A Definition of "Multitargeticity": Identifying Potential Multitarget and Selective Ligands Through a Vector Analysis.

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 in silico 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 in silico behavior. With this work, a new milestone is made in the way of defining multitargeticity and in drug design.

Synthesis, In Silico, and In Vitro Evaluation of Long Chain Alkyl Amides from 2-Amino-4-Quinolone Derivatives as Biofilm Inhibitors.

Infection from multidrug resistant bacteria has become a growing health concern worldwide, increasing the need for developing new antibacterial agents. Among the strategies that have been studied, biofilm inhibitors have acquired relevance as a potential source of drugs that could act as a complement for current and new antibacterial therapies. Based on the structure of 2-alkyl-3-hydroxy-4-quinolone and N-acylhomoserine lactone, molecules that act as mediators of quorum sensing and biofilm formation in Pseudomonas aeruginosa, we designed, prepared, and evaluated the biofilm inhibition properties of long chain amide derivatives of 2-amino-4-quinolone in Staphylococcus aureus and P. aeruginosa. All compounds had higher biofilm inhibition activity in P. aeruginosa than in S. aureus. Particularly, compounds with an alkyl chain of 12 carbons exhibited the highest inhibition of biofilm formation. Docking scores and molecular dynamics simulations of the complexes of the tested compounds within the active sites of proteins related to quorum sensing had good correlation with the experimental results, suggesting the diminution of biofilm formation induced by these compounds could be related to the inhibition of these proteins.

In Silico Studies on Compounds Derived from Calceolaria: Phenylethanoid Glycosides as Potential Multitarget Inhibitors for the Development of Pesticides.

An increasing occurrence of resistance in insect pests and high mammal toxicity exhibited by common pesticides increase the need for new alternative molecules. Among these alternatives, bioinsecticides are considered to be environmentally friendly and safer than synthetic insecticides. Particularly, plant extracts have shown great potential in laboratory conditions. However, the lack of studies that confirm their mechanisms of action diminishes their potential applications on a large scale. Previously, we have reported the insect growth regulator and insecticidal activities of secondary metabolites isolated from plants of the Calceolaria genus. Herein, we report an in silico study of compounds isolated from Calceolaria against acetylcholinesterase, prophenoloxidase, and ecdysone receptor. The molecular docking results are consistent with the previously reported experimental results, which were obtained during the bioevaluation of Calceolaria extracts. Among the compounds, phenylethanoid glycosides, such as verbascoside, exhibited good theoretical affinity to all the analyzed targets. In light of these results, we developed an index to evaluate potential multitarget insecticides based on docking scores.