预测系统毒理学
Predictive Systems Toxicology
摘要：
在这篇评论中，我们解决到什么程度的计算技术可以增强我们的能力来预测毒性。第一部分提供了一个简短的历史经验观察毒性追溯到黎明的苏美尔文明。有趣的是，剂量的概念很早就出现了，导致了现代对动力学特性的强调，这反过来又编码了毒性不仅仅是化合物的特性，而是取决于与宿主生物体的相互作用。下一个合乎逻辑的步骤是从个性化医疗的角度评估药物的当前概念。我们回顾了最近的工作整合什么可以被称为经典的药代动力学分析与新兴的系统生物学方法，将多组学数据。这些系统方法采用先进的统计分析数据处理，辅以机器学习技术，并使用药代动力学和组学数据。我们发现，这样的综合方法不仅提供了更好的预测毒性，但也使机制解释的基础毒性和耐药性的分子机制。我们通过讨论一些主要挑战来结束本章，例如如何平衡模型的预测能力之间的内在张力，这在实践中相当于限制模型中的特征数量，而不是允许丰富的机械可解释性，即为模型配备大量分子特征。这一挑战还需要对毒性进行患者特异性预测，这反过来又需要对患者进行适当的分层，以了解他们的反应，有或没有不良毒性作用。总而言之，目前使用编码在患者特定模型中的丰富综合数据成功实现了古老剂量概念的转变。
英文摘要：
In this review we address to what extent computational techniques can augment our ability to predict toxicity. The first section provides a brief history of empirical observations on toxicity dating back to the dawn of Sumerian civilization. Interestingly, the concept of dose emerged very early on, leading up to the modern emphasis on kinetic properties, which in turn encodes the insight that toxicity is not solely a property of a compound but instead depends on the interaction with the host organism. The next logical step is the current conception of evaluating drugs from a personalized medicine point-of-view. We review recent work on integrating what could be referred to as classical pharmacokinetic analysis with emerging systems biology approaches incorporating multiple omics data. These systems approaches employ advanced statistical analytical data processing complemented with machine learning techniques and use both pharmacokinetic and omics data. We find that such integrated approaches not only provide improved predictions of toxicity but also enable mechanistic interpretations of the molecular mechanisms underpinning toxicity and drug resistance. We conclude the chapter by discussing some of the main challenges, such as how to balance the inherent tension between the predictive capacity of models, which in practice amounts to constraining the number of features in the models versus allowing for rich mechanistic interpretability, i.e. equipping models with numerous molecular features. This challenge also requires patient-specific predictions on toxicity, which in turn requires proper stratification of patients as regards how they respond, with or without adverse toxic effects. In summary, the transformation of the ancient concept of dose is currently successfully operationalized using rich integrative data encoded in patient-specific models.
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