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Fuzzy Evaluation of Pharmacokinetic Models
Carlos Sepúlveda1, Oscar Montiel1, José M Cornejo Bravo2
1Instituto Politécnico Nacional, Centro de Investigación y Desarrollo de Tecnología Digital (CITEDI-IPN), Av. Instituto Politécnico Nacional, No. 1310, Col. Nueva Tijuana, 22435 Tijuana, BC, Mexico.
Computational Intelligence and Neuroscience
|February 8, 2019
Summary
This study introduces a software robot to automate the selection of the best population pharmacokinetic (PopPK) model. This fuzzy expert system reduces human error in model evaluation, ensuring accurate drug behavior analysis.
Area of Science:
- Pharmacokinetics
- Computational Biology
- Drug Development
Background:
- Population pharmacokinetic (PopPK) models analyze drug behavior and variability.
- Nonlinear mixed-effect (NLME) models are commonly used for PopPK development.
- Selecting the best PopPK model from multiple candidates is often a complex decision-making problem.
Purpose of the Study:
- To address the human error and misinterpretation issues in selecting the best PopPK model.
- To develop an automated system for evaluating and selecting optimal PopPK models.
- To integrate human expertise into the PopPK model selection process.
Main Methods:
- Development of a software robot utilizing a fuzzy expert system.
- Systematic evaluation of candidate PopPK models based on statistical criteria.
- Automation of the model selection process to minimize subjective bias.
Main Results:
- The developed software robot successfully automates the selection of the best PopPK model.
- The fuzzy expert system effectively evaluates multiple candidate models.
- The system ensures a more reliable and objective model selection process.
Conclusions:
- The software robot can be successfully employed to evaluate PopPK models.
- Automation via a fuzzy expert system enhances the accuracy and reliability of PopPK model selection.
- This approach mitigates human error in identifying the most suitable PopPK model.
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