Table 1.

Definitions and tutorials papers of key terminology

Term	Brief definition	Key tutorial papers
Pharmacometrics	The analytical science using mathematical models to quantify the relationship between drug exposure and response for safety and efficacy, patient characteristics, disease progression, and clinical outcomes to make inferences for optimal drug dosing during drug development and clinical practice	[6, 17, 52]
Pharmacokinetic–pharmacodynamic modeling	Pharmacokinetic/pharmacodynamic modeling links the time course of drug absorption, distribution, metabolism and excretion (expressed as a concentration–time relationship), with the consequent drug response (expressed as the concentration–effect relationship) to describe and predict drug exposure and response	[30, 53–55]
Sheiner’s learn and confirm paradigm	The paradigm of clinical drug development where each phase is designed for distinct purposes, i.e., learning and confirming. Phase I involves learning about general pharmacokinetics and tolerability in healthy patients, whereas phase IIA confirms early efficacy in a limited population. This is followed by a decision node, when positive efficacy can provide evidence to justify accelerating development. Phase IIB then involves learning about variations in PK and PD in target populations while phase III confirms safety and efficacy in a large patient population. This paradigm uses the Bayesian view, wherein prior knowledge from each phase is updated with the availability of new information from subsequent phases of trials using appropriate modeling strategies	[9]
Model-based drug development	The paradigm of drug development utilizing modeling and simulation of drug efficacy and safety, and associated uncertainty in these parameters across preclinical and clinical phases to inform decision making. The key components of model-based drug development include using PK/PD, disease models, meta-analysis of drug and competitor treatment effect sizes, trial execution models describing protocol deviations (e.g., dropout and non-compliance), statistical models describing treatment effect, and decision rules that describe the course of action (terminating or accelerating development) after trial completion	[8, 10]
Clinical trial simulations	Modeling disease progression, clinical pharmacology of drugs, patient covariates, and trial protocol deviations to enable efficient and cost-effective clinical trial design and implementation	[56, 57]
Model-based meta-analysis	Model-based meta-analysis is a quantitative tool that enables comparison of interventions, by aggregating efficacy and safety results from numerous clinical trials while accounting for between-study and between-study-arm variability. This approach utilizes non-linear mixed-effect models and allows characterization of dose–response relationships and the impact of covariates and study and dosing characteristics on patient outcome and efficacy	[58]
Disease progression models	Mathematical representations of the time course of a disease status and progression. These models can be empirical (data-driven descriptions of disease process), semi-mechanistic (data driven, but incorporate some knowledge about [patho]physiological and pharmacological processes), or systems biology (incorporate [patho]physiological and pharmacological processes in molecular detail from integration of in vitro, ex vivo, in vivo, non-clinical, and clinical data)	[59, 60]

PD pharmacodynamics, PK pharmacokinetics