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Prediction of Proteolysis-Targeting Chimeras Retention Time Using XGBoost Model Incorporated with Chromatographic Conditions.
J Chem Inf Model
January 2025
School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou 310053, People's Republic of China.
Proteolysis-targeting chimeras (PROTACs) are heterobifunctional molecules that target undruggable proteins, enhance selectivity and prevent target accumulation through catalytic activity. The unique structure of PROTACs presents challenges in structural identification and drug design. Liquid chromatography (LC), combined with mass spectrometry (MS), enhances compound annotation by providing essential retention time (RT) data, especially when MS alone is insufficient.
View Article and Find Full Text PDFLiquid Chromatography Combined With Tandem Mass Spectrometry for the Pharmacokinetic and Metabolism Studies of PROTAC ARV-471 in Rats.
Biomed Chromatogr
February 2025
Department of Breast and Thyroid Surgery, The First Affiliated Hospital of University of Science and Technology of China, Anhui Provincial Hospital, Hefei, Anhui Province, China.
Article Synopsis
- PROTAC is a novel therapeutic approach gaining traction in pharmaceuticals, particularly for targeting proteins like estrogen receptors in breast cancer treatment.
- ARV-471 is an orally bioavailable PROTAC that effectively degrades estrogen receptors by promoting their degradation through the proteasome.
- A sensitive liquid chromatography method was developed to measure ARV-471 in rat plasma, demonstrating robust validation and revealing key metabolic pathways, including hydrolysis and glucuronidation, with four metabolites identified.
Construction of a Wilms tumor risk model based on machine learning and identification of cuproptosis-related clusters.
BMC Med Inform Decis Mak
November 2024
Department of Pediatric Surgery, The First Affiliated Hospital of Guangxi Medical University, Guangxi Zhuang Autonomous Region, No. 6, Shuangyong Road, Nanning, 530022, China.
Article Synopsis
- Cuproptosis is a newly discovered form of programmed cell death influenced by copper, and this research aims to investigate its relationship with Wilms tumor (WT) and related genes, ultimately creating a predictive model for WT.
- Researchers analyzed four gene expression datasets from the GEO database to assess the expression of Cuproptosis-related genes (CRGs), uncover clusters of WT, and examine immune cell infiltration patterns in WT samples.
- The study identified 13 significant CRGs associated with WT, discovered differences in immune cell presence compared to normal tissue, and determined that a support vector machine (SVM) model performed best in predicting WT risk using five specific genes, with validation through calibration and decision curve analyses.
AI-DPAPT: a machine learning framework for predicting PROTAC activity.
Mol Divers
October 2024
Pharmaceutical Chemistry Department, Faculty of Pharmacy, Damanhour University, Damanhour, 22516, El-Buhaira, Egypt.
Article Synopsis
- Proteolysis Targeting Chimeras (PROTACs) are innovative tools in targeted protein degradation (TPD) that hold promise for drug development, but predicting their effectiveness is challenging.
- This study introduces an AI-powered PROTAC Activity Prediction Tool (AI-DPAPT) that leverages machine learning and molecular fingerprinting techniques to accurately predict PROTAC activity based on chemical structures.
- The research demonstrated strong performance across various machine learning classifiers, with the Random Forest model yielding an impressive area under the curve score of 0.97, enhancing the understanding of structure-activity relationships in TPD and potentially accelerating drug development processes.
Rapid and comprehensive analysis of complex proteomes across large sample sets is vital for unlocking the potential of systems biology. We present UFP-MS, an ultra-fast mass spectrometry (MS) proteomics method that integrates narrow-window data-independent acquisition (nDIA) with short-gradient micro-flow chromatography, enabling profiling of >240 samples per day. This optimized MS approach identifies 6,201 and 7,466 human proteins with 1- and 2-min gradients, respectively.
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