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

目的开发贝叶斯数据科学方法，以自动、准确地分析寡核苷酸杂质，并提供确定性的统计测量。细节寡核苷酸是大的复杂分子，它们目前很难分析杂质，因为分析是劳动密集型的，数据复杂性很高。这个项目的目标是应用数据科学方法对更多批次的质谱图数据进行杂质分析，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从纵向上将其从一些已知和未知的杂质中分离出来，以获得纵向上对这些杂质的详细见解；在确认概况的总体一致性的同时，确定任何变化模式、批次之间的趋势以及杂质之间的任何相关性。这种方法还可以提供半定量的结果，以帮助我们理解关键研究，如尽快、终末灭菌和药物物质和药物产品的强制降解。可交付通过建立数据分析管道并将其嵌入到我们的常规分析中，更紧密、更准确地监控杂质。应用这些知识来确定制造中可能存在的问题，并通过精确定位与合成不同步骤相关的杂质来改进过程化学。该项目属于EPSRC统计和应用概率研究领域，是与阿斯利康合作的项目。

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.