Petersen Brenden K, Ropella Glen E P, Hunt C Anthony
UCSF/UCB Joint Graduate Group in Bioengineering, University of California, Berkeley, California, United States of America.
Tempus Dictum, Inc., Portland, Oregon, United States of America.
PLoS One. 2016 May 26;11(5):e0155855. doi: 10.1371/journal.pone.0155855. eCollection 2016.
Hepatic cytochrome P450 levels are down-regulated during inflammatory disease states, which can cause changes in downstream drug metabolism and hepatotoxicity. Long-term, we seek sufficient new insight into P450-regulating mechanisms to correctly anticipate how an individual's P450 expressions will respond when health and/or therapeutic interventions change. To date, improving explanatory mechanistic insight relies on knowledge gleaned from in vitro, in vivo, and clinical experiments augmented by case reports. We are working to improve that reality by developing means to undertake scientifically useful virtual experiments. So doing requires translating an accepted theory of immune system influence on P450 regulation into a computational model, and then challenging the model via in silico experiments. We build upon two existing agent-based models-an in silico hepatocyte culture and an in silico liver-capable of exploring and challenging concrete mechanistic hypotheses. We instantiate an in silico version of this hypothesis: in response to lipopolysaccharide, Kupffer cells down-regulate hepatic P450 levels via inflammatory cytokines, thus leading to a reduction in metabolic capacity. We achieve multiple in vitro and in vivo validation targets gathered from five wet-lab experiments, including a lipopolysaccharide-cytokine dose-response curve, time-course P450 down-regulation, and changes in several different measures of drug clearance spanning three drugs: acetaminophen, antipyrine, and chlorzoxazone. Along the way to achieving validation targets, various aspects of each model are falsified and subsequently refined. This iterative process of falsification-refinement-validation leads to biomimetic yet parsimonious mechanisms, which can provide explanatory insight into how, where, and when various features are generated. We argue that as models such as these are incrementally improved through multiple rounds of mechanistic falsification and validation, we will generate virtual systems that embody deeper credible, actionable, explanatory insight into immune system-drug metabolism interactions within individuals.
在炎症性疾病状态下，肝脏细胞色素P450水平会下调，这可能导致下游药物代谢和肝毒性发生变化。长期来看，我们寻求对P450调节机制有足够的新见解，以便在健康状况和/或治疗干预发生变化时，正确预测个体的P450表达将如何反应。迄今为止，提高解释性机制见解依赖于从体外、体内和临床实验以及病例报告中收集的知识。我们正在努力通过开发进行科学有用的虚拟实验的方法来改善这种现状。这样做需要将免疫系统对P450调节的公认理论转化为计算模型，然后通过计算机模拟实验对该模型进行验证。我们基于两个现有的基于主体的模型——一个计算机模拟肝细胞培养模型和一个计算机模拟肝脏模型，它们能够探索和验证具体的机制假设。我们实例化了这个假设的计算机模拟版本：响应脂多糖，库普弗细胞通过炎性细胞因子下调肝脏P450水平，从而导致代谢能力下降。我们实现了从五个湿实验室实验中收集的多个体外和体内验证目标，包括脂多糖-细胞因子剂量反应曲线、P450下调的时间进程以及三种药物（对乙酰氨基酚、安替比林和氯唑沙宗）的几种不同药物清除率测量值的变化。在实现验证目标的过程中，每个模型的各个方面都被证伪并随后进行了改进。这种证伪-改进-验证的迭代过程产生了仿生但简约的机制，这可以为各种特征如何、在何处以及何时产生提供解释性见解。我们认为，随着这些模型通过多轮机制证伪和验证逐步得到改进，我们将生成虚拟系统，这些系统对个体内免疫系统-药物代谢相互作用体现出更深入、可信且可操作的解释性见解。
