Abstract: BACKGROUND AND OBJECTIVES: The genetic polymorphism of cytochrome P450 (CYP) 2D6 is characterized by an excessive impact on positive and adverse drug reactions to antipsychotics, such as risperidone. Consequently, the pharmacokinetics of the drug and metabolite can be substantially altered and exhibit a high variability between the different phenotypes. The goal of this study was to develop a physiologically based pharmacokinetic (PBPK) model considering the CYP2D6 genetic polymorphism for risperidone and 9-hydroxyrisperidone (9-OH-RIS) taking CYP3A4 into account. Additionally, risperidone dose adjustments, which would compensate for genetically caused differences in the plasma concentrations of the active moiety (sum of risperidone and 9-OH-RIS) were calculated. METHODS: Based on available knowledge about risperidone, 9-OH-RIS, and relevant physiological changes according to different CYP2D6 phenotypes, several PBPK models were built. In addition, an initial model was further evaluated based on the plasma concentrations of risperidone and 9-OH-RIS from a single-dose study including 71 genotyped healthy volunteers treated with 1 mg of oral risperidone. RESULTS: PBPK models were able to accurately describe risperidone exposure after single-dose administration, especially in the concentration range >/= 1 microg/L, illustrated by a minimal bias and a good precision. About 90.3% of all weighted residuals versus observed plasma concentrations >/= 1 microg/L were in the +/- 30% range. The risperidone/9-OH-RIS ratio increased progressively according to reduced CYP2D6 activity, resulting in a mean ratio of 4.96 for poor metabolizers. Simulations demonstrate that dose adjustment of the drug by - 25% for poor metabolizers and by - 10% for intermediate metabolizers results in a similar exposure to that of extensive metabolizers. Conversely, the risperidone/9-OH-RIS ratio can be used to determine the phenotype of individuals. CONCLUSION: PBPK modelling can provide a valuable tool to predict the pharmacokinetics of risperidone and 9-OH-RIS in healthy volunteers, according to the different CYP2D6 phenotypes taking CYP3A4 into account. These models are able to ultimately support decision-making regarding dose-optimization strategies, especially for subjects showing lower CYP2D6 activity.

Abstract: PURPOSE: In this work, we introduce CDGnet (Cancer-Drug-Gene Network), an evidence-based network approach for recommending targeted cancer therapies. CDGnet represents a user-friendly informatics tool that expands the range of targeted therapy options for patients with cancer who undergo molecular profiling by including the biologic context via pathway information. METHODS: CDGnet considers biologic pathway information specifically by looking at targets or biomarkers downstream of oncogenes and is personalized for individual patients via user-inputted molecular alterations and cancer type. It integrates a number of different sources of knowledge: patient-specific inputs (molecular alterations and cancer type), US Food and Drug Administration-approved therapies and biomarkers (curated from DailyMed), pathways for specific cancer types (from Kyoto Encyclopedia of Genes and Genomes [KEGG]), gene-drug connections (from DrugBank), and oncogene information (from KEGG). We consider 4 different evidence-based categories for therapy recommendations. Our tool is delivered via an R/Shiny Web application. For the 2 categories that use pathway information, we include an interactive Sankey visualization built on top of d3.js that also provides links to PubChem. RESULTS: We present a scenario for a patient who has estrogen receptor (ER)-positive breast cancer with FGFR1 amplification. Although many therapies exist for patients with ER-positive breast cancer, FGFR1 amplifications may confer resistance to such treatments. CDGnet provides therapy recommendations, including PIK3CA, MAPK, and RAF inhibitors, by considering targets or biomarkers downstream of FGFR1. CONCLUSION: CDGnet provides results in a number of easily accessible and usable forms, separating targeted cancer therapies into categories in an evidence-based manner that incorporates biologic pathway information.

Abstract: Over the past 15 years, genome-wide association studies (GWASs) have enabled the systematic identification of genetic loci associated with traits and diseases. However, due to resolution issues and methodological limitations, the true causal variants and genes associated with traits remain difficult to identify. In this post-GWAS era, many biological and computational fine-mapping approaches now aim to solve these issues. Here, we review fine-mapping and gene prioritization approaches that, when combined, will improve the understanding of the underlying mechanisms of complex traits and diseases. Fine-mapping of genetic variants has become increasingly sophisticated: initially, variants were simply overlapped with functional elements, but now the impact of variants on regulatory activity and direct variant-gene 3D interactions can be identified. Moreover, gene manipulation by CRISPR/Cas9, the identification of expression quantitative trait loci and the use of co-expression networks have all increased our understanding of the genes and pathways affected by GWAS loci. However, despite this progress, limitations including the lack of cell-type- and disease-specific data and the ever-increasing complexity of polygenic models of traits pose serious challenges. Indeed, the combination of fine-mapping and gene prioritization by statistical, functional and population-based strategies will be necessary to truly understand how GWAS loci contribute to complex traits and diseases.

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.

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.

Abstract: The clinical sampling of urine is noninvasive and unrestricted, whereby huge volumes can be easily obtained. This makes urine a valuable resource for the diagnoses of diseases. Urinary and renal proteomics have resulted in considerable progress in kidney-based disease diagnosis through biomarker discovery and treatment. This review summarizes the bioinformatics tools available for this area of proteomics and the milestones reached using these tools in clinical research. The scant research publications and the even more limited bioinformatic tool options available for urinary and renal proteomics are highlighted in this review. The need for more attention and input from bioinformaticians is highlighted, so that progressive achievements and releases can be made. With just a handful of existing tools for renal and urinary proteomic research available, this review identifies a gap worth targeting by protein chemists and bioinformaticians. The probable causes for the lack of enthusiasm in this area are also speculated upon in this review. This is the first review that consolidates the bioinformatics applications specifically for renal and urinary proteomics.

Abstract: BACKGROUND: Prevention of metabolic complications of long-term adjuvant endocrine therapy in breast cancers remained a challenge. We aimed to investigate the molecular mechanism in the development of tamoxifen (TAM)-induced fatty liver in both estrogen receptor (ER)-positive and ER-negative breast cancer. METHODS AND RESULTS: First, the direct protein targets (DPTs) of TAM were identified using DrugBank5.1.7. We found that mitogen-activated protein kinase 8 (MAPK8) was one DPT of TAM. We identified significant genes in breast cancer and fatty liver disease (FLD) using the MalaCards human disease database. Next, we analyzed the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways of those significant genes in breast cancer and FLD using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING). We found that overlapping KEGG pathways in these two diseases were MAPK signaling pathway, Forkhead box O (FoxO) signaling pathway, HIF-1 signaling pathway, AGE-RAGE signaling pathway in diabetic complications, and PI3K-Akt signaling pathway. Furthermore, the KEGG Mapper showed that the MAPK signaling pathway was related to the FoxO signaling pathway. Finally, the functional relevance of breast cancer and TAM-induced FLD was validated by Western blot analysis. We verified that TAM may induce fatty liver in breast cancer through the MAPK8/FoxO signaling pathway. CONCLUSION: Bioinformatics analysis combined with conventional experiments may improve our understanding of the molecular mechanisms underlying side effects of cancer drugs, thereby making this method a new paradigm for guiding future studies on this issue.

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.

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.

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.