Design, synthesis and biological evaluation of fluorinated selective estrogen receptor degraders (FSERDs) --- A promising strategy for advanced ER positive breast cancer

Lu, Yunlong; Liu, Chao; Wang, Xin; Liu, Lijuan; Zhao, Zhihao; Liang, Zhenlin; Liu, Yuanhao; Wen, Zhenfan; Du, Qianming; Liu, Wukun

doi:10.1016/j.ejmech.2023.115324

Cited by 6 publications

(2 citation statements)

References 40 publications

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“…The most prominent feature for ERα was identified as fluoromethane (Pubchem287; see Fig. 5 C and Table 7 ), a compound known for its ability to notably enhance various pharmaceutical properties, including potency, metabolic stability, hydrogen bonding, improved binding interactions, and pharmacokinetics, across a range of medications 63 . Furthermore, the inclusion of fluorine atoms in about 20–25% of known drugs highlights the element's importance in medicinal chemistry 64 , 65 .…”

Section: Resultsmentioning

confidence: 99%

“…Scott et al 66 conducted a study on the impact of fluorinated analogues on a clinical SERD candidate, and they concluded that the resulting molecules exhibited high quality and advanced profiling stages. Recently, Lu et al 63 reported their work on designing and synthesizing fluorinated SERDs based on the clinical drug candidate G1T48 (NCT03455270). Their findings suggested that introducing fluorine atom substitutions into SERDs enhanced overall therapeutic effectiveness, making them superior clinical candidates for orally treating ER-positive breast cancer.…”

Section: Resultsmentioning

confidence: 99%

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StackER: a novel SMILES-based stacked approach for the accelerated and efficient discovery of ERα and ERβ antagonists

Schaduangrat,

Homdee,

Shoombuatong

2023

Sci Rep

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The role of estrogen receptors (ERs) in breast cancer is of great importance in both clinical practice and scientific exploration. However, around 15–30% of those affected do not see benefits from the usual treatments owing to the innate resistance mechanisms, while 30–40% will gain resistance through treatments. In order to address this problem and facilitate community-wide efforts, machine learning (ML)-based approaches are considered one of the most cost-effective and large-scale identification methods. Herein, we propose a new SMILES-based stacked approach, termed StackER, for the accelerated and efficient identification of ERα and ERβ inhibitors. In StackER, we first established an up-to-date dataset consisting of 1,996 and 1,207 compounds for ERα and ERβ, respectively. Using the up-to-date dataset, StackER explored a wide range of different SMILES-based feature descriptors and ML algorithms in order to generate probabilistic features (PFs). Finally, the selected PFs derived from the two-step feature selection strategy were used for the development of an efficient stacked model. Both cross-validation and independent tests showed that StackER surpassed several conventional ML classifiers and the existing method in precisely predicting ERα and ERβ inhibitors. Remarkably, StackER achieved MCC values of 0.829–0.847 and 0.712–0.786 in terms of the cross-validation and independent tests, respectively, which were 5.92–8.29 and 1.59–3.45% higher than the existing method. In addition, StackER was applied to determine useful features for being ERα and ERβ inhibitors and identify FDA-approved drugs as potential ERα inhibitors in efforts to facilitate drug repurposing. This innovative stacked method is anticipated to facilitate community-wide efforts in efficiently narrowing down ER inhibitor screening.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%