Drug Database
CL

clobetasol (clobetasol, Mipharm / Dermovate / Dermoval)

✓ Approved

Pierre Fabre S.A. · NR3C1 · Small Molecule

What is clobetasol?

clobetasol is a small molecule developed by Pierre Fabre S.A.. It is approved for therapeutic indications via topical.

Drug Profile

Brand Namesclobetasol, Mipharm, Dermovate, Dermoval
CompanyPierre Fabre S.A.
Drug ClassSmall Molecule
Molecular TargetNR3C1
RouteTopical
StatusApproved
Sources: ClinicalTrials.gov, Pierre Fabre S.A.

Mechanism of Action

Molecular Targets

clobetasol acts on 1 molecular target:

NR3C1nuclear receptor subfamily 3 group C member 1 (GR, GCCR)
Want deeper analysis?Noah AI can explain complex mechanisms and compare to similar drugs.

Therapeutic Indications

clobetasol is developed for 4 unique indications across 1 therapeutic area.

Therapeutic AreaConditionPhase
Skin and subcutaneous tissue disordersDermatitis allergic✓ Approved
Skin and subcutaneous tissue disordersDermatitis atopic✓ Approved
Skin and subcutaneous tissue disordersPsoriasis✓ Approved
Skin and subcutaneous tissue disordersSeborrhoeic dermatitis✓ Approved

Related Research Articles

PubMedJournal of the Saudi Heart Association2026-09-19

Optimizing Heart Failure-Specific Disease Management Programs and Patient Care in Saudi Arabia: A Saudi Heart Association Position Statement.

Bakhsh Abeer A, Al Ayoubi Fakhr F, Elshaer Fayez F, Albackr Hanan B HB et al.

Heart failure (HF) represents a major and growing health burden in Saudi Arabia, with high morbidity, frequent hospitalizations, and substantial system-level costs. Despite advances in guideline-directed medical therapy, gaps in diagnosis, care coordination, and longitudinal management persist. Structured, multidisciplinary heart failure-specific disease management programs (HF-DMPs) have been shown to improve outcomes and healthcare efficiency, yet their implementation remains inconsistent across the Kingdom. The Saudi Heart Association (SHA) convened a multidisciplinary expert panel to develop a national position statement on HF-DMPs. Recommendations were formulated through structured consensus, informed by a comprehensive review of international guidelines, clinical trials, real-world evidence, and Saudi-specific data. The SHA recommends a patient-centered, multidisciplinary HF-DMP framework aligned with disease stage and institutional capacity. Core components include structured referral and return-of-care pathways, integration of primary care as longitudinal care owners, optimized use of multidisciplinary teams, and scalable models incorporating virtual care and home-based services. Special populations, including cardio-oncology, pregnancy-associated HF, and cardiomyopathies, are addressed through adaptive multidisciplinary pathways. Performance indicators and quality metrics are proposed to support benchmarking and continuous improvement. Crucially, this framework is designed to synergize with the Kingdom's Model of Care, ensuring that these clinical recommendations also offer a practical tool for the new healthcare system. Implementation of structured, capacity-based HF-DMPs is recommended as a pragmatic and scalable approach to enhance care coordination, optimize resource utilization, and reduce preventable HF morbidity and hospitalization at a national level.

PMID 42762004
Read full article →
PubMedJournal of pain & palliative care pharmacotherapy2026-09-19

Community Pharmacist Support for Complex Palliative Pharmacotherapy and Home Infusion in Advanced Heart Failure: A Case Report.

Suzuki Hiroshi H, Mori Makiko M, Hattori Yusuke Y, Koizumi Akari A et al.

Advanced heart failure (HF) may require complex palliative pharmacotherapy, including continuous home infusion of inotropes, diuretics, and opioids, together with careful management of infusion devices, injectable-drug quality, compatibility, and symptom monitoring. However, practical reports describing community pharmacist involvement in such care remain limited, particularly from the pre-discharge phase. We report a case of end-of-life pharmacotherapy and home infusion for advanced HF supported by an interdisciplinary team. A 76-year-old man with stage D HF and inotrope dependence chose home-based care after repeated hospitalizations for decompensated HF. Before discharge, the community pharmacist contributed to device-operation training, aseptic compounding procedures, cassette-exchange scheduling, and contingency planning for continuous dobutamine infusion. During home care, the pharmacist coordinated cassette preparation and exchange, proposed a lightweight disposable pump for high-dose continuous furosemide infusion, implemented light protection for furosemide, supported lumen separation to avoid incompatibility with dobutamine, and prepared a morphine cassette for continuous subcutaneous infusion. Home infusion was continued until the patient died peacefully at home on Day 45. This case highlights the multifaceted role of community pharmacists in supporting complex palliative pharmacotherapy and home infusion for advanced HF.

PMID 42762225
Read full article →
PubMedFrontiers in cardiovascular medicine2026-09-19

Development and external validation of an explainable machine learning model for in-hospital mortality risk stratification in intensive care unit patients with heart failure.

Xu Liusheng L, Cao Wei W, Huang Zefan Z, Lu Kefeng K et al.

Early risk stratification for in-hospital mortality remains challenging in intensive care unit (ICU) patients with heart failure (HF) because of substantial clinical heterogeneity and complex pathophysiological interactions. Explainable machine learning (ML) may offer a practical and transparent approach to prognostic assessment in this high-risk population. This retrospective dual-cohort study included 18,526 ICU patients with HF from the MIMIC-IV database (2008-2022) as the development cohort and 314 consecutive ICU patients with HF from an independent hospital-based cohort from August 1, 2023, to May 31, 2026, as the external validation cohort. Candidate predictors available within 24 h of ICU admission were screened using the Boruta algorithm and least absolute shrinkage and selection operator regression. Five ML algorithms were developed in the development cohort and compared using 5-fold nested cross-validation for internal validation. Model interpretability was assessed using Shapley Additive Explanations (SHAP). Nine routinely available predictors were retained for final model development. Among the candidate algorithms, Extreme Gradient Boosting (XGBoost) showed the most balanced overall performance and was selected as the final model. In internal validation using nested cross-validation, the XGBoost model achieved the highest validation area under the receiver operating characteristic curve (AUC), at 0.704 (95% CI 0.682-0.726). In the external validation cohort, the final model achieved an AUC of 0.877 (95% CI 0.820-0.933) and a Brier score of 0.079 (95% CI 0.061-0.099). However, this apparently stronger discrimination should be interpreted cautiously in light of cohort differences and the limited number of external events. SHAP analysis identified blood urea nitrogen, age, and white blood cell count as the most influential predictors. An explainable XGBoost-based model using nine routinely available early clinical variables showed promising performance for predicting in-hospital all-cause mortality in ICU patients with HF. This interpretable tool may support early risk stratification and individualized clinical management in critically ill patients with HF. Further large multicenter external validation is warranted.

PMID 42761085
Read full article →
PubMedMedicine2026-09-19

National trends in heart failure and hyperlipidemia related mortality in the United States, 1999-2024: Demographic and regional disparities with machine learning based forecasting to 2035.

Ayalew Biruk Demisse BD, Smith David N DN, Kumar Laksh L, Umar Muhammad M et al.

Cardiovascular disease remains the leading cause of death in the United States, with heart failure (HF) and hyperlipidemia substantially contributing to mortality. This study aimed to examine national trends in HF-related mortality with coexisting hyperlipidemia from 1999 to 2024 and project future mortality patterns through 2035. Mortality data were obtained from the Centers for Disease Control and Prevention Wide-ranging Online Data for Epidemiologic Research (CDC WONDER) database. Deaths were identified when heart failure and hyperlipidemia were listed on death certificates using the International Classification of Diseases, Tenth Revision (ICD-10) codes. Age-adjusted mortality rates (AAMRs) were calculated using the 2000 U.S. standard population and were stratified by sex, race/ethnicity, census region, and urbanization level. Temporal trends were evaluated using joinpoint regression analysis. Forecasts for 2025-2035 were generated using machine learning-based Seasonal Autoregressive Integrated Moving Average (SARIMA) time-series models. From 1999 to 2024, 283,161 deaths involving HF with hyperlipidemia were recorded on death certificates. The AAMR increased from 0.7 to 6.8 per 100,000 population, with a notable increase after 2018. Mortality rates were higher among men than women and were elevated in the non-Hispanic Black and American Indian/Alaska Native populations. Higher rates were also observed in nonmetropolitan counties and in the Midwest and West regions of the United States. Mortality involving HF with hyperlipidemia has increased over the past two decades, with persistent demographic and geographic differences. These findings highlight the importance of continued surveillance and targeted prevention strategies to address the cardiovascular health disparities in the United States.

PMID 42760716
Read full article →
PubMedMultiple sclerosis and related disorders2026-09-19

Reliability of neurophysiological measures in the upper- and lower-limb skeletal muscles of people experiencing high and low levels of multiple sclerosis fatigue.

Ellison Paula M PM, Goodall Stuart S, Howatson Glyn G, Kennedy Niamh N et al.

This study investigated the test-retest reliability of a broad range of knee extensor (KE) and wrist flexor (WF) neurophysiological measures in people with multiple sclerosis (PwMS) and matched healthy controls, before and after a submaximal intermittent exercise bout to task failure. PwMS were dichotomised according to whether they were experiencing high (MS-HF) or low (MS-LF) levels of perceived MS fatigue at baseline using the Fatigue Severity Scale (FSS) cut-points (MS-HF ≥5; MS-LF <4). Average (SD) FSS scores were 6.0 (0.6) vs 3.5 (1.1), respectively (p<0.001). With the caveat of wide 95% confidence intervals for some measures, intraclass correlation coefficients (ICC) for KE and WF in MS-HF and MS-LF (pre- and post-fatigue task) were good to excellent (ICC 0.76-0.98) for maximum voluntary force production and peak M-wave amplitude (Mmax); moderate to excellent for voluntary activation (ICC 0.57-0.96) and peak motor evoked potential (MEP)/Mmax (ICC 0.72-0.94) and poor to good for short‑interval intracortical inhibition (SICI; ICC 0.48-0.87) and cortical silent period (SP; ICC 0.23-0.89). Coefficient of variation scores for several measures pre- and post-fatigue task in MS-HF and MS-LF were <20%. This study provides a comprehensive dataset of reliability estimates for KE and WF neurophysiological measures in PwMS and many measures were shown to have good to excellent test-retest reliability, although some had moderate to poor reliability. There was no evidence that high levels of perceived MS fatigue adversely impact reliability estimates for these neurophysiological measures at baseline or after fatiguing muscular work.

PMID 42762584
Read full article →
PubMedImaging neuroscience (Cambridge, Mass.)2026-09-19

Brain-age in ultra-low-field MRI: How well does it work?

Biondo Francesca F, Bennallick Carly C, Martin Sophie A SA, Puglisi Lemuel L et al.

Brain-age estimates the brain's biological age from neuroimaging data and has been proposed as a biomarker of brain health and disease risk. While brain-age estimation commonly uses high-field (HF) magnetic resonance imaging (MRI) ( > 1.5 T), this is costly and inaccessible, limiting its applicability. Emerging ultra-low-field (ULF) MRI ( < 0.1 T) is cheaper and more accessible, but its lower resolution may limit the reliability of biomarkers such as brain-age. We assessed different brain-age pipelines in 23 adults scanned on one HF system (GE Signa Premier at 3 T) and two identical ULF systems (Hyperfine Swoop at 64 mT) located at two different sites, hereafter referred to as ULF1 and ULF2. We used 14 distinct acquisitions defined by T1- or T2-weighting, resolution, and preprocessing: raw anisotropic orientations (axial, coronal, sagittal), isotropic scans, and super-resolution derivatives from multi-resolution registration (MRR) and SynthSR. These inputs (a total of n = 573 scans) were analyzed with five brain-age software packages (BrainageR, SynthBA, MIDI, DeepBrainNet, PyBrainAge). Performance evaluation entailed validity (brain-age vs. actual age), correspondence (ULF brain-age vs. HF brain-age) and test-retest reliability (ULF1 brain-age vs. ULF2 brain-age). Overall, results were mixed across pipelines, although several ULF pipelines performed comparably to HF. The four best-performing combinations were SynthBA on T2 scans without SynthSR, MIDI on T2 scans without SynthSR, PyBrainAge on T1 scans with SynthSR and using FreeSurfer recon-all-clinical, and BrainageR on T1 scans with SynthSR. These showed moderate-to-strong validity ( r = 0.76 -0.92, R 2   = 0.54 -0.64, Mean Absolute Error [MAE] = 6.7 -8.21 years), moderate-to-strong correspondence to HF ( r = 0.84 -0.93, Intraclass Correlation Coefficient [ICC] = 0.72 -0.92), and excellent test-retest reliability ( r = 0.97 -0.99, ICC = 0.97 -0.99). Moreover, some anisotropic acquisitions achieved comparable validity and reliability to MRR images when tested with the best-performing model, SynthBA ( R 2   = 0.57 -0.62, ICC [CI] = 0.99 [0.97-1.00], for coronal T2). This first systematic evaluation of brain-age at ULF demonstrates that accurate and reliable estimates can be achieved across multiple pipelines, without necessarily requiring image enhancement. Performance depended on the combination of model, scan type, and preprocessing. ULF brain-age estimation could be a practical and scalable tool for clinical decision-making, population research, and long-term patient monitoring, thereby helping to make advanced neuroimaging biomarkers more accessible worldwide.

PMID 42761967
Read full article →

+9996 more articles available with a free account

Sign up free to view all articles →

Ask about clobetasol