companies aimed at including the patient's voice early in the medical product lifecycle.

Developing, optimizing, and evaluating patient infographics for diagnosing cardiac amyloidosis.

Objective: Advancements in diagnostics and treatment options for cardiac amyloidosis have improved patient outcomes, yet few patient education materials exist to help patients understand the disease and diagnosis process. We sought to develop and evaluate a set of plain language, patient-centered infographics describing the condition and common diagnostic tests. Methods: Using health literacy best practices, we developed 7 infographics which were further revised based on multilevel stakeholder feedback. To evaluate the materials, we recruited 100 patients from healthcare settings in Chicago, IL; participants completed a web-assisted interview during which they were randomized 1:1 to first view either our infographics or a standard material. Participants completed a knowledge assessment on their assigned material and subsequently reported impressions of both materials. Results: No differences were found between study arms in knowledge. The infographics took significantly less time to read and were more highly rated by participants in terms of appearance and understandability. Over two-thirds of participants preferred the infographics to the standard. Conclusions: The infographics created may improve the learning process about a complex condition and diagnosis process unknown to most adults. Innovation: These infographics are the first of their kind for cardiac amyloidosis and were created using health literacy best practices.

Genetic associations at regulatory phenotypes improve fine-mapping of causal variants for 12 immune-mediated diseases.

The resolution of causal genetic variants informs understanding of disease biology. We used regulatory quantitative trait loci (QTLs) from the BLUEPRINT, GTEx and eQTLGen projects to fine-map putative causal variants for 12 immune-mediated diseases. We identify 340 unique loci that colocalize with high posterior probability (≥98%) with regulatory QTLs and apply Bayesian frameworks to fine-map associations at each locus. We show that fine-mapping credible sets derived from regulatory QTLs are smaller compared to disease summary statistics. Further, they are enriched for more functionally interpretable candidate causal variants and for putatively causal insertion/deletion (INDEL) polymorphisms. Finally, we use massively parallel reporter assays to evaluate candidate causal variants at the ITGA4 locus associated with inflammatory bowel disease. Overall, our findings suggest that fine-mapping applied to disease-colocalizing regulatory QTLs can enhance the discovery of putative causal disease variants and enhance insights into the underlying causal genes and molecular mechanisms.

Multimethod quantitative benefit-risk assessment of treatments for moderate-to-severe osteoarthritis.

OBJECTIVE: Demonstrate how benefit-risk profiles of systemic treatments for moderate-to-severe osteoarthritis (OA) can be compared using a quantitative approach accounting for patient preference. STUDY DESIGN AND SETTING: This study used a multimethod benefit-risk modelling approach to quantifiably compare treatments of moderate-to-severe OA. In total four treatments and placebo were compared. Comparisons were based on four attributes identified as most important to patients. Patient Global Assessment of Osteoarthritis was included as a favourable effect. Unfavourable effects, or risks, included opioid dependence, nonfatal myocardial infarction and rapidly progressive OA leading to total joint replacement. Clinical data from randomized clinical trials, a meta-analysis of opioid dependence and a long-term study of celecoxib were mapped into value functions and weighted with patient preferences from a discrete choice experiment. RESULTS: Lower-dose NGFi had the highest weighted net benefit-risk score (0.901), followed by higher-dose NGFi (0.889) and NSAIDs (0.852), and the lowest score was for opioids (0.762). Lower-dose NGFi was the highest-ranked treatment option even when assuming a low incidence (0.34% instead of 4.7%) of opioid dependence (ie, opioid benefit-risk score 808) and accounting for both the uncertainty in clinical effect estimates (first rank probability 46% vs 20% for NSAIDs) and imprecision in patient preference estimates (predicted choice probability 0.26, 95% confidence interval [CI] 0.25-0.28 vs 0.21, 95% CI 0.19-0.23 for NSAIDs). CONCLUSION: The multimethod approach to quantitative benefit-risk modelling allowed the interpretation of clinical data from the patient perspective while accounting for uncertainties in the clinical effect estimates and imprecision in patient preferences.

Genetic perturbation of PU.1 binding and chromatin looping at neutrophil enhancers associates with autoimmune disease.

Neutrophils play fundamental roles in innate immune response, shape adaptive immunity, and are a potentially causal cell type underpinning genetic associations with immune system traits and diseases. Here, we profile the binding of myeloid master regulator PU.1 in primary neutrophils across nearly a hundred volunteers. We show that variants associated with differential PU.1 binding underlie genetically-driven differences in cell count and susceptibility to autoimmune and inflammatory diseases. We integrate these results with other multi-individual genomic readouts, revealing coordinated effects of PU.1 binding variants on the local chromatin state, enhancer-promoter contacts and downstream gene expression, and providing a functional interpretation for 27 genes underlying immune traits. Collectively, these results demonstrate the functional role of PU.1 and its target enhancers in neutrophil transcriptional control and immune disease susceptibility.

Paired rRNA-depleted and polyA-selected RNA sequencing data and supporting multi-omics data from human T cells.

Both poly(A) enrichment and ribosomal RNA depletion are commonly used for RNA sequencing. Either has its advantages and disadvantages that may lead to biases in the downstream analyses. To better access these effects, we carried out both ribosomal RNA-depleted and poly(A)-selected RNA-seq for CD4+ T naive cells isolated from 40 healthy individuals from the Blueprint Project. For these 40 individuals, the genomic and epigenetic data were also available. This dataset offers a unique opportunity to understand how library construction influences differential gene expression, alternative splicing and molecular QTL (quantitative loci) analyses for human primary cells.

No Evidence of Persistence or Inheritance of Mitochondrial DNA Copy Number in Holocaust Survivors and Their Descendants.

Mitochondrial DNA copy number has been previously shown to be elevated with severe and chronic stress, as well as stress-related pathology like Major Depressive Disorder (MDD) and post-traumatic stress disorder (PTSD). While experimental data point to likely recovery of mtDNA copy number changes after the stressful event, time needed for full recovery and whether it can be achieved are still unknown. Further, while it has been shown that stress-related mtDNA elevation affects multiple tissues, its specific consequences for oogenesis and maternal inheritance of mtDNA has never been explored. In this study, we used qPCR to quantify mtDNA copy number in 15 Holocaust survivors and 102 of their second- and third-generation descendants from the Czech Republic, many of whom suffer from PTSD, and compared them to controls in the respective generations. We found no significant difference in mtDNA copy number in the Holocaust survivors compared to controls, whether they have PTSD or not, and no significant elevation in descendants of female Holocaust survivors as compared to descendants of male survivors or controls. Our results showed no evidence of persistence or inheritance of mtDNA changes in Holocaust survivors, though that does not rule out effects in other tissues or mitigating mechanism for such changes.

Correction to: Functional variation in allelic methylomes underscores a strong genetic contribution and reveals novel epigenetic alterations in the human epigenome.

Following publication of the original article [1], the authors reported an error in Additional file 1.

Identification of Rigosertib for the Treatment of Recessive Dystrophic Epidermolysis Bullosa-Associated Squamous Cell Carcinoma.

PURPOSE: Squamous cell carcinoma (SCC) of the skin is the leading cause of death in patients with the severe generalized form of the genetic disease recessive dystrophic epidermolysis bullosa (RDEB). Although emerging data are identifying why patients suffer this fatal complication, therapies for treatment of RDEB SCC are in urgent need.Experimental Design: We previously identified polo-like kinase 1 (PLK1) as a therapeutic target in skin SCC, including RDEB SCC. Here, we undertake a screen of 6 compounds originally designated as PLK1 inhibitors, and detail the efficacy of the lead compound, the multipathway allosteric inhibitor ON-01910, for targeting RDEB SCC in vitro and in vivo. RESULTS: ON-01910 (or rigosertib) exhibited significant specificity for RDEB SCC: in culture rigosertib induced apoptosis in 10 of 10 RDEB SCC keratinocyte populations while only slowing the growth of normal primary skin cells at doses 2 orders of magnitude higher. Furthermore, rigosertib significantly inhibited the growth of two RDEB SCC in murine xenograft studies with no apparent toxicity. Mechanistically, rigosertib has been shown to inhibit multiple signaling pathways. Comparison of PLK1 siRNA with MEK inhibition, AKT inhibition, and the microtubule-disrupting agent vinblastine in RDEB SCC shows that only PLK1 reduction exhibits a similar sensitivity profile to rigosertib. CONCLUSIONS: These data support a "first in RDEB" phase II clinical trial of rigosertib to assess tumor targeting in patients with late stage, metastatic, and/or unresectable SCC.

Dose Titration of Pregabalin in Patients with Painful Diabetic Peripheral Neuropathy: Simulation Based on Observational Study Patients Enriched with Data from Randomized Studies.

INTRODUCTION: Achieving a therapeutic response to pregabalin in patients with painful diabetic peripheral neuropathy (pDPN) requires adequate upward dose titration. Our goal was to identify relationships between titration and response to pregabalin in patients with pDPN. METHODS: Data were integrated from nine randomized, placebo-controlled clinical trials as well as one 6-week open-label observational study conducted by 5808 physicians (2642 patients with pDPN) in standard outpatient settings in Germany. These studies evaluated pregabalin for treatment of pDPN. Using these data, we examined "what if" scenarios using a microsimulation platform that integrates data from randomized and observational sources as well as autoregressive-moving-average with exogenous inputs models that predict pain outcomes, taking into account weekly changes in pain, sleep interference, dose, and other patient characteristics that were unchanging. RESULTS: Final pain levels were significantly different depending on dose changes (P < 0.0001), with greater proportions improving with upward titration regardless of baseline pain severity. Altogether, 78.5% of patients with pDPN had 0-1 dose change, and 15.2% had ≥ 2 dose changes. Simulation demonstrated that the 4.8% of inadequately titrated patients who did not improve/very much improve their pain levels would have benefited from ≥ 2 dose changes. Patient satisfaction with tolerability (range 90.3-96.2%) was similar, regardless of baseline pain severity, number of titrations, or extent of improvement, suggesting that tolerability did not influence treatment response patterns. CONCLUSION: Upward dose titration reduced pain in patients with pDPN who actually received it. Simulation also predicted pain reduction in an inadequately titrated nonresponder subgroup of patients had they actually received adequate titration. The decision not to uptitrate must have been driven by factors other than tolerability. FUNDING: Pfizer, Inc.

Integrating data from randomized controlled trials and observational studies to predict the response to pregabalin in patients with painful diabetic peripheral neuropathy.

BACKGROUND: More patient-specific medical care is expected as more is learned about variations in patient responses to medical treatments. Analytical tools enable insights by linking treatment responses from different types of studies, such as randomized controlled trials (RCTs) and observational studies. Given the importance of evidence from both types of studies, our goal was to integrate these types of data into a single predictive platform to help predict response to pregabalin in individual patients with painful diabetic peripheral neuropathy (pDPN). METHODS: We utilized three pivotal RCTs of pregabalin (398 North American patients) and the largest observational study of pregabalin (3159 German patients). We implemented a hierarchical cluster analysis to identify patient clusters in the Observational Study to which RCT patients could be matched using the coarsened exact matching (CEM) technique, thereby creating a matched dataset. We then developed autoregressive moving average models (ARMAXs) to estimate weekly pain scores for pregabalin-treated patients in each cluster in the matched dataset using the maximum likelihood method. Finally, we validated ARMAX models using Observational Study patients who had not matched with RCT patients, using t tests between observed and predicted pain scores. RESULTS: Cluster analysis yielded six clusters (287-777 patients each) with the following clustering variables: gender, age, pDPN duration, body mass index, depression history, pregabalin monotherapy, prior gabapentin use, baseline pain score, and baseline sleep interference. CEM yielded 1528 unique patients in the matched dataset. The reduction in global imbalance scores for the clusters after adding the RCT patients (ranging from 6 to 63% depending on the cluster) demonstrated that the process reduced the bias of covariates in five of the six clusters. ARMAX models of pain score performed well (R 2 : 0.85-0.91; root mean square errors: 0.53-0.57). t tests did not show differences between observed and predicted pain scores in the 1955 patients who had not matched with RCT patients. CONCLUSION: The combination of cluster analyses, CEM, and ARMAX modeling enabled strong predictive capabilities with respect to pain scores. Integrating RCT and Observational Study data using CEM enabled effective use of Observational Study data to predict patient responses.

Functional variation in allelic methylomes underscores a strong genetic contribution and reveals novel epigenetic alterations in the human epigenome.

BACKGROUND: The functional impact of genetic variation has been extensively surveyed, revealing that genetic changes correlated to phenotypes lie mostly in non-coding genomic regions. Studies have linked allele-specific genetic changes to gene expression, DNA methylation, and histone marks but these investigations have only been carried out in a limited set of samples. RESULTS: We describe a large-scale coordinated study of allelic and non-allelic effects on DNA methylation, histone mark deposition, and gene expression, detecting the interrelations between epigenetic and functional features at unprecedented resolution. We use information from whole genome and targeted bisulfite sequencing from 910 samples to perform genotype-dependent analyses of allele-specific methylation (ASM) and non-allelic methylation (mQTL). In addition, we introduce a novel genotype-independent test to detect methylation imbalance between chromosomes. Of the ~2.2 million CpGs tested for ASM, mQTL, and genotype-independent effects, we identify ~32% as being genetically regulated (ASM or mQTL) and ~14% as being putatively epigenetically regulated. We also show that epigenetically driven effects are strongly enriched in repressed regions and near transcription start sites, whereas the genetically regulated CpGs are enriched in enhancers. Known imprinted regions are enriched among epigenetically regulated loci, but we also observe several novel genomic regions (e.g., HOX genes) as being epigenetically regulated. Finally, we use our ASM datasets for functional interpretation of disease-associated loci and show the advantage of utilizing naïve T cells for understanding autoimmune diseases. CONCLUSIONS: Our rich catalogue of haploid methylomes across multiple tissues will allow validation of epigenome association studies and exploration of new biological models for allelic exclusion in the human genome.

Genome-wide analysis of differential transcriptional and epigenetic variability across human immune cell types.

BACKGROUND: A healthy immune system requires immune cells that adapt rapidly to environmental challenges. This phenotypic plasticity can be mediated by transcriptional and epigenetic variability. RESULTS: We apply a novel analytical approach to measure and compare transcriptional and epigenetic variability genome-wide across CD14+CD16- monocytes, CD66b+CD16+ neutrophils, and CD4+CD45RA+ naïve T cells from the same 125 healthy individuals. We discover substantially increased variability in neutrophils compared to monocytes and T cells. In neutrophils, genes with hypervariable expression are found to be implicated in key immune pathways and are associated with cellular properties and environmental exposure. We also observe increased sex-specific gene expression differences in neutrophils. Neutrophil-specific DNA methylation hypervariable sites are enriched at dynamic chromatin regions and active enhancers. CONCLUSIONS: Our data highlight the importance of transcriptional and epigenetic variability for the key role of neutrophils as the first responders to inflammatory stimuli. We provide a resource to enable further functional studies into the plasticity of immune cells, which can be accessed from: http://blueprint-dev.bioinfo.cnio.es/WP10/hypervariability .

The methanogenic redox cofactor F420 is widely synthesized by aerobic soil bacteria.

F420 is a low-potential redox cofactor that mediates the transformations of a wide range of complex organic compounds. Considered one of the rarest cofactors in biology, F420 is best known for its role in methanogenesis and has only been chemically identified in two phyla to date, the Euryarchaeota and Actinobacteria. In this work, we show that this cofactor is more widely distributed than previously reported. We detected the genes encoding all five known F420 biosynthesis enzymes (cofC, cofD, cofE, cofG and cofH) in at least 653 bacterial and 173 archaeal species, including members of the dominant soil phyla Proteobacteria, Chloroflexi and Firmicutes. Metagenome datamining validated that these genes were disproportionately abundant in aerated soils compared with other ecosystems. We confirmed through high-performance liquid chromatography analysis that aerobically grown stationary-phase cultures of three bacterial species, Paracoccus denitrificans, Oligotropha carboxidovorans and Thermomicrobium roseum, synthesized F420, with oligoglutamate sidechains of different lengths. To understand the evolution of F420 biosynthesis, we also analyzed the distribution, phylogeny and genetic organization of the cof genes. Our data suggest that although the Fo precursor to F420 originated in methanogens, F420 itself was first synthesized in an ancestral actinobacterium. F420 biosynthesis genes were then disseminated horizontally to archaea and other bacteria. Together, our findings suggest that the cofactor is more significant in aerobic bacterial metabolism and soil ecosystem composition than previously thought. The cofactor may confer several competitive advantages for aerobic soil bacteria by mediating their central metabolic processes and broadening the range of organic compounds they can synthesize, detoxify and mineralize.

Pregabalin for the Treatment of Drug and Alcohol Withdrawal Symptoms: A Comprehensive Review.

Treatments for physical dependence and associated withdrawal symptoms following the abrupt discontinuation of prescription drugs (such as opioids and benzodiazepines), nicotine, alcohol, and cannabinoids are available, but there is still a need for new and more effective therapies. This review examines evidence supporting the potential use of pregabalin, an α2δ voltage-gated calcium channel subunit ligand, for the treatment of physical dependence and associated withdrawal symptoms. A literature search of the MEDLINE and Cochrane Library databases up to and including 11 December 2015 was conducted. The search term used was '(dependence OR withdrawal) AND pregabalin'. No other date limits were set and no language restrictions were applied. Works cited in identified articles were cross-referenced and personal archives of references also searched. Articles were included based on the expert opinions of the authors. There is limited evidence supporting the role of pregabalin for the treatment of physical dependence and accompanying withdrawal symptoms associated with opioids, benzodiazepines, nicotine, cannabinoids, and alcohol, although data from randomized controlled studies are sparse. However, the current evidence is promising and provides a platform for future studies, including appropriate randomized, placebo- and/or comparator-controlled studies, to further explore the efficacy and safety of pregabalin for the treatment of withdrawal symptoms. Given the potential for pregabalin misuse or abuse, particularly in individuals with a previous history of substance abuse, clinicians should exercise caution when using pregabalin in this patient population.