Artificial Neural Networks As a Novel Approach to Integrated Pharmacokinetic—Pharmacodynamic Analysis

Abstract

A novel model-independent approach to analyze pharmacokinetic (PK)-pharmacodynamic (PD) data using artificial neural networks (ANNs) is presented. ANNs are versatile computational tools that possess the attributes of adaptive learning and self-organization. The emulative ability of neural networks is evaluated with simulated PK-PD data, and the power of ANNs to extrapolate the acquired knowledge is investigated. ANNs of one architecture are shown to be flexible enough to accurately predict PD profiles for a wide variety of PK-PD relationships (e.g., effect compartment linked to the central or peripheral compartment and indirect response models). Also, an example is given of the ability of ANNs to accurately predict PD profiles without requiring any information regarding the active metabolite. Because structural details are not required, ANNs exhibit a clear advantage over conventional model-dependent methods. ANNs are proved to be robust toward error in the data and perturbations in the initial estimates. Moreover, ANNs were shown to handle sparse data well. Neural networks are emerging as promising tools in the field of drug discovery and development.

Prediction of Biliary Excretion in Rats and Humans Using Molecular Weight and Quantitative Structure–Pharmacokinetic Relationships

“…In the past, QSPKR has been successfully employed to predict a number of pharmacokinetic properties, including clearance, volume of distribution, bioavailability, and protein binding (13)(14)(15)(16)(17)(18)(19)(20)(21). To our knowledge, this study is the first to apply such a method to predict biliary excretion.…”

“…The concept can also be applied to pharmacokinetics by simply replacing the activities with pharmacokinetic parameters. Such quantitative structure-pharmacokinetic relationship (QSPKR) models have been successfully applied in several studies to predict clearance, volume of distribution, bioavailability and other pharmacokinetic parameters (13)(14)(15)(16)(17)(18)(19)(20)(21).…”

Application of Neural Networks to Population Pharmacokinetic Data Analysis

“…Application of this technique to biopharmaceutical data analysis has received considerable attention recently. [4][5][6][7] Brier et al 8 have examined the use of neural networks for population pharmacokinetic data analysis and concluded that neural networks and NON-MEM provided comparable predictions of plasma drug concentrations. The strength of neural networks is that they do not assume a specific model.…”

Artificial Neural Network in Drug Delivery and Pharmaceutical Research

“…ANN configurations are very useful in prediction of IVIVC from different formulations of same product [65]. ANNs can also be applied in quantitative structure-pharmacokinetic relationship (QSPR) of betablockers using ANNs demonstrating that ANNs are capable of prediction in vivo results from in vitro experiments [66]. IVIVC has been applied in prediction of absorption of salbutamol in the lungs in healthy and asthmatic volunteers based on published in vivo data [67,68].…”