Pharmacology is the science of the behavior and actions of drugs on biological systems, defining a “drug” as any entity able to exert an effect on such a system and elicit a physiological response. Drugs considered to be medicines are often called pharmaceuticals.
Pharmacotherapy is the discipline of treating disease through the use of pharmaceuticals or medicines as opposed to treatment by other forms of intervention – e.g. surgery, physiotherapy, counselling etc.
Pharmacokinetics (PK) is the branch of pharmacology that addresses the time course of the amount (or concentrations) of a drug in a biological system following administration of a drug to that system. Basic chemical laws of mass action dictate that the amount of drug in a system will drive the magnitude of the effects of that drug (often though in a complex manner), therefore an understanding of the PK of a drug is critical to understanding its dosage, efficacy and safety/tolerability.
Pharmacokinetic models are a type of mathematical model i.e. a description of a real world system using mathematical language. If PK data typically consists of a time course of observations of drug concentration in plasma/blood, urine or (more rarely) tissues, then PK data analysis aims to describe and summarise this data by means of a mathematical, pharmacokinetic model and its constituent parameters. Depending on the PK model design these parameters can range from being empirical in nature to having a genuine physiological meaning. Once fitted acceptably to a given dataset, a PK model has major potential applications in the design of dosing regimens and clinical trials, prediction of exposures under different regimens or with different study populations etc. while the PK model parameter values themselves can provide a useful summary as to the PK properties of a particular drug, particular for comparisons and screening of one drug vs. another.
A hierarchy of mathematical model can be defined according to the degree of detail (or mechanism) in their description of physiological processes (Aarons BJCP 60, 2005). As the mechanistic detail of a mathematical model increases, the data it can be applied to (or required to develop it in the first place) and its potential uses change. Broadly, the “Top-down” and“bottom up” approaches to PK modelling summarise the different ways of examining a given dataset.
A Top-downapproach will usually use empirical models with less physiological/mechanistic detail, applied in a fitting paradigm to simple data (e.g. plasma observations only). The model will describe and summarise the data, allow for objective comparison of one drug’s PK vs. another and define the dose to exposure relationship and potentially covariates that can explain variation in exposure. The model will have less rationale for prediction/extrapolation .
A Bottom up approach will often use more mechanistic models applied in a prediction/simulation paradigm. The more mechanistic model applied will require prior information not intrinsic to the data being described, and may be more challenging to fit to the data in question. Once developed however the model has greater biological rationale for prediction and extrapolation.
PBPK is a sub-field of PK modelling that applies mechanistically realistic, physiological models to PK data. PBPK extends the approach of compartmental PK models by taking a physiologically realistic scheme as the basis of the model’s compartmental structure. The model structure is essentially set a priori rather than being chosen based on the best empirical fit to data (as is usually the case for use of e.g. 1,2 compartment empirical models).
DMPK is the usual umbrella term within industry for the applied science of PK within drug discovery and development.
Pharmacodynamics is the study of the time course and magnitude of the pharmacological effect(s) of a drug and the dependence of this on drug exposure. In conjunction with the PK of a drug, understanding PD is critical to understanding a drug’s dosage, efficacy and safety/tolerability.
PK/PD typically refers to joint modelling of drug exposure and response to allow the fullest possible use of all the information in a given system to enable understanding of drug effect. Often the extra information provided by PK exposure data will “anchor” and stabilise the modelling of PD data and enable a better model to be applied than if PD data were analysed in isolation.
PKPD modelling is a similar exercise to PK modelling in that the aim is to describe, summarise and interpret quantitiative exposure and response data through use of a mathematical model.
An enormous variety of PKPD models exist however three core recurring concepts for PKPD modelling are:
– The need often to model a time delay between the concentration timecourse and effect timecourse. This reflects both PK distributional delays (i.e. it takes time for drug to get where it needs to go to elicit an effect) and also pharmacological delays in eliciting an effect (an effect might require e.g. signal transduction, protein synthesis, or a cascade chain of prior effects before being manifested). The effect compartment PKPD model is an example of an empirical PKPD model incorporating a delay between concentration and effect.
– The need to account for saturation effect. Pharmacological effects often saturate to a maximum value which can often be attributed to biological effects being manifested by receptor binding, which is a saturable process. Mathematical models describing PD data must often be able to account for this (e.g. the “Emax model” is a PD model commonly used to describe saturable processes).
– The need to account for endogenous baseline in effect. Often a drug’s effect manifests as a perturbation of an underlying physiological measure and this baseline effect needs to be accounted for.
Pharmacometrics can be defined as the quantitative study of the interactions between a drug and the subject it administered to. It has become an umbrella term to cover the disciplines of PK, PD, and PKPD modelling, but also to cover the application of specific techniques and approaches such population modelling, to PK and PD analysis.
Quantitative systems pharmacology can be considered an advanced form of PK and PKPD modelling, using extremely detailed and mechanistic mathematical models to describe physiological systems, diseases and pharmacological interventions. A QSP model will generally be considered to go beyond the degree of mechanistic detail of a PBPK model for example, and may incorporate descriptions for example to the level of protein-protein interactions in signalling cascades in order to describe the system in question.