Protozoan parasites have been one of the most significant public health problems for centuries and several human infections caused by them have massive global impact. Most of the current drugs used to treat these illnesses have been used for decades and have many limitations such as the emergence of drug resistance, severe side-effects, low-to-medium drug efficacy, administration routes, cost, etc. These drugs have been largely neglected as models for drug development because they are majorly used in countries with limited resources and as a consequence with scarce marketing possibilities. Nowadays, there is a pressing need to identify and develop new drug-based antiprotozoan therapies. In an effort to overcome this problem, the main purpose of this study is to develop a QSARs-based ensemble classifier for antiprotozoan drug-like entities from a heterogeneous compounds collection. Here, we use some of the TOMOCOMD-CARDD molecular descriptors and linear discriminant analysis (LDA) to derive individual linear classification functions in order to discriminate between antiprotozoan and non-antiprotozoan compounds as a way to enable the computational screening of virtual combinatorial datasets and/or drugs already approved. Firstly, we construct a wide-spectrum benchmark database comprising of 680 organic chemicals with great structural variability (254 of them antiprotozoan agents and 426 to drugs having other clinical uses). This series of compounds was processed by a k-means cluster analysis in order to design training and predicting sets. In total, seven discriminant functions were obtained, by using the whole set of atom-based linear indices. All the LDA-based QSAR models show accuracies above 85% in the training set and values of Matthews correlation coefficients (C) vary from 0.70 to 0.86. The external validation set shows rather-good global classifications of around 80% (92.05% for best equation). Later, we developed a multi-agent QSAR classification system, in which the individual QSAR outputs are the inputs of the aforementioned fusion approach. Finally, the fusion model was used for the identification of a novel generation of lead-like antiprotozoan compounds by using ligand-based virtual screening of 'available' small molecules (with synthetic feasibility) in our 'in-house' library. A new molecular subsystem (quinoxalinones) was then theoretically selected as a promising lead series, and its derivatives subsequently synthesized, structurally characterized, and experimentally assayed by using in vitro screening that took into consideration a battery of five parasite-based assays. The chemicals 11(12) and 16 are the most active (hits) against apicomplexa (sporozoa) and mastigophora (flagellata) subphylum parasites, respectively. Both compounds depicted good activity in every protozoan in vitro panel and they did not show unspecific cytotoxicity on the host cells. The described technical framework seems to be a promising QSAR-classifier tool for the molecular discovery and development of novel classes of broad-antiprotozoan-spectrum drugs, which may meet the dual challenges posed by drug-resistant parasites and the rapid progression of protozoan illnesses.
Keywords: Antimalarial; Antiprotozoan database; Antitoxoplasma; Antitrichomonas; Antitrypanosomal; Classification model; Cytotoxicity; In silico study; In vitro assay; Leishmanicide; Machine learning-based QSAR; Non-stochastic and stochastic linear indices; TOMOCOMD–CARDD software.
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