Drug Database
CL

clobetasol (DFD06 / DFD06 Cream / DFD 06)

✓ Approved

Encore Dermatology, Inc. · NR3C1 · Steroids

What is clobetasol?

clobetasol is a steroids developed by Encore Dermatology, Inc.. It is approved for therapeutic indications via topical.

Drug Profile

Brand NamesDFD06, DFD06 Cream, DFD 06
CompanyEncore Dermatology, Inc.
Drug ClassSteroids, Small Molecule
Molecular TargetNR3C1
RouteTopical
StatusApproved
Sources: ClinicalTrials.gov, Encore Dermatology, Inc.

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 1 unique indication across 1 therapeutic area.

Therapeutic AreaConditionPhase
Skin and subcutaneous tissue disordersPsoriasis✓ Approved

Related Research Articles

PubMedDiabetes, metabolic syndrome and obesity : targets and therapy2026-09-20

Metabolic Predictors and Diagnostic Performance of Fundus Autofluorescence for Diabetic Macular Edema: A Cross-Sectional Diagnostic Accuracy Study Using Swept-Source Optical Coherence Tomography as Reference Standard.

Amin Ramzi R, Pratama Adrian A, Ansyori Abdul Karim AK, Calisanie Mohammad Aulia Molid Ogest Putra MAMOP

To evaluate the diagnostic performance of fundus autofluorescence (FAF) imaging for detecting diabetic macular edema (DME) using swept-source optical coherence tomography (SS-OCT) as the reference standard, and to identify metabolic factors associated with FAF detection accuracy in patients with type 2 diabetes mellitus. This cross-sectional diagnostic accuracy study enrolled 120 eyes from 68 patients with type 2 diabetes mellitus at the Vitreoretina Subdivision, Department of Ophthalmology, RSUP Dr. Mohammad Hoesin, Palembang, Indonesia, between January 2024 and June 2025. All participants underwent comprehensive ophthalmic examination including FAF imaging and en face SS-OCT (DRI OCT Triton Plus, Topcon). Metabolic parameters including glycated hemoglobin (HbA1c), fasting lipid profile, body mass index (BMI), estimated glomerular filtration rate (eGFR), and diabetes duration were recorded. Diagnostic accuracy indices were calculated using 2×2 contingency tables. Multivariate logistic regression identified metabolic predictors of FAF detection concordance with SS-OCT. DME was present in 78 eyes (65.0%) by SS-OCT. FAF demonstrated sensitivity of 73.1% (95% CI: 61.8-82.5%), specificity of 76.2% (95% CI: 61.5-87.2%), positive predictive value of 85.1%, negative predictive value of 60.4%, positive likelihood ratio of 3.07, negative likelihood ratio of 0.35, and diagnostic odds ratio of 8.68. HbA1c ≥8.5% (OR 3.42, 95% CI: 1.28-9.14, p=0.014), diabetes duration ≥10 years (OR 2.87, 95% CI: 1.15-7.16, p=0.024), and BMI ≥30 kg/m2 (OR 2.31, 95% CI: 0.94-5.68, p=0.068) were independently associated with FAF-OCT concordance. Center-involved DME showed higher FAF sensitivity (82.4%) compared to non-center-involved DME (55.6%, p=0.012). FAF imaging demonstrates moderate diagnostic accuracy for DME detection, with performance significantly influenced by metabolic status and DME subtype. Integration of metabolic risk stratification may optimize FAF-based screening in diabetic populations.

PMID 42763671
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PubMedZhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences2026-09-19

Association between dietary folic acid intake and diabetic retinopathy among patients with diabetes: An analysis of 2005-2008 NHANES data.

Meng Zidi Z, Cao Huiyi H, Cao Jiamin J, Chen Yu Y et al.

Dietary folic acid (FA) intake may reduce homocysteine levels and alleviate oxidative stress, both of which are closely associated with the development and progression of diabetic retinopathy (DR). This study aims to investigate the association between dietary FA intake and DR among patients with diabetes aged ≥40 years in the United States. Data from the 2005-2008 National Health and Nutrition Examination Survey (NHANES) were used. The study population comprised 3 220 patients previously diagnosed with type 2 diabetes mellitus, after excluding patients with diabetes who had missing data on dietary FA intake or DR. Multivariable logistic regression was used to evaluate the association between dietary FA intake and the prevalence of DR, with subgroup analyses and interaction tests performed. Smooth curve fitting was used to explore the potential nonlinear relationship between dietary FA intake and DR. After adjustment for potential confounders, higher dietary FA intake was significantly associated with a lower prevalence of DR (OR=0.394, 95% CI 0.193 to 0.804, P=0.011). Smooth curve fitting revealed a predominantly linear, inverse dose-response relationship between dietary FA intake and the odds of DR, with no significant non-linearity detected (P value for non-linearity=0.6535). Subgroup analyses demonstrated that the inverse association remained consistent across subgroups stratified by sex, age, body mass index (BMI), education level, smoking status, alcohol consumption, hypertension, and hypercholesterolemia (all P values for interaction >0.05). Among adults with diabetes aged ≥40 years in the United States who were diagnosed clinically or based on biochemical testing, higher dietary FA intake is significantly associated with a lower prevalence of DR. Future prospective studies are needed to clarify the causal relationship and underlying mechanisms.

PMID 42763298
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PubMedThe Lancet regional health. Europe2026-09-19

Drug-resistance profiles, population structure, genomic clustering, and temporal trends in drug resistance among Mycobacterium tuberculosis complex isolates in Ukraine, 2019-2023: a multicentre cohort study.

Butov Dmytro D, Butova Tetiana T, Miasoiedov Valerii V, Feshchenko Yurii Y et al.

Ukraine has a high burden of rifampicin-resistant (RR) tuberculosis (TB). We characterised whole-genome sequencing (WGS)-inferred drug-resistance (DR) profiles, population structure, genomic clustering, and temporal trends in DR among Mycobacterium tuberculosis complex (Mtbc) isolates during 2019-2023, amid the COVID-19 pandemic, large-scale displacement, health-system disruptions, and changes in DR-TB-policy. In this multicentre cohort study, we analysed WGS data from pretreatment Mtbc isolates collected across 18 of 24 Ukrainian regions. WGS enabled phylogenetic classification, resistance prediction, clustering. Clinical data were prospectively collected. WGS was completed for 4162 Mtbc-isolates, of which 3112/4162 (74.8%) were at least RR. Among 3040 multidrug-resistant (MDR) isolates, 1101/3040 (36.2%) had WGS-inferred fluoroquinolone (FQ) resistance and 78/3040 (2.6%) were classified as extensively DR-TB (XDR-TB); 57 had bedaquiline resistance, 32 had linezolid resistance, and 11 had resistance to both drugs. Lineage 2 (L2) accounted for 1608/1939 (82.9%) of MDR, 891/1023 (87.1%) of pre-XDR, and 70/78 (89.7%) of XDR isolates. Overall, 1712/3112 (55.0%) of DR-isolates formed genomic clusters; the three largest comprised 671/3112 (21.6%) of DR-TB cases and consisted exclusively of L2-isolates. WGS-inferred FQ resistance declined from 276/675 (40.9%) to 216/767 (28.2%) during 2019-2023 (absolute difference -12.7 percentage points [95% CI -17.5 to -7.8]; FDR-adjusted q = 4.1 × 10-7). DR-TB in this cohort was characterised by the predominance of genomically clustered L2 strains and frequent FQ resistance. Although WGS-inferred bedaquiline resistance and XDR-TB were uncommon, combined FQ, bedaquiline, and linezolid resistance warrants continued genomic surveillance. Temporal trends may reflect concurrent epidemiological, diagnostic, and health-system changes. NIAID/USCRDF; BMBF; Deutsche Forschungsgemeinschaft; EvoLUNG.

PMID 42761426
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PubMedMethodist DeBakey cardiovascular journal2026-09-19

Dr. George P. Noon's Influence on Our Practice of Medicine.

Young James B JB, Bhimaraj Arvind A

Two physicians describe the impact Dr. George P. Noon had on his partners, trainees, referring colleagues, administrators, and Methodist Hospital overall. They share the many ways he influenced their careers at different points in the evolution of cardiovascular medical and surgical patient care. This essay accompanies the In Memoriam that also appears in this issue of the journal.1.

PMID 42761687
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PubMedInternational journal of ophthalmology2026-09-19

Angiogenesis-RNA modification in diabetic retinopathy: biomarkers and targets.

Wang Xue X, Wu Dan-Ping DP, Du Wei W, Li Rui R et al.

To identify biomarkers and potential therapeutic targets related to angiogenesis and RNA modification in diabetic retinopathy (DR). The Gene Expression Omnibus (GEO) datasets GSE12610, GSE87433, and GSE111465, which contain retinal samples from diabetic and normal mice, were analyzed to identify differentially expressed genes (DEGs). The DEGs were then intersected with angiogenesis- and RNA modification-related genes (A&RMRGs) obtained from GeneCards and other databases to identify differentially expressed A&RMRGs in DR. Hub genes were subsequently identified from the protein-protein interaction (PPI) network built on the enrichment results. Their diagnostic value was then estimated by receiver operating characteristic (ROC) analysis, and their expression levels were tested for associations with immune cell infiltration. Regulatory networks of hub genes were constructed using MicroRNA Target Prediction Database (miRDB), ChIPBase, and the Comparative Toxicogenomics Database (CTD). To validate the bioinformatic findings, hub gene transcript levels were quantified with reverse transcription quantitative polymerase chain reaction (RT-qPCR) in high-glucose-treated rat retinal microvascular endothelial cells (rRMECs). Forty-two A&RMRGs showed differential expression in the diabetic retina, with Gene Ontology (GO)/Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment implicating RNA modification and immune-inflammatory regulation, alongside Gene Set Enrichment Analysis (GSEA) evidence of activated RNA-silencing and fatty-acid-transport programs and suppressed neuronal and retinoid-cycle programs. Seven hub genes (Wdr3, Crebbp, Sdad1, Stat3, Gnl2, Nhp2, and Hsp90aa1) were selected, all of them were upregulated in DR. The DR retina showed higher levels of central memory CD8+ T cells and lower levels of monocytes, Tregs, and CD56bright natural killer (NK) cells. Monocyte infiltration was inversely associated with Stat3 expression (r=-0.604, P<0.001), and central memory CD8+ T cell levels were positively associated with Nhp2 expression (r=0.539, P=0.003). A total of 60 miRNAs, 43 transcription factors (TFs), and 47 drugs may regulate RNA modification of angiogenesis-associated genes in DR. Gnl2 [area under the ROC curve (AUC)=0.914, 95% confidence interval (CI): 0.810-1.000) and Stat3 (AUC=0.964, 95%CI: 0.892-1.000) showed high diagnostic value. Exposure of rRMECs to high glucose led to a marked rise in Stat3 expression and a fall in Sdad1, leaving the transcript levels of the remaining five hub genes unchanged. These findings identify Stat3 as a point of convergence between RNA modification, immune dysregulation, and angiogenesis, nominating the hub genes as candidate biomarkers and the associated drug-gene interactions as repurposing leads.

PMID 42761712
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PubMedZhonghua yi xue za zhi2026-09-19

[Analysis of influencing factors and construction of a predictive model for cardiovascular autonomic neuropathy in type 2 diabetes mellitus].

Jiang Y L YL, Wu H M HM, Zhu G G, Wang W W et al.

Objective: To investigate the influencing factors for cardiovascular autonomic neuropathy (CAN) in patients with type 2 diabetes mellitus (T2DM) and to develop a predictive model. Methods: A retrospective analysis was performed on clinical data of 831 patients with T2DM hospitalized in the Suqian Hospital Affiliated to Xuzhou Medical University from September 2024 to December 2025. Patients were divided into a training set (n=581) and a validation set (n=250) at a 7∶3 ratio using a random number table method. According to the presence of CAN, patients in the training set were further classified into the non-CAN group (n=307) and the CAN group (n=274). Compare the differences in various indicators between the two groups. Least absolute shrinkage and selection operator (LASSO) regression and multivariate logistic regression models were used to analyze influencing factors for CAN in patients with T2DM, and a nomogram prediction model was constructed. The area under the receiver operating characteristic curve (AUC), calibration curve and decision curve analysis were adopted to evaluate the predictive performance, accuracy and clinical applicability of the model. Results: In the training set, patients in the CAN group presented higher age, diabetes duration, glycated hemoglobin (HbA1c), neutrophil count, platelet count, neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), monocyte-to-lymphocyte ratio, systemic immune-inflammation index (SII), and systemic inflammatory response index compared with the non-CAN group. By contrast, height, body weight, total bilirubin, direct bilirubin, alanine aminotransferase, aspartate aminotransferase, fasting C-peptide and fasting insulin were lower in the CAN group (all P<0.05). The proportions of diabetic nephropathy, diabetic retinopathy (DR), diabetic peripheral neuropathy, history of hypertension, history of cardiovascular disease, history of stroke, anti-platelet agent use and insulin use were higher in the CAN group than in the non-CAN group (all P<0.05). Seven variables were screened out via LASSO regression analysis, including DR, age, diabetes duration, HbA1c, NLR, PLR and SII. Multivariate logistic regression analysis revealed that DR (OR=2.29, 95%CI: 1.48-3.56), longer diabetes duration (OR=1.04, 95%CI: 1.01-1.08), advancing age (OR=1.06, 95%CI: 1.04-1.09), elevated HbA1c (OR=1.23, 95%CI: 1.13-1.36), increased NLR (OR=2.23, 95%CI: 1.36-3.69) and elevated PLR (OR=1.01, 95%CI: 1.00-1.02) were influencing factors for CAN in patients with T2DM. The nomogram prediction model constructed with the above 6 variables yielded AUC values of 0.839 (95%CI: 0.807-0.871) and 0.787 (95%CI: 0.732-0.843) for predicting CAN among T2DM patients in the training set and validation set, respectively. The corresponding sensitivity was 81.4% and 71.3%, and the specificity was 71.7% and 73.3%. Calibration curves demonstrated good agreement between predicted and observed CAN outcomes in both the training and validation sets. The Hosmer-Lemeshow test showed satisfactory calibration for the training set (χ²=6.701, P=0.461) and validation set (χ²=12.139, P=0.096). The decision curve analysis demonstrated that the model exhibits favorable clinical applicability when the threshold probability of the training set ranges from 1% to 88%, and that of the validation set ranges from 3% to 80%. Conclusions: DR, longer diabetes duration, advanced age, elevated HbA1c, increased NLR and elevated PLR are risk factors for CAN in patients with T2DM. The nomogram prediction model established based on these variables can intuitively and individually assess the risk of CAN in patients with T2DM.

PMID 42763210
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