title: In silico prediction of blood brain barrier permeability: an Artificial Neural Network model.
abstract: This paper has two objectives: first to develop an in silico model for the prediction of blood brain barrier permeability of new chemical entities and second to find the role of active transport specific to the P-glycoprotein (P-gp) substrate probability in blood brain barrier permeability. An Artificial Neural Network (ANN) model has been developed to predict the ratios of the steady-state concentrations of drugs in the brain to those in the blood (logBB) from their molecular structural parameters. Seven descriptors including P-gp substrate probability have been used for model development. The developed model is able to capture a relationship between P-gp and logBB. The predictive ability of the ANN model has also been compared with earlier computational models.
authors.affiliation: National Institute of Pharmaceutical Education & Research, Sector 67, SAS Nagar, Punjab, India. prabhagarg@niper.ac.in
authors.forename: Prabha
authors.initials: P
authors.lastname: Garg
authors.forename: Jitender
authors.initials: J
authors.lastname: Verma
mesh_terms: D020168:ATP Binding Cassette Transporter, Subfamily B, Member 1 / Q000737:chemistry / Q000378:metabolism*; D001693:Biological Transport, Active; D001812:Blood-Brain Barrier / Q000737:chemistry / Q000378:metabolism*; D019295:Computational Biology; D003198:Computer Simulation; D008954:Models, Biological; D016571:Neural Networks, Computer; D010539:Permeability; D015203:Reproducibility of Results
chemical_list: D020168:ATP Binding Cassette Transporter, Subfamily B, Member 1