• Terms and Definitions 1
  • Variable
  • Parameter
  • Model
  • Linear and Nonlinear model
  • Fixed effect parameter
  • Random effect parameter
  • Mixed effects modeling
  • Structural model
  • Random effect model
  • Population PK and PD
• Terms and Definitions 2
  • Maximum likelihood
  • Objective function value
  • Log-likelihood ratio
  • Optimization procedure
  • Point estimate
  • Confidence intervals
  • Maximum a posteriori/post hoc estimates
  • Simulation
  • Clinical trial design
  • Pharmacodynamic terms
• Population analysis methods
  • Naive pooled data approach (NPD)
  • Naive averaged data approach (NAD)
  • Standard two-stage approach (S2S)
  • Three stage analysis (3S)
  • Bayesian estimation
  • One stage analysis
• Estimation methods
  • Ordinary least squares (OLS)
  • Weighted least squares (WLS)
  • Extended least squares (ELS)
• Good Modeling Practices (GMP)
  • Data management
  • Rational modeling process
  • Report

Good Modeling Practices (GMP)

Rational model building

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Preliminary data analysis

The main purpose is to get a general feel for the structural model.
Preliminary data analysis can be performed by one or combination of the following methods.

Literature survey

A search of the literature could be done to obtain information of the drug under study, refer to previous studies of the compound or similar compounds and understand the biology of the disease etc.,

Graphical analysis

General feel about the data could be obtained by plotting the data from all the individuals or few representative individuals during preliminary model building process.

Pharmacokinetics (PK)

In the initial step of model building exercise a semilog plot of concentration versus time could help in answering the following questions.

1. What is the general trend in the data? When does Tmax occur?
2. Are there any implausibility in the data? ie., any outliers ar any unexpected data
3. How many disposition phases are observed in the semilog plot of concentration versus time? ie., Is the decline mono-exponential (one compartment) or multi-exponential (two compartment or more)
4. Are there any multiple peaks observed in the oral data? If so, the reasons could be explored.
5. Do the curves superimpose when the data are dose-normalized? ie., Plot of concentrations divided by dose administered versus time would reveal non-linearity if the curves do not superimpose.

Pharmacodynamics

A plot of effect versus concentration and a plot of response versus time could help in answering the following questions.

1. Does the effect versus concentration curve show hysteresis? ie., If there is anti-clockwise hysteresis then there is a delayed effect observed for the drug.
2. When does maximum repsonse (T_Rmax or T_Rmin) or minmum response occur occurs? Does it match the Tmax of PK? If yes, then the drug exhibits a direct effect. If not, then the drug might be eliciting an indirect effect. The indirect effect may be due to a distributional delay to the effect site. Alternatively, the drug might be affecting the formation and dissipation of response. A rational choice of model can only be made by considering plausible mechanism of action of the drug.

Summary statistics - Non compartmental analysis (NCA)

1. Where applicable, NCA should be conducted, provided linear pharmacokinetics is assumed. NCA cannot be performed on sparse data.
2. The summary statistics of the results should serve as reasonable initial estimates