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Novel semi-supervised Bayesian learning for impurity detection in oligonucleotide drugs

用于寡核苷酸药物杂质检测的新型半监督贝叶斯学习

基本信息

批准号：
2597535
负责人：
金额：
--
依托单位：
University of Bristol
依托单位国家：
英国
项目类别：
Studentship
财政年份：
2021
资助国家：
英国
起止时间：
2021 至无数据
项目状态：
未结题

来源：
https://gtr.ukri.org/projects?ref=studentship-2597535
关键词：
Novel semi supervised Bayesian learning

项目摘要

ObjectivesDevelop Bayesian data science methodology to profile oligonucleotide impurities automatically, accurately, and delivers statistical measures of certainty.DetailsOligonucleotides are large complex molecules they are currently very difficult to profile for impurities, as the analysis is labour intensive and the data complexity is high. This project will aim to apply data science methodology to carry out impurity profiling over a larger number of batches of mass spectrometry data that will enable us to learn how to characterise the known oligonucleotide signal and deconvolute it from a number of known and unknown impurities longitudinally, in a semi-supervised learning framework, to gain detailed insights towards these impurities longitudinally; while confirming the overall consistency of the profile, identify any change patterns, trends over batches, and any correlation between impurities. This approach can also provide semi-quantitative result to aid our understanding in key studies such ASAP, terminal sterilisation and forced degradation for both drug substance and drug product. DeliverablesBy establishing a data analytics pipeline and embedding it as part of our routine analysis, monitor impurities more closely and more precisely. Apply the knowledge to identify possible issue in manufacturing and improve process chemistry by pinpointing impurities associated with different steps of the synthesis.This project falls within the EPSRC statistics and applied probability research area, and is a collaboration with AstraZeneca.

目的：发展贝叶斯数据科学方法来自动、准确地分析寡核苷酸杂质，并提供确定性的统计测量。核苷酸是大的复杂分子，目前很难分析杂质，因为分析是劳动密集型的，数据复杂性很高。该项目旨在应用数据科学方法对大量批次的质谱数据进行杂质分析，这将使我们能够学习如何表征已知的寡核苷酸信号，并在半监督学习框架中纵向地从许多已知和未知的杂质中解卷积它，以获得对这些杂质的详细见解；在确认概要的总体一致性的同时，确定任何变化模式、批次趋势以及杂质之间的任何相关性。该方法还可以提供半定量的结果，以帮助我们对原料药和制剂的ASAP、终端灭菌和强制降解等关键研究的理解。通过建立数据分析管道并将其嵌入到我们的日常分析中，可以更密切、更精确地监控杂质。运用所学知识识别制造过程中可能出现的问题，并通过精确定位合成过程中不同步骤的杂质来改进工艺化学。该项目属于EPSRC统计和应用概率研究领域，与阿斯利康合作。