New 1,3,4‒oxadiazole Quinazolines as Potential Anticancer Agents: Design, Synthesis, Biological Evaluation, and In silico Studies.
A novel series of 1,3,4‒oxadiazole connected to derivatives of quinazolinone (7a-e and 8a-f) was synthesized in the current investigation, and its anticancer and Topoisomerase‒ II inhibitory activity was evaluated.
These findings inspired the design, synthesis, and biological analysis of these 1,3,4‒oxadiazole-quinazolinone analogues as antiproliferative Topo‒II inhibitors.
The novel compound structures were determined using mass spectrometry and spectral methods (IR, NMR: 1H & 13C). The 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide colourimetric assay has been used to evaluate the anticancer efficacy of these drugs, and Autodock 4.2 provides a description of the docking results. For the more active members, additional biological tests, such as the Topo‒II inhibition experiment, were performed. These compounds' physicochemical and ADMET characteristics were examined in more detail.
In the experiment for antiproliferative activity, compounds 7d, 7e, 8c, 8e, and 8f demonstrated encouraging cytotoxicity findings against HCT‒116 and HepG2 cancer cell lines, with IC50 values ranging from 3.85 to 19.43 μM. Compounds 7d, 7e, and 8e were the most potent inhibitors of Topo II with IC50 values of 15.18, 17.55, and 12.59 μM, respectively. Additionally, the docked compound 8c showed the strongest conventional hydrogen bonds among the residues Leu507(B), Asn508(B), Asn520(B), and Glu522(B) in the Human topoisomerase‒IIβ active site in the DNA complex (4G0U) when compared to the findings of docking experiments.
New findings have discovered the fact that fused 1,3,4‒oxadiazole bearing quinazolinone contributed great significance in the field of medicinal chemistry due to their diverse biological properties. Finally, the in silico pharmacokinetic profile of all the synthesized derivatives was estimated using SwissADME, where some of the compounds followed Lipinski, Veber, Egan, and Muegge rules without deviation. The result of this activity advises that with a simple modification in structure, a potent anticancer agent can be generated with good efficacy.
Gujja V
，Sadineni K
，Koppula SK
，Basireddy A
，Venkanna B
，Gunda SK
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Comparison of Two Modern Survival Prediction Tools, SORG-MLA and METSSS, in Patients With Symptomatic Long-bone Metastases Who Underwent Local Treatment With Surgery Followed by Radiotherapy and With Radiotherapy Alone.
Survival estimation for patients with symptomatic skeletal metastases ideally should be made before a type of local treatment has already been determined. Currently available survival prediction tools, however, were generated using data from patients treated either operatively or with local radiation alone, raising concerns about whether they would generalize well to all patients presenting for assessment. The Skeletal Oncology Research Group machine-learning algorithm (SORG-MLA), trained with institution-based data of surgically treated patients, and the Metastases location, Elderly, Tumor primary, Sex, Sickness/comorbidity, and Site of radiotherapy model (METSSS), trained with registry-based data of patients treated with radiotherapy alone, are two of the most recently developed survival prediction models, but they have not been tested on patients whose local treatment strategy is not yet decided.
(1) Which of these two survival prediction models performed better in a mixed cohort made up both of patients who received local treatment with surgery followed by radiotherapy and who had radiation alone for symptomatic bone metastases? (2) Which model performed better among patients whose local treatment consisted of only palliative radiotherapy? (3) Are laboratory values used by SORG-MLA, which are not included in METSSS, independently associated with survival after controlling for predictions made by METSSS?
Between 2010 and 2018, we provided local treatment for 2113 adult patients with skeletal metastases in the extremities at an urban tertiary referral academic medical center using one of two strategies: (1) surgery followed by postoperative radiotherapy or (2) palliative radiotherapy alone. Every patient's survivorship status was ascertained either by their medical records or the national death registry from the Taiwanese National Health Insurance Administration. After applying a priori designated exclusion criteria, 91% (1920) were analyzed here. Among them, 48% (920) of the patients were female, and the median (IQR) age was 62 years (53 to 70 years). Lung was the most common primary tumor site (41% [782]), and 59% (1128) of patients had other skeletal metastases in addition to the treated lesion(s). In general, the indications for surgery were the presence of a complete pathologic fracture or an impending pathologic fracture, defined as having a Mirels score of ≥ 9, in patients with an American Society of Anesthesiologists (ASA) classification of less than or equal to IV and who were considered fit for surgery. The indications for radiotherapy were relief of pain, local tumor control, prevention of skeletal-related events, and any combination of the above. In all, 84% (1610) of the patients received palliative radiotherapy alone as local treatment for the target lesion(s), and 16% (310) underwent surgery followed by postoperative radiotherapy. Neither METSSS nor SORG-MLA was used at the point of care to aid clinical decision-making during the treatment period. Survival was retrospectively estimated by these two models to test their potential for providing survival probabilities. We first compared SORG to METSSS in the entire population. Then, we repeated the comparison in patients who received local treatment with palliative radiation alone. We assessed model performance by area under the receiver operating characteristic curve (AUROC), calibration analysis, Brier score, and decision curve analysis (DCA). The AUROC measures discrimination, which is the ability to distinguish patients with the event of interest (such as death at a particular time point) from those without. AUROC typically ranges from 0.5 to 1.0, with 0.5 indicating random guessing and 1.0 a perfect prediction, and in general, an AUROC of ≥ 0.7 indicates adequate discrimination for clinical use. Calibration refers to the agreement between the predicted outcomes (in this case, survival probabilities) and the actual outcomes, with a perfect calibration curve having an intercept of 0 and a slope of 1. A positive intercept indicates that the actual survival is generally underestimated by the prediction model, and a negative intercept suggests the opposite (overestimation). When comparing models, an intercept closer to 0 typically indicates better calibration. Calibration can also be summarized as log(O:E), the logarithm scale of the ratio of observed (O) to expected (E) survivors. A log(O:E) > 0 signals an underestimation (the observed survival is greater than the predicted survival); and a log(O:E) < 0 indicates the opposite (the observed survival is lower than the predicted survival). A model with a log(O:E) closer to 0 is generally considered better calibrated. The Brier score is the mean squared difference between the model predictions and the observed outcomes, and it ranges from 0 (best prediction) to 1 (worst prediction). The Brier score captures both discrimination and calibration, and it is considered a measure of overall model performance. In Brier score analysis, the "null model" assigns a predicted probability equal to the prevalence of the outcome and represents a model that adds no new information. A prediction model should achieve a Brier score at least lower than the null-model Brier score to be considered as useful. The DCA was developed as a method to determine whether using a model to inform treatment decisions would do more good than harm. It plots the net benefit of making decisions based on the model's predictions across all possible risk thresholds (or cost-to-benefit ratios) in relation to the two default strategies of treating all or no patients. The care provider can decide on an acceptable risk threshold for the proposed treatment in an individual and assess the corresponding net benefit to determine whether consulting with the model is superior to adopting the default strategies. Finally, we examined whether laboratory data, which were not included in the METSSS model, would have been independently associated with survival after controlling for the METSSS model's predictions by using the multivariable logistic and Cox proportional hazards regression analyses.
Between the two models, only SORG-MLA achieved adequate discrimination (an AUROC of > 0.7) in the entire cohort (of patients treated operatively or with radiation alone) and in the subgroup of patients treated with palliative radiotherapy alone. SORG-MLA outperformed METSSS by a wide margin on discrimination, calibration, and Brier score analyses in not only the entire cohort but also the subgroup of patients whose local treatment consisted of radiotherapy alone. In both the entire cohort and the subgroup, DCA demonstrated that SORG-MLA provided more net benefit compared with the two default strategies (of treating all or no patients) and compared with METSSS when risk thresholds ranged from 0.2 to 0.9 at both 90 days and 1 year, indicating that using SORG-MLA as a decision-making aid was beneficial when a patient's individualized risk threshold for opting for treatment was 0.2 to 0.9. Higher albumin, lower alkaline phosphatase, lower calcium, higher hemoglobin, lower international normalized ratio, higher lymphocytes, lower neutrophils, lower neutrophil-to-lymphocyte ratio, lower platelet-to-lymphocyte ratio, higher sodium, and lower white blood cells were independently associated with better 1-year and overall survival after adjusting for the predictions made by METSSS.
Based on these discoveries, clinicians might choose to consult SORG-MLA instead of METSSS for survival estimation in patients with long-bone metastases presenting for evaluation of local treatment. Basing a treatment decision on the predictions of SORG-MLA could be beneficial when a patient's individualized risk threshold for opting to undergo a particular treatment strategy ranged from 0.2 to 0.9. Future studies might investigate relevant laboratory items when constructing or refining a survival estimation model because these data demonstrated prognostic value independent of the predictions of the METSSS model, and future studies might also seek to keep these models up to date using data from diverse, contemporary patients undergoing both modern operative and nonoperative treatments.
Level III, diagnostic study.
Lee CC
，Chen CW
，Yen HK
，Lin YP
，Lai CY
，Wang JL
，Groot OQ
，Janssen SJ
，Schwab JH
，Hsu FM
，Lin WH
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Novel 5-Fluorouracil analogues versus perfluorophenyl ureas as potent anti-breast cancer agents: Design, robust synthesis, in vitro, molecular docking, pharmacokinetics ADMET analysis and dynamic simulations.
To investigate the therapeutic potential of 5-Fluorouracil-based analogues, a straightforward synthetic technique was employed to synthesize a novel series of 5-arylurea uracil derivatives (AUFU01-03) and aryl-urea derivatives bearing perfluorophenyl (AUPF01-03). Reliable tools such as infrared (IR), Nuclear Magnetic Resonance (NMR) spectra, and elemental analyses were utilized to confirm the chemical structures and purity of these compounds. In comparison to healthy noncancerous control skin fibroblast cells (BJ-1), we examined the antiproliferative efficacy of compounds (AUFU01-03) and (AUPF01-03) against specific human malignant cell lines of the breast (MCF-7), and colon (HCT-116). Based on the MTT experiment results, compounds AUFU03 and AUPF01-03 possessed highly cytotoxic effects. Among these, cytotoxicity was demonstrated by compounds AUPF01-03 with IC50 values (AUPF01, IC50 = 167 ± 0.57 µM, AUPF02, IC50 = 23.4 ± 0.68 µM and AUPF03, IC50 = 28.8 ± 1.13 µM, respectively, on MCF-7), relative to 5-Fluorouracil as reference drug (IC50 = 160.7 ± 0.22 µM). Compound AUPF01 showed safety on BJ-1 cells up to a concentration of 100 µM (% cytotoxicity = 3.9 ± 0.42 %), so AUPF01 was selected for further studies. At the gene, the expression levels of BCL-2 gene were decreased significantly in MCF-7 + 5-FU and reached the lowest level in MCF-7 + AUPF01. In contrast, the expression levels of pro-apoptotic genes (p53 and BAX) were increased in MCF-7 + 5-FU, and reached a significantly higher level in MCF-7 + AUPF01. Apoptosis/necrosis assays demonstrated that AUPF01 induced S and G2/M phase cell cycle arrest in MCF-7 cells. Moreover, the efficacy of these compounds against anti-cancer protein receptors was assessed using molecular docking. The results indicated that compound AUPF01 exhibited high binding energies, effectively interacting with the active sites of crucial proteins such as EGFR, CDK2, ERalfa, BAX1, BCL2, and P53. These interactions involved a diverse range of chemical bonding types, suggesting the potential of these substances to inhibit enzyme activities. Moreover, computational ADMET analyses of these compounds demonstrated compliance with Lipinski's criteria, indicating favorable physicochemical properties. Additionally, molecular dynamics (MD) simulations revealed stable complexes of AUPF01 with EGFR, CDK2, ERalfa, BAX1, BCL2, and P53, as evidenced by (RMSD) values, RMSF values, and (SASA) values for the respective complexes.
Sroor FM
，El-Sayed AF
，Mahmoud K
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ResearchGate
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钛学术
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