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"Reif, M"
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An Introduction to Terminology and Methodology of Chemical Synergy—Perspectives from Across Disciplines
The idea of synergistic interactions between drugs and chemicals has been an important issue in the biomedical world for over a century. As complex diseases, especially cancer, are being treated with various drug cocktails, understanding the interactions among these drugs is increasingly vital to ensuring successful treatment regimens. However, the idea of synergy is not limited to only the biomedical realm and these ideas have developed across many different disciplines, as well. In this review, we first discuss the various terminology surrounding the idea of synergy, providing a comprehensive list of terms defined across numerous disciplines. We then review the most common methodology for detection and quantification of synergy, including the two most prominent reference models for describing additive interactions: Loewe Additivity and Bliss Independence. We also discuss advantages and limitations to each method, with a focus on the Chou-Talalay Combination Index method. Finally, we describe how methods development and terminology have developed among disciplines outside of biomedicine and pharmacology, to synthesize the literature for readers.
Journal Article
ToxPi Graphical User Interface 2.0: Dynamic exploration, visualization, and sharing of integrated data models
Background
Drawing integrated conclusions from diverse source data requires synthesis across multiple types of information. The ToxPi (Toxicological Prioritization Index) is an analytical framework that was developed to enable integration of multiple sources of evidence by transforming data into integrated, visual profiles. Methodological improvements have advanced ToxPi and expanded its applicability, necessitating a new, consolidated software platform to provide functionality, while preserving flexibility for future updates.
Results
We detail the implementation of a new graphical user interface for ToxPi (Toxicological Prioritization Index) that provides interactive visualization, analysis, reporting, and portability. The interface is deployed as a stand-alone, platform-independent Java application, with a modular design to accommodate inclusion of future analytics. The new ToxPi interface introduces several features, from flexible data import formats (including legacy formats that permit backward compatibility) to similarity-based clustering to options for high-resolution graphical output.
Conclusions
We present the new ToxPi interface for dynamic exploration, visualization, and sharing of integrated data models. The ToxPi interface is freely-available as a single compressed download that includes the main Java executable, all libraries, example data files, and a complete user manual from
http://toxpi.org
.
Journal Article
Predictors of fatigue improvement in multimodal, multimodal-aerobic and aerobic exercise intervention studies in breast cancer survivors with cancer-related fatigue
Cancer-related fatigue (CRF) is common among breast cancer (BC) survivors. In addition to aerobic training, psychoeducation, sleep education/restriction, and mindfulness-based therapies are shown to reduce CRF. This study investigates the predictive effect of hygiogenetic and salutogenetic concepts, such as autonomic regulation (aR), self-regulation (SRS) and internal coherence (ICS) along with sleep quality (PSQI) and quality of life (EORTC QLQ C30, including cognitive, emotional and physical functioning) on the success of CRF therapies. Two studies are analyzed: a pilot (CRF-1) with 36 BC patients and a follow-up study (CRF2) with 126 patients either randomized or assigned to therapy by preference. All parameters were assessed at baseline and 10 weeks post-intervention (T1), and in CRF-2 also six months later (T2), and after four years (T3). Multiple linear regression models were applied. Trait aR and ICS are shown to be significant predictors of CRF when all timepoints of the CRF-2 study are included (β
Trait aR
= −0.170, df = 70,
p
< 0.001; β
ICS
= −0.210, df = 70,
p
< 0.01) as well as when combined with data of the CRF-1 study (β
Trait aR
= −0.144, df = 101,
p
= 0.001; β
ICS
= −0.211, df = 101,
p
< 0.01). Cognitive Function showed a borderline significance only at T3 and when all CRF-2 study time measurements were combined (β
CF
= −0.073, df = 70,
p
< 0.05). Using data from two studies with multimodal, aerobic and combined CRF treatments, this study highlights Trait aR and ICS at baseline as long-term predictors of CRF even four years after intervention. A stable autonomic regulation including rest/activity regulation and internal coherence are predictors for therapy response of a multimodal, combination or aerobic treatment in breast cancer survivors with CRF.
Journal Article
High-throughput screening and genome-wide analyses of 44 anticancer drugs in the 1000 Genomes cell lines reveals an association of the NQO1 gene with the response of multiple anticancer drugs
Cancer patients exhibit a broad range of inter-individual variability in response and toxicity to widely used anticancer drugs, and genetic variation is a major contributor to this variability. To identify new genes that influence the response of 44 FDA-approved anticancer drug treatments widely used to treat various types of cancer, we conducted high-throughput screening and genome-wide association mapping using 680 lymphoblastoid cell lines from the 1000 Genomes Project. The drug treatments considered in this study represent nine drug classes widely used in the treatment of cancer in addition to the paclitaxel + epirubicin combination therapy commonly used for breast cancer patients. Our genome-wide association study (GWAS) found several significant and suggestive associations. We prioritized consistent associations for functional follow-up using gene-expression analyses. The NAD(P)H quinone dehydrogenase 1 ( NQO1 ) gene was found to be associated with the dose-response of arsenic trioxide, erlotinib, trametinib, and a combination treatment of paclitaxel + epirubicin. NQO1 has previously been shown as a biomarker of epirubicin response, but our results reveal novel associations with these additional treatments. Baseline gene expression of NQO1 was positively correlated with response for 43 of the 44 treatments surveyed. By interrogating the functional mechanisms of this association, the results demonstrate differences in both baseline and drug-exposed induction.
Journal Article
Leveraging high-throughput screening data, deep neural networks, and conditional generative adversarial networks to advance predictive toxicology
There are currently 85,000 chemicals registered with the Environmental Protection Agency (EPA) under the Toxic Substances Control Act, but only a small fraction have measured toxicological data. To address this gap, high-throughput screening (HTS) and computational methods are vital. As part of one such HTS effort, embryonic zebrafish were used to examine a suite of morphological and mortality endpoints at six concentrations from over 1,000 unique chemicals found in the ToxCast library (phase 1 and 2). We hypothesized that by using a conditional generative adversarial network (cGAN) or deep neural networks (DNN), and leveraging this large set of toxicity data we could efficiently predict toxic outcomes of untested chemicals. Utilizing a novel method in this space, we converted the 3D structural information into a weighted set of points while retaining all information about the structure. In vivo toxicity and chemical data were used to train two neural network generators. The first was a DNN (Go-ZT) while the second utilized cGAN architecture (GAN-ZT) to train generators to produce toxicity data. Our results showed that Go-ZT significantly outperformed the cGAN, support vector machine, random forest and multilayer perceptron models in cross-validation, and when tested against an external test dataset. By combining both Go-ZT and GAN-ZT, our consensus model improved the SE, SP, PPV, and Kappa, to 71.4%, 95.9%, 71.4% and 0.673, respectively, resulting in an area under the receiver operating characteristic (AUROC) of 0.837. Considering their potential use as prescreening tools, these models could provide in vivo toxicity predictions and insight into the hundreds of thousands of untested chemicals to prioritize compounds for HT testing.
Journal Article
Four-year follow-up on fatigue and sleep quality of a three-armed partly randomized controlled study in breast cancer survivors with cancer-related fatigue
Cancer-related fatigue (CRF) is a frequent long-term symptom in non-metastasized breast cancer patients (BC). This 4-year follow-up intended to compare the long-term effects of a 10-week multimodal therapy (MT: sleep education, psychoeducation, eurythmy- and painting therapy) and combination therapy [CT: MT plus aerobic training (AT)] to AT-control. BC-patients were randomized or allocated by preference to three arms in a comprehensive cohort study. Primary outcome was a composite score including Pittsburgh Sleep Quality Index (PSQI) and Cancer Fatigue Scale (CFS-D), captured at baseline, after 10 weeks of intervention (T1), 6 months later (T2), and after 4 years (T3). We exploratively tested for superiority of MT and CT versus AT after 4 years (T3) based on the statistical model of the main analysis. Of 126 (65 randomized) BC-patients included, 105 started treatments and 79 were re-assessed for long-term effects (T3). MT and CT were superior over AT after 4 years regarding PSQI/CFS-D and PSQI sum-score, respectively (all
p
< 0.05), but not for CFS-D. The multimodal and combination treatment for breast cancer patients with CRF indicates sustainable long-term superiority over aerobic training for the outcomes sleep quality and combined sleep quality/fatigue. A confirmative randomized controlled trial is warranted.
Journal Article
Endocrine Profiling and Prioritization of Environmental Chemicals Using ToxCast Data
Background: The prioritization of chemicals for toxicity testing is a primary goal of the U.S. Environmental Protection Agency (EPA) ToxCast™ program. Phase I of ToxCast used a battery of 467 in vitro, high-throughput screening assays to assess 309 environmental chemicals. One important mode of action leading to toxicity is endocrine disruption, and the U.S. EPA's Endocrine Disraptor Screening Program (EDSP) has been charged with screening pesticide chemicals and environmental contaminants for their potential to affect the endocrine systems of humans and wildlife. Objective: The goal of this study was to develop a flexible method to facilitate the rational prioritization of chemicals for further evaluation and demonstrate its application as a candidate decisionsupport tool for EDSP. Methods: Focusing on estrogen, androgen, and thyroid pathways, we defined putative endocrine profiles and derived a relative rank or score for the entire ToxCast library of 309 unique chemicals. Effects on other nuclear receptors and xenobiotic metabolizing enzymes were also considered, as were pertinent chemical descriptors and pathways relevant to endocrine-mediated signaling. Results: Combining multiple data sources into an overall, weight-of-evidence Toxicological Priority Index (ToxPi) score for prioritizing further chemical testing resulted in more robust conclusions than any single data source taken alone. Conclusions: Incorporating data from in vitro assays, chemical descriptors, and biological pathways in this prioritization schema provided a flexible, comprehensive visualization and ranking of each chemical's potential endocrine activity. Importantly, ToxPi profiles provide a transparent visualization of the relative contribution of all information sources to an overall priority ranking, lhe method developed here is readily adaptable to diverse chemical prioritization tasks.
Journal Article
Deep autoencoder-based behavioral pattern recognition outperforms standard statistical methods in high-dimensional zebrafish studies
Zebrafish have become an essential model organism in screening for developmental neurotoxic chemicals and their molecular targets. The success of zebrafish as a screening model is partially due to their physical characteristics including their relatively simple nervous system, rapid development, experimental tractability, and genetic diversity combined with technical advantages that allow for the generation of large amounts of high-dimensional behavioral data. These data are complex and require advanced machine learning and statistical techniques to comprehensively analyze and capture spatiotemporal responses. To accomplish this goal, we have trained semi-supervised deep autoencoders using behavior data from unexposed larval zebrafish to extract quintessential “normal” behavior. Following training, our network was evaluated using data from larvae shown to have significant changes in behavior (using a traditional statistical framework) following exposure to toxicants that include nanomaterials, aromatics, per- and polyfluoroalkyl substances (PFAS), and other environmental contaminants. Further, our model identified new chemicals (Perfluoro-n-octadecanoic acid, 8-Chloroperfluorooctylphosphonic acid, and Nonafluoropentanamide) as capable of inducing abnormal behavior at multiple chemical-concentrations pairs not captured using distance moved alone. Leveraging this deep learning model will allow for better characterization of the different exposure-induced behavioral phenotypes, facilitate improved genetic and neurobehavioral analysis in mechanistic determination studies and provide a robust framework for analyzing complex behaviors found in higher-order model systems.
Journal Article