Drug Database
BE

betamethasone dipropionate (DFD01 / DFD 01 / Sernivo)

✓ Approved

Encore Dermatology, Inc. · Steroids · Steroids

What is betamethasone dipropionate?

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

Drug Profile

Brand NamesDFD01, DFD 01, Sernivo
CompanyEncore Dermatology, Inc.
Drug ClassSteroids, Small Molecule
RouteTopical
StatusApproved
Sources: ClinicalTrials.gov, Encore Dermatology, Inc.

Therapeutic Indications

betamethasone dipropionate is developed for 1 unique indication across 1 therapeutic area.

Therapeutic AreaConditionPhase
Skin and subcutaneous tissue disordersPsoriasis✓ Approved

Related Research Articles

PubMedJournal of clinical pharmacology2026-10-14

Mechanistic Prediction of Maternal, Fetal, and Amniotic Fluid Betamethasone Concentrations to Support Dosing Optimization in Pregnancy.

Mahdi Alaa A, Abduljalil Khaled K, Moreira Fernanda de Lima FL, Mehta Hiral Raju HR et al.

Respiratory distress syndrome (RDS) remains a major cause of neonatal mortality following premature birth. Antenatal betamethasone is the standard of care to accelerate fetal lung maturation and improve neonatal outcomes; however, current dosing recommendations assume uniform maternal and fetal exposure across pregnancies. Gestational age (GA)-dependent physiological changes and maternal obesity may alter betamethasone disposition and fetal exposure. This study developed a maternal-fetal physiologically based pharmacokinetic (m-f-PBPK) model to quantify the effects of GA and maternal obesity on betamethasone exposure and evaluate current dosing recommendations between 24 and 34 gestational weeks. The m-f-PBPK model incorporated maternal CYP3A-mediated metabolism, placental passive diffusion and active efflux via P-glycoprotein (P-gp), placental metabolic clearance, GA-dependent physiology, and formulation-specific release rates of betamethasone phosphate and acetate. Model performance was evaluated using maternal and fetal exposure metrics across gestation. At GA 30 weeks in non-obese pregnancies, placental metabolic clearance contributed approximately 17% of maternal clearance, whereas fetal hepatic metabolism accounted for only 2.4%. Maternal obesity decreased maternal and fetal exposure by approximately 9%, while morbid obesity reduced exposure by 23%. Simulated fetal concentrations remained above the proposed pre-clinical-derived surrogate marker of 2 ng/mL for a median duration of 38-40 h in non-obese, 37.5-39 h in obese, and 30-32 h in morbidly obese populations. This m-f-PBPK model successfully characterized maternal-fetal pharmacokinetics and provides a mechanistic basis for understanding betamethasone fetal exposure across GAs with and without maternal obesity, supporting optimization of antenatal corticosteroid therapy.

PMID 42872351
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PubMedFrontiers in pharmacology2026-10-14

Knowledge graph and machine learning-guided virtual screening prioritizes putative blockers of the PSMD11/Rpn6-EBOV glycoprotein interface.

Wang Yanshuang Y, Li Yuanjian Y, Zhu Lin L, Jiang Zhiguo Z et al.

Host-directed antiviral discovery requires a traceable route from host-factor identification to testable molecular hypotheses. This study developed such a workflow around a host factor associated with Ebola virus glycoprotein (EBOV GP). GFP-GP pulldown coupled with co-immunoprecipitation/mass spectrometry was used to identify GP-associated host candidates. A PSMD11/Rpn6-centred workflow integrated knowledge-graph inference, ligand-only machine learning, ADMETlab profiling and interface-guided docking. Candidate prioritization was assessed through score-weight sensitivity analysis, applicability-domain assessment and docking controls addressing seed reproducibility, search convergence, whole-receptor context and macrocycle ring-conformer sensitivity. Docking scores were contextualized against a matched 59-compound library. The analysis recovered 424 GP-pulldown-specific host proteins and nominated PSMD11/Rpn6 as a proteostasis anchor. The tier-gated knowledge-graph candidate membership was invariant across the tested weight perturbations, with Spearman rho = 0.998 under equal weighting. The selected ExtraTrees model achieved an AUC of 0.912 for the Ki endpoint. Applicability-domain assessment and ADMET profiling differentiated candidate confidence and predicted liabilities. Docking reproducibility checks yielded a maximum standard deviation of 0.05 kcal/mol for conformationally restricted ligands, while macrocyclic candidates showed greater protocol-dependent variability. Rule-based triage integrating upstream evidence, ADMET burden, chemotype diversity and mechanistic coverage returned five candidates: betamethasone, bafilomycin A1, everolimus, pitavastatin and CHEMBL1410015. The workflow links a proteomic host-factor signal to a compact set of chemically diverse hypotheses with explicit evidence sources and limitations. The candidates remain hypotheses for experimental assessment; direct target engagement, interface blockade and antiviral activity are not established by the present study.

PMID 42870962
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PubMedEClinicalMedicine2026-10-14

Global burden of dengue on healthcare systems: a meta-analysis of healthcare resource utilisation.

Kaushik Namita N, Subramanian Subhashini S, Gedif Kinfemichael K, Nallagangula Thej Kumar TK et al.

Dengue poses an increasing health challenge, yet contemporary global healthcare resource utilisation (HCRU) data remain limited. A systematic literature review (SLR) and meta-analysis of studies published 2013-2026 was conducted to estimate hospitalisation rate (HR) and length of stay (LOS) for dengue. Pooled proportions and means were analysed with 95% confidence intervals (CIs) under fixed- and random-effects models globally and by region. Meta-regression models estimated potential sources of heterogeneity. Additionally, sensitivity (excluding sample sizes <100, >50,000, and >50,000 and <100) and subgroup analyses were conducted for disease severity, patient characteristics, and region (PROSPERO# CRD42023480948). Of 341 studies included in the SLR, studies reporting HR (121) and LOS (204) were prioritised for analysis. Global HR among patients with dengue, irrespective of severity, was 36.7% (95% CI 29.0-44.4), under the random-effects model. HR was higher for severe versus non-severe dengue (79.4% [55.9-92.2] versus 26.6% [19.3-35.4]), with versus without warning signs (36.9% [23.3-52.9] versus 17.7% [10.2-28.9]), and in Asia versus Latin America (35.1% [18.0-57.2] versus 29.3% [18.4-43.2]). Global mean LOS among hospitalised patients with dengue, irrespective of severity, was 4.9 days (95% CI 4.6-5.2) under the random-effects model. Severe dengue had longer LOS versus non-severe dengue (6.5 [5.4-7.7] versus 4.6 [4.3-4.9] days), especially for patients with versus without comorbidities (6.1 [5.1-7.2] versus 5.1 [4.3-5.9] days), and in Asia versus Latin America (5.1 [4.8-5.5] versus 4.1 [3.4-4.7] days). Meta-regression showed disease severity was the single significant factor (p < 0.0001) associated with high HR and longer LOS (I2 ≥ 99.8%) among the factors analysed. Due to high heterogeneity between studies and interregional differences, pooled estimates should be interpreted with caution. These findings indicate the considerable burden of dengue driven by HCRU, offering valuable insights for health system preparedness, clinical trial design, and policy planning. Novartis Pharma AG.

PMID 42871990
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PubMedClinical case reports2026-10-13

Deep Gluteal Syndrome and Persistent Sciatica in a Patient With Unilateral Piriformis Muscle Hypertrophy: A Case Report.

Ghani Najoua N

A man of 47 had 6 months of left sciatica without root compression on lumbar imaging. Pelvic CT showed isolated left piriformis hypertrophy, 700 versus 450 mm2. Guided betamethasone injection relieved him within 48 h, sustained at 6 months. Asymmetry and response support, but do not prove, sciatic nerve entrapment.

PMID 42865704
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PubMedExpert opinion on drug discovery2026-10-13

From stochastic systems to governed intelligence: a neurosymbolic path for pharma.

Tan Shawn Zheng Kai SZK, Valdez Joshua Daniel JD, Bohr Adam A

Artificial intelligence is being deployed across the pharmaceutical value chain, yet returns have been inconsistent. One of the explanations for this inconsistency is the organizational focus on models rather than the data foundations needed to support reliable use. This Critical Perspective argues that pharma's AI challenge is not primarily model capability, but rather representation and governance. The authors examine why model-centric deployment strategies underdeliver in semantically weak and operationally complex setting. The authors follow this by outlining a semantic reference architecture in which heterogenous enterprise data are organized around explicit semantics while existing operational systems remain largely intact. Within this framework, ontologies function as computational substrates for conceptual clarity, interoperability, and machine reasoning. On this basis, the authors position neurosymbolic AI as a practical deployment model in which neural services provide flexible interpretation and generation, while symbolic substrates ensure semantic coherence with a knowledge base, therefore governing what may be accepted into real workflows. Durable value from AI in pharma will not come from layering increasingly capable generative models onto poorly governed data environments, nor from relying on human review alone to compensate for weak system design. It will come from operationalizing semantics as infrastructure so that neural outputs are inspectable, verifiable, and accountable before they affect real work.

PMID 42865013
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PubMedAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2026-10-13

LigHTS: Biomimetic Hydrogel Photo-Fabrication for Imaging-Based Ultra-High-Throughput Screening.

Enrico Alessandro A, Rigolli Sara S, Zimmermann Julius J, Pezzotti Melissa M et al.

Imaging-based ultra-high-throughput screening (UHTS) in pharma and biotech still relies on 384- and 1536-well plates whose rigid, flat substrates limit biological fidelity and screening efficiency. Highly biomimetic organoids and organs-on-chips improve relevance but at a cost that is incompatible with early screening campaigns. Photopatterned hydrogel scaffolds are potentially scalable, but current approaches lack plate-scale reproducibility and throughput. To address this gap, we present chemically-mediated Light-Intensity Gradients for structured hydrogel functionalization of ultra-High-Throughput Screening formats (LigHTS): an all-optical, in-well method for parallel photofabrication of biomimetic hydrogels in standard 384- and 1536-well plates. Adding ultraviolet-light absorber tartrazine to photo-curable solutions enables hydrogel thickness control by tuning illumination dose without focusing optics. Entire plates are functionalized in parallel with soft (∼1-10 kPa), thin (∼10-200 µm) hydrogels. Simultaneously, film photomasks that encode anisotropies enable control of well-level gel thickness and topography at UHTS scale. We demonstrate the utility of biomimetic gelatin-methacrylate-functionalized UHTS plates using mechanosensitive human cells (HT1080 fibrosarcoma cells, HaCaT epithelial cell colonies, and cardiomyocyte tissues derived from induced pluripotent stem cells), which display stiffness- and topography-dependent phenotypes. Finally, geometric uniformity across the plate meets industry requirements (coefficient of variation <20%), providing a readily available route to enhanced biomimicry in imaging-based UHTS pipelines.

PMID 42864779
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