Derivatives of nitrogen mustard anticancer agents with improved cytotoxicity

In previous studies, we demonstrated that esters of bendamustine containing a basic moiety are far more cytotoxic anticancer agents than their parent compound and that the substitution of the labile ester moiety by a branched ester or an amide markedly increases stability in the blood plasma. In the current study, we showed that this substitution was bioisosteric. Aiming at increased cytotoxicity, we introduced the same modification to related nitrogen mustards: 6‐isobendamustine, chlorambucil, and melphalan. The synthesis was accomplished using the coupling reagents N,N′‐dicyclohexylcarbodiimide or 2‐(1H‐benzotriazole‐1‐yl)‐1,1,3,3‐tetramethylaminium tetrafluoroborate. Cytotoxicity against a panel of diverse cancer cells (carcinoma, sarcoma, and malignant melanoma) was assessed in a kinetic chemosensitivity assay. The target compounds showed cytotoxic or cytocidal effects at concentrations above 1 µM: a striking enhancement over bendamustine and 6‐isobendamustine, both ineffective against the selected cancer cells at concentrations up to 50 µM, and a considerable improvement over chlorambucil, showing some potency only against the sarcoma cells. Melphalan was almost as effective as the target compounds—derivatization only provided a small improvement. The novel cytostatics are of interest as model compounds for analyzing a correlation between cytotoxicity and membrane transport and for the treatment of malignancies

neuralnet: Training of neural networks

Artificial neural networks are applied in many situations. neuralnet is built to train multi-layer perceptrons in the context of regression analyses, i.e. to approximate functional relationships between covariates and response variables. Thus, neural networks are used as extensions of generalized linear models. neuralnet is a very flexible package. The backpropagation algorithm and three versions of resilient backpropagation are implemented and it provides a custom-choice of activation and error function. An arbitrary number of covariates and response variables as well as of hidden layers can theoretically be included

A Multi-decadal Mediumresolution Wind, Wave and Storm Surge Hindcast Suitable for Coastal Applications. This Volume

Abstract A multi-decadal medium-resolution met-ocean hindcast for the North Sea and parts of the Northeast Atlantic is presented. The hindcast is based on a dynamical downscaling of the global NCEP/NCAR weather re-analyses using some simple data assimilation techniques. It is shown that the reconstructed wind, wave and storm surge climate agree reasonably with available in-situ observations. Analysis of the wind, wave and storm surge climate based on hindcast data reveals that they have undergone considerable variations from year to year and on longer time scales. An increase in storm activity from the beginning of the hindcast period has levelled off later and was replaced by a downward trend over the northeast North Atlantic. This behaviour closely corresponds to that based on the analysis of proxies for storm activity. Changes in extreme wave and storm surge conditions show a similar pattern over much of the North Sea area

Neural networks for modeling gene-gene interactions in association studies

<p>Abstract</p> <p>Background</p> <p>Our aim is to investigate the ability of neural networks to model different two-locus disease models. We conduct a simulation study to compare neural networks with two standard methods, namely logistic regression models and multifactor dimensionality reduction. One hundred data sets are generated for each of six two-locus disease models, which are considered in a low and in a high risk scenario. Two models represent independence, one is a multiplicative model, and three models are epistatic. For each data set, six neural networks (with up to five hidden neurons) and five logistic regression models (the null model, three main effect models, and the full model) with two different codings for the genotype information are fitted. Additionally, the multifactor dimensionality reduction approach is applied.</p> <p>Results</p> <p>The results show that neural networks are more successful in modeling the structure of the underlying disease model than logistic regression models in most of the investigated situations. In our simulation study, neither logistic regression nor multifactor dimensionality reduction are able to correctly identify biological interaction.</p> <p>Conclusions</p> <p>Neural networks are a promising tool to handle complex data situations. However, further research is necessary concerning the interpretation of their parameters.</p

Genetic loci and prioritization of genes for kidney function decline derived from a meta-analysis of 62 longitudinal genome-wide association studies

Estimated glomerular filtration rate (eGFR) reflects kidney function. Progressive eGFR-decline can lead to kidney failure, necessitating dialysis or transplantation. Hundreds of loci from genome-wide association studies (GWAS) for eGFR help explain population cross section variability. Since the contribution of these or other loci to eGFR-decline remains largely unknown, we derived GWAS for annual eGFR-decline and meta-analyzed 62 longitudinal studies with eGFR assessed twice over time in all 343,339 individuals and in high-risk groups. We also explored different covariate adjustment. Twelve genome-wide significant independent variants for eGFR-decline unadjusted or adjusted for eGFR-baseline (11 novel, one known for this phenotype), including nine variants robustly associated across models were identified. All loci for eGFR-decline were known for cross-sectional eGFR and thus distinguished a subgroup of eGFR loci. Seven of the nine variants showed variant-by-age interaction on eGFR cross section (further about 350,000 individuals), which linked genetic associations for eGFR-decline with age-dependency of genetic cross-section associations. Clinically important were two to four-fold greater genetic effects on eGFR-decline in high-risk subgroups. Five variants associated also with chronic kidney disease progression mapped to genes with functional in-silico evidence (UMOD, SPATA7, GALNTL5, TPPP). An unfavorable versus favorable nine-variant genetic profile showed increased risk odds ratios of 1.35 for kidney failure (95% confidence intervals 1.03-1.77) and 1.27 for acute kidney injury (95% confidence intervals 1.08-1.50) in over 2000 cases each, with matched controls). Thus, we provide a large data resource, genetic loci, and prioritized genes for kidney function decline, which help inform drug development pipelines revealing important insights into the age-dependency of kidney function genetics

Künstliche neuronale Netze in der genetischen Epidemiologie

Gene-gene and gene-environment interactions play an important role in the etiological pathway of many complex diseases. However, common statistical methods like regression models have problems to capture the complex interplay between genetic and non-genetic factors. Artificial neural networks provide a great flexibility to model functional relationships and thus are a promising statistical tool to handle the complexity of biological interactions. The aim of this thesis is to explore the ability of neural networks to capture different structures of gene-gene and gene-environment interactions and to identify gene-gene interactions in simulation studies. In addition, the consistency of the estimated weights is investigated for non-identified neural networks. In summary, neural networks prove successful for exploratory analyses and particularly can be used if limited information on the kind of functional relationship between influencing factors and the investigated outcome is available