In Silico Elucidation of Key Drivers of Staphyloccocus aureus-Staphyloccocus epidermidis-Induced Skin Damage in Atopic Dermatitis Lesions.

Staphylococcus aureus (SA) colonizes and can damage skin in atopic dermatitis lesions, despite being commonly found with Staphylococcus epidermidis (SE), a commensal that can inhibit SA's virulence and kill SA. In this study, we developed an in silico model, termed a virtual skin site, describing the dynamic interplay between SA, SE, and the skin barrier in atopic dermatitis lesions to investigate the mechanisms driving skin damage by SA and SE. We generated 106 virtual skin sites by varying model parameters to represent different skin physiologies and bacterial properties. In silico analysis revealed that virtual skin sites with no skin damage in the model were characterized by parameters representing stronger SA and SE growth attenuation than those with skin damage. This inspired an in silico treatment strategy combining SA-killing with an enhanced SA-SE growth attenuation, which was found through simulations to recover many more damaged virtual skin sites to a non-damaged state, compared with SA-killing alone. This study demonstrates that in silico modelling can help elucidate the key factors driving skin damage caused by SA-SE colonization in atopic dermatitis lesions and help propose strategies to control it, which we envision will contribute to the design of promising treatments for clinical studies.

Three stepwise pH progressions in stratum corneum for homeostatic maintenance of the skin.

The stratum corneum is the outermost skin layer with a vital role in skin barrier function. It is comprised of dead keratinocytes (corneocytes) and is known to maintain its thickness by shedding cells, although, the precise mechanisms that safeguard stratum corneum maturation and homeostasis remain unclear. Previous ex vivo studies have suggested a neutral-to-acidic pH gradient in the stratum corneum. Here, we use intravital pH imaging at single-corneocyte resolution to demonstrate that corneocytes actually undergo differentiation to develop three distinct zones in the stratum corneum, each with a distinct pH value. We identified a moderately acidic lower, an acidic middle, and a pH-neutral upper layer in the stratum corneum, with tight junctions playing a key role in their development. The upper pH neutral zone can adjust its pH according to the external environment and has a neutral pH under steady-state conditions owing to the influence of skin microbiota. The middle acidic pH zone provides a defensive barrier against pathogens. With mathematical modeling, we demonstrate the controlled protease activation of kallikrein-related peptidases on the stratum corneum surface that results in proper corneocyte shedding in desquamation. This work adds crucial information to our understanding of how stratum corneum homeostasis is maintained.

Inferring fungal growth rates from optical density data.

Quantifying fungal growth underpins our ability to effectively treat severe fungal infections. Current methods quantify fungal growth rates from time-course morphology-specific data, such as hyphal length data. However, automated large-scale collection of such data lies beyond the scope of most clinical microbiology laboratories. In this paper, we propose a mathematical model of fungal growth to estimate morphology-specific growth rates from easy-to-collect, but indirect, optical density (OD600) data of Aspergillus fumigatus growth (filamentous fungus). Our method accounts for OD600 being an indirect measure by explicitly including the relationship between the indirect OD600 measurements and the calibrating true fungal growth in the model. Therefore, the method does not require de novo generation of calibration data. Our model outperformed reference models at fitting to and predicting OD600 growth curves and overcame observed discrepancies between morphology-specific rates inferred from OD600 versus directly measured data in reference models that did not include calibration.

Evaluation of measurement errors in the Patient-Oriented Eczema Measure (POEM) outcome.

BACKGROUND: The Patient-Oriented Eczema Measure (POEM) is the recommended core outcome instrument for atopic dermatitis (AD) symptoms. POEM is reported by recalling the presence/absence of seven symptoms in the last 7 days. OBJECTIVE: To evaluate measurement errors in POEM recordings due to imperfect recall. METHODS: Using data from a clinical trial of 247 AD patients aged 12-65 years, we analysed the reported POEM score (r-POEM) and the POEM derived from the corresponding daily scores for the same seven symptoms without weekly recall (d-POEM). We quantified recall error by comparing the r-POEM and d-POEM for 777 patient-weeks collected from 207 patients, and estimated two components of recall error: (1) recall bias due to systematic errors in measurements and (2) recall noise due to random errors in measurements, using a bespoke statistical model. RESULTS: POEM scores have a relatively low recall bias, but a high recall noise. Recall bias was estimated at 1.2 points lower for the r-POEM on average than the d-POEM, with a recall noise of 5.7 points. For example, a patient with a recall-free POEM of 11 (moderate) could report their POEM score anywhere from 5 to 14 (with 95% probability) because of recall error. Model estimates suggested that patients tend to recall itch and dryness more often than experienced (positive bias of less than 1 day), but less often for the other symptoms (bleeding, cracking, flaking, oozing/weeping and sleep disturbance; negative bias ranging 1-4 days). CONCLUSIONS: In this clinical trial data set, we found that patients tended to slightly underestimate their symptoms when reporting POEM, with significant variation in how well they were able to recall the frequency of their symptoms every time they reported POEM. A large recall noise should be taken into consideration when interpreting POEM scores.

Role of epithelial barrier function in inducing type 2 immunity following early-life viral infection.

BACKGROUND: Preschool wheeze attacks triggered by recurrent viral infections, including respiratory syncytial virus (RSV), are associated with an increased risk of childhood asthma. However, mechanisms that lead to asthma following early-life viral wheezing remain uncertain. METHODS: To investigate a causal relationship between early-life RSV infections and onset of type 2 immunity, we developed a neonatal murine model of recurrent RSV infection, in vivo and in silico, and evaluated the dynamical changes of altered airway barrier function and downstream immune responses, including eosinophilia, mucus secretion and type 2 immunity. RESULTS: RSV infection of neonatal BALB/c mice at 5 and 15 days of age induced robust airway eosinophilia, increased pulmonary CD4+ IL-13+ and CD4+ IL-5+ cells, elevated levels of IL-13 and IL-5 and increased airway mucus at 20 days of age. Increased bronchoalveolar lavage albumin levels, suggesting epithelial barrier damage, were present and persisted following the second RSV infection. Computational in silico simulations demonstrated that recurrent RSV infection resulted in severe damage of the airway barrier (epithelium), triggering the onset of type 2 immunity. The in silico results also demonstrated that recurrent infection is not always necessary for the development of type 2 immunity, which could also be triggered with single infection of high viral load or when the epithelial barrier repair is compromised. CONCLUSIONS: The neonatal murine model demonstrated that recurrent RSV infection in early life alters airway barrier function and promotes type 2 immunity. A causal relationship between airway barrier function and type 2 immunity was suggested using in silico model simulations.

Reliable Detection of Eczema Areas for Fully Automated Assessment of Eczema Severity from Digital Camera Images.

Assessing the severity of eczema in clinical research requires face-to-face skin examination by trained staff. Such approaches are resource-intensive for participants and staff, challenging during pandemics, and prone to inter- and intra-observer variation. Computer vision algorithms have been proposed to automate the assessment of eczema severity using digital camera images. However, they often require human intervention to detect eczema lesions and cannot automatically assess eczema severity from real-world images in an end-to-end pipeline. We developed a model to detect eczema lesions from images using data augmentation and pixel-level segmentation of eczema lesions on 1,345 images provided by dermatologists. We evaluated the quality of the obtained segmentation compared with that of the clinicians, the robustness to varying imaging conditions encountered in real-life images, such as lighting, focus, and blur, and the performance of downstream severity prediction when using the detected eczema lesions. The quality and robustness of eczema lesion detection increased by approximately 25% and 40%, respectively, compared with that of our previous eczema detection model. The performance of the downstream severity prediction remained unchanged. Use of skin segmentation as an alternative to eczema segmentation that requires specialist labeling showed the performance on par with when eczema segmentation is used.

Determining the clinical applicability of machine learning models through assessment of reporting across skin phototypes and rarer skin cancer types: A systematic review.

Machine learning (ML) models for skin cancer recognition may have variable performance across different skin phototypes and skin cancer types. Overall performance metrics alone are insufficient to detect poor subgroup performance. We aimed (1) to assess whether studies of ML models reported results separately for different skin phototypes and rarer skin cancers, and (2) to graphically represent the skin cancer training datasets used by current ML models. In this systematic review, we searched PubMed, Embase and CENTRAL. We included all studies in medical journals assessing an ML technique for skin cancer diagnosis that used clinical or dermoscopic images from 1 January 2012 to 22 September 2021. No language restrictions were applied. We considered rarer skin cancers to be skin cancers other than pigmented melanoma, basal cell carcinoma and squamous cell carcinoma. We identified 114 studies for inclusion. Rarer skin cancers were included by 8/114 studies (7.0%), and results for a rarer skin cancer were reported separately in 1/114 studies (0.9%). Performance was reported across all skin phototypes in 1/114 studies (0.9%), but performance was uncertain in skin phototypes I and VI from minimal representation of the skin phototypes in the test dataset (9/3756 and 1/3756, respectively). For training datasets, although public datasets were most frequently used, with the most widely used being the International Skin Imaging Collaboration (ISIC) archive (65/114 studies, 57.0%), the largest datasets were private. Our review identified that most ML models did not report performance separately for rarer skin cancers and different skin phototypes. A degree of variability in ML model performance across subgroups is expected, but the current lack of transparency is not justifiable and risks models being used inappropriately in populations in whom accuracy is low.

A Gedunin-Type Limonoid, 7-Deacetoxy-7-Oxogedunin, from Andiroba (Carapa guianensis Aublet) Reduced Intracellular Triglyceride Content and Enhanced Autophagy in HepG2 Cells.

The seed oil of Carapa guianensis Aublet (Andiroba) has been used in folk medicine for its insect-repelling, anti-inflammatory, and anti-malarial activities. This study aimed to examine the triglyceride (TG) reducing effects of C. guianensis-derived limonoids or other commercially available limonoids in human hepatoblastoma HepG2 cells and evaluate the expression of lipid metabolism or autophagy-related proteins by treatment with 7-deacetoxy-7-oxogedunin (DAOG; 1), a principal limonoid of C. guianensis. The gedunin-type limonoids, such as DAOG (% of control at 20 µM: 70.9 ± 0.9%), gedunin (2, 74.0 ± 1.1%), epoxyazadiradione (4, 73.4 ± 2.0%), 17ß-hydroxyazadiradione (5, 79.9 ± 0.6%), 7-deacetoxy-7α-hydroxygedunin (6, 61.0 ± 1.2%), andirolide H (7, 87.4 ± 2.2%), and 6α-hydroxygedunin (8, 84.5 ± 1.1%), were observed to reduce the TG content at lower concentrations than berberine chloride (BBR, a positive control, 84.1 ± 0.3% at 30 µM) in HepG2 cells pretreated with high glucose and oleic acid. Andirobin-, obacunol-, nimbin-, and salannin-type limonoids showed no effect on the intracellular TG content in HepG2 cells. The TG-reducing effect of DAOG was attenuated by the concomitant use of compound C (dorsomorphin), an AMPK inhibitor. Further investigation on the detailed mechanism of action of DAOG at non-cytotoxic concentrations revealed that the expressions of autophagy-related proteins, LC3 and p62, were upregulated by treatment with DAOG. These findings suggested that gedunin-type limonoids from Andiroba could ameliorate fatty liver, and that the action of DAOG in particular is mediated by autophagy.

Detecting Eczema Areas in Digital Images: An Impossible Task?

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 intraclass correlation coefficient at the pixel and the area levels for different resolutions of the images. The average intraclass correlation coefficient was 0.45 ( standard error = 0.04 ) corresponding to a poor agreement between raters, whereas the degree of agreement for AD segmentation varied from image to image. The AD segmentation in digital images is highly rater dependent even among dermatologists. Such limitations need to be taken into consideration when AD segmentation data are used to train machine learning algorithms that assess eczema severity.

A protective role of HTLV-1 gp46-specific neutralizing and antibody-dependent cellular cytotoxicity-inducing antibodies in progression to adult T-cell leukemia (ATL).

Human T-cell leukemia virus type-1 (HTLV-1) establishes a long-term persistent infection in humans and causes malignant T-cell leukemia, adult T-cell leukemia (ATL). HTLV-1-specific cytotoxic T lymphocytes have been suggested to play a major role in the immunosurveillance of HTLV-1-infected T cells. However, it remains unclear whether HTLV-1-specific functional antibodies are also involved in the host defense. To explore the role of antibodies in the course of HTLV-1 infection, we quantitated HTLV-1-specific neutralizing and antibody-dependent cellular cytotoxicity (ADCC)-inducing antibody levels in plasma from asymptomatic carriers (ACs) and ATL patients. The levels of neutralizing antibodies, as determined by a syncytium inhibition assay, were significantly lower in acute and chronic ATL patients than in ACs. The levels of ADCC-inducing activity were tested using an autologous pair of HTLV-1-producing cells and cultured natural killer (NK) cells, which showed that the ADCC-inducing activity of IgG at a concentration of 100 µg/ml was comparable between ACs and acute ATL patients. The anti-gp46 antibody IgG levels, determined by ELISA, correlated with those of the neutralizing and ADCC-inducing antibodies. In contrast, the proviral loads did not correlate with any of these antibody levels. NK cells and a monoclonal anti-gp46 antibody reduced the number of HTLV-1 Tax-expressing cells in cultured peripheral blood mononuclear cells from patients with aggressive ATL. These results suggest a protective role for HTLV-1 neutralizing and ADCC-inducing antibodies during the course of HTLV-1 infection.

Machine learning to support visual auditing of home-based lateral flow immunoassay self-test results for SARS-CoV-2 antibodies.

Background: Lateral flow immunoassays (LFIAs) are being used worldwide for COVID-19 mass testing and antibody prevalence studies. Relatively simple to use and low cost, these tests can be self-administered at home, but rely on subjective interpretation of a test line by eye, risking false positives and false negatives. Here, we report on the development of ALFA (Automated Lateral Flow Analysis) to improve reported sensitivity and specificity. Methods: Our computational pipeline uses machine learning, computer vision techniques and signal processing algorithms to analyse images of the Fortress LFIA SARS-CoV-2 antibody self-test, and subsequently classify results as invalid, IgG negative and IgG positive. A large image library of 595,339 participant-submitted test photographs was created as part of the REACT-2 community SARS-CoV-2 antibody prevalence study in England, UK. Alongside ALFA, we developed an analysis toolkit which could also detect device blood leakage issues. Results: Automated analysis showed substantial agreement with human experts (Cohen's kappa 0.90-0.97) and performed consistently better than study participants, particularly for weak positive IgG results. Specificity (98.7-99.4%) and sensitivity (90.1-97.1%) were high compared with visual interpretation by human experts (ranges due to the varying prevalence of weak positive IgG tests in datasets). Conclusions: Given the potential for LFIAs to be used at scale in the COVID-19 response (for both antibody and antigen testing), even a small improvement in the accuracy of the algorithms could impact the lives of millions of people by reducing the risk of false-positive and false-negative result read-outs by members of the public. Our findings support the use of machine learning-enabled automated reading of at-home antibody lateral flow tests as a tool for improved accuracy for population-level community surveillance.

Model-Based Meta-Analysis to Optimize Staphylococcus aureus‒Targeted Therapies for Atopic Dermatitis.

Several clinical trials of Staphylococcus aureus (S. aureus)‒targeted therapies for atopic dermatitis (AD) have shown conflicting results about whether they improve AD severity scores. This study performs a model-based meta-analysis to investigate the 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 the application of S. hominis A9 and flucloxacillin on AD severity and showed that these effects disappeared if the bactericidal activity against CoNS was removed. A hypothetical (modeled) eradication of S. aureus by 3.0 log10 colony-forming unit per cm2 without killing CoNS achieved Eczema Area and Severity Index 75 comparable with that of dupilumab. This efficacy was potentiated if dupilumab was administered in conjunction with S. aureus eradication (Eczema Area and Severity Index 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 patients with AD, including dupilumab poor responders. This study will contribute to designing promising S. aureus‒targeted therapy.

Data-driven research on eczema: Systematic characterization of the field and recommendations for the future.

Background: The 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. Methods: We 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. Results: Five 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. Conclusions: Research 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 promising approach in AD and eczema research.

Elevation of the Plasma Levels of TNF Receptor 2 in Association with Those of CD25, OX40, and IL-10 and HTLV-1 Proviral Load in Acute Adult T-Cell Leukemia.

Adult T-cell leukemia/lymphoma (ATL) cells express TNF receptor type-2 (TNFR2) on their surface and shed its soluble form (sTNFR2). We previously reported that sTNFR2 levels were highly elevated in the plasma of patients with acute ATL. To investigate whether its quantitation would be helpful for the diagnosis or prediction of the onset of acute ATL, we examined the plasma levels of sTNFR2 in a large number of specimens obtained from a cohort of ATL patients and asymptomatic human T-cell leukemia virus type 1 (HTLV-1) carriers (ACs) and compared them to those of other candidate ATL biomarkers (sCD25, sOX40, and IL-10) by enzyme-linked immunosorbent assays (ELISA) and HTLV-1 proviral loads. We observed that sTNFR2 levels were significantly elevated in acute ATL patients compared to ACs and patients with other types of ATL (chronic, smoldering, and lymphoma). Importantly, sTNFR2 levels were significantly correlated with those of sCD25, sOX40, and IL-10, as well as proviral loads. Thus, the present study confirmed that an increase in plasma sTNFR2 levels is a biomarker for the diagnosis of acute ATL. Examination of plasma sTNFR2 alone or in combination with other ATL biomarkers may be helpful for the diagnosis of acute ATL.

A Fatal Case of Takotsubo Cardiomyopathy Secondary to Refractory Hypoglycemia in Severe Starvation: An Autopsy Case Report.

A 56-year-old, severely malnourished man presented with loss of consciousness due to hypoglycemia. Echocardiography revealed left ventricular apical ballooning, indicating takotsubo cardiomyopathy. Although his caloric intake was gradually increased to avoid refeeding syndrome, hypoglycemia was refractory, and repetitive glucose administration was required. On day 4 of admission, he developed severe refractory hypoglycemia with a progressive decrease in blood pressure. Consequently, pulseless ventricular tachycardia followed by pulseless electrical activity developed. Although venoarterial extracorporeal membrane oxygenation was introduced, the patient did not respond to the treatment and died. Autopsy revealed myocardial degeneration and contraction-band necrosis, indicative of takotsubo cardiomyopathy. No coronary stenosis was observed. The liver showed moderate hepatocyte atrophy and autophagosomes, consistent with starvation and not with refeeding syndrome. We speculated that refractory hypoglycemia induced extreme catecholamine secretion, which led to severe complications of takotsubo cardiomyopathy, such as fatal arrhythmia and extremely low cardiac output. Early recognition of these critically ill patients and timely therapeutic interventions, including strict glycemic control and adequate caloric intake, may improve patient outcomes.

EczemaPred: A computational framework for personalised prediction of eczema severity dynamics.

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.

Re-irradiation with interstitial brachytherapy in uterine cancer patients with vaginal recurrence after post-operative pelvic irradiation.

PURPOSE: This study aimed to evaluate the outcomes and dose-volume parameters of re-irradiation with interstitial brachytherapy (ISBT) in uterine cancer patients with vaginal recurrence after post-operative pelvic irradiation. MATERIAL AND METHODS: We evaluated six uterine cancer patients who received re-irradiation with ISBT between January, 2012 and December, 2016. As an initial treatment, all patients received post-operative whole pelvic irradiation of 45-50.4 Gy in 25-28 fractions. For vaginal recurrence, all patients were treated with ISBT alone at a dose of 38-42 Gy in 6-7 fractions for clinical target volumes (CTVs) for 3-4 days. RESULTS: Post-operative pelvic irradiation was delivered to five and one patients, using a three-dimensional conformal technique and intensity-modulated radiotherapy, respectively. Median duration from surgery to vaginal recurrence was 25.7 months. Median tumor size just before ISBT was 3.3 cm. Median time from completion of pelvic irradiation to ISBT initiation was 24.1 months. Mean doses per fraction of ISBT for CTV D90 (the minimum dose received by 90% of CTV) and minimum dose received by 2cc (D2cc) for the bladder and for rectum were 6.1 Gy, 4.4 Gy, and 3.8 Gy, respectively. Mean total equivalent dose in 2 Gy fractions (EQD2), including external beam radiotherapy and ISBT, for D2cc for the bladder, sigmoid, and rectum were 92.1 Gy, 50.4 Gy, and 81.6 Gy, respectively. Median follow-up duration was 53.3 months. Local recurrence was observed in two patients, and four of the six patients were alive. Grade 2 late rectal complications occurred in two patients, and no late grade ≥ 3 complications were observed in four alive patients. CONCLUSIONS: Re-irradiation with ISBT may be an effective treatment strategy for gynecological cancer patients with vaginal recurrence after post-operative pelvic irradiation.

A mathematical model to identify optimal combinations of drug targets for dupilumab poor responders in atopic dermatitis.

BACKGROUND: 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. METHODS: 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 system-level AD pathogenesis. Using this model, we simulated the clinical efficacy of hypothetical therapies on virtual patients. RESULTS: 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%). CONCLUSION: 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 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.

Essential Role of STAT3 Signaling in Hair Follicle Homeostasis.

Dominant-negative mutations associated with signal transducer and activator of transcription 3 (STAT3) signaling, which controls epithelial proliferation in various tissues, lead to atopic dermatitis in hyper IgE syndrome. This dermatitis is thought to be attributed to defects in STAT3 signaling in type 17 helper T cell　specification. However, the role of STAT3 signaling in skin epithelial cells remains unclear. We found that STAT3 signaling in keratinocytes is required to maintain skin homeostasis by negatively controlling the expression of hair follicle-specific keratin genes. These expression patterns correlated with the onset of dermatitis, which was observed in specific pathogen-free conditions but not in germ-free conditions, suggesting the involvement of Toll-like receptor-mediated inflammatory responses. Thus, our study suggests that STAT3-dependent gene expression in keratinocytes plays a critical role in maintaining the homeostasis of skin, which is constantly exposed to microorganisms.