Modeling Pharmacokinetics in Steatotic Livers (SteaPKMod)

* 挑战 * 肝脏疾病导致药物代谢受损，难以进行适当的药物给药。细胞代谢能力和疾病表现在不同的生理相关空间尺度上受到空间异质性分布的影响：整个器官不均匀，但在肝小叶（最小的肝功能单位）内也呈带状分布。如果疾病和代谢活性区域重合，药物代谢可能受到最大影响。目前的药代动力学（PK）模型考虑了累积形式的肝脏疾病，而没有代表与疾病相关的局部代谢能力变化的相关空间异质性。因此，它们缺乏调查不同异质性模式之间的相互作用和预测下列因素的具体影响的不可或缺的能力：分区疾病或手术对整体代谢能力的影响。*目标 * 我们将开发一种数学PK~模型，用于分区和非均匀分布的肝脏疾病（如脂肪变性）的个体化剂量优化和诊断支持。这种新模型将考虑空间非均匀代谢能力和细胞疾病状态的相互作用，从而允许以前所未有的准确度进行计算机研究。此外，它将成为未来决策支持和风险评估的临床应用的基础，例如，通过预测肝脏切除术后的残余代谢能力。方法 * 作为展示，我们将研究两种代表不同细胞色素P450酶（CYP）代谢的临床相关药物的药物。将在正常小鼠和患有酒精性或非酒精性脂肪变性的小鼠中获得PK数据，这是两种常见且充分表征的肝脏疾病，具有不同的脂质蓄积分区模式。基于我们的可行性研究[DOI 10.1016/j.compbiomed.2016.04.004]，我们将量化脂肪变性脂质积聚的分区和异质性以及酶的存在。对于使用普通和偏微分方程的数学模型，我们将开发一种新的脂肪变性如何影响肝细胞药物代谢的机制表示，例如，通过相关空间尺度上改变的细胞特性、正弦形态或血流动力学。根据模型的空间多尺度结构，我们将实施药物特异性PK模型。在成功验证模型预测后，我们将通过从药物代谢的变化中推导出潜在的区域改变来应用模拟进行剂量优化和诊断支持。通过这种临床前原理证明，我们将证明该方法适用于个体化治疗计划。

*Challenge* Liver diseases lead to impairment of drug metabolism, making appropriate drug dosing difficult. Cellular metabolic capability and disease manifestation are subject to a spatially heterogeneous distribution at different physiologically relevant spatial scales: inhomogeneous across the entire organ, but also zonated within liver lobules, the smallest hepatic functional units. Drug metabolism can be expected to be most affected if diseased and metabolically active regions coincide.Current pharmacokinetics (PK) models take into account liver diseases in cumulative form, without representing the relevant spatial heterogeneity of disease-related local changes of metabolic capability. They hence lack indispensable capabilities for investigating the interplay between different patterns of heterogeneity and for predicting the specific impact of, e.g., zonated diseases or surgery on the overall metabolic capability.*Goal* We will develop a mathematical PK~model for individualized dosage optimization and diagnosis support in zonated and heterogeneously distributed liver diseases such as steatosis.This novel model will consider the interplay of spatially inhomogeneous metabolic capabilities and diseased states of cells and thus permit in silico investigations with unprecedented accuracy. Moreover, it will be the foundation for future clinical applications of decision support and risk assessment, e.g., by predicting remnant metabolic capacity after liver resections.*Approach* As showcases, we will investigate two example drugs representing classes of clinically relevant drugs metabolized by different cytochrome P450 enzymes (CYPs). PK~data will be obtained in normal mice and mice with alcoholic or non-alcoholic steatosis, two frequent and well-characterized liver diseases with distinct zonation patterns of lipid accumulations. Based on our feasibility study [DOI 10.1016/j.compbiomed.2016.04.004], we will quantify zonation and heterogeneity of steatotic lipid accumulations and the presence of enzymes.For the mathematical model using ordinary and partial differential equations, we will develop a novel mechanistic representation of how steatosis affects the hepatic cellular drug metabolism, e.g., via altered cellular properties, sinusoidal morphology, or hemodynamics at the relevant spatial scales. Tailored to the spatial multiscale structure of the model, we will implement drug-specific PK models. After successful validation of model predictions, we will apply the simulations for dosage optimization and for diagnosis support by deducing the underlying zonal alteration from changes in drug metabolism.With this preclinical proof of principle we will show that the approach is applicable for individualized therapy planning.