Phoenix WNL Introduction

Phoenix WinNonlin

Phoenix WinNonlin is a non-compartmental analysis (NCA), pharmacokinetic/pharmacodynamic (PK/PD), and toxicokinetic (TK) modeling tool, with integrated tools for data processing, post-analysis processing including statistical tests and bioequivalence analysis, table creation, and graphics. It is highly suited for PK/PD modeling and noncompartmental analysis used to evaluate data from bioavailability and clinical pharmacology studies.

Phoenix WinNonlin includes a large library of pharmacokinetic, pharmacodynamic, noncompartmental, PK/PD linked, and indirect response models. It also supports creation of custom models to enable fitting and analysis of clinical data. It generates the graphs, tables, and output worksheets required for regulatory submission.

Operational objects that are part of WinNonlin include:

NCA
Bioequivalence
Linear Mixed Effects
Crossover
Convolution
Deconvolution
NonParametric Superposition
Semicompartmental Modeling
Modeling (Dissolution, Indirect Response, Linear, Michaelis-Menten, Pharmacodynamic, Pharmacokinetic, PK/PD Linked, User-defined ASCII)

References for the WinNonlin operational objects are provided in their descriptions. Additional references are provided below.

Pharmacokinetics References

Hooker, Staatz, and Karlsson (2007). Conditional Weighted Residuals (CWRES): A Model Diagnostic for the FOCE Method, Pharmaceutical Research, DOI:10.1007/s11095-007-9361-x.

Brown and Manno (1978). ESTRIP, A BASIC computer program for obtaining initial polyexponential parameter estimates. J Pharm Sci 67:1687–91.

Chan and Gilbaldi (1982). Estimation of statistical moments and steady-state volume of distribution for a drug given by intravenous infusion. J Pharm Bioph 10(5):551–8.

Cheng and Jusko (1991). Noncompartmental determination of mean residence time and steady-state volume of distribution during multiple dosing, J Pharm Sci 80:202.

Dayneka, Garg and Jusko (1993). Comparison of four basic models of indirect pharmacodynamic responses. J Pharmacokin Biopharm 21:457.

Endrenyi, ed. (1981). Kinetic Data Analysis: Design and Analysis of Enzyme and Pharmacokinetic Experiments. Plenum Press, New York.

Gabrielsson and Weiner (2016). Pharmacokinetic and Pharmacodynamic Data Analysis: Concepts and Applications, 5^th ed. Apotekarsocieteten, Stockholm.

Gibaldi and Perrier (1982). Pharmacokinetics, 2nd ed. Marcel Dekker, New York.

Gouyette (1983). Pharmacokinetics: Statistical moment calculations. Arzneim-Forsch/Drug Res 33 (1):173–6.

Holford and Sheiner (1982). Kinetics of pharmacological response. Pharmacol Ther 16:143.

Jusko (1990). Corticosteroid pharmacodynamics: models for a broad array of receptor-mediated pharmacologic effects. J Clin Pharmacol 30:303.

Koup (1981). Direct linear plotting method for estimation of pharmacokinetic parameters. J Pharm Sci 70:1093–4.

Kowalski and Karim (1995). A semicompartmental modeling approach for pharmacodynamic data assessment. J Pharmacokinet Biopharm 23(3):307–22.

Metzler and Tong (1981). Computational problems of compartmental models with Michaelis-Menten-type elimination. J Pharmaceutical Sciences 70:733–7.

Nagashima, O’Reilly and Levy (1969). Kinetics of pharmacologic effects in man: The anticoagulant action of warfarin. Clin Pharmacol Ther 10:22.

Wagner (1975). Fundamentals of Clinical Pharmacokinetics. Drug Intelligence, Illinois.

Regression and modeling References

Akaike (1978). Posterior probabilities for choosing a regression model. Annals of the Institute of Mathematical Statistics 30:A9–14.

Allen and Cady (1982). Analyzing Experimental Data By Regression. Lifetime Learning Publications, Belmont, CA.

Bard (1974). Nonlinear Parameter Estimation. Academic Press, New York.

Bates and Watts (1988). Nonlinear Regression Analyses and Its Applications. John Wiley & Sons, New York.

Beck and Arnold (1977). Parameter Estimation in Engineering and Science. John Wiley & Sons, New York.

Belsley, Kuh and Welsch (1980). Regression Diagnostics. John Wiley & Sons, New York.

Corbeil and Searle (1976). Restricted maximum likelihood (REML) estimation of variance components in the mixed models, Technometrics, 18:31–8.

Cornell (1962). A method for fitting linear combinations of exponentials. Biometrics 18:104–13.

Davies and Whitting (1972). A modified form of Levenberg's correction. Chapter 12 in Numerical Methods for Non-linear Optimization. Academic Press, New York.

DeLean, Munson and Rodbard (1978). Simultaneous analysis of families of sigmoidal curves: Application to bioassay, radioligand assay and physiological dose-response curves. Am J Physiol 235(2):E97–E102.

Draper and Smith (1981). Applied Regression Analysis, 2nd ed. John Wiley & Sons, NY.

Fai and Cornelius (1996). Approximate f-tests of multiple degree of freedom hypotheses in generalized least squares analysis of unbalanced split-plot experiments. J Stat Comp Sim 554:363–78.

Fletcher (1980). Practical methods of optimization, Vol. 1: Unconstrained Optimization. John Wiley & Sons, New York.

Foss (1970). A method of exponential curve fitting by numerical integration. Biometrics 26:815–21.

Giesbrecht and Burns (1985). Two-stage analysis based on a mixed model: Large sample asymptotic theory and small-sample simulation results. Biometrics 41:477–86.

Gill, Murray and Wright (1981). Practical Optimization. Academic Press.

Gomeni and Gomeni (1979). AUTOMOD: A Polyalgorithm for an integrated analysis of linear pharmacokinetic models. Comput Biol Med 9:39–48.

Hartley (1961). The modified Gauss-Newton method for the fitting of nonlinear regression functions by least squares. Technometrics 3:269–80.

Jennrich and Moore (1975). Maximum likelihood estimation by means of nonlinear least squares. Amer Stat Assoc Proceedings Statistical Computing Section 57–65.

Kennedy and Gentle (1980). Statistical Computing. Marcel Dekker, New York.

Koch (1972). The use of nonparametric methods in the statistical analysis of the two-period change-over design. Biometrics 577–84.

Kowalski and Karim (1995). A semicompartmental modeling approach for pharmacodynamic data assessment, J Pharmacokinet Biopharm 23:307–22.

Leferink and Maes (1978). STRIPACT, An interactive curve fit programme for pharmacokinetic analyses. Arzneim Forsch 29:1894–8.

Longley (1967). Journal of the American Statistical Association, 69:819–41.

Nelder and Mead (1965). A simplex method for function minimization. Computing Journal 7:308–13.

Parsons (1968). Biological problems involving sums of exponential functions of time: A mathematical analysis that reduces experimental time. Math Biosci 2:123–8.

PCNonlin. Scientific Consulting Inc., North Carolina, USA.

Peck and Barrett (1979). Nonlinear least-squares regression programs for microcomputers. J Pharmacokinet Biopharm 7:537–41.

Ratkowsky (1983). Nonlinear Regression Modeling. Marcel Dekker, New York.

Satterthwaite (1946). An approximate distribution of estimates of variance components. Biometrics Bulletin 2:110–4.

Schwarz (1978). Estimating the dimension of a model. Annals of Statistics 6:461–4.

Sedman and Wagner (1976). CSTRIP, A FORTRAN IV Computer Program for Obtaining Polyexponential Parameter Estimates. J Pharm Sci 65:1001–10.

Shampine, Watts and Davenport (1976). Solving nonstiff ordinary differential equations - the state of the art. SIAM Review 18:376–411.

Sheiner, Stanski, Vozeh, Miller and Ham (1979). Simultaneous modeling of pharmacokinetics and pharmacodynamics: application to d-tubocurarine. Clin Pharm Ther 25:358–71.

Smith and Nichols (1983). Improved resolution in the analysis of the multicomponent exponential signals. Nuclear Instrum Meth 205:479–83.

Tong and Metzler (1980). Mathematical properties of compartment models with Michaelis-Menten-type elimination. Mathematical Biosciences 48:293–306.

Wald (1943). Tests of statistical hypotheses concerning several parameters when the number of observations is large. Transaction of the American Mathematical Society 54.