Inactive Compounds Research Articles

Antioxidant and Antimicrobial Activities of Some Novel 2-Thiohydantoin Derivatives

Four new 2-thiohydantoin derivatives (4a-d) were synthesized from the reaction of maleimide derivatives (3a-d) with phenylisothiocyanate. The structures of synthetic compounds were characterized using Fourier-transform infrared spectroscopy (FT-IR), 1H- and 13C-nuclear magnetic resonance (NMR) spectroscopy, and mass spectrometry. The antioxidant activity of the synthesized compounds showed inactive 4b and 4c compounds, moderately active 4a, and strongly active 4d, compared with vitamin C as positive control (IC50 values = 1.380, 1.726, 4.147, 8.085, and 9.826 µM, respectively). Antibacterial and antifungal activities of compounds showed that only compound 4b had an effect on Pseudomonas aeruginosa, Candida albicans, and Aspergillus niger species.

Modelling of antiproliferative activity measured in HeLa cervical cancer cells in a series of xanthene derivatives

ABSTRACT Cancer remains one of the leading causes of death in humans, and new drug substances are therefore being developed. Thus, the anti-cancer activity of xanthene derivatives has become an important topic in the development of new and potent anti-cancer drug substances. Previously published novel series of xanthen-3-one and xanthen-1,8-dione derivatives have been synthesized in one of our laboratories and showed anti-proliferative activity in HeLa cancer cell lines. This series serves as a good basis to develop quantitative structure–activity relationship (QSAR), to study the relations between anti-proliferative activity and chemical structures. A QSAR model has been derived that relies only on two-dimensional molecular descriptors, providing mechanistic insight into the anti-proliferative activity of xanthene derivatives. The model is validated internally and externally and additionally with the set of inactive compounds of the original data, confirming model applicability for the design and discovery of novel xanthene derivatives. The QSAR model is available at the QsarDB repository (http://dx.doi.org/10.15152/QDB.237).

High Performance Electrode Architectures By Laser Structuring of Silicon-Graphite Anodes and Ultra-Thick Film NMC-Type Cathode Materials

Laser structuring of battery materials such as electrodes, current collectors, and separator materials becomes more and more a versatile tool for a flexible designing of battery performance with regard to high rate capability, reduced impedance and diffusion over-potential, enhanced battery lifetime, and a tremendous improvement of electrode wetting with liquid electrolyte. Especially structuring of thick film composite electrodes with film thickness up to 300 µm is of great interest for combining high energy and high power applications. Laser ablation by using ultrafast laser radiation ensures a high aspect ratio structuring without damaging or modifying the material properties of the active material, inactive compounds, and current collector. Thermal induced material modification and laser-induced material splatter on top of the electrode surfaces have been so far a major problem by using conventional nanosecond lasers. However, in recent years high power OEM-type ultrafast laser beam sources became available for industrial production. Those lasers provide powers of several 100 W up to the kW-regime with repetition rates of several tenfold MHz, which makes high-speed parallel material processing feasible. Finally, after introducing high-speed nanosecond laser cutting of electrodes almost one decade ago, we forecast that with regard to recent technical approaches quite soon high-speed electrode ultrafast laser structuring will entering the pilot line level. For this purpose, at KIT, parallel processing of cathode and anode materials with large foot print area is being developed in a roll-to-roll (R2R) environment in order to meet the process speed requirements of cell production. Furthermore, we are developing thick film electrode concepts for anodes made of graphite and silicon-graphite and cathodes based on Lithium-Nickel-Manganese-Cobalt-Oxide (NMC) with varying nickel content and water-based slurries. Electrode thicknesses in the range of 100-300 µm were synthesized for subsequent laser processing, cell assembly, and electrochemical characterization such as galvanostatic measurements, galvanostatic intermittent titration technique, cyclic voltammetry, and electrochemical impedance spectroscopy. Large area laser structuring in R2R environment for generation of three-dimensional (3D) electrode architectures with increased active surface area, enhanced mechanical integrity for high energy materials such as Si-graphite anodes and an overall boost in electrochemical performances on pouch cell level will be presented. Laser-induced breakdown spectroscopy (LIBS) was applied for elemental mapping of entire electrodes in order to characterize binder migration in ultra-thick film electrodes, and in post-mortem studies to evaluate degradation processes in unstructured, damaged, and structured thick film electrodes with large footprint (pouch cell level). Post-mortem studies show that significant chemical modifications across from electrodes with macroscopic surface defects could be detected while laser structured electrodes provide less inhomogeneity in lithium distribution in comparison to cells with unstructured electrodes. Furthermore, an inhomogeneity in mechanical load on cells could be identified as a main reason for cell degradation. LIBS is presented as a perfect tool to rapidly characterize and identify starting points for cell degradation on coin cell and pouch cell level. In summary, ultrafast laser structuring of electrodes provides a new manufacturing tool for next generation battery production to overcome current limitations in electrode design and cell performances.

Structure\u2013activity relationship-based chemical classification of highly imbalanced Tox21 datasets

The specificity of toxicant-target biomolecule interactions lends to the very imbalanced nature of many toxicity datasets, causing poor performance in Structure–Activity Relationship (SAR)-based chemical classification. Undersampling and oversampling are representative techniques for handling such an imbalance challenge. However, removing inactive chemical compound instances from the majority class using an undersampling technique can result in information loss, whereas increasing active toxicant instances in the minority class by interpolation tends to introduce artificial minority instances that often cross into the majority class space, giving rise to class overlapping and a higher false prediction rate. In this study, in order to improve the prediction accuracy of imbalanced learning, we employed SMOTEENN, a combination of Synthetic Minority Over-sampling Technique (SMOTE) and Edited Nearest Neighbor (ENN) algorithms, to oversample the minority class by creating synthetic samples, followed by cleaning the mislabeled instances. We chose the highly imbalanced Tox21 dataset, which consisted of 12 in vitro bioassays for > 10,000 chemicals that were distributed unevenly between binary classes. With Random Forest (RF) as the base classifier and bagging as the ensemble strategy, we applied four hybrid learning methods, i.e., RF without imbalance handling (RF), RF with Random Undersampling (RUS), RF with SMOTE (SMO), and RF with SMOTEENN (SMN). The performance of the four learning methods was compared using nine evaluation metrics, among which F1 score, Matthews correlation coefficient and Brier score provided a more consistent assessment of the overall performance across the 12 datasets. The Friedman’s aligned ranks test and the subsequent Bergmann-Hommel post hoc test showed that SMN significantly outperformed the other three methods. We also found that a strong negative correlation existed between the prediction accuracy and the imbalance ratio (IR), which is defined as the number of inactive compounds divided by the number of active compounds. SMN became less effective when IR exceeded a certain threshold (e.g., > 28). The ability to separate the few active compounds from the vast amounts of inactive ones is of great importance in computational toxicology. This work demonstrates that the performance of SAR-based, imbalanced chemical toxicity classification can be significantly improved through the use of data rebalancing.

Identification of novel αβ-tubulin modulators with antiproliferative activity directed to cancer therapy using ligand and structure-based virtual screening

Among several strategies related to cancer therapy targeting the modulation of αβ-tubulin has shown encouraging findings, more specifically when this is achieved by inhibitors located at the colchicine binding site. In this work, we aim to fish new αβ-tubulin modulators through a diverse and rational VS study, and thus, exhibiting the development of two VS pipelines. This allowed us to identify two compounds 5 and 9 that showed IC50 values of 19.69 and 21.97 μM, respectively, towards possible modulation of αβ-tubulin, such as assessed by in vitro assays in C6 glioma and HEPG2 cell lines. We also evaluated possible mechanisms of action of obtained hits towards the colchicine binding site of αβ-tubulin by using docking approaches. In addition, assessment of the stability of the active (5 and 9) and inactive compounds (3 and 13) within the colchicine binding site was carried out by molecular dynamics (MD) simulations, highlighting the solvent effect and revealing the compound 5 as the most stable in the complex. At last, deep analysis of these results provided some valuable insights on the importance of using mixed ligand- and structure-based strategies in VS campaigns, in order to achieve higher chemical diversity and biological effect as well.

Evaluation of QSAR Equations for Virtual Screening.

Quantitative Structure Activity Relationship (QSAR) models can inform on the correlation between activities and structure-based molecular descriptors. This information is important for the understanding of the factors that govern molecular properties and for designing new compounds with favorable properties. Due to the large number of calculate-able descriptors and consequently, the much larger number of descriptors combinations, the derivation of QSAR models could be treated as an optimization problem. For continuous responses, metrics which are typically being optimized in this process are related to model performances on the training set, for example, and . Similar metrics, calculated on an external set of data (e.g., ), are used to evaluate the performances of the final models. A common theme of these metrics is that they are context -” ignorant”. In this work we propose that QSAR models should be evaluated based on their intended usage. More specifically, we argue that QSAR models developed for Virtual Screening (VS) should be derived and evaluated using a virtual screening-aware metric, e.g., an enrichment-based metric. To demonstrate this point, we have developed 21 Multiple Linear Regression (MLR) models for seven targets (three models per target), evaluated them first on validation sets and subsequently tested their performances on two additional test sets constructed to mimic small-scale virtual screening campaigns. As expected, we found no correlation between model performances evaluated by “classical” metrics, e.g., and and the number of active compounds picked by the models from within a pool of random compounds. In particular, in some cases models with favorable and/or values were unable to pick a single active compound from within the pool whereas in other cases, models with poor and/or values performed well in the context of virtual screening. We also found no significant correlation between the number of active compounds correctly identified by the models in the training, validation and test sets. Next, we have developed a new algorithm for the derivation of MLR models by optimizing an enrichment-based metric and tested its performances on the same datasets. We found that the best models derived in this manner showed, in most cases, much more consistent results across the training, validation and test sets and outperformed the corresponding MLR models in most virtual screening tests. Finally, we demonstrated that when tested as binary classifiers, models derived for the same targets by the new algorithm outperformed Random Forest (RF) and Support Vector Machine (SVM)-based models across training/validation/test sets, in most cases. We attribute the better performances of the Enrichment Optimizer Algorithm (EOA) models in VS to better handling of inactive random compounds. Optimizing an enrichment-based metric is therefore a promising strategy for the derivation of QSAR models for classification and virtual screening.

LiNi0.8Co0.15Ti0.05O2: synthesis by solid state reaction and investigation of structural and electrochemical properties with enhanced battery performance

Solid state synthesis is an essential technique for large-scale production of electrode active materials in battery industry. However, solid state synthesis of LiNi0.8Co0.15Al0.05O2 (NCA), which is a well-known commercial cathode material for Li-ion batteries, provides electrochemically inactive compound. Here, we report the solid state synthesis of LixNi0.8Co0.15Ti0.05O2 where x = 1.03, 1.06, and 1.09, which is a modified version of conventional NCA. Our thorough studies consist of characterization of compounds by X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), and magnetization measurements. The results point out the significant effects of Li content on structural and magnetic properties of the samples. Battery performance tests show that Li1.06Ni0.8Co0.15Ti0.05O2 exhibits better cycling properties than conventional NCA. X-ray absorption spectroscopy (XAS) technique is utilized to determine structural modifications upon cycling of this compound via ex-situ analysis. We conclude that substitution of Ti ions in Li1.06Ni0.8Co0.15Ti0.05O2 improves the cycling capability of the cells by reducing the formation of NiO insulating layer which hinders the redox reactions. The capacity value of x = 1.06 sample increases up to 150 mAh g−1 at C/3 rate during cycling and the capacity fade is negative for the first 10 cycles. Possible mechanism for the negative capacity fade is also discussed.

New insight into the role of glutathione reductase in glutathione peroxidase-like activity determination by coupled reductase assay: Molecular Docking Study

Previously we have shown that among 15 substituted salicyloyl (2-hydroxybenzoyl) 5-seleninic acids (SSAs) 4 compounds with longer side chains or a cyclohexyl group exhibit no glutathione peroxidase (GPx)-like activity in the coupled reductase assay. Experimental inhibition of glutathione reductase (GR) by the selenenylsulfide (a main intermediate in the catalytic cycle for GPx-like activity determination) of one of the inactive compounds led us to assess the interactions between 15 selenenylsulfide compounds and the active site of GR by molecular docking. Docking results showed that S and Se atoms in selenenylsulfides of the compounds with no GPx-like activity were beyond 5 Å from S atom of Cys-58 or N atom of imidazole ring of His-467 (Root Mean Square Distances for general assessment of 3 major distances were over 4.8 Å) in the active site, so that they could not be catalyzed to be reduced by GR. Furthermore, their docking scores over 89 Kcal/mol meant that the selenenylsulfides were bound too strongly to the active site to leave it, leading eventually to inhibition of GR. We also applied the molecular docking to other GPx mimics such as ebselen, cyclic seleninate esters and di(propylaminomethylphenyl) diselenides to explain the differences in their GPx-like activity depending to the assays used. Our results suggest that the reduction of a selenenylsulfide by GR plays a positive role in GPx-like activity of GPx mimics in the coupled assay and recommended the prediction of possibility and strength of GPx-like activity by molecular docking before entering experimental research.

2-D chemical structure image-based in silico model to predict agonist activity for androgen receptor

BackgroundAbnormal activation of human nuclear hormone receptors disrupts endocrine systems and thereby affects human health. There have been machine learning-based models to predict androgen receptor agonist activity. However, the models were constructed based on limited numerical features such as molecular descriptors and fingerprints.ResultIn this study, instead of the numerical features, 2-D chemical structure images of compounds were used to build an androgen receptor toxicity prediction model. The images may provide unknown features that were not represented by conventional numerical features. As a result, the new strategy resulted in a construction of highly accurate prediction model: Mathews correlation coefficient (MCC) of 0.688, positive predictive value (PPV) of 0.933, sensitivity of 0.519, specificity of 0.998, and overall accuracy of 0.981 in 10-fold cross-validation. Validation on a test dataset showed MCC of 0.370, sensitivity of 0.211, specificity of 0.991, PPV of 0.882, and overall accuracy of 0.801. Our chemical image-based prediction model outperforms conventional models based on numerical features.ConclusionOur constructed prediction model successfully classified molecular images into androgen receptor agonists or inactive compounds. The result indicates that 2-D molecular mimetic diagram would be used as another feature to construct molecular activity prediction models.

Thermophysical modeling of selective laser ablation processing of lithium-ion battery cathodes

The microstructural optimization of lithium-ion battery (LiB) electrodes has recently gained a lot of interest. Versatile approaches to enhance fast charging abilities of LiB electrodes are the subject of current research. One of these approaches is the laser based photothermic removal of superficial inactive electrode components in order to improve the accessibility of the active material particles for the lithium-ions. In this work, we established a thermophysical model to describe the temperature fields within the electrode resulting from laser material processing. The model delivers satisfying results regarding the prediction of the removal of the top surface electrode layer that mainly consists of a binder and conductive additives. Lining up a simple approach of estimating the average depth in which the inactive binder-additive compound is selectively removed from the electrode's active mass layer led to a good agreement between the calculated and experimental results. Additionally, a potential negative thermal impact on the active material particles themselves due to the laser processing is evaluated. The established model can be used to optimize laser parameters in order to simultaneously maximize the selectively ablated inactive material and to minimize the thermal impact on the active material particles. Moreover, the model is capable of being transferred to laser processing of other types of composite materials such as LiB-anodes or carbon fiber reinforced polymers.

Dynamic-shared Pharmacophore Approach as Tool to Design New Allosteric PRC2 Inhibitors, Targeting EED Binding Pocket.

The Polycomb Repressive complex 2 (PRC2) maintains a repressive chromatin state and silences many genes, acting as methylase on histone tails. This enzyme was found overexpressed in many types of cancer. In this work, we have set up a Computer-Aided Drug Design approach based on the allosteric modulation of PRC2. In order to minimize the possible bias derived from using a single set of coordinates within the protein-ligand complex, a dynamic workflow was developed. In details, molecular dynamic was used as tool to identify the most significant ligand-protein interactions from several crystallized protein structures. The identified features were used for the creation of dynamic pharmacophore models and docking grid constraints for the design of new PRC2 allosteric modulators. Our protocol was retrospectively validated using a dataset of active and inactive compounds, and the results were compared to the classic approaches, through ROC curves and enrichment factor. Our approach suggested some important interaction features to be adopted for virtual screening performance improvement.

Binary and multi-class classification for androgen receptor agonists, antagonists and binders

Androgens and androgen receptor regulate a variety of biological effects in the human body. The impaired functioning of androgen receptor may have different adverse health effects from cancer to infertility. Therefore, it is important to determine whether new chemicals have any binding activity and act as androgen agonists or antagonists before commercial use. Due to the large number of chemicals that require experimental testing, the computational methods are a viable alternative. Therefore, the aim of the present study was to develop predictive QSAR models for classifying compounds according to their activity at the androgen receptor. A large data set of chemicals from the CoMPARA project was used for this purpose and random forest classification models have been developed for androgen binding, agonistic, and antagonistic activity. In addition, a unique effort has been made for multi-class approach that discriminates between inactive compounds, agonists and antagonists simultaneously. For the evaluation set, the classification models predicted agonists with 80% of accuracy and for the antagonists’ and binders’ the respective metrics were 72% and 78%. Combining agonists, antagonists and inactive compounds into a multi-class approach added complexity to the modelling task and resulted to 64% prediction accuracy for the evaluation set. Considering the size of the training data sets and their imbalance, the achieved evaluation accuracy is very good. The final classification models are available for exploring and predicting at QsarDB repository (https://doi.org/10.15152/QDB.236).

Identification of Novel Functionalized Carbohydrazonamides Designed as Chagas Disease Drug Candidates.

Although several research efforts have been made worldwide to discover novel drug candidates for the treatment of Chagas disease, the nitroimidazole drug benznidazol remains the only therapeutic alternative in the control of this disease. However, this drug presents reduced efficacy in the chronic form of the disease and limited safety after long periods of administration, making it necessary to search for new, more potent and safe prototypes. We described herein the synthesis and the trypanocidalaction of new functionalized carbohydrazonamides (2-10) against trypomastigote forms of Trypanosoma cruzi. These compounds were designed through the application of molecular hybridization concept between two potent anti-T. cruzi prototypes, the nitroimidazole derivative megazol (1) and the cinnamyl N-acylhydrazone derivative (14) which have been shown to be twice as potent in vitro as benznidazole. The most active compounds were the (Z)-N'-((E)-3-(4-nitrophenyl)-acryloyl)-1-methyl-5- nitro-1H-imidazol-2-carbohydrazonamide (6) (IC50=9.50 μM) and the (Z)-N'-((E)-3-(4- hydroxyphe-nyl)-acryloyl)-1-methyl-5-nitro-1H-imidazol-2-carbohydrazonamide (8) (IC50=12.85 μM), which were almost equipotent to benznidazole (IC50=10.26 μM) used as standard drug. The removal of the amine group attached to the imine subunit in the corresponding N-acylhydrazone derivatives (11-13) resulted in less potent or inactive compounds. The para-hydroxyphenyl derivative (8) presented also a good selectivity index (SI = 32.94) when tested against mammalian cells from Swiss mice. The promising trypanocidal profile of new carbohydrazonamide derivatives (6) and (8) was characterized. These compounds have proved to be a good starting point for the design of more effective trypanocidal drug candidates.

Evaluation of Artificial Intelligence in Participating Structure-Based Virtual Screening for Identifying Novel Interleukin-1 Receptor Associated Kinase-1 Inhibitors.

Interleukin-1 receptor associated kinase-1 (IRAK1) exhibits important roles in inflammation, infection, and autoimmune diseases; however, only a few inhibitors have been discovered. In this study, at first, a discriminatory structure-based virtual screening (SBVS) was employed, but only one active compound (compound 1, IC50 = 2.25 μM) was identified. The low hit rate (2.63%) which derives from the weak discriminatory power of docking among high-scored molecules was observed in our virtual screening (VS) process for IRAK1 inhibitor. Furthermore, an artificial intelligence (AI) method, which employed a support vector machine (SVM) model, integrated information of molecular docking, pharmacophore scoring and molecular descriptors was constructed to enhance the traditional IRAK1-VS protocol. Using AI, it was found that VS of IRAK1 inhibitors excluded by over 50% of the inactive compounds, which could significantly improve the prediction accuracy of the SBVS model. Moreover, four active molecules (two of which exhibited comparative IC50 with compound 1) were accurately identified from a set of highly similar candidates. Amongst, compounds with better activity exhibited good selectivity against IRAK4. The AI assisted workflow could serve as an effective tool for enhancement of SBVS.

Comparison of Scaling Methods to Obtain Calibrated Probabilities of Activity for Protein-Ligand Predictions.

In the context of bioactivity prediction, the question of how to calibrate a score produced by a machine learning method into a probability of binding to a protein target is not yet satisfactorily addressed. In this study, we compared the performance of three such methods, namely, Platt scaling (PS), isotonic regression (IR), and Venn-ABERS predictors (VA), in calibrating prediction scores obtained from ligand-target prediction comprising the Naïve Bayes, support vector machines, and random forest (RF) algorithms. Calibration quality was assessed on bioactivity data available at AstraZeneca for 40 million data points (compound-target pairs) across 2112 targets and performance was assessed using stratified shuffle split (SSS) and leave 20% of scaffolds out (L20SO) validation. VA achieved the best calibration performances across all machine learning algorithms and cross validation methods tested and also the lowest (best) Brier score loss (mean squared difference between the outputted probability estimates assigned to a compound and the actual outcome). In comparison, the PS and IR methods can actually degrade the assigned probability estimates, particularly for the RF for SSS and during L20SO. Sphere exclusion, a method to sample additional (putative) inactive compounds, was shown to inflate the overall Brier score loss performance, through the artificial requirement for inactive molecules to be dissimilar to active compounds, but was shown to result in overconfident estimators. VA was able to successfully calibrate the probability estimates for even small calibration sets. The multiprobability values (lower and upper probability boundary intervals) were shown to produce large discordance for test set molecules that are neither very similar nor very dissimilar to the active training set, which were hence difficult to predict, suggesting that multiprobability discordance can be used as an estimate for target prediction uncertainty. Overall, we were able to show in this work that VA scaling of target prediction models is able to improve probability estimates in all testing instances and is currently being applied for in-house approaches.

QSAR Modeling of SARS-CoV Mpro Inhibitors Identifies Sufugolix, Cenicriviroc, Proglumetacin, and other Drugs as Candidates for Repurposing against SARS-CoV-2.

The main protease (Mpro) of the SARS-CoV-2 has been proposed as one of the major drug targets for COVID-19. We have identified the experimental data on the inhibitory activity of compounds tested against the closely related (96 % sequence identity, 100 % active site conservation) Mpro of SARS-CoV. We developed QSAR models of these inhibitors and employed these models for virtual screening of all drugs in the DrugBank database. Similarity searching and molecular docking were explored in parallel, but docking failed to correctly discriminate between experimentally active and inactive compounds, so it was not relied upon for prospective virtual screening. Forty-two compounds were identified by our models as consensus computational hits. Subsequent to our computational studies, NCATS reported the results of experimental screening of their drug collection in SARS-CoV-2 cytopathic effect assay (https://opendata.ncats.nih.gov/covid19/). Coincidentally, NCATS tested 11 of our 42 hits, and three of them, cenicriviroc (AC50 of 8.9 μM), proglumetacin (tested twice independently, with AC50 of 8.9 μM and 12.5 μM), and sufugolix (AC50 12.6 μM), were shown to be active. These observations support the value of our modeling approaches and models for guiding the experimental investigations of putative anti-COVID-19 drug candidates. All data and models used in this study are publicly available via Supplementary Materials, GitHub (https://github.com/alvesvm/sars-cov-mpro), and Chembench web portal (https://chembench.mml.unc.edu/).

Synthesis, anti-cancer evaluation and molecular docking studies of aryl bisindole derivatives

Abstract A simple and efficient method has been developed for the synthesis of aryl bis-indolyl derivatives (9a-9k) by the catalytic amount of 18-Crown-6 and evaluated for their cytotoxicity using lung cancer, breast cancer, colon and melanoma cancer cell lines. Among them, compounds 9f and 9i with methoxy substitutions on phenyl ring was found be to be potent inhibitors (IC50 = 0.11 to 0.19 µM) of all the tested cell lines. Compound 9c with difluoro substitution on phenyl ring was inhibited first three cell lines (IC50 = 0.12 to 0.15 µM) except melanoma. Compound 9h with both chloro and fluoro on phenyl ring were found to be having the higher potency against only lung cancer cell line (IC50 = 0.11 µM). These compounds showed higher anticancer potency than Adriamycin drug (0.9 to 3.5 µM). Molecular docking studies have been carried out to understand the binding modes of the active and inactive compounds.

Cranberry extract-based formulations for preventing bacterial biofilms.

Generating formulations for the delivery of a mixture of natural compounds extracted from natural sources is a challenge because of unknown active and inactive ingredients and possible interactions between them. As one example, natural cranberry extracts have been proposed for the prevention of biofilm formation on dental pellicle or teeth. However, such extracts may contain phenolic acids, flavonol glycosides along with other constituents like coumaroyl iridoid glycosides, flavonoids, alpha-linolenic acid, n-6 (or n-3) fatty acids, and crude fiber. Due to the presence of a variety of compounds, determining which molecules (and how many molecules) are essential for preventing biofilm growth is nontrivial to ascertain. Therefore, a formulation that could contain natural, unrefined, cranberry extract (with all its constituent compounds) at high loading would be ideal. Accordingly, we have generated several candidate formulations including poly(lactic-co-glycolic) acid (PLGA)-based microencapsulation of cranberry extract (CE15) as well as formulations including stearic acid along with polyvinylpyrrolidone (PVP) or Ethyl lauroyl arginate (LAE) complexed with cranberry extracts (CE15). We found that stearic acid in combination with PVP or LAE as excipients led to higher loading of the active and inactive compounds in CE15 as compared with a PLGA microencapsulation and also sustained release of CE15 in a tunable manner. Using this method, we have been able to generate two successful formulations (one preventative based, one treatment based) that effectively inhibit biofilm growth when incubated with saliva. In addition to cranberry extract, this technique could also be a promising candidate for other natural extracts to form controlled release systems.Graphical abstract.

Video-based assessment of drug-induced effects on contractile motion properties using human induced pluripotent stem cell-derived cardiomyocytes

IntroductionDrug-induced inotropic change is a risk factor in drug development; thus, de-risking is desired in the early stages of drug development. Unlike proarrhythmic risk assessment using human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs), few in vitro models were validated to predict cardiac contractility. Motion field imaging (MFI), a high-resolution block matching-based optical flow technique, was expected to possess high quantitative predictivity in the detection of contraction speed. We aimed to establish an in vitro model to assess drug-induced contractile changes using hiPSC-CMs and MFI. MethodsMFI was designed to noninvasively characterize cardiomyocyte contractile behavior by analyzing light microscope video images, and maximum contraction speed (MCS) was used as the index of contractility. Using MFI, 9 inactive compounds, 10 negative inotropes, and 10 positive inotropes were tested. Two negative chronotropes, ZD7288 and ivabradine, were also tested. To determine the sensitivity and specificity of the assay, the minimum effective concentration of the MCS was compared with the human effective total therapeutic concentration for 28 compounds in clinical use. ResultsFor 8 negative and 8 positive inotropes, the effects observed in in vivo and clinical studies were detected in MFI assay. MFI assay showed negative chronotropic changes without inotropic changes. MFI assay presented sufficient specificity (83%) and sensitivity (88%), and RNA-sequence analysis provided an insight into the relationship between occurrence of the false compounds and target gene expression. DiscussionWe demonstrated the utility of MFI assay using hiPSC-CMs to assess drug-induced contractile function. These results will facilitate the de-risking of compounds during early drug development.

Secondary metabolites isolated from Agrimonia pilosa Ledeb.

A pilot study on the ethanol extracts of Agrimonia pilosa found to have anti-α-glucosidase and anti-inflammatory activities. Subsequent chemical study afforded a new phenylethyl isocoumarin glycoside (1) and eight known compounds (2-9). The structure of 1 was elucidated by comprehensive spectroscopic analysis and chemical transformations. All compounds showed modest α-glucosidase inhibitory activity (IC50 values ranging from 36.8 to 210.7 μM), which was lower than that of the positive control acarbose (IC50=301.9 μM). Those compounds except inactive compounds 3 and 6 showed weak anti-inflammatory activity.