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. Topics range from MIDD approaches in regulatory submission to pharmacometrics topics that are at the core of model development.  

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. 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

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

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

Authors

Maurice Ahsman, PharmD, PhD - Director, Quantitative Pharmacology and PMX, MSD Brazil; Jeffrey Sachs, ScB, ScM, PhD, ACDRS - Distinguished Scientist, Exec. Dir., Quantitative Pharmacology and PMX, Merck & Co., Inc., USA; Josiah Ryman, PharmD, PhD - Director, Former: Merck & Co., Inc., USA, Current: EMD Serono; Ka Lai Yee, PhD - Sr. Director, Quantitative Pharmacology and PMX, Merck & Co., Inc., USA; Natalie Banniettis, MD, FAAP - Sr. Prin. Scientist, Pneumococcal Vaccines Section, Clinical Research, Merck & Co., Inc., USA; Thomas Weiss, MPH, DrPH - Scientific Assoc. VP, PL Vaccines, Outcomes Research, Merck & Co., Inc., USA; Jessica Weaver, MPH, PhD - Prin. Scientist, PL Vaccines-Adult, Outcomes Research, Merck & Co., Inc., USA

Abstract

Objectives: Invasive pneumococcal disease incidence in children has decreased substantially since the introduction of pneumococcal conjugate vaccines (PCVs) against the 7 (PCV7) and, later, 6 more (PCV13) serotypes (STs) clinically relevant in the US. In the absence of placebo-controlled vaccine efficacy trials, new PCVs are evaluated based on the vaccine-induced, serotype-specific (SS) immune response levels (immunoglobulin G titers, IgGs), as they have been demonstrated to predict efficacy. New PCVs are approved if induced SS IgGs are non-inferior to those of PCVs with demonstrated efficacy. SS IgGs can decrease with increased PCV valency, so predicting vaccine effectiveness (VE) of new PCVs can be critical to supporting public health decisions. This work predicts (from IgGs) SS VEs for the 13 serotypes shared between earlier and new PCVs. Methods.The model-based meta-analysis combined real-world vaccine effectiveness (VE) data with IgG data from clinical trials. Reverse cumulative distribution curves (RCDCs) were simulated using SS IgGs observed in placebo, PCV7, and PCV13 recipients. These were combined with published SS VEs for PCV7 and PCV13 to derive the protective IgG "threshold" ("Cp") for each of the 13 serotypes. SS IgGs from V114 and PCV20 recipients in clinical trials gave RCDCs for each of the 13 serotypes shared with PCV13. The RCDCs were combined with Cp values to predict, for V114 and PCV20, VEs for the 13 shared serotypes [1]. Post-primary series titers were used for predicting SS Cp and VE for both 2+1 and 3+1 dosing regimens (two or three infant doses, respectively, followed by a toddler dose). 2+1 predictions used V114 and PCV20 titers from clinical trials in EU, Russian, and Australian pediatric populations for which this regimen is routinely recommended; PCV13 VEs were from an EU pediatric population. 3+1 predictions used V114 and PCV20 titers from clinical trials in the US and Puerto Rico (where 3+1 is routinely recommended); PCV13 VEs were from a US pediatric population. Resampling the relevant distributions accounted for variability and uncertainty in the available data.

Results: Predicted SS VEs against PCV13 serotypes are higher for V114 than PCV20, for both 2+1 and 3+1 dosing regimens, particularly for the still-prevalent serotype ST 3 (93% and 98% for V114, 47% and 64% for PCV20, respectively, for 2+1 and 3+1 dosing regimens). A V114 2+1 regimen had predicted VEs comparable to those of PCV13 for the other 12 shared serotypes, with VE for a V114 3+1 regimen predicted to be lower (than PCV13) for ST 6A and higher for ST 19F. The predictive power of this framework and its consistency (with observed data) is re-enforced with a vachette [2] visualization of the results considering serotype and regimen as covariates.

Conclusions: The approach can provide informative VE predictions in the absence of placebo-controlled clinical trials, and vachette visualization can help understand the results across regimens and serotypes.


Citations: [1] J Ryman, J Weaver, T Hu, DM Weinberger, KL Yee, JR Sachs, Predicting vaccine effectiveness against invasive pneumococcal disease in children using immunogenicity data. npj Vaccines 7, 140 (2022). https://doi.org/10.1038/s41541-022-00538-1

J Ryman, J Weaver, KL Yee, JR Sachs, Predicting effectiveness of the V114 vaccine against invasive pneumococcal disease in children. Expert Review of Vaccines, 21(10) (2022). https://doi.org/10.1080/14760584.2022.2112179

J Ryman, JR Sachs, KL Yee, N Banniettis, J Weaver, T Weiss, Predicted serotype-specific effectiveness of pneumococcal conjugate vaccines V114 and PCV20 against invasive pneumococcal disease in children. Expert Review of Vaccines, 23(1) (2024) https://doi.org/10.1080/14760584.2023.2292773

J Ryman, JR Sachs, N Banniettis, T Weiss, M Ahsman, KL Yee, J Weaver. Potential serotype-specific effectiveness against IPD of pneumococcal conjugate vaccines V114 and PCV20 in children given a 2+1 dosing regimen. Expert Review of Vaccines, 23(1), (2024) https://doi.org/10.1080/14760584.2024.2335323

[2] https://certara.shinyapps.io/vachette/

Keywords

Vaccine Pharmacometrics, pneumococcal PCV PCV15 V114 PCV20, model-based meta-analysis MBMA

Date of Conference

November 10-13, 2024

Conference Location

Phoenix, Arizona, USA

DOI

10.70534/ERLZ2065

Authors

Ken Liao, PhD – Executive Director, Clinical Pharmacology, Arcus Biosciences; Elaine Paterson, PhD – Director, Clinical Science, Arcus Biosciences; Paul Foster, PhD – Executive Director, Clinical Development, Arcus Biosciences; Lixia Jin, PhD – VP, DMPK & Bioanalytics, Arcus Biosciences; Balaji Agoram, PhD – VP, Clinical Pharmacology, Arcus Biosciences

Abstract

Objectives: Casdatifan (AB521), an orally bioavailable small-molecule inhibitor of HIF-2?, potently inhibits transcription of HIF-2?-dependent genes in cell lines and preclinical species. The objective of this analysis was to develop a population plasma and urine PK model describing the relationship between dose and casdatifan PK, conduct simulations to compare the exposure between subjects with normal renal function and those with moderate renal impairment, and support inclusion of moderate renally impaired patients in future clinical trials.

METHODS: Casdatifan plasma concentrations were obtained from 55 healthy participants in a Phase 1 study, ARC-14 (NCT05117554). The available plasma PK data included a casdatifan dose range of 3 to 100 mg single oral dose, and multiple oral doses of casdatifan from 15 mg daily to 50 mg daily. Casdatifan urine PK data (in terms of the cumulative urine excreted unchanged casdatifan) were collected from 25 participants across single dose of 100 mg (from predose to 144 h post dose) and at multiple oral doses of 15 to 50 mg daily (predose to 24 h post dose at Day 1 and Day 7). Population plasma and urine PK modeling was conducted using mixed effects methodology with NONMEM, v7.5.

RESULTS: Casdatifan showed favorable PK profiles with a dose-proportional increase in exposure in the dose range tested (3-100 mg) and an apparently half-life of approximately 21 h. Casdatifan renal clearance ranged from 21.8 to 25.2 mL/min at dose levels of 15-100 mg and was comparable across all dose levels, accounting for approximately 30% of total systemic clearance of casdatifan.

A two-compartment model with first-order absorption adequately described the casdatifan plasma PK across the dose range tested. A multi-compartment model including a urine compartment was chosen to describe both the plasma and urine data with between-subject variability on apparent non-renal clearance, renal clearance, apparent volume of distribution, and absorption rate constant. Body weight and creatinine clearance were found to be statistically significant covariates on apparent volume of distribution and on renal clearance, respectively.

Model simulations indicated that the overall difference in geometric means of PK parameters (steady-state peak and average concentrations) between participants with normal and moderate impairment renal function were approximately 20% across all dose levels and within the range of casdatifan PK variability. Therefore, this predicted small increase in exposure in patients with moderate renal impairment is expected to be clinically insignificant, thus supporting inclusion of these patients in casdatifan studies to allow a broader patient population to have access to this novel therapeutic candidate.

CONCLUSION: A preliminary population plasma and urine PK model was developed to adequately describe casdatifan plasma and urine PK profile. The model supports inclusion of moderate renal impaired patients in casdatifan studies.


Citations: 1

Keywords

Population PK, Urine PK, HIF-2?

Date of Conference

November 10-13, 2024

Conference Location

Phoenix, Arizona, USA

DOI

10.70534/DKDW2483

Authors

Jagdeep Podichetty, PhD - Senior Director - Predictive Analytics o QuantMed, The Critical Path Institute

Abstract

Objective: Drug-Drug Interaction (DDI) information can be found in varied sources of medical literature - doctors' notes and nursing records, research articles, case studies, and review articles. In this study we evaluate the effectiveness of Small Language Models (SLMs) targeting the task extraction of key-value pairs of DDIs from unstructured text from medical literature. Fast and efficient extraction of DDI information from unstructured text accelerates the identification of potential adverse interactions, enabling researchers to focus on safer and more effective drug formulations, potentially reducing trial failures due to unforeseen adverse drug reactions.

Method: SLMs claim to offer efficient solutions, requiring less computational resources than their larger counterparts without compromising on performance. We evaluate three recent SMLs, Gemma by Google DeepMind, Phi-3 by Microsoft and OpemELM by Apple. Our methodology includes two specific experiments -