Slide 1: Development and Application of PBPK Models to Support Transporter-mediated DDI Assessment
Development and Application of PBPK Models to Support Transporter-mediated Drug-Drug Interaction (tDDI) Assessment Revathi Chapa Suvarchala Avvari Simulations Plus, Inc. Lancaster, CA USA Spring 2024 1 NASDAQ: SLP
Slide 2: Outline of Presentation
Outline of Presentation • Types of Interactions • GastroPlus DDI Module Interface • Overview of relevant regulatory guidance documents 1. Investigation of Transporter Involvement in Drug Clearance 2. Investigation of Transporter Inhibitory Potential • PBPK Models of P-gp Substrates and tDDIs selecte
Slide 3: Types of Interactions
Types of Interactions • Steady-state competitive inhibition • Steady-state time-dependent inhibition • Steady-state induction may include metabolites effect with simulated perpetrator concentrations • Dynamic competitive inhibition • Dynamic time-dependent inhibition • Dynamic induction include effe
Slide 4: Dynamic Simulation – Equations
Dynamic Simulation – Equations 𝑇𝑟𝑎𝑛𝑠𝐴𝑐𝑡0 × 𝑉max × [𝑆]𝑢 Competitive Inhibition 𝜈= [𝐴]𝑢,𝑗 [𝐼]𝑢,𝑖 𝐾𝑚 1 + σ𝑁 𝑗=1 𝐾 + σ𝑀 𝑖=1 𝐾 + [𝑆]𝑢 𝑚,𝑗 𝑖,𝑗 multiple multiple inhibitors substrates of of given given transporter transporter 𝑇𝑟𝑎𝑛𝑠𝐴𝑐𝑡𝑡 × 𝑉max × [𝑆]𝑢 𝜈=…
Slide 5: Let’s not forget …
Let’s not forget … In GastroPlus , the PBPK model is linked to the ACAT physiological intestinal model …. These phenomena: • are happening simultaneously • are repeated in each of the compartments of the gastrointestinal tract 5 5 NASDAQ: SLP
Slide 6: Fa% FDp% (not Fa%)
… and getting the correct dissolution/absorption is the prerequisite for getting correct PBPK & DDI predictions for oral dosage forms! Fa% FDp% (not Fa%) F% D A PV SC Metabolism Metabolism * Modified from van de Waterbeemd, H, and Gifford, E. ADMET In Silico Modelling: 6 Towards Prediction Paradise?
Slide 7: Developing a DDI Qualification Matrix
Developing a DDI Qualification Matrix: PBPK Models of Probe Substrates, Inhibitors, and Inducers in Various Stages of Validation • The GastroDDIStandards database includes PBPK and compartmental PK models of many inhibitors, inducers, and probe substrates Alfentanil Dolutegravir Metformin Ranitidine
Slide 8: DDI Module Interface
DDI Module Interface 8 NASDAQ: SLP
Slide 9: Basic Interface Layout
Basic Interface Layout The left side of the DDI window displays the current compound – record which was opened on main GastroPlus window at the time of accessing DDI module. You select the designation for current compound as a perpetrator or victim. 9 NASDAQ: SLP
Slide 10: Basic Interface Layout
Basic Interface Layout The right side of the DDI window displays the interacting compound – this may be a record in the same database that is opened in the main GastroPlus window or a different GastroPlus database. By default, the database of standard compounds supplied with DDI Module will open whe
Slide 11: Substrate Settings
Substrate Settings The substrate metabolic profile (fm and Fg values) needs to be specified for steady-state predictions. fm values: - may be calculated from in vitro assays using built-in converter or entered manually by user and saved in database. - if Km and Vmax values are already present in the
Slide 12: Substrate Settings
Substrate Settings Metabolic profile in gut and liver is displayed in a pie-chart. 12 NASDAQ: SLP
Slide 13: Substrate Settings
Substrate Settings Time of victim’s administration relative to perpetrator dosing and simulation length are required for all predictions involving ‘simulation’ concentrations (dynamic simulation or steady-state prediction involving simulated perpetrator concentration) 13 NASDAQ: SLP
Slide 14: Substrate Settings
Substrate Settings 14 NASDAQ: SLP
Slide 15: Perpetrator Settings
Perpetrator Settings Inhibition/Induction constants for all proteins (enzymes or transporters) affected by a given compound and its metabolites need to be specified for all predictions (steady-state and/or dynamic simulations). Multiple constants for the same compound- protein pair may be saved in t
Slide 16: Perpetrator Settings
Perpetrator Settings Perpetrator dosing information needs to be specified for all predictions (steady- state and/or dynamic). Missing values for dose or dosing interval (or any of the perpetrator properties) may result in failure in obtaining effective perpetrator concentration (the concentration va
Slide 17: Prediction Results
Prediction Results With full dynamic simulations, the simulation results are shown for every compound in the system (substrate, perpetrator and their metabolites). To obtain the AUC ratio, you need to run a full simulation as well as “baseline” (i.e., simulation where the interactions are ignored).
Slide 18: Prediction Results
Prediction Results 18 NASDAQ: SLP
Slide 19: FDA Guidance Document: DDI & PBPK
FDA Guidance Document: DDI & PBPK In Silico DDI Studies: Physiologically based pharmacokinetic (PBPK) models can be used in lieu of some prospective DDI studies. For example, PBPK models have predicted the impact of weak and moderate inhibitors on the substrates of some CYP isoforms (e.g., CYP2D6, C
Slide 20: Utilization of PBPK models for DDIs in regulatory documents
Utilization of PBPK models for DDIs studies is present in regulatory documents for different jurisdictions 20 NASDAQ: SLP
Slide 21: FDA Guidance Document: Transporter mediated DDI
FDA Guidance Document : Transporter mediated DDI The USFDA Guidance regulates that the sponsor should consider evaluating DDI of the NCE mediated by the following transporters: • P‐glycoprotein (P‐gp) which also known as multidrug resistance 1 (MDR1); • Breast cancer resistance protein (BCRP); • Org
Slide 22: Regulatory Requirements of Transporter Evaluation
Regulatory Requirements of Transporter Evaluation Fu. S et al., Medicine in Drug Discovery. (2021) 22 NASDAQ: SLP
Slide 23: Possible Model for Decision Making: Transporter-Based DDI Studies
Possible Model for Decision Making: Transporter-Based Drug- Drug Interaction Studies US FDA EMA, Guideline on the investigation of drug interactions 23 NASDAQ: SLP
Slide 24: Decision Tree: Substrate for P-gp
Decision Tree to Determine Whether An Investigational Drug is A Substrate for P-gp https://www.fda.gov/drugs/drug-interactions-labeling/drug-development-and-drug-interactions-possible-models-decision-making 24 NASDAQ: SLP
Slide 25: Decision Tree: Substrate for OATP1B1 or OATP1B3
Decision Tree to Determine Whether An Investigational Drug is A Substrate for OATP1B1 or OATP1B3 https://www.fda.gov/drugs/drug-interactions-labeling/drug-development-and-drug-interactions-possible-models- 25 decision-making NASDAQ: SLP
Slide 26: Decision Tree: Substrate for OCT2, OAT1 or OAT3
Decision Tree to Determine Whether An Investigational Drug is A Substrate for OCT2, OAT1 or OAT3 https://www.fda.gov/drugs/drug-interactions-labeling/drug-development-and-drug-interactions-possible-models-decision-making 26 NASDAQ: SLP
Slide 27: Regulatory Requirements for Inhibition Studies
Regulatory Requirements of Transporter Evaluation for Inhibition Studies Fu. S et al., Medicine in Drug Discovery. (2021) 27 NASDAQ: SLP
Slide 28: Decision Tree: Inhibitor of P-gp
Decision Tree to Determine Whether An Investigational Drug is An Inhibitor of P-gp https://www.fda.gov/drugs/drug-interactions-labeling/drug-development-and-drug-interactions-possible-models-decision-making 28 NASDAQ: SLP
Slide 29: Decision Tree: Inhibitor of OATP1B1 or OATP1B3
Decision Tree to Determine Whether An Investigational Drug is An Inhibitor of OATP1B1 or OATP1B3 https://www.fda.gov/drugs/drug-interactions-labeling/drug-development-and-drug-interactions-possible-models-decision-making 29 NASDAQ: SLP
Slide 30: Decision Tree: Inhibitor of OCT2, OAT1 or OAT3
Decision Tree to Determine Whether An Investigational Drug is An Inhibitor of OCT2, OAT1 or OAT3 https://www.fda.gov/drugs/drug-interactions-labeling/drug-development-and-drug-interactions-possible-models-decision-making 30 NASDAQ: SLP
Slide 31: Determining if the Investigational Drug is an Inducer of a Transporter
Determining if the Investigational Drug is an Inducer of a Transporter • Transporters such as P-gp are induced through mechanisms similar to those for CYP enzymes (ex: by activation of specific nuclear receptors). • Because of these similarities, information from CYP3A inductions studies can inform
Slide 32: Analysis and Interpretation of Transporter Inhibition Assay
Analysis and Interpretation of Transporter Inhibition Assay Fu. S et al., Medicine in Drug Discovery. (2021) 32 NASDAQ: SLP
Slide 33: Regulatory Guidance Assessing tDDIs PBPK Modeling Considerations
Regulatory Guidance Assessing tDDIs PBPK Modeling Considerations Vijaywargi G., Biopharm Drug Dispos. 2023 33 NASDAQ: SLP
Slide 34: Literature – tDDI and PBPK Modeling
Literature – tDDI and PBPK Modeling Vijaywargi G., Biopharm Drug Dispos. 2023 34 NASDAQ: SLP
Slide 35: Transporter based PBPK Models in Healthy
Transporter based PBPK Models in Healthy Vijaywargi G., Biopharm Drug Dispos. 2023 35 NASDAQ: SLP
Slide 36: Transporter based PBPK Models in Diseased and Geriatric Population
Transporter based PBPK Models in Diseased and Geriatric Population Vijaywargi G., Biopharm Drug Dispos. 2023 36 NASDAQ: SLP
Slide 37: Transporter Mediated DDI in Regulatory Submissions
Transporter Mediated DDI in Regulatory Submissions Shebley Clin Pharm Ther 2018 Taskar Clin Pharm Ther 2019 37 37 NASDAQ: SLP
Slide 38: Strategies for Validating tDDI Models
Strategies for Validating tDDI Models Vijaywargi G., Biopharm Drug Dispos. 2023 38 NASDAQ: SLP
Slide 39: Challenges – PBPK Modeling Involving Drug Transporters
Challenges – PBPK Modeling Involving Drug Transporters • There has been considerable effort in the area of transporters over the last decade to understand and build robust IVIVE for transporters • Some information is still lacking like • in vitro data (ex: Km, Jmax, CLPD), • Protein abundance/ expre
Slide 40: Development, Validation, and Application of PBPK Models of Transporter-Mediated Processes
Development, Validation, and Application of PBPK Models of Transporter- Mediated Processes 40 NASDAQ: SLP
Slide 41: DDI Qualification Approach
DDI Qualification Approach • Literature collection collated in a spreadsheet • Model building and verification of single doses and multiple doses • Verification for all mechanisms of DDI • PowerPoint • MS-Word Reports (context of use) that can be submitted along with PBPK reports for investigational
Slide 42: In Silico DDI Predictive Performance: Guest Criteria
In Silico DDI Predictive Performance: Guest Criteria Guest EJ, Aarons L, Houston JB, Rostami-Hodjegan A, Galetin A. Critique of the two-fold measure of prediction success for ratios: application for the assessment of drug-drug interactions. Drug Metab Dispos. 2011 Feb;39(2):170-3 Predictive performa
Slide 43: PBPK Model for P-gp Substrate Digoxin
Development of a Physiologically Based Pharmacokinetic (PBPK) Model for the P-gp Substrate Digoxin, and Model Validation of Known Drug-Drug Interactions (DDIs) with Rifampicin and Itraconazole 43 NASDAQ: SLP
Slide 44: Literature collection
Literature collection 44 NASDAQ: SLP
Slide 45: Overview of Modeling Strategy of Digoxin
Overview of Modeling Strategy of Digoxin 45 NASDAQ: SLP
Slide 46: Key Parameters for Digoxin Used in GastroPlus Simulations
Key Physicochemical and Biopharmaceutical Parameters for Digoxin Used in GastroPlus Simulations Parameter Value Reference Parameter Value Reference logP 1.26 [ PUBCHEM] OATP4C1 (kidney) Diffusion coefficient 0.44x10-5 cm2/s ADMET Predictor Km (µM) 7.8 [Mikkaichi et al. 2004] Vmax (mg/s/mg trans prot
Slide 47: Digoxin Characteristic Properties
Digoxin Characteristic Properties • P-gp substrate that reaches Cmax 1-3 hrs after oral administration • Mainly excreted unchanged in human urine (only16 – 25 % of a dose is metabolized) • The fact that renal CL of Digoxin is greater than creatinine CL indicates that it is excreted by tubular secret
Slide 48: Transporters Involved in Absorption and Disposition of Digoxin
Schematic View of Transporters Involved in Absorption and Disposition of Digoxin in Gut, Liver, Muscle and Kidney Lee CA. CPT- 96(3)-298-301 48 NASDAQ: SLP
Slide 49: Digoxin and MDR3 P-gp Transporter
Digoxin and MDR3 P-gp Transporter ` ` Smith J- J Biolo Chemistry-275(31)-23530-2000 49 NASDAQ: SLP
Slide 50: Model Validation of Digoxin
Model Validation of Digoxin Observed vs Predicted Values for Cmax and AUC of Digoxin Purple Circles and Blue Circles represent Cmax and AUC0-inf , respectively. Red lines (──) represent 2-fold prediction error, Black lines (──) represent the 1.25-fold prediction error. 50 NASDAQ: SLP
Slide 51: DDI Accuracy
DDI Accuracy Observed vs Predicted AUC0-t and Cmax Ratios for DDI Between Digoxin, Rifampicin, and Itraconazole Green (circles) represent the AUC and Cmax for DDI with Rifampicin, and Orange (Circles) represent the AUC and Cmax for DDI with Itraconazole. Red lines (──) represent 2-fold prediction er
Slide 52: PBPK Model for Fexofenadine
Development of a Physiologically Based Pharmacokinetic (PBPK) Model for the P-gp, OATP2B1, OAT3 Substrate Fexofenadine and Model Validation of Known Drug-Drug Interactions (DDIs) with Rifampicin, Itraconazole, Verapamil, and Efavirenz 52 NASDAQ: SLP
Slide 53: Literature Collection
Literature Collection 53 NASDAQ: SLP
Slide 54: Overview of Modeling Strategy of Fexofenadine
Overview of Modeling Strategy of Fexofenadine 54 NASDAQ: SLP
Slide 55: Key Parameters for Fexofenadine Used in GastroPlus Simulations
Key Physicochemical and Biopharmaceutical Parameters for Fexofenadine Used in GastroPlus Simulations Parameter Value Reference Parameter Value Reference logP 0.5 (Chen chen et al. 2007) Blood:plasma concentration ratio 0.74 (Takano et al.,2016) (Rbp) Diffusion coefficient 0.53x10-5 cm2/s ADMET Predi
Slide 56: GastroPlus PBPK Model: Fexofenadine
GastroPlus PBPK Model : Fexofenadine • Used the log P = 0.5 • Changed the kidney and muscle model to Permeability-limited with SpecPstc =6E-4 mL/s/mL • PBPK Vmax values (P-gp & OATP2B1) and SpecPStc were optimized against IV dose • Gut transporter Vmax values were fitted against the PO dose 56 NASDA
Slide 57: Model Validation of Fexofenadine
Model Validation of Fexofenadine Observed vs Predicted Values for Cmax and AUC of Fexofenadine Purple Circles and Blue Circles represent Cmax and AUC0-inf , respectively. Red lines (──) represent 2-fold prediction error, Black lines (──) represent the 1.25-fold prediction error. 57 NASDAQ: SLP
Slide 58: DDI Accuracy
DDI Accuracy Observed vs Predicted AUC0-t and Cmax Ratios for DDI Between Fexofenadine, Itraconazole, Verapamil, Rifampicin, and Efavirenz. Green (circles), Blue (Circles), and Orange (Circles) represent the AUC and Cmax respectively. Red lines (──) represent 2-fold prediction error, and black lines
Slide 59: PBPK Model for Rosuvastatin
Development of a Physiologically Based Pharmacokinetic (PBPK) Model for the BCRP, OATP1B1, and OATP1B3 Substrate Rosuvastatin, and Model Validation of Known Drug-Drug Interactions (DDIs) with Rifampicin and Gemfibrozil 59 NASDAQ: SLP
Slide 60: Enzymes and Transporters Involved in Disposition of Rosuvastatin
Schematic View of Enzymes and Transporters Involved in Absorption and Disposition of Rosuvastatin in Gut, Liver and Kidney of Human Body CYP2C9 NTCP sodium-taurocholate co-transporting polypeptide UGT1A1 OAT3 organic anion-transporter CYP2C9 BCRP breast cancer resistance protein UGT1A1 OATP1B1 organ
Slide 61: Overview of Modeling Strategy of Rosuvastatin
Overview of Modeling Strategy of Rosuvastatin 61 NASDAQ: SLP
Slide 62: Key Parameters for Rosuvastatin Used in GastroPlus Simulations
Key Physicochemical and Biopharmaceutical Parameters for Rosuvastatin Used in GastroPlus Simulations Parameter Value Reference Parameter Value Reference logD -0.33 @ pH=7.4 (Jones et al. 2012) Transporters Diffusion coefficient 0.57x10-5 cm2/s ADMET Predictora Influx: Basolateral side 4.329 (acid),
Slide 63: Model Validation of Rosuvastatin
Model Validation of Rosuvastatin Goodness-of-Fit Plots Showing Observed vs Predicted Values for Cmax and AUC of Rosuvastatin 63 NASDAQ: SLP
Slide 64: Model Validation of Rosuvastatin: DDI Accuracy
Model Validation of Rosuvastatin: DDI Accuracy Observed vs Predicted DDI Ratios for Cmax and AUC of Rosuvastatin with Perpetrators (Rifampicin and Gemfibrozil) 64 NASDAQ: SLP
Slide 65: PBPK Model for Pravastatin
Development of a Physiologically Based Pharmacokinetic (PBPK) Model for the OATP1B Substrate Pravastatin, and Model Validation of Known Drug- Drug Interactions (DDIs) with Rifampicin, Gemfibrozil, Fluconazole, and Itraconazole 65 NASDAQ: SLP
Slide 66: Enzymes and Transporters Involved in Disposition of Pravastatin
Schematic View of Enzymes and Transporters Involved in Absorption and Disposition of Pravastatin in Gut, Liver and Kidney of Human Body OATP1B1 organic anion-transporting polypeptide 1B1 OATP1B3 organic anion-transporting polypeptide 1B3 OAT3 organic anion-transporter MRP2 multi-drug resistance prot
Slide 67: Overview of Modeling Strategy of Pravastatin
Overview of Modeling Strategy of Pravastatin 67 NASDAQ: SLP
Slide 68: Key Parameters for Pravastatin Used in GastroPlus Simulations
Key Physicochemical and Biopharmaceutical Parameters for Pravastatin Used in GastroPlus Simulations Parameter Value Reference Parameter Value Reference Molecular weight 424.5 ADMET predictor Transporters LogD at pH 7 0.59 (FDA) Influx: Basolateral side Optimized to describe IV Cp- OATP1B1 (liver) Lo
Slide 69: Model Validation of Pravastatin
Model Validation of Pravastatin Goodness-of-Fit Plots Showing Observed vs Predicted Values for Cmax and AUC of Pravastatin 69 NASDAQ: SLP
Slide 70: Model Validation of Pravastatin: DDI Accuracy
Model Validation of Pravastatin: DDI Accuracy Observed vs Predicted DDI Ratios for Cmax and AUC of Pravastatin with Perpetrators (Rifampicin, Gemfibrozil, and Its Metabolite, Itraconazole, and Fluconazole) 70 NASDAQ: SLP
Slide 71: Investigational Drugs as Victim Drug-Findings/Guidance
Investigational Drugs as Victim Drug-Findings/Guidance For Investigational drugs, initial Based on our findings, it is likely that a Thereafter, we can test the simulations can be carried out clinical DDI study with strong inhibitor untested scenarios like the using in vitro metabolism and and or ma
Slide 72: Investigational Drugs as Transporter Inhibitors-Findings/Guidance
Investigational Drugs as Transporter Inhibitors- Findings/Guidance For Investigational drugs, using the in vitro Ki Depending on the predicted magnitude of values, if the R values calculated are higher than interaction, whether significant or not. cut-off values, then relevant substrates (our In the
Slide 73: Additional Literature on GastroPlus DDI Applications
Additional Literature on GastroPlus DDI Applications • Perrier Jeremy, Gualano V, Helmer E, Namour F, Lukacova V, Taneja A. Drug-drug interaction prediction of Ziritaxestat using a physiologically based enzyme and transporter pharmacokinetic network interaction model. 2023 Sep; Clin Transl Sci. 16:2
Slide 74: Additional Literature on GastroPlus DDI Applications
Additional Literature on GastroPlus DDI Applications • Sohlenius-Sternbeck AK, Meyerson G, Hagbjörk AL, Juric S, Terelius Y. A strategy for early-risk predictions of clinical drug-drug interactions involving the GastroPlusTM DDI module for time- dependent CYP inhibitors. Xenobiotica. 2018 Apr;48(4):
Slide 75: Important Resources
Important Resources • In Vitro Drug Interaction Studies — Cytochrome P450 Enzyme- and Transporter-Mediated Drug Interactions Guidance for Industry | FDA • Clinical Drug Interaction Studies — Cytochrome P450 Enzyme- and Transporter-Mediated Drug Interactions Guidance for Industry | FDA • Drug Develop
Slide 76: Additional Information
Additional Information 76 NASDAQ: SLP
Slide 77: DDI Inhibition Parameters
DDI Inhibition Parameters 77 NASDAQ: SLP
Slide 78: DDI Inhibition Parameters Cont.,
DDI Inhibition Parameters Cont., 78 NASDAQ: SLP
Slide 79: DDI Inhibition Parameters Cont.,
DDI Inhibition Parameters Cont., 79 NASDAQ: SLP
Slide 80: DDI Inhibition Parameters Cont.,
DDI Inhibition Parameters Cont., 80 NASDAQ: SLP