This paper describes two general methods for estimating ADIs that circumvent some of the limitations inherent in current approaches. The first method is based on a graphic presentation of toxicity data and is also shown to be useful for estimating acceptable intakes for durations of toxicant

) HFA mice, and (iii) human volunteers. The ‘No Microbial Effect Level ’ (NoMEL) of an antibiotic on human ora, measured in one of these models, is used to set the acceptable daily intake (ADI) for human consumers. Human volunteer trials are most relevant to set microbiological ADIs, and may

The minimal inhibitory concentration (MIC) of an antimicrobial agent for a microbial population (MIC50, obs and MIC90, obs) is an interpolated value determined for antibacterial drugs by in vitro methods. Many studies have tried to determine the correlation between the MIC50, obs or MIC90, obs value and the physicochemical parameters to allow quantitaive structure activity relationship (QSAR) predictions of efficacy. A rigorous evaluation of approaches to this problem is presented here. In order to find a cor-relation between chemical structure and the derivatives of the MIC values for 9 indicatory bacterial strains, it is necessary to employ a number of physicochemical parameters in combination. Only an arithmetic expression composed of many features illustrating the chemical structure of the molecule can be linked to the fMIC50, obs value. This article demonstrated that, despite the complexity of the MIC value used as the end point, it is possible to validate the model in a limited extent.

The minimal inhibitory concentration (MIC) of an antimicrobial agent for a microbial population (MIC50, obs and MIC90, obs) is an interpolated value determined for antibacterial drugs by in vitro methods. Many studies have tried to determine the correlation between the MIC50, obs or MIC90, obs value and the physicochemical parameters to allow quantitaive structure activity relationship (QSAR) predictions of efficacy. A rigorous evaluation of approaches to this problem is presented here. In order to find a cor-relation between chemical structure and the derivatives of the MIC values for 9 indicatory bacterial strains, it is necessary to employ a number of physicochemical parameters in combination. Only an arithmetic expression composed of many features illustrating the chemical structure of the molecule can be linked to the fMIC50, obs value. This article demonstrated that, despite the complexity of the MIC value used as the end point, it is possible to validate the model in a limited extent.

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