Kyowa Kirin (Japan)

Hypoxia-inducible factor prolyl hydroxylase inhibitors (HIF-PHIs) are a novel therapeutic alternative to erythropoiesis-stimulating agents (ESAs) for anemia in chronic kidney disease. Evidence regarding their real-world effectiveness is still limited. We analyzed data from the Japanese Dialysis Outcomes and Practice Patterns Study (J-DOPPS, 2019–2022) to assess the real-world effectiveness of HIF-PHIs among maintenance hemodialysis patients. We compared patients who transitioned from ESAs to HIF-PHIs with those who continued ESAs, focusing on baseline characteristics and changes in anemia-related, iron metabolism, and inflammatory parameters. Among 1433 patients on ESAs, only 56 (3.9%) were switched to HIF-PHIs. Compared with the ESA continuation group, patients in the HIF-PHI group had poorer nutritional status (albumin: 3.3 vs. 3.6 g/dL; cholesterol: 142 vs. 156 mg/dL; respectively) and greater ESA resistance (erythropoietin resistance index: 18.4 vs. 8.5 IU/kg/week/g/dL, respectively), despite higher ESA doses and iron prescription rates. After switching, a significant increase in hemoglobin was observed at 1 month (+0.44 g/dL; 95% confidence interval: 0.05–0.82; p = 0.025). Serum iron and total iron-binding capacity levels also increased, while transferrin saturation and ferritin remained unchanged. A subgroup analysis suggested greater hemoglobin response in patients with high C-reactive protein levels (p for interaction = 0.074), but no difference by erythropoietin resistance index (p for interaction = 0.372). In routine clinical practice, HIF-PHIs were selectively prescribed to patients with treatment-refractory anemia. Treatment increased hemoglobin levels, with a suggestive trend toward greater effect among those with inflammation, supporting the potential utility of HIF-PHIs in complex anemia management.

The health benefits of apple polyphenols are well-documented. Phlorizin (Pz), an apple-derived polyphenol, is a known inhibitor of sodium-glucose cotransporters (SGLTs); however, the effects of other apple constituents on SGLTs remain unclear. In this study, we examined the inhibitory effects of an unripe apple polyphenol extract, ApplePhenon® (AP), and its constituents-including Pz, phloretin, procyanidins B1 and B2, chlorogenic acid, gallic acid, catechin, epicatechin, epigallocatechin gallate, and quercetin-on SGLTs using the Ussing chamber in mouse small intestinal mucosa. AP exhibited noncompetitive inhibition of glucose-induced short-circuit current (Isc), reflecting electrogenic Na+ transport via SGLTs (apparent Ki = 22.23 ± 3.06 μg·mL-1). A similar inhibitory effect was observed in the case of Pz (Ki = 1.88 ± 0.32 μg·mL-1), phloretin (Ki = 18.82 ± 3.52 μg·mL-1), and quercetin (Ki = 220.9 ± 3.52 μg·mL-1) but not in other components. Based on the total Pz content of 6.2%, 86.0% of the inhibitory effect can be attributed to Pz. Although Pz is known to inhibit SGLT1 competitively, AP and its constituents, including Pz, phloretin, and quercetin, showed noncompetitive inhibition in this study. These findings warrant further investigation into the mechanisms underlying SGLT inhibition by AP.

Viruses, like influenza and Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), remain major causes of upper respiratory tract infections worldwide, with symptoms ranging from asymptomatic to lethal outcomes. While antivirals and vaccines have helped ameliorate disease morbidity and mortality, these infections still pose significant challenges. Probiotics, including Lactococcus lactis strain plasma (LC-Plasma), have recently shown antiviral effects by activating plasmacytoid dendritic cells (pDCs), though their detailed mechanism remains unclear. In this study, we stimulated peripheral blood mononuclear cells (PBMCs) collected from healthy participants with LC-Plasma and conducted immunological analyses to investigate the immunomodulatory mechanisms of LC-Plasma. The supernatant derived from LC-Plasma-stimulated PBMCs (LCP Sup) exhibited dose-dependent inhibition of replication in Influenza A virus subtype H1N1 (H1N1) and SARS-CoV-2. LCP Sup significantly reduced the SARS-CoV-2 viral load in Huh-7 cells. However, in the H1N1 antiviral assay using A549 cells, LCP Sup was required at a higher concentration against H1N1 in A549 cells compared with SARS-CoV-2 in Huh-7 cells. Treatment with LCP Sup significantly upregulated interferon-stimulated genes (ISG) expression, particularly MxA, in A549 cells. While MxA showed the most notable increase, other ISGs also exhibited elevated expression levels compared with the negative control. Other cytokines, chemokines, and growth factors were also induced by LC-Plasma and CpG-DNA stimulation, and the effects of LC-Plasma were much higher than those of CpG-DNA. These results provide in vitro evidence of the antiviral mechanisms of LC-Plasma via upregulation of interferon-α (IFN-α) and related ISGs for host defense against respiratory viruses.

Accurately predicting novel compound-protein interactions (CPIs) is essential for accelerating drug discovery. The generalizability of machine learning-based CPI prediction models relies significantly on the availability and diversity of CPI datasets. To maximize data utility, particularly for highly confidential datasets maintained by industries, federated learning (FL)—which integrates multi-site data while preserving privacy—has emerged as a promising approach. Nonetheless, its effectiveness when applied to heterogeneous data from diverse molecular domains, a common real-world scenario, remains unclear, thereby limiting its broader adoption. This study evaluates FL for CPI prediction using datasets spanning multiple chemical and protein domains, providing practical guidance for optimizing the FL approach. Results indicate that the FL model enhanced out-of-domain prediction performance but was surpassed by local models for in-domain data under data heterogeneity. Drawing on these findings, a new strategy was developed to achieve robust performance for in- and out-of-domain tasks: a similarity-guided ensemble that combines the global FL model with fine-tuned models based on each client's local data. This method demonstrated effectiveness with real-world industry data, including samples from the public database and 13 pharmaceutical companies. Cumulatively, these findings offer practical guidance for implementing FL in contemporary drug discovery processes.

Inter-individual variability in fine-grained functional topographies poses challenges for scalable data analysis and modeling. Functional alignment techniques can help mitigate these individual differences but they typically require paired brain data with the same stimuli between individuals, which are often unavailable. Here we present a neural code conversion method that overcomes this constraint by optimizing conversion parameters based on the discrepancy between the stimulus contents represented by original and converted brain activity patterns. This approach, combined with hierarchical features of deep neural networks as latent content representations, achieves conversion accuracies that are comparable with methods using shared stimuli. The converted brain activity from a source subject can be accurately decoded using the target's pre-trained decoders, producing high-quality visual image reconstructions that rival within-individual decoding, even with data across different sites and limited training samples. Our approach offers a promising framework for scalable neural data analysis and modeling and a foundation for brain-to-brain communication.

ABSTRACTAn exploratory analysis of past clinical trials was conducted to propose a predictive scoring system for the efficacy of mogamulizumab, an anti‐CC chemokine receptor 4 (CCR4) antibody, based on easily measurable parameters. Factors affecting progression‐free survival (PFS) were investigated using data from three clinical trials (NCT00920790, NCT01626664, and NCT01173887) and one clinical study (UMIN000013294) conducted in patients with relapsed/refractory (R/R) or untreated CCR4‐positive aggressive adult T‐cell leukemia‐lymphoma (ATL) receiving mogamulizumab treatment. Twelve routinely measured clinical parameters and three calculated indices—lymphocyte‐to‐neutrophil count ratio, platelet‐to‐lymphocyte count ratio, and lymphocyte‐to‐monocyte count ratio (LMR)—were selected as variables. Univariate Cox proportional hazards analysis identified albumin level, disease type, lactate dehydrogenase (LDH), monocyte count, neutrophil count, and LMR as relevant factors in R/R ATL patients treated with mogamulizumab monotherapy (p < 0.05). A predictive model constructed from multivariate analysis results stratified the monotherapy group (n = 69) into three subgroups, with scores of 0 (n = 5), 1 (n = 25), and 2 (n = 39), based on LDH (0 for < 265 and 1 for ≥ 265) and LMR (0 for ≥ 3.571 and 1 for < 3.571). Median PFS values were 0.57, 0.46, and 0.07 years for scores 0, 1, and 2, respectively (log‐rank test: p = 0.005 for score 0 vs. 2; p < 0.001 for score 1 vs. 2). The simple model combining LDH and LMR may predict PFS in patients with R/R aggressive ATL receiving mogamulizumab treatment. Since LDH and LMR are easily measurable in clinical practice, this model could help predict mogamulizumab efficacy and guide treatment decisions in this patient population.Trial Registration: Registration number: UMIN000049135. Date of registration: October 17, 2022

Immunoglobulin A nephropathy (IgA-N) is a primary glomerulonephritis that is characterized by mesangial cell proliferation and expansion. Although glomerular endothelial cells (GE) are implicated in the pathogenesis of IgA-N, their role remains poorly understood. We conduct single-cell and spatial transcriptomic analysis using human specimens with mild IgA-N compared with normal. We integrate single-cell and spatial transcriptome analyses in human samples of mild IgA-N compared to normal samples, revealing several novel findings: (1) identification of clusters of GE and their expressed gene profiles, (2) identification of novel inflammation-related molecules newly implicated in GE in mild IgA-N, (3) activation of inflammatory pathways characteristic of IgA-N. Therefore, we suggest the importance of GE in the mechanism of IgA-N, as GE initiates more active inflammatory response prior to mesangial cells in patients with mild IgA-N. These findings provide useful information for understanding the pathogenesis and choosing the best drug for IgA nephropathy.