Abstract: BACKGROUND: The treatment of complex diseases by taking multiple drugs becomes increasingly popular. However, drug-drug interactions (DDIs) may give rise to the risk of unanticipated adverse effects and even unknown toxicity. DDI detection in the wet lab is expensive and time-consuming. Thus, it is highly desired to develop the computational methods for predicting DDIs. Generally, most of the existing computational methods predict DDIs by extracting the chemical and biological features of drugs from diverse drug-related properties, however some drug properties are costly to obtain and not available in many cases. RESULTS: In this work, we presented a novel method (namely DPDDI) to predict DDIs by extracting the network structure features of drugs from DDI network with graph convolution network (GCN), and the deep neural network (DNN) model as a predictor. GCN learns the low-dimensional feature representations of drugs by capturing the topological relationship of drugs in DDI network. DNN predictor concatenates the latent feature vectors of any two drugs as the feature vector of the corresponding drug pairs to train a DNN for predicting the potential drug-drug interactions. Experiment results show that, the newly proposed DPDDI method outperforms four other state-of-the-art methods; the GCN-derived latent features include more DDI information than other features derived from chemical, biological or anatomical properties of drugs; and the concatenation feature aggregation operator is better than two other feature aggregation operators (i.e., inner product and summation). The results in case studies confirm that DPDDI achieves reasonable performance in predicting new DDIs. CONCLUSION: We proposed an effective and robust method DPDDI to predict the potential DDIs by utilizing the DDI network information without considering the drug properties (i.e., drug chemical and biological properties). The method should also be useful in other DDI-related scenarios, such as the detection of unexpected side effects, and the guidance of drug combination.

Abstract: Since the emergence of SARS-CoV2, to date, no effective antiviral drug has been approved to treat the disease, and no vaccine against SARS-CoV2 is available. Under this scenario, the combination of two HIV-1 protease inhibitors, lopinavir and ritonavir, has attracted attention since they have been previously employed against the SARS-CoV main proteinase (M(pro)) and exhibited some signs of effectiveness. Recently, the 3D structure of SARS-CoV2 M(pro) was constructed based on the monomeric SARS-CoV M(pro) and employed to identify potential approved small inhibitors against SARS-CoV2 M(pro), allowing the selection of 15 drugs among 1903 approved drugs to be employed. In this study, we performed docking of these 15 approved drugs against the recently solved X-ray crystallography structure of SARS-CoV2 M(pro) in the monomeric and dimeric states; the latter is the functional state that was determined in a biological context, and these were submitted to molecular dynamics (MD) simulations coupled with the molecular mechanics generalized Born surface area (MM/GBSA) approach to obtain insight into the inhibitory activity of these compounds. Similar studies were performed with lopinavir and ritonavir coupled to monomeric and dimeric SARS-CoV M(pro) and SARS-CoV2 M(pro) to compare the inhibitory differences. Our study provides the structural and energetic basis of the inhibitory properties of lopinavir and ritonavir on SARS-CoV M(pro) and SARS-CoV2 M(pro), allowing us to identify two FDA-approved drugs that can be used against SARS-CoV2 M(pro). This study also demonstrated that drug discovery requires the dimeric state to obtain good results.

Abstract: Objective: Hepatocellular carcinoma (HCC) is frequently diagnosed in patients with late-stage disease who are ineligible for curative surgical therapies. The majority of patients become resistant to sorafenib, the only approved first-line therapy for advanced cancer, underscoring the need for newer, more effective drugs. The purpose of this study is to expedite identification of novel drugs against sorafenib resistant (SR)-HCC. Methods: We employed a transcriptomics-based drug repurposing method termed connectivity mapping using gene signatures from in vitro-derived SR Huh7 HCC cells. For proof of concept validation, we focused on drugs that were FDA-approved or under clinical investigation and prioritized two anti-neoplastic agents (dasatinib and fostamatinib) with targets associated with HCC. We also prospectively validated predicted gene expression changes in drug-treated SR Huh7 cells as well as identified and validated the targets of Fostamatinib in HCC. Results: Dasatinib specifically reduced the viability of SR-HCC cells that correlated with up-regulated activity of SRC family kinases, its targets, in our SR-HCC model. However, fostamatinib was able to inhibit both parental and SR HCC cells in vitro and in xenograft models. Ingenuity pathway analysis of fostamatinib gene expression signature from LINCS predicted JAK/STAT, PI3K/AKT, ERK/MAPK pathways as potential targets of fostamatinib that were validated by Western blot analysis. Fostamatinib treatment reversed the expression of genes that were deregulated in SR HCC. Conclusion: We provide proof of concept evidence for the validity of this drug repurposing approach for SR-HCC with implications for personalized medicine.

Abstract: BACKGROUND: A variety of organic materials (biomass) are burned for cooking and heating purposes in poorly ventilated houses; smoke from biomass combustion is considered an environmental risk factor for chronic obstructive pulmonary disease COPD. In this study, we attempted to determine the participation of single-nucleotide variants in the HHIP (hedgehog-interacting protein) gene in lung function, HHIP serum levels, and HHIP sputum supernatant levels in Mexican women with and without COPD who were exposed to biomass-burning smoke. METHODS: In a case-control study (COPD-BS, n = 186, BBES, n = 557) in Mexican women, three SNPs (rs13147758, rs1828591, and rs13118928) in the HHIP gene were analyzed by qPCR; serum and supernatant sputum protein levels were determined through ELISA. RESULTS: The rs13118928 GG genotype is associated with decreased risk (p = 0.021, OR = 0.51, CI95% = 0.27-0.97) and the recessive genetic model (p = 0.0023); the rs1828591-rs13118928 GG haplotype is also associated with decreased risk (p = 0.04, OR = 0.65, CI95% 0.43-0.98). By the dominant model (rs13118928), the subjects with one or two copies of the minor allele (G) exhibited higher protein levels. Additionally, two correlations with the AG genotype were identified: BBES with FEV1 (p = 0.03, r(2) = 0.53) and COPD-BS with FEV1/FVC (p = 0.012, r(2) = 0.54). CONCLUSIONS: Single-nucleotide variants in the HHIP gene are associated with decreased COPD risk, higher HHIP serum levels, and better lung function in Mexican women exposed to biomass burning.

Abstract: Fentanyl is widely used for analgesia, sedation, and anesthesia both in adult and pediatric populations. Yet, only few pharmacokinetic studies of fentanyl in pediatrics exist as conducting clinical trials in this population is especially challenging. Physiologically-based pharmacokinetic (PBPK) modeling is a mechanistic approach to explore drug pharmacokinetics and allows extrapolation from adult to pediatric populations based on age-related physiological differences. The aim of this study was to develop a PBPK model of fentanyl and norfentanyl for both adult and pediatric populations. The adult PBPK model was established in PK-Sim((R)) using data from 16 clinical studies and was scaled to several pediatric subpopulations. ~93% of the predicted AUClast values in adults and ~88% in pediatrics were within 2-fold of the corresponding value observed. The adult PBPK model predicted a fraction of fentanyl dose metabolized to norfentanyl of ~33% and a fraction excreted in urine of ~7%. In addition, the pediatric PBPK model was used to simulate differences in peak plasma concentrations after bolus injections and short infusions. The novel PBPK models could be helpful to further investigate fentanyl pharmacokinetics in both adult and pediatric populations.

Abstract: Angiotensin converting enzyme 2 (ACE2) downregulation is a key negative factor for the severity of lung edema and acute lung failure observed in patients infected with SARS-CoV-2. ACE2 downregulation affects the levels of diverse peptide mediators of the renin-agiotensin-aldestosterone and kallikrein-kinin systems, compromising vascular hemostasis. Increasing evidence suggests that the inflammatory response observed in covid-19 patients is initiated by the action of kinins on the bradykinin receptors. Accordingly, the use of bradykinin antagonists should be considered as a strategy for therapeutic intervention against covid-19 illness progression. Presently, icatibant is the only bradykinin antagonist drug approved. In the present report, we investigated the molecular features characterizing non-selective antagonists targeting the bradykinin receptors and carried out a in silico screening of approved drugs, aimed at the identification of compounds with a non-selective bradykinin antagonist profile that can be evaluated for drug repurposing. The study permitted to identify eight compounds as prospective non-selective antagonists of the bradykinin receptors, including raloxifene; sildenafil; cefepime; cefpirome; imatinib; ponatinib; abemaciclib and entrectinib.

Abstract: BACKGROUND: Though accounts for 2.5% of all cancers in female, the death rate of ovarian cancer is high, which is the fifth leading cause of cancer death (5% of all cancer death) in female. The 5-year survival rate of ovarian cancer is less than 50%. The oncogenic molecular signaling of ovarian cancer are complicated and remain unclear, and there is a lack of effective targeted therapies for ovarian cancer treatment. METHODS: In this study, we propose to investigate activated signaling pathways of individual ovarian cancer patients and sub-groups; and identify potential targets and drugs that are able to disrupt the activated signaling pathways. Specifically, we first identify the up-regulated genes of individual cancer patients using Markov chain Monte Carlo (MCMC), and then identify the potential activated transcription factors. After dividing ovarian cancer patients into several sub-groups sharing common transcription factors using K-modes method, we uncover the up-stream signaling pathways of activated transcription factors in each sub-group. Finally, we mapped all FDA approved drugs targeting on the upstream signaling. RESULTS: The 427 ovarian cancer samples were divided into 3 sub-groups (with 100, 172, 155 samples respectively) based on the activated TFs (with 14, 25, 26 activated TFs respectively). Multiple up-stream signaling pathways, e.g., MYC, WNT, PDGFRA (RTK), PI3K, AKT TP53, and MTOR, are uncovered to activate the discovered TFs. In addition, 66 FDA approved drugs were identified targeting on the uncovered core signaling pathways. Forty-four drugs had been reported in ovarian cancer related reports. The signaling diversity and heterogeneity can be potential therapeutic targets for drug combination discovery. CONCLUSIONS: The proposed integrative network analysis could uncover potential core signaling pathways, targets and drugs for ovarian cancer treatment.

Abstract: Modern drug discovery through de novo drug discovery entails high financial costs, low success rates, and lengthy trial periods. Drug repositioning presents a suitable approach for overcoming these issues by re-evaluating biological targets and modes of action of approved drugs. Coupling high-throughput technologies with genome-wide essentiality screens, network analysis, genome-scale metabolic modeling, and machine learning techniques enables the proposal of new drug-target signatures and uncovers unanticipated modes of action for available drugs. Here, we discuss the current issues associated with drug repositioning in light of curated high-throughput multi-omic databases, genome-wide screening technologies, and their application in systems biology/medicine approaches.

Abstract: Genome-wide association studies (GWAS) have identified tens of genetic variants associated with Parkinson's disease (PD). Nevertheless, the genes or DNA elements that affect traits through these genetic variations are usually undiscovered. This study was the first to combine meta-analysis GWAS data and expression data to identify PD risk genes. Four known genes, CRHR1, KANSL1, NSF and LRRC37A, and two new risk genes, STX4 and BST1, were identified. Among them, CRHR1 is a known drug target, indicating that hydrocortisone may become a potential drug for the treatment of PD. Furthermore, the potential pathogenesis of CRHR1 and LRRC37A was explored by applying DNA methylation (DNAm) data, indicating a pathogenesis whereby the effect of a genetic variant on PD is mediated by genetic regulation of transcription through DNAm. Overall, this research identified the risk genes and pathogenesis that affect PD through genetic variants, which has significance for the diagnosis and treatment of PD.

Abstract: Malaria, a global threat to the human population, remains a challenge partly due to the fast-growing drug-resistant strains of Plasmodium species. New therapeutics acting against the pathogenic asexual and sexual stages, including liver-stage malarial infection, have now attained more attention in achieving malaria eradication efforts. In this paper, two previously identified potent antiplasmodial hydroxyethylamine (HEA) compounds were investigated for their activity against the malaria parasite's multiple life stages. The compounds exhibited notable activity against the artemisinin-resistant strain of P. falciparum blood-stage culture with 50% inhibitory concentrations (IC50) in the low micromolar range. The compounds' cytotoxicity on HEK293, HepG2 and Huh-7 cells exhibited selective killing activity with IC50 values > 170 muM. The in vivo efficacy was studied in mice infected with P. berghei NK65, which showed a significant reduction in the blood parasite load. Notably, the compounds were active against liver-stage infection, mainly compound 1 with an IC50 value of 1.89 muM. Mice infected with P. berghei sporozoites treated with compound 1 at 50 mg kg(-1) dose had markedly reduced liver stage infection. Moreover, both compounds prevented ookinete maturation and affected the developmental progression of gametocytes. Further, systematic in silico studies suggested both the compounds have a high affinity towards plasmepsin II with favorable pharmacological properties. Overall, the findings demonstrated that HEA and piperidine possessing compounds have immense potential in treating malarial infection by acting as multistage inhibitors.