Low pH condition impairs BP-IgG binding to the basement membrane zone.

Bullous pemphigoid (BP), an autoimmune subepidermal blistering disease, shows tense blisters associated with urticarial erythema. Tissue-bound Immunoglobulin G (IgG) at the basement membrane zone (BMZ) detected by direct immunofluorescence (DIF) is strong evidence for a diagnosis of BP. The sensitivity of DIF is higher in complement component 3 (C3) than in IgG, but the reason for this different sensitivity is not fully understood. In this study, we performed several ex vivo studies to investigate the possible mechanism of IgG negativity and C3 positivity at the BMZ by DIF in some BP cases. First, sera from BP patients showing IgG negativity by DIF were found to clearly react to the BMZ in their own DIF skin samples. Next, indirect immunofluorescence (IIF) was performed using sera diluted with different pH phosphate-buffered saline (PBS), pH 7.4, 6.0, and 3.0. Patients' sera diluted with pH 7.4 PBS showed linear staining at the BMZ, but sera diluted with pH 6.0 PBS and pH 3.0 PBS showed lower fluorescence intensities. Finally, sections of skin from BP patients were pre-incubated with different pH PBS (pH 3.0, 6.0, and 7.4), followed by staining with anti-human IgG and C3. The fluorescence intensities were notably lower for IgG and C3 that had been pre-incubated with pH 3.0 PBS and pH 6.0 PBS than for IgG and C3 that had been pre-incubated with pH 7.4 PBS. These results suggest that a low pH condition hinders the binding of autoantibodies to the BMZ, that is, the drop in tissue pH induced by inflammation inhibits autoantibodies from depositing at the BMZ. Furthermore, the drop in tissue pH causes tissue-bound autoantibodies to detach from the BMZ. Complement fragments are activated not only on IgG but also on the cell surface of cells close to IgG during complement activation. IgG may detach from the BMZ under low pH condition induced by inflammation, but some complement fragments remain at the BMZ. These phenomena may help to explain why C3 is more sensitive than IgG when DIF is used to diagnose BP.

Developing a GNN-based AI model to predict mitochondrial toxicity using the bagging method.

Mitochondrial toxicity has been implicated in the development of various toxicities, including hepatotoxicity. Therefore, mitochondrial toxicity has become a major screening factor in the early discovery phase of drug development. Several models have been developed to predict mitochondrial toxicity based on chemical structures. However, they only provide a binary classification of positive or negative results and do not provide the substructures that contribute to a positive decision. Therefore, we developed an artificial intelligence (AI) model to predict mitochondrial toxicity and visualize structural alerts. To construct the model, we used the open-source software library kMoL, which employs a graph neural network approach that allows learning from chemical structure data. We also utilized the integrated gradient method, which enables the visualization of substructures that contribute to positive results. The dataset used to construct the AI model exhibited a significant imbalance, with significantly more negative than positive data. To address this, we employed the bagging method, which resulted in a model with high predictive performance, as evidenced by an F1 score of 0.839. This model can also be used to visualize substructures that contribute to mitochondrial toxicity using the integrated gradient method. Our AI model predicts mitochondrial toxicity based on chemical structures and may contribute to screening mitochondrial toxicity in the early stages of drug discovery.

Inducing Melanoma Cell Apoptosis by ERp57/PDIA3 Antibody in the Presence of CPI-613 and Hydroxychloroquine.

The combination of the cancer mitochondrial metabolic inhibitor CPI-613 and hydroxychloroquine has tumor-suppressive effects on clear cell sarcoma, which shares pathobiological properties with melanoma. Therefore, we intended to examine the effects of a combination of CPI-613 and hydroxychloroquine on the growth of melanoma cells in the present study. However, cell death was not induced in melanoma cells. Therefore, a monoclonal antibody, ICT, that induced apoptosis in melanoma cells in combination with CPI-613 and hydroxychloroquine was developed. Immunoprecipitation, mass spectrometry, and small interfering RNA (siRNA)-mediated gene silencing demonstrated that ICT targeted Endoplasmic Reticulum Resident Protein 57/ Protein Disulfide Isomerase Family A Member 3 (ERp57/PDIA3), which was first identified as being upregulated by metabolic depletion stress and is localized on the cell surface during immunogenic cell death. The combination of CPI-613 and hydroxychloroquine enhanced the localization of ERp57/PDIA3 to the surface of melanoma cells. siRNA-mediated downregulation of ERp57/PDIA3 did not significantly induce ICT-mediated apoptosis in melanoma cells in the presence of CPI-613 and hydroxychloroquine. Therefore, the ICT antibody acts as a tumor suppressor in melanoma cells by targeting the cell membrane ERp57/PDIA3, expression of which was enhanced by the combination of CPI-613 and hydroxychloroquine.

Feature extraction of particle morphologies of pharmaceutical excipients from scanning electron microscope images using convolutional neural networks.

Scanning electron microscopy (SEM) images are the most widely used tool for evaluating particle morphology; however, quantitative evaluation using SEM images is time-consuming and often neglected. In this study, we aimed to extract features related to particle morphology of pharmaceutical excipients from SEM images using a convolutional neural network (CNN). SEM images of 67 excipients were acquired and used as models. A classification CNN model of the excipients was constructed based on the SEM images. Further, features were extracted from the middle layer of this CNN model, and the data was compressed to two dimensions using uniform manifold approximation and projection. Lastly, hierarchical clustering analysis (HCA) was performed to categorize the excipients into several clusters and identify similarities among the samples. The classification CNN model showed high accuracy, allowing each excipient to be identified with a high degree of accuracy. HCA revealed that the 67 excipients were classified into seven clusters. Additionally, the particle morphologies of excipients belonging to the same cluster were found to be very similar. These results suggest that CNN models are useful tools for extracting information and identifying similarities among the particle morphologies of excipients.

Multiple courses of steroid pulse therapy are required in treating acquired idiopathic generalized anhidrosis patients with a large anhidrotic area: A retrospective study of 28 cases.

Patients with acquired idiopathic generalized anhidrosis (AIGA) demonstrate a sudden loss of sweating function without neurological or endocrine abnormalities. The main treatment is steroid pulse therapy. However, the number of courses required for improvement has been unclear. This study aims to clarify the factors associated with AIGA disease severity and with AIGA patients' responses to steroid pulse therapy. We retrospectively analysed the clinical information of 28 patients with AIGA in our department from the last 10 years. Univariate analysis revealed that patients with a large anhidrotic area need multiple courses of steroid pulse therapy.

Papular Umbilicated Granuloma Annulare in a Patient With Systemic Lupus Erythematosus.

Granuloma annulare (GA) is characterized by palisading granuloma, which is histopathologically distinguished by histiocytes arrayed in a palisade configuration encircling insoluble entities associated with degenerated collagen fibrils. The present case demonstrated multiple cutaneous papules showing palisading granuloma in a patient with SLE. A 39-year-old woman has been taking oral prednisolone daily, hydroxychloroquine sulfate, and belimumab for systemic lupus erythematosus (SLE). A few papules appeared on the lateral side of the left arm and gradually increased around both sides. Physical examination found multiple firm skin-colored papules ranging in diameter from 2 to 3 mm on both forearms. Some of the papules had umbilicated tops. Histopathological examination showed degenerated collagen fibers with mucin deposition surrounded by histiocyte infiltrates in the dermis. These findings are characteristic of palisading granuloma. There are several GA variants, such as generalized, subcutaneous, and perforating GA. We considered several possibilities of the mechanisms underlying characteristic histological changes; atypical generalized GA variants, dermatofibroma, and granuloma associated with cutaneous vasculitis. We made the final diagnosis of papular umbilicated GA in the context of SLE.

VGAE-MCTS: A New Molecular Generative Model Combining the Variational Graph Auto-Encoder and Monte Carlo Tree Search.

Molecular generation is crucial for advancing drug discovery, materials science, and chemical exploration. It expedites the search for new drug candidates, facilitates tailored material creation, and enhances our understanding of molecular diversity. By employing artificial intelligence techniques such as molecular generative models based on molecular graphs, researchers have tackled the challenge of identifying efficient molecules with desired properties. Here, we propose a new molecular generative model combining a graph-based deep neural network and a reinforcement learning technique. We evaluated the validity, novelty, and optimized physicochemical properties of the generated molecules. Importantly, the model explored uncharted regions of chemical space, allowing for the efficient discovery and design of new molecules. This innovative approach has considerable potential to revolutionize drug discovery, materials science, and chemical research for accelerating scientific innovation. By leveraging advanced techniques and exploring previously unexplored chemical spaces, this study offers promising prospects for the efficient discovery and design of new molecules in the field of drug development.

Improving Compound-Protein Interaction Prediction by Self-Training with Augmenting Negative Samples.

Identifying compound-protein interactions (CPIs) is crucial for drug discovery. Since experimentally validating CPIs is often time-consuming and costly, computational approaches are expected to facilitate the process. Rapid growths of available CPI databases have accelerated the development of many machine-learning methods for CPI predictions. However, their performance, particularly their generalizability against external data, often suffers from a data imbalance attributed to the lack of experimentally validated inactive (negative) samples. In this study, we developed a self-training method for augmenting both credible and informative negative samples to improve the performance of models impaired by data imbalances. The constructed model demonstrated higher performance than those constructed with other conventional methods for solving data imbalances, and the improvement was prominent for external datasets. Moreover, examination of the prediction score thresholds for pseudo-labeling during self-training revealed that augmenting the samples with ambiguous prediction scores is beneficial for constructing a model with high generalizability. The present study provides guidelines for improving CPI predictions on real-world data, thus facilitating drug discovery.

Association of Genetic Variants of HLA-DQA1 with Bullous Pemphigoid Induced by Dipeptidyl Peptidase-4 Inhibitors.

Bullous pemphigoid (BP) is the most common autoimmune blistering disorder. Several factors, including an antidiabetic (dipeptidyl peptidase-4 inhibitor [DPP-4i]), have been reported to trigger BP. To identify the genetic variants associated with BP, GWAS and HLA fine-mapping analyses were conducted. The 21 cases of noninflammatory BP induced by DPP-4i (i.e., DPP-4i-induced noninflammatory BP) and 737 controls (first cohort) and the 8 cases and 164 controls (second cohort) were included in the GWAS. Combining GWAS satisfied the genome-wide significant association of HLA-DQA1 (chromosome 6, rs3129763 [T/C]) with the risk of DPP-4i-induced noninflammatory BP (allele T carrier of 72.4% [21 of 29] in cases vs. 15.3% [138 of 901] in controls; dominant model, OR = 14, P = 1.8 × 10-9). HLA fine mapping revealed that HLA-DQA1∗05 with serine at position 75 of HLA-DQα1 (Ser75) had the most significant association with the combined cohort of DPP-4i-induced noninflammatory BP (79.3% [23 of 29] cases vs. 16.1% [145 of 901] controls; dominant model, OR = 21, P = 2.0 × 10-10). HLA-DQα1 Ser75 polymorphism was located inside the functional pocket of HLA-DQ molecules, suggesting the impact of HLA-DQα1 Ser75 on DPP-4i-induced noninflammatory BP.

Application of in Silico Technologies for Drug Target Discovery and Pharmacokinetic Analysis.

Drug discovery is researched and developed through many processes, but its overall success rate is extremely low, requiring a very long period of development and considerable costs. Clearly, there is a need to reduce research and development costs by improving the probability of success and increasing process efficiency. One promising approach to this challenge is so-called "in silico drug discovery," which is drug discovery utilizing information and communications technologies (ICT) such as artificial intelligence (AI) and molecular simulation. In recent years, ICT-based science and technology, such as bioinformatics, systems biology, cheminformatics, and molecular simulation, which have been developed mainly in the life science and chemistry fields, have changed the face of drug development. AI-based methods have been developed in the drug discovery process, mainly in relation to drug target discovery and pharmacokinetic analysis. In drug target discovery, an in silico method has been developed that uses a probabilistic framework that eliminates the problems of conventional experimental approaches and provides a key to understanding the pathways and mechanisms from compounds to phenotypes. In the field of pharmacokinetic analysis, we have seen the development of a method using nonclinical data to predict human pharmacokinetic parameters, which are important for predicting drug efficacy and toxicity in clinical trials. In this article, we provide an overview of these methods.

Zonula occludens-1 distribution and barrier functions are affected by epithelial proliferation and turnover rates.

Zonula occludens-1 (ZO-1) is a scaffolding protein of tight junctions, which seal adjacent epithelial cells, that is also expressed in adherens junctions. The distribution pattern of ZO-1 differs among stratified squamous epithelia, including that between skin and oral buccal mucosa. However, the causes for this difference, and the mechanisms underlying ZO-1 spatial regulation, have yet to be elucidated. In this study, we showed that epithelial turnover and proliferation are associated with ZO-1 distribution in squamous epithelia. We tried to verify the regulation of ZO-1 by comparing normal skin and psoriasis, known as inflammatory skin disease with rapid turnover. We as well compared buccal mucosa and oral lichen planus, known as an inflammatory oral disease with a longer turnover interval. The imiquimod (IMQ) mouse model, often used as a psoriasis model, can promote cell proliferation. On the contrary, we peritoneally injected mice mitomycin C, which reduces cell proliferation. We examined whether IMQ and mitomycin C cause changes in the distribution and appearance of ZO-1. Human samples and mouse pharmacological models revealed that slower epithelial turnover/proliferation led to the confinement of ZO-1 to the uppermost part of squamous epithelia. In contrast, ZO-1 was widely distributed under conditions of faster cell turnover/proliferation. Cell culture experiments and mathematical modelling corroborated these ZO-1 distribution patterns. These findings demonstrate that ZO-1 distribution is affected by epithelial cell dynamics.

Rituximab therapy for intractable pemphigus: A multicenter, open-label, single-arm, prospective study of 20 Japanese patients.

This was a multicenter clinical trial of rituximab, a chimeric monoclonal IgG antibody directed against CD20, for the treatment of refractory pemphigus vulgaris and pemphigus foliaceus. In total, 20 patients were treated with two doses of rituximab (1000 mg; 2 weeks apart) on days 0 and 14. The primary end point was the proportion of patients who achieved complete or partial remission on day 168 following the first rituximab dose. Of the 20 enrolled patients, 11 (55%) and four (20%) achieved complete and partial remission, respectively; therefore, remission was achieved in a total of 15 patients (75.0% [95% confidence interval, 50.9%-91.3%]). It was demonstrated that the remission rate was greater than the prespecified threshold (5%). In addition, a significant improvement in clinical score (Pemphigus Disease Area Index) and decrease in serum anti-desmoglein antibody level were observed over time. Four serious adverse events (heart failure, pneumonia, radial fracture, and osteonecrosis) were recorded in two patients, of which only pneumonia was considered causally related with rituximab. The level of peripheral blood CD19-positive B lymphocytes was decreased on day 28 after rituximab treatment and remained low throughout the study period until day 168. Our results confirm the efficacy and safety of rituximab therapy for refractory pemphigus in Japanese patients.

Mutational analysis of epidermolysis bullosa in Taiwan by whole-exome sequencing complemented by RNA sequencing: a series of 77 patients.

BACKGROUND: Epidermolysis bullosa (EB) is a heterogeneous group of hereditary skin diseases characterized by skin fragility. Primary data on Taiwanese population remain scarce. METHODS: We gathered clinical information from EB patients at National Cheng Kung University Hospital from January, 2012, to June, 2021. Diagnostic tests including transmission electron microscopy, immunofluorescence studies, and whole-exome sequencing (WES) were performed. The pathogenicity of novel splice-site mutations was determined through reverse transcriptase-PCR of skin mRNA followed by Sanger and/or RNA sequencing. RESULTS: Seventy-seven EB patients from 45 families were included: 19 EB simplex, six junctional EB, and 52 dystrophic EB. Pathogenic variants were identified in 37 of 38 families (97.4%), in which WES was used as a first-line tool for mutational analysis; RNA sequencing determined pathogenic variants in the remaining one family. A total of 60 mutations in EB-related genes were identified, including 22 novel mutations. The mutations involved KRT5, KRT14, PLEC, COL17A1, LAMB3, LAMA3, ITGB4, and COL7A1. Over one-quarter of DEB patients had EB pruriginosa. CONCLUSIONS: The distinct clinical presentation and molecular pathology of EB in Taiwan expand our understanding of this disorder. WES was an effective first-line diagnostic tool for identifying EB-associated variants. RNA sequencing complemented WES when multiple potentially pathogenic splice-site mutations were found.

A case of VEXAS syndrome (vacuoles, E1 enzyme, X-linked, autoinflammatory, somatic) with decreased oxidative stress levels after oral prednisone and tocilizumab treatment.

VEXAS (vacuoles, E1 enzyme, X-linked, autoinflammatory, somatic) syndrome has recently been described as an autoinflammatory disease associated with severe adult-onset inflammatory manifestations. The various clinical manifestations include recurrent high-grade fever, neutrophilic dermatoses, cutaneous vasculitis, chondritis of the ear and nose, pulmonary infiltrates, cytopenia, uveitis, gastrointestinal pain or inflammation, aortitis, hepatosplenomegaly, and hematological disorders. VEXAS syndrome is caused by somatic mutations of the ubiquitin-like modifier activating enzyme 1 (UBA1) gene in myeloid-lineage cells. It is characterized by vacuolated myeloid and erythroid progenitor cells seen by bone marrow biopsy. We report the case of a 64-year-old Japanese man with VEXAS syndrome. At age 63, he was referred to us with a recurrent erythema on the hands associated with a general fever of 38-40°C that had persisted for 4 or 5 days and had recurred about once a month for a year. The skin rash appeared 2 or 3 days after the onset of each fever episode. Computed tomography (CT) of the chest revealed bilateral hilar lymphadenopathy (BHL), and the mediastinal lymph nodes were swollen. Sarcoidosis was suspected but was ruled out by several tests. Laboratory examinations showed elevated inflammatory markers. Bone marrow examination showed the vacuolization of myeloid precursor cells. A skin biopsy revealed dense dermal, predominantly perivascular, infiltrates. These consisted of mature neutrophils admixed with myeloperoxidase-positive CD163-positive myeloid cells, lymphoid cells and eosinophils. Sequencing analysis identified the somatic UBA1 variant c.122T > C, which results in p.Met41Thr. Treatment with oral prednisone (15 mg/day) and monthly intravenous tocilizumab injections (400 mg) completely resolved the symptoms. Neutrophils are a major source of reactive oxygen species, and the present case demonstrated numerous neutrophilic infiltrates. We hypothesize that the patient might have had elevated derivatives of reactive oxygen metabolites (d-ROMs). d-ROM quantification is a simple method for detecting hydroperoxide levels, and clinical trials have proven it useful for evaluating oxidative stress. In this study, we measured serum d-ROM before and after oral prednisone and tocilizumab treatment. The levels decreased significantly during treatment.

Classification of scanning electron microscope images of pharmaceutical excipients using deep convolutional neural networks with transfer learning.

Convolutional Neural Networks (CNNs) are image analysis techniques that have been applied to image classification in various fields. In this study, we applied a CNN to classify scanning electron microscopy (SEM) images of pharmaceutical raw material powders to determine if a CNN can evaluate particle morphology. We tested 10 pharmaceutical excipients with widely different particle morphologies. SEM images for each excipient were acquired and divided into training, validation, and test sets. Classification models were constructed by applying transfer learning to pretrained CNN models such as VGG16 and ResNet50. The results of a 5-fold cross-validation showed that the classification accuracy of the CNN model was sufficiently high using either pretrained model and that the type of excipient could be classified with high accuracy. The results suggest that the CNN model can detect differences in particle morphology, such as particle size, shape, and surface condition. By applying Grad-CAM to the constructed CNN model, we succeeded in finding particularly important regions in the particle image of the excipients. CNNs have been found to have the potential to be applied to the identification and characterization of raw material powders for pharmaceutical development.