Genetic programming for computational pharmacokinetics in drug discovery and development

The success of a drug treatment is strongly correlated with the ability of a molecule to reach its target in the patient’s organism without inducing toxic effects. Moreover the reduction of cost and time associated with drug discovery and development is becoming a crucial requirement for pharmaceutical industry. Therefore computational methods allowing reliable predictions of newly synthesized compounds properties are of outmost relevance. In this paper we discuss the role of genetic programming in predictive pharmacokinetics, considering the estimation of adsorption, distribution, metabolism, excretion and toxicity processes (ADMET) that a drug undergoes into the patient’s organism. We compare genetic programming with other well known machine learning techniques according to their ability to predict oral bioavailability (%F), median oral lethal dose (LD50) and plasma-protein binding levels (%PPB). Since these parameters respectively characterize the percentage of initial drug dose that effectively reaches the systemic blood circulation, the harmful effects and the distribution into the organism of a drug, they are essential for the selection of potentially good molecules. Our results suggest that genetic programming is a valuable technique for predicting pharmacokinetics parameters, both from the point of view of the accuracy and of the generalization ability.

[1]  Yuanyuan Wang,et al.  Predictive Toxicology: Benchmarking Molecular Descriptors and Statistical Methods , 2003, J. Chem. Inf. Comput. Sci..

[2]  I. Kola,et al.  Can the pharmaceutical industry reduce attrition rates? , 2004, Nature Reviews Drug Discovery.

[3]  Igor V. Tetko,et al.  Virtual Computational Chemistry Laboratory – Design and Description , 2005, J. Comput. Aided Mol. Des..

[4]  Han van de Waterbeemd,et al.  Lipophilicity in PK design: methyl, ethyl, futile , 2001, J. Comput. Aided Mol. Des..

[5]  Vidroha Debroy,et al.  Genetic Programming , 1998, Lecture Notes in Computer Science.

[6]  A. Beresford,et al.  ADME/PK as part of a rational approach to drug discovery. , 2000, Drug discovery today.

[7]  Marco Pintore,et al.  Prediction of oral bioavailability by adaptive fuzzy partitioning. , 2003, European journal of medicinal chemistry.

[8]  Maarten Keijzer,et al.  Improving Symbolic Regression with Interval Arithmetic and Linear Scaling , 2003, EuroGP.

[9]  H. Akaike,et al.  Information Theory and an Extension of the Maximum Likelihood Principle , 1973 .

[10]  J. Topliss,et al.  QSAR model for drug human oral bioavailability. , 2000, Journal of medicinal chemistry.

[11]  S Gottlieb.  US doctors say sex selection acceptable for non-medical reasons , 2001, BMJ : British Medical Journal.

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

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

[14]  David S. Wishart,et al.  DrugBank: a comprehensive resource for in silico drug discovery and exploration , 2005, Nucleic Acids Res..

[15]  Leonardo Vanneschi.  Investigating Problem Hardness of Real Life Applications , 2008 .

[16]  Sébastien Vérel,et al.  Fitness Clouds and Problem Hardness in Genetic Programming , 2004, GECCO.

[17]  I. Jolliffe.  Principal Component Analysis , 2002 .

[18]  Andreas Zell,et al.  Kernel Functions for Attributed Molecular Graphs – A New Similarity‐Based Approach to ADME Prediction in Classification and Regression , 2006 .

[19]  Mark A. Hall,et al.  Correlation-based Feature Selection for Machine Learning , 2003 .

[20]  Bernhard Schölkopf,et al.  A tutorial on support vector regression , 2004, Stat. Comput..

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

[22]  T M Martin,et al.  Prediction of the acute toxicity (96-h LC50) of organic compounds to the fathead minnow (Pimephales promelas) using a group contribution method. , 2001, Chemical research in toxicology.

[23]  Terence Soule,et al.  Genetic Programming: Theory and Practice , 2003 .

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

[25]  Sébastien Vérel,et al.  Negative Slope Coefficient: A Measure to Characterize Genetic Programming Fitness Landscapes , 2006, EuroGP.

[26]  Peter J. Rousseeuw,et al.  Robust Regression and Outlier Detection , 2005, Wiley Series in Probability and Statistics.

[27]  Roberto Todeschini,et al.  Handbook of Molecular Descriptors , 2002 .

[28]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[29]  Nigel Greene,et al.  Computer systems for the prediction of toxicity: an update. , 2002, Advanced drug delivery reviews.

[30]  Johann Gasteiger,et al.  Neural networks in chemistry and drug design , 1999 .

[31]  William B. Langdon,et al.  Genetic Programming in Data Mining for Drug Discovery , 2005 .

[32]  Annette Tuffs.  Bayer faces shake up after Lipobay withdrawn , 2001, BMJ : British Medical Journal.

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

[34]  Manfred Kansy,et al.  Predicting plasma protein binding of drugs: a new approach. , 2002, Biochemical pharmacology.

[35]  Leonardo Vanneschi,et al.  Genetic programming for human oral bioavailability of drugs , 2006, GECCO.

[36]  L. Berezhkovskiy,et al.  Determination of Drug Binding to Plasma Proteins Using Competitive Equilibrium Binding to Dextran-Coated Charcoal , 2006, Journal of Pharmacokinetics and Pharmacodynamics.

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