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更新于：2026-02-27

Sulfaphenazole

磺胺苯吡唑

更新于：2026-02-27

概要

基本信息

药物类型

小分子化药

别名

1-phenyl-5-sulfanilamidopyrazole、3-(p-aminobenzenesulfonamido)-2-phenylpyrazole、5-sulfanilamido-1-phenylpyrazole

+ [6]

靶点

DHPS

作用方式

抑制剂

作用机制

DHPS抑制剂(二氢叶酸合成酶抑制剂)

治疗领域

感染

在研适应症-

非在研适应症

细菌感染

原研机构

Purdue Pharma LP

在研机构-

非在研机构

The Purdue Frederick Co., Inc.Pharmaceutical Research Associates, Inc.

权益机构-

最高研发阶段撤市

首次获批日期

美国 (1974-04-02),

细菌感染

最高研发阶段(中国)-

特殊审评-

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结构/序列

分子式C15H14N4O2S

InChIKeyQWCJHSGMANYXCW-UHFFFAOYSA-N

CAS号526-08-9

关联

100 项与磺胺苯吡唑相关的临床结果

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100 项与磺胺苯吡唑相关的转化医学

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100 项与磺胺苯吡唑相关的专利（医药）

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1,103

项与磺胺苯吡唑相关的文献（医药）

2026-06-01·COMPUTATIONAL BIOLOGY AND CHEMISTRY

Repurposing sulfonamide drugs as anticancer ligands and understanding its properties through density functional theory

Article

作者: Deepa, Palanisamy ; Sundarakannan, Balasubramanian ; Thirumeignanam, Duraisamy

Drug repurposing represents a promising approach towards drug discovery that has the potential to improve patient outcomes and address unmet medical needs. This study attempted to repurpose existing sulfonamide drugs in search of novel anticancer drugs because of their effectiveness in treating bacterial infections. A search was made in DrugBank for Sulfonamide, and 25 drugs with functional groups like SH, OSO, CS, and -S- were chosen for our study. The drug properties, such as dipole moment, volume, polarisability, highest occupied molecular orbital (HOMO), lowest unoccupied molecular orbital (LUMO), and electrostatic potential map, were analysed through a quantum mechanical approach at different functionals: M062X, M06HF, and B3LYP with basis sets (6-31 +G*, LANL2DZ). The electrostatic potential map was analyzed to determine the magnitude, size, and distribution of the electron cloud surrounding the sulfur atoms. Analysis of NBO (Natural Bond Orbital) and NCI (Non-Covalent Interaction) plots confirmed the presence of intramolecular hydrogen bonding in the sulfonamide drugs. Furthermore, the frontier molecular orbitals (HOMO and LUMO) and the band gap were thoroughly examined for all drugs to identify the best electron acceptors and donors. Docking analysis was performed to have a lock-and-key model of 25 sulfonamide drugs with the most promising cancer-targeted protein (1ZZ1): histone deacetylases (HDACs). The best drug orientation (optimal position) was discussed and compared with the control ligand SHH based on the analysis of binding affinity and root mean square deviation (RMSD). Binding affinity of control ligand SHH is -8.1 kcal/mol for the 2nd pose, which matches exactly with 1ZZ1 SHH ligand. The drugs Tolazamide, Fezolinetant, Ensulizole, Taurolidine, Acetohexamide, Isoxicam, Sulfamethizole, Sulfamethoxazole, Sulfapyridine, Sulfaphenazole, and Dodecyl sulphate were observed to exhibit high molecular volume, polarizability, dipole moment and significant HOMO, LUMO values, which are recommended for further quantum mechanical calculations. The findings of this study will be essential for evaluating the properties of sulfonamide drugs from a drugbank using a variety of analyses in order to repurpose them as novel anticancer drugs. Quantum mechanical calculations will be performed on the optimal docking poses in future work. Keywords: Sulfonamide drugs, Docking, Histone deacetylases, Lipinsk's rule, Binding affinity.

2026-03-01·ENVIRONMENTAL RESEARCH

Causal machine learning with interpretability deciphers the impact of micropollutants and socioeconomic factors on ARGs in Chinese urban drinking water

Article

作者: Zhao, Yifan ; Niu, Zhiguang ; Yu, Yang ; Wang, Chenchen ; Zhang, Ying

The widespread co-occurrence of antibiotic resistance genes (ARGs) with diverse micropollutants in drinking water distribution systems poses a critical public health threat by potentially facilitating ARG dissemination, yet the underlying causal drivers remain poorly understood. In this study, we employed H2O automated machine learning to profile ARGs and nearly 100 micropollutants, alongside socioeconomic indicators, in drinking water samples collected from 67 Chinese cities. Moving beyond traditional correlation and interpretability analyses, we adopted double machine learning (DML), a causal inference framework, to quantified causal effects (CATE) and elucidate pollutant-ARG relationships. Results revealed synergistic effects among major ARG types (e.g., sul1, tetB and blaTEM), with integron genes (intI1, intI2) serving as key genetic vectors. Antibiotics (e.g., Sulfaphenazole) and PAHs (e.g., Acenaphthene) significantly drove the proliferation of ARGs, while PCBs (e.g., Perfluoro-n-dodecanoic acid) and advanced urban development generally suppressed their prevalence, with GDP exhibiting a nonlinear U-shaped association in detail. The integration of interpretable machine learning with DML effectively deciphered these intricate relationships. Our findings provide new causal insights into ARGs drivers in drinking water and supports evidence-based risk assessment and targeted strategies to mitigate antimicrobial resistance dissemination via water systems.

2026-02-01·DRUG METABOLISM AND DISPOSITION

Fraction metabolized by cytochrome P450 enzymes: A comprehensive evaluation of the translatability of an in vitro HepatoPac assay

Article

作者: Remus, Tobias ; Schriewer, Alexander ; Shanmugalingam, Thanusa ; Parrott, Neil ; Umehara, Kenichi ; Walter, Isabelle ; Klammers, Florian

In our previous study, long-term cocultured hepatocytes were used to estimate the fraction of a drug metabolized by CYP3A4 (f_m,CYP3A4). Metabolic turnover was measured with and without a CYP3A4 selective inhibitor, and the results were verified against in vivo reference data. The current study followed a similar approach using direct or time-dependent inhibitors to evaluate f_m,CYP1A2, f_m,CYP2C8, f_m,CYP2C9, f_m,CYP2C19, and f_m,CYP2D6 for a set of marketed drugs. The used inhibitors were for CYP1A2 (20 μM furafylline), CYP2C8 (40 μM montelukast), CYP2C9 (40 μM sulfaphenazole), CYP2C19 (3 μM (-)N-3-benzyl-phenobarbital), and CYP2D6 (5 μM quinidine). We found that in vitro f_m values above 0.5 were comparable to in vivo values, falling within a 0.5 to 2-fold error in 9 of 11 CYP1A2 substrates, 5 of 8 CYP2C8 substrates, 5 of 8 CYP2C9 substrates, 2 of 3 CYP2C19 substrates, and 11 of 20 CYP2D6 substrates. The study also showed how uncertainty in measured metabolic turnover affects the estimated f_m,CYP_s, revealing that when estimated f_m errors are <25%, 89% of predictions are within 2-fold of in vivo f_m, but this drops to 40% when there is higher uncertainty in measured turnover. Although some f_m values were poorly predicted and clinical studies revealed off-target inhibition by certain inhibitors, the chemical inhibition approach using human long-term cocultured hepatocytes showed useful prediction performance for early drug discovery enabling moderate-to-sensitive drug-drug interaction risk assessments, when metabolic turnover is adequate, and inhibitor selectivity is well defined. SIGNIFICANCE STATEMENT: Calculating in vitro fraction metabolized by cytochrome P450 enzymes in liver is vital in drug discovery for assessing the object drug-drug interaction risk of new chemical entities metabolized by cytochrome P450 enzymes before clinical data are available. Despite some limitations, the current study demonstrated that using long-term cocultured hepatocytes with chemical inhibitors is a reliable method for estimating fraction metabolized by cytochrome P450 enzymes in liver, complementing the drug interaction risk assessment.

100 项与磺胺苯吡唑相关的药物交易

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