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tion.Mesenchymal stem cells (MSCs), a form of adult stem cells, are known to have a self-renewing property and the potential to specialize into a multitude of cells and tissues such as adipocytes, cartilage cells, and fibroblasts. MSCs can migrate and home to the desired target zone where inflammation is present. The unique characteristics of MSCs in repairing, differentiation, regeneration, and its high capacity of immune modulation has attracted tremendous attention for exerting them in clinical purposes, as they contribute to tissue regeneration process and anti-tumor activity. The MSCs-based treatment has demonstrated remarkable applicability towards various diseases such as heart and bone malignancies, and cancer cells. Importantly, genetically engineered MSCs, as a state-of-the-art therapeutic approach, could address some clinical hurdles by systemic secretion of cytokines and other agents with a short half-life and high toxicity. Therefore, understanding the biological aspects and the characteristics of MSCs is an imperative issue of concern. Herein, we provide an overview of the therapeutic application and the biological features of MSCs against different inflammatory diseases and cancer cells. We further shed light on MSCs physiological interaction, such as migration, homing, and tissue repairing mechanisms with different healthy and inflamed tissues.

Tuberculosis (TB) is an infectious disease caused by Mycobacterium tuberculosis, which still has high prevalence worldwide. In addition, cases of drug resistance are frequently observed. In the search for new anti-TB drugs, compounds with antimycobacterial activity have been developed, such as derivatives of pyrazinoic acid, which is the main pyrazinamide metabolite. In a previous study, the compounds were evaluated and showed moderate antimycobacterial activity and no important cytotoxic profile; however, information about their pharmacokinetic profile is lacking.

The aim of this work was to perform physicochemical, permeability, and metabolic properties of four pyrazinoic acid esters.

The compounds were analyzed for their chemical stability, n-octanolwater partition coefficient (logP) and apparent permeability (Papp) in monolayer of Caco-2 cells. The stability of the compounds in rat and human microsomes and in rat plasma was also evaluated.

The compounds I, II and IV were found to be hydrophilic, while compound III was the most lipophilic (logP 1.59) compound. All compounds showed stability at the three evaluated pHs (1.2, 7.4 and 8.8). The apparent permeability measured suggests good intestinal absorption of the compounds. Additionally, the compounds showed metabolic stability under action of human and rat microsomal enzymes and stability in rat plasma for at least 6 hours.

The results bring favorable perspectives for the future development of the evaluated compounds and other pyrazinoic acid derivatives.

The results bring favorable perspectives for the future development of the evaluated compounds and other pyrazinoic acid derivatives.Pregnant women are often excluded from routine clinical trials. Consequently, appropriate dosing regimens for majority of drugs are unknown in this population, which may lead to unexpected safety issue or insufficient efficacy in this un-studied population. Establishing evidence through the conduct of clinical studies in pregnancy is still a challenge. In recent decades, physiologically-based pharmacokinetic (PBPK) modeling has proven to be useful to support dose selection under various clinical scenarios, such as renal and/or liver impairment, drug-drug interactions, and extrapolation from adult to children. By integrating gestational-dependent physiological characteristics and drug-specific information, PBPK models can be used to predict PK during pregnancy. Population pharmacokinetic (PopPK) modeling approach also could complement pregnancy clinical studies by its ability to analyze sparse sampling data. read more In the past five years, PBPK and PopPK approaches for pregnancy have made significant progress. We reviewed recent progress, challenges and potential solutions for the application of PBPK, PopPK, and exposure-response analysis in clinical drug development for pregnancy.Drug repurposing, known also as drug repositioning/reprofiling, is a relatively new strategy for identification of alternative uses of well-known therapeutics that are outside the scope of their original medical indications. Such an approach might entail a number of advantages compared to standard de novo drug development, including less time needed to introduce the drug to the market, and lower costs. The group of compounds that could be considered as promising candidates for repurposing in oncology includes the central nervous system drugs, especially selected antidepressant and antipsychotic agents. In this article, we provide an overview of some antidepressants (citalopram, fluoxetine, paroxetine, sertraline) and antipsychotics (chlorpromazine, pimozide, thioridazine, trifluoperazine) that have the potential to be repurposed as novel chemotherapeutics in cancer treatment, as they have been found to exhibit preventive and/or therapeutic action in cancer patients. Nevertheless, although drug repurposing seems to be an attractive strategy to search for oncological drugs, we would like to clearly indicate that it should not replace the search for new lead structures, but only complement de novo drug development.Drug-target Interactions (DTIs) prediction plays a central role in drug discovery. Computational methods in DTIs prediction have gotten more attention because carrying out in vitro and in vivo experiments on a large scale is costly and time-consuming. Machine learning methods, especially deep learning, are widely applied to DTIs prediction. In this study, the main goal is to provide a comprehensive overview of deep learning-based DTIs prediction approaches. Here, we investigate the existing approaches from multiple perspectives. We explore these approaches to find out which deep network architectures are utilized to extract features from drug compound and protein sequences. Also, the advantages and limitations of each architecture are analyzed and compared. Moreover, we explore the process of how to combine descriptors for drug and protein features. Likewise, a list of datasets that are commonly used in DTIs prediction is investigated. Finally, current challenges are discussed and a short future outlook of deep learning in DTI prediction is given.