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Do AutoML-Based QSAR Models Fulfill OECD Principles for Regulatory Assessment? A 5-HT_1A Receptor Case.

ABSTRACT: The drug discovery and development process requires a lot of time, financial, and workforce resources. Any reduction in these burdens might benefit all stakeholders in the healthcare domain, including patients, government, and companies. One of the critical stages in drug discovery is a selection of molecular structures with a strong affinity to a particular molecular target. The possible solution is the development of predictive models and their application in the screening process, but due to the complexity of the problem, simple and statistical models might not be sufficient for practical application. The manuscript presents the best-in-class predictive model for the serotonin 1A receptor affinity and its validation according to the Organization for Economic Co-operation and Development guidelines for regulatory purposes. The model was developed based on a database with close to 9500 molecules by using an automatic machine learning tool (AutoML). The model selection was conducted based on the Akaike information criterion value and 10-fold cross-validation routine, and later good predictive ability was confirmed with an additional external validation dataset with over 700 molecules. Moreover, the multi-start technique was applied to test if an automatic model development procedure results in reliable results.

SUBMITTER: Czub N

PROVIDER: S-EPMC9319483 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundMajor depressive disorder (MDD) is a severe mental disorder related to the deficiency of monoamine neurotransmitters, particularly to abnormalities of 5-HT (5-hydroxytryptamine, serotonin) and its receptors. Our previous study suggested that acute treatment with a novel curcumin derivative J147 exhibited antidepressant-like effects by increasing brain derived neurotrophic factor (BDNF) level in the hippocampus of mice. The present study expanded upon our previous findings and investigated the antidepressant-like effects of sub-acute treatment of J147 for 3 days in male ICR mice and its possible relevancy to 5-HT1A and 5-HT1B receptors and downstream cAMP-BDNF signaling.MethodsJ147 at doses of 1, 3, and 9 mg/kg (via gavage) was administered for 3 days, and the anti-immobility time in the forced swimming and tail suspension tests (FST and TST) was recorded. The radioligand binding assay was used to determine the affinity of J147 to 5-HT1A and 5-HT1B receptor. Moreover, 5-HT1A or 5-HT1B agonist or its antagonist was used to determine which 5-HT receptor subtype is involved in the antidepressant-like effects of J147. The downstream signaling molecules such as cAMP, PKA, pCREB, and BDNF were also measured to determine the mechanism of action.ResultsThe results demonstrated that sub-acute treatment of J147 remarkably decreased the immobility time in both the FST and TST in a dose-dependent manner. J147 displayed high affinity in vitro to 5-HT1A receptor prepared from mice cortical tissue and was less potent at 5-HT1B receptor. These effects of J147 were blocked by pretreatment with a 5-HT1A antagonist NAD-299 and enhanced by a 5-HT1A agonist 8-OH-DPAT. However, 5-HT1B receptor antagonist NAS-181 did not appreciably alter the effects of J147 on depression-like behaviors. Moreover, pretreatment with NAD-299 blocked J147-induced increases in cAMP, PKA, pCREB, and BDNF expression in the hippocampus, while 8-OH-DPAT enhanced the effects of J147 on these proteins' expression.ConclusionThe results suggest that J147 induces rapid antidepressant-like effects during a 3-day treatment period without inducing drug tolerance. These effects might be mediated by 5-HT1A-dependent cAMP/PKA/pCREB/BDNF signaling.

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Do AutoML-Based QSAR Models Fulfill OECD Principles for Regulatory Assessment? A 5-HT_1A Receptor Case.

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Dataset Information

Do AutoML-Based QSAR Models Fulfill OECD Principles for Regulatory Assessment? A 5-HT1A Receptor Case.

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OmicsDI is part of the ELIXIR infrastructure

Tweets

Do AutoML-Based QSAR Models Fulfill OECD Principles for Regulatory Assessment? A 5-HT_1A Receptor Case.