Toxicity predictors

Arthritis causing inflammatory and degenerative changes around joints affects 43 million in the United States, and CDC projects that this will rise to 60 million by 2020. It can be caused by more than 100 different diseases, but the commonest are osteoarthritis and rheumatoid arthritis. New medications, such as the anti-tumor necrotic factor a-blockers, raise fresh challenges to clinical study methodology because of limitations on nonclinical toxicity predictors and the application of biologic measurements on a traditional drug appraisal system. 

Toxicity Amelioration. Cancer researchers traditionally have not focused their attention on the question of toxicity amehoration. This is partiy attributed to the lack of predictive animal models for human toxicities. For example, the preclinical rat model, used as a predictor of myelosuppression, has failed to predict myelosuppression in humans in clinical trials. In addition, reduction of one toxicity may result in the emergence of another, more serious problem. Research efforts to address the problem of toxicity amelioration has progressed in several directions. The three most prominent areas are analogue synthesis, chemoprotection, and dmg targeting. 

The most important potential complication of phenol-based peels is cardiotoxicity. Phenol is directly toxic to myocardium. Studies in rats have shown a decrease in myocardial contraction and in electrical activity following systemic exposure to phenol [i6]. Since fatal doses ranged widely in these studies, it seems that individual sensitivity of myocardium to this chemical exists. In humans neither sex/age nor previous cardiac history/blood phenol levels are accurate predictors for cardiac arrhythmia susceptibility [17]. 

No. The FDA went too far. Aflatoxins can indeed cause liver toxicity in animals and are also carcinogenic. But they produce these adverse effects only at levels far above the FDA set limit. We should ensure some safety margin to protect humans, but 20 ppb is unnecessarily low and the policy that there is no safe level is not supported by scientific studies. Indeed, it s not even certain that aflatoxins represent a cancer risk to humans because animal testing is not known to be a reliable predictor of human risk. Moreover, the carcinogenic potency of aflatoxins varies greatly even among the several animal species in which they have been tested. Human evidence that aflatoxins cause cancer is unsubstantiated. There is no sound scientific basis for the FDA s position. 

A reliable druglikeness predictor would give high prediction scores only to compounds that have satisfactory properties based on aU of these criteria, or in other words, there would be few false positives. There has been considerable effort expended over the last 10 to 20 years in modeling individual components of this process, including solubility [18-36], ADME properties [53-70], and toxicities [71-88]. Individually, each of tliese predictions has false positives and false negatives, so it is difficult to expect... 

Pharmagene offers various services on metabolism predictors, drug-drug interactions, toxicity, Cytochrome P450 (CYP450), hepatocytes, MetMatrix , and drug metabolism (333). 

From a purely pragmatic perspective, it is clear that reactive metabolites are linked with toxicity and that a circumstantial link can be made to idiosyncratic toxicides. Consequently, even though the mechanism of this toxicity is not fully understood, since assays are available to measure the potential for bioactivation in an ideal world one would not carry this liability forward. Conversely, it is not an ideal world, all drug molecules have challenges and the definition of therapeutic index (i.e., the ratio between the toxic exposure and the therapeutic exposure) is critical. Covalent binding of reactive metabolites to macromolecules is a crude measure and not a full predictor of toxicity and it is well known that toxicity can be ameliorated by a lower dose. Furthermore, the so-called definitive assays require radiolabeled drug material which is expensive and generally slow to produce. 

Since most chemicals caused different phenotypic outcomes between the rats and rabbits, species-specific models were analyzed, with 251 chemicals evaluated in the rat model and 234 in the rabbit (Fig. 2). Cross-validation balanced accuracies in the resulting classification models were 71% for the rat model (12 features), and 74% for the rabbit model (7 features). Each model contained positive predictors or assay features generally affected by the developmental toxicants (as defined above) and negative predictors or assay features that were generally affected by the nondevelopmen-tal toxicants (as defined above). 

Zebrafish embryo assay results were compared to the ToxCast in vitro assay features from the predictive model of developmental toxicity (50). A majority of the features were significant between the zebrafish data and predictive models, despite the fact that the zebrafish assay did not correlate with global developmental toxicity defined by species-specific ToxRefDB data. The top 15 chemicals predicted to be developmental toxicants and bottom 15 chemicals predicted not to be developmental toxicants varied in their endpoint responses and logP values. Padilla et al. (35) noted that chemical-physical characteristics could limit the amount of chemical seen by the embryo due to poor solubility or poor uptake. This may be the reason that a majority of the bottom 15 chemicals with no zebrafish embryo activity had logP values less than 1.0. The bottom 15 chemicals with zebrafish embryo activity could almost exclusively be characterized by the negative predictors of the species-specific developmental toxicity models, which may be indicating that these predictors have differing roles between mammalian and zebrafish development. 

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