QSAR-Driven Discovery of Novel Chemical Scaffolds Active against Schistosoma mansoni.

Schistosomiasis is a neglected tropical disease that affects millions of people worldwide. Thioredoxin glutathione reductase of Schistosoma mansoni (SmTGR) is a validated drug target that plays a crucial role in the redox homeostasis of the parasite. We report the discovery of new chemical scaffolds against S. mansoni using a combi-QSAR approach followed by virtual screening of a commercial database and confirmation of top ranking compounds by in vitro experimental evaluation with automated imaging of schistosomula and adult worms. We constructed 2D and 3D quantitative structure-activity relationship (QSAR) models using a series of oxadiazoles-2-oxides reported in the literature as SmTGR inhibitors and combined the best models in a consensus QSAR model. This model was used for a virtual screening of Hit2Lead set of ChemBridge database and allowed the identification of ten new potential SmTGR inhibitors. Further experimental testing on both shistosomula and adult worms showed that 4-nitro-3,5-bis(1-nitro-1H-pyrazol-4-yl)-1H-pyrazole (LabMol-17) and 3-nitro-4-{[(4-nitro-1,2,5-oxadiazol-3-yl)oxy]methyl}-1,2,5-oxadiazole (LabMol-19), two compounds representing new chemical scaffolds, have high activity in both systems. These compounds will be the subjects for additional testing and, if necessary, modification to serve as new schistosomicidal agents

QSAR-Based Virtual Screening: Advances and Applications in Drug Discovery

Virtual screening (VS) has emerged in drug discovery as a powerful computational approach to screen large libraries of small molecules for new hits with desired properties that can then be tested experimentally. Similar to other computational approaches, VS intention is not to replace in vitro or in vivo assays, but to speed up the discovery process, to reduce the number of candidates to be tested experimentally, and to rationalize their choice. Moreover, VS has become very popular in pharmaceutical companies and academic organizations due to its time-, cost-, resources-, and labor-saving. Among the VS approaches, quantitative structure–activity relationship (QSAR) analysis is the most powerful method due to its high and fast throughput and good hit rate. As the first preliminary step of a QSAR model development, relevant chemogenomics data are collected from databases and the literature. Then, chemical descriptors are calculated on different levels of representation of molecular structure, ranging from 1D to nD, and then correlated with the biological property using machine learning techniques. Once developed and validated, QSAR models are applied to predict the biological property of novel compounds. Although the experimental testing of computational hits is not an inherent part of QSAR methodology, it is highly desired and should be performed as an ultimate validation of developed models. In this mini-review, we summarize and critically analyze the recent trends of QSAR-based VS in drug discovery and demonstrate successful applications in identifying perspective compounds with desired properties. Moreover, we provide some recommendations about the best practices for QSAR-based VS along with the future perspectives of this approach

Modern drug discovery technologies: opportunities and challenges in lead discovery

The identification of promising hits and the generation of high quality leads are crucial steps in the early stages of drug discovery projects. The definition and assessment of both chemical and biological space have revitalized the screening process model and emphasized the importance of exploring the intrinsic complementary nature of classical and\ud modern methods in drug research. In this context, the widespread use of combinatorial chemistry and sophisticated screening methods for the discovery of lead compounds has created a large demand for small organic molecules that act on specific drug targets.\ud Modern drug discovery involves the employment of a wide variety of technologies and expertise in multidisciplinary research teams. The synergistic effects between experimental and computational approaches on the selection and optimization of bioactive compounds emphasize the importance of the integration of advanced technologies in drug discovery programs. These technologies (VS, HTS, SBDD, LBDD, QSAR, and so on) are complementary in the sense that they have mutual goals, thereby the combination of both empirical and in silico efforts is feasible at many different levels of lead optimization and new chemical entity (NCE) discovery. This paper provides a brief perspective on the evolution and use of key drug design technologies, highlighting opportunities and challenges

Schistosomiasis Drug Discovery in the Era of Automation and Artificial Intelligence.

Schistosomiasis is a parasitic disease caused by trematode worms of the genus Schistosoma and affects over 200 million people worldwide. The control and treatment of this neglected tropical disease is based on a single drug, praziquantel, which raises concerns about the development of drug resistance. This, and the lack of efficacy of praziquantel against juvenile worms, highlights the urgency for new antischistosomal therapies. In this review we focus on innovative approaches to the identification of antischistosomal drug candidates, including the use of automated assays, fragment-based screening, computer-aided and artificial intelligence-based computational methods. We highlight the current developments that may contribute to optimizing research outputs and lead to more effective drugs for this highly prevalent disease, in a more cost-effective drug discovery endeavor

Identification of anti-schistosomal, anthelmintic and anti-parasitic compounds curated and text-mined from the scientific literature

More than a billion people are infected with parasitic worms, including nematodes, such as hookworms, and flatworms, such as blood flukes. Few drugs are available to treat worm infections, but high-throughput screening approaches hold promise to identify novel drug candidates. One problem for researchers who find an interesting ‘hit’ from a high-throughput screen is to identify whether that compound, or a similar compound has previously been published as having anthelmintic or anti-parasitic activity. Here, we present (i) data sets of 2,828 anthelmintic compounds, and 1,269 specific anti-schistosomal compounds, manually curated from scientific papers and books, and (ii) a data set of 24,335 potential anthelmintic and anti-parasitic compounds identified by text-mining PubMed abstracts. We provide their structures in simplified molecular-input line-entry system (SMILES) format so that researchers can easily compare ‘hits’ from their screens to these anthelmintic compounds and anti-parasitic compounds and find previous literature on them to support/halt their progression in drug discovery pipelines

In silico screening strategies for novel inhibitors of parasitic diseases

Introduction: Parasitic diseases are a major global problem causing long-term disability and death, with severe medical and psychological consequences around the world. Despite the prevalence of parasitic disease, the treatment options for many of these illnesses are still inadequate and there is a dire need for new antiparasitic drugs. In silico screening techniques, which are powerful strategies for hit generation, are widely being applied in the design of new ligands for parasitic diseases.\ud Areas covered: This article analyses the application of ligand- and structure-based virtual screening strategies against a variety of parasitic diseases and discusses the benefits of the integration between computational and experimental approaches toward the discovery of new antiparasitic agents. The analysis is illustrated by recent examples, with emphasis on the strategies reported within the past 2 years.\ud Expert opinion: Virtual screening techniques are powerful tools commonly used in drug discovery against parasitic diseases, which have provided new opportunities for the identification of several novel compound classes with antiparasitic activity

Computationally-guided drug repurposing enables the discovery of kinase targets and inhibitors as new schistosomicidal agents.

The development of novel therapeutics is urgently required for diseases where existing treatments are failing due to the emergence of resistance. This is particularly pertinent for parasitic infections of the tropics and sub-tropics, referred to collectively as neglected tropical diseases, where the commercial incentives to develop new drugs are weak. One such disease is schistosomiasis, a highly prevalent acute and chronic condition caused by a parasitic helminth infection, with three species of the genus Schistosoma infecting humans. Currently, a single 40-year old drug, praziquantel, is available to treat all infective species, but its use in mass drug administration is leading to signs of drug-resistance emerging. To meet the challenge of developing new therapeutics against this disease, we developed an innovative computational drug repurposing pipeline supported by phenotypic screening. The approach highlighted several protein kinases as interesting new biological targets for schistosomiasis as they play an essential role in many parasite's biological processes. Focusing on this target class, we also report the first elucidation of the kinome of Schistosoma japonicum, as well as updated kinomes of S. mansoni and S. haematobium. In comparison with the human kinome, we explored these kinomes to identify potential targets of existing inhibitors which are unique to Schistosoma species, allowing us to identify novel targets and suggest approved drugs that might inhibit them. These include previously suggested schistosomicidal agents such as bosutinib, dasatinib, and imatinib as well as new inhibitors such as vandetanib, saracatinib, tideglusib, alvocidib, dinaciclib, and 22 newly identified targets such as CHK1, CDC2, WEE, PAKA, MEK1. Additionally, the primary and secondary targets in Schistosoma of those approved drugs are also suggested, allowing for the development of novel therapeutics against this important yet neglected disease

Unveiling the kinomes of leishmania infantum and L. braziliensis empowers the discovery of new kinase targets and antileishmanial compounds

Leishmaniasis is a neglected tropical disease caused by parasites of the genus Leishmania (NTD) endemic in 98 countries. Although some drugs are available, current treatments deal with issues such as toxicity, low efficacy, and emergence of resistance. Therefore, there is an urgent need to identify new targets for the development of new antileishmanial drugs. Protein kinases (PKs), which play an essential role in many biological processes, have become potential drug targets for many parasitic diseases. A refined bioinformatics pipeline was applied in order to define and compare the kinomes of L. infantum and L. braziliensis, species that cause cutaneous and visceral manifestations of leishmaniasis in the Americas, the latter being potentially fatal if untreated. Respectively, 224 and 221 PKs were identified in L. infantum and L. braziliensis overall. Almost all unclassified eukaryotic PKs were assigned to six of nine major kinase groups and, consequently, most have been classified into family and subfamily. Furthermore, revealing the kinomes for both Leishmania species allowed for the prioritization of potential drug targets that could be explored for discovering new drugs against leishmaniasis. Finally, we used a drug repurposing approach and prioritized seven approved drugs and investigational compounds to be experimentally tested against Leishmania. Trametinib and NMS-1286937 inhibited the growth of L. infantum and L. braziliensis promastigotes and amastigotes and therefore might be good candidates for the drug repurposing pipeline17352361FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE GOIÁS - FAPE

tackling malaria

Malaria is an infectious disease that affects over 216 million people worldwide, killing over 445,000 patients annually. Due to the constant emergence of parasitic resistance to the current antimalarial drugs, the discovery of new drug candidates is a major global health priority. Aiming to make the drug discovery processes faster and less expensive, we developed binary and continuous Quantitative Structure-Activity Relationships (QSAR) models implementing deep learning for predicting antiplasmodial activity and cytotoxicity of untested compounds. Then, we applied the best models for a virtual screening of a large database of chemical compounds. The top computational predictions were evaluated experimentally against asexual blood stages of both sensitive and multi-drug-resistant Plasmodium falciparum strains. Among them, two compounds, LabMol-149 and LabMol-152, showed potent antiplasmodial activity at low nanomolar concentrations (EC50 <500 nM) and low cytotoxicity in mammalian cells. Therefore, the computational approach employing deep learning developed here allowed us to discover two new families of potential next generation antimalarial agents, which are in compliance with the guidelines and criteria for antimalarial target candidates.publishersversionpublishe

Physicochemical, biological and β-haematin inhibiting activity of pyrido-dibemequines, pyrido[1,2-α]benzimidazoles and their derivatives

There is an urgent need for new antimalarials following the emergence of Plasmodium falciparum strains with reduced sensitivity to the currently used artemisinin combination therapies. Classical aminoquinoline-based drugs inhibit the formation of haemozoin (HZ) thereby causing parasite death from the cellular accumulation of toxic 'free' haem. Coincidentally, this immutable pathway also exists in Schistosoma mansoni, and presents a vulnerable target for drug design in these haematophagus organisms. Therefore, it would be of interest to explore novel scaffolds that can inhibit HZ formation as well as exploit the merits of established drugs via structural modifications that would harness their pharmacological and pharmacokinetic advantages while circumventing their therapeutic shortcomings. This project investigated the physicochemical, biological and mechanistic profiles of pyrido-dibemequine (pDBQ) and pyrido[1,2-α]benzimidazole (PBI) derivatives whose structural motifs were informed by previously synthesised prototype molecules. Specifically, the aqueous solubility, membrane permeability, lipophilicity, metabolic stability and potential for cardiotoxicity of seven pDBQs, their metabolites and ten PBIs were tested through computational and experimental methods. In addition, their antiplasmodial and antischistosomal activities were determined and correlated with their respective physicochemical properties. As regards mechanistic evaluation, their ability to inhibit formation of abiotic HZ, β-haematin (βH), was assessed and intracellular inhibition of HZ formation probed. The pDBQs constitute reversed chloroquines with a 4-aminoquinoline nucleus hybridised to a dibenzylmethylamine side group that serves as a chemosensitising moiety. The pDBQ derivatives showed moderate to high solubility (52 - 197 μM) and permeability (LogPₐₚₚ: -4.6 - -3.6) at pH 6.5. Their lipophilicity, indexed by cLogP, ranged between 3.7 and 5.6 while the mean LogD at both cytosolic (7.4) and vacuolar (5.0) pH was 3.15 and 0.93, respectively. The compounds also showed low-nanomolar range antiplasmodial activity against both chloroquine (CQ)-sensitive (CQS) and resistant (CQR) strains (IC₅₀ range CQS: 14.4 - 126.6 nM, CQRᴰᵈ²: 44.5 - 162 nM and CQR⁷ᴳ⁸: 69.6 - 307.1 nM), with no discernible cross-resistance with CQ and the antiplasmodial activity directly correlated with lipophilicity. Mechanistically, all the pDBQs inhibited βH formation (IC₅₀: 13 - 25 μM) and haem-pyridine fractionation profiles revealed they produced a CQ-like dose-dependent increase in toxic 'free' haem with corresponding decrease in HZ levels. Predicted human-Ether-a-Go-Go-Related Gene (hERG) channel inhibition pIC₅₀ ranged between 6.2 and 6.6, and correlated strongly with the cLogP and molecular weight. The derivatives were also highly susceptibility to microsomal metabolism, with N-dealkylation identified as the main biotransformation route. The pDBQ metabolites exhibited solubility and membrane permeability profiles similar to the parent compounds at pH 6.5, albeit with reduced lipophilicity (cLogP range: 2.3 - 3.5). Their βH inhibition activity (IC₅₀: 15 - 24 μM) was also comparable to the parent compounds as were the haem-pyridine fractionation profiles. However, they showed greater antiplasmodial activity, with 4/7 derivatives exhibiting IC₅₀ < 80 nM against PƒDd2 (CQR strain). The metabolites had reduced hERG channel inhibition potential (pIC₅₀: 5.0 - 5.7) and significantly improved metabolic stability upon incubation with mouse and human liver microsomes. The PBIs comprise molecules with structural likeness to CQ, including a planar heterocyclic moiety and a basic amine side group. PBI analogues showed low to moderate solubility (<5 - 80 μM) and were moderately lipophilic (mean LogD7.4: 3.04). Although most of the derivatives were stable in liver microsomes, their predicted hERG channel inhibition potential was higher (pIC₅₀: 6.11 - 7.50), presumably due to their high molecular weights. All but one derivative had submicromolar activity against CQS and CQR strains, with analogues bearing halo-substituents on the left of the PBI core showing the best antiplasmodial activity (mean IC₅₀: CQS = 26.7 nM and CQR = 30.0 nM), highest selectivity (188 - 341) as well as complete cures in P. berghei-infected mice. The PBIs also inhibited βH formation (IC₅₀: 6.8 - 120 μM) but did not all display intracellular inhibition of HZ formation. All derivatives were active against juvenile (mean IC₅₀: 1.97 μM) and adult (mean IC₅₀: 4.38 μM) schistosomes, with the 3, 4-dichloro-substituted analogue exhibiting 48% reduction of worm burden in vivo. In summary, the pDBQs evaluated in this project constitute potent antiplasmodial inhibitors of HZ formation but whose activity is compromised by metabolic and hERG liability while their metabolites seem to possess improved biological and physicochemical features. The observed activity of the PBIs against P. falciparum and S. mansoni complements the already-established broad antimicrobial potency of this chemotype