A model of competing saturable kinetic processes with application to the pharmacokinetics of the anticancer drug paclitaxel.

A saturable multi-compartment pharmacokinetic model for the anti-cancer drug paclitaxel is proposed based on a meta-analysis of pharmacokinetic data published over the last two decades. We present and classify the results of time series for the drug concentration in the body to uncover the underlying power laws. Two dominant fractional power law exponents were found to characterize the tails of paclitaxel concentration-time curves. Short infusion times led to a power exponent of -1.57 ± 0.14, while long infusion times resulted in tails with roughly twice the exponent. Curves following intermediate infusion times were characterized by two power laws. An initial segment with the larger slope was followed by a long-time tail characterized by the smaller exponent. The area under the curve and the maximum concentration exhibited a power law dependence on dose, both with compatible fractional power exponents. Computer simulations using the proposed model revealed that a two-compartment model with both saturable distribution and elimination can reproduce both the single and crossover power laws. Also, the nonlinear dose-dependence is correlated with the empirical power law tails. The longer the infusion time the better the drug delivery to the tumor compartment is a clinical recommendation we propose.

Emergence of power laws in the pharmacokinetics of paclitaxel due to competing saturable processes.

PURPOSE: This study presents the results of power law analysis applied to the pharmacokinetics of paclitaxel. Emphasis is placed on the role that the power exponent can play in the investigation and quantification of nonlinear pharmacokinetics and the elucidation of the underlying physiological processes. METHODS: Forty-one sets of concentration-time data were inferred from 20 published clinical trial studies, and 8 sets of area-under-the-curve (AUC) and maximum concentration (Cmax) values as a function of dose were collected. Both types of data were tested for a power law relationship using least squares regression analysis. RESULTS: Thirty-nine of the concentration-time curves were found to exhibit power law tails, and two dominant fractional exponents emerged. Short infusion times led to asymptotic tails with a single power exponent of - 1.57 +/- 0.14, while long infusion times resulted in steeper tails characterized by roughly twice the exponent. The curves following intermediate infusion times were characterized by two consecutive power laws; an initial short slope with the larger alpha value was followed by a crossover to a long-time tail characterized by the smaller Beta exponent. The AUC and Cmax parameters exhibited a power law dependence on the dose, with fractional power exponents that agreed with each other and with the exponent characterizing the shallow decline. Computer simulations revealed that a two- or three-compartment model with both saturable distribution and saturable elimination can produce the observed behaviour. Analogous linear models did not provide good fits over the range of values collected empirically. Furthermore, there is preliminary evidence that the nonlinear dose-dependence is correlated with the power law tails. CONCLUSIONS: Assessment of data from published clinical trials suggests that power laws accurately describe the concentration-time curves and nonlinear dose-dependence of paclitaxel, and the power exponents provide new\ insights into the underlying drug mechanisms. The interplay between two saturable processes can produce a wide range of behaviour, including concentration-time curves with exponential, power law, and dual power law tails.

Use of a simulated annealing algorithm to fit compartmental models with an application to fractal pharmacokinetics.

Increasingly, fractals are being incorporated into pharmacokinetic models to describe transport and chemical kinetic processes occurring in confined and heterogeneous spaces. However, fractal compartmental models lead to differential equations with power-law time-dependent kinetic rate coefficients that currently are not accommodated by common commercial software programs. This paper describes a parameter optimization method for fitting individual pharmacokinetic curves based on a simulated annealing (SA) algorithm, which always converged towards the global minimum and was independent of the initial parameter values and parameter bounds. In a comparison using a classical compartmental model, similar fits by the Gauss-Newton and Nelder-Mead simplex algorithms required stringent initial estimates and ranges for the model parameters. The SA algorithm is ideal for fitting a wide variety of pharmacokinetic models to clinical data, especially those for which there is weak prior knowledge of the parameter values, such as the fractal models.

Modeling fractal-like drug elimination kinetics using an interacting random-walk model.

We introduce an interacting random-walk model to describe the residence time of drug molecules undergoing a series of sojourn times in the body before being permanently eliminated under either homogeneous or heterogeneous conditions. We show that short-term correlations between drug molecules lead to Michaelis-Menten kinetics while long-term correlations lead to transient fractal-like kinetics. By combining both types of correlation, fractal-like Michaelis-Menten kinetics are achieved, and the simulations confirm previous analytical results.

Fractal michaelis-menten kinetics under steady state conditions: Application to mibefradil.

PURPOSE: To provide the first application of fractal kinetics under steady state conditions to pharmacokinetics as a model for the enzymatic elimination of a drug from the body. MATERIALS AND METHODS: A one-compartment model following fractal Michaelis-Menten kinetics under a steady state is developed and applied to concentration-time data for the cardiac drug mibefradil in dogs. The model predicts a fractal reaction order and a power law asymptotic time-dependence of the drug concentration, therefore a mathematical relationship between the fractal reaction order and the power law exponent is derived. The goodness-of-fit of the model is assessed and compared to that of four other models suggested in the literature. RESULTS: The proposed model provided the best fit to the data. In addition, it correctly predicted the power law shape of the tail of the concentration-time curve. CONCLUSION: A simple one-compartment model with steady state fractal Michaelis-Menten kinetics describing drug elimination from the body most accurately describes the pharmacokinetics of mibefradil in dogs. The new fractal reaction order can be explained in terms of the complex geometry of the liver, the organ responsible for eliminating the drug.