| Title | Quantitative structure-pharmacokinetic relationships for drug clearance by using statistical learning methods. | | Author(s) | Yap CW, Li ZR, Chen YZ | | Institution | Department of Computational Science, National University of Singapore, Blk SOC1, Level 7, 3 Science Drive 2, Singapore 117543, Singapore. | | Source | J Mol Graph Model 2006 Mar; 24(5):383-95. | | MeSH | Adult
Algorithms
Allopurinol
Antipyrine
Carbidopa
Chlorpheniramine
Fendiline
Humans
Male
Pharmaceutical Preparations
Pharmacokinetics
Predictive Value of Tests
Quantitative Structure-Activity Relationship
Quinazolines
Reproducibility of Results
Statistics as Topic
Thiophenes
Tocainide
| | Abstract | Quantitative structure-pharmacokinetic relationships (QSPkR) have increasingly been used for the prediction of the pharmacokinetic properties of drug leads. Several QSPkR models have been developed to predict the total clearance (CL(tot)) of a compound. These models give good prediction accuracy but they are primarily based on a limited number of related compounds which are significantly lesser in number and diversity than the 503 compounds with known CL(tot) described in the literature. It is desirable to examine whether these and other statistical learning methods can be used for predicting the CL(tot) of a more diverse set of compounds. In this work, three statistical learning methods, general regression neural network (GRNN), support vector regression (SVR) and k-nearest neighbour (KNN) were explored for modeling the CL(tot) of all of the 503 known compounds. Six different sets of molecular descriptors, DS-MIXED, DS-3DMoRSE, DS-ATS, DS-GETAWAY, DS-RDF and DS-WHIM, were evaluated for their usefulness in the prediction of CL(tot). GRNN-, SVR- and KNN-developed models have average-fold errors in the range of 1.63 to 1.96, 1.66-1.95 and 1.90-2.23, respectively. For the best GRNN-, SVR- and KNN-developed models, the percentage of compounds with predicted CL(tot) within two-fold error of actual values are in the range of 61.9-74.3% and are comparable or slightly better than those of earlier studies. QSPkR models developed by using DS-MIXED, which is a collection of constitutional, geometrical, topological and electrotopological descriptors, generally give better prediction accuracies than those developed by using other descriptor sets. These results suggest that GRNN, SVR, and their consensus model are potentially useful for predicting QSPkR properties of drug leads. | | Language | eng | | Pub Type(s) | Journal Article
Research Support, Non-U.S. Gov't
| | PubMed ID | 16290201 |
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