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Background: In many countries, neuro-muscular blocking agents (NMBAs) are the first cause of perioperative anaphylaxis. Epidemiological studies identified pholcodine, a quaternary ammonium-containing opiate as one of the sensitization sources. However, NMBA anaphylaxis exists in countries where pholcodine was unavailable, prompting the hypothesis of other sensitizing molecules, most likely quaternary ammonium compounds (QACs).Indeed, QACs are commonly used as disinfectants, antiseptics, preservatives, and detergents. Occupational exposure to QACs has been reported as a risk factor for NMBA anaphylaxis, but little is known about the sensitization mechanism and the capacity of these molecules to elicit an immune response. We aimed to establish the immunogenicity of QACs representative of the main existing chemical structures. Methods: We measured the sensitization potential of seven QACs (two polyquaterniums, three alkyl-ammoniums and two aromatic ammoniums) by using two standard dendritic cells (DCs) models (THP-1 cell line and monocyte derived-dendritic cells). The allergenicity of the sensitizing compounds was further tested in heterologous and autologous T-cell-DC co-culture models. Results: Amongst the seven molecules tested, four could modulate activation markers on DCs, and thus can be classified as chemical sensitizers (polyquaterniums-7 and -10, ethylhexadecyldimethylammonium and benzethonium). This activation was accompanied by the secretion of pro-inflammatory and maturation cytokines. Furthermore, activation by polyquaternium-7 could induce T-cell proliferation in heterologous and autologous coculture models, demonstrating that this molecule can induce a specific CD4+ T cell response. Conclusions: We provide evidence at the cellular level that some QACs can elicit an immune response, which could be in line with the hypothesis of these molecules' role in NMBA sensitization.

BackgroundKey characteristics (KCs), properties of agents or exposures that confer potential hazard, have been developed for carcinogens and other toxicant classes. KCs have been used in the systematic assessment of hazards and to identify assay and data gaps that limit screening and risk assessment. Many of the mechanisms through which pharmaceuticals and occupational or environmental agents modulate immune function are well recognized. Thus KCs could be identified for immunoactive substances and applied to improve hazard assessment of immunodulatory agents.ObjectivesThe goal was to generate a consensus-based synthesis of scientific evidence describing the KCs of agents known to cause immunotoxicity and potential applications, such as assays to measure the KCs.MethodsA committee of 18 experts with diverse specialties identified 10 KCs of immunotoxic agents, namely, 1) covalently binds to proteins to form novel antigens, 2) affects antigen processing and presentation, 3) alters immune cell signaling, 4) alters immune cell proliferation, 5) modifies cellular differentiation, 6) alters immune cell-cell communication, 7) alters effector function of specific cell types, 8) alters immune cell trafficking, 9) alters cell death processes, and 10) breaks down immune tolerance. The group considered how these KCs could influence immune processes and contribute to hypersensitivity, inappropriate enhancement, immunosuppression, or autoimmunity.DiscussionKCs can be used to improve efforts to identify agents that cause immunotoxicity via one or more mechanisms, to develop better testing and biomarker approaches to evaluate immunotoxicity, and to enable a more comprehensive and mechanistic understanding of adverse effects of exposures on the immune system. https://doi.org/10.1289/EHP10800.

Although an extrapolation from the clinical experience in adults can often be considered to support the pediatric use for most pharmaceutical compounds, differences in safety profiles between adult and pediatric patients can be observed. The developing immune system may be affected due to exaggerated pharmacological or non-expected effects of a new drug. Toxicology studies in juvenile animals could therefore be required to better evaluate the safety profile of any new pharmaceutical compound targeting the pediatric population. The Göttingen minipig is now considered a useful non-rodent species for non-clinical safety testing of human pharmaceuticals. However, knowledge on the developing immune system in juvenile minipigs is still limited. The objective of the work reported here was to evaluate across-age proportions of main immune cells circulating in blood or residing in lymphoid organs (thymus, spleen, lymph nodes) in Göttingen Minipigs. In parallel, the main immune cell populations from healthy and immunocompromised piglets were compared following treatment with cyclosporin A (CsA) at 10 mg/kg/day for 4 wk until weaning. The study also assessed functionality of immune responses using an in-vivo model after “Keyhole limpet hemocyanin” (KLH) immunization and an ex-vivo lymph proliferation assay after stimulation with Concanavalin A. The results demonstrated variations across age in circulating immune cell populations including CD21+ B-cells, αβ-T- and γδ-T-cells, NK cells, and monocytes. CsA-induced changes in immune functions were only partially recovered by 5 mo after the end of treatment, whereas the immune cell populations affected by the treatment returned to normal levels in animals of the same age. Taken together, the study here shows that in this model, the immune function endpoints were more sensitive than the immunophenotyping endpoints.

Biogenic isoprene emissions play a very important role in atmospheric chemistry. These emissions are strongly dependent on various environmental conditions, such as temperature, solar radiation, plant water stress, ambient ozone and CO2 concentrations, and soil moisture. Current biogenic emission models (i.e., Model of Emissions of Gases and Aerosols from Nature, MEGAN) can simulate emission responses to some of the major driving variables, such as short-term variations in temperature and solar radiation, but the other factors are either missing or poorly represented. In this paper, we propose a new modeling approach that considers the physiological effects of drought stress on plant photosynthesis and isoprene emissions for use in the MEGAN3 biogenic emission model. We test the MEGAN3 approach by integrating the algorithm into the existing MEGAN2.1 biogenic emission model framework embedded into the global Community Land Model of the Community Earth System Model (CLM4.5/CESM1.2). Single-point simulations are compared against available field measurements at the Missouri Ozarks AmeriFlux (MOFLUX) field site. The modeling results show that the MEGAN3 approach of using of a photosynthesis parameter (Vcmax) and soil wetness factor (βt) to determine the drought activity factor leads to better simulated isoprene emissions in non-drought and drought periods. The global simulation with the MEGAN3 approach predicts a 17% reduction in global annual isoprene emissions, in comparison to the value predicted using the default CLM4.5/MEGAN2.1 without any drought effect. This reduction leads to changes in surface ozone and oxidants in the areas where the reduction of isoprene emissions is observed. Based on the results presented in this study, we conclude that it is important to simulate the drought-induced response of biogenic isoprene emission accurately in the coupled Earth System model.

Soon after the release of the new anti-COVID mRNA vaccines, reports came in from the US and the UK of anaphylactic reactions. Fueled by the necessary caution toward these new vaccine platforms, these reports had a great impact and were largely commented upon in the scientific literature and global media. The current estimated frequency is of 5 cases per million doses. Very little biological data are presented in the literature to support the anaphylaxis diagnosis in these patients in addition to skin tests. Allergic reactions to vaccines are rare and mostly due to vaccine excipient. Therefore, the poly-ethylene-glycol (PEG) present in both mRNA formulation, and already known to be immunogenic, was soon suspected to be the potential culprit. Several hypersensitivity mechanisms to PEG or to other vaccine components can be suspected, even if the classical IgE-dependent anaphylaxis seems to be one of the most plausible candidates. In the early 2022, the international guidelines recommended to perform skin prick tests and basophil activation tests (BAT) in people experiencing allergic reaction to the first dose of COVID-19 vaccine or with a history of PEG allergy. The aim of this review is to discuss the main potential mechanisms of immediate allergy to COVID19 vaccines based on published data, together with the various techniques used to confirm or not sensitization to one component.

Objectives Around 30% of patients with rheumatoid arthritis (RA) do not respond to tumour necrosis factor inhibitors (TNFi). We aimed to predict patient response to TNFi using machine learning on simple clinical and biological data.Methods We used data from the RA ESPOIR cohort to train our models. The endpoints were the EULAR response and the change in Disease Activity Score (DAS28). We compared the performances of multiple models (linear regression, random forest, XGBoost and CatBoost) on the training set and cross-validated them using the area under the receiver operating characteristic curve (AUROC) or the mean squared error. The best model was then evaluated on a replication cohort (ABIRISK).Results We included 161 patients from ESPOIR and 118 patients from ABIRISK. The key selected features were DAS28, lymphocytes, ALT (aspartate aminotransferase), neutrophils, age, weight, and smoking status. When predicting EULAR response, CatBoost achieved the best performances of the four tested models. It reached an AUROC of 0.72 (0.68–0.73) on the train set (ESPOIR). Better results were obtained on the train set when etanercept and monoclonal antibodies were analysed separately. On the test set (ABIRISK), these models respectively achieved on AUROC of 0.70 (0.57–0.82) and 0.71 (0.55–0.86). Two decision thresholds were tested. The first prioritised a high confidence in identifying responders and yielded a confidence up to 90% for predicting response. The second prioritised a high confidence in identifying inadequate responders and yielded a confidence up to 70% for predicting non-response. The change in DAS28 was predicted with an average error of 1.1 DAS28 points.Conclusion The machine learning models developed allowed predicting patient response to TNFi exclusively using data available in clinical routine.

Abstract Background Upon treatment with biopharmaceuticals, the immune system may produce anti-drug antibodies (ADA) that inhibit the therapy. Up to 40% of multiple sclerosis patients treated with interferon β (IFNβ) develop ADA, for which a genetic predisposition exists. Here, we present a genome-wide association study on ADA and predict the occurrence of antibodies in multiple sclerosis patients treated with different interferon β preparations. Methods We analyzed a large sample of 2757 genotyped and imputed patients from two cohorts (Sweden and Germany), split between a discovery and a replication dataset. Binding ADA (bADA) levels were measured by capture-ELISA, neutralizing ADA (nADA) titers using a bioassay. Genome-wide association analyses were conducted stratified by cohort and treatment preparation, followed by fixed-effects meta-analysis. Results Binding ADA levels and nADA titers were correlated and showed a significant heritability (47% and 50%, respectively). The risk factors differed strongly by treatment preparation: The top-associated and replicated variants for nADA presence were the HLA-associated variants rs77278603 in IFNβ-1a s.c.- (odds ratio (OR) = 3.55 (95% confidence interval = 2.81–4.48), p = 2.1 × 10−26) and rs28366299 in IFNβ-1b s.c.-treated patients (OR = 3.56 (2.69–4.72), p = 6.6 × 10−19). The rs77278603-correlated HLA haplotype DR15-DQ6 conferred risk specifically for IFNβ-1a s.c. (OR = 2.88 (2.29–3.61), p = 7.4 × 10−20) while DR3-DQ2 was protective (OR = 0.37 (0.27–0.52), p = 3.7 × 10−09). The haplotype DR4-DQ3 was the major risk haplotype for IFNβ-1b s.c. (OR = 7.35 (4.33–12.47), p = 1.5 × 10−13). These haplotypes exhibit large population-specific frequency differences. The best prediction models were achieved for ADA in IFNβ-1a s.c.-treated patients. Here, the prediction in the Swedish cohort showed AUC = 0.91 (0.85–0.95), sensitivity = 0.78, and specificity = 0.90; patients with the top 30% of genetic risk had, compared to patients in the bottom 30%, an OR = 73.9 (11.8–463.6, p = 4.4 × 10−6) of developing nADA. In the German cohort, the AUC of the same model was 0.83 (0.71–0.92), sensitivity = 0.80, specificity = 0.76, with an OR = 13.8 (3.0–63.3, p = 7.5 × 10−4). Conclusions We identified several HLA-associated genetic risk factors for ADA against interferon β, which were specific for treatment preparations and population backgrounds. Genetic prediction models could robustly identify patients at risk for developing ADA and might be used for personalized therapy recommendations and stratified ADA screening in clinical practice. These analyses serve as a roadmap for genetic characterizations of ADA against other biopharmaceutical compounds.

Despite being assayed on commercialized DNA chips, the X chromosome is commonly excluded from genome-wide association studies (GWAS). One of the reasons is the complexity to analyze the data taking into account the X-chromosome inactivation (XCI) process in women and in particular the XCI process with a potentially skewed pattern. This is the case when investigating the role of X-linked genetic variants in the occurrence of anti-drug antibodies (ADAs) in patients with autoimmune diseases treated by biotherapies. In this context, we propose a novel test statistic for selecting loci of interest harbored by the X chromosome that are associated with time-to-event data taking into account skewed X-inactivation (XCI-S). The proposed statistic relies on a semi-parametric additive hazard model and is straightforward to implement. Results from the simulation study show that the test provides higher power gains than the score tests from the Cox model (under XCI process or its escape) and the Xu et al.'s XCI-S likelihood ratio test. We applied the test to the data from the real-world observational multicohort study set-up by the IMI-funded ABIRISK consortium for identifying X chromosome susceptibility loci for drug immunogenicity in patients with autoimmune diseases treated by biotherapies. The test allowed us to select two single nucleotide polymorphisms (SNPs) with high linkage disequilibrium (rs5991366 and rs5991394) located in the cytoband Xp22.2 that would have been overlooked by the Cox score tests and the Xu et al.'s XCI-S likelihood ratio test. Both SNPs showed a similar protective effect for drug immunogenicity without any occurrence of ADA positivity for the homozygous females and hemizygous males for the alternative allele. To our knowledge, this is the first study to investigate the association between X chromosome loci and the occurrence of anti-drug antibodies. We think that more X-Chromosome GWAS should be performed and that the test is well-suited for identifying X-Chromosome SNPs, while taking into account all patterns of the skewed X-Chromosome inactivation process.

Allergic contact dermatitis (ACD) is a complex skin pathology occurring in reaction against environmental substances found in the workplace (cement, hair dyes, textile dyes), in the private environment (e.g., household products, cosmetic ingredients), or following skin exposure to drugs. Many cells are involved in the initiation of ACD during the sensitization phase. The four key events (KE) of skin sensitization AOP are covalent binding to skin proteins (KE1), keratinocyte activation (KE2), activation of DCs (KE3), and T-cell activation and proliferation (KE4), leading to the adverse outcome of ACD. Dendritic cells (DCs) are thus playing a key role in ACD pathophysiology. Indeed, in the presence of chemical sensitizers, DCs migrate from the skin to the draining lymph nodes and present peptide-chemical conjugates to T cells, leading to their activation and proliferation. In vitro methods have been actively developed to assess the activation of DCs by chemicals to establish a reliable in vitro sensitization test. Therefore, this review will detail the most used methods and protocols to develop DC models in vitro. Three different models of DCs will be addressed: 1) DCs derived from Cord Blood (CD34-DCs), 2) DCs derived from Monocytes (Mo-DCs), and 3) DCs derived from mice Bone-Marrow (BM-DCs). In addition, a model of exposition to contact sensitizers to assess KE3 of skin sensitization will be detailed for each of the models presented.

IntroductionWe previously reported a specific defect of rheumatoid arthritis (RA) monocyte polarization to anti-inflammatory M2-like macrophages related to increased miR-155 expression in all RA patients except those receiving adalimumab (ADA). In this longitudinal study, we examined whether different tumor necrosis factor inhibitors were able to restore monocyte polarization to M2-like macrophages and their effect on the transcriptomic signature.MethodsM2-like polarization induced by human serum AB was studied in 7 healthy donors and 20 RA patients included in the ABIRA cohort before and 3 months after starting ADA or etanercept (ETA). The differential gene expression of M2- and M1-related transcripts was studied in macrophage-derived monocytes after differentiation.ResultsAt baseline, RA monocytes showed a defect of polarization to M2-like macrophages as compared with healthy donor monocytes, which was negatively correlated with disease activity. M2-like polarization from circulating monocytes was restored only with ADA and not ETA treatment. The transcriptomic signature demonstrated downregulation of M2-related transcripts and upregulation of M1-related transcripts in active RA. In patients receiving ADA, the transcriptomic signature of M2-related transcripts was restored.ConclusionThis longitudinal study demonstrates that ADA but not ETA is able to restore the M2-like polarization of monocytes that is defective in RA.

Considerable amounts and varieties of biogenic volatile organic compounds (BVOCs) are exchanged between vegetation and the surrounding air. These BVOCs play key ecological and atmospheric roles that must be adequately represented for accurately modeling the coupled biosphere-atmosphere-climate earth system. One key uncertainty in existing models is the response of BVOC fluxes to an important global change process: drought. We describe the diurnal and seasonal variation in isoprene, monoterpene, and methanol fluxes from a temperate forest ecosystem before, during, and after an extreme 2012 drought event in the Ozark region of the central USA. BVOC fluxes were dominated by isoprene, which attained high emission rates of up to 35.4 mg m(-2)  h(-1) at midday. Methanol fluxes were characterized by net deposition in the morning, changing to a net emission flux through the rest of the daylight hours. Net flux of CO2 reached its seasonal maximum approximately a month earlier than isoprenoid fluxes, which highlights the differential response of photosynthesis and isoprenoid emissions to progressing drought conditions. Nevertheless, both processes were strongly suppressed under extreme drought, although isoprene fluxes remained relatively high compared to reported fluxes from other ecosystems. Methanol exchange was less affected by drought throughout the season, confirming the complex processes driving biogenic methanol fluxes. The fraction of daytime (7-17 h) assimilated carbon released back to the atmosphere combining the three BVOCs measured was 2% of gross primary productivity (GPP) and 4.9% of net ecosystem exchange (NEE) on average for our whole measurement campaign, while exceeding 5% of GPP and 10% of NEE just before the strongest drought phase. The meganv2.1 model correctly predicted diurnal variations in fluxes driven mainly by light and temperature, although further research is needed to address model BVOC fluxes during drought events.

Abstract Background With the growth in use of biotherapic drugs in various medical fields, the occurrence of anti-drug antibodies represents nowadays a serious issue. This immune response against a drug can be due either to pre-existing antibodies or to the novel production of antibodies from B-cell clones by a fraction of the exposed subjects. Identifying genetic markers associated with the immunogenicity of biotherapeutic drugs may provide new opportunities for risk stratification before the introduction of the drug. However, real-world investigations should take into account that the population under study is a mixture of pre-immune, immune-reactive and immune-tolerant subjects. Method In this work, we propose a novel test for assessing the effect of genetic markers on drug immunogenicity taking into account that the population under study is a mixed one. This test statistic is derived from a novel two-part semiparametric improper survival model which relies on immunological mechanistic considerations. Results Simulation results show the good behavior of the proposed statistic as compared to a two-part logrank test. In a study on drug immunogenicity, our results highlighted findings that would have been discarded when considering classical tests. Conclusion We propose a novel test that can be used for analyzing drug immunogenicity and is easy to implement with standard softwares. This test is also applicable for situations where one wants to test the equality of improper survival distributions of semi-continuous outcomes between two or more independent groups.

Drug hypersensitivity reactions (DHRs) are a type of adverse drug reaction that can occur with different classes of drugs and affect multiple organ systems and patient populations. DHRs can be classified as allergic or non-allergic based on the cellular mechanisms involved. Whereas nonallergic reactions rely mainly on the innate immune system, allergic reactions involve the generation of an adaptive immune response. Consequently, drug allergies are DHRs for which an immunological mechanism, with antibody and/or T cell, is demonstrated. Despite decades of research, methods to predict the potential for a new chemical entity to cause DHRs or to correctly attribute DHRs to a specific mechanism and a specific molecule are not well-established. This review will focus on allergic reactions induced by systemically administered low-molecular weight drugs with an emphasis on drug- and patient-specific factors that could influence the development of DHRs. Strategies for predicting and diagnosing DHRs, including potential tools based on the current state of the science, will also be discussed. [ABSTRACT FROM AUTHOR]

Allergic reactions to drugs and chemicals are mediated by an adaptive immune response involving specific T cells. During thymic selection, T cells that have not yet encountered their cognate antigen are considered naive T cells. Due to the artificial nature of drug/chemical-T-cell epitopes, it is not clear whether thymic selection of drug/chemical-specific T cells is a common phenomenon or remains limited to few donors or simply does not exist, suggesting T-cell receptor (TCR) cross-reactivity with other antigens. Selection of drug/chemical-specific T cells could be a relatively rare event accounting for the low occurrence of drug allergy. On the other hand, a large T-cell repertoire found in multiple donors would underline the potential of a drug/chemical to be recognized by many donors. Recent observations raise the hypothesis that not only the drug/chemical, but also parts of the haptenated protein or peptides may constitute the important structural determinants for antigen recognition by the TCR. These observations may also suggest that in the case of drug/chemical allergy, the T-cell repertoire results from particular properties of certain TCR to recognize hapten-modified peptides without need for previous thymic selection. The aim of this review is to address the existence and the role of a naive T-cell repertoire in drug and chemical allergy. Understanding this role has the potential to reveal efficient strategies not only for allergy diagnosis but also for prediction of the immunogenic potential of new chemicals.