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Monte Carlo Optimization Method Based QSAR Modeling of Placental Barrier Permeability
Predrag Vukomanović1,2, Milan Stefanović1,2, Jelena Milošević Stevanović1,2
1Faculty of Medicine, University of Niš, Niš, Serbia.
Quantitative structure-activity relationship (QSAR) models can predict drug placental transfer, offering a faster, cheaper alternative to in vitro perfusion. This study developed a robust QSAR model for high-throughput screening of drug placental permeability.
Area of Science:
- Pharmacology
- Computational Chemistry
- Drug Development
Background:
- Predicting placental drug transfer is vital for safe pregnancy medication.
- In vitro human placental perfusion is the standard but is costly and time-consuming.
Purpose of the Study:
- To develop a Quantitative Structure-Activity Relationship (QSAR) model as an alternative to in vitro methods for assessing drug placental transfer.
- To enable prediction of drug placental permeability for safer drug administration during pregnancy.
Main Methods:
- Developed conformation-independent QSAR models using SMILES notation descriptors and local molecular graph invariants.
- Employed Monte Carlo optimization with three independent molecular splits for model development.
Main Results:
- Validated the QSAR model using various statistical parameters, demonstrating excellent predictive potential and robustness.
- Identified molecular fragments influencing drug placental permeability, derived from SMILES descriptors.
Conclusions:
- The developed QSAR model serves as a valuable tool for high-throughput screening of drug placental permeability.
- This approach can significantly aid in evaluating drug safety during pregnancy.
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