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Accelerating medicine development timelines through new approaches in knowledge extraction from diverse biological data sets

通过从不同生物数据集中提取知识的新方法加快药物开发进程

基本信息

批准号：
MR/W003996/1
负责人：
Neil Carragher
金额：
$ 29.73万
依托单位：
University of Edinburgh
依托单位国家：
英国
项目类别：
Research Grant
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=MR%2FW003996%2F1
关键词：
Accelerating medicine development timelines through

项目摘要

Never has the impact of the development time of a new medicine been better understood with the current situation in the UK relating to COVID. This remains and has always been a big driver for Pharmaceutical companies, to reduce the time it takes from identification of an interesting compound or vaccine to a marketed medicine. The main focus of this project is to develop new Artificial Intelligence and Machine Learning (AI/ML) analytics tools to speed up the identification of immunological medicines, including small molecules, biologics and vaccines. One of the biggest challenges we face in drug development that delays our ability to bring medicines to market quickly is the high rates of candidate molecule termination due to poor clinical efficacy and poor pre-clinical safety. To reduce the high rates of compound attrition, we are implementing disease relevant and physiological human cellular models in early stage discovery. We are using these cellular models to identify new therapeutic targets and screen our pharmacological agents to prioritise those with a better chance of success and to stop projects earlier with either efficacy or safety liabilities. These assays often rely on cellular imaging, in recent years automated microscopy has opened up the ability to characterise the state and phenotypes of cells, at single cell and even subcellular resolution. Thus, biological diversity can be visualized and the effects of perturbations on cells can be quantified more richly than by almost any other means. Relating this back to the challenge of compound failure we can use this imaging data to study compound effects in complex human in vitro systems to enable us to identify potential efficacy and safety risks much earlier in drug discovery enabling us to prioritise those medicines with a higher chance of success. As well as automated imaging, advancements have also been made to study other high content data sets such as transcriptomic, and proteomic information. This used to be completed in a screening cascade of assays all run independently to optimise our pharmacology, we are now able to multiplex these endpoints in the same system enabling us to pull together a complete fingerprint of cellular activity much faster.With these models in place, the current challenge in drug discovery is that the level of complexity of both cellular models as well as the data derived from these models is often beyond the level of data analytics technologies we have available. There is an interesting relationship between data volume and understanding. Initially if you increase the amount of information you have on a system, your understanding increases before reaching a point where understanding rapidly decreases and increased data actually causes confusion as it becomes impossible to interpret. This project is focused on resolving this bottleneck in two phases. Firstly, in recent years, AI methods, specifically convolutional deep learning methods, have revolutionised the field of computer vision by achieving performance often better than humans in image interpretation. In phase one of this project we intend to use AI methods to build a toolbox of image analytics solutions that will be used across our cellular imaging studies, to gather more information from our high-resolution images and transform millions of pixels into parameters. Similar deep learning networks have also now demonstrated the ability to explore vast data sets to interpret and deliver biological understanding. Thus, the second phase of this project will be applying AI tools to transform the millions of parameters obtained from all our high content technologies into features and mechanistic information that can be used to support project decisions.By utilising the immense power of AI approaches in image analysis and big data analytics we hope to enable a better understanding of both chemical and genetic perturbations, ultimately improving our success rate in the clinic.

新药开发时间的影响从来没有在英国与COVID有关的当前情况下得到更好的理解。这仍然是制药公司的一个重要驱动力，以减少从识别感兴趣的化合物或疫苗到上市药物所需的时间。该项目的主要重点是开发新的人工智能和机器学习（AI/ML）分析工具，以加快识别免疫药物，包括小分子，生物制剂和疫苗。我们在药物开发中面临的最大挑战之一是，由于临床疗效差和临床前安全性差，候选分子终止率很高。为了降低化合物的高损耗率，我们正在早期发现阶段实施疾病相关和生理人类细胞模型。我们正在使用这些细胞模型来识别新的治疗靶点，并筛选我们的药理学药物，以优先考虑那些有更好成功机会的药物，并尽早停止具有疗效或安全性责任的项目。这些分析通常依赖于细胞成像，近年来自动显微镜已经开辟了在单细胞甚至亚细胞分辨率下观察细胞状态和表型的能力。因此，生物多样性可以可视化，扰动对细胞的影响可以比几乎任何其他手段更丰富地量化。将其与化合物失败的挑战联系起来，我们可以使用这些成像数据来研究复杂的人类体外系统中的化合物效应，使我们能够在药物发现中更早地识别潜在的有效性和安全性风险，从而使我们能够优先考虑那些成功机会更高的药物。除了自动成像，研究其他高内容数据集（如转录组学和蛋白质组学信息）也取得了进展。这曾经是在筛选级联试验中完成的，所有试验都是独立运行的，以优化我们的药理学，我们现在能够在同一系统中多路复用这些终点，使我们能够更快地将细胞活性的完整指纹集中在一起。目前药物发现的挑战是，细胞模型以及从这些模型得到的数据的复杂程度通常是超越了我们现有的数据分析技术水平。数据量和理解力之间有一种有趣的关系。最初，如果你增加了系统中的信息量，你的理解力就会增加，直到理解力迅速下降，增加的数据实际上会导致混乱，因为它变得无法解释。该项目的重点是分两个阶段解决这一瓶颈。首先，近年来，人工智能方法，特别是卷积深度学习方法，通过在图像解释方面实现比人类更好的性能，彻底改变了计算机视觉领域。在这个项目的第一阶段，我们打算使用人工智能方法来构建一个图像分析解决方案工具箱，用于我们的细胞成像研究，从我们的高分辨率图像中收集更多信息，并将数百万像素转换为参数。类似的深度学习网络现在也证明了探索大量数据集以解释和提供生物学理解的能力。因此，该项目的第二阶段将应用人工智能工具，将从我们所有高内涵技术中获得的数百万个参数转换为可用于支持项目决策的特征和机械信息。通过利用人工智能方法在图像分析和大数据分析中的巨大力量，我们希望能够更好地理解化学和遗传扰动，最终提高我们在临床上的成功率。