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)
Example of Applications for MIDD (Model-Informed Drug Development)
___________:Determining the optimal dosing regimen using Pk/PD modeling.
___________:Simulating potential outcomes to guide clinical trial designs.
_____________:Developing personalized treatment strategies based on individual variability.
Dose Optimization
Predictive Modeling
Personalized Medicine
Model-Informed Drug Development (MIDD)
Integration of ________: combines preclinical, clinical, and real-world drug behavior
____________:Uses PK/PD models, physiologically-based pharmacokinetic (PBPK) models, and quantitative systems
Decision support: Helps in dose selection, optimizing clinical trial designs, and improving success rates in drug development
data
quantitative models
Benefits of Model-Informed Drug Development (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 ________ for informed decision-making
reduced time and cost
Precision
FDA
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
Digital Twin
Applications in Drug development with Digital Twin
__________ Medicine: tailors treatments to individual patient profiles, predicting responses to therapies.
_________Trials: Simulates clinical trials in silico, reducing the need for expensive human trials
Optimized Dosing:Determines the most effective and safe dosing strategies by testing scenarios on the Digital Twin
Personalized
Virtual
How Digital Twins Emerge from MIDD
_________Integration: Combines multi-source data (e.g. genomics, PK/PD, imaging) to create a detailed virtual replica
________Modeling: Utilizes MIDD tools like PBPK and QSP models to simulate physiological responses.
________Learning: Updates with new data, refining predictions and enhancing accuracy over time
Data
Advanced
Continuous
Applications in Drug Development
_____________ Medicine: Tailors treatments to individual patient profiles predicting responses to therapies.
______________: Simulates clinical trials in silico, reducing the need for extensive human trials.
Optimized Dosing: Determines the most effective and safe dosing strategies by testing scenarios on the Digital Twin.
Personalized
Virtual models
What is Quantitative Systems Pharmacology (QSP)?
A modeling and analysis approach that combines systems ___________ with pharmacokinetics (PK) and pharmacodynamics (PD) to understand drug action.
Purpose: To predict the effects of drugs across different biological scales (molecular, cellular, organ, organism).
Application: Used in drug development and precision medicine.
biology
Key Components of QSP
___________:
* Study of interactions within biological systems.
* Uses computational models to simulate complex biological processes.
__________________:
How the drug is absorbed, distributed, metabolized, and excreted (ADME).
Pharmacodynamics (PD):
The biochemical and physiological effects of drugs and their mechanisms of action.
Systems Biology
Pharmacokinetics (PK)
QSP Modeling Workflow
_____________: Experimental data from in vitro, in vivo, and clinical studies.
_______________: Construct mathematical models representing biological systems.
Parameter Estimation: Calibrate model parameters using experimental data.
Simulation & Validation: Simulate drug behavior and validate models with additional data.
Prediction: Predict outcomes for different scenarios and guide decisionmaking.
Data collection
Model Development
Advantages of QSP
______________:Provides detailed understanding of drug mechanisms.
_______________: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 insight
Predictive Power
_______________: 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 (PBPK) Modeling
Physiologically Based Pharmacokinetic (PBPK) Modeling
Importance:
_____________: PBPK models are used to inform drug dosing, predict drug interactions, and support regulatory submissions.
______________: Regulatory agencies, such as the FDA and EMA,increasingly rely on PBPK models to evaluate the safety and efficacy of new drugs.
_______________: Unlike empirical models, PBPK models can predict drug behavior in various scenarios, including different species, special populations, and disease states.
Drug development
Regulatory science
Predictive Power
Physiologically Based Pharmacokinetic (PBPK) Modeling
Mechanistic models based on __________ and _____________ data
Characteristics:
Uses multiple compartments representing actual organs and tissues
Incorporates physiological parameters like ___________rates, tissue volumes, and binding affinities
Predicts ________________ in various tissues
physiological and anatomical
blood flow
drug concentrations
