Towards better BBB passage prediction using an extensive and curated data set
In the present report, the challenging task of drug delivery across the blood-brain barrier (BBB) is addressed via a computational approach. The BBB passage was modeled using classification and regression schemes on a novel extensive and curated data set (the largest to the best of our knowledge) in...
- Autores:
- Tipo de recurso:
- Fecha de publicación:
- 2015
- Institución:
- Universidad Tecnológica de Bolívar
- Repositorio:
- Repositorio Institucional UTB
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.utb.edu.co:20.500.12585/9016
- Acceso en línea:
- https://hdl.handle.net/20.500.12585/9016
- Palabra clave:
- BBB endpoint
Blood£brain barrier
Dragon descriptor
Linear discriminant analysis
Multiple linear regression
P-glycoprotein
Quantitative structure pharmacokinetic (property) relationship
Central nervous system agents
Multidrug resistance protein
Octanol
Water
Article
Blood-Brain Barrier
Brain disease
Central nervous system
Chemical structure
Cluster analysis
Computer program
Data analysis
Discriminant analysis
Drug penetration
Drug research
Drug targeting
Drug transport
Genetic algorithm
High throughput screening
Human
Linear discriminant analysis
Machine learning
Molecular weight
Molecule
Multiple linear regression analysis
Partition coefficient
Prediction
Priority journal
Quantitative structure activity relation
Quantitative structure pharmacokinetic relation
Statistical analysis
Statistical model
Statistical parameters
Animal
Biological model
Blood-Brain Barrier
Computer simulation
Physiology
Animals
Blood-Brain Barrier
Computer simulation
Humans
Models, Cardiovascular
Models, Neurological
- Rights
- restrictedAccess
- License
- http://creativecommons.org/licenses/by-nc-nd/4.0/
| Summary: | In the present report, the challenging task of drug delivery across the blood-brain barrier (BBB) is addressed via a computational approach. The BBB passage was modeled using classification and regression schemes on a novel extensive and curated data set (the largest to the best of our knowledge) in terms of log BB. Prior to the model development, steps of data analysis that comprise chemical data curation, structural, cutoff and cluster analysis (CA) were conducted. Linear Discriminant Analysis (LDA) and Multiple Linear Regression (MLR) were used to fit classification and correlation functions. The best LDA-based model showed overall accuracies over 85% and 83% for the training and test sets, respectively. Also a MLR-based model with acceptable explanation of more than 69% of the variance in the experimental log BB was developed. A brief and general interpretation of proposed models allowed the estimation on how 'near' our computational approach is to the factors that determine the passage of molecules through the BBB. In a final effort some popular and powerful Machine Learning methods were considered. Comparable or similar performance was observed respect to the simpler linear techniques. Most of the compounds with anomalous behavior were put aside into a set denoted as controversial set and discussion regarding to these compounds is provided. Finally, our results were compared with methodologies previously reported in the literature showing comparable to better results. The results could represent useful tools available and reproducible by all scientific community in the early stages of neuropharmaceutical drug discovery/development projects. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim. |
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