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Im an applied statistical student and Im very interested in random
coeficients models to estimate the dosage parameters.
How many methods exist to calculate the dosage of a given drug for a given
patient?
Please send any references to
jgomez1.aaa.nemak.com
Thanks.
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Dear Jose Luis:
Probably most methods use conventional PK models and simply compute the
dose needed to achieve a desired target peak or trough value. In addition,
some do this to achieve desired target concentrations in the peripheral
(nonserum) compartment of a patient's PK model. A review can be found in
Therapeutic Drug Monitoring 15: 380-393, 1993, and in Int. J. Biomed
Cumputing, 36: 1-23, 1994. These are all based on Maximum Aposteriori
Probability (MAP)Bayesian approaches to adaptive control of dosage regimens
using parameteric PK models in which the parameters have only a single
value (ie, the Vd is xxx and the Kel or clearance is yyy. etc.).
Another approach to computing dosage regimens is the "Multiple Model"
approach. it is based on nonparametric PK models. Each parameter has many
possible values. The nonparametric maximum likelihood (NPML) approach of
Mallet is one such approach. This is described in Mallet A: A maximum
likelihood regression estimation method for random coefficient regression
models, in Biometrika, 73: 645-656, 1986. It is reviwed by Steimer JL,
Mallet A, and Mentre F in: Estimating Interindividual Pharmacokinetic
Variability, in Variability in Drug Therapy: Description, Estimation, and
Control, ed by Rowland M, et al, Raven Press, New York, pp 65-111, 1985.
Another nonparametric approach is the nonparametric EM (NPEM)
approach of
Schumitzky, as described in Nonparametric EM Algorithms for Estimating
Prior Distributions, in Appl. Math. Comput. 45: 143-157, 1991.
Both methods of population modeling are well suited to the multiple
model
(MM) control approach The NPML method has been used with MM control by
Taright et al, Therapeutic Drug Monitoring 16: 258-269, 1994.
The NPEM models have been used in MM approaches by Bayard, Milman, and
Schumitzky, in Int. J. Bio-Med Comput. 36: 103-115, 1994, and descdibed in
more detail in a chapter in Selected Topics in Mathematical Physics
(Professor R. Vasudeval Memorial Volume) ed by Sridhar, Rao, and
Lakshminarayanan, Allied Publishers Ltd, Madras, pp 407-426, 1995. A
clinical version of this approach is now in development.
Since multiple versions (models) of the patient are present, a
candidate
regimen can be developed. It will predict a trajectory of serum levels into
the future for each model. These predicted concentrations can be compared
with a desired concentration at a desired time, and a penalty function such
as a weighted least squares one can then be minimized, thus choosing the
regimen which specifically minimizes the penalty function. This method,
therefore, can conside the cost or penalty of not achieving the desired
goal (a feature not possible with parametric models), and can specifically
compute the dosage regimen which most precisely achieves the desired goal.
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USC Lab of Applied Pharmacokinetics
CSC 134-B, 2250 Alcazar St, Los Angeles CA 90033
Phone (213)342-1300, Fax (213)342-1302
email=jelliffe.-a-.hsc.usc.edu
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