Search

Characterizing the gut microbiota in terms of their capacity to interfere with drug metabolism is necessary to achieve drug efficacy and safety. Although examples of drug-microbiome interactions are well-documented, little has been reported about a computational pipeline for systematically identifying and characterizing bacterial enzymes that process particular classes of drugs. The goal of our study is to develop a computational approach that compiles drugs whose metabolism may be influenced by a particular class of microbial enzymes and that quantifies the variability in the collective level of those enzymes among individuals. The present paper describes this approach, with microbial β-glucuronidases as an example, which break down drug-glucuronide conjugates and reactivate the drugs or their metabolites. We identified 100 medications that may be metabolized by β-glucuronidases from the gut microbiome. These medications included morphine, estrogen, ibuprofen, midazolam, and their structural analogues. The analysis of metagenomic data available through the Sequence Read Archive (SRA) showed that the level of β-glucuronidase in the gut metagenomes was higher in males than in females, which provides a potential explanation for the sex-based differences in efficacy and toxicity for several drugs, reported in previous studies. Our analysis also showed that infant gut metagenomes at birth and 12 months of age have higher levels of β-glucuronidase than the metagenomes of their mothers and the implication of this observed variability was discussed in the context of breastfeeding as well as infant hyperbilirubinemia. Overall, despite important limitations discussed in this paper, our analysis provided useful insights on the role of the human gut metagenome in the variability in drug response among individuals. Importantly, this approach exploits drug and metagenome data available in public databases as well as open-source cheminformatics and bioinformatics tools to predict drug-metagenome interactions.

Some concepts in nuclear radiation physics are abstract and intellectually demanding. In the present paper, an “MCHP platform” (MCHP was an acronym for Monte Carlo simulations + Human Phantoms) was proposed to provide assistance to the students through visualization. The platform involved Monte Carlo simulations of interactions between ionizing radiations and the Oak Ridge National Laboratory (ORNL) adult male human phantom. As an example to demonstrate the benefits of the proposed MCHP platform, the present paper investigated the variation of the absorbed photon dose per photon from a 137Cs source in three selected organs, namely, brain, spine and thyroid of an adult male for concrete and lead shields with varying thicknesses. The results were interesting but not readily comprehensible without direct visualization. Graphical visualization snapshots as well as video clips of real time interactions between the photons and the human phantom were presented for the involved cases, and the results were explained with the help of such snapshots and video clips. It is envisaged that, if the platform is found useful and effective by the readers, the readers can also propose examples to be gradually added onto this platform in future, with the ultimate goal of enhancing students’ understanding and learning the concepts in an undergraduate nuclear radiation physics course or a related course.

ABH antigens are histo-antigens, but were first described on the surface of human erythrocytes. They are found in those cells only in great apes and humans, while in more primitive animals they are found in tissues and body fluids. ABH antigens are mainly distributed in tissues that are in contact with the external environment and may serve as ligands for pathogens in tissues or block their connection. Description of the distribution of these molecules in non-human primate tissues is restricted to a few tissues and species. This paper describes the expression of human A, B and H type antigens in different organs from four species of New World Primates, obtained from the Centro Nacional de Primatas, as well as comparing that expression with what has been described for humans. In this study, although the tissue description of the antigens is similar to the genetic model for humans, some differences in expression between some organs from those species and those of humans were found. The differences occurred mainly in endodermal organs that have secretory functions and are probably under the control of the human-type FUT-2 enzyme. In the mesodermal-origin organs there was a reduction or absence of A and B antigen marking, particularly in the H precursor substance, indicating that those organs are under the control of the human-type FUT-1 enzyme. These findings have demonstrated that there is similar ABH antigen reactivity in tissue distribution between the species, although there are some species-specific cases.

Metabolites play a significant role in various complex human disease. The exploration of the relationship between metabolites and diseases can help us to better understand the underlying pathogenesis. Several network-based methods have been used to predict the association between metabolite and disease. However, some methods ignored hierarchical differences in disease network and failed to work in the absence of known metabolite-disease associations. This paper presents a bi-random walks based method for disease-related metabolites prediction, called MDBIRW. First of all, we reconstruct the disease similarity network and metabolite functional similarity network by integrating Gaussian Interaction Profile (GIP) kernel similarity of diseases and GIP kernel similarity of metabolites, respectively. Then, the bi-random walks algorithm is executed on the reconstructed disease similarity network and metabolite functional similarity network to predict potential disease-metabolite associations. At last, MDBIRW achieves reliable performance in leave-one-out cross validation (AUC of 0.910) and 5-fold cross validation (AUC of 0.924). The experimental results show that our method outperforms other existing methods for predicting disease-related metabolites.

Chronic obstructive pulmonary disease (COPD) is a multi-factor disease, in which metabolic disturbances played important roles. In this paper, functional information was integrated into a COPD-related metabolic network to assess similarity between genes. Then a gene prioritization method was applied to the COPD-related metabolic network to prioritize COPD candidate genes. The gene prioritization method was superior to ToppGene and ToppNet in both literature validation and functional enrichment analysis. Top-ranked genes prioritized from the metabolic perspective with functional information could promote the better understanding about the molecular mechanism of this disease. Top 100 genes might be potential markers for diagnostic and effective therapies.

A hemoglobin wrapped covalently by three human serum albumins, a Hb-HSA3 cluster, is an artificial O2-carrier with the potential to function as a red blood cell substitute. This paper describes the synthesis and O2-binding properties of new hemoglobin‒albumin clusters (i) bearing four HSA units at the periphery (Hb-HSA4, large-size variant) and (ii) containing an intramolecularly crosslinked Hb in the center (XLHb-HSA3, high O2-affinity variant). Dynamic light scattering measurements revealed that the Hb-HSA4 diameter is greater than that of either Hb-HSA3 or XLHb-HSA3. The XLHb-HSA3 showed moderately high O2-affinity compared to the others because of the chemical linkage between the Cys-93(β) residues in Hb. Furthermore, the blood circulation behavior of 125I-labeled clusters was investigated by assay of blood retention and tissue distribution after intravenous administration into anesthetized rats. The XLHb-HSA3 was metabolized faster than Hb-HSA3 and Hb-HSA4. Results suggest that the molecular structure of the protein cluster is a factor that can influence in vivo circulation behavior.

Many different physiological processes affect the growth of malignant lesions and their response to therapy. Each of these processes is spatially and genetically heterogeneous; dynamically evolving in time; controlled by many other physiological processes, and intrinsically random and unpredictable. The objective of this paper is to show that all of these properties of cancer physiology can be treated in a unified, mathematically rigorous way via the theory of random processes. We treat each physiological process as a random function of position and time within a tumor, defining the joint statistics of such functions via the infinite-dimensional characteristic functional. The theory is illustrated by analyzing several models of drug delivery and response of a tumor to therapy. To apply the methodology to precision cancer therapy, we use maximum-likelihood estimation with Emission Computed Tomography (ECT) data to estimate unknown patient-specific physiological parameters, ultimately demonstrating how to predict the probability of tumor control for an individual patient undergoing a proposed therapeutic regimen.

Poor patient compliance, untoward reactions and unstable blood drug levels after the bolus administration are impeding the pharmacotherapy for insobriety. A long-acting preparation may address these limitations. The aim of this paper was to further investigate the in vitro characteristics and in vivo performances of nalmefene microspheres. Nalmefene was blended with poly (lactide-co-glycolide) (PLGA) to prepare the target microspheres by an O/O emulsification solvent evaporation method. The prepared microspheres exhibited a controlled release profile of nalmefene in vitro over 4 weeks, which was well fitted with a first-order model. In vitro degradation study showed that the drug release in vitro was dominated by both drug diffusion and polymer degradation mechanisms. Pharmacokinetics study indicated that the prepared microspheres could provide a relatively constant of nalmefene plasma concentration for at least one month in rats. The in vivo pharmacokinetics profile was well correlated with the in vitro drug release. Pharmacodynamics studies revealed that the drug loaded microspheres could produce a long-acting antagonism efficacy on rats. These results demonstrated the promising application of injectable PLGA microspheres containing nalmefene for the long-term treatment of alcohol dependence.

Identifying drug-drug interaction (DDI) is an important topic for the development of safe pharmaceutical drugs and for the optimization of multidrug regimens for complex diseases such as cancer and HIV. There have been about 150,000 publications on DDIs in PubMed, which is a great resource for DDI studies. In this paper, we introduced an automatic computational method for the systematic analysis of the mechanism of DDIs using MeSH (Medical Subject Headings) terms from PubMed literature. MeSH term is a controlled vocabulary thesaurus developed by the National Library of Medicine for indexing and annotating articles. Our method can effectively identify DDI-relevant MeSH terms such as drugs, proteins and phenomena with high accuracy. The connections among these MeSH terms were investigated by using co-occurrence heatmaps and social network analysis. Our approach can be used to visualize relationships of DDI terms, which has the potential to help users better understand DDIs. As the volume of PubMed records increases, our method for automatic analysis of DDIs from the PubMed database will become more accurate.

In this paper, we develop a spatio-temporal modeling approach to describe blood and drug flow, as well as drug uptake and elimination, on an approximation of the liver. Extending on previously developed computational approaches, we generate an approximation of a liver, which consists of a portal and hepatic vein vasculature structure, embedded in the surrounding liver tissue. The vasculature is generated via constrained constructive optimization, and then converted to a spatial grid of a selected grid size. Estimates for surrounding upscaled lobule tissue properties are then presented appropriate to the same grid size. Simulation of fluid flow and drug metabolism (hepatic clearance) are completed using discretized forms of the relevant convective-diffusive-reactive partial differential equations for these processes. This results in a single stage, uniformly consistent method to simulate equations for blood and drug flow, as well as drug metabolism, on a 3D structure representative of a liver.