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Mechanistic Profiling of Inhalation Sensory Irritants with a Computational Model for Transient Receptor Potential Vanilloid Subfamily Type 1

Mechanistic Profiling of Inhalation Sensory Irritants with a Computational Model for Transient... Introduction: We are using big data mining to develop computational models that predict potential interaction with important biological pathways. Transient receptor potential vanilloid subfamily type 1 (TRPV1) is one of several nociceptors that contribute to sensory irritation. Because sensory irritation is frequently used as a critical effect in setting occupational exposure limits (OELs), we developed a model that predicts interaction with TRPV1 and used it to mechanistically profile two inhalation databases (DBs).Methods: We built a random forest machine learning model to predict whether a novel compound will or will not interact with TRPV1 by fingerprinting a large DB curated primarily from public in vitro data. Our model has high sensitivity (90.2%), specificity (99.2%), and balanced accuracy (94.8%). We mechanistically profiled (1) a rodent RD50 DB (concentrations causing a 50% decrease in respiratory rate; N = 190) and (2) a subset of the American Conference of Governmental Industrial Hygienists DB with OELs that were primarily based on sensory irritation (N = 109).Results: For both DBs, a high percentage of compounds were identified for potential interaction with TRPV1. Further screening of DBs with a profiler for facile chemical reactivity gave similar results, with many compounds flagged for both mechanisms. The more potent compounds in either DB were often chemically reactive, suggesting potential involvement of a related nociceptor known to serve as a sentinel for electrophiles—transient receptor potential ankyrin subfamily type 1.Conclusion: Our findings emphasize the need for an integrated testing approach using tiered in silico and in vitro screening for these nociceptors to derive OELs without using animals. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Applied In Vitro Toxicology Mary Ann Liebert

Mechanistic Profiling of Inhalation Sensory Irritants with a Computational Model for Transient Receptor Potential Vanilloid Subfamily Type 1

Mechanistic Profiling of Inhalation Sensory Irritants with a Computational Model for Transient Receptor Potential Vanilloid Subfamily Type 1

Applied In Vitro Toxicology , Volume 7 (4): 8 – Dec 1, 2021

Abstract

Introduction: We are using big data mining to develop computational models that predict potential interaction with important biological pathways. Transient receptor potential vanilloid subfamily type 1 (TRPV1) is one of several nociceptors that contribute to sensory irritation. Because sensory irritation is frequently used as a critical effect in setting occupational exposure limits (OELs), we developed a model that predicts interaction with TRPV1 and used it to mechanistically profile two inhalation databases (DBs).Methods: We built a random forest machine learning model to predict whether a novel compound will or will not interact with TRPV1 by fingerprinting a large DB curated primarily from public in vitro data. Our model has high sensitivity (90.2%), specificity (99.2%), and balanced accuracy (94.8%). We mechanistically profiled (1) a rodent RD50 DB (concentrations causing a 50% decrease in respiratory rate; N = 190) and (2) a subset of the American Conference of Governmental Industrial Hygienists DB with OELs that were primarily based on sensory irritation (N = 109).Results: For both DBs, a high percentage of compounds were identified for potential interaction with TRPV1. Further screening of DBs with a profiler for facile chemical reactivity gave similar results, with many compounds flagged for both mechanisms. The more potent compounds in either DB were often chemically reactive, suggesting potential involvement of a related nociceptor known to serve as a sentinel for electrophiles—transient receptor potential ankyrin subfamily type 1.Conclusion: Our findings emphasize the need for an integrated testing approach using tiered in silico and in vitro screening for these nociceptors to derive OELs without using animals.

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Publisher
Mary Ann Liebert
Copyright
Copyright 2021, Mary Ann Liebert, Inc., publishers
ISSN
2332-1512
eISSN
2332-1539
DOI
10.1089/aivt.2021.0007
Publisher site
See Article on Publisher Site

Abstract

Introduction: We are using big data mining to develop computational models that predict potential interaction with important biological pathways. Transient receptor potential vanilloid subfamily type 1 (TRPV1) is one of several nociceptors that contribute to sensory irritation. Because sensory irritation is frequently used as a critical effect in setting occupational exposure limits (OELs), we developed a model that predicts interaction with TRPV1 and used it to mechanistically profile two inhalation databases (DBs).Methods: We built a random forest machine learning model to predict whether a novel compound will or will not interact with TRPV1 by fingerprinting a large DB curated primarily from public in vitro data. Our model has high sensitivity (90.2%), specificity (99.2%), and balanced accuracy (94.8%). We mechanistically profiled (1) a rodent RD50 DB (concentrations causing a 50% decrease in respiratory rate; N = 190) and (2) a subset of the American Conference of Governmental Industrial Hygienists DB with OELs that were primarily based on sensory irritation (N = 109).Results: For both DBs, a high percentage of compounds were identified for potential interaction with TRPV1. Further screening of DBs with a profiler for facile chemical reactivity gave similar results, with many compounds flagged for both mechanisms. The more potent compounds in either DB were often chemically reactive, suggesting potential involvement of a related nociceptor known to serve as a sentinel for electrophiles—transient receptor potential ankyrin subfamily type 1.Conclusion: Our findings emphasize the need for an integrated testing approach using tiered in silico and in vitro screening for these nociceptors to derive OELs without using animals.

Journal

Applied In Vitro ToxicologyMary Ann Liebert

Published: Dec 1, 2021

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