Tingjun Hou1, Junmei Wang. 1. University of California at San Diego, Department of Chemistry and Biochemistry, Center for Theoretical Biological Physics, 9500 Gilman Drive, La Jolla, CA 92093-0359, USA. tingjunhou@hotmail.com
Abstract
BACKGROUND: Theoretical models for predicting absorption, distribution, metabolism and excretion (ADME) properties play increasingly important roles in support of the drug development process. OBJECTIVE: We briefly review the in silico prediction models for three important ADME properties, namely, aqueous solubility, human intestinal absorption, and oral bioavailability. METHODS: Rather than giving detailed descriptions of the ADME prediction models, we focus on the discussions of the prediction accuracies of the in silico models. RESULTS/ CONCLUSION: We find that the robustness and predictive capability of the ADME models are directly associated with the complexity of the ADME property. For the ADME properties involving complex phenomena, such as bioavailability, the in silico models usually cannot give satisfactory predictions. Moreover, the lack of large and high-quality data sets also greatly hinder the reliability of ADME predictions. While considerable progress has been achieved in ADME predictions, many challenges remain to be overcome.
BACKGROUND: Theoretical models for predicting absorption, distribution, metabolism and excretion (ADME) properties play increasingly important roles in support of the drug development process. OBJECTIVE: We briefly review the in silico prediction models for three important ADME properties, namely, aqueous solubility, human intestinal absorption, and oral bioavailability. METHODS: Rather than giving detailed descriptions of the ADME prediction models, we focus on the discussions of the prediction accuracies of the in silico models. RESULTS/ CONCLUSION: We find that the robustness and predictive capability of the ADME models are directly associated with the complexity of the ADME property. For the ADME properties involving complex phenomena, such as bioavailability, the in silico models usually cannot give satisfactory predictions. Moreover, the lack of large and high-quality data sets also greatly hinder the reliability of ADME predictions. While considerable progress has been achieved in ADME predictions, many challenges remain to be overcome.
Authors: C Anthony Hunt; Glen E P Ropella; Tai Ning Lam; Jonathan Tang; Sean H J Kim; Jesse A Engelberg; Shahab Sheikh-Bahaei Journal: Pharm Res Date: 2009-09-09 Impact factor: 4.200
Authors: María Belén Jiménez-Díaz; Sara Viera; Javier Ibáñez; Teresa Mulet; Noemí Magán-Marchal; Helen Garuti; Vanessa Gómez; Lorena Cortés-Gil; Antonio Martínez; Santiago Ferrer; María Teresa Fraile; Félix Calderón; Esther Fernández; Leonard D Shultz; Didier Leroy; David M Wilson; José Francisco García-Bustos; Francisco Javier Gamo; Iñigo Angulo-Barturen Journal: PLoS One Date: 2013-06-25 Impact factor: 3.240