Faculty of Medical and Health Sciences
Department of Pharmacology & Clinical Pharmacology, University of Auckland
Faculty of Medical and Health Sciences
Department of Pharmacology & Clinical Pharmacology, University of Auckland


The purpose of this page is to provide general background and links on the internet for learning more about Pharmacometrics.

What is Pharmacometrics?

Pharmacometrics involves the analysis and interpretation of data produced in pre-clinical and clinical trials. Studies in pre-clinical and clinical pharmacology, pharmacokinetics, pharmacodynamics, and toxicology typically involve collection of various types of experimental data in individual and groups (populations) of biologic preparations, animals, or human subjects. Appropriate methods of analysis of such data requires an understanding of the underlying science including: biostatistics, computational methods, and pharmacokinetic/pharmacodynamic modelling. Scientists with proficiency in pharmacometrics assist in the design and analysis of protocols and studies related to drug therapy questions, and provide insights into the processes which control the time course of drug concentrations and clinical, pharmacologic and toxicologic responses.

Population pharmacokinetic and pharmacodynamic modelling

A simple definition of pharmacokinetics (PK) is "how the body processes a drug", resulting in a drug concentration in the body. Pharmacodynamics (PD) can similarly be defined as "how the drug acts on the body", resulting in a measurable drug effect. Combining these two ideas leads to the concept of dose-concentration-effect, which can be modelled using PKPD modelling. The result of such modelling is a mathematical description of a drug's fate in the body, for an individual. Population modelling involves the analysis of data from a group (population) of individuals, with all their data analyzed simultaneously to provide information about the variability of the model's parameters.

Population pharmacokinetics (popPK), according to the definition of the US Food and Drug Administration (FDA), is "the study of the sources and correlates of variability in drug concentrations among individuals who are the target patient population receiving clinically relevant doses of a drug of interest" (FDA, 1999). This definition summarizes the basic characteristics of popPK. It is concerned with patients the drug intends to treat rather than healthy volunteers. It studies the variability in drug exposure for clinically safe and effective doses by focusing on identification of patient characteristics, which significantly affect or are highly correlated to this variability.

Disease progress modelling

Disease progress modelling uses mathematical models to describe, explain, investigate and predict the changes in disease status as a function of time. A disease progress model incorporates functions of natural disease progression and drug action. Natural disease progression is the change in disease status solely attributed to the progression of the disease. Drug action reflects the effect of a drug on disease status. In degenerative diseases, treatments can be classified into symptomatic and protective. Protective treatment can slow down, halt, or even reverse disease progress. Symptomatic treatments can only reduce symptom severity.

A treatment may have both symptomatic and protective benefits, but the dominant effect is more likely to be expressed and mask the subdominant effect. Disease progress modeling can be used to separate symptomatic and protective actions, provided that the time course of onset of these effects is sufficiently different. Disease progress models, combined with pharmacokinetic-pharmacodynamic models, and hierarchical random effects statistical models, provide insights into understanding the time course and management of degenerative disease.

Clinical trial simulation

Computer simulation is the process of building a mathematical model that mimics a real-world situation and then using the model to conduct experiments in order to describe, explain, investigate, and predict the behavior of that situation. Simulation furnishes scientists with a conceptual tool for translating often complex, real-world subject matter into a simplified form (a mathematical model), generalizing detail and exposing important assumptions. The model should capture all crucial aspects of the physical situation being described. By employing the model, simulation experiments can explore assumptions made about the model's structure and parameters. Additionally, model-based simulations may enable the investigation of actual experiment designs, which, in turn, might shed light on the model's assumptions.

The clinical trial is the preferred modern strategy for empirical evaluation of medical therapy. As such, it serves as a key component of the drug development process, when adequately designed and conducted, by providing information with which to weigh the risks and benefits of a compound. This information is used in risk management at various levels: at a regulatory level when a government agency determines, based on this information, whether or not a candidate compound may be marketed; and in a clinical setting when a physician decides whether, and if so, how a drug should be administered to a patient. Clinical trial simulation is the abstraction of the clinical trial process. It is used to investigate assumptions and to influence trial design in order to maximize the amount of pertinent information gained throughout this process about the drug.

Simulation is applicable to many areas of the clinical trial process. The focus often centers on the use of simulation with models based upon the dose-concentration-effect relationship that reflect the disposition and effect of drugs as observed in clinical trials. The recognition that traditional methods of drug development often lead to many clinical trials that contribute little to regulatory approval has aroused interest in exploring other methods. Clinical trial simulation has been advocated as a way of getting better insight into the real questions that need to be answered by a clinical trial. The process of model building in itself is a powerful method of understanding what is known and what remains to be discovered. Simulation of a clinical trial can provide a data set that will resemble the results of an actual trial. This can be used for preparing clinical trial databases and rehearsing analysis plans. Multiple replications of a clinical trial simulation can be used as a form of meta-analysis to refine clinical trial designs.


Pharmacometric related conferences and meetings

Pharmacometric Resources

Resources may be found at the PAGE site and also at the ISoP site.