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.
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Notes
We have not reported statistics, like the average best fitness and the fitness standard deviation on the test set over the 20 independent runs in this paper, because we want to compare the best results obtained by GP with the best ones obtained by the other non-evolutionary ML methods. For %F some of these statistics are reported in Ref. [27], where we can see that results are enough “stable” over the 20 runs, i.e. the variance of the best fitness found in the different runs is “small”. The same also holds for LD50 and %PPB.
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Acknowledgments
During the development of this research, we presented our initial results at the 8th annual conference on Genetic and Evolutionary Computation, GECCO 2006 [27]. We are grateful for the discussion and suggestions by conference attendees and reviewers, who honored our work with the Best Paper Award for the “Biological Applications” conference track.
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Archetti, F., Lanzeni, S., Messina, E. et al. Genetic programming for computational pharmacokinetics in drug discovery and development. Genet Program Evolvable Mach 8, 413–432 (2007). https://doi.org/10.1007/s10710-007-9040-z
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DOI: https://doi.org/10.1007/s10710-007-9040-z