Genetic Programming and Evolvable Machines

, Volume 8, Issue 4, pp 413–432

Genetic programming for computational pharmacokinetics in drug discovery and development

  • Francesco Archetti
  • Stefano Lanzeni
  • Enza Messina
  • Leonardo Vanneschi
Original Paper

DOI: 10.1007/s10710-007-9040-z

Cite this article as:
Archetti, F., Lanzeni, S., Messina, E. et al. Genet Program Evolvable Mach (2007) 8: 413. doi:10.1007/s10710-007-9040-z

Abstract

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.

Keywords

Computational pharmacokinetics Drug discovery Genetic programming 

Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  • Francesco Archetti
    • 1
    • 2
  • Stefano Lanzeni
    • 1
  • Enza Messina
    • 1
  • Leonardo Vanneschi
    • 1
  1. 1.D.I.S.Co., Department of Informatics, Systems and CommunicationUniversity of Milano-BicoccaMilanItaly
  2. 2.Consorzio Milano RicercheMilanItaly

Personalised recommendations