Abstract: Introduction: Numerous drugs with potent toxicity against cancer cells are available for treating malignancies, but therapeutic efficacies are limited due to their inefficient tumor targeting and deleterious effects on non-cancerous tissue. Therefore, two improvements are mandatory for improved chemotherapy 1) novel delivery techniques that can target cancer cells to deliver anticancer drugs and 2) methods to specifically enhance drug efficacy within tumors. The loading of inert drug carriers with anticancer agents and peptides which are able to bind (target) tumor-related proteins to enhance tumor drug accumulation and local cytotoxicity is a most promising approach. Objective: To evaluate the anticancer efficacy of Chitosan nanoparticles loaded with human growth hormone hGH fragment 176-191 peptide plus the clinical chemotherapeutic doxorubicin in comparison with Chitosan loaded with doxorubicin alone. Methods: Two sets of in silico experiments were performed using molecular docking simulations to determine the influence of hGH fragment 176-191 peptide on the anticancer efficacy of doxorubicin 1) the binding affinities of hGH fragment 176-191 peptide to the breast cancer receptors, 2) the effects of hGH fragment 176-191 peptide binding on doxorubicin binding to these same receptors. Further, the influence of hGH fragment 176-191 peptide on the anticancer efficacy of doxorubicin was validated using viability assay in Human MCF-7 breast cancer cells. Results: In silico analysis suggested that addition of the hGH fragment to doxorubicin-loaded Chitosan nanoparticles can enhance doxorubicin binding to multiple breast cancer protein targets, while photon correlation spectroscopy revealed that the synthesized dual-loaded Chitosan nanoparticles possess clinically favorable particle size, polydispersity index, as well as zeta potential. Conclusion: These dual-loaded Chitosan nanoparticles demonstrated greater anti-proliferative activity against a breast cancer cell line (MCF-7) than doxorubicin-loaded Chitosan. This dual-loading strategy may enhance the anticancer potency of doxorubicin and reduce the clinical side effects associated with non-target tissue exposure.

Abstract: Taohong Siwu Decoction (TSD), a classical gynecological prescription that was firstly reported 600 years ago, has been widely used in the adjuvant treatment of breast cancer (BRCA) in China. However, the mechanism of action of TSD in treating BRCA has remained unclear. Here, high-throughput sequencing-based high-throughput screening (HTS(2)) technology was used to reveal the molecular mechanism of TSD, combination with bioinformatics and systems pharmacology in this study. Firstly, our results showed that TSD exerts an anticancer effect on BRCA cells by inhibiting cell proliferation, migration and inducing apoptosis as well as cell-cycle arrest. And our results from HTS(2) suggested that herbs of TSD could significantly inhibit KRAS pathway and pathway in cancer, and activate apoptosis pathway, p53 pathway and hypoxia pathway, which may lead to the anticancer function of TSD. Further, we found that TSD clearly regulates MYC, BIRC5, EGF, and PIK3R1 genes, which play an important role in the development and progression of tumor and have significant correlation with overall survival in BRCA patients. By molecular docking, we discovered that Pentagalloylglucose, a compound derived from TSD, might directly bind to and inhibit the function of BRD4, which is a reported transcriptional activator of MYC gene, and thus repress the expression of MYC. Taken together, this study explores the mechanism of TSD in anti-BRCA by combining HTS(2) technology, bioinformatics analysis and systems pharmacology.

Abstract: Background: Diabetic nephropathy (DN) represents a major complication of diabetes, and podocyte injury has a critical function in DN development. TangShenWeiNing formula (TSWN) has been demonstrated to efficiently decrease proteinuria and protect podocytes in DN. This work aimed to explore the mechanism by which TSWN alleviates DN and protects podocytes. Methods: The major bioactive components of TSWN were detected by mass spectrometry (MS) and pharmacological databases. Eight-week-old male C57BLKS/J db/m and db/db mice were provided pure water, valsartan, low dose TSWN, middle dose TSWN and high dose TSWN by gavage for 12 weeks, respectively. Results: MS and network pharmacology analyses suggested that TSWN might prevent DN through the sirtuin (SIRT)1/hypoxia-inducible factor (HIF)-1alpha pathway. Diabetic mice showed elevated urinary albumin in comparison with non-diabetic mice, and TSWN decreased urinary albumin in diabetic mice. Histological injury increased in the kidney in diabetic mice, which could be improved by TSWN. Fibrosis and collagen I expression were induced in the diabetic mouse kidney in comparison with the non-diabetic mouse kidney; TSWN alleviated these effects. Apoptosis and cleaved caspase-3 were induced in the diabetic mouse kidney in comparison with the non-diabetic mouse kidney, and TSWN blunted these effects. Podocytes were damaged in the diabetic mouse kidney, which was improved by TSWN. Podocin and nephrin amounts were decreased in the diabetic mouse kidney in comparison with the non-diabetic mouse kidney, and podocalyxin was increased in urine of diabetic animals in comparison with non-diabetic counterparts. After TSWN treatment, podocin and nephrin were raised in the diabetic mouse kidney, and urinary podocalyxin was depressed in diabetic animals. Diabetic mice had lower SIRT1 and higher HIF-1alpha amounts in kidney specimens in comparison with non-diabetic mice, and TSWN promoted SIRT1 and inhibited HIF-1alpha in the diabetic mouse kidney. Moreover, co-staining of SIRT1 and podocin revealed that SIRT1 decreased in podocytes from diabetic mice in comparison with those from non-diabetic mice, and TSWN elevated SIRT1 in podocytes. Conclusions: This study indicated that TSWN alleviates DN by improving podocyte injury through the SIRT1/HIF-1alpha pathway in diabetic mouse kidneys.

Abstract: Computational drug repositioning aims to identify potential applications of existing drugs for the treatment of diseases for which they were not designed. This approach can considerably accelerate the traditional drug discovery process by decreasing the required time and costs of drug development. Tensor decomposition enables us to integrate multiple drug- and disease-related data to boost the performance of prediction. In this study, a nonnegative tensor decomposition for drug repositioning, NTD-DR, is proposed. In order to capture the hidden information in drug-target, drug-disease, and target-disease networks, NTD-DR uses these pairwise associations to construct a three-dimensional tensor representing drug-target-disease triplet associations and integrates them with similarity information of drugs, targets, and disease to make a prediction. We compare NTD-DR with recent state-of-the-art methods in terms of the area under the receiver operating characteristic (ROC) curve (AUC) and the area under the precision and recall curve (AUPR) and find that our method outperforms competing methods. Moreover, case studies with five diseases also confirm the reliability of predictions made by NTD-DR. Our proposed method identifies more known associations among the top 50 predictions than other methods. In addition, novel associations identified by NTD-DR are validated by literature analyses.

Abstract: Acute myeloid leukemia (AML) is one of the deadly cancers. Chemotherapy is the first-line treatment and the only curative intervention is stem cell transplantation which are intolerable for aged and comorbid patients. Therefore, finding complementary treatment is still an active research area. For this, empirical knowledge driven search for therapeutic agents have been carried out by long and arduous wet lab processes. Nonetheless, currently there is an accumulated bioinformatics data about natural products that enabled the use of efficient and cost effective in silico methods to find drug candidates. In this work, therefore, we set out to computationally investigate the phytochemicals from Brucea antidysentrica to identify therapeutic phytochemicals for AML. We performed in silico molecular docking of compounds against AML receptors IDH2, MCL1, FLT3 and BCL2. Phytochemicals were docked to AML receptors at the same site where small molecule drugs were bound and their binding affinities were examined. In addition, random compounds from PubChem were docked with AML targets and their docking score was compared with that of phytochemicals using statistical analysis. Then, non-covalent interactions between phytochemicals and receptors were identified and visualized using discovery studio and Protein-Ligand Interaction Profiler web tool (PLIP). From the statistical analysis, most of the phytochemicals exhibited significantly lower (p-value = 0.05) binding energies compared with random compounds. Using cutoff binding energy of less than or equal to one standard deviation from the mean of the phytochemicals' binding energies for each receptor, 12 phytochemicals showed considerable binding affinity. Especially, hydnocarpin (-8.9 kcal/mol) and yadanzioside P (-9.4 kcal/mol) exhibited lower binding energy than approved drugs AMG176 (-8.6 kcal/mol) and gilteritinib (-9.1 kcal/mol) to receptors MCL1 and FLT3 respectively, indicating their potential to be lead molecules. In addition, most of the phytochemicals possessed acceptable drug-likeness and absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties. Based on the binding affinities as exhibited by the molecular docking studies supported by the statistical analysis, 12 phytochemicals from Brucea antidysentrica (1,11-dimethoxycanthin-6-one, 1-methoxycanthin-6-one, 2-methoxycanthin-6-one, beta-carboline-1-propionic acid, bruceanol A, bruceanol D, bruceanol F, bruceantarin, bruceantin, canthin-6-one, hydnocarpin, and yadanzioside P) can be considered as candidate compounds to prevent and manage AML. However, the phytochemicals should be further studied using in vivo & in vitro experiments on AML models. Therefore, this study concludes that combination of empirical knowledge, in silico molecular docking and ADMET profiling is useful to find natural product-based drug candidates. This technique can be applied to other natural products with known empirical efficacy.

Abstract: Alzheimer's disease (AD), the most common neurodegenerative disease, remains unclear in terms of its underlying causative genes and effective therapeutic approaches. Meanwhile, abnormalities in iron metabolism have been demonstrated in patients and mouse models with AD. Therefore, this study sought to find hub genes based on iron metabolism that can influence the diagnosis and treatment of AD. First, gene expression profiles were downloaded from the GEO database, including non-demented (ND) controls and AD samples. Fourteen iron metabolism-related gene sets were downloaded from the MSigDB database, yielding 520 iron metabolism-related genes. The final nine hub genes associated with iron metabolism and AD were obtained by differential analysis and WGCNA in brain tissue samples from GSE132903. GO analysis revealed that these genes were mainly involved in two major biological processes, autophagy and iron metabolism. Through stepwise regression and logistic regression analyses, we selected four of these genes to construct a diagnostic model of AD. The model was validated in blood samples from GSE63061 and GSE85426, and the AUC values showed that the model had a relatively good diagnostic performance. In addition, the immune cell infiltration of the samples and the correlation of different immune factors with these hub genes were further explored. The results suggested that these genes may also play an important role in immunity to AD. Finally, eight drugs targeting these nine hub genes were retrieved from the DrugBank database, some of which were shown to be useful for the treatment of AD or other concomitant conditions, such as insomnia and agitation. In conclusion, this model is expected to guide the diagnosis of patients with AD by detecting the expression of several genes in the blood. These hub genes may also assist in understanding the development and drug treatment of AD.

Abstract: Metal-dependent histone deacetylases (HDACs) are essential epigenetic regulators; their molecular and pharmacological roles in medically critical diseases such as neuropsychiatric disorders, neurodegeneration, and cancer are being studied globally. HDAC2's differential expression in the central nervous system makes it an appealing therapeutic target for chronic neurological diseases like autism spectrum disorder. In this study, we identified H3R inhibitor molecules that are computationally effective at binding to the HDAC2 metal-coordinated binding site. The study highlights the importance of pitolisant in screening the potential H3R inhibitors by using a hybrid workflow of ligand and receptor-based drug discovery. The screened lead compounds with PubChem SIDs 103179850, 103185945, and 103362074 show viable binding with HDAC2 in silico. The importance of ligand contacts with the Zn2+ ion in the HDAC2 catalytic site is also discussed and investigated for a significant role in enzyme inhibition. The proposed H3R inhibitors 103179850, 103185945, and 103362074 are estimated as dual-active molecules to block the HDAC2-mediated deacetylation of the EAAT2 gene (SLC1A2) and H3R-mediated synaptic transmission irregularity and are, therefore, open for experimental validation.

Abstract: India confines more than 17% of the world's population and has a diverse genetic makeup with several clinically relevant rare mutations belonging to many sub-group which are undervalued in global sequencing datasets like the 1000 Genome data (1KG) containing limited samples for Indian ethnicity. Such databases are critical for the pharmaceutical and drug development industry where diversity plays a crucial role in identifying genetic disposition towards adverse drug reactions. A qualitative and comparative sequence and structural study utilizing variant information present in the recently published, largest curated Indian genome database (IndiGen) and the 1000 Genome data was performed for variants belonging to the kinase coding genes, the second most targeted group of drug targets. The sequence-level analysis identified similarities and differences among different populations based on the nsSNVs and amino acid exchange frequencies whereas a comparative structural analysis of IndiGen variants was performed with pathogenic variants reported in UniProtKB Humsavar data. The influence of these variations on structural features of the protein, such as structural stability, solvent accessibility, hydrophobicity, and the hydrogen-bond network was investigated. In-silico screening of the known drugs to these Indian variation-containing proteins reveals critical differences imparted in the strength of binding due to the variations present in the Indian population. In conclusion, this study constitutes a comprehensive investigation into the understanding of common variations present in the second largest population in the world and investigating its implications in the sequence, structural and pharmacogenomic landscape. The preliminary investigation reported in this paper, supporting the screening and detection of ADRs specific to the Indian population could aid in the development of techniques for pre-clinical and post-market screening of drug-related adverse events in the Indian population.

Abstract: Drug repurposing is the process of discovering new indications (i.e., diseases or conditions) for already approved drugs. Many computational methods have been proposed for predicting new associations between drugs and diseases. In this article, we proposed a new method, called DR-HGNN, an integrative heterogeneous graph neural network-based method for multi-labeled drug repurposing, to discover new indications for existing drugs. For this purpose, we first used the DTINet dataset to construct a heterogeneous drug-protein-disease (DPD) network, which is a graph composed of four types of nodes (drugs, proteins, diseases, and drug side effects) and eight types of edges. Second, we labeled each drug-protein edge, dp i,j = (d i , p j ), of the DPD network with a set of diseases, {delta i,j,1, ... , delta i,j,k } associated with both d i and p j and then devised multi-label ranking approaches which incorporate neural network architecture that operates on the heterogeneous graph-structured data and which leverages both the interaction patterns and the features of drug and protein nodes. We used a derivative of the GraphSAGE algorithm, HinSAGE, on the heterogeneous DPD network to learn low-dimensional vector representation of features of drugs and proteins. Finally, we used the drug-protein network to learn the embeddings of the drug-protein edges and then predict the disease labels that act as bridges between drugs and proteins. The proposed method shows better results than existing methods applied to the DTINet dataset, with an AUC of 0.964.

Abstract: Of the patients infected with coronavirus disease 2019 (COVID-19), approximately 14-53% developed liver injury resulting in poor outcomes. Drug-induced liver injury (DILI) is the primary cause of liver injury in COVID-19 patients. In this study, we elucidated liver injury mechanism induced by drugs of pharmacologic treatments against SARS-CoV-2 (DPTS) using bioinformatics and systems biology. Totally, 1209 genes directly related to 216 DPTS (DPTSGs) were genes encoding pharmacokinetics and therapeutic targets of DPTS and enriched in the pathways related to drug metabolism of CYP450s, pregnane X receptor (PXR), and COVID-19 adverse outcome. A network, constructed by 110 candidate targets which were the shared part of DPTSGs and 445 DILI targets, identified 49 key targets and four Molecular Complex Detection clusters. Enrichment results revealed that the 4 clusters were related to inflammatory responses, CYP450s regulated by PXR, NRF2-regualted oxidative stress, and HLA-related adaptive immunity respectively. In cluster 1, IL6, IL1B, TNF, and CCL2 of the top ten key targets were enriched in COVID-19 adverse outcomes pathway, indicating the exacerbation of COVID-19 inflammation on DILI. PXR-CYP3A4 expression of cluster 2 caused DILI through inflammation-drug interaction and drug-drug interactions among pharmaco-immunomodulatory agents, including tocilizumab, glucocorticoids (dexamethasone, methylprednisolone, and hydrocortisone), and ritonavir. NRF2 of cluster 3 and HLA targets of cluster four promoted DILI, being related to ritonavir/glucocorticoids and clavulanate/vancomycin. This study showed the pivotal role of PXR associated with inflammation-drug and drug-drug interactions on DILI and highlighted the cautious clinical decision-making for pharmacotherapy to avoid DILI in the treatment of COVID-19 patients.