Application of data mining approach to identify drug subclasses based on solubility and permeability
Abstract
Solubility and permeability are recognized as key parameters governing drug intestinal absorption and represent the basis for biopharmaceutics drug classification. The Biopharmaceutics Classification System (BCS) is widely accepted and adopted by regulatory agencies. However, currently established low/high permeability and solubility boundaries are the subject of the ongoing scientific discussion. The aim of the present study was to apply data mining analysis on the selected drugs data set in order to develop a human permeability predictive model based on selected molecular descriptors, and to perform data clustering and classification to identify drug subclasses with respect to dose/solubility ratio (D/S) and effective permeability (P-eff). The P-eff values predicted for 30 model drugs for which experimental human permeability data are not available were in good agreement with the reported fraction of drug absorbed. The results of clustering and classification analysis indicate the pr...edominant influence of P-eff over D/S. Two P-eff cut-off values (1 x 10(-4) and 2.7 x 10(-4) cm/s) have been identified indicating the existence of an intermediate group of drugs with moderate permeability. Advanced computational analysis employed in the present study enabled the recognition of complex relationships and patterns within physicochemical and biopharmaceutical properties associated with drug bioperformance.
Keywords:
biopharmaceutics classification system (BCS) / data mining / human intestinal permeability / solubilitySource:
Biopharmaceutics & Drug Disposition, 2019, 40, 2, 51-61Publisher:
- Wiley, Hoboken
Funding / projects:
DOI: 10.1002/bdd.2170
ISSN: 0142-2782
PubMed: 30635908
WoS: 000459936200001
Scopus: 2-s2.0-85061434877
Collections
Institution/Community
PharmacyTY - JOUR AU - Gatarić, Biljana AU - Parojčić, Jelena PY - 2019 UR - https://farfar.pharmacy.bg.ac.rs/handle/123456789/3294 AB - Solubility and permeability are recognized as key parameters governing drug intestinal absorption and represent the basis for biopharmaceutics drug classification. The Biopharmaceutics Classification System (BCS) is widely accepted and adopted by regulatory agencies. However, currently established low/high permeability and solubility boundaries are the subject of the ongoing scientific discussion. The aim of the present study was to apply data mining analysis on the selected drugs data set in order to develop a human permeability predictive model based on selected molecular descriptors, and to perform data clustering and classification to identify drug subclasses with respect to dose/solubility ratio (D/S) and effective permeability (P-eff). The P-eff values predicted for 30 model drugs for which experimental human permeability data are not available were in good agreement with the reported fraction of drug absorbed. The results of clustering and classification analysis indicate the predominant influence of P-eff over D/S. Two P-eff cut-off values (1 x 10(-4) and 2.7 x 10(-4) cm/s) have been identified indicating the existence of an intermediate group of drugs with moderate permeability. Advanced computational analysis employed in the present study enabled the recognition of complex relationships and patterns within physicochemical and biopharmaceutical properties associated with drug bioperformance. PB - Wiley, Hoboken T2 - Biopharmaceutics & Drug Disposition T1 - Application of data mining approach to identify drug subclasses based on solubility and permeability VL - 40 IS - 2 SP - 51 EP - 61 DO - 10.1002/bdd.2170 ER -
@article{ author = "Gatarić, Biljana and Parojčić, Jelena", year = "2019", abstract = "Solubility and permeability are recognized as key parameters governing drug intestinal absorption and represent the basis for biopharmaceutics drug classification. The Biopharmaceutics Classification System (BCS) is widely accepted and adopted by regulatory agencies. However, currently established low/high permeability and solubility boundaries are the subject of the ongoing scientific discussion. The aim of the present study was to apply data mining analysis on the selected drugs data set in order to develop a human permeability predictive model based on selected molecular descriptors, and to perform data clustering and classification to identify drug subclasses with respect to dose/solubility ratio (D/S) and effective permeability (P-eff). The P-eff values predicted for 30 model drugs for which experimental human permeability data are not available were in good agreement with the reported fraction of drug absorbed. The results of clustering and classification analysis indicate the predominant influence of P-eff over D/S. Two P-eff cut-off values (1 x 10(-4) and 2.7 x 10(-4) cm/s) have been identified indicating the existence of an intermediate group of drugs with moderate permeability. Advanced computational analysis employed in the present study enabled the recognition of complex relationships and patterns within physicochemical and biopharmaceutical properties associated with drug bioperformance.", publisher = "Wiley, Hoboken", journal = "Biopharmaceutics & Drug Disposition", title = "Application of data mining approach to identify drug subclasses based on solubility and permeability", volume = "40", number = "2", pages = "51-61", doi = "10.1002/bdd.2170" }
Gatarić, B.,& Parojčić, J.. (2019). Application of data mining approach to identify drug subclasses based on solubility and permeability. in Biopharmaceutics & Drug Disposition Wiley, Hoboken., 40(2), 51-61. https://doi.org/10.1002/bdd.2170
Gatarić B, Parojčić J. Application of data mining approach to identify drug subclasses based on solubility and permeability. in Biopharmaceutics & Drug Disposition. 2019;40(2):51-61. doi:10.1002/bdd.2170 .
Gatarić, Biljana, Parojčić, Jelena, "Application of data mining approach to identify drug subclasses based on solubility and permeability" in Biopharmaceutics & Drug Disposition, 40, no. 2 (2019):51-61, https://doi.org/10.1002/bdd.2170 . .