Blackcurrant press cake by-product: Increased chemical bioaccessibility and reduced antioxidant protection after in vitro simulation of gastrointestinal digestion.

This study describes the bioaccessibility in terms of total phenolic content (TPC) and antioxidant capacity before and after in vitro digestion from blackcurrant press cake extracts (BPC) and the bioactivity in cell culture, human erythrocytes as well as the in silico analysis. Chemical analysis of BPC presented an increase in TPC (270%) and anthocyanins (136%) after in vitro digestion, resulting in an improvement of antioxidant activity (DPPH 112%; FRAP: 153%). This behavior may be related to the highest activity of cyanidin-3-rutinoside, as confirmed by in silico analysis. The digested BPC did not exert cytotoxicity in cells and showed less antioxidant activity against the oxidative damage induced in endothelial cells and human erythrocytes compared to the non-digested extract. The results raise a question about the reliability we should place on results obtained only from crude samples, especially those that will be used to produce foods or nutraceuticals.

In silico evaluation of pharmacokinetics properties of withanolides and simulation of their biological activities against Alzheimer's disease.

The withanolides are naturally occurring steroidal lactones found mainly in plants of the Solanaceae family. The subtribe Withaninae includes species like Withania sominifera, which are a source of many bioactive withanolides. In this work, we selected and evaluate the ADMET-related properties of 91 withanolides found in species of the subtribe Withaninae computationally, to predict the relationship between their structures and their pharmacokinetic profiles. We also evaluated the interaction of these withanolides with known targets of Alzheimer's disease (AD) through molecular docking and molecular dynamics. Withanolides presented favorable pharmacokinetic properties, like high gastrointestinal absorption, lipophilicity (logP ≤ 5), good distribution and excretion parameters, and a favorable toxicity profile. The specie Withania aristata stood out as an interesting source of the promising withanolides classified as 5-ene with 16-ene or 17-ene. These withanolides presented a favourable pharmacokinetic profile and were also highlighted as the best candidates for inhibition of AD-related targets. Our results also suggest that withanolides are likely to act as cholinesterase inhibitors by interacting with the catalytic pocket in an energy favorable and stable way.Communicated by Ramaswamy H. Sarma.

Designing drugs when there is low data availability: one-shot learning and other approaches to face the issues of a long-term concern.

INTRODUCTION: Modern drug discovery is generally accessed by useful information from previous large databases or uncovering novel data. The lack of biological and/or chemical data tends to slow the development of scientific research and innovation. Here, approaches that may help provide solutions to generate or obtain enough relevant data or improve/accelerate existing methods within the last five years were reviewed. AREAS COVERED: One-shot learning (OSL) approaches, structural modeling, molecular docking, scoring function space (SFS), molecular dynamics (MD), and quantum mechanics (QM) may be used to amplify the amount of available data to drug design and discovery campaigns, presenting methods, their perspectives, and discussions to be employed in the near future. EXPERT OPINION: Recent works have successfully used these techniques to solve a range of issues in the face of data scarcity, including complex problems such as the challenging scenario of drug design aimed at intrinsically disordered proteins and the evaluation of potential adverse effects in a clinical scenario. These examples show that it is possible to improve and kickstart research from scarce available data to design and discover new potential drugs.

RI75, a curcumin analogue, inhibits tumor necrosis factor-α and interleukin-6 production and exhibits antiallodynic and antiedematogenic activities in mice.

Curcumin and its analogues exhibited anti-inflammatory activity in different experimental models. Recently, we synthesized (2E,3E)-3-buten-2-one-4-(4-hydroxy-3-methoxyphenyl)-2-(4-(4-methoxyphenyl)-2-thiazolyl)hydrazone (RI75), a curcumin analogue with a thiazolyl hydrazone moiety. In the present study, we investigated the effects induced by RI75 in different models of inflammation and pain in mice, as well as some underlying mechanisms. Pre-treatment with RI75 (40 mg/kg, intraperitoneal; i.p.) or curcumin (40 mg/kg, i.p.) reduced the mechanical allodynia and paw edema induced by intraplantar (i.pl) injection of carrageenan. RI75 antiallodynic activity was reduced by pre-treatment with naltrexone (5 and 10 mg/kg, i.p.) and cyproheptadine (10 mg/kg, i.p.), but not glibenclamide (20 and 40 mg/kg, i.p.). In a model of neuropathic pain, a single i.p. administration of RI75 (40 mg/kg) or curcumin (40 mg/kg) attenuated the ongoing mechanical allodynia induced by repeated administrations of paclitaxel. Pre-treatment with RI75 (40 mg/kg, i.p.) or curcumin (40 mg/kg, i.p.) also reduced tumor necrosis factor-α and interleukin-6 production and myeloperoxidase activity induced by carrageenan. The results of the present study demonstrate that RI75, a synthetic curcumin analogue, exhibits antiallodynic and antiedematogenic activities. Activation of opioidergic and serotonergic mechanisms and reduced production of inflammatory mediators and neutrophil recruitment may underlie RI75 activities.

Recent Advances in the Prediction of Pharmacokinetics Properties in Drug Design Studies: A Review.

This review presents the main aspects related to pharmacokinetic properties, which are essential for the efficacy and safety of drugs. This topic is very important because the analysis of pharmacokinetic aspects in the initial design stages of drug candidates can increase the chances of success for the entire process. In this scenario, experimental and in silico techniques have been widely used. Due to the difficulties encountered with the use of some experimental tests to determine pharmacokinetic properties, several in silico tools have been developed and have shown promising results. Therefore, in this review, we address the main free tools/servers that have been used in this area, as well as some cases of application. Finally, we present some studies that employ a multidisciplinary approach with synergy between in silico, in vitro, and in vivo techniques to assess ADME properties of bioactive substances, achieving successful results in drug discovery and design.

Knowing and combating the enemy: a brief review on SARS-CoV-2 and computational approaches applied to the discovery of drug candidates.

Since the emergence of the new severe acute respiratory syndrome-related coronavirus 2 (SARS-CoV-2) at the end of December 2019 in China, and with the urge of the coronavirus disease 2019 (COVID-19) pandemic, there have been huge efforts of many research teams and governmental institutions worldwide to mitigate the current scenario. Reaching more than 1,377,000 deaths in the world and still with a growing number of infections, SARS-CoV-2 remains a critical issue for global health and economic systems, with an urgency for available therapeutic options. In this scenario, as drug repurposing and discovery remains a challenge, computer-aided drug design (CADD) approaches, including machine learning (ML) techniques, can be useful tools to the design and discovery of novel potential antiviral inhibitors against SARS-CoV-2. In this work, we describe and review the current knowledge on this virus and the pandemic, the latest strategies and computational approaches applied to search for treatment options, as well as the challenges to overcome COVID-19.

Do Drug-likeness Rules Apply to Oral Prodrugs?

This paper describes a comparative analysis of the physicochemical and structural properties of prodrugs and their corresponding drugs with regard to drug-likeness rules. The dataset used in this work was obtained from the DrugBank. Sixty-five pairs of prodrugs/drugs were retrieved and divided into the following categories: carrier-linked to increase hydrophilic character, carrier-linked to increase absorption, and bioprecursors. We compared the physicochemical properties related to drug-likeness between prodrugs and drugs. Our results show that prodrugs do not always follow Lipinski's Rule of 5, especially as we observed 15 prodrugs with more than 10 hydrogen bond acceptors and 18 with a molecular weight greater than 500 Da. This fact highlights the importance of extending Lipinski's rules to encompass other parameters as both strategies (filtering of drug-like chemical libraries and prodrug design) aim to improve the bioavailability of compounds. Therefore, critical reasoning is fundamental to determine whether a structure has drug-like properties or could be considered a potential orally active compound in the drug-design pipeline.

Improving the solubility of an antifungal thiazolyl hydrazone derivative by cyclodextrin complexation.

RN104, named 2-[2-(cyclohexylmethylene)hydrazinyl)]-4-phenylthiazole, is a thiazolyl hydrazone derivative with promising antifungal activity. A HPLC-DAD method was carried out using C18 end-capped column (250 × 4.6 mm, 5 µm) and a mobile phase composed of water and acetonitrile (15:85 v/v) at a flow rate of 1.2 mL/min, injection volume 25 µL and DAD detection at 240 nm. The method showed to be selective, linear in the range of 20 to 240 µg/mL, precise, accurate and robust.Due to the low solubility of RN104 in water, the development of inclusion complexes using different cyclodextrins (ß-CD, γ-CD and 2-HP-ß-CD) was investigated, as well as the interaction mode between RN104 and cyclodextrins using molecular docking followed by semi-empirical calculations. Among tested cyclodextrins, the best results were obtained with 2-HP-ß-CD, which promoted an 18-fold increase in RN104's aqueous solubility.

Synthesis of quinoline derivatives as potential cysteine protease inhibitors.

Aim: Cysteine proteases are important molecular targets involved in the replication, virulence and survival of parasitic organisms, including Trypanosoma and Leishmania species. Methodology & results: Analogs of the 7-chloro-N-[3-(morpholin-4-yl)propyl]quinolin-4-amine were synthesized and their inhibitory activity against the enzymes cruzain and rhodesain as well as against promastigotes forms of Leishmania species and epimastigotes forms of Trypanosoma cruzi were evaluated. Five compounds showed activity against both enzymes with half maximal inhibitory concentration (IC50) values ranging from 23 to 123 µM. Among these, compounds 3 and 4 displayed leishmanicidal activity; compound 4 was the most promising with IC50 values <10 µM and no cytotoxicity for uninfected cells. Conclusion: The results obtained indicate that cysteine proteases are likely to be the molecular target of compounds 3 and 4.

Structure-Based Virtual Screening, Molecular Dynamics and Binding Free Energy Calculations of Hit Candidates as ALK-5 Inhibitors.

Activin-like kinase 5 (ALK-5) is involved in the physiopathology of several conditions, such as pancreatic carcinoma, cervical cancer and liver hepatoma. Cellular events that are landmarks of tumorigenesis, such as loss of cell polarity and acquisition of motile properties and mesenchymal phenotype, are associated to deregulated ALK-5 signaling. ALK-5 inhibitors, such as SB505154, GW6604, SD208, and LY2157299, have recently been reported to inhibit ALK-5 autophosphorylation and induce the transcription of matrix genes. Due to their ability to impair cell migration, invasion and metastasis, ALK-5 inhibitors have been explored as worthwhile hits as anticancer agents. This work reports the development of a structure-based virtual screening (SBVS) protocol aimed to prospect promising hits for further studies as novel ALK-5 inhibitors. From a lead-like subset of purchasable compounds, five molecules were identified as putative ALK-5 inhibitors. In addition, molecular dynamics and binding free energy calculations combined with pharmacokinetics and toxicity profiling demonstrated the suitability of these compounds to be further investigated as novel ALK-5 inhibitors.

Antibacterial activity of synthetic 1,3-bis(aryloxy)propan-2-amines against Gram-positive bacteria.

Synthetic 1,3-bis(aryloxy)propan-2-amines have been shown in previous studies to possess several biological activities, such as antifungal and antiprotozoal. In the present study, we describe the antibacterial activity of new synthetic 1,3-bis(aryloxy)propan-2-amines against Gram-positive pathogens (Streptococcus pyogenes, Enterococcus faecalis and Staphylococcus aureus) including Methicillin-resistant S. aureus strains. Our compounds showed minimal inhibitory concentrations (MIC) in the range of 2.5-10 µg/ml (5.99-28.58 µM), against different bacterial strains. The minimal bactericidal concentrations found were similar to MIC, suggesting a bactericidal mechanism of action of these compounds. Furthermore, possible molecular targets were suggested by chemical similarity search followed by docking approaches. Our compounds are similar to known ligands targeting the cell division protein FtsZ, Quinolone resistance protein norA and the Enoyl-[acyl-carrier-protein] reductase FabI. Taken together, our data show that synthetic 1,3-bis(aryloxy)propan-2-amines are active against Gram-positive bacteria, including multidrug-resistant strains and can be a promising lead in the development of new antibacterial compounds for the treatment of these infections.

Advances and Perspectives in Applying Deep Learning for Drug Design and Discovery.

Discovering (or planning) a new drug candidate involves many parameters, which makes this process slow, costly, and leading to failures at the end in some cases. In the last decades, we have witnessed a revolution in the computational area (hardware, software, large-scale computing, etc.), as well as an explosion in data generation (big data), which raises the need for more sophisticated algorithms to analyze this myriad of data. In this scenario, we can highlight the potentialities of artificial intelligence (AI) or computational intelligence (CI) as a powerful tool to analyze medicinal chemistry data. According to IEEE, computational intelligence involves the theory, the design, the application, and the development of biologically and linguistically motivated computational paradigms. In addition, CI encompasses three main methodologies: neural networks (NN), fuzzy systems, and evolutionary computation. In particular, artificial neural networks have been successfully applied in medicinal chemistry studies. A branch of the NN area that has attracted a lot of attention refers to deep learning (DL) due to its generalization power and ability to extract features from data. Therefore, in this mini-review we will briefly outline the present scope, advances, and challenges related to the use of DL in drug design and discovery, describing successful studies involving quantitative structure-activity relationships (QSAR) and virtual screening (VS) of databases containing thousands of compounds.

Transfer and Multi-task Learning in QSAR Modeling: Advances and Challenges.

Medicinal chemistry projects involve some steps aiming to develop a new drug, such as the analysis of biological targets related to a given disease, the discovery and the development of drug candidates for these targets, performing parallel biological tests to validate the drug effectiveness and side effects. Approaches as quantitative study of activity-structure relationships (QSAR) involve the construction of predictive models that relate a set of descriptors of a chemical compound series and its biological activities with respect to one or more targets in the human body. Datasets used to perform QSAR analyses are generally characterized by a small number of samples and this makes them more complex to build accurate predictive models. In this context, transfer and multi-task learning techniques are very suitable since they take information from other QSAR models to the same biological target, reducing efforts and costs for generating new chemical compounds. Therefore, this review will present the main features of transfer and multi-task learning studies, as well as some applications and its potentiality in drug design projects.

GQ-11: A new PPAR agonist improves obesity-induced metabolic alterations in LDLr-/- mice.

BACKGROUND: Obesity and insulin resistance/diabetes are important risk factors for cardiovascular diseases and demand safe and efficacious therapeutics. OBJECTIVE: To assess the effects of a new thiazolidine compound-GQ-11-on obesity and insulin resistance induced by a diabetogenic diet in LDL receptor-deficient (LDLr-/-) mice. METHODS: Molecular docking simulations of GQ-11, PPARα and PPARγ structures were performed. Male C57BL/6J LDLr-/- mice fed a diabetogenic diet for 24 weeks were treated with vehicle, GQ-11 or pioglitazone or (20 mg/kg/day) for 28 days by oral gavage. Glucose tolerance test, insulin, HOMA-IR, adipokines (leptin, adiponectin) and the lipid profile were assessed after treatment. Adipose tissue was analysed by X-ray analysis and morphometry; gene and protein expression were evaluated by real-time PCR and western blot, respectively. RESULTS: GQ-11 showed partial agonism to PPARγ and PPARα. In vivo, treatment with GQ-11 ameliorated insulin sensitivity and did not modify subcutaneous adipose tissue and body weight gain. In addition, GQ-11 restored adipokine imbalance induced by a diabetogenic diet and enhanced Glut-4 expression in the adipose tissue. Improved insulin sensitivity was also associated with lower levels of MCP-1 and higher levels of IL-10. Furthermore, GQ-11 reduced triglycerides and VLDL cholesterol and increased HDL-cholesterol by upregulation of Apoa1 and Abca1 gene expression in the liver. CONCLUSION: GQ-11 is a partial/dual PPARα/γ agonist that demonstrates anti-diabetic effects. Additionally, it improves the lipid profile and ameliorates chronic inflammation associated with obesity in atherosclerosis-prone mice.

On the relationship of anthranilic derivatives structure and the FXR (Farnesoid X receptor) agonist activity.

Farnesoid X receptor (FXR) is a nuclear receptor related to lipid and glucose homeostasis and is considered an important molecular target to treatment of metabolic diseases as diabetes, dyslipidemia, and liver cancer. Nowadays, there are several FXR agonists reported in the literature and some of it in clinical trials for liver disorders. Herein, a compound series was employed to generate QSAR models to better understand the structural basis for FXR activation by anthranilic acid derivatives (AADs). Furthermore, here we evaluate the inclusion of the standard deviation (SD) of EC50 values in QSAR models quality. Comparison between the use of experimental variance plus average values in model construction with the standard method of model generation that considers only the average values was performed. 2D and 3D QSAR models based on the AAD data set including SD values showed similar molecular interpretation maps and quality (Q2LOO, Q2(F2), and Q2(F3)), when compared to models based only on average values. SD-based models revealed more accurate predictions for the set of test compounds, with lower mean absolute error indices as well as more residuals near zero. Additionally, the visual interpretation of different QSAR approaches agrees with experimental data, highlighting key elements for understanding the biological activity of AADs. The approach using standard deviation values may offer new possibilities for generating more accurate QSAR models based on available experimental data.

The Role of QSAR and Virtual Screening Studies in Type 2 Diabetes Drug Discovery.

BACKGROUND: Due to the increasing number of diabetes cases worldwide, there is an international concern to provide even more effective treatments to control this condition. METHODS: This review brings together a selection of studies that helped to broaden the comprehension of various biological targets and associated mechanisms involved in type 2 diabetes mellitus. RESULTS: Such studies demonstrated that QSAR techniques and virtual screenings have been successfully employed in drug design projects. CONCLUSIONS: Therefore, the main goal of this review is to give the state-of-art for the development of new drugs for the treatment of type 2 diabetes mellitus and to evaluate how computational tools, such as virtual screening and 3D-QSAR, can aid the development of new drugs with reduced adverse side effects.

Study on molecular structure, spectroscopic properties (FTIR and UV-Vis), NBO, QTAIM, HOMO-LUMO energies and docking studies of 5-fluorouracil, a substance used to treat cancer.

Cancer cells can expand to other parts of body through blood system and nodes from a mechanism known as metastasis. Due to the large annual growth of cancer cases, various biological targets have been studied and related to this disorder. A very interesting target related to cancer is human epidermal growth factor receptor 2 (HER2). In this study, we analyzed the main intermolecular interactions between a drug used in the cancer treatment (5-fluorouracil) and HER2. Molecular modeling methods were also employed to assess the molecular structure, spectroscopic properties (FTIR and UV-Vis), NBO, QTAIM and HOMO-LUMO energies of 5-FU. From the docking simulations it was possible to analyze the interactions that occur between some residues in the binding site of HER2 and 5-FU. To validate the choice of basis set that was used in the NBO and QTAIM analyses, theoretical calculations were performed to obtain FT-IR and UV/Vis spectra, and the theoretical results are consistent with the experimental data, showing that the basis set chosen is suitable. For the maximum λ from the theoretical calculation (254.89nm) of UV/Vis, the electronic transition from HOMO to LUMO occurs at 4.89eV. From NBO analyses, we observed interactions between Asp863 and 5-FU, i.e. the orbitals with high transfer of electrons are LP O15 (donor NBO) and BD* (π) N1-H10 (acceptor NBO), being that the value of this interaction is 7.72kcal/mol. Results from QTAIM indicate one main intermolecular H bond, which is necessary to stabilize the complex formed between the ligands and the biological target. Therefore, this study allowed a careful evaluation on the main structural, spectroscopic and electronic properties involved in the interaction between 5-FU and HER2, an important biological complex related to the cancer treatment.

Application of bioisosterism in design of the semicarbazone derivatives as cruzain inhibitors: a theoretical and experimental study.

A series of semicarbazone, thiosemicarbazone, and aminoguanidine derivatives were synthesized and tested as antitrypanosomal agents. The theoretical NMR of the compounds was calculated using molecular modeling techniques (density functional theory (DFT) calculations) and confirmed the formation of the compounds. The ability to inhibit cruzain and Trypanosoma cruzi epimastigote replication was evaluated. Cruzain inhibition ranged between 70 and 75% (100 µM), and IC50 values observed in epimastigote forms of T. cruzi ranged from 20 to 140 µM. Furthermore, the compounds did not present cytotoxicity at concentrations up to 50 and 250 µM in MTT tests. Molecular modeling studies were conducted using DFT method (B3LYP functional and the basis set 6-311G(d,p)) to understand the activity of the compounds, corroborating the observed cruzain inhibitory activity. In docking studies, the obtained analogs showed good complementarity with cruzain active site. In addition, docking results are in accordance with the susceptibility of these analogs to nucleophilic attack of the catalytic Cys25. Taken together, this study shows that this class of compounds can be used as a prototype in the identification of new antichagasic drugs.

Understanding PPAR-δ affinity and selectivity using hologram quantitative structure-activity modeling, molecular docking and GRID calculations.

AIM: Type 2 diabetes mellitus and metabolic syndrome are two diseases related to disorders of lipid and carbohydrate metabolism and insulin resistance. Peroxisome proliferator-activated receptors (PPARs) are a class of nuclear receptors that control the metabolism of lipids/carbohydrates and are considered targets for both diseases. PPAR affinity and selectivity are critical points to design drug candidates with appropriated pharmacodynamic/kinetic profiles. MATERIALS & METHODS: Hologram quantitative structure-activity relationships studies were conducted, as well molecular docking and molecular interaction field calculations, in order to explain affinity and selectivity of selected compounds. RESULTS: The constructed hologram quantitative structure-activity relationship models are robust and predictive (values of q2 and r2test above 0.70). CONCLUSION: The quantitative structure-activity relationship models and docking/GRID analyses indicated that carboxyl group of indole-sulfonamide derivatives could interact at helix-3 region, being considered important point of PPAR-δ selectivity.