Di Veroli Giovanni Y, Fornari Chiara, Goldlust Ian, Mills Graham, Koh Siang Boon, Bramhall Jo L, Richards Frances M, Jodrell Duncan I
CRUK Cambridge Institute, University of Cambridge, UK.
NIH Chemical Genomics Center, National Institutes of Health, Bethesda, USA.
Sci Rep. 2015 Oct 1;5:14701. doi: 10.1038/srep14701.
In cancer pharmacology (and many other areas), most dose-response curves are satisfactorily described by a classical Hill equation (i.e. 4 parameters logistical). Nevertheless, there are instances where the marked presence of more than one point of inflection, or the presence of combined agonist and antagonist effects, prevents straight-forward modelling of the data via a standard Hill equation. Here we propose a modified model and automated fitting procedure to describe dose-response curves with multiphasic features. The resulting general model enables interpreting each phase of the dose-response as an independent dose-dependent process. We developed an algorithm which automatically generates and ranks dose-response models with varying degrees of multiphasic features. The algorithm was implemented in new freely available Dr Fit software (sourceforge.net/projects/drfit/). We show how our approach is successful in describing dose-response curves with multiphasic features. Additionally, we analysed a large cancer cell viability screen involving 11650 dose-response curves. Based on our algorithm, we found that 28% of cases were better described by a multiphasic model than by the Hill model. We thus provide a robust approach to fit dose-response curves with various degrees of complexity, which, together with the provided software implementation, should enable a wide audience to easily process their own data.
在癌症药理学（以及许多其他领域）中，大多数剂量-反应曲线都能通过经典的希尔方程（即四参数逻辑方程）得到令人满意的描述。然而，在某些情况下，存在不止一个拐点的明显特征，或者存在激动剂和拮抗剂的联合效应，这使得无法通过标准的希尔方程对数据进行直接建模。在此，我们提出一种改进的模型和自动拟合程序，以描述具有多相特征的剂量-反应曲线。由此产生的通用模型能够将剂量-反应的每个阶段解释为一个独立的剂量依赖性过程。我们开发了一种算法，该算法能自动生成并对具有不同程度多相特征的剂量-反应模型进行排序。该算法已在新的免费软件Dr Fit（sourceforge.net/projects/drfit/）中实现。我们展示了我们的方法如何成功地描述具有多相特征的剂量-反应曲线。此外，我们分析了一个涉及11650条剂量-反应曲线的大型癌细胞活力筛选实验。基于我们的算法，我们发现28%的情况用多相模型描述比用希尔模型更好。因此，我们提供了一种稳健的方法来拟合具有不同复杂程度的剂量-反应曲线，连同所提供的软件实现，应该能使广大用户轻松处理他们自己的数据。
