Lidocaine self-sacrificially improves the skin permeation of the acidic and poorly water-soluble drug etodolac via its transformation into an ionic liquid

Poor transdermal penetration of active pharmaceutical ingredients (APIs) impairs both bioavailability and therapeutic benefits and is a major challenge in the development of transdermal drug delivery systems. Here, we transformed a poorly water-soluble drug, etodolac, into an ionic liquid in order to improve its hydrophobicity, hydrophilicity and skin permeability. The ionic liquid was prepared by mixing etodolac with lidocaine (1:1, mol/mol). Both the free drug and the transformed ionic liquid were characterized by differential scanning colorimetry (DSC), infrared spectroscopy (IR), and saturation concentration measurements. In addition, in vitro skin-permeation testing was carried out via an ionic liquid-containing patch (Etoreat patch). The lidocaine and etodolac in ionic liquid form led to a relatively lower melting point than either lidocaine or etodolac alone, and this improved the lipophilicity/hydrophilicity of etodolac. In vitro skin-permeation testing demonstrated that the Etoreat patch significantly increased the skin permeation of etodolac (9.3-fold) compared with an etodolac alone patch, although an Etoreat patch did not increase the skin permeation of lidocaine, which was consistent with the results when using a lidocaine alone patch. Lidocaine appeared to self-sacrificially improve the skin permeation of etodolac via its transformation into an ionic liquid. The data suggest that ionic liquids composed of approved drugs may substantially expand the formulation preparation method to meet the challenges of drugs which are characterized by poor rates of transdermal absorption

レキシテキ ニンゲンガク カラ ミタ ニホン ノ シンタイ ブンカ : ベルリン ジユウ ダイガク デノ コクサイ ワーク ショップ ニホン ノ シンタイ ブンカ カタ ト ソノ ブンカ オウダンテキ デンタツ ホウコク ロンブン 1

Am 22. September 2008 führten Hirota und Ishida die internationale Werkstatt "Japanische Kultur -durch 'KATA'(Form/Muster) erleben" in der Freien Universität Berlin durch. An diesem Tag gab es ungefähr 20 Teilnehmer: Professor Ch. Wulf und die 'Historische Anthropologie' Studenten/innen，die einige Übungen erfuhren. In unserer Werkstatt haben sie erst unter Leitung von Ishida die Schwertkunst，Stockkunst und Aikido erfahren. Dann hat Hirota ihnen einigen Bewegungsübung als japanische kulturelle Essenz angeboten. Dieser Artikel besteht aus einem Bericht dieser Werkstatt und dem Nachdenken von KATA，sonst (1) ist der Teil von Ishida

AI-Driven Synthetic Route Design Incorporated with Retrosynthesis Knowledge

Computer-aided synthesis planning (CASP) aims to assist chemists in performing retrosynthetic analysis for which they utilize their experiments, intuition, and knowledge. Recent breakthroughs in machine learning (ML) techniques, including deep neural networks, have significantly improved data-driven synthetic route designs without human intervention. However, learning chemical knowledge by ML for practical synthesis planning has not yet been adequately achieved and remains a challenging problem. In this study, we developed a data-driven CASP application integrated with various portions of retrosynthesis knowledge called “ReTReK” that introduces the knowledge as adjustable parameters into the evaluation of promising search directions. The experimental results showed that ReTReK successfully searched synthetic routes based on the specified retrosynthesis knowledge, indicating that the synthetic routes searched with the knowledge were preferred to those without the knowledge. The concept of integrating retrosynthesis knowledge as adjustable parameters into a data-driven CASP application is expected to enhance the performance of both existing data-driven CASP applications and those under development

キョウイク ニオケル タシャ ナル シンタイ : ベルリン ジユウ ダイガク デノ コクサイ ワーク ショップ ニホン ノ シンタイ ブンカ カタ ト ソノ ブンカ オウダンテキ デンタツ ホウコク ロンブン 2

Am 22. September 2008 führten Hirota und Ishida die internationale Werkstatt "Japanische Kultur -durch 'KATA'(Form/Muster) erleben" in der Freien Universität Berlin durch. An diesem Tag gab es ungefähr 20 Teilnehmer: Professor Ch. Wulf und die "Historische Anthropologie"Studenten/innen，die einige Übungen erfuhren. In unserer Werkstatt haben sie erst unter Leitung von Ishida die Schwertkunst，Stockkunst und Aikido erfahren. Dann hat Hirota ihnen einigen Bewegungsübung als japanische kulturelle Essenz angeboten. Dieser Artikel besteht aus einem Bericht und Nachdenken dieser Werkstatt，sonst (2) ist der Teil von Hirota. Weiterhin werden dem Artikel der Eindruck und eine Überlegung zu der Werkstatt von Staudacher，die eine Teilnehmerin dort war，beigefügt

Synthesis of Dinaphtho[2,3-d:2',3'-d']anthra[1,2-b:5,6-b']dithiophene (DNADT) Derivatives: Effect of Alkyl Chains on Transistor Properties

To investigate organic field-effect transistor (OFET) properties, a new thienoacene-type molecule, 4,14-dihexyldinaphtho[2,3-d:2',3'-d']anthra[1,2-b:5,6-b']dithiophene (C6-DNADT), consisting of pi-conjugated nine aromatic rings and two hexyl chains along the longitudinal molecular axis has been successfully synthesized by sequential reactions, including Negishi coupling, epoxidation, and cycloaromatization. The fabricated OFET using thin films of C6-DNADT exhibited p-channel FET properties with field-effect mobilities (mu) of up to 2.6 x 10(-2) cm(2) V-1 s(-1), which is ca. three times lower than that of the parent DNADT molecule (8.5 x 10(-2) cm(2) V-1 s(-1)). Although this result implies that the installation of relatively short alkyl chains into the DNADT core is not suitable for transistor application, the origins for the FET performance obtained in this work is fully discussed, based on theoretical calculations and solid-state structure of C6-DNADT by grazing incidence wide-angle X-ray scattering (GIWAXS) and atomic force microscopy (AFM) analyses. The results obtained in this study disclose the effect of alkyl chains introduced onto the molecule on transistor characteristics

kGCN: a graph-based deep learning framework for chemical structures

Deep learning is developing as an important technology to perform various tasks in cheminformatics. In particular, graph convolutional neural networks (GCNs) have been reported to perform well in many types of prediction tasks related to molecules. Although GCN exhibits considerable potential in various applications, appropriate utilization of this resource for obtaining reasonable and reliable prediction results requires thorough understanding of GCN and programming. To leverage the power of GCN to benefit various users from chemists to cheminformaticians, an open-source GCN tool, kGCN, is introduced. To support the users with various levels of programming skills, kGCN includes three interfaces: a graphical user interface (GUI) employing KNIME for users with limited programming skills such as chemists, as well as command-line and Python library interfaces for users with advanced programming skills such as cheminformaticians. To support the three steps required for building a prediction model, i.e., pre-processing, model tuning, and interpretation of results, kGCN includes functions of typical pre-processing, Bayesian optimization for automatic model tuning, and visualization of the atomic contribution to prediction for interpretation of results. kGCN supports three types of approaches, single-task, multi-task, and multi-modal predictions. The prediction of compound-protein interaction for four matrixmetalloproteases, MMP-3, -9, -12 and -13, in the inhibition assays is performed as a representative case study using kGCN. Additionally, kGCN provides the visualization of atomic contributions to the prediction. Such visualization is useful for the validation of the prediction models and the design of molecules based on the prediction model, realizing “explainable AI” for understanding the factors affecting AI prediction. kGCN is available at https://github.com/clinfo