Lecture 1 Flashcards
__________________ is an approach that leverages mathematical and computational models to enhance the drug development process. It integrates pharmacokinetics (PK), pharmacodynamics (PD), and other biological data to inform decision-making
Model-Informed Drug Development
(MIDD)
Model-Informed Drug Development
Example Applications
______________: Determining the optimal dosing regimen using PK/PD modeling.
___________ Modeling: Simulating potential outcomes to
guide clinical trial designs.
Personalized Medicine: Developing personalized
treatment strategies based on individual variability.
Dose Optimization
Predictive modeling
Model-Informed Drug Development (MIDD)
______________of Data: Combines preclinical, clinical, and real-world data to predict drug behavior.
______________Models: Uses PK/PD models, Physiologically-Based Pharmacokinetic (PBPK) models, and Quantitative Systems Pharmacology (QSP) models.
Decision Support: Helps in dose selection, optimizing clinical trial designs, and improving success rates in drug development.
Integration
Quantitative
Benefits of MIDD:
Reduced _______ and ______: Speeds up the drug development process by reducing trial-and-error approaches
Increased _________: Tailors therapies to individual patient characteristics, enhancing efficacy and safety
Regulatory Support: Increasingly recognized and supported by regulatory agencies like the FDA for informed decision-making.
reduced time and cost
Precision
A ___________ is a highly accurate virtual model of a biological system, patient, or organ, created using data from MIDD. It replicates real-world characteristics, allowing for simulation and prediction of drug behavior in a virtual environment.
Digitial Twin
How Digital Twins Emerge from MIDD:
Data ______________: Combines multi-source data (e.g., genomics, PK/PD, imaging) to create a detailed virtual replica.
Advanced Modeling: Utilizes MIDD tools like PBPK and QSP models to simulate physiological responses.
__________________: Updates with new data, refining predictions and enhancing accuracy over time.
integration
continuous learning
Applications in Drug Development for Digital Twin
_____________ Medicine: Tailors treatments to individual patient profiles, predicting responses to therapies.
__________ Trials: Simulates clinical trials in silico, reducing the need for extensive human trials.
___________ Dosing: Determines the most effective and safe dosing strategies by testing scenarios on the Digital Twin
Personalized, virtual, optimized
____________: A modeling and analysis approach that combines systems biology with pharmacokinetics (PK) and pharmacodynamics (PD) to understand drug action
Quantitative Systems Pharmacology
Quantitative Systems Pharmacology
Purpose: To predict the ________ of drugs across different biological scales (molecular, cellular,organ,organism)
Application: Used in drug development and precision medicine
effects
Key Components of QSP
___________________:Study of interactions within biological systems.Uses computational models to simulate complex biological processes.
________________ (PK):How the drug is absorbed, distributed, metabolized, and excreted (ADME).
_________________ (PD):The biochemical and physiological effects of drugs and their mechanisms of action
systems biology
pharmacokinetics
pharmcodynamics
QSP Modeling Workflow
_____________: Experimental data from in vitro, in vivo, and clinical studies.
Model Development: Construct mathematical models representing biological systems.
____________ Estimation: Calibrate model parameters using experimental data.
Simulation & Validation: Simulate drug behavior and validate models with additional data.
__________: Predict outcomes for different scenarios and guide decisionmaking.
Data collection, parameter, prediction
Advantages of QSP
_____________ Insight:
Provides detailed understanding of drug mechanisms.
____________ Power:
Forecasts drug responses and potential side effects.
Optimization:
Aids in optimizing dosing regimens and therapeutic strategies.
Personalization:
Facilitates personalized medicine by accounting for patient variability
mechanistic, predictive
____________________ modeling is a mechanistic modeling approach that uses mathematical descriptions of anatomical, physiological, and biochemical processes to predict the absorption, distribution, metabolism, and excretion (ADME) of chemical compounds in humans and animals
Physiologically Based Pharmacokinetic
Importance of Physiologically Based Pharmacokinetic Modeling
________________: PBPK models are used to inform drug dosing,predict drug interactions, and support regulatory submissions.
Regulatory Science: Regulatory agencies, such as the FDA and EMA, increasingly rely on PBPK models to evaluate the safety and efficacy of new drugs.
____________Power: Unlike empirical models, PBPK models can predict drug behavior in various scenarios, including different species, special populations, and disease states.
Drug development
Predictive Power
Physiologically Based Pharmacokinetic (PBPK) Modeling
Description: Mechanistic models based on
physiological and anatomical data
Characteristics:
Uses multiple compartments representing
actual ___________ and tissues
Incorporates physiological parameters like
_____________ rates, tissue volumes, and binding
affinities
Predicts drug concentrations in various
_____________
organs
blood flow
tissues
