Machine Learning Applied to Automated Planar Patch Clamps

描述（由申请人提供）：在过去两年中，自动化平面膜片钳仪器的引入将电压钳离子通道分析的吞吐量提高了至少十倍。这是可能的，因为自动化系统可以使用16孔板和384孔板并行进行分析。虽然药物发现行业已经接受了这项新技术，但由于检测的适度成功率和可消耗的贴片基板的高成本，热情有所减弱。目前，标准离子通道检测的典型成功率约为50%，例如使用来自Sophion Biosciences的Q-Patch，来自Nanion Biosciences的Port-a-Patch系统，或来自Molecular Devices Corp.的PatchXpress。换句话说，对于这些系统中使用的每16个通道芯片，只有8个将产生可用的数据。这实际上使每个数据点的价格比理想情况下的价格翻了一倍。为了使平面膜片钳实验成功，需要发生几个事件（假设细胞在功能状态下表达适当的离子通道）：感兴趣的细胞必须与平面衬底形成高阻力密封，必须实现全细胞配置，流体通路必须完整，以便感兴趣的化合物可以应用于细胞。这些步骤中的任何一个失败都将导致该井无法收集数据。我们建议优化该过程的前两个步骤，即密封形成和进入全细胞记录配置。我们将使用机器学习方法来研究人类膜片钳专家如何与膜片钳系统交互，以开发一个模型，该模型将提供可用于更有效和成功地提供可用的全细胞记录配置的参数。值得注意的是，我们从我们的方法中得出的模型实际上不会复制专家所做的事情，而是试图根据专家可能没有意识到的线索来优化过程。第一阶段组件的具体目标将是：(1)将记录功能集成到Nanion现有的自动化膜片钳软件中，(2)评估我们机器学习分析指定的程序的成功率，以及(3)开发专门用于手动膜片钳设置的独立软件，并通过专家培训探索在其他应用中使用机器学习的潜在好处。在第二阶段，我们建议将概念验证软件开发成用户友好的商业软件模块，我们将提供给现有和潜在的自动化膜片钳公司。我们还将简化和简化该软件的用户界面，作为手动膜片钳系统的独立组件。开发靶向离子通道的药物一直受到用于自动膜片钳筛选设备的耗材费用的阻碍。我们建议开发一种方法，使用机器学习技术，这可能会提高这些仪器的成功率，从而降低离子通道药物发现的总体成本。

DESCRIPTION (provided by applicant): The introduction of automated planar patch clamp instruments over the past two years has increased the throughput of voltage clamp ion channel assays by a factor of at least ten. This is possible because the automated systems can perform assays in parallel using16 and 384-well plates. While the drug discovery industry has embraced this new technology, the enthusiasm has been tempered by the modest success rates of the assays and by the high cost of the consumable patch substrate. Currently, typical success rates for a standard ion channel assay, using, for example, the Q-Patch from Sophion Biosciences, the Port-a-Patch system from Nanion Biosciences, or the PatchXpress from Molecular Devices Corp., is around 50%. In other words, for every16 channel chip used in these systems, only eight will produce useable data. This effectively doubles the price of each data point over what is ideally possible. In order for a planar patch clamp experiment to succeed, several events need to occur (assuming that the cell expresses the appropriate ion channels in functional states): the cell of interest must form a high-resistance seal with the planar substrate, the whole-cell configuration must be achieved, and fluidic pathways must be intact so that compounds of interest maybe applied to the cell. A failure of anyone of these steps will result in no data collected from that well. We propose to optimize the first two steps in this process, namely, seal formation and entry into whole-cell recording configuration. We will use machine learning approaches to examine how a human patch clamp expert interacts with the patch clamp system in order to develop a model that will provide parameters that can be used to more efficiently and successfully provide useable whole-cell recording configuration. It is important to note that the model that we derive from our approach will not actually copy what the expert does, but will attempt to optimize the process based on cues that mayor may not be consciously monitored by the expert. The Specific Aims of the Phase I component will be to: (1) integrate recording capabilities into existing automated patch clamp software from Nanion, (2) evaluate the success rate of the procedure specified by our machine learning analysis, and (3) develop stand-alone software for use specifically with manual patch clamp setups and for exploration of the potential benefits of using machine learning via expert training in other applications. In Phase II we propose to develop the proof-of-concept software into a user-friendly commercial software module which we will offer to existing and potential automated patch clamp companies. We will also simplify and streamline the user interface of this software as a stand-alone component for manual patch clamp systems. Developing drugs that target ion channels has been hindered by the expense of the consumables used in automated patch clamp screening devices. We propose to develop a method, using machine learning techniques which may increase the success rate of these instruments and therefore lower the overall cost of ion channel drug discovery.