Tozasertib

The formation of charge-transfer complexes (CTCs) of iodine with five chemotherapeutic drugs used for the treatment of different types of cancer has not been investigated. These drugs are olaparib, seliciclib, vandetanib, dasatinib, and tozasertib. Additionally, these drugs need an appropriate general spectrophotometric assay for their analysis in the dosage forms regardless of the differences in their chemical structures.

The aim of this study was the development of a novel microwell spectrophotometric assay (MW-SPA) for one-step determination of these drugs via their interactions with iodine, which resulted in instantaneous production of bright lemon-yellow CTCs.

A spectrophotometric study of the CTCs was conducted, and all CTCs were characterized. Site(s) of interaction on each drug were assigned, and the MW-SPA was developed and applied to the analysis of dosage forms.

The findings confirmed that the reactions proceeded via CTC formation. Beer's law was obeyed over a general concentration range of 1–6 µg/mL. The LODs and LOQs were in the ranges of 0.5–2.1 and 1.5–6.4 µg/mL, respectively. The proposed MW-SPA demonstrated excellent precisions as the relative standard deviations were < 2.24 and 2.23% for the intra- and inter-assay precision, respectively. Recovery studies demonstrated the accuracy of MW-SPA. Successful determination of all drugs in bulk and tablet forms was achieved using the MW-SPA. The environmental sustainability of the proposed methodology was determined, providing evidence of the assay's alignment with the basis of green analytical chemistry. The high throughput of the assay was documented.

In contrast to other existing methods, the MW-SPA described herein was valid for analyzing all drugs at the same wavelength.

The assay is useful for routine analysis of drugs in their formulations in QC laboratories.

The immunophenoscore (IPS) is an important indicator for evaluating immunotherapy response. This work was designed to establish a prognostic model based on IPS-related genes in cervical cancer. Weighted correlation network analysis (WGCNA) was utilized to identify key modules related to IPS in cervical cancer data from The Cancer Genome Atlas (TCGA). The results show that the yellow module (158 genes) had a high correlation with both IPS_CTLA4_blocker and IPS_CTLA4_and PC1/PDL1/PDL2 blocker. Univariate cox regression analysis and LASSO regression analysis were performed based on 158 genes, and 9 characteristic genes were finally identified to construct the model. According to the differentially expressed genes, cervical cancer samples were divided into high-risk and low-risk groups and cluster 1.2.3. Higher risk scores associated with poorer prognosis. cluster2 and cluster3 were identified as cervical cancer subtypes with significant survival differences. cluster2 had higher immune cell infiltration levels and better prognosis, with greater sensitivity to Cyclopamine, Imatinib, MG-13, Paclitaxel, PHA-665752, Rapamycin, Sorafenib, Sunitinib, and VX-680. In contrast, cluster3 had higher TTN and PIK3CA mutations and greater sensitivity to AZ628, Dasatinib, Doxorubicin, HG-6-64-1, JQ12, Midostaurin, PF-562271, TAE684, and WH-4-023. In conclusion, we developed a feasible risk score model based on IPS-related genes for cervical cancer prognosis and identified potential drugs for different cervical cancer subtypes.