Miscibility of poly(ethylene terephthalate)/poly(ethylene 2,6-naphthalate) blends by transesterification

The effects of transesterification on the miscibility of poly(ethylene terephthalate)/poly(ethylene 2,6-naphthalate) were studied. Blends were obtained by solution precipitation at room temperature to avoid transesterification during blend preparation. The physical blends and transesterified products were analyzed by wide-angle x-ray scattering, differential scanning calorimetry, and nuclear magnetic resonance spectroscopy. It was found that the physical blends are immiscible and when the extent of transesterification reaches 50% of the completely randomized state, independent of blend composition, the blends are not crystallizable and show a single glass transition temperature between those of starting polymers. The interchange reactions were significantly influenced by annealing temperature and time but negligibly by blend composition. © 1996 John Wiley & Sons, Inc.     

Miscibility in a ternary system of poly(ether imide) with semicrystalline poly(ethylene terephthalate) and poly(butylene terephthalate)

A rare case of thermodynamic miscibility has been demonstrated in the amorphous state (quenched glass as well as molten state) of a ternary blend system formed by poly(ether imide) and semicrystalline poly(ethylene terephthalate) and poly-(butylene terephthalate). A single glass transition temperature (T_g) in the ternary blends was observed using differential scanning calorimetry and dynamic mechanical analysis.     

Drug-likeness scoring based on unsupervised learning

Drug-likeness prediction is important for the virtual screening of drug candidates. It is challenging because the drug-likeness is presumably associated with the whole set of necessary properties to pass through clinical trials, and thus no definite data for regression is available. Recently, binary classification models based on graph neural networks have been proposed but with strong dependency of their performances on the choice of the negative set for training. Here we propose a novel unsupervised learning model that requires only known drugs for training. We adopted a language model based on a recurrent neural network for unsupervised learning. It showed relatively consistent performance across different datasets, unlike such classification models. In addition, the unsupervised learning model provides drug-likeness scores that well separate distributions with increasing mean values in the order of datasets composed of molecules at a later step in a drug development process, whereas the classification model predicted a polarized distribution with two extreme values for all datasets presumably due to the overconfident prediction for unseen data. Thus, this new concept offers a pragmatic tool for drug-likeness scoring and further can be applied to other biochemical applications.

A new quantification method of drug-likeness based on unsupervised learning. The method only uses drug molecules as training set without any non-drug-like molecules.     

Recent Advances in Divergent Synthetic Strategies for Indole-Based Natural Product Libraries

Considering the potential bioactivities of natural product and natural product-like compounds with highly complex and diverse structures, the screening of collections and small-molecule libraries for high-throughput screening (HTS) and high-content screening (HCS) has emerged as a powerful tool in the development of novel therapeutic agents. Herein, we review the recent advances in divergent synthetic approaches such as complexity-to-diversity (Ctd) and biomimetic strategies for the generation of structurally complex and diverse indole-based natural product and natural product-like small-molecule libraries.     

Preparation and characterization of self-assembled nanoparticles based on glycol chitosan bearing adriamycin

An anthracycline drug, adriamycin, was chemically conjugated onto the backbone of glycol chitosan via an acid-labile cis-aconityl linkage. The physicochemical characteristics of the glycol chitosan–adriamycin (GC–ADR) conjugates were investigated by dynamic light scattering, atomic force microscopy, and fluorescence spectroscopy. The GC–ADR conjugates were capable of forming nano-sized self-aggregates in an aqueous medium, when the adriamycin content in the conjugate was in the range of 2.0–5.0 wt.%. The self-aggregates were spherical in shape, and had mean diameters of 238–304 nm, depending on the adriamycin content. The critical aggregation concentrations of the conjugates, estimated by the fluorescence quenching method, were as low as 1.0–2.5×10⁻² mg/ml. The size of self-aggregates was not affected by the polymer concentration in the range from 50 to 2,000 μg/ml, and was maintained up to 8 days in phosphate-buffered saline (pH 7.4), indicating high colloidal stability. The release of adriamycin from self-aggregates was significantly dependent on the pH of the medium due to the cis-aconityl linkage; e.g., the amount of adriamycin released for 4 days was 7.3±0.3% at pH 7, whereas it was 29.3±1.9% at pH 4. The cell viability results demonstrated that free adriamycin shows more potent cytotoxicity than the conjugates, primarily attributed to the sustained release of adriamycin from self-aggregates. In conclusion, the self-aggregates, formed by GC–ADR conjugates, might be useful for the site-specific delivery of adriamycin in a sustained manner.     

Soft‐Chemical Exfoliation Route to Layered Cobalt Oxide Monolayers and Its Application for Film Deposition and Nanoparticle Synthesis

A colloidal suspension of exfoliated, layered cobalt oxide nanosheets has been synthesized through the intercalation of quaternary tetramethylammonium ions into protonated lithium cobalt oxide. According to atomic force microscopy, exfoliated nanosheets of layered cobalt oxide show a plateau‐like height profile with nanometer‐level height, underscoring the formation of unilamellar 2D nanosheets. The exfoliation of layered cobalt oxide was cross‐confirmed by X‐ray diffraction, UV/Vis spectroscopy, and transmission electron microscopy. The maintenance of the hexagonal in‐plane structure of the cobalt oxide lattice after the exfoliation process was evidenced by selected‐area electron diffraction and Co K‐edge X‐ray absorption near‐edge structure analysis. The zeta‐potential measurements clearly demonstrated the negative surface charge of cobalt oxide nanosheets. Adopting the nanosheets of layered cobalt oxide as a precursor, we were able to prepare the monodisperse CoO nanocrystals with a particle size of ≈10 nm as well as the heterolayered film composed of cobalt oxide monolayer and polycation.     

Computational Approaches to Discover Novel Natural Compounds for SARS-CoV-2 Therapeutics

Scientists all over the world are facing a challenging task of finding effective therapeutics for the coronavirus disease (COVID-19). One of the fastest ways of finding putative drug candidates is the use of computational drug discovery approaches. The purpose of the current study is to retrieve natural compounds that have obeyed to drug-like properties as potential inhibitors. Computational molecular modelling techniques were employed to discover compounds with potential SARS-CoV-2 inhibition properties. Accordingly, the InterBioScreen (IBS) database was obtained and was prepared by minimizing the compounds. To the resultant compounds, the absorption, distribution, metabolism, excretion and toxicity (ADMET) and Lipinski's Rule of Five was applied to yield drug-like compounds. The obtained compounds were subjected to molecular dynamics simulation studies to evaluate their stabilities. In the current article, we have employed the docking based virtual screening method using InterBioScreen (IBS) natural compound database yielding two compounds has potential hits. These compounds have demonstrated higher binding affinity scores than the reference compound together with good pharmacokinetic properties. Additionally, the identified hits have displayed stable interaction results inferred by molecular dynamics simulation results. Taken together, we advocate the use of two natural compounds, STOCK1N-71493 and STOCK1N-45683 as SARS-CoV-2 treatment regime.     

Electrochemical Detection of Lead Ion Based on a Peptide Modified Electrode

A novel biosensor harnessing a peptide layer which has specific affinity to lead ion proved to be highly effective for electrochemical analysis of lead ions. The peptide modified electrode was used for the electrochemical analysis of various trace metal ions by square wave voltammetry. Compared to the other ions investigated, the peptide modified electrode was found to be highly selective to Pb²⁺ in the range of 50–700 nM. Furthermore, the biosensor exhibited a high reusability and good spike recovery in the tap water containing various concentration of Pb²⁺.