Dataset Information

Imidazolylpyrrolone-Based Small Molecules as Anticancer Agents for Renal Cell Carcinoma.

ABSTRACT: An in silico study focused on known cancer-related target proteins, identified a selection of imidazo[4,5-b]pyrrolo[3,4-d]pyridines as potentially active. These compounds were prepared by a novel synthetic approach, designed and developed in-house, based on the reaction of 5-amino-4-cyanoformimidoyl imidazoles with N-substituted cyanoacetamides. The substituted imidazolylpyrrolones obtained, were cyclized intramolecularly to generate the intended imidazo[4,5-b]pyrrolo[3,4-d]pyridines in a process catalyzed by DBU. Treating the imidazolylpyrrolones with an excess of triethyl orthoformate and heating at 80 °C in the presence of acid catalysis led to imidazopyrrolodiazepines. These compounds were screened for their anticancer potential, using the renal cell carcinoma cell line model (A498 and 786-O cell lines). Two compounds exhibited IC₅₀ values in the low micromolar range with a good selectivity index, when compared to non-neoplastic kidney cell line HK2 and the reference compounds rapamycin, cediranib and sunitinib.

SUBMITTER: Sousa A

PROVIDER: S-EPMC10098907 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature

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Imidazolylpyrrolone-Based Small Molecules as Anticancer Agents for Renal Cell Carcinoma.

Sousa Ana A Pontes Olívia O Andrade Juliana J Baltazar Fátima F Costa Marta M Proença Fernanda F

ChemMedChem 20221115 2

An in silico study focused on known cancer-related target proteins, identified a selection of imidazo[4,5-b]pyrrolo[3,4-d]pyridines as potentially active. These compounds were prepared by a novel synthetic approach, designed and developed in-house, based on the reaction of 5-amino-4-cyanoformimidoyl imidazoles with N-substituted cyanoacetamides. The substituted imidazolylpyrrolones obtained, were cyclized intramolecularly to generate the intended imidazo[4,5-b]pyrrolo[3,4-d]pyridines in a proces ...[more]

PMID: 36310147

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Project description:PurposeIn an era of rapid expansion of FDA approvals for oral anticancer agents (OAAs), it is important to understand the factors associated with survival among real-world populations, which include groups not well-represented in pivotal clinical trials of OAAs, such as the elderly, racial minorities, and medically complex patients. Our objective was to evaluate patient- and provider-level characteristics' associations with mortality among a multi-payer cohort of metastatic renal cell carcinoma (mRCC) patients who initiated OAAs.MethodsThis retrospective cohort study was conducted using data from the North Carolina state cancer registry linked to multi-payer claims data for the years 2004 to 2015. Provider data were obtained from North Carolina Health Professions Data System and the National Plan & Provider Enumeration System. Included patients were individuals with mRCC who initiated an OAA and survived ≥90 days after beginning treatment. We estimated hazard ratios (HR) and corresponding 95% confidence limits (CL) using Cox hazard models for associations between patient demographics, patient clinical characteristics, provider-level factors, and 2-year all-cause mortality.ResultsThe cohort included 207 patients with mRCC who received OAAs. In multivariable models, clinical variables such as frailty (HR: 1.36, 95% CL: 1.11-1.67) and de novo metastatic diagnosis (HR: 2.63, 95%CL: 1.67-4.16) were associated with higher all-cause mortality. Additionally, patients solely on Medicare had higher adjusted all-cause mortality compared with patients with any private insurance (HR: 2.35, 95% CL: 1.32-4.18). No provider-level covariates investigated were associated with all-cause mortality.ConclusionsWithin a real-world population of mRCC patients taking OAAs, survival differed based on patient characteristics. In an era of rapid expansion of FDA approvals for OAAs, these real-world data underscore the continued importance of access to high-quality care, particularly for medically complex patients with limited resources.

Project description:The development of new, effective, and safe drugs to treat cancer remains a challenging and time-consuming task due to limited hit rates, restraining subsequent development efforts. Despite the impressive progress of quantitative structure-activity relationship and machine learning-based models that have been developed to predict molecule pharmacodynamics and bioactivity, they have had mixed success at identifying compounds with anticancer properties against multiple cell lines. Here, we have developed a novel predictive tool, pdCSM-cancer, which uses a graph-based signature representation of the chemical structure of a small molecule in order to accurately predict molecules likely to be active against one or multiple cancer cell lines. pdCSM-cancer represents the most comprehensive anticancer bioactivity prediction platform developed till date, comprising trained and validated models on experimental data of the growth inhibition concentration (GI50%) effects, including over 18,000 compounds, on 9 tumor types and 74 distinct cancer cell lines. Across 10-fold cross-validation, it achieved Pearson's correlation coefficients of up to 0.74 and comparable performance of up to 0.67 across independent, non-redundant blind tests. Leveraging the insights from these cell line-specific models, we developed a generic predictive model to identify molecules active in at least 60 cell lines. Our final model achieved an area under the receiver operating characteristic curve (AUC) of up to 0.94 on 10-fold cross-validation and up to 0.94 on independent non-redundant blind tests, outperforming alternative approaches. We believe that our predictive tool will provide a valuable resource to optimizing and enriching screening libraries for the identification of effective and safe anticancer molecules. To provide a simple and integrated platform to rapidly screen for potential biologically active molecules with favorable anticancer properties, we made pdCSM-cancer freely available online at http://biosig.unimelb.edu.au/pdcsm_cancer.

Dataset Information

Imidazolylpyrrolone-Based Small Molecules as Anticancer Agents for Renal Cell Carcinoma.

Publications

Imidazolylpyrrolone-Based Small Molecules as Anticancer Agents for Renal Cell Carcinoma.

Similar Datasets

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