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The highly polymorphic human cytochrome P450 2D6 enzyme is involved in the metabolism of up to 25% of all marketed drugs and accounts for significant individual differences in response to CYP2D6 substrates. Because of the differences in the multiplicity and substrate specificity of CYP2D family members among species, it is difficult to predict pathways of human CYP2D6-dependent drug metabolism on the basis of animal studies. To create animal models that reflect the human situation more closely and that allow an in vivo assessment of the consequences of differential CYP2D6 drug metabolism, we have developed a novel straightforward approach to delete the entire murine Cyp2d gene cluster and replace it with allelic variants of human CYP2D6. By using this approach, we have generated mouse lines expressing the two frequent human protein isoforms CYP2D6.1 and CYP2D6.2 and an as yet undescribed variant of this enzyme, as well as a Cyp2d cluster knockout mouse. We demonstrate that the various transgenic mouse lines cover a wide spectrum of different human CYP2D6 metabolizer phenotypes. The novel humanization strategy described here provides a robust approach for the expression of different CYP2D6 allelic variants in transgenic mice and thus can help to evaluate potential CYP2D6-dependent interindividual differences in drug response in the context of personalized medicine.
Modulation of muscle-tendon interaction in the human triceps surae during an energy dissipation task
(2017)
Molecular Modeling Approach to the Prediction of Mechanical Properties of Silica-Reinforced Rubbers
(2014)
Recently, we have suggested a nanomechanical model for dissipative loss in filled elastomer networks in the context of the Payne effect. The mechanism is based on a total interfiller particle force exhibiting an intermittent loop, due to the combination of short-range repulsion and dispersion forces with a long-range elastic attraction. The sum of these forces leads, under external strain, to a spontaneous instability of “bonds” between the aggregates in a filler network and attendant energy dissipation. Here, we use molecular dynamics simulations to obtain chemically realistic forces between surface modified silica particles. The latter are combined with the above model to estimate the loss modulus and the low strain storage modulus in elastomers containing the aforementioned filler-compatibilizer systems. The model is compared to experimental dynamic moduli of silica filled rubbers. We find good agreement between the model predictions and the experiments as function of the compatibilizer's molecular structure and its bulk concentration.