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Whiting B, Kelman AW, Grevel J. Population pharmacokinetics. Theory and clinical application. Clin Pharmacokinet. 1986;11(5):387-401doi: 10.2165/00003088-198611050-00004.
Whiting, B., Kelman, A. W., & Grevel, J. (1986). Population pharmacokinetics. Theory and clinical application. Clinical pharmacokinetics, 11(5), 387-401. https://doi.org/10.2165/00003088-198611050-00004
Whiting, B, et al. "Population pharmacokinetics. Theory and clinical application." Clinical pharmacokinetics vol. 11,5 (1986): 387-401. doi: https://doi.org/10.2165/00003088-198611050-00004
Whiting B, Kelman AW, Grevel J. Population pharmacokinetics. Theory and clinical application. Clin Pharmacokinet. 1986 Sep-Oct;11(5):387-401. doi: 10.2165/00003088-198611050-00004. PMID: 3536257.
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Clin Pharmacokinet. 1986 Sep-Oct;11(5):387-401. doi: 10.2165/00003088-198611050-00004.

Population pharmacokinetics. Theory and clinical application.

Clinical pharmacokinetics

B Whiting, A W Kelman, J Grevel

PMID: 3536257 DOI: 10.2165/00003088-198611050-00004

Abstract

Good therapeutic practice should always be based on an understanding of pharmacokinetic variability. This ensures that dosage adjustments can be made to accommodate differences in pharmacokinetics due to genetic, environmental, physiological or pathological factors. The identification of the circumstances in which these factors play a significant role depends on the conduct of pharmacokinetic studies throughout all stages of drug development. Advances in pharmacokinetic data analysis in the last 10 years have opened up a more comprehensive approach to this subject: early traditional small group studies may now be complemented by later population-based studies. This change in emphasis has been largely brought about by the development of appropriate computer software (NONMEM: Nonlinear Mixed Effects Model) and its successful application to the retrospective analysis of clinical data of a number of commonly used drugs, e.g. digoxin, phenytoin, gentamicin, procainamide, mexiletine and lignocaine (lidocaine). Success has been measured in terms of the provision of information which leads to increased efficiency in dosage adjustment, usually based on a subsequent Bayesian feedback procedure. The application of NONMEM to new drugs, however, raises a number of interesting questions, e.g. 'what experimental design strategies should be employed?' and 'can kinetic parameter distributions other than those which are unimodal and normal be identified?' An answer to the later question may be provided by an alternative non-parametric maximum likelihood (NPML) approach. Population kinetic studies generate a considerable amount of demographic and concentration-time data; the effort involved may be wasted unless sufficient attention is paid to the organisation and storage of such information. This is greatly facilitated by the creation of specially designed clinical pharmacokinetic data bases, conveniently stored on microcomputers. A move towards the adoption of population pharmacokinetics as a routine procedure during drug development should now be encouraged. A number of studies have shown that it is possible to organise existing, routine data in such a way that valuable information on pharmacokinetic variability can be obtained. It should be relatively easy to organise similar studies prospectively during drug development and, where appropriate, proceed to the establishment of control systems based on Bayesian feedback.

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