Prediction of pharmacokinetic parameters.

In silico tools specifically developed for prediction of pharmacokinetic parameters are of particular interest to pharmaceutical industry because of the high potential of discarding inappropriate molecules during an early stage of drug development itself with consequent saving of vital resources and valuable time. The ultimate goal of the in silico models of absorption, distribution, metabolism, and excretion (ADME) properties is the accurate prediction of the in vivo pharmacokinetics of a potential drug molecule in man, whilst it exists only as a virtual structure. Various types of in silico models developed for successful prediction of the ADME parameters like oral absorption, bioavailability, plasma protein binding, tissue distribution, clearance, half-life, etc. have been briefly described in this chapter.

[1]  Mario Lobell,et al.  In silico prediction of aqueous solubility, human plasma protein binding and volume of distribution of compounds from calculated pKa and AlogP98 values , 2004, Molecular Diversity.

[2]  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.

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

[4]  Maykel Pérez González,et al.  A topological sub-structural approach for predicting human intestinal absorption of drugs. , 2004, European journal of medicinal chemistry.

[5]  John Hodgson,et al.  ADMET—turning chemicals into drugs , 2001, Nature Biotechnology.

[6]  Y. Yano,et al.  Prediction of human pharmacokinetics from animal data and molecular structural parameters using multivariate regression analysis: oral clearance. , 2003, Journal of pharmaceutical sciences.

[7]  Su Young Choi,et al.  Prediction of the permeability of drugs through study on quantitative structure-permeability relationship. , 2006, Journal of pharmaceutical and biomedical analysis.

[8]  Panos Macheras,et al.  In silico prediction of ADME and pharmacokinetics. Report of an expert meeting organised by COST B15. , 2002, European Journal of Pharmaceutical Sciences.

[9]  L. Aarons,et al.  Quantitative Structure–Pharmacokinetics Relationships: II. A Mechanistically Based Model to Evaluate the Relationship Between Tissue Distribution Parameters and Compound Lipophilicity , 1998, Journal of Pharmacokinetics and Biopharmaceutics.

[10]  J. D. Elliott,et al.  Prediction of the Intestinal Absorption of Endothelin Receptor Antagonists Using Three Theoretical Methods of Increasing Complexity , 1999, Pharmaceutical Research.

[11]  H Lennernäs,et al.  Correlation of human jejunal permeability (in vivo) of drugs with experimentally and theoretically derived parameters. A multivariate data analysis approach. , 1998, Journal of medicinal chemistry.

[12]  J. V. Turner,et al.  Pharmacokinetic parameter prediction from drug structure using artificial neural networks. , 2004, International journal of pharmaceutics.

[13]  A. N. Jain,et al.  Molecular hashkeys: a novel method for molecular characterization and its application for predicting important pharmaceutical properties of molecules. , 1999, Journal of medicinal chemistry.

[14]  Irene Luque Ruiz,et al.  QSAR models based on isomorphic and nonisomorphic data fusion for predicting the blood brain barrier permeability , 2007, J. Comput. Chem..

[15]  Ulf Norinder,et al.  Prediction of ADMET Properties , 2006, ChemMedChem.

[16]  Prashant S. Kharkar,et al.  Two-dimensional (2D) in silico models for absorption, distribution, metabolism, excretion and toxicity (ADME/T) in drug discovery. , 2010 .

[17]  Alexander Golbraikh,et al.  QSAR Modeling of the Blood–Brain Barrier Permeability for Diverse Organic Compounds , 2008, Pharmaceutical Research.

[18]  Snezana Agatonovic-Kustrin,et al.  Multiple pharmacokinetic parameter prediction for a series of cephalosporins. , 2003, Journal of pharmaceutical sciences.

[19]  Sandeep Modi Positioning ADMET in silico tools in drug discovery. , 2004, Drug discovery today.

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

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

[22]  Paulo Paixão,et al.  Prediction of the in vitro intrinsic clearance determined in suspensions of human hepatocytes by using artificial neural networks. , 2010, European journal of pharmaceutical sciences : official journal of the European Federation for Pharmaceutical Sciences.

[23]  Tongtong Liu,et al.  Development of In Vitro Pharmacokinetic Screens Using Caco-2, Human Hepatocyte, and Caco-2/Human Hepatocyte Hybrid Systems for the Prediction of Oral Bioavailability in Humans , 2007, Journal of biomolecular screening.

[24]  S. Ekins,et al.  Progress in predicting human ADME parameters in silico. , 2000, Journal of pharmacological and toxicological methods.

[25]  Panos Macheras,et al.  Multivariate Statistics of Disposition Pharmacokinetic Parameters for Structurally Unrelated Drugs Used in Therapeutics , 2002, Pharmaceutical Research.

[26]  Bruno Boulanger,et al.  Towards a new age of virtual ADME/TOX and multidimensional drug discovery , 2002, J. Comput. Aided Mol. Des..

[27]  Antti Poso,et al.  Quantitative structure-activity relationship analysis of inhibitors of the nicotine metabolizing CYP2A6 enzyme. , 2005, Journal of medicinal chemistry.

[28]  Wei Zhang,et al.  Recent advances in computational prediction of drug absorption and permeability in drug discovery. , 2006, Current medicinal chemistry.

[29]  Remigijus Didziapetris,et al.  Ionization-specific prediction of blood-brain permeability. , 2009, Journal of pharmaceutical sciences.

[30]  Paulo Paixão,et al.  Prediction of drug distribution within blood. , 2009, European journal of pharmaceutical sciences : official journal of the European Federation for Pharmaceutical Sciences.

[31]  L. A. Fenu,et al.  The Prediction of Drug Metabolism, Tissue Distribution, and Bioavailability of 50 Structurally Diverse Compounds in Rat Using Mechanism-Based Absorption, Distribution, and Metabolism Prediction Tools , 2007, Drug Metabolism and Disposition.

[32]  Haiyan Li,et al.  First-principle, structure-based prediction of hepatic metabolic clearance values in human. , 2009, European journal of medicinal chemistry.

[33]  Donald E Mager,et al.  Quantitative structure-pharmacokinetic/pharmacodynamic relationships. , 2006, Advanced drug delivery reviews.

[34]  A. Li,et al.  Screening for human ADME/Tox drug properties in drug discovery. , 2001, Drug discovery today.

[35]  M Paul Gleeson,et al.  Plasma protein binding affinity and its relationship to molecular structure: an in-silico analysis. , 2007, Journal of medicinal chemistry.

[36]  D. Butina,et al.  Predicting ADME properties in silico: methods and models. , 2002, Drug discovery today.

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

[38]  Franco Lombardo,et al.  In silico ADME prediction: data, models, facts and myths. , 2003, Mini reviews in medicinal chemistry.

[39]  C. Lipinski Drug-like properties and the causes of poor solubility and poor permeability. , 2000, Journal of pharmacological and toxicological methods.

[40]  Pierre Tufféry,et al.  FAF-Drugs: free ADME/tox filtering of compound collections , 2006, Nucleic Acids Res..

[41]  K. Luthman,et al.  Correlation of drug absorption with molecular surface properties. , 1996, Journal of pharmaceutical sciences.

[42]  J J Baldwin,et al.  Prediction of drug absorption using multivariate statistics. , 2000, Journal of medicinal chemistry.

[43]  L. A. Fenu,et al.  Predicting Oral Clearance in Humans , 2008, Clinical pharmacokinetics.

[44]  Harish Dureja,et al.  Topological Models for Prediction of Pharmacokinetic Parameters of Cephalosporins using Random Forest, Decision Tree and Moving Average Analysis , 2008 .

[45]  C. Hansch Quantitative Relationships Between Lipophilic Character and Drug Metabolism , 1972 .

[46]  A. K. Madan,et al.  Topological models for prediction of physico-chemical, pharmacokinetic and toxicological properties of antihistaminic drugs using decision tree and moving average analysis , 2009, Int. J. Comput. Biol. Drug Des..

[47]  Prabha Garg,et al.  In Silico Prediction of Blood Brain Barrier Permeability: An Artificial Neural Network Model , 2006, J. Chem. Inf. Model..

[48]  G. Grass,et al.  Physiologically-based pharmacokinetic simulation modelling. , 2002, Advanced drug delivery reviews.

[49]  A. Ghose,et al.  A knowledge-based approach in designing combinatorial or medicinal chemistry libraries for drug discovery. 1. A qualitative and quantitative characterization of known drug databases. , 1999, Journal of combinatorial chemistry.

[50]  Melvin J. Yu Predicting Total Clearance in Humans from Chemical Structure , 2010, J. Chem. Inf. Model..

[51]  A. Madan,et al.  Validation of topochemical models for the prediction of permeability through the blood-brain barrier , 2007, Acta pharmaceutica.

[52]  M. Morris,et al.  Quantitative Structure–Activity Relationship and Quantitative Structure–Pharmacokinetics Relationship of 1,4-Dihydropyridines and Pyridines as Multidrug Resistance Modulators , 2005, Pharmaceutical Research.

[53]  G Beck,et al.  Evaluation of human intestinal absorption data and subsequent derivation of a quantitative structure-activity relationship (QSAR) with the Abraham descriptors. , 2001, Journal of pharmaceutical sciences.

[54]  Remigijus Didziapetris,et al.  Classification structure-activity relations (C-SAR) in prediction of human intestinal absorption. , 2003, Journal of pharmaceutical sciences.

[55]  Peter C. Jurs,et al.  Prediction of Human Intestinal Absorption of Drug Compounds from Molecular Structure , 1998, J. Chem. Inf. Comput. Sci..

[56]  Yi Li,et al.  Constructing Optimum Blood Brain Barrier QSAR Models Using a Combination of 4D-Molecular Similarity Measures and Cluster Analysis , 2004, J. Chem. Inf. Model..

[57]  Bhupinder Singh,et al.  In silico quantitative structure pharmacokinetic relationship modeling of quinolones: Apparent volume of distribution , 2009 .

[58]  Edward H. Kerns,et al.  Insights for Predicting Blood-Brain Barrier Penetration of CNS Targeted Molecules Using QSPR Approaches , 2010, J. Chem. Inf. Model..

[59]  T. Kennedy Managing the drug discovery/development interface , 1997 .

[60]  T. Grabowski,et al.  Bioavailability of veterinary drugs in vivo and in silico. , 2009, Journal of veterinary pharmacology and therapeutics.

[61]  J. Lavandera,et al.  Cheminformatic models to predict binding affinities to human serum albumin. , 2001, Journal of medicinal chemistry.

[62]  P. Leeson,et al.  A comparison of physiochemical property profiles of development and marketed oral drugs. , 2003, Journal of medicinal chemistry.

[63]  Xin Wu,et al.  Predicting the volume of distribution of drugs in humans. , 2008, Current drug metabolism.

[64]  Jie Shen,et al.  Estimation of ADME Properties with Substructure Pattern Recognition , 2010, J. Chem. Inf. Model..

[65]  Yoshitaka Yano,et al.  Prediction of human clearance from animal data and molecular structural parameters using multivariate regression analysis. , 2002, Journal of pharmaceutical sciences.

[66]  Z R Li,et al.  Quantitative structure-pharmacokinetic relationships for drug clearance by using statistical learning methods. , 2006, Journal of molecular graphics & modelling.

[67]  Wilhelm Huisinga,et al.  The virtual laboratory approach to pharmacokinetics: design principles and concepts. , 2006, Drug discovery today.

[68]  M. Bayliss,et al.  Combining high-throughput pharmacokinetic screens at the hits-to-leads stage of drug discovery. , 2000, Drug discovery today.

[69]  Gordon M. Crippen,et al.  Use of Classification Regression Tree in Predicting Oral Absorption in Humans , 2004, J. Chem. Inf. Model..

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

[71]  Parminder Singh,et al.  In Silico Prediction of Drug Permeability Across Buccal Mucosa , 2009, Pharmaceutical Research.

[72]  Marjo Yliperttula,et al.  Computational prediction of oral drug absorption based on absorption rate constants in humans. , 2006, Journal of medicinal chemistry.

[73]  Guo-Ping Wang,et al.  Predicting blood-brain barrier penetration from molecular weight and number of polar atoms. , 2008, European journal of pharmaceutics and biopharmaceutics : official journal of Arbeitsgemeinschaft fur Pharmazeutische Verfahrenstechnik e.V.

[74]  Franco Lombardo,et al.  In silico prediction of volume of distribution in human using linear and nonlinear models on a 669 compound data set. , 2009, Journal of medicinal chemistry.

[75]  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..

[76]  A Rostami-Hodjegan,et al.  Utilization of estimated physicochemical properties as an integrated part of predicting hepatic clearance in the early drug-discovery stage: Impact of plasma and microsomal binding , 2009, Xenobiotica; the fate of foreign compounds in biological systems.

[77]  Jianzhong Liu,et al.  Prediction and mechanistic interpretation of human oral drug absorption using MI-QSAR analysis. , 2007, Molecular pharmaceutics.

[78]  Gabriele Cruciani,et al.  Predicting human serum albumin affinity of interleukin-8 (CXCL8) inhibitors by 3D-QSPR approach. , 2005, Journal of medicinal chemistry.

[79]  A K Madan,et al.  Topochemical models for the prediction of permeability through blood-brain barrier. , 2006, International journal of pharmaceutics.

[80]  Thomas Hofmann,et al.  Predicting CNS Permeability of Drug Molecules: Comparison of Neural Network and Support Vector Machine Algorithms , 2002, J. Comput. Biol..

[81]  Structure-Based Prediction of the Nonspecific Binding of Drugs to Hepatic Microsomes , 2009, The AAPS Journal.

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

[83]  D. E. Clark,et al.  In Silico Predictions of Blood-Brain Barrier Penetration: Considerations to “Keep in Mind” , 2005, Journal of Pharmacology and Experimental Therapeutics.

[84]  I Moriguchi,et al.  Non-congeneric structure-pharmacokinetic property correlation studies using fuzzy adaptive least-squares: oral bioavailability. , 1994, Biological & pharmaceutical bulletin.

[85]  Arun K Mandagere,et al.  Graphical model for estimating oral bioavailability of drugs in humans and other species from their Caco-2 permeability and in vitro liver enzyme metabolic stability rates. , 2002, Journal of medicinal chemistry.

[86]  E. Gifford,et al.  The development and validation of a computational model to predict rat liver microsomal clearance. , 2009, Journal of pharmaceutical sciences.

[87]  H. van de Waterbeemd,et al.  ADMET in silico modelling: towards prediction paradise? , 2003, Nature reviews. Drug discovery.

[88]  T. A. McIntyre,et al.  Prediction of animal clearance using naïve Bayesian classification and extended connectivity fingerprints , 2009, Xenobiotica; the fate of foreign compounds in biological systems.

[89]  Meihua Tu,et al.  Development of a computational approach to predict blood-brain barrier permeability. , 2004, Drug metabolism and disposition: the biological fate of chemicals.

[90]  A. Tropsha,et al.  Development and validation of k-nearest-neighbor QSPR models of metabolic stability of drug candidates. , 2003, Journal of medicinal chemistry.

[91]  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.

[92]  Yong Seo Cho,et al.  In Silico Renal Clearance Model Using Classical Volsurf Approach , 2006, J. Chem. Inf. Model..

[93]  M Paul Gleeson,et al.  In silico human and rat Vss quantitative structure-activity relationship models. , 2006, Journal of medicinal chemistry.

[94]  Tingjun Hou,et al.  ADME Evaluation in Drug Discovery, 8. The Prediction of Human Intestinal Absorption by a Support Vector Machine , 2007, J. Chem. Inf. Model..

[95]  Zhi-Wei Cao,et al.  Effect of Selection of Molecular Descriptors on the Prediction of Blood-Brain Barrier Penetrating and Nonpenetrating Agents by Statistical Learning Methods , 2005, J. Chem. Inf. Model..

[96]  Marjo Yliperttula,et al.  Passive oral drug absorption can be predicted more reliably by experimental than computational models--fact or myth. , 2008, European journal of pharmaceutical sciences : official journal of the European Federation for Pharmaceutical Sciences.