End-to-end application of model-informed drug development for ertugliflozin, a novel sodium-glucose cotransporter 2 inhibitor

Role of Clinical Pharmacology in Diversity and Inclusion in Global Drug Development: Current Practices and Industry Perspectives: White Paper

The 2022 United States Food and Drug Administration (US FDA) draft guidance on diversity plan (DP), which will be implemented through the Diversity Action Plans by December 2025, under the 21st Century Cures Act, marks a pivotal effort by the FDA to ensure that registrational studies adequately reflect the target patient populations based on diversity in demographics and baseline characteristics. This white paper represents the culminated efforts of the International Consortium of Quality and Innovation (IQ) Diversity and Inclusion (D&I) Working Group (WG) to assess the implementation of the draft FDA guidance by members of the IQ consortium in the discipline of clinical pharmacology (CP). This article describes current practices in the industry and emphasizes the tools and techniques of quantitative pharmacology that can be applied to support the inclusion of a diverse population during global drug development, to support diversity and inclusion of underrepresented patient populations, in multiregional clinical trials (MRCTs). It outlines strategic and technical recommendations to integrate demographics, including age, sex/gender, race/ethnicity, and comorbidities, in multiregional phase III registrational studies, through the application of quantitative pharmacology. Finally, this article discusses the challenges faced during global drug development, which may otherwise limit the enrollment of a broader, potentially diverse population in registrational trials. Based on the outcomes of the IQ survey that provided the current awareness of diversity planning, it is envisioned that in the future, industry efforts in the inclusion of previously underrepresented populations during global drug development will culminate in drug labels that apply to the intended patient populations at the time of new drug application or biologics license application rather than through post-marketing requirements.

Applying quantitative and systems pharmacology to drug development and beyond: An introduction to clinical pharmacologists

ABSTRACT

Quantitative and systems pharmacology (QSP) is an innovative and integrative approach combining physiology and pharmacology to accelerate medical research. This review focuses on QSP's pivotal role in drug development and its broader applications, introducing clinical pharmacologists/researchers to QSP's quantitative approach and the potential to enhance their practice and decision-making. The history of QSP adoption reveals its impact in diverse areas, including glucose regulation, oncology, autoimmune disease, and HIV treatment. By considering receptor-ligand interactions of various cell types, metabolic pathways, signaling networks, and disease biomarkers simultaneously, QSP provides a holistic understanding of interactions between the human body, diseases, and drugs. Integrating knowledge across multiple time and space scales enhances versatility, enabling insights into personalized responses and general trends. QSP consolidates vast data into robust mathematical models, predicting clinical trial outcomes and optimizing dosing based on preclinical data. QSP operates under a "learn and confirm paradigm," integrating experimental findings to generate testable hypotheses and refine them through precise experimental designs. An interdisciplinary collaboration involving expertise in pharmacology, biochemistry, genetics, mathematics, and medicine is vital. QSP's utility in drug development is demonstrated through integration in various stages, predicting drug responses, optimizing dosing, and evaluating combination therapies. Challenges exist in model complexity, communication, and peer review. Standardized workflows and evaluation methods ensure reliability and transparency.

Applications of Model-Based Meta-Analysis in Drug Development

Model-based meta-analysis (MBMA) is a quantitative approach that leverages published summary data along with internal data and can be applied to inform key drug development decisions, including the benefit-risk assessment of a treatment under investigation. These risk-benefit assessments may involve determining an optimal dose compared against historic external comparators of a particular disease indication. MBMA can provide a flexible framework for interpreting aggregated data from historic reference studies and therefore should be a standard tool for the model-informed drug development (MIDD) framework.In addition to pairwise and network meta-analyses, MBMA provides further contributions in the quantitative approaches with its ability to incorporate longitudinal data and the pharmacologic concept of dose-response relationship, as well as to combine individual- and summary-level data and routinely incorporate covariates in the analysis.A common application of MBMA is the selection of optimal dose and dosing regimen of the internal investigational molecule to evaluate external benchmarking and to support comparator selection. Two case studies provided examples in applications of MBMA in biologics (durvalumab + tremelimumab for safety) and small molecule (fenebrutinib for efficacy) to support drug development decision-making in two different but well-studied disease areas, i.e., oncology and rheumatoid arthritis, respectively.Important to the future directions of MBMA include additional recognition and engagement from drug development stakeholders for the MBMA approach, stronger collaboration between pharmacometrics and statistics, expanded data access, and the use of machine learning for database building. Timely, cost-effective, and successful application of MBMA should be part of providing an integrated view of MIDD.

Applications of Quantitative Systems Pharmacology (QSP) in Drug Development for NAFLD and NASH and Its Regulatory Application

Nonalcoholic steatohepatitis (NASH) is a widely prevalent disease, but approved pharmaceutical treatments are not available. As such, there is great activity within the pharmaceutical industry to accelerate drug development in this area and improve the quality of life and reduce mortality for NASH patients. The use of quantitative systems pharmacology (QSP) can help make this overall process more efficient. This mechanism-based mathematical modeling approach describes both the pathophysiology of a disease and how pharmacological interventions can modify pathophysiologic mechanisms. Multiple capabilities are provided by QSP modeling, including the use of model predictions to optimize clinical studies. The use of this approach has grown over the last 20 years, motivating discussions between modelers and regulators to agree upon methodologic standards. These include model transparency, documentation, and inclusion of clinical pharmacodynamic biomarkers. Several QSP models have been developed that describe NASH pathophysiology to varying extents. One specific application of NAFLDsym, a QSP model of NASH, is described in this manuscript. Simulations were performed to help understand if patient behaviors could help explain the relatively high rate of fibrosis stage reductions in placebo cohorts. Simulated food intake and body weight fluctuated periodically over time. The relatively slow turnover of liver collagen allowed persistent reductions in predicted fibrosis stage despite return to baseline for liver fat, plasma ALT, and the NAFLD activity score. Mechanistic insights such as this that have been derived from QSP models can help expedite the development of safe and effective treatments for NASH patients.

Glycemic efficacy and safety of the SGLT2 inhibitor ertugliflozin in patients with type 2 diabetes and stage 3 chronic kidney disease: an analysis from the VERTIS CV randomized trial

INTRODUCTION

Here we report the glycemic efficacy and safety of ertugliflozin in patients in the VERTIS CV cardiovascular outcome trial with chronic kidney disease (CKD) stage 3.

RESEARCH DESIGN AND METHODS

Prespecified and post-hoc analyses were performed in patients with an estimated glomerular filtration rate (eGFR) 30-<60 mL/min/1.73 m² at screening. The primary endpoint was glycemic efficacy at week 18. Longer term glycemic efficacy and changes in body weight, systolic blood pressure (SBP), and eGFR were also evaluated.

RESULTS

Among 8246 patients in VERTIS CV, 1776 patients had CKD stage 3; 1319 patients had CKD stage 3A (eGFR 45-<60 mL/min/1.73 m²); 457 patients had CKD stage 3B (eGFR 30-<45 mL/min/1.73 m²). Week 18 least squares (LS)-mean (95% CI) placebo-adjusted changes from baseline in glycated hemoglobin (HbA1c) for 5 mg and 15 mg ertugliflozin were -0.27% (-0.37% to -0.17%) and -0.28% (-0.38% to -0.17%), respectively, for CKD stage 3 overall and -0.27% (-0.38% to -0.15%) and -0.31% (-0.43% to -0.19%), respectively, for CKD stage 3A (all p<0.001). For CKD stage 3B, the reduction in HbA1c for 5 mg ertugliflozin was -0.28% (-0.47% to -0.08%) (p=0.006) and for 15 mg ertugliflozin was -0.19% (-0.39% to 0.01%) (p=0.064). LS-mean placebo-adjusted reductions in body weight (range: -1.32 to -1.95 kg) and SBP (range: -2.42 to -3.41 mm Hg) were observed across CKD stage 3 categories with ertugliflozin. After an initial dip, eGFR remained above or near baseline with ertugliflozin treatment. The incidence of overall adverse events (AEs), symptomatic hypoglycemia, hypovolemia, and kidney-related AEs did not differ between ertugliflozin and placebo across CKD stage 3 subgroups.

CONCLUSIONS

In VERTIS CV patients with CKD stage 3A, ertugliflozin resulted in reductions in HbA1c, body weight and SBP, maintenance of eGFR, and was generally well tolerated. Results in the CKD stage 3B subgroup were generally similar except for an attenuated HbA1c response with the 15 mg dose.

TRIAL REGISTRATION NUMBER

NCT01986881.

Meta-Analysis of Noncompartmental Pharmacokinetic Parameters of Ertugliflozin to Evaluate Dose Proportionality and UGT1A9 Polymorphism Effect on Exposure

Ertugliflozin, a sodium‐glucose cotransporter 2 inhibitor, is primarily metabolized via glucuronidation by the uridine 5′‐diphospho‐glucuronosyltransferase (UGT) isoform UGT1A9. This noncompartmental meta‐analysis of ertugliflozin pharmacokinetics evaluated the relationship between ertugliflozin exposure and dose, and the effect of UGT1A9 genotype on ertugliflozin exposure. Pharmacokinetic data from 25 phase 1 studies were pooled. Structural models for dose proportionality described the relationship between ertugliflozin area under the plasma concentration‐time curve (AUC) or maximum observed plasma concentration (C_max) and dose. A structural model for the UGT1A9 genotype described the relationship between ertugliflozin AUC and dose, with genotype information on 3 UGT1A9 polymorphisms (UGT1A9‐2152, UGT1A9*3, UGT1A9*1b) evaluated as covariates from the full model. Ertugliflozin AUC and C_max increased in a dose‐proportional manner over the dose range of 0.5‐300 mg, and population‐predicted AUC and C_max values for the 5‐ and 15‐mg ertugliflozin tablets administered in the fasted state demonstrated good agreement with the observed data. The largest change in ertugliflozin AUC was in subjects carrying the UGT1A9*3 heterozygous variant, with population‐predicted AUC (90% confidence interval) values of 485 ng·h/mL (458 to 510 ng·h/mL) and 1560 ng·h/mL (1480 to 1630 ng·h/mL) for ertugliflozin 5 and 15 mg, respectively, compared with 436 ng·h/mL (418 to 455 ng·h/mL) and 1410 ng·h/mL (1350 to 1480 ng·h/mL), respectively, in wild‐type subjects. Overall, the mean effects of the selected UGT1A9 variants on ertugliflozin AUC were within ±10% of the wild type. UGT1A9 genotype did not have any clinically meaningful effects on ertugliflozin exposure in healthy subjects. No ertugliflozin dose adjustment would be required in patients with the UGT1A9 variants assessed in this study.

End-to-end application of model-informed drug development for ertugliflozin, a novel sodium-glucose cotransporter 2 inhibitor

Model-informed drug development (MIDD) is critical in all stages of the drug-development process and almost all regulatory submissions for new agents incorporate some form of modeling and simulation. This review describes the MIDD approaches used in the end-to-end development of ertugliflozin, a sodium-glucose cotransporter 2 inhibitor approved for the treatment of adults with type 2 diabetes mellitus. Approaches included (1) quantitative systems pharmacology modeling to predict dose-response relationships, (2) dose-response modeling and model-based meta-analysis for dose selection and efficacy comparisons, (3) population pharmacokinetics (PKs) modeling to characterize PKs and quantify population variability in PK parameters, (4) regression modeling to evaluate ertugliflozin dose-proportionality and the impact of uridine 5'-diphospho-glucuronosyltransferase (UGT) 1A9 genotype on ertugliflozin PKs, and (5) physiologically-based PK modeling to assess the risk of UGT-mediated drug-drug interactions. These end-to-end MIDD approaches for ertugliflozin facilitated decision making, resulted in time/cost savings, and supported registration and labeling.