Meta-Analysis of Permeability Literature Data Shows Possibilities and Limitations of Popular Methods

Permeability is an important molecular property in drug discovery, as it co-determines pharmacokinetics whenever a drug crosses the phospholipid bilayer, e.g., into the cell, in the gastrointestinal tract, or across the blood–brain barrier. Many methods for the determination of permeability have been developed, including cell line assays (CACO-2 and MDCK), cell-free model systems like parallel artificial membrane permeability assay (PAMPA) mimicking, e.g., gastrointestinal epithelia or the skin, as well as the black lipid membrane (BLM) and submicrometer liposomes. Furthermore, many in silico approaches have been developed for permeability prediction: meta-analysis of publicly available databases for permeability data (MolMeDB and ChEMBL) was performed to establish their usability. Four experimental and two computational methods were evaluated. It was shown that repeatability of the reported permeability measurement is not great even for the same method. For the PAMPA method, two different permeabilities are reported: intrinsic and apparent. They can vary in degrees of magnitude; thus, we suggest being extra cautious using literature data on permeability. When we compared data for the same molecules using different methods, the best agreement was between cell-based methods and between BLM and computational methods. Existence of unstirred water layer (UWL) permeability limits the data agreement between cell-based methods (and apparent PAMPA) with data that are not limited by UWL permeability (computational methods, BLM, intrinsic PAMPA). Therefore, different methods have different limitations. Cell-based methods provide results only in a small range of permeabilities (−8 to −4 in cm/s), and computational methods can predict a wider range of permeabilities beyond physical limitations, but their precision is therefore limited. BLM with liposomes can be used for both fast and slow permeating molecules, but its usage is more complicated than standard transwell techniques. To sum up, when working with in-house measured or published permeability data, we recommend caution in interpreting and combining them.

膜通透性（Permeability）是药物发现领域的关键分子属性，当药物跨磷脂双分子层（如进入细胞、透过胃肠道或血脑屏障）时，其可共同决定药物的代谢动力学特性。目前已开发出多种通透性测定方法，包括细胞系检测法（CACO-2与MDCK）、模拟胃肠道上皮或皮肤的无细胞模型体系（如平行人工膜通透性检测法（PAMPA）），以及黑脂膜（BLM）与亚微米脂质体。此外，学界已开发出多种用于通透性预测的计算机辅助（in silico）方法：研究人员对公开的通透性数据库（MolMeDB与ChEMBL）开展了荟萃分析，以验证其可用性；本研究共评估了4种实验方法与2种计算方法。研究表明，即便采用同一方法，已报道的通透性测量结果的重复性也欠佳。对于PAMPA方法，存在两种不同的通透性指标：内在通透性与表观通透性，二者的数值量级可能存在显著差异，因此在使用文献中的通透性数据时需格外谨慎。当对同一分子采用不同方法获取通透性数据时，一致性最佳的配对分别为细胞类方法之间，以及BLM与计算方法之间。无搅拌水层（UWL）的通透性限制效应，会导致细胞类方法（及表观PAMPA）的数据与不受UWL限制的方法（计算方法、BLM、内在PAMPA）的数据之间一致性较差。由此可见，不同的通透性测定方法均存在各自的局限性。细胞类方法仅能在狭窄的通透性范围内输出结果（量程为-8至-4 cm/s）；而计算方法可预测超出物理极限的更广范围通透性，但因此其精度受限。结合脂质体的BLM方法可适用于快速与慢速渗透的分子，但该方法的操作复杂度高于标准Transwell检测技术。综上，在使用实验室自研测定或已发表的通透性数据时，建议在解读与整合数据过程中保持审慎。