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

ObjectiveDevelopedBayesian数据科学方法，自动，准确地分析寡核苷酸杂质，并提供确定性的统计测量。DetailsOligonucleotides是大型复杂分子，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在半监督学习框架中纵向消除已知寡核苷酸信号并将其从一些已知和未知杂质中解卷积，以纵向获得对这些杂质的详细见解;在确认图谱的总体一致性时，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结果，以帮助我们理解关键研究，例如原料药和制剂的ASAP、最终灭菌和强制降解。通过建立数据分析管道并将其作为常规分析的一部分，更密切、更精确地监控杂质。应用这些知识来识别生产中可能存在的问题，并通过精确定位与合成不同步骤相关的杂质来改进工艺化学。该项目福尔斯EPSRC统计和应用概率研究领域，是与阿斯利康的合作项目。

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.