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Published in January 2020
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Small molecule screening identifies inhibitors of the Epstein-Barr virus deubiquitinating enzyme, BPLF1.

Authors: Atkins SL, Motaib S, Wiser LC, Hopcraft SE, Hardy PB, Shackelford J, Foote P, Wade AH, Damania B, Pagano JS, Pearce KH, Whitehurst CB

Abstract: Herpesviral deubiquitinating enzymes (DUBs) were discovered in 2005, are highly conserved across the family, and are proving to be increasingly important players in herpesviral infection. EBV's DUB, BPLF1, is known to regulate both cellular and viral target activities, yet remains largely unstudied. Our work has implicated BPLF1 in a wide range of processes including infectivity, viral DNA replication, and DNA repair. Additionally, knockout of BPLF1 delays and reduces human B-cell immortalization and lymphoma formation in humanized mice. These findings underscore the importance of BPLF1 in viral infectivity and pathogenesis and suggest that inhibition of EBV's DUB activity may offer a new approach to specific therapy for EBV infections. We set out to discover and characterize small molecule inhibitors of BPLF1 deubiquitinating activity through high-throughput screening. An initial small pilot screen resulted in discovery of 10 compounds yielding >80% decrease in BPLF1 DUB activity at a 10muM concentration. Follow-up dose response curves of top hits identified several compounds with an IC50 in the low micromolar range. Four of these hits were tested for their ability to cleave ubiquitin chains as well as their effects on viral infectivity and cell viability. Further characterization of the top hit, commonly known as suramin was found to not be selective yet decreased viral infectivity by approximately 90% with no apparent effects on cell viability. Due to the conserved nature of Herpesviral deubiquitinating enzymes, identification of an inhibitor of BPLF1 may prove to be an effective and promising new avenue of therapy for EBV and other herpesviral family members.
Published in January 2020
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Gastric cancer during pregnancy: A report on 13 cases and review of the literature with focus on chemotherapy during pregnancy.

Authors: Maggen C, Lok CA, Cardonick E, van Gerwen M, Ottevanger PB, Boere IA, Koskas M, Halaska MJ, Fruscio R, Gziri MM, Witteveen PO, Van Calsteren K, Amant F

Abstract: INTRODUCTION: Gastric cancer during pregnancy is extremely rare and data on optimal treatment and possible chemotherapeutic regimens are scarce. The aim of this study is to describe the obstetric and maternal outcome of women with gastric cancer during pregnancy and review the literature on antenatal chemotherapy for gastric cancer. MATERIAL AND METHODS: Treatment and outcome of patients registered in the International Network on Cancer, Infertility and Pregnancy database with gastric cancer diagnosed during pregnancy were analyzed. RESULTS: In total, 13 women with gastric cancer during pregnancy were registered between 2002 and 2018. Median gestational age at diagnosis was 22 weeks (range 6-30 weeks). Twelve women were diagnosed with advanced disease and died within 2 years after pregnancy, most within 6 months. In total, eight out of 10 live births ended in a preterm delivery because of preeclampsia, maternal deterioration, or therapy planning. Two out of six women who initiated chemotherapy during pregnancy delivered at term. Two neonates prenatally exposed to chemotherapy were growth restricted and one of them developed a systemic infection with brain abscess after preterm delivery for preeclampsia 2 weeks after chemotherapy. No malformations were reported. CONCLUSIONS: The prognosis of gastric cancer during pregnancy is poor, mainly due to advanced disease at diagnosis, emphasizing the need for early diagnosis. Antenatal chemotherapy can be considered to reach fetal maturity, taking possible complications such as growth restriction, preterm delivery, and hematopoietic suppression at birth into account.
Published in January 2020
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Prediction of Drug-Drug Interactions by Using Profile Fingerprint Vectors and Protein Similarities.

Authors: Dere S, Ayvaz S

Abstract: Objectives: Drug-drug interaction (DDI) is a vital problem that threatens people's health. However, the prediction of DDIs through in-vivo experiments is not only extremely costly but also difficult as many serious side effects are hard to detect in in-vivo and in-vitro settings. The aim of this study was to assess the effectiveness of similarity-based in-silico computational DDI prediction approaches and to provide a cost effective and scalable solution to predict potential DDIs. Methods: In this study, widely known similarity-based computational DDI prediction methods were utilized to discover novel potential DDIs. More specifically, known interactions, drug targets, adverse effects, and protein similarities of drug pairs were used to construct drug fingerprints for the prediction of DDIs. Results: Using the drug interaction profile, our approach achieved an area under the curve (AUC) of 0.975 in the prediction of a potential DDI. The drug adverse effect profile and protein profile similarity-based methods resulted in AUC values of 0.685 and 0.895, respectively, in the prediction of DDIs. Conclusions: In this study, we developed a computational approach to the prediction of potential drug interactions. The performance of the similarity-based computational methods was comparatively evaluated using a comprehensive real-world DDI dataset. The evaluations showed that the drug interaction profile information is a better predictor of DDIs compared to drug adverse effects and protein similarities among DDI pairs.
Published in January 2020
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Optimal doses of antidepressants in dependence on age: Combined covariate actions in Bayesian network meta-analysis.

Authors: Holper L

Abstract: Background: The meta-analysis by Furukawa et al. (The Lancet Psychiatry 2019, 6(7)) reported optimal doses for antidepressants in adult major depressive disorder (MDD). The present reanalysis aimed to adjust optimal doses in dependence on age. Methods: Analysis was based on the same dataset by Cipriani et al. (The Lancet 2018, 391(10128)) comparing 21 antidepressants in MDD. Random-effects Bayesian network meta-analysis was implemented to estimate the combined covariate action using restricted cubic splines (RCS). Balanced treatment recommendations were derived for the outcomes efficacy (response), acceptability (dropouts for any reason), and tolerability (dropouts due to adverse events). Findings: The combined covariate action of dose and age suggested agomelatine and escitalopram as the best-balanced antidepressants in terms of efficacy and tolerability that may be escalated until 40 and 60 mg/day fluoxetine equivalents (mg/day FE ), respectively, for ages 30-65 years. Desvenlafaxine, duloxetine, fluoxetine, milnacipran, and vortioxetine may be escalated until 20-40 mg/day FE , whereas bupropion, citalopram, mirtazapine, paroxetine, and venlafaxine may not be given in doses > 20 mg/day FE . Amitriptyline, clomipramine, fluvoxamine, levomilnacipran, reboxetine, sertraline, and trazodone revealed no relevant balanced benefits and may therefore not be recommended for antidepressant treatment. None of the antidepressants was observed to provide balanced benefits in patients >70 years because of adverse events exceeding efficacy. Interpretation: Findings suggest that the combined covariate action of dose and age provides a better basis for judging antidepressant clinical benefits than considering dose or age separately, and may thus inform decision makers to accurately guide antidepressant dosing recommendations in MDD. Funding: No funding.
Published in January 2020
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Tissue-guided LASSO for prediction of clinical drug response using preclinical samples.

Authors: Huang EW, Bhope A, Lim J, Sinha S, Emad A

Abstract: Prediction of clinical drug response (CDR) of cancer patients, based on their clinical and molecular profiles obtained prior to administration of the drug, can play a significant role in individualized medicine. Machine learning models have the potential to address this issue but training them requires data from a large number of patients treated with each drug, limiting their feasibility. While large databases of drug response and molecular profiles of preclinical in-vitro cancer cell lines (CCLs) exist for many drugs, it is unclear whether preclinical samples can be used to predict CDR of real patients. We designed a systematic approach to evaluate how well different algorithms, trained on gene expression and drug response of CCLs, can predict CDR of patients. Using data from two large databases, we evaluated various linear and non-linear algorithms, some of which utilized information on gene interactions. Then, we developed a new algorithm called TG-LASSO that explicitly integrates information on samples' tissue of origin with gene expression profiles to improve prediction performance. Our results showed that regularized regression methods provide better prediction performance. However, including the network information or common methods of including information on the tissue of origin did not improve the results. On the other hand, TG-LASSO improved the predictions and distinguished resistant and sensitive patients for 7 out of 13 drugs. Additionally, TG-LASSO identified genes associated with the drug response, including known targets and pathways involved in the drugs' mechanism of action. Moreover, genes identified by TG-LASSO for multiple drugs in a tissue were associated with patient survival. In summary, our analysis suggests that preclinical samples can be used to predict CDR of patients and identify biomarkers of drug sensitivity and survival.
Published in January 2020
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Pharmacological manipulations of judgement bias: A systematic review and meta-analysis.

Authors: Neville V, Nakagawa S, Zidar J, Paul ES, Lagisz M, Bateson M, Lovlie H, Mendl M

Abstract: Validated measures of animal affect are crucial to research spanning numerous disciplines. Judgement bias, which assesses decision-making under ambiguity, is a promising measure of animal affect. One way of validating this measure is to administer drugs with affect-altering properties in humans to non-human animals and determine whether the predicted judgement biases are observed. We conducted a systematic review and meta-analysis using data from 20 published research articles that use this approach, from which 557 effect sizes were extracted. Pharmacological manipulations overall altered judgement bias at the probe cues as predicted. However, there were several moderating factors including the neurobiological target of the drug, whether the drug induced a relatively positive or negative affective state in humans, dosage, and the presented cue. This may partially reflect interference from adverse effects of the drug which should be considered when interpreting results. Thus, the overall pattern of change in animal judgement bias appears to reflect the affect-altering properties of drugs in humans, and hence may be a valuable measure of animal affective valence.
Published in January - December 2020
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Regulation of antibody-mediated complement-dependent cytotoxicity by modulating the intrinsic affinity and binding valency of IgG for target antigen.

Authors: Wang B, Yang C, Jin X, Du Q, Wu H, Dall'Acqua W, Mazor Y

Abstract: Complement-dependent cytotoxicity (CDC) is a potent effector mechanism, engaging both innate and adaptive immunity. Although strategies to improve the CDC activity of antibody therapeutics have primarily focused on enhancing the interaction between the antibody crystallizable fragment (Fc) and the first subcomponent of the C1 complement complex (C1q), the relative importance of intrinsic affinity and binding valency of an antibody to the target antigen is poorly understood. Here we show that antibody binding affinity to a cell surface target antigen evidently affects the extent and efficacy of antibody-mediated complement activation. We further report the fundamental role of antibody binding valency in the capacity to recruit C1q and regulate CDC. More specifically, an array of affinity-modulated variants and functionally monovalent bispecific derivatives of high-affinity anti-epidermal growth factor receptor (EGFR) and anti-human epidermal growth factor receptor 2 (HER2) therapeutic immunoglobulin Gs (IgGs), previously reported to be deficient in mediating complement activation, were tested for their ability to bind C1q by biolayer interferometry using antigen-loaded biosensors and to exert CDC against a panel of EGFR and HER2 tumor cells of various histological origins. Significantly, affinity-reduced variants or monovalent derivatives, but not their high-affinity bivalent IgG counterparts, induced near-complete cell cytotoxicity in tumor cell lines that had formerly been shown to be resistant to complement-mediated attack. Our findings suggest that monovalent target engagement may contribute to an optimal geometrical positioning of the antibody Fc to engage C1q and deploy the complement pathway.
Published on January 30, 2020
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Identification and characterization of methylation-mediated transcriptional dysregulation dictate methylation roles in preeclampsia.

Authors: Zhao S, Lv N, Li Y, Liu T, Sun Y, Chu X

Abstract: BACKGROUND: Preeclampsia (PE) is a heterogeneous, hypertensive disorder of pregnancy, with no robust biomarkers or effective treatments. PE increases the risk of poor outcomes for both the mother and the baby. Methylation-mediated transcriptional dysregulation motifs (methTDMs) could contribute the PE development. However, precise functional roles of methTDMs in PE have not been globally described. METHODS: Here, we develop a comprehensive and computational pipeline to identify PE-specific methTDMs following TF, gene, methylation expression profile, and experimentally verified TF-gene interactions. RESULTS: The regulation patterns of methTDMs are multiple and complex in PE and contain relax inhibition, intensify inhibition, relax activation, intensify activation, reverse activation, and reverse inhibition. A core module is extracted from global methTDM network to further depict the mechanism of methTDMs in PE. The common and specific features of any two kinds of regulation pattern are also analyzed in PE. Some key methylation sites, TFs, and genes such as IL2RG are identified in PE. Functional analysis shows that methTDMs are associated with immune-, insulin-, and NK cell-related functions. Drug-related network identifies some key drug repurposing candidates such as NADH. CONCLUSION: Collectively, the study highlighted the effect of methylation on the transcription process in PE. MethTDMs could contribute to identify specific biomarkers and drug repurposing candidates for PE.
Published on January 28, 2020
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Integrated Computational Approaches and Tools forAllosteric Drug Discovery.

Authors: Sheik Amamuddy O, Veldman W, Manyumwa C, Khairallah A, Agajanian S, Oluyemi O, Verkhivker G, Tastan Bishop O

Abstract: Understanding molecular mechanisms underlying the complexity of allosteric regulationin proteins has attracted considerable attention in drug discovery due to the benefits and versatilityof allosteric modulators in providing desirable selectivity against protein targets while minimizingtoxicity and other side effects. The proliferation of novel computational approaches for predictingligand-protein interactions and binding using dynamic and network-centric perspectives has ledto new insights into allosteric mechanisms and facilitated computer-based discovery of allostericdrugs. Although no absolute method of experimental and in silico allosteric drug/site discoveryexists, current methods are still being improved. As such, the critical analysis and integration ofestablished approaches into robust, reproducible, and customizable computational pipelines withexperimental feedback could make allosteric drug discovery more efficient and reliable. In this article,we review computational approaches for allosteric drug discovery and discuss how these tools can beutilized to develop consensus workflows for in silico identification of allosteric sites and modulatorswith some applications to pathogen resistance and precision medicine. The emerging realization thatallosteric modulators can exploit distinct regulatory mechanisms and can provide access to targetedmodulation of protein activities could open opportunities for probing biological processes and insilico design of drug combinations with improved therapeutic indices and a broad range of activities.
Published on January 27, 2020
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Investigations of Piperazine Derivatives as Intestinal Permeation Enhancers in Isolated Rat Intestinal Tissue Mucosae.

Authors: Stuettgen V, Brayden DJ

Abstract: A limiting factor for oral delivery of macromolecules is low intestinal epithelial permeability. 1-Phenylpiperazine (PPZ), 1-(4-methylphenyl) piperazine (1-4-MPPZ) and 1-methyl-4-phenylpiperazine (1-M-4-PPZ) have emerged as potential permeation enhancers (PEs) from a screen carried out by others in Caco-2 monolayers. Here, their efficacy, mechanism of action and potential for epithelial toxicity were further examined in Caco-2 cells and isolated rat intestinal mucosae. Using high-content analysis, PPZ and 1-4-MPPZ decreased mitochondrial membrane potential and increased plasma membrane potential in Caco-2 cells to a greater extent than 1-M-4-PPZ. The Papp of the paracellular marker, [(14)C]-mannitol, and of the peptide, [(3)H]-octreotide, was measured across rat colonic mucosae following apical addition of the three piperazines. PPZ and 1-4-MPPZ induced a concentration-dependent decrease in transepithelial electrical resistance (TEER) and an increase in the Papp of [(14)C]-mannitol without causing histological damage. 1-M-4-PPZ was without effect. The piperazines caused the Krebs-Henseleit buffer pH to become alkaline, which partially attenuated the increase in Papp of [(14)C]-mannitol caused by PPZ and 1-4-MPPZ. Only addition of 1-4-MPPZ increased the Papp of [(3)H]-octreotide. Pre-incubation of mucosae with two 5-HT4 receptor antagonists, a loop diuretic and a myosin light chain kinase inhibitor, reduced the permeation enhancement capacity of PPZ and 1-4-MPP for [(14)C]-mannitol. 1-4-MPPZ holds most promise as a PE, but intestinal physiology may also be impacted due to multiple mechanisms of action.