Setting course for a translational pharmacology and a predictive toxicology based on the numerical probability of clinical relevance.

Autor: Suarez-Torres JD; Department of Pharmacy, Faculty of Sciences, Universidad Nacional de Colombia, Bogotá D.C., Colombia; Department of Toxicology, Faculty of Medicine, Universidad Nacional de Colombia, Bogotá, D.C., Colombia. Electronic address: jdsuarezto@unal.edu.co., Ciangherotti CE; Laboratory of Neuropeptides. Institute of Pharmaceutical Research, Faculty of Pharmacy, Universidad Central de Venezuela, Caracas, Venezuela., Orozco CA; Department of Animal Health, Faculty of Veterinary Medicine and Zootechnics, Universidad Nacional de Colombia, Bogotá, D.C., Colombia.
Jazyk: angličtina
Zdroj: Environmental toxicology and pharmacology [Environ Toxicol Pharmacol] 2023 Jan; Vol. 97, pp. 103968. Date of Electronic Publication: 2022 Sep 06.
DOI: 10.1016/j.etap.2022.103968
Abstrakt: For a significant share of the chemicals, current bioassays mispredicted the outcomes in the reference methods they simulate. For any drug or chemical, and depending on the regulatory or corporate situation, three different approaches calculate the numerical probability by which agreement (or discrepancy) can be statistically expected between (1) the result of a predictive bioassay, and (2) the outcome on its reference method. If such concordance is expected with enough confidence based on a sufficient percentage probability, then specific results from that bioassay can be considered as correctly predictive. The statistical approaches analyzed in this article assist in valuable tasks, including (1) a better translation of the clinical relevance (or insignificance) of specific preclinical findings; (2) waiving unnecessary animal testing (or any other unpredictive testing; e.g., a given in vitro bioassay), and (3) in advancing only the most promising candidates in the pharmaceutical, pesticide, or chemical development process.
Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2022 The Authors. Published by Elsevier B.V. All rights reserved.)
Databáze: MEDLINE
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