Pharmacometrics is a field of study of the methodology and application of models for disease and pharmacological measurement. It uses mathematical models of biology, pharmacology, disease, and physiology to describe and quantify interactions between xenobiotics and patients (human and non-human), including beneficial effects and adverse effects.
It is typically used to quantify drug, disease, and trial information to aid efficient drug development, regulatory decisions, and rational drug treatment in patients. Pharmacometrics uses models based on pharmacology, physiology, and disease for quantitative analysis of interactions between drugs and patients. This involves Systems pharmacology, pharmacokinetics, pharmacodynamics and disease progression with a focus on populations and variability. Mould and Upton provide an overview of basic concepts in population modeling, simulation, and model-based drug development.
A major focus of pharmacometrics is to understand variability in drug response. Variability may be predictable (e.g., due to differences in body weight or kidney function) or apparently unpredictable (reflecting the current knowledge gap).
Origins The term "pharmacometrics" first appeared in literature in the preface of the 1964 book "Evaluation of Drug Activities: Pharmacometrics":
The sub-title of the book is, as far as we are aware, a neologism, coined by one of us (A.L.B.), and the word is defined by the main title of the book, which could have been even more explicitly, if more verbosely, expressed as "The Identification and the Comparative Evaluation, Qualitative and Quantitative, of Drug Activities". The term has an etymological precedent in the now widely accepted "Econometrics". We hope it will prove useful for distinguishing the kind of measurement discussed and described in this book from what is nowadays called bioassay; although the same techniques sometimes serve for both, their objectives are not at all identical.
However, the editors later state at the end of the preface:
...we have learned with interest and humility that Dr. Karl Beyer, a vice-president of Merck, Sharpe and Dohme, Rahway, New Jersey, U.S.A., and current president of the American Pharmacological Society, "coined the word (Pharmacometrics) in the early '50s and has been using it in internal reports ever since" (J. R. Vane, personal communication). Moreover, one of the laboratories in the pharmacological department of his Company is "labeled 'Pharmacometrics'"! We do not know in exactly what sense Dr. Beyer has been using the word, though we find it difficult to think of any other legitimate one than that advanced above. We can only hope that he also thinks so and that its use in the title of this book may help to give it the wider currency that we believe it to deserve and all the "priority" rights to Dr. Beyer.
Types of models
Pharmacokinetics (PK) Pharmacokinetic models are constructs aimed at characterizing the average pharmacokinetic behavior of a drug within a population. By incorporating inter-individual variability, these models provide insights into central tendencies and variabilities in drug responses across diverse patient groups. Their application extends to the optimization of dosing regimens at a population level.
Pharmacodynamics (PD) Pharmacodynamic models focus on elucidating the intricate relationship between drug concentration and its effects on the body. This includes both the desired therapeutic effects and potential side effects. By delineating the time course of drug effects, these models contribute to the prediction of efficacy and adverse events, aiding in the identification of optimal dosing strategies.
Physiologically based Pharmacokinetics Physiologically-Based Pharmacokinetic models integrate physiological information to simulate drug behavior in various tissues and organs. These models consider organ-specific blood flow, tissue permeability, and drug properties, facilitating predictions of drug concentration at specific sites. PBPK models are instrumental in understanding complex drug behaviors.
Exposure-response Exposure-Response models establish the relationship between drug exposure and clinical response. They play a crucial role in determining the optimal therapeutic range and predicting the likelihood of efficacy or adverse events. These models not only guide dose individualization based on desired clinical outcomes but also provide information on population exposure-response relationships for effects and adverse effects.
Drug-Drug Interaction Drug-Drug Interaction models explore the impact of interactions between different drugs on their pharmacokinetics or pharmacodynamics. These models help predict the effects of co-administered drugs on each other, aiding in the identification of potential risks and the adjustment of dosages in the presence of multiple medications.
Disease progression The natural time course of a disease is often dynamic, with the tendency to become worse without treatment. Disease progression models are mainly used to understand the relationship between treatment, biomarker changes and clinical outcomes. These models describe the disease trajectory, by observing the change in the biomarker level, or the other clinically relevant endpoint that reflects the disease status, over time.
There are three key classes of disease progression models: empirical, semi-mechanistic, and systems biology.
Most of the disease progression models are empirical, describing disease trajectory rather than the physiological background of the disease.
The simplest model that is used to describe disease progression is a linear model when the change of disease status over time is assumed to be constant.
Systems Pharmacology Systems Pharmacology models integrate pharmacokinetics, pharmacodynamics, and systems biology to provide a comprehensive understanding of drug effects. By considering the intricate interplay between drugs, biological systems, and disease pathways, these models contribute to a holistic approach to drug development and personalized medicine.
Mechanistic Models Mechanistic models provide a detailed understanding of the underlying biological and physiological processes governing drug behavior. These models offer insights into the mechanisms influencing drug absorption, distribution, metabolism, and elimination, aiding in predicting drug responses in diverse scenarios.
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