INDEX human, 239 predictability

North, R. A., and RUey, V. (1989), W/INDEX A Predictive Model of Operator Workload, in Applications of Human Performance Models to System Design, G. McMillan et al., Eds., Plenum Press, New York. 

For the A (10" ) index, the training set contains 45.2 % compounds with an ejqtressed activity, including 24.0% componnds with a high activity. Hie primary screening did not employ in silico methods, so the percentage of the obtained active substances can be regarded as an indication of the accnracy of the intuitive human prediction performed... 

Extrapolations of therapeutic index and toxicity data from animals to humans are reasonably predictive for many but not for all toxicities. Seeking an improved process, a Predictive Safety Testing Consortium of five of America s largest pharmaceutical companies with an advisory role by the Food and Drug Administration (FDA) has been formed to share internally developed laboratory methods to predict the safety of new treatments before they are tested in humans. In 2007, this group presented to the FDA a set of biomarkers for early kidney damage. 

Recently, some models have been derived to analyze the occurrence of interactive joint action in binary single-species toxicity experiments (Jonker 2003). Such detailed analysis models are well equipped to serve as null models for a precision analysis of experimental data, next to the generalized use of concentration addition and response addition as alternative null models. However, in our opinion these models are not applicable to quantitatively predict the combined toxicity of mixtures with a complexity that is prevalent in a contaminated environment, because the parameters of such models are typically not known. Recently a hazard index (Hertzberg and Teus-chler 2002) was developed for human risk assessment for exposure to multiple chemicals. Based on a weight-of-evidence approach, this index can be equipped with an option to adjust the index value for possible interactions between toxicants. It seems plausible that a comparable kind of technique could be applied in ecotoxicological risk assessments of mixtures for single species. However, at present, the widespread application of this approach is prevented by lack of available information. 

Figure 2. Diagnostics for the Wonderland approximating model (a) actual human development index (HDI) values versus their cross-validation predictions (b) standardized cross-validation residuals versus cross-validation predictions.

Certainly, most in vitro assays cannot predict (a) metabolite effects, (b) adverse effects associated by accumulation, and (c) drug actions at targets with mutations. Nevertheless, early safety-profiling assays have the power to detect trends toward certain adverse reactions and organ toxicity and to navigate drug discovery teams away from them. The major difficulty is estimating the impact of the data on the clinical performance determined by the human therapeutic index. 

EPA recommends three approaches (1) if the toxicity data on mixture of concern are available, the quantitative risk assessment is done directly form these preferred data (2) when toxicity data are not available for the mixture of concern, data of a sufficiently similar mixture can be used to derive quantitative risk assessment for mixture of concern and (3) if the data are not available for both mixture of concern and the similar mixture, mixture effects can be evaluated from the toxicity data of components. According to EPA, the dose-additive models reasonably predict the systemic toxicity of mixtures composed of similar (dose addition) and dissimilar (response addition) compounds. Therefore, the potential health risk of a mixture can be estimated using a hazard index (HI) derived by summation of the ratios of the actual human exposure level to estimated maximum acceptable level of each toxicant. A HI near to unity is suggestive of concern for public health. This approach will hold true for the mixtures that do not deviate from additivity and do not consider the mode of action of chemicals. Modifications of the standard HI approach are being developed to take account of the data on interactions. 

Figure 2. Estimation of DCS class based on the GastroPlus software of SimulationPlus. Based on the dimensionless Dose, Absorption and Dissolution Numbers, drugs can be defined as DCS Class 1 (A), 11 (B), 111 (C) and IV (D) based solely on structural information. The program gives predicted values for solubility, pKa and effective human intestinal permeability in order to suggest a fraction absorbed into the portal vein. (See color insert after Index.)...

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