Computational models to assign biopharmaceutics drug disposition classification from molecular structure.
OBJECTIVE
We applied in silico methods to automatically classify drugs according to the Biopharmaceutics Drug Disposition Classification System (BDDCS).


METHODS
Models were developed using machine learning methods including recursive partitioning (RP), random forest (RF) and support vector machine (SVM) algorithms with ChemDraw, clogP, polar surface area, VolSurf and MolConnZ descriptors. The dataset consisted of 165 training and 56 test set molecules.


RESULTS
RF model 3, RP model 1, and SVM model 1 can correctly predict 73.1, 63.6 and 78.6% test compounds in classes 1, 2 and 3, respectively. Both RP and SVM models can be used for class 4 prediction. The inclusion of consensus analysis resulted in improved test set predictions for class 2 and 4 drugs.


CONCLUSIONS
The models can be used to predict BDDCS class for new compounds from molecular structure using readily available molecular descriptors and software, representing an area where in silico approaches could aid the pharmaceutical industry in speeding drugs to the patient and reducing costs. This could have significant applications in drug discovery to identify molecules that may have future developability issues.
Khandelwal
Akash
A
Department of Pharmaceutical Sciences, University of Maryland, 20 Penn Street, Baltimore, Maryland 21201, USA.
Bahadduri
Praveen M
PM
Chang
Cheng
C
Polli
James E
JE
Swaan
Peter W
PW
Ekins
Sean
S
D000465:Algorithms; D001185:Artificial Intelligence; D001702:Biopharmaceutics / Q000379:methods*; D003198:Computer Simulation; D008956:Models, Chemical; D015394:Molecular Structure; D004364:Pharmaceutical Preparations / Q000737:chemistry* / Q000145:classification; D015203:Reproducibility of Results; D012984:Software; D013329:Structure-Activity Relationship
D004364:Pharmaceutical Preparations