Improving the use of ranking in virtual screening against HIV-1 integrase with triangular numbers and including ligand profiling with antitargets

Flavonoids as tyrosinase inhibitors in in silico and in vitro models: basic framework of SAR using a statistical modelling approach

Flavonoids are widely distributed in plants and constitute the most common polyphenolic phytoconstituents in the human diet. In this study, the in vitro inhibitory activity of 44 different flavonoids (1-44) against mushroom tyrosinase was studied, and an in silico study and type of inhibition for the most active compounds were evaluated too. Tyrosinase inhibitors block melanogenesis and take part in melanin production or distribution leading to pigmentation diseases. The in vitro study showed that quercetin was a competitive inhibitor (IC50=44.38 ± 0.13 µM) and achieved higher antityrosinase activity than the control inhibitor kojic acid. The in silico results highlight the importance of the flavonoid core with a hydroxyl at C7 as a strong contributor of interference with tyrosinase activity. According to the developed statistical model, the activity of molecules depends on hydroxylation at C3 and methylation at C8, C7, and C3 in the benzo-γ-pyrane ring of the flavonoids.

Flavonoids as inhibitors of human neutrophil elastase

Elastase is a proteolytic enzyme belonging to the family of hydrolases produced by human neutrophils, monocytes, macrophages, and endothelial cells. Human neutrophil elastase is known to play multiple roles in the human body, but an increase in its activity may cause a variety of diseases. Elastase inhibitors may prevent the development of psoriasis, chronic kidney disease, respiratory disorders (including COVID-19), immune disorders, and even cancers. Among polyphenolic compounds, some flavonoids and their derivatives, which are mostly found in herbal plants, have been revealed to influence elastase release and its action on human cells. This review focuses on elastase inhibitors that have been discovered from natural sources and are biochemically characterised as flavonoids. The inhibitory activity on elastase is a characteristic of flavonoid aglycones and their glycoside and methylated, acetylated and hydroxylated derivatives. The presented analysis of structure-activity relationship (SAR) enables the determination of the chemical groups responsible for evoking an inhibitory effect on elastase. Further study especially of the in vivo efficacy and safety of the described natural compounds is of interest in order to gain better understanding of their health-promoting potential.

Combined Naïve Bayesian, Chemical Fingerprints and Molecular Docking Classifiers to Model and Predict Androgen Receptor Binding Data for Environmentally- and Health-Sensitive Substances

Many chemicals that enter the environment, food chain, and the human body can disrupt androgen-dependent pathways and mimic hormones and therefore, may be responsible for multiple diseases from reproductive to tumor. Thus, modeling and predicting androgen receptor activity is an important area of research. The aim of the current study was to find a method or combination of methods to predict compounds that can bind to and/or disrupt the androgen receptor, and thereby guide decision making and further analysis. A stepwise procedure proceeded from analysis of protein structures from human, chimp, and rat, followed by docking and subsequent ligand, and statistics based techniques that improved classification gradually. The best methods used multivariate logistic regression of combinations of chimpanzee protein structural docking scores, extended connectivity fingerprints, and naïve Bayesians of known binders and non-binders. Combination or consensus methods included data from a variety of procedures to improve the final model accuracy.

Benford's law in medicinal chemistry: Implications for drug design

Aim: The explosion of data based technology has accelerated pattern mining. However, it is clear that quality and bias of data impacts all machine learning and modeling. Results & methodology: A technique is presented for using the distribution of first significant digits of medicinal chemistry features: logP, logS, and pK_a. experimental and predicted, to assess their following of Benford's law as seen in many natural phenomena. Conclusion: Quality of data depends on the dataset sizes, diversity, and magnitudes. Profiling based on drugs may be too small or narrow; using larger sets of experimentally determined or predicted values recovers the distribution seen in other natural phenomena. This technique may be used to improve profiling, machine learning, large dataset assessment and other data based methods for better (automated) data generation and designing compounds.

Towards a Treatment for Gulf War Illness: A Consensus Docking Approach

Introduction

Gulf War Illness (GWI) currently has no known cure and affects soldiers deployed during the Persian Gulf War. It is thought to originate from exposure to neurotoxicants combined with battlefield stress, and previous research indicates that treatment first involves inhibition of interleukin-2 and tumor necrosis factor alpha, followed by the glucocorticoid receptor. However, the off-target effects of pharmaceuticals hinder development of a drug treatment therapy.

Materials and Methods

AutoDock 4.2, AutoDock Vina, and Schrodinger’s Glide were used to perform consensus docking, a computational technique where pharmaceuticals are screened against targets using multiple scoring algorithms to obtain consistent binding affinities. FDA approved pharmaceuticals were docked against the above-mentioned immune and stress targets to determine a drug therapy for GWI. Additionally, the androgen and estrogen targets were screened to avoid pharmaceuticals with off-target interactions.

Results

While suramin bound to both immune targets with high affinity, top binders of the hormonal and glucocorticoid targets were non-specific towards their respective proteins, possibly due to high structure similarity between these proteins.

Conclusions

Development of a drug treatment therapy for GWI is threatened by the tight interplay between the immune and hormonal systems, often leading to drug interactions. Increasing knowledge of these interactions can lead to break-through therapies.

Synthesis, In Silico, and In Vitro Evaluation of Anti-Leishmanial Activity of Oxadiazoles and Indolizine Containing Compounds Flagged against Anti-Targets

Due to the lack of approved vaccines against human leishmaniasis and the limitations of the current chemotherapy inducing side effects and drug resistance, development of new, effective chemotherapeutic agents is essential. This study describes the synthesis of a series of novel oxadiazoles and indolizine-containing compounds. The compounds were screened in silico using an EIIP/AQVN filter followed by ligand-based virtual screening and molecular docking to parasite arginase. Top hits were further screened versus human arginase and finally against an anti-target battery to tag their possible interactions with proteins essential for the metabolism and clearance of many substances. Eight candidate compounds were selected for further experimental testing. The results show measurable in vitro anti-leishmanial activity for three compounds. One compound with an IC₅₀ value of 2.18 µM on Leishmania donovani intramacrophage amastigotes is clearly better positioned than the others as an interesting molecular template for further development of new anti-leishmanial agents.

Using a Consensus Docking Approach to Predict Adverse Drug Reactions in Combination Drug Therapies for Gulf War Illness

Gulf War Illness (GWI) is a chronic multisymptom illness characterized by fatigue, musculoskeletal pain, and gastrointestinal and cognitive dysfunction believed to stem from chemical exposures during the 1990⁻1991 Persian Gulf War. There are currently no treatments; however, previous studies have predicted a putative multi-intervention treatment composed of inhibiting Th1 immune cytokines followed by inhibition of the glucocorticoid receptor (GCR) to treat GWI. These predictions suggest the use of specific monoclonal antibodies or suramin to target interleukin-2 and tumor necrosis factor α , followed by mifepristone to inhibit the GCR. In addition to this putative treatment strategy, there exist a variety of medications that target GWI symptomatology. As pharmaceuticals are promiscuous molecules, binding to multiple sites beyond their intended targets, leading to off-target interactions, it is key to ensure that none of these medications interfere with the proposed treatment avenue. Here, we used the drug docking programs AutoDock 4.2, AutoDock Vina, and Schrödinger's Glide to assess the potential off-target immune and hormone interactions of 43 FDA-approved drugs commonly used to treat GWI symptoms in order to determine their putative polypharmacology and minimize adverse drug effects in a combined pharmaceutical treatment. Several of these FDA-approved drugs were predicted to be novel binders of immune and hormonal targets, suggesting caution for their use in the proposed GWI treatment strategy symptoms.

Comparison of Quantitative and Qualitative (Q)SAR Models Created for the Prediction of Ki and IC50 Values of Antitarget Inhibitors

Estimation of interaction of drug-like compounds with antitargets is important for the assessment of possible toxic effects during drug development. Publicly available online databases provide data on the experimental results of chemical interactions with antitargets, which can be used for the creation of (Q)SAR models. The structures and experimental Ki and IC50 values for compounds tested on the inhibition of 30 antitargets from the ChEMBL 20 database were used. Data sets with Ki and IC50 values including more than 100 compounds were created for each antitarget. The (Q)SAR models were created by GUSAR software using quantitative neighborhoods of atoms (QNA), multilevel neighborhoods of atoms (MNA) descriptors, and self-consistent regression. The accuracy of (Q)SAR models was validated by the fivefold cross-validation procedure. The balanced accuracy was higher for qualitative SAR models (0.80 and 0.81 for Ki and IC50 values, respectively) than for quantitative QSAR models (0.73 and 0.76 for Ki and IC50 values, respectively). In most cases, sensitivity was higher for SAR models than for QSAR models, but specificity was higher for QSAR models. The mean R 2 and RMSE were 0.64 and 0.77 for Ki values and 0.59 and 0.73 for IC50 values, respectively. The number of compounds falling within the applicability domain was higher for SAR models than for the test sets.

Recovering Actives in Multi-Antitarget and Target Design of Analogs of the Myosin II Inhibitor Blebbistatin

In multitarget drug design, it is critical to identify active and inactive compounds against a variety of targets and antitargets. Multitarget strategies thus test the limits of available technology, be that in screening large databases of compounds vs. a large number of targets, or in using in silico methods for understanding and reliably predicting these pharmacological outcomes. In this paper, we have evaluated the potential of several in silico approaches to predict the target, antitarget and physicochemical profile of (S)-blebbistatin, the best-known myosin II ATPase inhibitor, and a series of analogs thereof. Standard and augmented structure-based design techniques could not recover the observed activity profiles. A ligand-based method using molecular fingerprints was, however, able to select actives for myosin II inhibition. Using further ligand- and structure-based methods, we also evaluated toxicity through androgen receptor binding, affinity for an array of antitargets and the ADME profile (including assay-interfering compounds) of the series. In conclusion, in the search for (S)-blebbistatin analogs, the dissimilarity distance of molecular fingerprints to known actives and the computed antitarget and physicochemical profile of the molecules can be used for compound design for molecules with potential as tools for modulating myosin II and motility-related diseases.

Arginase Flavonoid Anti-Leishmanial in Silico Inhibitors Flagged against Anti-Targets

Arginase, a drug target for the treatment of leishmaniasis, is involved in the biosynthesis of polyamines. Flavonoids are interesting natural compounds found in many foods and some of them may inhibit this enzyme. The MetIDB database containing 5667 compounds was screened using an EIIP/AQVN filter and 3D QSAR to find the most promising candidate compounds. In addition, these top hits were screened in silico versus human arginase and an anti-target battery consisting of cytochromes P450 2a6, 2c9, 3a4, sulfotransferase, and the pregnane-X-receptor in order to flag their possible interactions with these proteins involved in the metabolism of substances. The resulting compounds may have promise to be further developed for the treatment of leishmaniasis.