Welcome to the Pharmacometrics webpage maintained by Dr Nick Holford and graduate
students at The University of Auckland. The purpose of this page is to provide general
background and links to useful resources on the internet for learning more about
Pharmacometrics. Like most pages on the internet, it is under constant development,
so check back periodically to see what's new. If you have useful information or
links related to topics on this page that we have not included, drop us an email
and let us know. Cheers.
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. There is currently
no similar definition for population pharmadynamics defined by the FDA.
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 around the world - in no particular order
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Related links to Pharmacometrics - in no particular order
There are many academic fields of interest to pharmacokinetic and pharmacodynamic
modellers including: mathematics, statistics, computer science, bioinformatics,
etc. The links below are to some of these useful fields:
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