Ruxolitinib binding to human serum albumin: bioinformatics, biochemical and functional characterization in JAK2V617F+ cell models

A novel approach to study multi-domain motions in JAK1's activation mechanism based on energy landscape

The family of Janus Kinases (JAKs) associated with the JAK-signal transducers and activators of transcription signaling pathway plays a vital role in the regulation of various cellular processes. The conformational change of JAKs is the fundamental steps for activation, affecting multiple intracellular signaling pathways. However, the transitional process from inactive to active kinase is still a mystery. This study is aimed at investigating the electrostatic properties and transitional states of JAK1 to a fully activation to a catalytically active enzyme. To achieve this goal, structures of the inhibited/activated full-length JAK1 were modelled and the energies of JAK1 with Tyrosine Kinase (TK) domain at different positions were calculated, and Dijkstra's method was applied to find the energetically smoothest path. Through a comparison of the energetically smoothest paths of kinase inactivating P733L and S703I mutations, an evaluation of the reasons why these mutations lead to negative or positive regulation of JAK1 are provided. Our energy analysis suggests that activation of JAK1 is thermodynamically spontaneous, with the inhibition resulting from an energy barrier at the initial steps of activation, specifically the release of the TK domain from the inhibited Four-point-one, Ezrin, Radixin, Moesin-PK cavity. Overall, this work provides insights into the potential pathway for TK translocation and the activation mechanism of JAK1.

Serum albumin and nucleic acids biodistribution: from molecular aspects to biotechnological applications

Serum albumin (SA) is the most abundant protein in plasma and represents the main carrier of endogenous and exogenous compounds. Several evidence supports the notion that SA binds single and double stranded deoxy- and ribonucleotides at two sites, with values of the dissociation equilibrium constant (i.e., K_d ) ranging from micromolar to nanomolar values. This can be relevant from a physiological and pathological point of view as in human plasma circulate cell-free nucleic acids (cfNAs), which are single and double stranded NAs released by different tissues via apoptosis, necrosis, and secretions. Albeit SA shows low hydrolytic reactivity toward DNA and RNA, the high plasma concentration of this protein and the occurrence of several SA receptors may be pivotal for sequestering and hydrolyzing cfNAs. Therefore, pathological conditions like cancer, characterized by altered levels of human SA or by altered SA post-translational modifications, may influence cfNAs distribution and metabolism. Besides, the stability, solubility, biocompatibility, and low immunogenicity make SA a golden share for biotechnological applications related to the delivery of therapeutic NAs (TNAs). Indeed, pre-clinical studies report the therapeutic potential of SA:TNAs complexes in precision cancer therapy. Here, the molecular and biotechnological implications of SA:NAs interaction are discussed, highlighting new perspectives into SA plasmatic functions. This article is protected by copyright. All rights reserved.

Assessment of Skin Toxicity in an in Vitro Reconstituted Human Epidermis Model Using Deep Learning

Skin toxicity is a common safety concern associated with drugs that inhibit epidermal growth factor receptors as well as other targets involved in epidermal growth and differentiation. Recently, the use of a three-dimensional reconstructed human epidermis model enabled large-scale drug screening and showed potential for predicting skin toxicity. Although a decrease in epidermal thickness was often observed when the three-dimensional reconstructed tissues were exposed to drugs causing skin toxicity, the thickness evaluation of epidermal layers from a pathologist was subjective and not easily reproducible or scalable. In addition, the subtle differences in thickness among tissues, as well as the large number of samples tested, made cross-study comparison difficult when a manual evaluation strategy was used. The current study used deep learning and image-processing algorithms to measure the viable epidermal thickness from multiple studies and found that the measured thickness was not only significantly correlated with a pathologist's semi-quantitative evaluation but was also in close agreement with the quantitative measurement performed by pathologists. Moreover, a sensitivity of 0.8 and a specificity of 0.75 were achieved when predicting the toxicity of 18 compounds with clinical observations with these epidermal thickness algorithms. This approach is fully automated, reproducible, and highly scalable. It not only shows reasonable accuracy in predicting skin toxicity but also enables cross-study comparison and high-throughput compound screening.

Studying metabolism with multi-organ chips: new tools for disease modelling, pharmacokinetics and pharmacodynamics

Non-clinical models to study metabolism including animal models and cell assays are often limited in terms of species translatability and predictability of human biology. This field urgently requires a push towards more physiologically accurate recapitulations of drug interactions and disease progression in the body. Organ-on-chip systems, specifically multi-organ chips (MOCs), are an emerging technology that is well suited to providing a species-specific platform to study the various types of metabolism (glucose, lipid, protein and drug) by recreating organ-level function. This review provides a resource for scientists aiming to study human metabolism by providing an overview of MOCs recapitulating aspects of metabolism, by addressing the technical aspects of MOC development and by providing guidelines for correlation with in silico models. The current state and challenges are presented for two application areas: (i) disease modelling and (ii) pharmacokinetics/pharmacodynamics. Additionally, the guidelines to integrate the MOC data into in silico models could strengthen the predictive power of the technology. Finally, the translational aspects of metabolizing MOCs are addressed, including adoption for personalized medicine and prospects for the clinic. Predictive MOCs could enable a significantly reduced dependence on animal models and open doors towards economical non-clinical testing and understanding of disease mechanisms.

Synthesis and In Silico Docking Studies of Ethyl 2-(2-Arylidene-1-Alkylhydrazinyl)Thiazole-4-Carboxylates as Antiglycating agents

Ethyl 2-(2-arylidene-1-alkylhydrazinyl)thiazole-4-carboxylates ( 1a-k ) were synthesized by alkylation on HN- of ethyl 2-(2-arylidenehydrazinyl)thiazole-4-carboxylates. The proposed structures ( 1a-k ) are corroborated by spectro-analytical techniques like UV, FT-IR, 1 H-, 13 C-NMR and HRMS. The compounds ( 1a-k ) were screened for their antiglycation and antioxidant assays. The in vitro antiglycation results revealed promising activity of compounds 1a , 1b , 1d , 1e , 1f , 1g , 1j and 1k with IC 50 values 0.0004 ± 1.097-17.22 ± 0.538 µM/mL when compared to standard, aminoguanidine (IC 50 = 25.50 ± 0.337 µM/mL). Among all tested compounds 1j and 1k are the best antiglycating agents with IC 50 values 1.848 ± 0.646 and 0.0004 ± 1.097 µM/mL, respectively. The in-silico studies also agree with these results where binding energy for 1j and 1k was found to be -9.25 and -8.42 kcal/mol with calculated dissociation constants of 0.16 and 0.67 µM. The antiglycation results demonstrate the application of these compounds in reducing diabetic complications.