Outcome of nonmem analysis depends on modeling strategy
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CLINICAL PHARMACOLOGY & THERAPEUTICS P52 American Societyfor Clinical Pharmacology and Therapeutics FEBaVARY2003 PII-86 OUTCOME OF NONMEM ANALYSIS DEPENDS ON MOD- ELING STRATEGY. R.R. Bies, PharmD, PhD, M. E. Sale, MD, G. Smith, PhD, M. Muldoon, MD, F. E. Lotrich, MD, PhD, B. G. Pollock, MD, PhD, Schools of Pharmacy and Medicine, University of Pittsburgh, GlaxoSmithKline, Hillside Hospital and Albert Einstein College of Medicine, School of Medicine, University of Pittsburgh, Pittsburgh, PA. Purpose: To evaluate the impact of modeling strategy on the identification of the model structure and covariates comparing for- ward stepwise, reverse stepwise and genetic algorithm approaches. Methods: A two-.compartment model to describe citalopram phar- macokinetics (PK) after IV infusion was implemented using NON- MEM. Weight, age and sex were evaluated for their impact on the parameters of the PK model using three different methods for co- variate identification: 1. Forward stepwise evaluation; 2. Backward elimination from the fnll model, 3. Forward and backward stepwise evaluation from the "final model" identified with method #1 and; 4. Genetic algorithm search (GA). All of these methods were evaluated using the first order estimation settings, methods one and three were also evaluated using the first order conditional estimation method. Results: All four of these methods identified different covariates that had a significant effect on the PK model. The GA approach (using the first order estimation method) found a model with the lowest objective function that was significantly lower than the lowest found (<30 points) with the standard search methods. This approach identified sex, weight and age as having a significant impact on clearance and sex and weight having a significant impact on the peripheral volume of distribution. Conclusions: The genetic algorithm identified significant covari- ates that were missed when using other approaches. PII-87 IMPACT OF ERRORS IN DOSE TIME AND DOSE AMOUNT ASSUMPTIONS ON THE POPULATION PHARMACOKINETIC ANALYSIS OF A PEDIATRIC DATA SET: A SIMULATION STUDY. P. Rajagopalan, PhD, M. R. Gastonguay, PhD, Bayer Pharmaceuticals, University of Connecticut School of Pharmacy, West Haven, CT. The accuracy of dosing times (DT1M) and dose amounts (AMT) in a population (POP) pharmacokinetic (PK) database can be suspect, especially in outpatient pediatric trials with oral dosage forms. A simulation study was undertaken to assess the impact of errors in assumptions about DTIME and AMT on the POP parameter estimates from a nonlinear mixed-effects PK analysis of a pediatric data set. Simulations were based on a known 2-compartment structural model for an investigational drug, parameterized in terms of allometrically- scaled clearances and volumes. Three different sampling schemes (SI 2 extensive sampling occasions; $2 = 1 extensive sampling occasion; $3 = 1 sparse and 1 extensive sampling occasion) and 4 types of data errors (DTE = +/- 3 hour dose time error; DAE = +/- 10% dose amount error; COM = combined DTE & DAE; NOE - no errors) were investigated in 500 replicate simulations of 60 patients, for each scenario or combination thereof. Parameters were estimated using NONMEM with FOCE-INTERACTION. All error scenarios resulted in accurate (<10% mean prediction error) and precise (<30% mean absolute prediction error) fixed-effect parameter esti- mates, with S1 demonstrating the best performance. Random effect parameters were estimated with less accuracy and precision with performance in decreasing order: S1, $3, $2. For this particular PK model and study design, errors in dose amount and timing assump- tions had minimal impact on POP mean and variance parameter estimates. PII-88 SCALING IN HOSPITAL PRESCRIPTION ORDERS. D.A. Frattarelli, MD, J. V. Aranda, MD, PhD, Children's Hospital of Michigan, Detroit, MI. To determine whether scaling occurs in the practices of hospital physicians, we reviewed the medication orders received by our in- patient pharmacy and ranked the 100 most used drugs in order of 1 frequency. Scaling, described mathematically by Zipf's law, 1°, oc n has been found in the patterns of a variety of complex systems ranging from the populations of cities to human heartbeat variability. Stated another way, Zipf's law says that the second most commonly encountered event happens half as often as the first, the third most common event happens a third as often, and so on. As shown in this graph, the observed data correlated with the values predicted by Zipf's law (p<0.0001). This scaling is likely the result of both the habitual nature of prescribing habits and the fact that some drugs are more broadly useful than others. For example, our most used medi- cation was morphine, which is not the treatment for any specific disease but for the pain associated with many diagnoses, while albuterol, the specific therapy for our most frequently encountered admission of asthma exacerbation (but not for anything else) ranked 29th. Zipf's "Principle of least effort" explains some scaling as well. While one drug may be better than another, the other may work well enough often enough that there is no pressure on the physician to change his habits. Thus, scaling occurs in prescribing practices, as familiar medications are used frequently, even in situations where they may be sub-c ~timal. \ _i tO00 ...... 100 1o loo Log Rank
Transcript of Outcome of nonmem analysis depends on modeling strategy
C L I N I C A L P H A R M A C O L O G Y & T H E R A P E U T I C S
P 5 2 American Society for Clinical Pharmacology and Therapeutics FEBaVARY2003
PII-86 OUTCOME OF NONMEM ANALYSIS DEPENDS ON MOD-
ELING STRATEGY. R.R. Bies, PharmD, PhD, M. E. Sale, MD, G. Smith, PhD, M. Muldoon, MD, F. E. Lotrich, MD, PhD, B. G. Pollock, MD, PhD, Schools of Pharmacy and Medicine, University of Pittsburgh, GlaxoSmithKline, Hillside Hospital and Albert Einstein College of Medicine, School of Medicine, University of Pittsburgh, Pittsburgh, PA.
Purpose: To evaluate the impact of modeling strategy on the identification of the model structure and covariates comparing for- ward stepwise, reverse stepwise and genetic algorithm approaches.
Methods: A two-.compartment model to describe citalopram phar- macokinetics (PK) after IV infusion was implemented using NON- MEM. Weight, age and sex were evaluated for their impact on the parameters of the PK model using three different methods for co- variate identification: 1. Forward stepwise evaluation; 2. Backward elimination from the fnll model, 3. Forward and backward stepwise evaluation from the "final model" identified with method #1 and; 4. Genetic algorithm search (GA). All of these methods were evaluated using the first order estimation settings, methods one and three were also evaluated using the first order conditional estimation method.
Results: All four of these methods identified different covariates that had a significant effect on the PK model. The GA approach (using the first order estimation method) found a model with the lowest objective function that was significantly lower than the lowest found (<30 points) with the standard search methods. This approach identified sex, weight and age as having a significant impact on clearance and sex and weight having a significant impact on the peripheral volume of distribution.
Conclusions: The genetic algorithm identified significant covari- ates that were missed when using other approaches.
PII-87 IMPACT OF ERRORS IN DOSE TIME AND DOSE AMOUNT
ASSUMPTIONS ON THE POPULATION PHARMACOKINETIC ANALYSIS OF A PEDIATRIC DATA SET: A SIMULATION STUDY. P. Rajagopalan, PhD, M. R. Gastonguay, PhD, Bayer Pharmaceuticals, University of Connecticut School of Pharmacy, West Haven, CT.
The accuracy of dosing times (DT1M) and dose amounts (AMT) in a population (POP) pharmacokinetic (PK) database can be suspect, especially in outpatient pediatric trials with oral dosage forms. A simulation study was undertaken to assess the impact of errors in assumptions about DTIME and AMT on the POP parameter estimates from a nonlinear mixed-effects PK analysis of a pediatric data set. Simulations were based on a known 2-compartment structural model for an investigational drug, parameterized in terms of allometrically- scaled clearances and volumes. Three different sampling schemes (SI 2 extensive sampling occasions; $2 = 1 extensive sampling occasion; $3 = 1 sparse and 1 extensive sampling occasion) and 4 types of data errors (DTE = +/- 3 hour dose time error; DAE = +/- 10% dose amount error; COM = combined DTE & DAE; NOE - no errors) were investigated in 500 replicate simulations of 60 patients, for each scenario or combination thereof. Parameters were estimated using NONMEM with FOCE-INTERACTION. All error scenarios resulted in accurate (<10% mean prediction error) and precise (<30% mean absolute prediction error) fixed-effect parameter esti- mates, with S1 demonstrating the best performance. Random effect parameters were estimated with less accuracy and precision with performance in decreasing order: S1, $3, $2. For this particular PK model and study design, errors in dose amount and timing assump- tions had minimal impact on POP mean and variance parameter estimates.
PII-88 SCALING IN HOSPITAL PRESCRIPTION ORDERS. D . A .
Frattarelli, MD, J. V. Aranda, MD, PhD, Children's Hospital of Michigan, Detroit, MI.
To determine whether scaling occurs in the practices of hospital physicians, we reviewed the medication orders received by our in- patient pharmacy and ranked the 100 most used drugs in order of
1 frequency. Scaling, described mathematically by Zipf's law, 1°, oc
n has been found in the patterns of a variety of complex systems ranging from the populations of cities to human heartbeat variability. Stated another way, Zipf's law says that the second most commonly encountered event happens half as often as the first, the third most common event happens a third as often, and so on. As shown in this graph, the observed data correlated with the values predicted by Zipf's law (p<0.0001). This scaling is likely the result of both the habitual nature of prescribing habits and the fact that some drugs are more broadly useful than others. For example, our most used medi- cation was morphine, which is not the treatment for any specific disease but for the pain associated with many diagnoses, while albuterol, the specific therapy for our most frequently encountered admission of asthma exacerbation (but not for anything else) ranked 29th. Zipf's "Principle of least effort" explains some scaling as well. While one drug may be better than another, the other may work well enough often enough that there is no pressure on the physician to change his habits. Thus, scaling occurs in prescribing practices, as familiar medications are used frequently, even in situations where they may be sub-c ~timal.
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