Profiling of Flavonol Derivatives for the Development of Antitrypanosomatidic Drugs

Flavonoids represent a potential source of new antitrypanosomatidic leads. Starting from a library of natural products, we combined target-based screening on pteridine reductase 1 with phenotypic screening on Trypanosoma brucei for hit identification. Flavonols were identified as hits, and a library of 16 derivatives was synthesized. Twelve compounds showed EC50 values against T. brucei below 10 \u3bcM. Four X-ray crystal structures and docking studies explained the observed structure-activity relationships. Compound 2 (3,6-dihydroxy-2-(3-hydroxyphenyl)-4H-chromen-4-one) was selected for pharmacokinetic studies. Encapsulation of compound 2 in PLGA nanoparticles or cyclodextrins resulted in lower in vitro toxicity when compared to the free compound. Combination studies with methotrexate revealed that compound 13 (3-hydroxy-6-methoxy-2-(4-methoxyphenyl)-4H-chromen-4-one) has the highest synergistic effect at concentration of 1.3 \u3bcM, 11.7-fold dose reduction index and no toxicity toward host cells. Our results provide the basis for further chemical modifications aimed at identifying novel antitrypanosomatidic agents showing higher potency toward PTR1 and increased metabolic stability

Analysis of the confidence in the prediction of the protein folding by artificial intelligence

6 p.-4 fig.-1 tab.The determination of protein structure has been facilitated using deep learning models, which can predict protein folding from protein sequences. In some cases, the predicted structure can be compared to the already-known distribution if there is information from classic methods such as nuclear magnetic resonance (NMR) spectroscopy, X-ray crystallography, or electron microscopy (EM). However, challenges arise when the proteins are not abundant, their structure is heterogeneous, and protein sample preparation is difficult. To determine the level of confidence that supports the prediction, different metrics are provided. These values are important in two ways: they offer information about the strength of the result and can supply an overall picture of the structure when different models are combined. This work provides an overview of the different deep-learning methods used to predict protein folding and the metrics that support their outputs. The confidence of the model is evaluated in detail using two proteins that contain four domains of unknown function.This work is a result of the project "Data-driven drug repositioning applying graph neural networks (3DR-GNN)", that is being developed under grant "PID2021-122659OB-I00" from the Spanish Ministerio de Ciencia e Innovación. This work was funded partially by Knowledge Spaces project (Grant PID2020-118274RB-I00 funded by MCIN/AEI/10.13039/501100011033)Peer reviewe

Analysis of the confidence in the prediction of the protein folding by artificial intelligence

6 p.-4 fig.The determination of protein structure has been facilitated using deep learning models, which can predict protein folding from protein sequences. In some cases, the predicted structure can be compared to the already-known distribution if there is information from classic methods such as nuclear magnetic resonance (NMR) spectroscopy, X-ray crystallography, or electron microscopy (EM). However, challenges arise when the proteins are not abundant, their structure is heterogeneous, and protein sample preparation is difficult. To determine the level of confidence that supports the prediction, different metrics are provided. These values are important in two ways: they offer information about the strength of the result and can supply an overall picture of the structure when different models are combined. This work provides an overview of the different deep-learning methods used to predict protein folding and the metrics that support their outputs. The confidence of the model is evaluated in detail using two proteins that contain four domains of unknown function.This work is a result of the project "Data-driven drug repositioning applying graph neural networks (3DR-GNN)", that is being developed under grant "PID2021-122659OB-I00" from the Spanish Ministerio de Ciencia e Innovación. This work was funded partially by Knowledge Spaces project (Grant PID2020-118274RB-I00 funded by MCIN/AEI/10.13039/501100011033)Peer reviewe

Unlocking the power of AI models: exploring protein folding prediction through comparative analysis

14 p.-8 fig.-3 tab.Protein structure determination has made progress with the aid of deep learning models, enabling the prediction of protein folding from protein sequences. However, obtaining accurate predictions becomes essential in certain cases where the protein structure remains undescribed. This is particularly challenging when dealing with rare, diverse structures and complex sample preparation. Different metrics assess prediction reliability and offer insights into result strength, providing a comprehensive understanding of protein structure by combining different models. In a previous study, two proteins named ARM58 and ARM56 were investigated.These proteins contain four domains of unknown function and are present in Leishmania spp. ARM refers to an antimony resistance marker. The study’s main objective is to assess the accuracy of the model’s predictions,thereby providing insights into the complexities and supportingmetrics underlying these findings. The analysis also extends to the comparison of predictions obtained from other species and organisms. Notably, one of these proteins shares an ortholog with Trypanosoma cruzi and Trypanosoma brucei, leading further significance to our analysis. This attempt underscored the importance of evaluating the diverse outputs from deep learning models, facilitating comparisons across different organisms and proteins. This becomes particularly pertinent in cases where no previous structural information is available.This work is a result of the project “Data-driven drug repositioning applying graph neural networks (3DR-GNN)”, that is being developed under grant “PID2021-122659OB-I00” from the Spanish Ministerio de Ciencia e Innovación. This work was funded partially by Knowledge Spaces project (Grant PID2020-118274RB-I00 funded by MCIN/AEI/ 10.13039/501100011033).Peer reviewe

Discovery of a benzothiophene-flavonol halting miltefosine and antimonial drug resistance in Leishmania parasites through the application of medicinal chemistry, screening and genomics

Leishmaniasis, a major health problem worldwide, has a limited arsenal of drugs for its control. The appearance of resistance to first- and second-line anti-leishmanial drugs confirms the need to develop new and less toxic drugs that overcome spontaneous resistance. In the present study, we report the design and synthesis of a novel library of 38 flavonol-like compounds and their evaluation in a panel of assays encompassing parasite killing, pharmacokinetics, genomics and ADME-Toxicity resulting in the progression of a compound in the drug discovery value chain. Compound 19, 2-(benzo[b]thiophen-3-yl)- 3-hydroxy-6-methoxy-4H-chromen-4-one, exhibited a broad-spectrum activity against Leishmania spp. (EC50 1.9 mM for Leishmania infantum, 3.4 mM for L. donovani, 6.7 mM for L. major), Trypanosoma cruzi (EC50 7.5 mM) and T. brucei (EC50 0.8 mM). Focusing on anti-Leishmania activity, compound 19 challenge in vitro did not select for resistance markers in L. donovani, while a Cos-Seq screening for dominant resistance genes identified a gene locus on chromosome 36 that became ineffective at concentrations beyond EC50. Thus, compound 19 is a promising scaffold to tackle drug resistance in Leishmania infection. In vivo pharmacokinetic studies indicated that compound 19 has a long half-life (intravenous (IV): 63.2 h; per os (PO): 46.9 h) with an acceptable ADME-Toxicity profile. When tested in Leishmania infected hamsters, no toxicity and limited efficacy were observed. Low solubility and degradation were investigated spectroscopically as possible causes for the sub-optimal pharmacokinetic properties. Compound 19 resulted a specific compound based on the screening against a protein set, following the intrinsic fluorescence changes

Methoxylated 2'-hydroxychalcones as antiparasitic hit compounds

Chalcones display a broad spectrum of pharmacological activities. Herein, a series of 2â-hydroxy methoxylated chalcones was synthesized and evaluated towards Trypanosoma brucei, Trypanosoma cruzi and Leishmania infantum. Among the synthesized library, compounds 1, 3, 4, 7 and 8 were the most potent and selective anti-T. brucei compounds (EC50 = 1.3â4.2 μM, selectivity index >10-fold). Compound 4 showed the best early-tox and antiparasitic profile. The pharmacokinetic studies of compound 4 in BALB/c mice using hydroxypropil-β-cyclodextrins formulation showed a 7.5 times increase in oral bioavailability