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Population modelling.
Modeling and simulation have emerged as important tools for integrating data,
knowledge, and mechanisms to aid in arriving at rational decisions regarding drug use
and development.

Population modelling methods provide a framework for quantitating and explaining
variability in drug exposure and response.

All drugs exhibit between-subject variability (BSV) in exposure and response, and many
studies performed during drug development are aimed at identifying and quantifying this

A sound understanding of the influence of factors such as body weight, age, genotype,
renal/hepatic function, and concomitant medications on drug exposure and response is
important for refining dosage recommendations, thereby improving the safety and
efficacy of a drug agent by appropriately controlling variability in drug exposure.

Population modeling is a tool to identify and describe relationships between a subject’s
physiologic characteristics and observed drug exposure or response.

Population parameters were originally estimated either by fitting the combined data from
all the individuals, ignoring individual differences (the “naive pooled approach”), or by
fitting each individual’s data separately and combining individual parameter estimates to
generate mean (population) parameters (the “two-stage approach”).

Both methods have inherent problems, which become worse when deficiencies such as
dosing compliance, missing samples, and other data errors are present, resulting in
biased parameter estimates.

The approach developed by Sheiner et al. addressed the problems associated with both
the earlier methods and allowed pooling of sparse data from many subjects to estimate
population mean parameters, BSV, and the covariate effects that quantitate and explain
variability in drug exposure. This approach also allowed a measure of parameter
precision by generation of SE.

The Components of Population Models
Population modeling requires accurate information on dosing, measurements, and
covariates. Population models are comprised of several components: structural models,
stochastic models, and covariate models.

Structural models are functions that describe the time course of a measured response
and can be represented as algebraic or differential equations.

Stochastic models describe the variability or random effects in the observed data, and

Covariate models describe the influence of factors such as demographics or disease on
the individual time course of the response.