Imperial College London

Dr Reiko J. Tanaka

Faculty of EngineeringDepartment of Bioengineering

Reader in Computational Systems Biology & Medicine
 
 
 
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Contact

 

+44 (0)20 7594 6374r.tanaka Website

 
 
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Location

 

RSM 3.10Royal School of MinesSouth Kensington Campus

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Summary

 

Publications

Publication Type
Year
to

108 results found

Thomas BR, Steele L, Tanaka RJ, O'Toole EAet al., 2022, Prediction of FLG genotype using human and computer-aided phenotype extraction with random forest machine learning, 102nd Annual Meeting of the British-Association-of-Dermatologists (BAD), Publisher: WILEY, Pages: 44-45, ISSN: 0007-0963

Conference paper

Sun Y, Hurault G, Kezic S, Irvine AD, Tanaka RJet al., 2022, From univariate analysis to machine learning: identifying atopic dermatitis-related biomarkers, Publisher: WILEY, Pages: E20-E20, ISSN: 0007-0963

Conference paper

Miyano T, Irvine AD, Tanaka RJ, 2022, A mathematical model to investigate drug targets for dupilumab poor responders in atopic dermatitis, Publisher: WILEY, Pages: E39-E40, ISSN: 0007-0963

Conference paper

Thomas BR, Steele L, Tanaka RJ, O'Toole EAet al., 2022, Prediction of FLG genotype using human and computer-aided phenotype extraction with random forest machine learning, Publisher: WILEY, Pages: 195-196, ISSN: 0007-0963

Conference paper

Lee J, Miyano T, Tanaka RJ, 2022, Staphylococcus aureus eradication can cause skin barrier damage due to S. epidermidis overgrowth in atopic dermatitis: a mathematical model study, Publisher: WILEY, Pages: E238-E239, ISSN: 0007-0963

Conference paper

Duverdier A, Custovic A, Tanaka RJ, 2022, Data-driven research on eczema: systematic characterization of the field and recommendations for the future, Publisher: WILEY

Working paper

Duverdier A, Custovic A, Tanaka R, 2022, Data-driven research on eczema: systematic characterization of the field and recommendations for the future, Clinical and Translational Allergy, Vol: 12, Pages: 1-10, ISSN: 2045-7022

BackgroundThe past decade has seen a substantial rise in the employment of modern data-driven methods to study atopic dermatitis (AD)/eczema. The objective of this study is to summarise the past and future of data-driven AD research, and identify areas in the field that would benefit from the application of these methods.MethodsWe retrieved the publications that applied multivariate statistics (MS), artificial intelligence (AI, including machine learning-ML), and Bayesian statistics (BS) to AD and eczema research from the SCOPUS database over the last 50 years. We conducted a bibliometric analysis to highlight the publication trends and conceptual knowledge structure of the field, and applied topic modelling to retrieve the key topics in the literature.ResultsFive key themes of data-driven research on AD and eczema were identified: (1) allergic co-morbidities, (2) image analysis and classification, (3) disaggregation, (4) quality of life and treatment response, and (5) risk factors and prevalence. ML&AI methods mapped to studies investigating quality of life, prevalence, risk factors, allergic co-morbidities and disaggregation of AD/eczema, but seldom in studies of therapies. MS was employed evenly between the topics, particularly in studies on risk factors and prevalence. BS was focused on three key topics: treatment, risk factors and allergy. The use of AD or eczema terms was not uniform, with studies applying ML&AI methods using the term eczema more often. Within MS, papers using cluster and factor analysis were often only identified with the term AD. In contrast, those using logistic regression and latent class/transition models were “eczema” papers.ConclusionsResearch areas that could benefit from the application of data-driven methods include the study of the pathogenesis of the condition and related risk factors, its disaggregation into validated subtypes, and personalised severity management and prognosis. We highlight BS as a new and pro

Journal article

Hurault G, Pan K, Ricardo M, Bayanne O, Eleanor E, Lloyd S, Williams H, Tanaka Ret al., 2022, Detecting eczema areas in digital images: an impossible task?, JID innovations, ISSN: 2667-0267

Assessing the severity of atopic dermatitis (AD, or eczema) traditionally relies on a face-to-face assessment by healthcare professionals, and may suffer from inter- and intra-rater variability. With the expanding role of telemedicine, several machine learning algorithms have been proposed to automatically assess AD severity from digital images. Those algorithms usually detect and then delineate (“segment”) AD lesions before assessing lesional severity, and are trained using the data of AD areas detected by healthcare professionals. To evaluate the reliability of such data, we estimated the inter-rater reliability of AD segmentation in digital images. Four dermatologists independently segmented AD lesions in 80 digital images collected in a published clinical trial. We estimated the inter-rater reliability of the AD segmentation using the intra-class correlation coefficients (ICCs) at the pixel-level and the area-levels for different resolutions of the images. The average ICC was 0.45 (SE=0.04) corresponding to a “poor” agreement between raters, while the degree of agreement for AD segmentation varied from image to image. The AD segmentation in digital images is highly rater-dependent even between dermatologists. Such limitations need to be taken into consideration when the AD segmentation data are used to train machine learning algorithms that assess eczema severity.

Journal article

Miyano T, Irvine A, Tanaka R, 2022, Model-based meta-analysis to optimise S. aureus-targeted therapies for atopic dermatitis, JID Innovations, Vol: 2, ISSN: 2667-0267

Several clinical trials of Staphylococcus aureus (S. aureus)-targeted therapies for atopic dermatitis (AD) have demonstrated conflicting results regarding whether they improve AD severity scores. This study performs a model-based meta-analysis to investigate possible causes of these conflicting results and suggests how to improve the efficacies of S. aureus-targeted therapies.We developed a mathematical model that describes systems-level AD pathogenesis involving dynamic interactions between S. aureus and Coagulase Negative Staphylococcus (CoNS). Our model simulation reproduced the clinically observed detrimental effects of application of S. hominis A9 (ShA9) and flucloxacillin on AD severity and showed that these effects disappeared if the bactericidal activity against CoNS was removed. A hypothetical (modelled) eradication of S. aureus by 3.0 log10 CFU/cm2, without killing CoNS, achieved comparable EASI-75 to dupilumab. This efficacy was potentiated if dupilumab was administered in conjunction with S. aureus eradication (EASI-75 at week 16; S. aureus eradication: 66.7%, dupilumab 61.6% and combination: 87.8%). The improved efficacy was also seen for virtual dupilumab poor responders.Our model simulation suggests that killing CoNS worsens AD severity and that S. aureus-specific eradication without killing CoNS could be effective for AD patients, including dupilumab poor responders. This study will contribute to design promising S. aureus-targeted therapy.

Journal article

Hurault G, Stalder, Mery, Delarue, Saint Aroman, Josse, Tanaka Ret al., 2022, EczemaPred: a computational framework for personalised prediction of eczema severity dynamics, Clinical and Translational Allergy, Vol: 12, ISSN: 2045-7022

Background:Atopic dermatitis (AD) is a chronic inflammatory skin disease leading to substantial quality of life impairment with heterogeneous treatment responses. People with AD would benefit from personalised treatment strategies, whose design requires predicting how AD severity evolves for each individual.Objective:This study aims to develop a computational framework for personalised prediction of AD severity dynamics.Methods:We introduced EczemaPred, a computational framework to predict patient-dependent dynamic evolution of AD severity using Bayesian state-space models that describe latent dynamics of AD severity items and how they are measured. We used EczemaPred to predict the dynamic evolution of validated patient-oriented scoring atopic dermatitis (PO-SCORAD) by combining predictions from the models for the nine severity items of PO-SCORAD (six intensity signs, extent of eczema, and two subjective symptoms). We validated this approach using longitudinal data from two independent studies: a published clinical study in which PO-SCORAD was measured twice weekly for 347 AD patients over 17 weeks, and another one in which PO-SCORAD was recorded daily by 16 AD patients for 12 weeks.Results:EczemaPred achieved good performance for personalised predictions of PO-SCORAD and its severity items daily to weekly. EczemaPred outperformed standard time-series forecasting models such as a mixed effect autoregressive model. The uncertainty in predicting PO-SCORAD was mainly attributed to that in predicting intensity signs (75% of the overall uncertainty).Conclusions:EczemaPred serves as a computational framework to make a personalised prediction of AD severity dynamics relevant to clinical practice. EczemaPred is available as an R package.

Journal article

Miyano T, Irvine A, Tanaka R, 2022, A mathematical model to identify optimal combinations of drug targets for dupilumab poor responders in atopic dermatitis, Allergy, Vol: 77, Pages: 582-594, ISSN: 0105-4538

BackgroundSeveral 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.MethodsWe 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 system‐level AD pathogenesis. Using this model, we simulated the clinical efficacy of hypothetical therapies on virtual patients.ResultsOur 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%).ConclusionOur 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 model will serve as a computational platform for model‐informed drug development for precision medicine, as it allows evaluation of the effects of new potential drug targets and the mechanisms behind patient variability in drug response.

Journal article

Miyano T, Irvine AD, Tanaka RJ, 2022, Model-Based Meta-Analysis to Optimize Staphylococcus aureus‒Targeted Therapies for Atopic Dermatitis, JID Innovations, Pages: 100110-100110, ISSN: 2667-0267

Journal article

Hurault G, Roekevisch, Schram, Szegedi, Kezic, Middelkamp-Hup, Spuls, Tanaka Ret al., 2022, Can serum biomarkers predict the outcome of systemic immunosuppressive therapy in adult atopic dermatitis patients?, Skin Health and Disease, Vol: 2, ISSN: 2690-442X

Background: Atopic dermatitis (AD or eczema) is a most common chronic skin disease. Designing personalised treatment strategies for AD based on patient stratification is of high clinical relevance, given a considerable variation in the clinical phenotype and responses to treatments among patients. It has been hypothesised that the measurement of biomarkers could help predict therapeutic responses for individual patients.Objective: We aim to assess whether serum biomarkers can predict the outcome of systemic immunosuppressive therapy in adult AD patients.Methods: We developed a statistical machine learning model using the data of an already published longitudinal study of 42 patients who received azathioprine or methotrexate for over 24 weeks. The data contained 26 serum cytokines and chemokines measured before the therapy. The model described the dynamic evolution 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 the prediction of AD severity scores.Results: We validated our model in a forward chaining setting and confirmed that it outperformed standard time-series forecasting models. Adding biomarkers did not improve predictive performance.Conclusions: In this study, biomarkers had a negligible and non-significant effect for predicting the future AD severity scores and the outcome of the systemic therapy.

Journal article

Miyauchi, Ki, Ukai, Suzuki, Inoue, Suda, Ito, Honda, Koseki, Ohara, Tanaka R, Okada-Hatakeyama, Kuboet al., 2021, Essential role of STAT3 signaling in hair follicle homeostasis, Frontiers in Immunology, ISSN: 1664-3224

Journal article

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

Conference paper

Steele L, Thomas B, O'Toole EA, Tanaka RJet 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

Conference paper

Steele L, Thomas BR, O'Toole EA, Tanaka RJet al., 2021, Computer-aided quantification of palmar hyperlinearity in atopic dermatitis: a proof-of-concept study, Publisher: WILEY, Pages: 68-69, ISSN: 0007-0963

Conference paper

Holm JG, Hurault G, Agner T, Clausen ML, Kezic S, Tanaka RJ, Thomsen SFet 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.

Journal article

Lovell S, Zhang L, Kryza T, Neodo A, Bock N, De Vita E, Williams E, Engelsberger E, Xu C, Bakker A, Maneiro M, Tanaka R, Bevan C, Clements J, Tate Eet 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.

Journal article

Jurakic Toncic R, Jakasa I, Sun Y, Hurault G, Ljubojevic Hadzavdic S, Tanaka R, Kezic S, Marinovic Bet 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

Journal article

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

Journal article

Hurault G, Delorieux V, Kim Y-M, Ahn K, Williams HC, Tanaka Ret 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.

Journal article

Nousbeck J, McAleer MA, Hurault G, Kenny E, Harte K, Kezic S, Tanaka RJ, Irvine ADet 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

Conference paper

Nousbeck J, McAleer MA, Hurault G, Kenny E, Harte K, Kezic S, Tanaka RJ, Irvine ADet 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.

Journal article

Miyano T, Irvine AD, Tanaka RJ, 2021, A mathematical model to identify optimal combinations of drug targets for dupilumab poor responders in atopic dermatitis, Publisher: Cold Spring Harbor Laboratory

<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

Working paper

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

Conference paper

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

Conference paper

Hurault G, Domínguez-Hüttinger E, Langan S, Williams H, Tanaka Ret 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.

Journal article

Hurault G, Delorieux V, Kim Y-M, Ahn K, Williams HC, Tanaka RJet al., 2020, Impact of environmental factors in predicting daily severity scores of atopic dermatitis, Publisher: Cold Spring Harbor Laboratory

<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

Working paper

Pan K, Hurault G, Arulkumaran K, Williams H, Tanaka Ret 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.

Conference paper

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