| TERMINOLOGY | DESCRIPTION | URL TO ADDITIONAL INFO |
|---|---|---|
| ADME | ADME is an abbreviation in pharmacokinetics and pharmacology for “absorption, distribution, metabolism, and excretion”, and describes the disposition of a pharmaceutical compound within an organism. The four criteria all influence the drug levels and kinetics of drug exposure to the tissues and hence influence the performance and pharmacological activity of the compound as a drug. | https://en.wikipedia.org/wiki/ADME |
| AUC | Area under the curve. In pharmacokinetics, the area under the concentration-time curve. | |
| bin | Statistical data binning is a way to group numbers of more or less continuous values into a smaller number of “bins”. For example, if you have data about a group of people, you might want to arrange their ages into a smaller number of age intervals (for example, grouping every five years together). | https://en.wikipedia.org/wiki/Data_binning |
| CI | Confidence interval: interval that gives the probability with which the estimated interval will contain the true parameter. | |
| ciVPC | Model diagnostic: Confidence interval visual predictive check. This type of visual predictive check does not only show the distribution of the data and simulations but also shows the confidence intervals around these distributions. | https://www.pmxsolutions.com/2018/06/24/a-step-by-step-guide-to-confidence-interval-visual-predictive-checks-vpc-of-nonmem-models/ |
| CL | Clearance (volume/time) | |
| Cmax | Maximal concentration reached in a pharmacokinetic profile | |
| CMT | Compartment. This can refer to a mathematical compartment used for the modelling of the distribution of a compound or have a physiological meaning in physiologically based pharmacokinetic models. | |
| CTS | Clinical trial simulation: Approach in which population PKPD models can be used to simulate new scenarios and judge the most effective design and dosing regimen of new clinical trials. | |
| CV | Coefficient of variation (%) is the ratio of the standard deviation to the mean and shows the level of inter-individual variability on a parameter. However, as the inter-individual variability is usually included as a log-normal distribution in non-linear mixed effects models, a modification should be applied in its calculation: sqrt(exp(omega squared)-1) | https://www.page-meeting.org/pdf_assets/4964-Elassaiss-Schaap%20-%20Equations%20variability%20reporting%20PK-PD%20-%20Final.pdf |
| CWRES | Model diagnostic: conditional weighted residuals. Presents the deviation from data and model predictions, divided by the root of the covariance of the data given the model. The smaller the CWRES, the better the fit of the data. Commonly used to study whether a misspecification in the model over time or over the concentration range is present. | https://doi.org/10.1007/s11095-007-9361-x |
| EBE | Bayesian Emperical Bayes Estimates: Individual parameter estimates of a model-derived distribution. | |
| EC50 | Concentration at which 50% of the maximal effect is reached. Commonly used in combination with an EMAX or sigmoid EMAX equation. | |
| EMAX (equation) | Parameter that informs on the maximal effect that can be reached. Can also correspond with an EMAX equation in pharmacokinetic/pharmacodynamic (PK/PD) models which links the concentration of a drug with the observed effect = EMAX*Concentration/(EC50 + Concentration) | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7033401/ |
| eta | In NONMEM, eta represents the addition of a random variable drawn from a normal distribution on a parameter. This results in inter-individual variability on this parameter. | |
| F | Bioavailability | |
| GOF | Goodness-of-fit: figures or numerical values that present how well a model is capable of describing the data. | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5321813/ |
| hill coefficient | Used in a sigmoid EMAX equation in order to set the steepness of the concentration-effect curve. If the hill coefficient is not given, it is usually fixed to 1. | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7033401/ |
| IIV | Inter-individual variability: Random effects on an individual level which can cause differences between individuals | |
| IPRE/IPRED | Individual predictions. Predictions of the model while including both fixed and random effects. The IPRED is equal to the PRED if no inter-individual variability (IIV) is present in the model. | |
| k12/k21 | First-order rate constants (/time) that give the exchange between central and peripheral compartments (e.g. k12 = from compartment 1 to compartment 2). Can be derived from the volume of distribution and Q: k12 = Q/V central, k21 = Q/V peripheral | |
| ka | Absorption rate constant (/time): Parameter that represent the speed of absorption from a depot to a central compartment | |
| ke/kel | Elimination rate constant. First-order rate constant that describes the elimination of a drug. Can be derived from clearance (CL) and central volume of distribution (Vd) by kel= CL/Vd | |
| NCA | Non-compartmental analysis: Analysis based on the data while not making any assumptions on underlying compartmental disposition of a compound. Pharmacokinetics profiles in a NCA are commonly be quantified with a Cmax, tmax, area under the curve, half-life, etc. | https://www.nuventra.com/resources/blog/what-is-nca/ |
| NLME | Non-linear mixed effects: Modeling methodology that contain both fixed and random effects. | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3606044/ |
| NONMEM | Commonly used software package used for the development of population models. | https://www.iconplc.com/innovation/nonmem/ |
| OFV | Objective Function Value: a goodness of fit statistic; the lower the value, the better the fit. Negative values are possible and have no special significance. Under certain assumptions, the (default) objective function value is minus twice the log likelihood. | https://nonmem.iconplc.com/nonmem/Nonmem_7_Project/nmvi2.0beta/htmlplus/minimumv.htm |
| PAGE | Population Approach Group in Europe. A community with very smart people with a shared interest in data analysis using the population approach. Organises yearly meetings. | https://www.page-meeting.org/ |
| pcVPC | Model diagnostic: Prediction corrected visual predictive check. This type of visual predictive check does not only show the distribution of the data and simulations but also shows the confidence intervals around these distributions, while correcting for different dosing regimens between individuals. | https://www.pmxsolutions.com/2018/09/21/a-step-by-step-guide-to-prediction-corrected-visual-predictive-checks-vpc-of-nonmem-models/ |
| PD | Pharmacodynamics – What the drug does to the body | |
| PGx | The study of how genes affect the body’s response to medications. | |
| Pharmacometrics | Pharmacometrics is mathematical models of biology, pharmacology, disease, and physiology used to describe and quantify interactions between xenobiotics and patients (human and non-human), including beneficial effects and adverse effects. | |
| PI | Prediction interval: interval between which x% of the observations lie | |
| PK | Pharmacokinetics – What the body does to the drug | |
| PMX | 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 normally applied to quantify drug, disease and trial information to aid efficient drug development, regulatory decisions and rational drug treatment in patients. | https://en.wikipedia.org/wiki/Pharmacometrics |
| popPK | Population pharmacokinetic (models). Read more about the basic concepts of population PK(/PD) models in the paper in the URL. | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3636497/ |
| PRED | Population predictions. Prediction of the model while only including the fixed effects | |
| Q | Inter-compartmental clearance (volume/time), gives the exchange rate between central and peripheral compartments. | |
| R | Programming language and software package used for statistics and data analysis. | https://www.r-project.org/ |
| RSE | Relative standard error: metric that shows the level of certainty in a parameter. Acceptable RSE’s are below 50%. Calculate as the standard error divided by the population parameter. | |
| TAD | Time after dose. Important to use when multiple or irregular doses are given. | |
| TDM | Therapeutic drug monitoring | |
| tmax | Time at which the maximal concentration (Cmax) is reached | |
| Vd/Vc | Central or peripheral volume of distribution. Represents a theoretical volume that is required to scale between the amounts (e.g. mg) administered and the concentrations (e.g. mg/L) observed. May represent a physiological volume. | |
| VPC | Model diagnostic: Visual predictive check. Used to check how simulations with the developed model correspond with the original (or external) data. | https://www.pmxsolutions.com/category/tutorials/vpc-tutorial/ |
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