Biomedical subjects
Publications and source records attributed to J Sühnel.
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In the field of combination experiments there is wide-spread confusion over definitions, terminology and methods for the evaluation of interaction between biologically active agents. According to our view the widely used isobole approach is the method of choice. In this contribution it is shown how the combination of the classical isobole approach with response surface modeling and computer graphics leads to powerful new methods for the assessment of interaction of biologically active agents. In particular, zero interaction response surfaces, difference response surfaces and interaction functions are proposed. Zero interaction response surfaces represent surfaces which display zero interaction in the whole dose range. Difference response surfaces display the difference between an actual response surface and the corresponding zero interaction response surface. Interaction functions are a generalization of the index of interaction, which describe the dose dependence of this quantity.
In this study, the influence of different stages and transplantation routes of the experimentally widely used solid tumor melanoma B16 on the pharmacokinetics of the antineoplastic agent mitoguazone was investigated in B6D2F1 mice. It could be shown that changes of the pharmacokinetic parameters as well as the distribution pattern of this drug were clearly influenced and dependent on the tumor stage but not by the tumor inoculation route. Advanced melanoma (d16) led to a sharp decrease in the terminal elimination half-life as well as to decreased spleen levels and increased initial liver concentrations of the drug. With respect to the results obtained in leukemia P388-bearing mice it can be concluded that the tumor stage as well as the tumor model are to be considered as important factors in which way and to which extent a tumor may alter the pharmacokinetics of antineoplastic agents.
The criteria used for the evaluation of synergism or antagonism for the combined action of antiviral agents are critically reviewed. The isobole method is the only generally applicable approach which does not require any assumptions about the shape of dose-response curves. A microcomputer-based isobole method is proposed which constructs response surfaces and a complete set of isoboles in the dose range under study from a relatively small number of experiments. The objective of this work is to take a step towards standardization of definitions, terminology, and methods for the evaluation of synergism or antagonism in antiviral drug combination experiments.
The aim of the present study was to investigate the influence of different stages of leukemia P388 on the pharmacokinetics of the antineoplastic agent mitoguazone in mice. It could be shown that, independent of the tumor stage investigated, the total clearance of mitoguazone is slightly reduced reflecting a moderate increase of AUC in the serum of leukemia-bearing animals. Furthermore, in an advanced tumor stage the drug levels in kidneys, liver, spleen and serum were found to be elevated to some extent in comparison to tumor-free controls in contrast to an earlier stage of leukemia. In conclusion, the tumor stage has to be considered as an important factor to which extent a neoplasia may alter the pharmacokinetics of drugs used for anticancer chemotherapy.
In a recent paper (Sühnel & Veckenstedt, 1989, J. theor. Biol. 137, 27) we have proposed a new method of plotting survival data from experimentally virus-infected laboratory animals; the survival diagram. In this diagram two experiments, for which the mean number of virions inoculated is kept fixed but other parameters may vary, are compared. The variations in two basic quantities of survival analysis are simultaneously displayed: the standard mean survival time and the relative mean challenge virus dose, which is via a dose-response relation interrelated with the fraction of animals dying. It is analyzed in which manner variations in the kinetic parameters and the critical virus level necessary to produce a particular effect influence the location of the points of comparison in the survival diagram. The analysis presented is a prerequisite for further applications of this diagram and of the underlying mathematical model.
Although survival analysis is a well-established mathematical discipline, there seem to be almost no attempts in survival modeling for experimentally virus-infected laboratory animals. We have taken up a stochastic approach originally developed by Shortley in the sixties and have applied it to three different types of experimental data: to virus titer determination, to the dose dependence of the mean survival time and to single survival curves. Experience concerning parameter estimation is reported and new ways of working with the model parameters are proposed. A standard mean survival time is defined and suggested as a new quantitative measure of virulence. Moreover, for the comparison of two experiments for which the amount of virions inoculated is kept fixed, but for which other parameters may vary, a new scheme of systematizing survival data from experimentally virus-infected laboratory animals is proposed. It is very likely that the model can be also applied to cancer survival data or any other infectious pathogen.
Modified logistic, Weibull and Gompertz survival models are described which are suitable for the model-based analysis of survival experiments with both zero and non-zero final survivors. They are applied to 3 examples: the influence of food restriction and body fat on the life span of mice and the effect of an immunosuppressant and of an antiviral agent on experimentally virus-infected mice. It is shown that besides the median survival time and the fraction of animals finally surviving the hazard rate at the median survival time, the ratio of maximum to minimum survival time and the area under the survival curve are useful parameters for a more detailed analysis of survival experiments. Hazard plots display differences between experiments more clearly than survival curves. The models described provide a powerful tool which is appropriate for a great variety of different survival experiments. The model-based analysis of survival data is also a prerequisite for the development of computerized data bases on the survival behaviour of laboratory animals. The approach presented can be easily generalized to cases for which spontaneous mortality and mortality due to an experimental challenge interfere.