Laboratory of Membrane and Liposome Research, Department of Biochemistry, IMRIC, The Hebrew University - Hadassah Medical School, Jerusalem, Israel; Molecular Modeling and Drug Design Laboratory, The Institute for Drug Research, The Hebrew University of Jerusalem, Israel.
European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom.
J Control Release. 2017 Apr 28;252:18-27. doi: 10.1016/j.jconrel.2017.02.015. Epub 2017 Feb 16.
Remote drug loading into nano-liposomes is in most cases the best method for achieving high concentrations of active pharmaceutical ingredients (API) per nano-liposome that enable therapeutically viable API-loaded nano-liposomes, referred to as nano-drugs. This approach also enables controlled drug release. Recently, we constructed computational models to identify APIs that can achieve the desired high concentrations in nano-liposomes by remote loading. While those previous models included a broad spectrum of experimental conditions and dealt only with loading, here we reduced the scope to the molecular characteristics alone. We model and predict API suitability for nano-liposomal delivery by fixing the main experimental conditions: liposome lipid composition and size to be similar to those of Doxil® liposomes. On that basis, we add a prediction of drug leakage from the nano-liposomes during storage. The latter is critical for having pharmaceutically viable nano-drugs. The "load and leak" models were used to screen two large molecular databases in search of candidate APIs for delivery by nano-liposomes. The distribution of positive instances in both loading and leakage models was similar in the two databases screened. The screening process identified 667 molecules that were positives by both loading and leakage models (i.e., both high-loading and stable). Among them, 318 molecules received a high score in both properties and of these, 67 are FDA-approved drugs. This group of molecules, having diverse pharmacological activities, may be the basis for future liposomal drug development.
远程加载纳米脂质体是将高浓度活性药物成分（API）载入纳米脂质体的最佳方法，从而实现治疗有效浓度的载药纳米脂质体，即纳米药物。这种方法还可以实现药物的控制释放。最近，我们构建了计算模型来识别通过远程加载可以实现高浓度纳米脂质体的 API。虽然之前的模型包含了广泛的实验条件，并仅处理加载问题，但在这里，我们将范围缩小到仅考虑分子特征。我们通过固定主要实验条件（脂质体的脂质组成和大小与 Doxil®脂质体相似）来模拟和预测 API 适合纳米脂质体递药的特性。在此基础上，我们增加了对纳米脂质体在储存过程中药物泄漏的预测。这对于具有治疗可行性的纳米药物至关重要。“加载和泄漏”模型用于筛选两个大型分子数据库，以寻找可通过纳米脂质体递药的候选 API。两个筛选数据库中，加载和泄漏模型中阳性实例的分布相似。筛选过程确定了 667 种分子在加载和泄漏模型中均为阳性（即高载药量和稳定性）。其中，318 种分子在这两种特性上都获得了高分，其中 67 种是 FDA 批准的药物。这组具有不同药理活性的分子可能是未来脂质体药物开发的基础。
