Summary
This article examines the use of population pharmacokinetic models to store experiences about drugs in patients and to apply that experience to the care of new patients. Population models are the Bayesian prior. For truly individualised therapy, it is necessary first to select a specific target goal, such as a desired serum or peripheral compartment concentration, and then to develop the dosage regimen individualised to best hit that target in that patient.
One must monitor the behaviour of the drug by measuring serum concentrations or other responses, hopefully obtained at optimally chosen times, not only to see the raw results, but to also make an individualised (Bayesian posterior) model of how the drug is behaving in that patient. Only then can one see the relationship between the dose and the absorption, distribution, effect and elimination of the drug, and the patient’s clinical sensitivity to it; one must always look at the patient. Only by looking at both the patient and the model can it be judged whether the target goal was correct or needs to be changed. The adjusted dosage regimen is again developed to hit that target most precisely starting with the very next dose, not just for some future steady state.
Nonparametric population models have discrete, not continuous, parameter distributions. These lead naturally into the multiple model method of dosage design, specifically to hit a desired target with the greatest possible precision for whatever past experience and present data are available on that drug — a new feature for this goal-oriented, model-based, individualised drug therapy. As clinical versions of this new approach become available from several centres, it should lead to further improvements in patient care, especially for bacterial and viral infections, cardiovascular therapy, and cancer and transplant situations.
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References
Rowland M, Sheiner L, Steimer JL, editors. Variability in drug therapy: description, estimation, and control. New York: Raven Press, 1985.
Reuning R, Sams R, Notari R. Role of pharmacokinetics in drug dosage adjustment: 1. pharmacologic effects, kinetics, and apparent volume of distribution of digoxin. J Clin Pharmacol 1973; 13: 127–41.
Sheiner L, Beal S, Rosenberg B, et al. Forecasting individual pharmacokinetics. Clin Pharmacol Ther 1979; 26: 294–305.
Aarons L. The estimation of population pharmacokinetic parameters using an EM algorithm. Comput Methods Programs Biomed 1993; 41: 9–16.
Beal S, Sheiner L. NONMEM user’s guide I: users basic guide. San Francisco: Division of Clinical Pharmacology, University of California, 1979.
Sheiner L. The population approach to pharmacokinetic data analysis: rationale and standard data analysis methods. Drug Metab Rev 1984; 15: 153–71.
Beal S. Population pharmacokinetic data and parameter estimation based on their first two statistical moments. Drug Metab Rev 1984; 15: 173–93.
De Groot M. Probability and statistics. 2nd ed. Reading (MA): Addison-Wesley, 1986: 334–6.
Spieler G, Schumitzky A. Asymptotic properties of extended least squares estimators with approximate models [technical report]. Los Angeles: Laboratory of Applied Pharmacokinetics, University of Southern California School of Medicine, 1992: 92–4.
Spieler G, Schumitzky A. Asymptotic properties of extended least squares estimates with application to population pharmacokinetics. San Francisco: Proceedings of the American Statistical Society, Biopharmaceutical Section, 1993: 177–82.
Vonesh E, Chinchilla V. Linear and nonlinear models for analysis of repeated measurements. New York: Marcel Dekker, 1997: 354–5.
Rodman J, Silverstein K. Comparison of two stage (TS) and first order (FO) methods for estimation of population parameters in an intensive pharmacokinetic (PK) study. Clin Pharmacol Ther 1990; 47: 151.
Maire P, Barbaut X, Girard P, et al. Preliminary results of three methods for population pharmacokinetic analysis (NONMEM, NPML, NPEM) of amikacin in geriatric and general medicine patients. Int J Biomed Comput 1994; 36: 139–41.
Lindstrom M, Bates D. Nonlinear mixed-effects models for repeated measures data. Biometrics 1990; 46: 673–87.
Vonesh E, Carter R. Mixed effects nonlinear regressions for unbalanced repeated measures. Biometrics 1992; 48: 1–17.
Wakefield J, Smith A, Racine-Poon A, et al. Bayesian ananysis of linear and nonlinear population models. Applied Stats 1994; 43: 201–22.
Davidian M, Gallant A. The nonlinear mixed effects model with a smooth random effects density. Biometrika 1993; 80: 475–88.
Bertilsson L. Geographic/interracial differences in polymorphic drug oxidation. Clin Pharmacokinet 1995; 29: 192–209.
Lindsay B. The geometry of mixture likelihoods: a general theory. Ann Statist 1983; 11: 86–94.
Mallet A. A maximum likelihood estimation method for random coefficient regression models. Biometrika 1986; 73: 645–56.
Schumitzky A. The nonparametric maximum likelihood approach to pharmacokinetic population analysis: proceedings of the 1993 Western Simulation Multiconference: simulation for health care. San Deigo: Society for Computer Simulation, 1993: 95–100.
Schumitzky A. Nonparametric EM algorithms for estimating prior distributions. App Math Comput 1991; 45: 143–57.
Hurst A, Yoshinaga M, Mitani G, et al. Application of a bayesian method to monitor and adjust vancomycin dosage regimens. Antimicrob Agents Chemother 1990; 34: 1165–71.
Bayard D, Milman M, Schumitzky A. Design of dosage regimens: a multiple model stochastic approach. Int J Biomed Comput 1994; 36: 103–15.
Bayard D, Jelliffe R, Schumitzky A, et al. Precision drug dosage regimens using multiple model adaptive control: theory and application to simulated vancomycin therapy. In: Sridhar R, Srinavasa RK, Vasudevan L, editor. Selected topics in mathematical physics. Madras: Allied Publishers Inc, 1995: 407–26.
Mallet A, Mentre F, Giles J, et al. Handling covariates in population pharmacokinetics with an application to gentamicin. Biomed Meas Infor Contr 1988; 2: 138–46.
Taright N, Mentre F, Mallet A, et al. Nonparametric estimation of population characteristics of the kinetics of lithium from observational and experimental data: individualization of chronic dosing regimen using a new bayesian approach. Ther Drug Monit 1994; 16: 258–69.
Jerling M. Population kinetics of antidepressant and neuroleptic drugs: studies of therapeutic drug monitoring data to evaluate kinetic variability, drug interactions, nonlinear kinetics, and the influence of genetic factors Ph.D. thesis]. Stockholm: Karolinska Institute at Huddinge University Hospital, 1995: 28–9.
D’Argenio D. Optimal sampling times for pharmacokinetic experiments. J Pharmacokin Biopharm 1981; 9: 739–56.
Jelliffe R, Iglesias T, Hurst A, et al. Individualising gentamicin dosage regimens: a comparative review of selected models, data filling methods and monitoring strategies. Clin Pharmacokinet 1991; 21: 461–78.
Jelliffe R, Schumitzky A, Van Guilder M, et al. Individualizing drug dosage regimens: roles of population pharmacokinetic models, bayesian fitting, and adaptive control. Ther Drug Monit 1993; 15: 380–93.
Jelliffe R, Maire P, Sattler F, et al. Adaptive control of drug dosage regimens: basic foundations, relevant issues, and clinical examples. Int J Biomed Comput 1994; 36: 1–23.
Sheiner L, Beal S. Bayesian individualization of pharmacokinetics: simple implementation and comparison with non-bayesian methods. J Pharm Sci 71: 1344–8.
Vozeh S, Berger M, Wenk M, et al. Rapid prediction of individual dosage requirements for lignocaine. Clin Pharmacokinet 1984; 9: 354–63.
Sawchuk R, Zaske D. Pharmacokinetics of dosing regimens which utilize multiple intravenous infusions: gentamicin in burn patients. J Pharmacokin Biopharm 1976; 4: 183–95.
Zaske D, Bootman JL, Solem L, et al. Increased burn patient survival with individualized dosages of gentamicin. Surgery 1982; 91: 142–9.
Jelliffe R. Explicit determination of laboratory assay error patterns: a useful aid in therapeutic drug monitoring; No. DM 89-4 (DM56). Drug Monit Toxicol 1989; 10(4): 1–6.
Neider J, Mead R. A simplex method for function minimization. Comput J 1965; 4: 308–13.
Caceci M, Cacheris W. Fitting curves to data: the simplex algorithm is the answer. BYTE Magazine 1984; 9: 340–62.
Lainiotis D. Partitioning: a unifying framework for adaptive systems. Pt 1: estimation. Proc IEEE 1976; 64: 1126–43.
Dodge W, Jelliffe R, Richardson CJ, et al. Population pharmacokinetic models: measures of central tendency. Drug Invest 1993; 5: 206–11.
Bertsekas D. Dynamic programming: deterministic and stochastic models. Englewood Cliffs (NJ): Prentice-Hall, 1987: 144–6.
Jelliffe R, Schumitzky A, Van Guilder M, et al. User manual for version 10.7 of the USC*PACK collection of PC programs. Los Angeles: Laboratory of Applied Pharmacokinetics, University of Southern California School of Medicine, 1995: 57–69.
Destache C, Meyer K, Bittner M, et al. Impact of a clinical pharmacokinetic service on patients treated with aminoglycosides: a cost-benefit analysis. Ther Drug Monit 1990; 12: 419–26.
Destache C, Meyer S, Rowley K. Does accepting pharmacokinetic recommendations impact hospitalization?: a costbenefit analysis. Ther Drug Monit 1990; 12: 427–33.
Vinks AATMM, Evers NAEM, Mathot R, et al. Impact of goaloriented model-based TDM of aminoglycosides on clinical outcome: a cost-effectiveness study [abstract]. Submitted for consideration for presentation at the 5th International Congress of Therapeutic Drug Monitoring and Clinical Toxicology; 1997 Nov; Vancouver; 10–4.
Jelliffe R, Buell J, Kalaba R. Reduction of digitalis toxicity by computer-assisted glycoside dosage regimens. Ann Int Med 1972; 77: 891–906.
Rodman J, Jelliffe R, Kolb E, et al. Clinical studies with computer-assisted lidocaine therapy. Arch Int Med 1984; 144: 703–9.
Van Guilder M, Leary R, Schumitzky A, et al. Nonlinear nonparametric population codeling on a supercomputer. Supercomputer 1997 Conference; 1997 Nov 17–20; San Jose (CA).
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Jelliffe, R.W., Schumitzky, A., Bayard, D. et al. Model-Based, Goal-Oriented, Individualised Drug Therapy. Clin Pharmacokinet 34, 57–77 (1998). https://doi.org/10.2165/00003088-199834010-00003
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DOI: https://doi.org/10.2165/00003088-199834010-00003