Simcyp Focused Workshops

Transporters

The ABC of Modeling Drug Transporter Data: Mechanistic Approaches to Predict the Impact of Drug Transport Proteins on ADME/Pharmacokinetics and Toxicity.

  • Re-visiting common in vitro transporter-based assays
  • Characterization of key parameters required to model in vitro data
  • Understanding complex data sets generated from in vitro assays
  • Approaches to optimizing and modeling complex data sets derived from in vitro assays
  • Scaling of in vitro data for gut, liver, brain and kidney to in vivo
  • Practical examples involving the applications of the modelling approach to answer observations related to:
    • Oral bioavailability
    • Hepatic clearance
    • Brain disposition
    • Renal clearance
    • Drug-drug interactions
  • Predicting the impact of transporters on tissue-drug concentrations (efficacy/toxicity)
  • Impact of population variability on transporter-based drug disposition
  • Approaches to handling genetic variations
  • Modeling the impact of transporters on pharmacodynamics

The course is ideal for modelers and scientists (both bench and clinical) who want to enhance their knowledge of mechanistic approaches to predicting the impact of drug transport proteins on ADME/pharmacokinetics and toxicity.

Biologics

Predicting Pharmacokinetics and Pharmacodynamics of Therapeutic Proteins. This workshop will highlight how the Simcyp Simulator can be used to predict the pharmacokinetics and pharmacodynamics of Therapeutic proteins.

  • The use of minimal physiologically based pharmacokinetic models for both mAbs and other therapeutic proteins
  • A description of and hands on exercises utilising the full (PBPK) models for therapeutic proteins available within Simcyp
  • Prediction of pharmacokinetics of therapeutic proteins in populations as well as in an average individual
  • Use of the allometric calculator tool
  • The effect of FcRn affinity on the pharmacokinetics of monoclonal antibodies
  • A description of and hands on exercises demonstrating the utility of the target mediated drug disposition models (TMDD) within Simcyp
  • The use of TMDD models that can account for the shedding of target from the cell surface into the interstitial space and plasma
  • Linking the biologics/TMDD models to simulate pharmacodynamic effects
  • Drug-biologics interactions

Absorption

Prediction of Oral Drug Bioavailability and Development of Mechanistic Physiologically-based IVIVCs: Applications of the Simcyp Advanced Dissolution, Absorption and Metabolism (ADAM) Model.

  • Gut wall permeability
  • Drug solubility, dissolution and precipitation
  • Oral drug absorption incorporating food effects
  • Bioavailability and the impact of dosage form
  • Gut first-pass metabolism
  • Transporter effects
  • Entero-hepatic recirculation
  • Inter-individual variability

Drug-Drug Interactions (DDIs)

Predicting and Evaluating Complex DDIs: Application of the Simcyp Population-based Simulator to Real-life Cases. Participants will learn good practices in combining data from both in vitro and clinical studies, whilst gaining experience in using such data within physiologically-based dynamic models to evaluate the DDI liability of drug candidates.

  • Static vs. dynamic models (including discussion around regulatory guidance)
  • Metabolic and transporter mediated DDIs
  • Competitive inhibition, mechanism-based inhibition, induction, suppression
  • Complex DDIs involving combined interaction mechanisms and multiple inhibitors (including inhibitory metabolites)
  • Importance of in vitro study design
  • The role of non-hepatic metabolism in DDIs
  • Use of parameter estimation and sensitivity analysis
  • Optimal clinical study design
  • Special populations – identification of individuals at risk of DDI

Best Practice in PBPK model building

The main aim of this focused workshop is to demonstrate the application of best practices in developing PBPK models, and to indicate how to qualify and refine model performance. Various cases studies including some real-life examples will be presented. The case studies will cover various elements of model development including DDIs, special populations, transporters and different formulations. Optimal in vitro and clinical data sets will be discussed. Participants will learn how to choose the most suitable models and also investigate the impact of various relevant assumptions on model performance. Requirements for regulatory submissions will also be discussed. It is assumed that the participants are familiar with the fundamentals of PBPK modeling and have hands on experience.

Key aspects covered in this course

  • Quick overview of models within the Simcyp Simulator
  • Model selection and how to pick the most suitable models
  • What are the most appropriate in vitro data
  • Leveraging clinical data to verify and refine PBPK model performance
  • Application of special population models
  • Application of sensitivity analysis in assessing the impact of uncertain parameters

Parameter Estimation and Pharmacodynamics (PEPD)

A Systems Pharmacology Approach to Modeling and Simulation: Accelerating Model Building and Covariate Recognition in Drug Development by Combining Top-down and Bottom-up Modeling of Pharmacokinetics Linked to Drug Response. The workshop covers the concepts and the practical applications of integrating in vitro data and physiological knowledge with clinical observations for the purpose of estimating unknown/uncertain model parameters. Delegates will work through different parameter estimation examples using fitting tools and explore various PD models.

Key aspects covered in this course:

  • The theoretical basis for combining bottom-up and top-down modelling and simulation
  • Parameter estimation:
    • Step-by step guidance on data entry, fitting and interpretation of results
    • Simultaneous fitting of PK and PD parameters
    • Covariate recognition
  • Pharmacodynamics:
    • The models available
    • The use of preliminary clinical data to model and simulate various covariate effects (e.g. genotypic/phenotypic differences, effects of diseases such as renal impairment or cirrhosis)
    • PD at the relevant effect site (eg, liver)
    • Custom scripting of user-defined PD models

Discovery

Use of Simcyp in Drug Discovery: Case Study-based Workshop Focusing on Using the Dynamic Models in the Simcyp Simulator to Prioritize Compounds Based on Data Available in Early Drug Discovery. The workshop will highlight how the Simcyp Simulator can be used to prioritize compounds based on data available early in the drug discovery process. Working through case studies, attendees will use the dynamic models within Simcyp to select compounds which should be progressed to the next stage. As more information becomes available during the exercise predictions will be refined allowing some compounds to be progressed to humans. Data from first-in-human (FIH) studies will be compared with the simulations and any differences will be rationalized using parameter estimation.

  • Prediction of Simcyp inputs from physicochemical properties
  • Use of limited clearance data to prioritise compounds for further investigation (in vitroin vivo extrapolation (IVIVE))
  • Consideration of drug-drug interaction potential of compounds
  • Use of Simcyp animal modules for predicting exposure in toxicity studies
  • Use of the mechanistic Peff model that allows prediction of effective permeability from physicochemical properties alone
  • Prediction of tissue distribution from physicochemical data
  • Selection of appropriate distribution models in human using pre-clinical data

Pediatrics

Predicting Age Related Changes to Pharmacokinetics (PK) and Drug-drug Interactions Including Associated Variability: Linking this Information to Drug Response in the Pediatric Population. Participants will learn how PK behavior can be modeled in neonates, infants and children and how it can be linked to PD. Hands-on exercises explore how this valuable information is relevant to early and informed decisions to assist in the improved design of pediatric clinical studies.

Key aspects covered in this course:

  • The impact of developmental physiology and ontogeny of drug elimination systems
  • How changing ontogeny influences the level of drug-drug interactions in paediatrics
  • In vitroin vivo extrapolation (IVIVE) and how it applies to neonates, infants and children
  • Development and utilisation of a paediatric full physiologically based pharmacokinetic (PBPK) model
  • How the PBPK model can be linked to PD
  • Handling unknown values for pediatric PBPK/PD model parameters
  • Pediatric oral drug absorption
  • Specialized study design considerations (eg, formulation, diet)
  • The role of modeling and simulation in pediatric drug development

Cardiac Safety

Mechanistic Approaches for the Assessment of a Drug’s Pro-arrhythmic Potency within Target Populations. Drug-induced cardiovascular adverse events were one of the leading causes of drug withdrawals from the market and of drug label restrictions. Nowadays, these safety concerns are among the main reasons compounds development have stopped. Participants will utilize in vitro measured drug-triggered cardiac ion-current disruption data in combination with in vivo drug exposure information to evaluate the factors influencing potential drug cardiac risk.

Key aspects covered in this course:

  • Enhanced QSAR methodology and models for the drug triggered IKr, IKs, INa and ICa currents inhibition prediction
  • Mathematical models of human heart ventricular cells describing cardiac electrophysiology
  • Role of the multiple cardiac ion channels inhibition to assess pro-arrhythmic potency
  • Drug and physiology related parameters of the pro-arrhythmic risk in population
  • Various endpoints of the drugs pro-arrhythmic potency – their simulation and interpretation
  • Novel biomarkers of the cardiac risk including electro-mechanical window
  • How the PBPK models can be linked to mechanistic electrophysiology models to perform full IVIV extrapolation
  • Role of the drug-drug and drug-physiology interactions in the cardiac toxicity and their prediction
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