Publications
127 results found
Hurault G, Tanaka RJ, 2022, PERSONALIZED PREDICTIONS OF ATOPIC DERMATITIS SEVERITY DYNAMICS AND TREATMENT RECOMMENDATIONS USING A BAYESIAN MACHINE LEARNING APPROACH, Publisher: ACTA DERMATO-VENEREOLOGICA, Pages: 15-15, ISSN: 0001-5555
Hurault G, Attar R, Pan K, et al., 2022, FULLY AUTOMATED ASSESSMENT OF ATOPIC DERMATITIS SEVERITY FROM REAL-WORLD DIGITAL IMAGES, Publisher: ACTA DERMATO-VENEREOLOGICA, Pages: 15-15, ISSN: 0001-5555
Duverdier- A, Hurault G, Custovic A, et al., 2022, RECENCY BIAS IN WEEKLY POEM RECORDING AND ITS EFFECTS ON POEM PREDICTION, Publisher: ACTA DERMATO-VENEREOLOGICA, Pages: 14-15, ISSN: 0001-5555
Miyauchi, Ki, Ukai, et al., 2021, Essential role of STAT3 signaling in hair follicle homeostasis, Frontiers in Immunology, ISSN: 1664-3224
Steele L, Thomas B, O'Toole EA, et al., 2021, Machine learning prediction of filaggrin mutation status from palmar images: a proof-of- concept study, Publisher: ELSEVIER SCIENCE INC, Pages: S157-S157, ISSN: 0022-202X
Miyano T, Irvine AD, Tanaka RJ, 2021, A computational model suggested potential therapies for dupilumab poor responders in atopic dermatitis, Publisher: ELSEVIER SCIENCE INC, Pages: S156-S156, ISSN: 0022-202X
Holm JG, Hurault G, Agner T, et al., 2021, Immunoinflammatory biomarkers in serum are associated with disease severity in atopic dermatitis, Dermatology: international journal for clinical and investigative dermatology, Vol: 237, Pages: 513-520, ISSN: 1018-8665
Background: A growing body of evidence links various biomarkers to atopic dermatitis (AD). Still, little is known about the association of specific biomarkers to disease characteristics and severity in AD. Objective: To explore the relationship between various immunological markers in the serum and disease severity in a hospital cohort of AD patients. Methods: Outpatients with AD referred to the Department of Dermatology, Bispebjerg Hospital, Copenhagen, Denmark, were divided into groups based on disease severity (SCORAD). Serum levels of a preselected panel of immunoinflammatory biomarkers were tested for association with disease characteristics. Two machine learning models were developed to predict SCORAD from the measured biomarkers. Results: A total of 160 patients with AD were included; 53 (33.1%) with mild, 73 (45.6%) with moderate, and 34 (21.3%) with severe disease. Mean age was 29.2 years (range 6–70 years) and 84 (52.5%) were females. Numerous biomarkers showed a statistically significant correlation with SCORAD, with the strongest correlations seen for CCL17/thymus and activation-regulated chemokine (chemokine ligand-17/TARC) and CCL27/cutaneous T cell-attracting-chemokine (CTACK; Spearman R of 0.50 and 0.43, respectively, p < 0.001). Extrinsic AD patients were more likely to have higher mean SCORAD (p < 0.001), CCL17 (p < 0.001), CCL26/eotaxin-3 (p < 0.001), and eosinophil count (p < 0.001) than intrinsic AD patients. Predictive models for SCORAD identified CCL17, CCL27, serum total IgE, IL-33, and IL-5 as the most important predictors for SCORAD, but with weaker associations than single cytokines. Conclusions: Specific immunoinflammatory biomarkers in the serum, mainly of the Th2 pathway, are correlated with disease severity in patients with AD. Predictive models identified biomarkers associated with disease severity but this finding warrants further investigation.
Steele L, Thomas BR, O'Toole EA, et al., 2021, Computer-aided quantification of palmar hyperlinearity in atopic dermatitis: a proof-of-concept study, Publisher: WILEY, Pages: 68-69, ISSN: 0007-0963
Lovell S, Zhang L, Kryza T, et al., 2021, A suite of activity-based probes to dissect the KLK activome in drug-resistant prostate cancer, Journal of the American Chemical Society, Vol: 143, Pages: 8911-8924, ISSN: 0002-7863
Kallikrein-related peptidases (KLKs) are a family of secreted serine proteases, which form a network (the KLK activome) with an important role in proteolysis and signaling. In prostate cancer (PCa), increased KLK activity promotes tumor growth and metastasis through multiple biochemical pathways, and specific quantification and tracking of changes in the KLK activome could contribute to validation of KLKs as potential drug targets. Herein we report a technology platform based on novel activity-based probes (ABPs) and inhibitors enabling simultaneous orthogonal analysis of KLK2, KLK3, and KLK14 activity in hormone-responsive PCa cell lines and tumor homogenates. Importantly, we identifed a significant decoupling of KLK activity and abundance and suggest that KLK proteolysis should be considered as an additional parameter, along with the PSA blood test, for accurate PCa diagnosis and monitoring. Using selective inhibitors and multiplexed fluorescent activity-based protein profiling (ABPP), we dissect the KLK activome in PCa cells and show that increased KLK14 activity leads to a migratory phenotype. Furthermore, using biotinylated ABPs, we show that active KLK molecules are secreted into the bone microenvironment by PCa cells following stimulation by osteoblasts suggesting KLK-mediated signaling mechanisms could contribute to PCa metastasis to bone. Together our findings show that ABPP is a powerful approach to dissect dysregulation of the KLK activome as a promising and previously underappreciated therapeutic target in advanced PCa.
Miyano T, Tanaka R, 2021, Identification of keratinocyte subpopulations in transcriptome to evaluate drug effects in atopic dermatitis, British Journal of Dermatology, Vol: 184, Pages: 798-799, ISSN: 0007-0963
Jurakic Toncic R, Jakasa I, Sun Y, et al., 2021, Stratum corneum markers of innate and T helper cell-related immunity and their relation to the disease severity in Croatian patients with atopic dermatitis, Journal of the European Academy of Dermatology and Venereology, Vol: 35, Pages: 1186-1196, ISSN: 0926-9959
BackgroundAtopic dermatitis (AD) presents with the wide spectrum of clinical phenotypes within and between various populations. Recent study showed low frequency of filaggrin loss‐of‐function (FLG LOF) mutations in Croatian AD patients. At present, there are no data on biomarkers of immune response in Croatian AD patients that might be useful in the selection and monitoring of novel immune therapies.ObjectivesTo investigate levels of cytokines of various signature in the stratum corneum (SC) collected from lesional and non‐lesional skin of AD patients and healthy controls and to evaluate their relationship with the severity of disease and skin barrier function.MethodsSC samples were collected from 100 adult patients with moderate‐to‐severe AD and 50 healthy controls. The levels of 21 cytokines were measured by multiplex immunoassay. We conducted machine learning analysis to assess whether a small number of cytokine measurements can discriminate between healthy controls and AD patients and can predict AD severity (SCORAD).ResultsThe SC levels of thirteen cytokines representing innate immunity, Th‐1, Th‐2 and Th‐17/22 immune response showed significant differences between healthy and AD skin. Our analysis demonstrated that as few as three cytokines measured in lesional skin can discriminate healthy controls and AD with an accuracy of 99% and that the predictive models for SCORAD did not achieve a high accuracy. Cytokine levels were highly correlated with the levels of filaggrin degradation products and skin barrier function.ConclusionsStratum corneum analysis revealed aberrant levels of cytokines representing innate immunity, Th‐1‐, Th‐2‐ and Th‐17/22‐mediated immune response in Croatian AD patients. Increased Th‐2 cytokines and their strong association with natural moisturizing factor (NMF) can explain low NMF levels despite of low frequency of FLG LOF mutations in Croatian population. Predictive models for SCORAD identified cytokines associated with SCORAD but warra
Hurault G, Delorieux V, Kim Y-M, et al., 2021, Impact of environmental factors in predicting daily severity scores of atopic dermatitis, Clinical and Translational Allergy, Vol: 11, ISSN: 2045-7022
BackgroundAtopic dermatitis (AD) is a chronic inflammatory skin disease that affects 20% of children worldwide. Environmental factors including weather and air pollutants have been shown to be associated with AD symptoms. However, the time-dependent nature of such a relationship has not been adequately investigated. This paper aims to assess whether real-time data on weather and air pollutants can make short-term prediction of AD severity scores.MethodsUsing longitudinal data from a published panel study of 177 paediatric patients followed up daily for 17 months, we developed a statistical machine learning model to predict daily AD severity scores for individual study participants. Exposures consisted of daily meteorological variables and concentrations of air pollutants, and outcomes were daily recordings of scores for six AD signs. We developed a mixed-effect autoregressive ordinal logistic regression model, validated it in a forward-chaining setting and evaluated the effects of the environmental factors on the predictive performance.ResultsOur model successfully made daily prediction of the AD severity scores, and the predictive performance was not improved by the addition of measured environmental factors. Potential short-term influence of environmental exposures on daily AD severity scores was outweighed by the underlying persistence of preceding scores.ConclusionsOur data does not offer enough evidence to support a claim that weather or air pollutants can make short-term prediction of AD signs. Inferences about the magnitude of the effect of environmental factors on AD severity scores require consideration of their time-dependent dynamic nature.
Nousbeck J, McAleer MA, Hurault G, et al., 2021, miRNA analysis of childhood atopic dermatitis reveals a role for miR-451a, British Journal of Dermatology, Vol: 184, Pages: 514-523, ISSN: 0007-0963
BACKGROUND: MicroRNAs (miRNAs), important regulators of gene expression, have been implicated in a variety of disorders. The expression pattern of miRNAs in pediatric atopic dermatitis (AD) has not been well studied. OBJECTIVE: We sought to investigate miRNA expression profiles in different blood compartments of infants with AD. METHODS: Small RNA and HTG-Edge sequencing were performed to identify differentially expressed miRNAs in PBMCs and plasma of AD infants versus age-matched healthy controls, with reverse transcription quantitative real-time PCR used for validation and measurement of miRNA targets. Logistic regression models with AUROC estimation was used to evaluate the diagnostic potential of chosen miRNAs for AD. RESULTS: RNA sequencing was performed to access miRNA expression profile in pediatric AD. We identified ten differentially expressed miRNAs in PBMCs and eight dysregulated miRNAs in plasma of AD infants compared to controls. Upregulated miRNAs in PBMCs included miRNAs known to be involved in inflammation: miR-223-3p, miR-126-5p and miR-143-3p. Differential expression of only one miRNA, miR-451a, was observed in both PBMCs and plasma of children with AD. Dysregulation of three miRNAs: miR-451a, miR-143-3p and miR-223-3p was validated in larger number of samples and miR-451a was identified as a predictive biomarker for the early diagnosis of the disease. Experimentally verified targets of miR-451a, IL6R and PSMB8, were increased in AD patients, negatively correlated with miR-451a levels and upregulated following inhibition of miR-451a in PBMCs. CONCLUSION: In infants with AD, a distinct peripheral blood miRNA signature is seen, highlighting the systemic effects of the disease. miR-451a is uniquely expressed in different blood compartments of AD patients and may serve as a promising novel biomarker for the early diagnosis of AD.
Nousbeck J, McAleer MA, Hurault G, et al., 2021, Analysis of small RNA molecules in childhood atopic dermatitis reveals a role for miR-451a, Publisher: WILEY, Pages: E88-E88, ISSN: 0007-0963
Miyano T, Irvine AD, Tanaka RJ, 2021, A mathematical model to identify optimal combinations of drug targets for dupilumab poor responders in atopic dermatitis
<jats:title>Abstract</jats:title><jats:sec><jats:title>Background</jats:title><jats:p>Several biologics for atopic dermatitis (AD) have demonstrated good efficacy in clinical trials, but with a substantial proportion of patients being identified as poor responders. This study aims to understand the pathophysiological backgrounds of patient variability in drug response, especially for dupilumab, and to identify promising drug targets in dupilumab poor responders.</jats:p></jats:sec><jats:sec><jats:title>Methods</jats:title><jats:p>We conducted model-based meta-analysis of recent clinical trials of AD biologics and developed a mathematical model that reproduces reported clinical efficacies for nine biological drugs (dupilumab, lebrikizumab, tralokinumab, secukinumab, fezakinumab, nemolizumab, tezepelumab, GBR 830, and recombinant interferon-gamma) by describing systems-level AD pathogenesis. Using this model, we simulated the clinical efficacy of hypothetical therapies on virtual patients.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>Our model reproduced reported time courses of %improved EASI and EASI-75 of the nine drugs. The global sensitivity analysis and model simulation indicated the baseline level of IL-13 could stratify dupilumab good responders. Model simulation on the efficacies of hypothetical therapies revealed that simultaneous inhibition of IL-13 and IL-22 was effective, whereas application of the nine biologic drugs was ineffective, for dupilumab poor responders (EASI-75 at 24 weeks: 21.6% vs. max. 1.9%).</jats:p></jats:sec><jats:sec><jats:title>Conclusion</jats:title><jats:p>Our model identified IL-13 as a potential predictive biomarker to stratify dupilumab good responders, and simultaneous inhibition of IL-13 and IL-22 as a promising drug therapy for dupilumab poor responders. This mod
Miyano T, Irvine AD, Tanaka RJ, 2021, A COMPUTATIONAL MODEL TO INVESTIGATE DRUG TARGETS IN AD PATIENTS WITH HETEROGENOUS RESPONSE TO BIOLOGIC DRUGS, Publisher: ACTA DERMATO-VENEREOLOGICA, Pages: 45-45, ISSN: 0001-5555
Hurault G, Tanaka RJ, 2021, COMPUTATIONAL TOOLS FOR DATA-DRIVEN PERSONALISED MEDICINE FOR ATOPIC DERMATITIS, Publisher: ACTA DERMATO-VENEREOLOGICA, Pages: 33-33, ISSN: 0001-5555
Hurault G, Roekevisch E, Schram ME, et al., 2020, Can serum biomarkers predict the outcome of systemic therapy for atopic dermatitis?
<jats:title>SUMMARY</jats:title><jats:sec><jats:title>Background</jats:title><jats:p>Atopic dermatitis (AD or eczema) is a most common chronic skin disease. Designing personalised treatment strategies for AD based on patient stratification, rather than the “one-size-fits-all” treatments, is of high clinical relevance. It has been hypothesised that the measurement of biomarkers could help predict therapeutic response for individual patients.</jats:p></jats:sec><jats:sec><jats:title>Objective</jats:title><jats:p>We aim to assess whether biomarkers can predict the outcome of systemic therapy.</jats:p></jats:sec><jats:sec><jats:title>Methods</jats:title><jats:p>We developed a statistical machine learning predictive model using the data of an already published longitudinal study of 42 patients who received systemic therapy. The data contained 26 serum cytokines measured before the therapy. The model described the dynamics of the latent disease severity and measurement errors to predict AD severity scores (EASI, (o)SCORAD and POEM) two-weeks ahead. We conducted feature selection to identify the most important biomarkers for predicting the AD severity scores.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>We validated our model and confirmed that it outperformed standard time-series forecasting models. Adding biomarkers did not improve predictive performance. Our estimates of the minimum detectable change for the AD severity scores were larger than already published estimates of the minimal clinically important difference.</jats:p></jats:sec><jats:sec><jats:title>Conclusions</jats:title><jats:p>Biomarkers had a negligible and non-significant effect for predicting the future AD severity scores and the outcome of the systemic therapy. Instead, a historical record o
Hurault G, Domínguez-Hüttinger E, Langan S, et al., 2020, Personalised prediction of daily eczema severity scores using a mechanistic machine learning model, Clinical and Experimental Allergy, Vol: 50, Pages: 1258-1266, ISSN: 0954-7894
Background: A topic dermatitis (AD) is a chronic inflammatory skin disease with periods of flares and remission. Designing personalised treatment strategies for AD is challenging, given the apparent unpredictability and large variation in AD symptoms and treatment responses within and across individuals.Better prediction of AD severity over time for individual patients could help to select optimum timing and type of treatment for improving disease control.Objective: We aimed to develop a proof-of-principle mechanistic machine learning model that predicts the patient-specific evolution of AD severity scores on a daily basis.Methods: We designed a probabilistic predictive model and trained it using Bayesian inference with the longitudinal data from two published clinical studies. The data consisted of daily recordings of AD severity scores and treatments used by 59 and 334 AD children ove r6 months and 16 weeks, respectively. Validation of the predictive model was conducted in a forward-chaining setting.Results: Our model was able to predict future severity scores at the individual level and improved chance-level forecast by 60%. Heterogeneous patterns in severity trajectories were captured with patient-specific parameters such as the short-term persistence of AD severity and responsiveness to topical steroids, calcineurin inhibitors and step-up treatment.Conclusions: Our proof of principle model successfully predicted the daily evolution of AD severity scores at an individual level,and could inform the design of personalised treatment strategies that can be tested in future studies.Our model-based approach can be applied to other diseases such as asthma with apparent unpredictability and large variation in symptoms and treatment responses.
Hurault G, Delorieux V, Kim Y-M, et al., 2020, Impact of environmental factors in predicting daily severity scores of atopic dermatitis
<jats:title>ABSTRACT</jats:title><jats:sec><jats:title>Background</jats:title><jats:p>Atopic dermatitis (AD) is a chronic inflammatory skin disease that affects 20% of children worldwide. Although environmental factors including weather and air pollutants have been shown to be associated with AD symptoms, the time-dependent nature of such a relationship has not been adequately investigated.</jats:p></jats:sec><jats:sec><jats:title>Objective</jats:title><jats:p>This paper aims to assess the short-term impact of weather and air pollutants on AD severity scores.</jats:p></jats:sec><jats:sec><jats:title>Methods</jats:title><jats:p>Using longitudinal data from a published panel study of 177 paediatric patients followed up for 17 months, we developed statistical machine learning models to predict daily AD severity scores for individual study participants. Exposures consisted of daily meteorological variables and concentrations of air pollutants and outcomes were daily recordings of scores for six AD signs. We developed a mixed effect autoregressive ordinal logistic regression model, validated it in a forward-chaining setting, and evaluated the effects of the environmental factors on the predictive performance.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>Our model outperformed benchmark models for daily prediction of the AD severity scores. The predictive performance of AD severity scores was not improved by the addition of measured environmental factors. Any potential short-term influence of environmental exposures on AD severity scores was outweighed by the underlying persistence of preceding scores.</jats:p></jats:sec><jats:sec><jats:title>Conclusions</jats:title><jats:p>Our data does not offer enough evidence to support a claim that AD symptoms are associated with wea
Pan K, Hurault G, Arulkumaran K, et al., 2020, EczemaNet: automating detection and severity assessment of atopic dermatitis, International Workshop on Machine Learning in Medical Imaging, Publisher: Springer Verlag, Pages: 220-230, ISSN: 0302-9743
Atopic dermatitis (AD), also known as eczema, is one of themost common chronic skin diseases. AD severity is primarily evaluatedbased on visual inspections by clinicians, but is subjective and has largeinter- and intra-observer variability in many clinical study settings. Toaid the standardisation and automating the evaluation of AD severity,this paper introduces a CNN computer vision pipeline, EczemaNet, thatfirst detects areas of AD from photographs and then makes probabilisticpredictions on the severity of the disease. EczemaNet combines trans-fer and multitask learning, ordinal classification, and ensembling overcrops to make its final predictions. We test EczemaNet using a set of im-ages acquired in a published clinical trial, and demonstrate low RMSEwith well-calibrated prediction intervals. We show the effectiveness of us-ing CNNs for non-neoplastic dermatological diseases with a medium-sizedataset, and their potential for more efficiently and objectively evaluatingAD severity, which has greater clinical relevance than mere classification.
Hurault G, Domínguez-Hüttinger E, Langan SM, et al., 2020, Personalised prediction of daily eczema severity scores using a mechanistic machine learning model
<jats:title>ABSTRACT</jats:title><jats:sec><jats:title>Background</jats:title><jats:p>Atopic dermatitis (AD) is a chronic inflammatory skin disease with periods of flares and remission. Designing personalised treatment strategies for AD is challenging, given the apparent unpredictability and large variation in AD symptoms and treatment responses within and across individuals. Better prediction of AD severity over time for individual patients could help to select optimum timing and type of treatment for improving disease control.</jats:p></jats:sec><jats:sec><jats:title>Objective</jats:title><jats:p>We aimed to develop a mechanistic machine learning model that predicts the patient-specific evolution of AD severity scores on a daily basis.</jats:p></jats:sec><jats:sec><jats:title>Methods</jats:title><jats:p>We designed a probabilistic predictive model and trained it using Bayesian inference with the longitudinal data from two published clinical studies. The data consisted of daily recordings of AD severity scores and treatments used by 59 and 334 AD children over 6 months and 16 weeks, respectively. Internal and external validation of the predictive model was conducted in a forward-chaining setting.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>Our model was able to predict future severity scores at the individual level and improved chance-level forecast by 60%. Heterogeneous patterns in severity trajectories were captured with patient-specific parameters such as the short-term persistence of AD severity and responsiveness to topical steroids, calcineurin inhibitors and step-up treatment.</jats:p></jats:sec><jats:sec><jats:title>Conclusion</jats:title><jats:p>Our proof of principle model successfully predicted the daily evolution of AD severity scores at an individual
Hurault G, Tanaka RJ, 2019, A Bayesian machine learning model to identify patient-specific dynamic responses to eczema treatments, 49th Annual Meeting of the European-Society-for-Dermatological-Research (ESDR), Publisher: ELSEVIER SCIENCE INC, Pages: S239-S239, ISSN: 0022-202X
McAleer MA, Jakasa I, Hurault G, et al., 2019, Systemic and stratum corneum biomarkers of severity in infant AD include markers of innate and Th-related immunity and angiogenesis, British Journal of Dermatology, Vol: 180, Pages: 586-596, ISSN: 1365-2133
BACKGROUND: Biomarkers of atopic dermatitis (AD) are largely lacking, especially in infant AD. Those that have been examined to date have focused mostly on serum cytokines with few on non-invasive biomarkers in the skin. OBJECTIVES: We aimed to explore biomarkers obtainable from non-invasive sampling of infant skin. We compared these to plasma biomarkers and structural and functional measures of the skin barrier. METHODS: We recruited 100 infants at first presentation with AD, who were treatment naïve to topical or systemic anti-inflammatory therapies and 20 healthy children. We sampled clinically unaffected skin by tape stripping the stratum corneum (SC). Multiple cytokines and chemokines and natural moisturizing factors (NMF) were measured in the SC and plasma. We recorded disease severity and skin barrier function. RESULTS: 19 SC and 12 plasma biomarkers showed significant difference between healthy and AD skin. Some biomarkers were common to both the SC and plasma, and others were compartment-specific. Identified biomarkers of AD severity included Th2 skewed markers (IL-13, CCL17, CCL22, IL-5), markers of innate activation (IL-18, Il-1α, IL1β, CXCL8), angiogenesis (Flt-1, VEGF) and others (sICAM-1, vCAM-1, IL-16, IL-17A). CONCLUSIONS: We identified clinically relevant biomarkers of AD, including novel markers, easily sampled and typed in infants. These markers may provide objective assessment of disease severity and suggest new therapeutic targets, or response measurement targets for AD. Future studies will be required to determine if these biomarkers, seen in very early AD, can predict disease outcomes or comorbidities.
Eyerich K, Brown S, Perez White B, et al., 2019, Human and computational models of atopic dermatitis: a review and perspectives by an expert panel of the International Eczema Council, Journal of Allergy and Clinical Immunology, Vol: 143, Pages: 36-45, ISSN: 0091-6749
Atopic dermatitis (AD) is a prevalent disease worldwide and is associated with systemic comorbidities representing a significant burden on patients, their families, and society. Therapeutic options for AD remain limited, in part because of a lack of well-characterized animal models. There has been increasing interest in developing experimental approaches to study the pathogenesis of human AD in vivo, in vitro, and in silico to better define pathophysiologic mechanisms and identify novel therapeutic targets and biomarkers that predict therapeutic response. This review critically appraises a range of models, including genetic mutations relevant to AD, experimental challenge of human skin in vivo, tissue culture models, integration of “omics” data sets, and development of predictive computational models. Although no one individual model recapitulates the complex AD pathophysiology, our review highlights insights gained into key elements of cutaneous biology, molecular pathways, and therapeutic target identification through each approach. Recent developments in computational analysis, including application of machine learning and a systems approach to data integration and predictive modeling, highlight the applicability of these methods to AD subclassification (endotyping), therapy development, and precision medicine. Such predictive modeling will highlight knowledge gaps, further inform refinement of biological models, and support new experimental and systems approaches to AD.
Hurault G, Schram M, Roekevisch E, et al., 2018, Relationship and probabilistic stratification of EASI and oSCORAD severity scores for atopic dermatitis, British Journal of Dermatology, Vol: 179, Pages: 1003-1005, ISSN: 1365-2133
The Harmonizing Outcome Measures for Eczema (HOME) recommended the Eczema Area and Severity Index (EASI) as the core outcome instrument for measuring the clinical signs of atopic dermatitis (AD). However, EASI may not have been used in previous clinical trials, and other scores, e.g. SCORAD (SCORing Atopic Dermatitis), the objective component of SCORAD (oSCORAD) and the Investigator Global Assessment (IGA), remain widely used. It is useful to establish a method to convert these scores into EASI to compare the results from different studies effectively. Indeed, EASI and oSCORAD have been found to be strongly correlated (rSpearman=0.92)7, suggesting a possibility to find a relationship between the two scores.
Tanaka G, Dominguez-Huttinger E, Christodoulides P, et al., 2018, Bifurcation analysis of a mathematical model of atopic dermatitis to determine patient-specific effects of treatments on dynamic phenotypes, Journal of Theoretical Biology, Vol: 448, Pages: 66-79, ISSN: 0022-5193
Atopic dermatitis (AD) is a common inflammatory skin disease, whose incidence is currently increasing worldwide. AD has a complex etiology, involving genetic, environmental, immunological, and epidermal factors, andits pathogenic mechanisms have not yet been fully elucidated. Identificationof AD risk factors and systematic understanding of their interactions arerequired for exploring effective prevention and treatment strategies for AD.We recently developed a mathematical model for AD pathogenesis to clarifymechanisms underlying AD onset and progression. This model describes adynamic interplay between skin barrier, immune regulation, and environmental stress, and reproduced four types of dynamic behaviour typically observed in AD patients in response to environmental triggers. Here, we analyse bifurcations of the model to identify mathematical conditions for the system to demonstrate transitions between different types of dynamic behaviour that reflect respective severity of AD symptoms. By mathematically modelling effects of topical application of antibiotics, emollients, corticosteroids, and their combinations with different application schedules and doses, bifurcation analysis allows us to mathematically evaluate effects of the treatments on improving AD symptoms in terms of the patients' dynamic behaviour. The mathematical method developed in this study can be used to explore and improve patient-specific personalised treatment strategies to control AD symptoms.
Hurault G, Roekevisch E, Szegedi K, et al., 2018, Predicting short- and long-term outcomes of a systemic therapy for atopic dermatitis using machine learning methods, 10th George Rajka International Symposium on Atopic Dermatitis, Publisher: Wiley, Pages: E17-E18, ISSN: 1365-2133
Giannari AG, van Logtestijn MDA, Christodoulides P, et al., 2018, Model Predictive Control for Designing Proactive Therapy of Atopic Dermatitis, European Control Conference (ECC), Publisher: IEEE, Pages: 2387-2392
Hurault G, Roekevisch E, Szegedi K, et al., 2018, Development of computational tools to convert severity scores of atopic dermatitis for a probabilistic classification of symptom severity, Annual Meeting of the British-Society-for-Investigative-Dermatology, Publisher: WILEY, Pages: E429-E429, ISSN: 0007-0963
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