Abstract: Essential oils (EOs) have been used therapeutically for centuries. In recent decades, randomized controlled (clinical) trials have supported efficacy in specific therapeutic indications for a few of them. Some EOs, their components or derivatives thereof have been approved as drugs. Nevertheless, they are still considered products that are mainly used in complementary and alternative medicine. EO components occupy a special niche in chemical space, that offers unique opportunities based on their unusual physicochemical properties, because they are typically volatile and hydrophobic. Here we evaluate selected physicochemical parameters, used in conventional drug discovery, of EO components present in a range of commercially available EOs. We show that, contrary to generally held belief, most EO components meet current-day requirements of medicinal chemistry for good drug candidates. Moreover, they also offer attractive opportunities for lead optimization or even fragment-based drug discovery. Because their therapeutic potential is still under-scrutinized, we propose that this be explored more vigorously with present-day methods.

Abstract: Integration of multiple sources and data levels provides a great insight into the complex associations between human and malaria systems. In this study, a meta-analysis framework was developed based on a heterogeneous network model for integrating human-malaria protein similarities, a human protein interaction network, and a Plasmodium vivax protein interaction network. An iterative network propagation was performed on the heterogeneous network until we obtained stabilized weights. The association scores were calculated for qualifying a novel potential human-malaria protein association. This method provided a better performance compared to random experiments. After that, the stabilized network was clustered into association modules. The potential association candidates were then thoroughly analyzed by statistical enrichment analysis with protein complexes and known drug targets. The most promising target proteins were the succinate dehydrogenase protein complex in the human citrate (TCA) cycle pathway and the nicotinic acetylcholine receptor in the human central nervous system. Promising associations and potential drug targets were also provided for further studies and designs in therapeutic approaches for malaria at a systematic level. In conclusion, this method is efficient to identify new human-malaria protein associations and can be generalized to infer other types of association studies to further advance biomedical science.

Abstract: Aim: High-throughput phenotypic screens have emerged as a promising avenue for small-molecule drug discovery. The challenge faced in high-throughput phenotypic screens is target deconvolution once a small molecule hit is identified. Chemogenomics libraries have emerged as an important tool for meeting this challenge. Here, we investigate their target-specificity by deriving a 'polypharmacology index' for broad chemogenomics screening libraries. Methods: All known targets of all the compounds in each library were plotted as a histogram and fitted to a Boltzmann distribution, whose linearized slope is indicative of the overall polypharmacology of the library. Results & conclusion: Comparison of libraries clearly distinguished the most target-specific library, which might be assumed to be more useful for target deconvolution in a phenotypic screen.

Abstract: The activation of the PI3K/AKT/mTOR pathway is a main driver of cell growth, proliferation, survival, and chemoresistance of cancer cells, and, for this reason, represents an attractive target for developing targeted anti-cancer drugs. There are plenty of preclinical data sustaining the anti-tumor activity of dual PI3K/mTOR inhibitors as single agents and in combination in lymphomas. Clinical responses, including complete remissions (especially in follicular lymphoma patients), are also observed in the very few clinical studies performed in patients that are affected by relapsed/refractory lymphomas or chronic lymphocytic leukemia. In this review, we summarize the literature on dual PI3K/mTOR inhibitors focusing on the lymphoma setting, presenting both the three compounds still in clinical development and those with a clinical program stopped or put on hold.

Abstract: Tuberculosis (TB) remains a serious threat to global public health, responsible for an estimated 1.5 million mortalities in 2018. While there are available therapeutics for this infection, slow-acting drugs, poor patient compliance, drug toxicity, and drug resistance require the discovery of novel TB drugs. Discovering new and more potent antibiotics that target novel TB protein targets is an attractive strategy towards controlling the global TB epidemic. In silico strategies can be applied at multiple stages of the drug discovery paradigm to expedite the identification of novel anti-TB therapeutics. In this paper, we discuss the current TB treatment, emergence of drug resistance, and the effective application of computational tools to the different stages of TB drug discovery when combined with traditional biochemical methods. We will also highlight the strengths and points of improvement in in silico TB drug discovery research, as well as possible future perspectives in this field.

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: FOLFOX (5-fluorouracil, leucovorin and oxaliplatin) is one of the main chemotherapy regimens for colorectal cancer (CRC), but only half of CRC patients respond to this regimen. Using gene expression profiles of 96 metastatic CRC patients treated with FOLFOX, we first selected gene pairs whose within-sample relative expression orderings (REO) were significantly associated with the response to FOLFOX using the exact binomial test. Then, from these gene pairs, we applied an optimization procedure to obtain a subset that achieved the largest F-score in predicting pathological response of CRC to FOLFOX. The REO-based qualitative transcriptional signature, consisting of five gene pairs, was developed in the training dataset consisting of 96 samples with an F-score of 0.90. In an independent test dataset consisting of 25 samples with the response information, an F-score of 0.82 was obtained. In three other independent survival datasets, the predicted responders showed significantly better progression-free survival than the predicted non-responders. In addition, the signature showed a better predictive performance than two published FOLFOX signatures across different datasets and is more suitable for CRC patients treated with FOLFOX than 5-fluorouracil-based signatures. In conclusion, the REO-based qualitative transcriptional signature can accurately identify metastatic CRC patients who may benefit from the FOLFOX regimen.

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: 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.