Drug Database
IN

indometacin (IV) (Indocin IV)

✓ Approved

Merck & Co. · PTGS1 · Small Molecule

What is indometacin (IV)?

indometacin (IV) is a small molecule developed by Merck & Co.. It is approved for therapeutic indications via injectable (others) or intravenous (iv).

Drug Profile

Brand NamesIndocin IV
CompanyMerck & Co.
Drug ClassSmall Molecule
Molecular TargetPTGS1, PTGS2
RouteInjectable (Others), Intravenous (IV)
StatusApproved
Sources: ClinicalTrials.gov, Merck & Co.

Mechanism of Action

Molecular Targets

indometacin (IV) acts on 2 molecular targets:

PTGS1prostaglandin-endoperoxide synthase 1 (COX3, PCOX1)
PTGS2prostaglandin-endoperoxide synthase 2 (GRIPGHS, hCox-2)
Want deeper analysis?Noah AI can explain complex mechanisms and compare to similar drugs.

Therapeutic Indications

indometacin (IV) is developed for 1 unique indication across 1 therapeutic area.

Therapeutic AreaConditionPhase
Congenital, familial and genetic disordersPatent ductus arteriosus✓ Approved

Related Research Articles

PubMedJACC. Heart failure2026-08-30

Effect of In-Hospital Initiation of Dapagliflozin on Decongestion in Patients Hospitalized for Heart Failure: An Analysis of the DAPA ACT HF-TIMI 68 Trial.

Small Andre M AM, Patel Siddharth M SM, Thomas Colleen C, Palazzolo Michael G MG et al.

Safe and effective decongestion remains an important therapeutic goal in patients hospitalized for heart failure. The authors evaluated the effect of dapagliflozin on multiple clinically relevant measures of congestion among patients hospitalized for heart failure. This was a prespecified analysis of DAPA ACT HF-TIMI 68 (Dapagliflozin Effect on Cardiovascular Events in Acute Heart Failure-Thrombolysis in Myocardial Infarction 68), a randomized, placebo-controlled trial evaluating in-hospital initiation of dapagliflozin on clinical outcomes through 2 months. The authors assessed placebo-adjusted changes from baseline in modified EVEREST (Efficacy of Vasopressin Antagonism in Heart Failure) Composite Congestion Score (CCS), body weight, mean daily loop diuretic dose (furosemide 40 mg intravenous [IV] equivalents), and body weight adjusted for mean daily loop diuretic dose (diuretic efficiency) at 1 week, 1 month, and 2 months using linear mixed-effects models for repeated measures. At randomization, 12%, 63%, and 24% had no congestion (CCS 0), mild-to-moderate congestion (CCS 1-3), and severe congestion (CCS 4-9), respectively. Compared with placebo, dapagliflozin improved all decongestion-related endpoints by 1 week, including CCS (least squares mean difference [LSMD]: -0.18; 95% CI: -0.33 to -0.04; P = 0.011), body weight (LSMD: -0.60 kg; 95% CI: -0.95 to -0.26; P < 0.001), mean daily loop diuretic dose (LSMD: -0.12 furosemide 40-mg IV equivalents; 95% CI: -0.22 to -0.02; P = 0.014), and diuretic efficiency (LSMD: -0.93 kg/furosemide 40 mg IV equivalents; 95% CI: -1.61 to -0.25; P = 0.008). These improvements were sustained through 2 months, with progressive increases in diuretic efficiency through 2 months (LSMD: -1.99 kg/furosemide 40 mg IV equivalents; 95% CI: -3.19 to -0.78; P < 0.001). In-hospital initiation of dapagliflozin led to modest but significant improvements across multiple measures of congestion within 1 week that were sustained through 2 months. Diuretic efficiency progressively improved through 2 months. (Dapagliflozin Effect on Cardiovascular Events in Acute Heart Failure-Thrombolysis in Myocardial Infarction 68 [DAPA ACT HF-TIMI 68]; NCT04363697).

PMID 42669075
Read full article →
PubMedCaspian journal of internal medicine2026-08-30

Survival rate in patients with pancreatic cancer in Guilan province, Iran.

Almasi Mohammad M, Faraji Niloofar N, Joukar Farahnaz F, Eslami Narges N et al.

Pancreatic cancer is among the deadliest malignancies globally, marked by poor prognosis and limited survival. This research investigated the demographic and clinical determinants affecting overall survival in patients diagnosed with pancreatic cancer in Guilan Province, Iran. A total of 50 pathologically and radiologically confirmed cases of pancreatic cancer were analyzed. Demographic, anthropometric, and clinical data were extracted from medical records. Survival probabilities were estimated using the Kaplan--Meier method, and intergroup differences were tested using the log-rank test. Cox proportional hazards modeling identified predictors of mortality, with significance defined at p < 0.05. Statistical analyses were conducted in SPSS (v16) and GraphPad Prism (v8). The majority of patients were male (60%) and over 60 years old (58%), while 46% had normal BMI and 40% had blood group O. Comorbidities were present in 42%, and 32% were diagnosed at stage IV. Mean and median survival were 39.6 and 9.0 months, respectively. One-, three-, and five-year survival rates were 45.2%, 31.3%, and 23.5%. Advanced tumor stage (III/IV) significantly predicted shorter survival (p < 0.001). Underweight individuals had higher mortality risk (HR = 2.79; 95% CI: 1.21--6.42; P = 0.016). In multivariate models, male gender, blood group O, and advanced disease remained independent negative prognostic indicators. Male sex, advanced stage, low BMI, and blood group O were major determinants of poorer survival in pancreatic cancer. These results emphasize the need for individualized treatment and closer follow-up among high-risk groups.

PMID 42668993
Read full article →
PubMedPeerJ2026-08-30

Deep learning-based in-hospital mortality prediction using long-term sequential data in ICU patients: a multi-center validation study.

Yan Zihao Z, Wang Xueyu X, Xu Xinran X, Guo Yu Y et al.

Intensive Care Unit (ICU) patients are at high risk of acute clinical deterioration and in-hospital mortality. Accurate prediction of in-hospital mortality is essential for optimizing clinical decision-making and resource allocation. However, most existing models rely primarily on short-term physiological data from a single hospitalization and lack adequate external validation. This study develops a deep learning-based model that integrates long-term temporal clinical sequences derived from patients' historical ICU records with diagnostic information from the current ICU admission to predict in-hospital all-cause mortality during the current hospitalization. The proposed framework is applicable to both patients with a single ICU admission and those with multiple ICU admissions. We used current and historical International Classification of Diseases (ICD) codes, temporal features, and basic demographics to construct the prediction models. Single-admission patients were represented as one-event sequences, while repeated-admission patients were represented as longitudinal diagnostic trajectories. Model training and internal validation were conducted based on the MIMIC-IV database, while the eICU and MIMIC-III databases were used for external independent testing. Five deep learning (DL) models, including Informer and Transformer, were constructed for comparative analysis. On this basis, a stacked model integrating these five base models was further developed. The Area Under the Receiver Operating Characteristic Curve (AUROC), calibration curves, and clinical epidemiological analyses involving meta-analyses across different datasets were used to comprehensively evaluate the model's predictive performance, generalizability, and specific clinical application value. A total of 22,176 patients from Medical Information Mart for Intensive Care IV (MIMIC-IV), MIMIC-III, and eICU were included, and five deep learning models as well as one stacked ensemble model were constructed. In the internal validation set, the AUROC of most deep learning models exceeded 0.89, with the exception of the RNN. Among them, the Informer model achieved the best internal performance, with an AUROC of 0.95 (95% CI [0.928-0.967]). In the dual external validation across different databases, model performance exhibited apparent heterogeneity across cohorts. Although no single model consistently outperformed others, the stacked ensemble model showed relatively competitive overall predictive accuracy and stable generalizability. Compared with traditional models based on short-term biochemical indicators, the proposed deep learning models and the stacked ensemble model demonstrated competitive predictive performance for in-hospital mortality. This study demonstrates the potential of deep learning for ICU mortality prediction and proposes a novel framework for integrating longitudinal diagnostic trajectories into predictive modeling.

PMID 42668985
Read full article →
PubMedJournal of the Intensive Care Society2026-08-30

The LoVe score: Development and validation of a bedside clinical index to identify patients at risk for prolonged invasive mechanical ventilation.

Hilders Paul A PA, Lijović Lada L, Otten Martijn M, Biesheuvel Laurens A LA et al.

Early management decisions after intubation, such as humidification strategy or initiation of prevention bundles for ventilator-associated pneumonia, often depend on the expected duration of invasive mechanical ventilation (IMV). However, no simple, standardised method exists to support such estimates at the bedside. We aimed to develop and validate the Length of Ventilation (LoVe) index, a score to predict prolonged IMV beyond 48 h using peri-intubation data. We conducted a retrospective cohort study using two large, publicly available ICU databases: AmsterdamUMCdb for model development and internal validation, and MIMIC-IV for external validation. The primary outcome was prolonged IMV, defined as support beyond 48 h or death within 48 h. Two LoVe variants were derived: a ventilator-inclusive version incorporating ventilator settings, and a ventilator-agnostic version without ventilator settings. Predictors identified via logistic regression were discretised into clinically interpretable ranges and summed into an additive score. Clinical utility was assessed using decision curve analysis. In the cardiac surgery and non-cardiac surgery cohorts, the ventilator-inclusive LoVe index achieved AUROC scores of 0.82 and 0.75, respectively. The ventilator-agnostic version reached 0.72 in the non-cardiac surgery cohort. Validation in MIMIC-IV yielded 0.71 and 0.68 for the ventilator-inclusive version, and 0.71 and 0.65 for the ventilator-agnostic index. Decision curve analysis demonstrated that both LoVe indices provided meaningful clinical utility. The LoVe index provides a simple, interpretable bedside tool to estimate the likelihood of prolonged IMV early after intubation, showing consistent external validity and potential to support early ICU decision-making.

PMID 42668746
Read full article →
PubMedMarine life science & technology2026-08-30

Inhibitory effect of gut microbiota-fermented trisaccharide GuFGa (Glc-Fuc-Gal) on Campylobacter jejuni: insights from transcriptomic and metabolomic analyses.

Ren Xinmiao X, Li Shuang S, Wang Changyun C, Secundo Francesco F et al.

Fucose-rich carbohydrates, such as 2'-fucosyllactose and fucoidan, are recognized as anti-infective components that protect the host from pathogens. In this study, the response of the common enteric pathogen Campylobacter jejuni to a specific fucose-containing trisaccharide (GuFGa, β-D-Glcp-(1 → 4)-[β-D-Galp-(1 → 3)]-α-ʟ-Fucp) fermented with human fecal microbiota was investigated using metabolomic and transcriptomic analyses. The protective effect of GuFGa-derived microbial metabolites against C. jejuni was assessed in vitro using a cell-based model. No directly inhibitory effect of GuFGa was observed with the growth of C. jejuni during single-strain cultivation. However, the supernatant of GuFGa fermented with human fecal microbiota (F-GuFGa) reduced the relative abundance of C. jejuni by tenfold within the microbial community. Transcriptome data showed that 128 differentially expressed genes of C. jejuni induced by F-GuFGa treatment were mainly enriched in oxidative phosphorylation and bacterial secretion systems (type IV). Fecal fermentation of GuFGa altered 452 differentially abundant metabolites, which were mainly enriched in phenylalanine and tryptophan metabolism. Correlation analysis indicated that the expression of type IV secretion system genes was significantly negatively correlated with the abundance of phenylacetic acid (PAA) and D-3-phenyllactic acid (D-PLA) (P < 0.05). Adhesion of C. jejuni to Caco-2 cells was reduced by treatment with F-GuFGa, PAA and D-PLA, with the highest inhibition rate observed for F-GuFGa (44.4%), followed by D-PLA (33.3%). This study provides a new perspective for developing GuFGa and similarly fucose-rich oligosaccharides as innovative dietary interventions to inhibit bacterial infections and improve gut health. The online version contains supplementary material available at https://doi.org/10.1007/s42995-026-00394-1.

PMID 42668736
Read full article →
PubMediLIVER2026-08-30

Comparison of surgical treatment versus transcatheter arterial chemoembolization combined with systemic treatment for spontaneously ruptured hepatocellular carcinoma.

Liu Jianwei J, Pan Xiaorong X, Bai Shilei S, Lu Caixia C et al.

This study aimed to evaluate the efficacy of hepatectomy versus transcatheter arterial chemoembolization (TACE), both combined with targeted therapy and immunotherapy, in patients with spontaneously ruptured hepatocellular carcinoma (HCC). We retrospectively reviewed the data of 78 patients with spontaneously ruptured HCC. Patients received targeted therapy and immunotherapy and were stratified into the hepatectomy group (n = 43) or TACE group (n = 35) based on the local treatment modality. Postoperative complications and long-term prognosis were compared. Multivariable analysis identified TACE (hazard ratio [HR]: 3.394, 95% confidence interval [CI]: 1.777-6.483 for overall survival [OS]; HR: 4.763, 95% CI: 2.601-8.723 for event-free survival [EFS] and vascular invasion (VI) (HR: 3.141, 95% CI: 1.554-6.346 for OS; HR: 3.277, 95% CI: 1.713-6.269 for EFS) as independent risk factors for OS and EFS. An AFP level > 400 ng/mL (HR: 1.944, 95% CI: 1.075-3.518) was identified as an independent risk factor for EFS. The 1-, 3-, and 5-year OS rates for the hepatectomy and TACE groups were 88.4%, 53.9%, and 31.8% and 71.4%, 22.9%, and NA (p = 0.001), respectively, and the 1-, 3-, and 5-year EFS rates were 72.1%, 26.3%, and 23.0% and 45.7%, 0%, and 0% (p < 0.001). Multivariable analysis following propensity score matching showed that TACE, VI, and AFP > 400 ng/mL remained independent risk factors for OS and EFS. The incidence of complications associated with hepatectomy was significantly lower than that associated with TACE (p = 0.031). There were no significant differences between the groups regarding Grade III/IV complications related to hepatectomy or TACE or any grade or Grade III/IV complications related to targeted therapy and immunotherapy (p > 0.05). Compared with TACE combined with targeted therapy and immunotherapy, hepatectomy combined with targeted therapy and immunotherapy is a safe and effective treatment for patients with spontaneously ruptured HCC. It does not increase the incidence of complications and offers improved long-term prognosis.

PMID 42668473
Read full article →

+9996 more articles available with a free account

Sign up free to view all articles →

Ask about indometacin (IV)