Drug Database
AZ

azithromycin (SJP0118 / ISV401 / AzaSite)

✓ Approved

Sun Pharmaceutical Industries Ltd. · Small Molecule · Small Molecule

What is azithromycin?

azithromycin is a small molecule developed by Sun Pharmaceutical Industries Ltd.. It is approved for therapeutic indications via others.

Drug Profile

Brand NamesSJP0118, ISV401, AzaSite
CompanySun Pharmaceutical Industries Ltd.
Drug ClassSmall Molecule
RouteOthers
StatusApproved
Sources: ClinicalTrials.gov, Sun Pharmaceutical Industries Ltd.

Therapeutic Indications

azithromycin is developed for 3 unique indications across 2 therapeutic areas.

Therapeutic AreaConditionPhase
Infections and infestationsEye infection✓ Approved
Eye disordersBlepharitis✓ Approved
Eye disordersChalazion✓ Approved

Related Research Articles

PubMedThe journals of gerontology. Series B, Psychological sciences and social sciences2026-11-11

Ageist attitudes as a moderator of objective and subjective vision: A longitudinal study.

Mironi Nava N, Weissberger Gali H GH, Bergman Yoav S YS

This longitudinal study examined the relationship between objective visual acuity and subjective vision over a six-month period. Specifically, it investigated the moderating role of internalized ageist attitudes in shaping how objective vision relates to perceived sensory vision difficulties over time. A sample of 139 community-dwelling participants (M=70.25, SD=5.57) underwent objective vision assessments (distance and near visual acuity, and contrast sensitivity) and completed the Fraboni Scale of Ageism at baseline (T1). Subjective vision assessments were conducted at T1 and six months later (T2). Hierarchical regression analyses were used to predict T2 subjective vision, controlling for baseline subjective vision, age, sex, relationship status, and self-rated health. No direct main effect of T1 objective vision on T2 subjective vision was found. However, higher baseline ageist attitudes significantly predicted poorer subjective vision at T2 (β=.14, p=.041). Crucially, a significant interaction emerged (β=.16, p=.018); objective vision at T1 predicted poorer subjective vision at T2 only among individuals with high levels of ageist attitudes. For those with low ageist attitudes, this association was non-significant. These findings support Stereotype Embodiment Theory, suggesting that internalized ageist stereotypes asct as a cognitive lens through which individuals interpret biological sensory cues. The results indicate that subjective vision is not merely a reflection of physical decline but is significantly shaped by psychological frameworks. Clinical interventions aimed at reducing internalized ageist attitudes may mitigate the negative subjective experience of vision loss, potentially reducing functional vulnerabilities and enhancing overall well-being.

PMID 43010731
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PubMedMicrobiology spectrum2026-11-11

A dual-action combination therapy targeting bacteria and host immunity against bacterial infections.

Liu Yanjun Y, Zheng Anni A, Zhang Hongfang H, Lu Xingyue X et al.

The escalating crisis of antimicrobial resistance, coupled with a stagnant novel antibiotic discovery pipeline, underscores the urgent need for innovative strategies to combat bacterial infections. The development of antibiotic adjuvants that increase the efficacy of existing drugs represents a critical approach for addressing this challenge. We demonstrate that combining the conventional antibiotic azithromycin with the quorum-sensing inhibitor norharmane exerts potent antibacterial effects through both direct bactericidal activity and host immune activation. This combination not only disrupts bacterial structural integrity but also promotes macrophage polarization toward the M1-type phenotype and upregulates the expression of the important mediator, Muc5b, through activation of the IL-17 signaling pathway, ultimately enhancing mucosal immune defense and tissue repair functions. This regimen shows potent antibacterial efficacy against a range of pathogens in vitro and in vivo, along with a favorable safety profile. These findings highlight a promising combinatorial therapeutic strategy against the growing threat of antibiotic resistance. Antibiotic resistance has become one of the most urgent threats to global public health, yet the development of new antibiotics has slowed dramatically. In this study, we propose a promising alternative strategy that combines azithromycin with a small-molecule compound capable of inhibiting bacterial quorum sensing. This combination not only kills bacteria more effectively than the antibiotic alone but also harnesses the body's own immune system to fight infection. Its mechanisms of action include disrupting bacterial structure, promoting the polarization of macrophages toward the M1-type phenotype, and enhancing bactericidal capacity by activating the IL-17 signaling pathway to stimulate the production of the Muc5b protein. Our findings suggest that this dual-pronged approach could help revitalize existing antibiotics, offering a practical pathway to combat drug-resistant infections.

PMID 43012424
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PubMedNano letters2026-11-11

Bioinspired Self-Powered Polarization-Adaptive Visual Sensors Enabled by PbS Quantum-Dots/Oriented ReSe2 Heterojunctions.

Zhu Yongxing Y, Hu Haifeng H, Tao Ye Y, Chen Peipei P et al.

Polarization-sensitive visual adaptation, the biological capability to autonomously adjust to dynamic polarized light stimuli, is a highly desirable feature for next-generation intelligent vision systems. Integrating polarized light detection and adaptive processing into a single hardware device effectively overcomes the traditional bottlenecks of artificial vision, namely, the reliance on bulky optical components and the latency caused by separated sensing and computing. Here, we report a bioinspired, self-powered, polarization-adaptive visual system based on a PbS quantum-dots (QDs)/oriented ReSe2 heterojunction. Interfacial charge trapping enables adaptive behavior, while oriented etching enhances polarization-dependent photocurrent anisotropy. Under 980 nm illumination in self-powered mode, the device achieves a photocurrent anisotropy ratio of ∼8. It further demonstrates autonomous polarization adjustment, broadband spectral responsivity, and polarization-synergistic functionalities. This work establishes a compact hardware platform for multidimensional perception and adaptive processing in intelligent vision systems.

PMID 43012578
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PubMedAnalytical chemistry2026-11-11

Machine Vision-Assisted Self-Verifying Bipolar Electrochromic Distance Sensor for Instrument-Free Detection.

Jia Mingxuan M, Liu Zhenzhou Z, Wei Kehui K, Heng Huadong H et al.

Distance-based electrochromic visual sensing has attracted considerable attention for point-of-care testing (POCT) due to its simple operation and intuitive signal readout. However, the analytical reliability of existing platforms is frequently limited by environmental disturbances and poorly defined electrochromic signal boundaries, which can lead to inaccurate interpretation of visual responses. Herein, we report a machine vision-assisted dynamic self-verifying bipolar electrochromic distance sensor for the highly sensitive visual detection of ochratoxin A (OTA). A closed bipolar electrode (CBPE) architecture was rationally designed to generate two spatially separated electrochromic distance signals through coupled anodic and cathodic redox reactions, enabling an intrinsic self-verification mechanism by continuously evaluating the consistency between dual distance-based signals. To achieve objective signal interpretation, a machine vision-based analytical algorithm was integrated into the sensing system to automatically perform electrochromic boundary identification, bidirectional distance measurement, signal-ratio calculation, and quantitative analysis. Compared with conventional image-processing methods based on color intensity extraction, the developed approach provides standardized and automated quantification of spatially resolved electrochromic signals without subjective visual judgment. Under optimized conditions, the sensor exhibited a linear response between the color-change distance and the logarithm of OTA concentration over a range of 0.04-850 ng/mL, with a detection limit of 0.003 ng/mL. By synergistically integrating electrochromic signal generation with machine vision-enabled intelligent interpretation, the proposed strategy establishes a self-validating analytical framework that transforms conventional distance-based visual sensing from empirical image readout into an automated and quantitatively reliable analytical process, providing a versatile platform for portable food safety monitoring, environmental analysis, and point-of-care diagnostics.

PMID 43010423
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PubMedJournal of medical imaging (Bellingham, Wash.)2026-11-11

Representation geometry and task performance in vision-language modeling for computed tomography enterography.

Minoccheri Cristian C, Wittrup Emily E, Najarian Kayvan K, Stidham Ryan R

Computed tomography (CT) enterography is central to inflammatory bowel disease evaluation, but optimal representation choices for vision-language learning on this volumetric modality are unclear. We establish baseline performance and quantify how aggregation, slice encoding, and retrieval context affect classification, retrieval, and report generation. We fine-tuned vision-language models on abdominal CT enterography with parameter-efficient adapters and compared (i) mean versus attention pooling of slice embeddings, (ii) multiwindow red-green-blue Hounsfield-unit encoding versus multiplanar sampling for added spatial coverage, and (iii) report generation with versus without retrieval-augmented generation (RAG). A three-teacher pseudolabel framework enabled evaluation without expert annotations. Classification and retrieval comparisons used three training seeds. Mean and attention pooling achieved the same mean three-class disease classification accuracy (55.7%; standard deviations of 2.0 and 2.4 percentage points) at the largest low-rank adaptation capacity. Neither consistently outperformed the other at matched capacity with central sampling. Sixteen slices at 20% to 80% had higher classification accuracy than 8 slices, 32 slices, and 10% to 90% sampling in all three seeds for both pooling methods. The tested multiplanar configurations had lower classification accuracy. For report generation, fine-tuning without retrieval performed at prevalence-matched chance for within-1 report-label agreement (70.4% versus 71.0% random). RAG improved within-1 report-label agreement by 7 to 14 points over chance and reduced ordinal mean absolute error from 0.98 to 0.80-0.89. Slice sampling and tissue-contrast-focused encoding affect performance in CT enterography vision-language systems, and RAG improved report-label agreement over the fine-tuned baseline. These results provide initial baselines and practical guidance for multimodal learning in volumetric CT.

PMID 43011715
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PubMedSurgical laparoscopy, endoscopy & percutaneous techniques2026-11-11

Measurement of Early Gastric Cancer Size Using a Computer Vision-Assisted Virtual Ruler: A Prospective Clinical Study.

Wang Zhihong Z, Kuai Yaxian Y, Yang Xu X, Zeng Zhuang Z et al.

Accurate measurement of early gastric cancer (EGC) size is important for clinical management. We evaluated a computer vision-assisted virtual ruler (VR) for measuring lesion size during endoscopy. This prospective clinical trial included 78 patients who underwent endoscopic submucosal dissection (ESD) from January to December 2024. The maximum diameter of the post-ESD specimen, measured with a physical ruler, was used as the reference. VR measurements obtained during ESD and preoperative visual estimates were compared with the reference using mean absolute error (MAE), percentage relative error (PRE), Bland-Altman analysis, and the intraclass correlation coefficient (ICC). Lesion size was also compared between patients with high-grade intraepithelial neoplasia (HGIN) and adenocarcinoma. Visual estimation underestimated lesion size in the overall cohort and in the HGIN and adenocarcinoma groups, whereas VR measurements did not differ significantly from the reference. MAE was lower with VR than with visual estimation (1.00 vs. 3.00 mm, P=0.001), as was PRE (3.28% vs. 7.89%, P=0.001). The difference was most apparent for flat lesions and adenocarcinoma. VR also showed narrower Bland-Altman limits of agreement (-4.23 to 4.19 mm vs. -3.85 to 9.49 mm) and a higher ICC (0.979 vs. 0.909, P<0.001). The computer vision-assisted VR showed better agreement with the reference measurement and less error than visual estimation for EGC lesion size.

PMID 43012733
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