Estratégias computacionais como métodos alternativos para avaliação da sensibilização cutânea
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2017-05-12
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Universidade Federal de Goiás
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Introduction: Skin sensitization is a major environmental and human health hazard.
Although many chemicals have been evaluated in humans, there have been no efforts to
model these data to date. Skin sensitization is commonly evaluated using structural alerts.
However, there has been a growing concern that alerts disproportionally flag too many
chemicals as toxic, which questions their reliability as toxicity markers. The main goal of this
thesis was to develop and apply new cheminformatics methods to predict skin sensitization of
chemical compounds that lack experimental data. Methodology: It has been compiled,
curated, analyzed, and compared the available human data and the murine (performed in
mice) animal model data, named LLNA (local lymph node assay). Using these data, it was
developed reliable computational models and applied them for virtual screening of chemical
libraries to identify putative skin sensitizers. It was developed a freely accessible web-based
application for the identification of potential skin sensitizers. In addition, it was demonstrated
that contrary to the common perception of QSAR models as “black boxes” they can be used to
identify statistically significant chemical substructures (QSAR-based alerts) that influence
toxicity. Results and discussion: The overall concordance between murine LLNA and human
skin sensitization responses for a set of 135 unique chemicals was low (R = 28-43%),
although several chemical classes had high concordance. We have succeeded to develop
predictive QSAR models of all available human data with the external correct classification
rate of 71%. A consensus model integrating concordant QSAR predictions and LLNA results
afforded a higher correct classification rate of 82% but at the expense of the reduced external
dataset coverage (52 %). We used the developed QSAR models for virtual screening of
CosIng database and identified 1,061 putative skin sensitizers; for seventeen of these
compounds, we found published evidence of their skin sensitization effects. The developed
Pred-Skin web app (http://www.labmol.com.br/predskin/) is based on binary QSAR models of
human (109 compounds) and LLNA (515 compounds) data with good external correct
classification rate (70-81% and 72-84%, respectively). It is also included a multiclass potency
model based on LLNA data (accuracy ranging between 73-76%). Conclusions: Models
reported herein provide more accurate alternative to LLNA testing for human skin sensitization
assessment across diverse chemical data. In addition, they can also be used to guide the
structural optimization of toxic compounds to reduce their skin sensitization potential. The
Pred-Skin web app is a fast, reliable, and user-friendly tool for early assessment of
chemically-induced skin sensitization. A new approach that synergistically integrates structural
alerts and rigorously validated QSAR models for a more transparent and accurate safety
assessment of new chemicals was also proposed.
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ALVES, Vinícius M. Estratégias computacionais como métodos alternativos para avaliação da sensibilização cutânea. 2017. 114 f. Tese (Doutorado em Inovação Farmacêutica em Rede) - Universidade Federal de Goiás, Goiânia, 2017.