An important skill for quantitative scientists within the pharmaceutical industry is the need effectively communicate complex technical information.  This is especially challenging when the audience includes high-level stakeholders such as senior managers and regulatory representatives who may not have a deep technical understanding but must rely on modeling and simulation analyses to drive decision making.  In this webinar, important points to consider will be discussed that can help attendees develop and deliver successful presentations that are tailored to meet the needs of the target audience and their objectives. 

The MIDD Webinar Series, coordinated by Yash Kapoor and Fulya Akpinar Singh, is a series of webinars focused on shaping the future of drug development and regulatory decision-making sponsored by the ISoP Education Committee. Topics range from MIDD approaches in regulatory submission to pharmacometrics topics that are at the core of model development.  

We consider nonlinear mixed effects models including high-dimensional covariates to model individual parameters variability. The objective is to identify relevant covariates and estimate model parameters. We combine a penalized LASSO-type estimator with an eBIC model choice criterion to select the covariates of interest. Then we estimate the parameters by maximum likelihood in the reduced model. We calculate the LASSO-type penalized estimator by a weighted proximal gradient descent algorithm with an Adagrad-type adaptive step. This choice allows us in particular to consider models that do not necessarily belong to the curved exponential family. We illustrate the performance of the method in a nonlinear mixed-effects logistic growth model.

The MIDD Webinar Series, coordinated by Yash Kapoor and Fulya Akpinar Singh, is a series of webinars focused on shaping the future of drug development and regulatory decision-making sponsored by the ISoP Education Committee. Topics range from MIDD approaches in regulatory submission to pharmacometrics topics that are at the core of model development.  

In November 2024, the ICH Assembly endorsed the draft guideline entitled “M15 General Principles for Model-Informed Drug Development” and agreed to make the guideline available for public comment. This marks a major milestone for model-informed drug development (MIDD) and its maturity as a globally accepted standard for evidence generation. The draft guideline is the product of the multidisciplinary Expert Working Group (EWG) of the ICH. It establishes a harmonized assessment framework (including associated terminology) for MIDD evidence and provides an assessment table as a tool for communication. In this session, members of the ICH M15 EWG from regulatory agencies and industry will provide an overview of the draft guideline. The presentations will cover key contents of the M15 guideline, relevant perspectives, and examples.

The MIDD Webinar Series, coordinated by Yash Kapoor and Fulya Akpinar Singh, is a series of webinars focused on shaping the future of drug development and regulatory decision-making sponsored by the ISoP Education Committee. Topics range from MIDD approaches in regulatory submission to pharmacometrics topics that are at the core of model development.  

Abstracts and Posters from ACoP 2024

Date of Conference

November 10-13, 2024

Conference Location

Phoenix, Arizona, USA

Overview of the 2024 MIDD Webinar Series

The 2024 MIDD Webinar Series, coordinated by Ana Ruiz and Sihem Bihorel, is a series of webinars focused on shaping the future of drug development and regulatory decision-making. Topics range from regulatory aspects from a European perspective to methods for dose selection in rare diseases. Topics range from regulatory aspects from a European perspective to methods for dose selection in rare diseases.

Authors

Yegwon An, The State University of New York at Buffalo; Min Hee Kang, Pharm.D - Associate Vice President, Office of Research, Department of Medicine, Texas Tech University Health Sciences Center; Sukyung Woo, Ph.D - Associate Professor, Pharmaceutical Sciences, The State University of New York at Buffalo, School of Pharmacy & Pharmaceutical Sciences

Abstract

Objectives: Nal-IRI is a nano-liposomal irinotecan approved for the management of metastatic colorectal cancer. The aim of this study is to characterize the disposition and biodistribution of irinotecan and SN-38 following Nal-IRI administration in Ewing's Family tumor xenografts, and quantify the determinant factors of pharmacokinetics (PK) and the benefits of nanoparticle formulation Nal-IRI by developing a physiologically based pharmacokinetic (PBPK) model. Methods: The PK data of CPT-11 and its metabolite SN-38 in plasma and various tissues (liver, spleen, kidney, brain, lung, tumor) were obtained following single intravenous doses of 5, 10, 20 mg/kg of Nal-IRI (5 mg/mL, nano-liposomal irinotecan) and irinotecan (Camptosar, 20 mg/mL, free drug formulation) in both non-tumor-bearing and tumor-bearing mice. A dual PBPK model for irinotecan and SN-38 was sequentially developed. Initially, the model described the disposition of the free drug formulation in non-tumor bearing mice, followed by the disposition of the nano-liposomal formation in tumor-bearing mice. Results: The developed dual PBPK model well described the observed plasma and tissue PK profiles of both irinotecan and SN-38. Key features of the model for irinotecan and SN-38 disposition included biliary clearance and enterohepatic circulation of irinotecan and SN-38 in the liver, metabolic conversion of irinotecan to SN-38 by carboxylesterases in the liver and plasma, and renal clearance of SN-38. Specific components of the Nal-IRI formulation encompassed phagocytic uptake into the interstitial space of the liver, spleen, kidney, and lung, which exhibited linearity, alongside non-linear uptake in plasma; liposomal release of irinotecan, metabolic conversion in phagocytic cells, and liposomal stability in plasma. Global sensitivity analysis revealed that key parameters included plasma-tissue partition coefficients, biliary clearance, and metabolic clearance, all estimated with good precision. Significant elimination mechanisms included biliary clearance (0.999 mL/hr) and metabolic clearance (51.94 mL/hr) for irinotecan, while for SN-38, biliary clearance (6.31 mL/hr) and renal clearance (6.21 mL/hr) were notable. Critical parameters of the Nal-IRI PBPK model were associated with phagocytic cellular uptake, slow liposomal release kinetics, partition coefficient, and prolonged drug residence time in plasma and tissue, including tumors. The estimated value of the maximum Nal-IRI uptake rate by phagocytic cells was 13.6 mL/hr. Conclusions: The quantification and characterization of drug disposition for both Nal-IRI and non-liposomal formulation irinotecan using a PBPK modeling approach suggest that phagocytic cellular uptake, prolonged drug residence time in tissues and plasma, and slow liposomal release kinetics are key aspects influencing the disposition and tissue distribution of nano-liposomal irinotecan.


Citations: [1] Kang MH, Wang J, Makena MR, et al. Activity of MM-398, nanoliposomal irinotecan (nal-IRI), in Ewing's family tumor xenografts is associated with high exposure of tumor to drug and high SLFN11 expression. Clin Cancer Res. 2015;21(5):1139-1150. doi:10.1158/1078-0432.CCR-14-1882

Keywords

PBPK, Nano-liposomal formulation, Ewing's Family Tumor

Date of Conference

November 10-13, 2024

Conference Location

Phoenix, Arizona, USA

DOI

10.70534/CNZG6162

Authors

Hannah Huang, Arcus; Ken Liao, Ph.D. - Sr. Director, Clinical Pharmacology, Arcus Biosciences; Molly Zhao, Ph.D. - Director, Clinical Pharmacology, Gilead Sciences; Balaji Agoram, Ph.D. - Vice President, Clinical Pharmacology, Arcus Biosciences; Ayyappa Chaturvedula, Ph.D. - Sr. Director, Clinical Pharmacology, Arcus Biosciences

Abstract

OBJECTIVES: The objective of this analysis is to demonstrate a general simulation-based methodology to assess sample size and power for clinical studies with exposure metric based end points. METHODS: A two-compartment model following intravenous administration with moderate between subject variability on pharmacokinetic parameters was used for pharmacokinetic (PK) simulations. An example drug development scenario of infusion time change from 1-hour to 0.5-hour was used. PK profiles of reference and test populations were simulated following single dose and steady-state using 1-hour and 0.5-hour infusion times, respectively. Non-compartmental analysis was conducted to derive test and reference population-based exposure metrics, including maximum concentration [C_max] and average concentration [C_avg] from individual predictions (IPRED). Random sampling was conducted from the test and reference populations to represent a parallel design PK evaluation trial with various sample sizes (n=6, 12, 20, 30, 40 or 50). The geometric mean ratios (GMR) of test to reference C_max and C_avg were calculated for each trial. The process was repeated 1000 times to generate virtual clinical trials for each sample size scenario. Power to detect a nominal 20% difference between test and reference population was calculated as percentage of trials with GMR within 0.8-1.2. In addition, the entire power calculation process was repeated to evaluate scenarios with an assumed true difference of 25% lower exposure in test compared to reference population. RESULTS: Sample size of >=6 can achieve >80% probability of correctly concluding that Cmax and Cavg for 0.5-h co-admin are within +-20% of those for 1-h infusion when no true differences between test and reference populations are assumed. Sample size of >=40 patients would provide >80% probability of correctly concluding that Cmax and Cavg for 0.5-h co-admin are outside +-20% of those for 1-h infusion when the true difference is 25% between test and reference population. CONCLUSIONS: Clinical trial simulation using population PK models can provide an assessment of sample size and power for studies with PK exposure metric driven endpoints. It is more advantageous than traditional power calculation as it is more intuitive, adaptable to different study designs, and incorporates known PK variability related to intrinsic and extrinsic factors as well as differences in dosing. The framework presented can be generally applied in various scenarios in drug development with drug specific models of varying complexities with simple simulation workflows.


Citations: N/A

Keywords

Sample size, clinical trial simulation

Date of Conference

November 10-13, 2024

Conference Location

Phoenix, Arizona, USA

DOI

10.70534/YJSU8236