Dataset Information

Ensemble learning for predicting ex vivo human placental barrier permeability.

ABSTRACT:

Background

The placental barrier protects the fetus from exposure to some toxicants and is vital for drug development and risk assessment of environmental chemicals. However, in vivo experiments for assessing the placental barrier permeability of chemicals is not ethically acceptable. Although ex vivo placental perfusion methods provide good alternatives for the assessment of placental barrier permeability, the application to a large number of test chemicals could be time- and resource-consuming. Computational prediction models for ex vivo placental barrier permeability are therefore desirable.

Methods

A total of 87 chemicals and corresponding 1444 physicochemical properties were divided into training and test datasets. Three types of algorithms including linear regression, random forest, and ensemble models were applied to develop prediction models for ex vivo placental barrier permeability.

Results

Among the tested models, the ensemble model integrating the previous two methods performed best for predicting ex vivo human placental barrier permeability with correlation coefficients of 0.887 and 0.825 when considering the applicability domain. An additional test on seven newly curated chemicals from the literature showed a good correlation coefficient of 0.879 which was further improved to 0.921 by considering the variation of experiments.

Conclusion

In this study, the first valid predicting model for ex vivo human placental barrier permeability was developed following the OECD guideline. The model is expected to be useful for assessing the human placental barrier permeability and can be integrated with developmental toxicity prediction models for investigating the toxic effects of chemicals on the fetus.

SUBMITTER: Chou CY

PROVIDER: S-EPMC9502578 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature

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Publications

Ensemble learning for predicting ex vivo human placental barrier permeability.

Chou Che-Yu CY Lin Pinpin P Kim Jongwoon J Wang Shan-Shan SS Wang Chia-Chi CC Tung Chun-Wei CW

BMC bioinformatics 20220922 Suppl 10

<h4>Background</h4>The placental barrier protects the fetus from exposure to some toxicants and is vital for drug development and risk assessment of environmental chemicals. However, in vivo experiments for assessing the placental barrier permeability of chemicals is not ethically acceptable. Although ex vivo placental perfusion methods provide good alternatives for the assessment of placental barrier permeability, the application to a large number of test chemicals could be time- and resource-c ...[more]

PMID: 36138350

Similar Datasets

Project description:BackgroundGold nanoparticles (AuNPs) are promising candidates to design the next generation NP-based drug formulations specifically treating maternal, fetal or placental complications with reduced side effects. Profound knowledge on AuNP distribution and effects at the human placental barrier in dependence on the particle properties and surface modifications, however, is currently lacking. Moreover, the predictive value of human placental transfer models for NP translocation studies is not yet clearly understood, in particular with regards to differences between static and dynamic exposures. To understand if small (3-4 nm) AuNPs with different surface modifications (PEGylated versus carboxylated) are taken up and cross the human placental barrier, we performed translocation studies in a static human in vitro co-culture placenta model and the dynamic human ex vivo placental perfusion model. The samples were analysed using ICP-MS, laser ablation-ICP-MS and TEM analysis for sensitive, label-free detection of AuNPs.ResultsAfter 24 h of exposure, both AuNP types crossed the human placental barrier in vitro, although in low amounts. Even though cellular uptake was higher for carboxylated AuNPs, translocation was slightly increased for PEGylated AuNPs. After 6 h of perfusion, only PEGylated AuNPs were observed in the fetal circulation and tissue accumulation was similar for both AuNP types. While PEGylated AuNPs were highly stable in the biological media and provided consistent results among the two placenta models, carboxylated AuNPs agglomerated and adhered to the perfusion device, resulting in different cellular doses under static and dynamic exposure conditions.ConclusionsGold nanoparticles cross the human placental barrier in limited amounts and accumulate in placental tissue, depending on their size- and/or surface modification. However, it is challenging to identify the contribution of individual characteristics since they often affect colloidal particle stability, resulting in different biological interaction in particular under static versus dynamic conditions. This study highlights that human ex vivo and in vitro placenta models can provide valuable mechanistic insights on NP uptake and translocation if accounting for NP stability and non-specific interactions with the test system.

Dataset Information

Ensemble learning for predicting ex vivo human placental barrier permeability.

Background

Methods

Results

Conclusion

Publications

Ensemble learning for predicting ex vivo human placental barrier permeability.

Similar Datasets

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