Statistical analysis of concentration-dependent high-dimensional gene expression data

Understanding the behavior of genes as a response to external influences, such as radiation or chemicals, on a fundamental level is one of the great challenges of modern biology. In specific, the investigation of chemically-induced toxicity is of major importance since it is crucial for the identification of biomarkers and the development of drugs. One approach to accomplish this objective utilizes toxicogenomics which is based upon the combination of toxicology and the analysis of genome-wide gene expression data. This research field uses the technology of microarrays which allows the simultaneous measurement of the expression of tens of thousands of genes. The thesis focuses on three topics which often arise in the context of gene expression analysis: First, the identification and characterization of genes associated with certain modes of action, second, the detection of biomarker candidates in the in vitro system for the prediction of toxicity in vivo and third, the identification of critical concentrations at which a pre-specified effect level is exceeded. To better understand the key principles of transcriptome changes, a genome-wide gene expression analysis is performed. For this, the Open TG-GATEs database is used which contains data for more than 150 compounds applied to cells from rats (liver, kidney) and to human hepatocytes using different concentration and time sets. Special attention is drawn to statistical challenges arising from working with large data sets. Besides the curse of dimensionality (many more variables than observations) and the small number of replicates, the statistical analysis is faced with additional complexity including batch effects and implausible concentration progressions. To address this issue in a general manner, a pipeline involving several curation steps and a systematic strategy for the identification of consensus genes is proposed. Regarding, the third topic of this thesis, a model-based approach is applied to gene expression data to detect concentrations with critical changes in gene expression. Typically, only measured concentrations are considered as potential candidates for alert concentrations. Based on the assumption that the response dependency of the dose can be described by a sigmoidal function, a four-parameter log-logistic (4pLL) model is fitted to the data. Two alert concentrations referring to critical compound concentrations are estimated from the fitted average trend and compared with those of the classical naïve approach where for each measured concentration separately it is tested if the critical effect level is exceeded. The results are evaluated in a simulation study and in a real dose-response study. The thesis serves to gain a better understanding to whether a model-based approach yields more accurate results in terms of predicting critical concentrations than the classical one which is often used for the analysis of large-scale toxicogenomics data sets

The Core of Care Management: The Role of Authentic Relationships in Caring for Patients with Frequent Hospitalizations.

In the movement to improve the health of patients with multiple chronic conditions and vulnerabilities, while reducing the need for hospitalizations, care management programs have garnered wide attention and support. The qualitative data presented in this paper sheds new light on key components of successful chronic care management programs. By going beyond a task- and temporal-based framework, this analysis identifies and defines the importance of authentic healing relationships in driving individual and systemic change. Drawing on the voices of 30 former clients of the Camden Coalition of Healthcare Providers, the investigators use qualitative methods to identify and elaborate the core elements of the authentic healing relationship-security, genuineness, and continuity-a relationship that is linked to patient motivation and active health management. Although not readily found in the traditional health care delivery system, these authentic healing relationships present significant implications for addressing the persistent health-related needs of patients with frequent hospitalizations. (Population Health Management 2016;19:248-256)

Prediction of human drug-induced liver injury (DILI) in relation to oral doses and blood concentrations

Drug-induced liver injury (DILI) cannot be accurately predicted by animal models. In addition, currently available in vitro methods do not allow for the estimation of hepatotoxic doses or the determination of an acceptable daily intake (ADI). To overcome this limitation, an in vitro/in silico method was established that predicts the risk of human DILI in relation to oral doses and blood concentrations. This method can be used to estimate DILI risk if the maximal blood concentration (Cmax) of the test compound is known. Moreover, an ADI can be estimated even for compounds without information on blood concentrations. To systematically optimize the in vitro system, two novel test performance metrics were introduced, the toxicity separation index (TSI) which quantifies how well a test differentiates between hepatotoxic and non-hepatotoxic compounds, and the toxicity estimation index (TEI) which measures how well hepatotoxic blood concentrations in vivo can be estimated. In vitro test performance was optimized for a training set of 28 compounds, based on TSI and TEI, demonstrating that (1) concentrations where cytotoxicity first becomes evident in vitro (EC10) yielded better metrics than higher toxicity thresholds (EC50); (2) compound incubation for 48 h was better than 24 h, with no further improvement of TSI after 7 days incubation; (3) metrics were moderately improved by adding gene expression to the test battery; (4) evaluation of pharmacokinetic parameters demonstrated that total blood compound concentrations and the 95%-population-based percentile of Cmax were best suited to estimate human toxicity. With a support vector machine-based classifier, using EC10 and Cmax as variables, the cross-validated sensitivity, specificity and accuracy for hepatotoxicity prediction were 100, 88 and 93%, respectively. Concentrations in the culture medium allowed extrapolation to blood concentrations in vivo that are associated with a specific probability of hepatotoxicity and the corresponding oral doses were obtained by reverse modeling. Application of this in vitro/in silico method to the rat hepatotoxicant pulegone resulted in an ADI that was similar to values previously established based on animal experiments. In conclusion, the proposed method links oral doses and blood concentrations of test compounds to the probability of hepatotoxicity

Practical implementation of the partial ordering continual reassessment method in a Phase I combination-schedule dose-finding trial.

Funder: Merck; Id: http://dx.doi.org/10.13039/100009945There is a growing medical interest in combining several agents and optimizing their dosing schedules in a single trial in order to optimize the treatment for patients. Evaluating at doses of several drugs and their scheduling in a single Phase I trial simultaneously possess a number of statistical challenges, and specialized methods to tackle these have been proposed in the literature. However, the uptake of these methods is slow and implementation examples of such advanced methods are still sparse to date. In this work, we share our experience of proposing a model-based partial ordering continual reassessment method (POCRM) design for three-dimensional dose-finding in an oncology trial. In the trial, doses of two agents and the dosing schedule of one of them can be escalated/de-escalated. We provide a step-by-step summary on how the POCRM design was implemented and communicated to the trial team. We proposed an approach to specify toxicity orderings and their a-priori probabilities, and developed a number of visualization tools to communicate the statistical properties of the design. The design evaluation included both a comprehensive simulation study and considerations of the individual trial behavior. The study is now enrolling patients. We hope that sharing our experience of the successful implementation of an advanced design in practice that went through evaluations of several health authorities will facilitate a better uptake of more efficient methods in practice

Comparison of observation-based and model-based identification of alert concentrations from concentration–expression data

MOTIVATION: An important goal of concentration–response studies in toxicology is to determine an ‘alert’ concentration where a critical level of the response variable is exceeded. In a classical observation-based approach, only measured concentrations are considered as potential alert concentrations. Alternatively, a parametric curve is fitted to the data that describes the relationship between concentration and response. For a prespecified effect level, both an absolute estimate of the alert concentration and an estimate of the lowest concentration where the effect level is exceeded significantly are of interest. RESULTS: In a simulation study for gene expression data, we compared the observation-based and the model-based approach for both absolute and significant exceedance of the prespecified effect level. Results show that, compared to the observation-based approach, the model-based approach overestimates the true alert concentration less often and more frequently leads to a valid estimate, especially for genes with large variance

Toxicogenomics directory of chemically exposed human hepatocytes

A long-term goal of numerous research projects is to identify biomarkers for in vitro systems predicting toxicity in vivo. Often, transcriptomics data are used to identify candidates for further evaluation. However, a systematic directory summarizing key features of chemically influenced genes in human hepatocytes is not yet available. To bridge this gap, we used the Open TG-GATES database with Affymetrix files of cultivated human hepatocytes incubated with chemicals, further sets of gene array data with hepatocytes from human donors generated in this study, and publicly available genome-wide datasets of human liver tissue from patients with non-alcoholic steatohepatitis (NASH), cirrhosis, and hepatocellular cancer (HCC). After a curation procedure, expression data of 143 chemicals were included into a comprehensive biostatistical analysis. The results are summarized in the publicly available toxicotranscriptomics directory ( http://wiki.toxbank.net/toxicogenomics-map/ ) which provides information for all genes whether they are up- or downregulated by chemicals and, if yes, by which compounds. The directory also informs about the following key features of chemically influenced genes: (1) Stereotypical stress response. When chemicals induce strong expression alterations, this usually includes a complex but highly reproducible pattern named 'stereotypical response.' On the other hand, more specific expression responses exist that are induced only by individual compounds or small numbers of compounds. The directory differentiates if the gene is part of the stereotypical stress response or if it represents a more specific reaction. (2) Liver disease-associated genes. Approximately 20 % of the genes influenced by chemicals are up- or downregulated, also in liver disease. Liver disease genes deregulated in cirrhosis, HCC, and NASH that overlap with genes of the aforementioned stereotypical chemical stress response include CYP3A7, normally expressed in fetal liver; the phase II metabolizing enzyme SULT1C2; ALDH8A1, known to generate the ligand of RXR, one of the master regulators of gene expression in the liver; and several genes involved in normal liver functions: CPS1, PCK1, SLC2A2, CYP8B1, CYP4A11, ABCA8, and ADH4. (3) Unstable baseline genes. The process of isolating and the cultivation of hepatocytes was sufficient to induce some stress leading to alterations in the expression of genes, the so-called unstable baseline genes. (4) Biological function. Although more than 2,000 genes are transcriptionally influenced by chemicals, they can be assigned to a relatively small group of biological functions, including energy and lipid metabolism, inflammation and immune response, protein modification, endogenous and xenobiotic metabolism, cytoskeletal organization, stress response, and DNA repair. In conclusion, the introduced toxicotranscriptomics directory offers a basis for a rationale choice of candidate genes for biomarker evaluation studies and represents an easy to use source of background information on chemically influenced genes

Impairment of human neural crest cell migration by prolonged exposure to interferon-beta

Human cell-based toxicological assays have been used successfully to detect known toxicants, and to distinguish them from negative controls. However, there is at present little experience on how to deal with hits from screens of compounds with yet unknown hazard. As a case study to this issue, we characterized human interferon-beta (IFNβ) as potential developmental toxicant affecting neural crest cells (NCC). The protein was identified as a hit during a screen of clinically used drugs in the 'migration inhibition of neural crest' (MINC) assay. Concentration-response studies in the MINC combined with immunocytochemistry and mRNA quantification of cellular markers showed that IFNβ inhibited NCC migration at concentrations as low as 20 pM. The effective concentrations found here correspond to levels found in human plasma, and they were neither cytostatic nor cytotoxic nor did they did they affect the differentiation state and overall phenotype of NCC. Data from two other migration assays confirmed that picomolar concentration of IFNβ reduced the motility of NCC, while other interferons were less potent. The activation of JAK kinase by IFNβ, as suggested by bioinformatics analysis of the transcriptome changes, was confirmed by biochemical methods. The degree and duration of pathway activation correlated with the extent of migration inhibition, and pharmacological block of this signaling pathway before, or up to 6 h after exposure to the cytokine prevented the effects of IFNβ on migration. Thus, the reduction of vital functions of human NCC is a hitherto unknown potential hazard of endogenous or pharmacologically applied interferons.publishe