Quantitative structure−pharmacokinetic parameters relationships (QSPKR) analysis of antimicrobial agents in humans using simulated annealing k‐nearest‐neighbor and partial least‐square analysis methods

doi:10.1002/jps.20117

Journal of Pharmaceutical Sciences

Volume 93, Issue 10, October 2004, Pages 2535-2544

https://doi.org/10.1002/jps.20117 Get rights and content

Abstract

We have developed quantitative structure−pharmacokinetic parameters relationship (QSPKR) models using k‐nearest‐neighbor (k‐NN) and partial least‐square (PLS) methods to predict the volume of distribution at steady state (Vss) and clearance (CL) of 44 antimicrobial agents in humans. The performance of QSPKR was determined by the values of the internal leave‐one‐out, crossvalidated coefficient of determination q² for the training set and external predictive r² for the test set. The best simulated annealing (SA)‐kNN model was highly predictive for Vss and provided q² and r² values of 0.93 and 0.80, respectively. For all compounds, the model produced average fold error values for Vss of 1.00 and for 93% of the compounds provided predictions that were within a twofold error of actual values. The best SA‐kNN model for prediction of CL yielded q² and r² values of 0.77 and 0.94, respectively, and had an average fold rror of 1.05. Use of PLS methods resulted in inferior QSPKR models. The SA‐kNN QSPKR approach has utility in drug discovery and development in the identification of compounds that possess appropriate pharmacokinetic characteristics in humans, and will assist in the selection of a suitable starting dose for Phase I, first‐time‐in‐man studies.

Section snippets

INTRODUCTION

During the drug development process, prediction of human pharmacokinetic parameters such as clearance and volume of distribution is fundamental in the design, optimization, and selection of lead drug candidates and in the determination of optimal dosing regimens for early phase clinical trials. In recent years, the application of combinatorial chemistry methods in drug discovery has produced vast numbers of drug candidates, which have dramatically increased the demand for rapid and efficient

Data

Pharmacokinetic data in humans for clearance (CL) and volume of distribution at steady state (Vss) were obtained from published literature for 44 antibiotics from different structural and therapeutic classes and are listed in Table 1.¹⁷ Parameters used in this analysis were derived from data following iv administration.

Generation of Molecular Descriptors

All chemical structures were generated using Sybyl version 6.7 (Tripos Associate, St. Louis, MO). Molecular Operating Environment (MOE) descriptors were generated for variable

Data and Molecular Descriptors

The 44 antibiotics included in the complete data set consisted of 18 cephalosporins (40.9%), nine penicillins (20.5%), four aminoglycosides (9.1%), two tetracyclines (4.5%), one macrolide (2.3%), one quinolone (2.3%), and nine antibiotics from nine other classes (20.4%) (Table 1). Scaled data were used for model building (standardized by mean and standard deviation). One hundred eighty‐one MOE descriptors were determined for each drug in the original data set using the QuaSAR‐descriptor option

DISCUSSION

In this study, we used a recently developed, variable‐selection kNN method¹⁸ to obtain QSPKR models for 44 antimicrobial agents. We compared these results to those obtained with models developed with the more commonly used PLS method. Using a combination of 2D and 3D MOE descriptors of chemical structures, we built a QSPKR model with high values of q² (for the training and complete sets) and r² (for the test set) using the SA‐kNN method. Efforts to build similar quality models with the PLS

Acknowledgements

This paper was presented in part at the Annual Meeting of the American Association of Pharmaceutical Scientists held in Toronto in 2003.

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^*: This paper was presented in part at the Annual Meeting of the American Association of Pharmaceutical Scientists in Toronto in 2003.

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Research ArticlesQuantitative structure−pharmacokinetic parameters relationships (QSPKR) analysis of antimicrobial agents in humans using simulated annealing k‐nearest‐neighbor and partial least‐square analysis methods*

Abstract

Section snippets

INTRODUCTION

Data

Generation of Molecular Descriptors

Data and Molecular Descriptors

DISCUSSION

Acknowledgements

Life Sci

J Pharm Sci

J Pharm Sci

J Pharm Sci

J Pharm Sci

J Pharm Sci

Anal Chem Acta

Applications of combinatorial technologies to drug discovery. 1. Background and peptide combinatorial libraries

J Med Chem

Applications of combinatorial technologies to drug discovery. 2. Combinatorial organic synthesis, library screening strategies, and future directions

J Med Chem

Applications and limitations of interspecies scaling and in vitro extrapolation in pharmacokinetics

Drug Metab Dispos

Research Articles
Quantitative structure−pharmacokinetic parameters relationships (QSPKR) analysis of antimicrobial agents in humans using simulated annealing k‐nearest‐neighbor and partial least‐square analysis methods*