Multidisciplinary View of Modeling and Simulation: Fundamentals, Case Examples, and Future Directions | An ISSX 2023 Virtual Short Course Event
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This course was developed by the ISSX Modeling and Simulation Focus Group and was moderated by Maria Posada, Ph.D., Eli Lilly and Company, USA, Oliver Hatley, Ph.D., Certara UK, United Kingdom, and Jaydeep Yadav, Ph.D. Merck, USA.
This short course focuses on the fundamentals of modeling and simulation, spanning several modeling disciplines, including PBPK, QSP, PopPK and Biopharmaceutics, (small molecules, peptides, proteins, or new modalities). The course includes the fundamentals for beginners, advancement, and application, as well as case examples.
SC 1.1 Principles of Population PKPD Modeling
Stacey Tannenbaum, Ph.D., Metrum Research Group, USA
SC 1.2 Informing Drug Discovery and Development using Quantitative Systems Pharmacology Approaches
Jason Chan, Ph.D., Eli Lilly and Company, USA
SC 1.3 PBPK Modelling of Drug-Drug Interactions – Challenges and Opportunities
Aleksandra Galetin, Ph.D., University of Manchester, United Kingdom
SC 1.4 Physiologically Based Mechanistic Oral Absorption Modeling
Kazuko Sagawa, Ph.D., Pfizer, USA
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SC 1.1 Principles of Population PKPD Modeling | Stacey Tannenbaum
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Pharmacokinetics (PK) and Pharmacodynamics (PD) helps in the understanding of drug disposition (absorption, distribution, metabolism, and elimination) and effects (both safety and efficacy). PK and PKPD analyses support and inform key drug discovery and development decisions. Population PK (PopPK) is a quantitative approach that simultaneously analyzes PK (dose, concentration, time) data from individuals, allowing understanding of how the population behaves, estimates of each subject’s PK parameters, and an evaluation of the sources of variability in the population. PopPK allows both rich and/or sparse PK sampling from subjects, making it a suitable choice for analyzing PK across studies from early to late phase. Because it is model-based, non-linearities and complex disposition mechanisms can be described. An important component of PopPK analysis is the identification of covariates (patient characteristics and external factors such as weight, age, and other medications) which explain the variability within a population.
PKPD models link the time course of the concentrations to the time course of the effects. Exposure-response models are a more general term in which other metrics of exposure (such as AUC or Cmax) are correlated with endpoints which may not have a temporal correlation with drug concentrations. E-R models ideally include an understanding of mechanism, and selection of the appropriate exposure metrics for the various drug effects (safety and efficacy). A population modeling approach may be applied to PKPD data as well, allowing an assessment of the between-subject variability in potency and efficacy, and identification of the covariates that drive these differences. Simulations of the concentrations or exposures from the PK model can be fed into the E-R model to estimate the drug effect in individuals and to help to identify subpopulations that require a dose adjustment.
SC 1.2 Informing Drug Discovery and Development using Quantitative Systems Pharmacology Approaches | Jason Chan
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Quantitative Systems Pharmacology (QSP) is an interdisciplinary approach that integrates mathematical modeling, biological knowledge, and experimental data to gain insights into the complex dynamics of drug action and disease progression on the human physiological system. This short course will cover the workflow for building, qualifying, and applying a QSP model to pharmaceutical drug development. Topics of discussion will include: types of data used, the concept of virtual patients and virtual populations, model qualification, and communication of QSP models and simulation results. A case study will be presented for illustration.
SC1.3 PBPK Modelling of Drug-Drug Interactions – Challenges and Opportunities | Aleksandra Galetin
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Physiologically-based pharmacokinetic (PBPK) modelling is now the key translational tool in drug development. PBPK models provide a dynamic framework for integration of physiological or system data with drug-dependent parameters. As such, PBPK models allow mechanistic ‘bottom up’ prediction of both systemic and tissue exposure and evaluation of the interplay of multiple processes governing drug distribution and clearance. PBPK has gained broad acceptance in regulatory submissions and has been used for a variety of diverse applications, from prediction of drug-drug interactions (DDI), absorption/ food effects to prediction of pharmacokinetics in special populations (e.g., organ impairment, paediatrics etc).
This short course lecture will illustrate methodological best practices in PBPK modelling, focusing specifically on prediction of drug-drug interactions. Importance of appropriate clinical PK data for either verification of the developed PBPK models (‘learn and confirm’) or
refinement of model parameters associated with high uncertainty (a “middle-out” approach) will be highlighted. Complexity of the PBPK model (e.g., use of either whole body or reduced/ semiPBPK model) and the individual organ models will be discussed with examples. Points above
will be illustrated using examples of PBPK modelling of complex transporter-metabolism DDI, non-linear interactions or DDIs in special populations.
SC 1.4 Physiologically Based Mechanistic Oral Absorption Modeling | Kazuko Sagawa
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The first part of this lecture will cover the fundamentals of drug absorption. In this section, how basic physicochemical properties of drugs, such as solubility, logP, pKa’s will influence drug absorption and how these factors are incorporated into absorption modeling will be reviewed. Drug dissolution is one of the key elements of absorption modeling. Drug dissolution modeling will be reviewed with a fundamental equation with API particle size distribution as input parameters. Drug precipitation can also play a role for drug absorption of basic molecules. An example of how drug precipitation was incorporated into the absorption model will be introduced. Further, fundamentals of drug permeation, food effect prediction and bile solubilization in relation to GI physiology will be discussed with a few examples. The second part of the lecture will review absorption modeling applications where virtual bioequivalence study will be discussed with a few examples. Lastly oral absorption modeling in pediatric population will be discussed with an example.