Sánchez-Tejeda Juan Francisco, Sánchez-Ruiz Juan F, Salazar Juan Rodrigo, Loza-Mejía Marco A
Facultad de Ciencias Químicas, Universidad La Salle, Mexico City, Mexico.
Ciencia y Estrategia S.A. de C.V., Mexico City, Mexico.
Front Chem. 2020 Mar 13;8:176. doi: 10.3389/fchem.2020.00176. eCollection 2020.
The design of multitarget drugs is an essential area of research in Medicinal Chemistry since they have been proposed as potential therapeutics for the management of complex diseases. However, defining a multitarget drug is not an easy task. In this work, we propose a vector analysis for measuring and defining "multitargeticity." We developed terms, such as order and force of a ligand, to finally reach two parameters: multitarget indexes 1 and 2. The combination of these two indexes allows discrimination of multitarget drugs. Several training sets were constructed to test the usefulness of the indexes: an experimental training set, with real affinities, a docking training set, within theoretical values, and an extensive database training set. The indexes proved to be useful, as they were used independently and experimental data, identifying actual multitarget compounds and even selective ligands in most of the training sets. We then applied these indexes to evaluate a virtual library of potential ligands for targets related to multiple sclerosis, identifying 10 compounds that are likely leads for the development of multitarget drugs based on their behavior. With this work, a new milestone is made in the way of defining multitargeticity and in drug design.
多靶点药物设计是药物化学研究的一个重要领域，因为它们已被提议作为治疗复杂疾病的潜在疗法。然而，定义一种多靶点药物并非易事。在这项工作中，我们提出了一种用于测量和定义“多靶点性”的向量分析方法。我们开发了诸如配体的阶次和作用力等术语，最终得出两个参数：多靶点指数1和2。这两个指数的组合能够区分多靶点药物。构建了几个训练集来测试这些指数的实用性：一个具有实际亲和力的实验训练集、一个理论值范围内的对接训练集以及一个广泛的数据库训练集。这些指数被证明是有用的，因为它们被独立使用并结合实验数据，在大多数训练集中识别出了实际的多靶点化合物甚至选择性配体。然后，我们应用这些指数来评估一个与多发性硬化症相关靶点的潜在配体虚拟库，根据其行为识别出10种可能成为多靶点药物开发先导的化合物。通过这项工作，在定义多靶点性和药物设计方面迈出了新的里程碑。
