基于细胞膜模拟技术的药物诱发磷脂沉积风险体外预警模型研究

Drug-induced phospholipidosis (DIPLD), which is an FDA recognized potentially adverse response, represents a potential major liability for compounds transitioning between the drug discovery and development phases, prior to entering final regulatory approval. Consequently, it is highly desirable to identify any potential liability to induce PLD as early as possible in the research phase, prior to candidates entering expensive development. The reason for PLD is believed to be the formation of drug-phospholipid complexes, which cannot be subsequently metabolized by phospholipid-degrading enzymes.This mechanism has been utilized for developing early screening tool for DIPLD potential of drug candidates. Chromatographic techniques we have been developing hold great potential for studying DIPLD compound liabilities, since they can effectively mimic drug-membrane interactions in an in vitro analytical environment. Artificial cell membrane chromatography (ACMC), such as immobilized artificial membrane chromatography (IAM) and liposome electrokinetic chromatography (LEKC), have been used in estimating membrane affinity related properties of drug candidates, such as intestinal absorption, volume distribution, blood brain barrier permeability. These techniques also offer a fast, reliable, high-throughput and cost-effective screening tool for early prediction of the PLD risk. In this proposal, both LEKC and IAMC were used to mimic the lysosome membrane environment in order to develop an successful PLD screening model. A series of novel monolithic columns containing different phospholipids were first prepared by co-polymerizing specific designed phospholipid containing monomers. The influence of acidic phospholipids on the predictivity was also investigated by varying the type and amount of acidic phospholipids within ACMC system. Additionally, the influence of the running buffer pH and the role of the phospholipid bilayer structure were also studied. The initial results showed that there is a significant correlation between the retention of compounds on the ACMC system and its DIPLD potential. 450 chemicals of which 93 induced PLD and 357 were shown not to induce PLD, were analyzed in this study. The QSAR will be carried out in order to find any key structural features of PLD inducers, and the binding sites between compounds and phospholipids related. This research can provide not only a rapid, robust, reproducible and sensitive in vitro trend analysis for a large number of drug candidates early in the drug discovery process with minimal resources, but also a strong evidence for drug-phospholipid complex formation mechanism of PLD. It has important practical and theoretical significance in solving problems in related to PLD drug development.

如何在药物研发的早期对候选药物诱发磷脂沉积（DIPLD）的风险进行有效的预测是新药研发中一个亟待解决的问题。人工模拟细胞膜色谱法（ACMC）用于药物DIPLD风险的筛选具有操作简单、花费低廉、自动化程度高和易于推广等优点，在新药研发中具有广泛的应用前景。我们在前期工作中对脂质体电动色谱和磷脂膜整体色谱柱进行了系统的研究，结果显示，这些ACMC系统能有效地预测一些与药物磷脂作用相关的药理作用。本课题拟首次采用ACMC方法模拟溶酶体磷脂层膜环境，对药物DIPLD风险进行预测和筛选研究，同时还将对酸性磷脂成分在DIPLD过程中的重要性以及药物立体结构与DIPLD风险之间的构效关系进行研究。本课题的意义不仅在于首次探讨色谱模型预测药物DIPLD风险的可行性，而且在于其将对药物磷脂作用抑制磷脂酶活性的DIPLD机理提供强有力的支持，因此在解决新药研发中有关DIPLD的问题具有重要的现实和理论意义。

如何在药物研发早期对候选药物的PLD风险进行有效预测是新药研发中一个亟待解决的问题。本研究基于人工模拟细胞膜色谱技术的特点，构建了多个脂质体电动色谱（LEKC）和磷脂膜整体柱系统来模拟溶酶体磷脂层膜环境，对药物DIPLD 风险进行预测和筛选研究。所取得的重要成果如下所示：.1. 以细胞膜中常见的磷脂POPC、POPS等为组分，成功建立了15个稳定的LEKC系统，并应用于DIPLD风险的定性预测。研究结果证明LEKC模型能够很好地预测DIPLD风险。通过与之前的体内实验结果对比，LEKC模型误差值较少，准确性较高。与多种体外预测模型对比，LEKC的准确度相当，甚至高过其他体外模型。系统考察了酸性磷脂组分POPS对LEKC 模型有效性的影响发现其比例对DIPLD风险预测的准确性有显著影响，进一步揭示了溶酶体中酸性磷脂成分在诱发磷脂沉积中的重要作用。.2. 首次成功合成了含单/双脂肪酸链的PC、PE、PA和PS等7种磷脂单体，制备了12种性能优良的不同磷脂组成的磷脂膜整体柱。对于随机选择的70多个药物，其在MDPC80PS20和MDPC80PA20整体柱上的色谱保留行为与其在商业化IAM柱上保留行为具有很好的相关性。同时它们也对DIPLD风险具有较好的预测能力。与已报道基于人体/动物水平、细胞水平的DIPLD风险预测平台相比，混合磷脂膜整体柱筛选模型预测准确性显著增高，可达到94%。与两种体外模型相比较，相关性分别达到0.82和0.92。同时发现随着整体柱中酸性磷脂的含量从0%增加到20%，其预测准确性依次提高，当酸性磷脂的含量达到30%时，其预测能力则明显降低。在此基础之上，利用计算机辅助设计等手段，还明确了阳离子两亲药物与DIPLD 风险之间的构效关系。.3. 成功建立了一系列基于磷脂膜整体柱的药物细胞膜作用预测模型。研究发现MDPC整体柱模型有望替代现有商业化IAM柱用于新药研发早期的药物细胞膜作用预测研究，而MDPE和MDPS整体柱模型均对药物血脑屏障渗透性具有一定的预测能力。.综上所述，本研究成功建立了两大类新DIPLD 风险筛选平台和一系列药物细胞膜作用预测模型，用于新药研发早期药物细胞膜作用预测等领域，从而为提高新药研发的效率和降低成本等提供了技术保障。