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. 2011;9(3):387-409.
doi: 10.2203/dose-response.09-030.Beam. Epub 2010 Jun 25.

Optimization of nonlinear dose- and concentration-response models utilizing evolutionary computation

Andrew L Beam  1 , Alison A Motsinger-Reif
Affiliations

Optimization of nonlinear dose- and concentration-response models utilizing evolutionary computation

Andrew L Beam et al. Dose Response. 2011.

Abstract

An essential part of toxicity and chemical screening is assessing the concentrated related effects of a test article. Most often this concentration-response is a nonlinear, necessitating sophisticated regression methodologies. The parameters derived from curve fitting are essential in determining a test article's potency (EC(50)) and efficacy (E(max)) and variations in model fit may lead to different conclusions about an article's performance and safety. Previous approaches have leveraged advanced statistical and mathematical techniques to implement nonlinear least squares (NLS) for obtaining the parameters defining such a curve. These approaches, while mathematically rigorous, suffer from initial value sensitivity, computational intensity, and rely on complex and intricate computational and numerical techniques. However if there is a known mathematical model that can reliably predict the data, then nonlinear regression may be equally viewed as parameter optimization. In this context, one may utilize proven techniques from machine learning, such as evolutionary algorithms, which are robust, powerful, and require far less computational framework to optimize the defining parameters. In the current study we present a new method that uses such techniques, Evolutionary Algorithm Dose Response Modeling (EADRM), and demonstrate its effectiveness compared to more conventional methods on both real and simulated data.

Keywords: Evolutionary Algorithm; Hill-Slope Model; Nonlinear Regression; Parameter Estimation.

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Figures

FIGURE 1:
FIGURE 1:
Example Nonlinear Dose-Response
FIGURE 2:
FIGURE 2:
Evolutionary Algorithm Overview
FIGURE 3:
FIGURE 3:
Effect of Initial Population Size on Convergence Rate
FIGURE 4:
FIGURE 4:
Effect of Equilibrium Population Size on Convergence Rate
FIGURE 5:
FIGURE 5:
Effect of Initial Tournament Size on Convergence Rate
FIGURE 6:
FIGURE 6:
Execution Time vs. Initial Population Size
FIGURE 7:
FIGURE 7:
Execution Time vs. Equilibrium Population Size
FIGURE 8:
FIGURE 8:
Execution Time vs. Equilibrium Population Size
FIGURE 9:
FIGURE 9:
Resulting Fit from Exponential Model
FIGURE 10:
FIGURE 10:
Result from Simulating both Exponential and Hillslope individuals on Hillslope Data
FIGURE 11:
FIGURE 11:
Concentration-Response for CYP3A4/RIF
FIGURE 12:
FIGURE 12:
HMGCS2 Suppression by CDCA. Note that the green and red lines overlap, and are not distinguishable visually.
See this image and copyright information in PMC

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