Abstract: Abnormal activation of microglia promotes neuroinflammation (NI) in Alzheimer's disease (AD). Callicarpa nudiflora Hook et Arn. (CN) is a traditional Chinese herb with a wide range of clinical applications and definite anti-inflammatory effects. However, the anti-inflammatory action and mechanism of NI are not known. The purpose of this research was to survey whether CN could inhibit lipopolysaccharide (LPS)-induced inflammatory activation in BV-2 microglia. This study used a network pharmacology and pharmacophore model-based approach to explore the molecular mechanism of CN anti-NI by combining molecular docking and experimental validation. First, we screened the key active components and targets of CN anti-NI by network pharmacology. Then, the common structural features of these functional molecules in the treatment of neuroinflammation were predicted by 3D-QSAR pharmacodynamic modeling. Finally, the molecular mechanism of the active ingredient 5-hydroxy-3,7,4'-trimethoxyflavone (THF) against neuroinflammation was validated by molecular docking and in vitro experiments. In conclusion, this study established the structure-activity relationships of the active components of CN anti-NI and provided new insights into the pharmacological mechanisms of CN anti-NI at an integrative level.

Abstract: DrugTax is an easy-to-use Python package for small molecule detailed characterization. It extends a previously explored chemical taxonomy making it ready-to-use in any Artificial Intelligence approach. DrugTax leverages small molecule representations as input in one of their most accessible and simple forms (SMILES) and allows the simultaneously extraction of taxonomy information and key features for big data algorithm deployment. In addition, it delivers a set of tools for bulk analysis and visualization that can also be used for chemical space representation and molecule similarity assessment. DrugTax is a valuable tool for chemoinformatic processing and can be easily integrated in drug discovery pipelines. DrugTax can be effortlessly installed via PyPI ( https://pypi.org/project/DrugTax/ ) or GitHub ( https://github.com/MoreiraLAB/DrugTax ).

Abstract: Fourteen compounds were isolated from Pueraria lobata (Willd.) Ohwi by column chromatography and preparative thin-layer chromatography; the structures were identified by spectroscopic analysis and compared with data reported in the literature. Seven compounds were isolated and identified from Pueraria lobata for the first time: Linoleic acid, Sandwicensin, Isovanillin, Ethyl ferulate, Haginin A, Isopterofuran, 3'.7-Dihydroxyisoflavan. The other 10 compounds were structurally identified as follows: Lupenone, Lupeol, beta-sitosterol, Genistein, Medicarpin, Coniferyl Aldehyde, Syringaldehyde. All compounds were evaluated for their ability to inhibit SW480 and SW620 cells using the CCK-8 method; compound 5 (Sandwicensin) had the best activity, and compounds 6, 9, 11 and 12 exhibited moderate inhibitory activity. In addition, the targets and signaling pathways of Sandwicensin treatment for CRC were mined using network pharmacology, and MAPK3, MTOR, CCND1 and CDK4 were found to be closely associated with Sandwicensin treatment for CRC; the GO and KEGG analysis showed that Sandwicensin may directly regulate the cycle, proliferation and apoptosis of CRC cells through cancer-related pathways.

Abstract: We present the Pharmacorank search tool as an objective means to obtain prioritized protein drug targets and their associated medications according to user-selected diseases. This tool could be used to obtain prioritized protein targets for the creation of novel medications or to predict novel indications for medications that already exist. To prioritize the proteins associated with each disease, a gene similarity profiling method based on protein functions is implemented. The priority scores of the proteins are found to correlate well with the likelihoods that the associated medications are clinically relevant in the disease's treatment. When the protein priority scores are plotted against the percentage of protein targets that are known to bind medications currently indicated to treat the disease, which we termed the pertinency score, a strong correlation was observed. The correlation coefficient was found to be 0.9978 when using a weighted second-order polynomial fit. As the highly predictive fit was made using a broad range of diseases, we were able to identify a general threshold for the pertinency score as a starting point for considering drug repositioning candidates. Several repositioning candidates are described for proteins that have high predicated pertinency scores, and these provide illustrative examples of the applications of the tool. We also describe focused reviews of repositioning candidates for Alzheimer's disease. Via the tool's URL, https://protein.som.geisinger.edu/Pharmacorank/, an open online interface is provided for interactive use; and there is a site for programmatic access.

Abstract: One of the strategies for the treatment of advanced cancer diseases is targeting the energy metabolism of the cancer cells. The compound 2,4-DNP (2,4-dinitrophenol) disrupts the cell energy metabolism through the ability to decouple oxidative phosphorylation. The aim of the study was to determine the ability of 2,4-DNP to sensitize prostate cancer cells with different metabolic phenotypes to the action of known anthracyclines (doxorubicin and epirubicin). The synergistic effect of the anthracyclines and 2,4-DNP was determined using an MTT assay, apoptosis detection and a cell cycle analysis. The present of oxidative stress in cancer cells was assessed by CellROX, the level of cellular thiols and DNA oxidative damage. The study revealed that the incubation of LNCaP prostate cancer cells (oxidative phenotype) with epirubicin and doxorubicin simultaneously with 2,4-DNP showed the presence of a synergistic effect for both the cytostatics. Moreover, it contributes to the increased induction of oxidative stress, which results in a reduced level of cellular thiols and an increased number of AP sites in the DNA. The synergistic activity may consist of an inhibition of ATP synthesis and the simultaneous production of toxic amounts of ROS, destroying the mitochondria. Additionally, the sensitivity of the LNCaP cell line to the anthracyclines is relatively higher compared to the other two (PC-3, DU-145).

Abstract: Transmissible and infectious viruses can cause large-scale epidemics around the world. This is because the virus can constantly mutate and produce different variants and subvariants to counter existing treatments. Therefore, a variety of treatments are urgently needed to keep up with the mutation of the viruses. To facilitate the research of such treatment, we updated our Virus-CKB 1.0 to Virus-CKB 2.0, which contains 10 kinds of viruses, including enterovirus, dengue virus, hepatitis C virus, Zika virus, herpes simplex virus, Andes orthohantavirus, human immunodeficiency virus, Ebola virus, Lassa virus, influenza virus, coronavirus, and norovirus. To date, Virus-CKB 2.0 archived at least 65 antiviral drugs (such as remdesivir, telaprevir, acyclovir, boceprevir, and nelfinavir) in the market, 178 viral-related targets with 292 available 3D crystal or cryo-EM structures, and 3766 chemical agents reported for these target proteins. Virus-CKB 2.0 is integrated with established tools for target prediction and result visualization; these include HTDocking, TargetHunter, blood-brain barrier (BBB) predictor, Spider Plot, etc. The Virus-CKB 2.0 server is accessible at https://www.cbligand.org/g/virus-ckb. By using the established chemogenomic tools and algorithms and newly developed tools, we can screen FDA-approved drugs and chemical compounds that may bind to these proteins involved in viral-associated disease regulation. If the virus strain mutates and the vaccine loses its effect, we can still screen drugs that can be used to treat the mutated virus in a fleeting time. In some cases, we can even repurpose FDA-approved drugs through Virus-CKB 2.0.

Abstract: Despite the growing number of G protein-coupled receptor (GPCR) structures, only 39 structures have been cocrystallized with allosteric inhibitors. These structures have been studied by protein mapping using the FTMap server, which determines the clustering of small organic probe molecules distributed on the protein surface. The method has found druggable sites overlapping with the cocrystallized allosteric ligands in 21 GPCR structures. Mapping of Alphafold2 generated models of these proteins confirms that the same sites can be identified without the presence of bound ligands. We then mapped the 394 GPCR X-ray structures available at the time of the analysis (September 2020). Results show that for each of the 21 structures with bound ligands there exist many other GPCRs that have a strong binding hot spot at the same location, suggesting potential allosteric sites in a large variety of GPCRs. These sites cluster at nine distinct locations, and each can be found in many different proteins. However, ligands binding at the same location generally show little or no similarity, and the amino acid residues interacting with these ligands also differ. Results confirm the possibility of specifically targeting these sites across GPCRs for allosteric modulation and help to identify the most likely binding sites among the limited number of potential locations. The FTMap server is available free of charge for academic and governmental use at https://ftmap.bu.edu/.

Abstract: Tsetse flies are a type of blood-sucking insect living in diverse locations in sub-Saharan Africa. These insects can transmit the unicellular parasite Trypanosoma brucei (T. brucei) which causes African trypanosomiasis in mammals. There remain huge unmet needs for prevention, early detection, and effective treatments for this disease. Currently, few studies have investigated the molecular mechanisms of parasite-host interactions underlying African trypanosomiasis, mainly due to a lack of understanding of the T. brucei genome. In this study, we dissected the genomic and transcriptomic profiles of T. brucei by annotating the genome and analyzing the gene expression. We found about 5% of T. brucei proteins in the human proteome, while more than 80% of T. brucei protein in other trypanosomes. Sequence alignment analysis showed that 142 protein homologs were shared among T. brucei and mammalian genomes. We identified several novel proteins with pathogenic potential supported by their molecular functions in T. brucei, including 24 RNA-binding proteins and six variant surface glycoproteins. In addition, 26 novel microRNAs were characterized, among which five miRNAs were not found in the mammalian genomes. Topology analysis of the miRNA-gene network revealed three genes (RPS27A, UBA52 and GAPDH) involved in the regulation of critical pathways related to the development of African trypanosomiasis. In conclusion, our work opens a new door to understanding the parasite-host interaction mechanisms by resolving the genome and transcriptome of T. brucei.

Abstract: Artificial Intelligence (AI)-based formulation development is a promising approach for facilitating the drug product development process. AI is a versatile tool that contains multiple algorithms that can be applied in various circumstances. Solid dosage forms, represented by tablets, capsules, powder, granules, etc., are among the most widely used administration methods. During the product development process, multiple factors including critical material attributes (CMAs) and processing parameters can affect product properties, such as dissolution rates, physical and chemical stabilities, particle size distribution, and the aerosol performance of the dry powder. However, the conventional trial-and-error approach for product development is inefficient, laborious, and time-consuming. AI has been recently recognized as an emerging and cutting-edge tool for pharmaceutical formulation development which has gained much attention. This review provides the following insights: (1) a general introduction of AI in the pharmaceutical sciences and principal guidance from the regulatory agencies, (2) approaches to generating a database for solid dosage formulations, (3) insight on data preparation and processing, (4) a brief introduction to and comparisons of AI algorithms, and (5) information on applications and case studies of AI as applied to solid dosage forms. In addition, the powerful technique known as deep learning-based image analytics will be discussed along with its pharmaceutical applications. By applying emerging AI technology, scientists and researchers can better understand and predict the properties of drug formulations to facilitate more efficient drug product development processes.

Abstract: G protein-coupled receptors (GPCRs) are amongst the most pharmaceutically relevant and well-studied protein targets, yet unanswered questions in the field leave significant gaps in our understanding of their nuanced structure and function. Three-dimensional pharmacophore models are powerful computational tools in in silico drug discovery, presenting myriad opportunities for the integration of GPCR structural biology and cheminformatics. This review highlights success stories in the application of 3D pharmacophore modeling to de novo drug design, the discovery of biased and allosteric ligands, scaffold hopping, QSAR analysis, hit-to-lead optimization, GPCR de-orphanization, mechanistic understanding of GPCR pharmacology and the elucidation of ligand-receptor interactions. Furthermore, advances in the incorporation of dynamics and machine learning are highlighted. The review will analyze challenges in the field of GPCR drug discovery, detailing how 3D pharmacophore modeling can be used to address them. Finally, we will present opportunities afforded by 3D pharmacophore modeling in the advancement of our understanding and targeting of GPCRs.