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  • 1
    Electronic Resource
    Electronic Resource
    Springer
    European journal of clinical pharmacology 47 (1995), S. 417-421 
    ISSN: 1432-1041
    Keywords: Phase I trials ; Alanine amino transferase ; healthy volunteers ; adverse experience ; normal range ; liver function test
    Source: Springer Online Journal Archives 1860-2000
    Topics: Chemistry and Pharmacology , Medicine
    Notes: Abstract In Phase I clinical studies, the maximum tolerated dose has to be determined by a case by case analysis sometimes using a laboratory adverse effect, e.g. an increase in alanine amino transferase (ALT). For this reason a threshold to discriminate between significant or non significant adverse changes in ALT is required particularly in Phase I studies, in order to deal with the very common “close to the limit values”. Previous methods (limit of normal range or normal range plus an arbitrary margin) do not solve this problem. The authors propose a new method taking into account the threshold used as inclusion criteria for ALT (R) and the range of spontaneous variations measured under identical Phase I study conditions (V). The (R) and (V) thresholds, respectively, are defined as 50 IU·1−1 and a 50% increase, from baseline. Thus an ALT value is recognized as a “significant adverse experience” if it exceeds 50 IU·1−1 above an increase from baseline exceeding 50% of the baseline value. To highlight the value of the method, it was implemented in a one year period including 8 studies and 134 subjects. The sensitivity, specificity and positive predictive value of various methods were compared. The results showed the following: Six out of 134 subjects had significant adverse changes in ALT (4%); and all these 6 subjects were detected by the proposed new method without error. Eight subjects including two false positives, were detected by an use of the normal range limit, and only 4 were detected using, the 10% margin. Thus, use of the new method showed 1. keeping the normal range limit as the detection threshold led to preserved sensitivity; 2. it reduced the background noise of false positive results related to chance variation around the upper limit, mainly in subjects with a baseline value close to the limit; 3. it allowed better judgment of the significance of a value which lay just beyond the limit when variation from the baseline exceeding the normal range. The new method produced the best combination of sensitivity, specificity and positive predictive value. Given the small number of subjects in the study, further evaluation with a larger population is required. Finally, the proposed new method seems to be a tool easy to use determining the significance of adverse changes in ALT when the values are close to the limit that is common in Phase I studies.
    Type of Medium: Electronic Resource
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  • 2
    Electronic Resource
    Electronic Resource
    Springer
    Journal of optimization theory and applications 90 (1996), S. 139-159 
    ISSN: 1573-2878
    Keywords: Nonlinear parameter estimation ; associative memory ; adaptive training ; linear associative memory matrix ; weighted cost function
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    Notes: Abstract The method of linear associative memory (LAM), a notion from the field of artificial neural nets, has been applied recently in nonlinear parameter estimation. In the LAM method, a model response, nonlinear with respect to the parameters, is approximated linearly by a matrix, which maps inversely from a response vector to a parameter vector. This matrix is determined from a set of initial training parameter vectors and their response vectors, and can be update recursively and adaptively with a pair of newly generated parameter response vectors. The LAM advantage is that it can yield a good estimation of the true parameters from a given observed response, even if the initial training parameter vectors are far from the true values. In this paper, we present a weighted linear associative memory (WLAM) for nonlinear parameter estimation. WLAM improves LAM by taking into account an observed response vector oriented weighting. The basic idea is to weight each pair of parameter response vectors in the cost function such that, if a response vector is closer to the observed one, then this pair plays a more important role in the cost function. This weighting algorithm improves significantly the accuracy of parameter estimation as compared to a LAM without weighting. In addition, we are able to construct the associative memory matrix recursively, while taking the weighting procedure into account, and simultaneously update the ridge parameter α of the cost function further improving the efficiency of the WLAM estimation. These features enable WLAM to be a powerful tool for nonlinear parameter simulation.
    Type of Medium: Electronic Resource
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