ADME evaluation in drug discovery. 9. Prediction of oral bioavailability in humans based on molecular properties and structural fingerprints.

Oral bioavailability is an essential parameter in drug screening cascades and a good indicator of the capability of the delivery of a given compound to the systemic circulation by oral administration. In the present work, we report a database of oral bioavailability of 1014 molecules determined in humans. A systematic examination of the relationships between various physicochemical properties and oral bioavailability were carried out to investigate the influence of these properties on oral bioavailability. A number of property-based rules for bioavailability classification were generated and evaluated. We found that no rule was an effective predictor for oral bioavailability because these simple rules cannot characterize the influence of important metabolic processes on bioavailability. Finally, the genetic function approximation (GFA) technique was employed to construct the multiple linear regression models for oral bioavailability using structural fingerprints as the basic parameters, together with several important molecular properties. The best model is able to predict human oral bioavailability with an r of 0.79, a q of 0.72, and a RMSE (root-mean-square error) of 22.30% of the compounds from the training set. The analysis of the descriptors chosen by GFA shows that the important structural fingerprints are primarily related to important intestinal absorption and well-known metabolic processes. The predictive power of the models was further evaluated using a separate test set of 80 compounds, and the consensus model can predict the oral bioavailability with r(test) = 0.71 and RMSE = 23.55% for the tested compounds. Since the necessary molecular properties and structural fingerprints can be calculated easily and quickly, the models we proposed here may help speed up the process of finding or designing compounds with improved oral bioavailability.

[1]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[2]  Tingjun Hou,et al.  Recent developments of in silico predictions of intestinal absorption and oral bioavailability. , 2009, Combinatorial chemistry & high throughput screening.

[3]  Tingjun Hou,et al.  Recent development and application of virtual screening in drug discovery: an overview. , 2004, Current pharmaceutical design.

[4]  Junmei Wang,et al.  Structure – ADME relationship: still a long way to go? , 2008, Expert opinion on drug metabolism & toxicology.

[5]  E. Lien Structure-activity relationships and drug disposition. , 1981, Annual review of pharmacology and toxicology.

[6]  Tingjun Hou,et al.  ADME Evaluation in Drug Discovery. 4. Prediction of Aqueous Solubility Based on Atom Contribution Approach , 2004, J. Chem. Inf. Model..

[7]  D. E. Clark Rapid calculation of polar molecular surface area and its application to the prediction of transport phenomena. 1. Prediction of intestinal absorption. , 1999, Journal of pharmaceutical sciences.

[8]  John G. Topliss,et al.  QSAR Model for Drug Human Oral Bioavailability1 , 2000 .

[9]  Adriano D Andricopulo,et al.  Hologram QSAR model for the prediction of human oral bioavailability. , 2007, Bioorganic & medicinal chemistry.

[10]  Tingjun Hou,et al.  ADME Evaluation in Drug Discovery. 3. Modeling Blood-Brain Barrier Partitioning Using Simple Molecular Descriptors , 2003, J. Chem. Inf. Comput. Sci..

[11]  Tingjun Hou,et al.  ADME Evaluation in Drug Discovery. 5. Correlation of Caco-2 Permeation with Simple Molecular Properties , 2004, J. Chem. Inf. Model..

[12]  Hxugo Kubiny Variable Selection in QSAR Studies. I. An Evolutionary Algorithm , 1994 .

[13]  Tingjun Hou,et al.  ADME Evaluation in Drug Discovery, 6. Can Oral Bioavailability in Humans Be Effectively Predicted by Simple Molecular Property-Based Rules? , 2007, J. Chem. Inf. Model..

[14]  H. Kubinyi Variable Selection in QSAR Studies. II. A Highly Efficient Combination of Systematic Search and Evolution , 1994 .

[15]  D. Rogers,et al.  Using Extended-Connectivity Fingerprints with Laplacian-Modified Bayesian Analysis in High-Throughput Screening Follow-Up , 2005, Journal of biomolecular screening.

[16]  Tingjun Hou,et al.  ADME evaluation in drug discovery , 2002, Journal of molecular modeling.

[17]  Anton J. Hopfinger,et al.  Application of Genetic Function Approximation to Quantitative Structure-Activity Relationships and Quantitative Structure-Property Relationships , 1994, J. Chem. Inf. Comput. Sci..

[18]  F. Lombardo,et al.  Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings , 1997 .

[19]  Y. Martin,et al.  A bioavailability score. , 2005, Journal of medicinal chemistry.

[20]  Lawrence M. Seiford,et al.  Recent developments in dea : the mathematical programming approach to frontier analysis , 1990 .

[21]  Tingjun Hou,et al.  ADME Evaluation in Drug Discovery, 7. Prediction of Oral Absorption by Correlation and Classification , 2007, J. Chem. Inf. Model..

[22]  T. Halgren MMFF VI. MMFF94s option for energy minimization studies , 1999, J. Comput. Chem..

[23]  Yuquan Wei,et al.  Prediction models of human plasma protein binding rate and oral bioavailability derived by using GA-CG-SVM method. , 2008, Journal of pharmaceutical and biomedical analysis.

[24]  Lawrence X. Yu,et al.  Predicting Human Oral Bioavailability of a Compound: Development of a Novel Quantitative Structure-Bioavailability Relationship , 2000, Pharmaceutical Research.

[25]  Stephen R. Johnson,et al.  Molecular properties that influence the oral bioavailability of drug candidates. , 2002, Journal of medicinal chemistry.

[26]  Tingjun Hou,et al.  Applications of Genetic Algorithms on the Structure-Activity Relationship Analysis of Some Cinnamamides , 1999, J. Chem. Inf. Comput. Sci..

[27]  Junmei Wang,et al.  Genetic Algorithm-Optimized QSPR Models for Bioavailability, Protein Binding, and Urinary Excretion , 2006, J. Chem. Inf. Model..