Model-Based Meta-Analysis for Multiple Myeloma: A Quantitative Drug-Independent Framework for Efficient Decisions in Oncology Drug Development

An adaptive seamless 2-in-1 design with biomarker-driven subgroup enrichment

Adaptive seamless phase 2/3 subgroup enrichment design plays a pivotal role in streamlining efficient drug development within a competitive landscape, while also enhancing patient access to promising treatments. This design approach identifies biomarker subgroups with the highest potential to benefit from investigational regimens. The seamless integration of Phase 2 and Phase 3 ensures a timely confirmation of clinical benefits. One significant challenge in adaptive enrichment decisions is determining the optimal timing and maturity of the primary endpoint. In this paper, we propose an adaptive seamless 2-in-1 biomarker-driven subgroup enrichment design that addresses this challenge by allowing subgroup selection using an early intermediate endpoint that predicts clinical benefits (i.e. a surrogate endpoint). The proposed design initiates with a Phase 2 stage involving all participants and can potentially expand into a Phase 3 study focused on the subgroup demonstrating the most favorable clinical outcomes. We will show that, under certain correlation assumptions, the overall type I error may not be inflated at the end of the study. In scenarios where the assumptions may not hold, we present a general framework to control the multiplicity. The flexibility and efficacy of the proposed design are highlighted through an extensive simulation study and illustrated in a case study in multiple myeloma.

Longitudinal Meta-Analysis of Historical Parkinson's Disease Trials to Inform Future Trial Design

BACKGROUND

The outcome of clinical trials in neurodegeneration can be highly uncertain due to the presence of a strong placebo effect.

OBJECTIVES

To develop a longitudinal model that can enhance the success of future Parkinson's disease trials by quantifying trial-to-trial variations in placebo and active treatment response.

METHODS

A longitudinal model-based meta-analysis was conducted on the total score of Unified Parkinson's Disease Rating Scale (UPDRS) Parts 1, 2, and 3. The analysis included aggregate data from 66 arms (observational [4], placebo [28], or investigational-drug-treated [34]) from 4 observational studies and 17 interventional trials. Inter-study variabilities in key parameters were estimated. Residual variability was weighted by the size of study arms.

RESULTS

The baseline total UPDRS was estimated to average at 24.5 points. Disease score was estimated to worsen by 3.90 points/year for the duration of the treatments; whilst notably, arms with a lower baseline progressed faster. The model captured the transient nature of the placebo response and sustained symptomatic drug effect. Both placebo and drug effects peaked within 2 months; although, 1 year was needed to observe the full treatment difference. Across these studies, the progression rate varied by 59.4%, the half-life for offset of placebo response varied by 79.4%, and the amplitude for drug effect varied by 105.3%.

CONCLUSION

The longitudinal model-based meta-analysis describes UPDRS progression rate, captures the dynamics of the placebo response, quantifies the effect size of the available therapies, and sets the expectation of uncertainty for future trials. The findings provide informative priors to enhance the rigor and success of future trials of promising agents, including potential disease modifiers. © 2023 GSK. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

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.

Pharmacometrics meets statistics-A synergy for modern drug development

Modern drug development problems are very complex and require integration of various scientific fields. Traditionally, statistical methods have been the primary tool for design and analysis of clinical trials. Increasingly, pharmacometric approaches using physiology-based drug and disease models are applied in this context. In this paper, we show that statistics and pharmacometrics have more in common than what keeps them apart, and collectively, the synergy from these two quantitative disciplines can provide greater advances in clinical research and development, resulting in novel and more effective medicines to patients with medical need.

Model-Based Meta-Analysis Compares DAS28 Rheumatoid Arthritis Treatment Effects and Suggests an Expedited Trial Design for Early Clinical Development

A nonlinear mixed effects modeling approach was used to conduct a model‐based meta‐analysis (MBMA) of longitudinal, summary‐level, baseline‐corrected 28‐joint Disease Activity Score (ΔDAS28) clinical trial data from seven approved rheumatoid arthritis (RA) drugs (abatacept, adalimumab, certolizumab, etanercept, rituximab, tocilizumab, and tofacitinib), representing 130 randomized clinical trials in 27,355 patients. All of the drugs except tocilizumab were found to have relatively similar ΔDAS28 time courses and efficacy (baseline‐corrected and placebo‐corrected) at 24 weeks and beyond of approximately 0.87–1.3 units in the typical RA patient population. Tocilizumab was estimated to have a differentially greater response of 1.99 at 24 weeks, likely due to its disproportionate effect on the acute‐phase cytokine interleukin‐6. Baseline DAS28, disease duration, percentage of male participants, and the year of conduct of the trial were found to have statistically significant effects on the timing and/or magnitude of ΔDAS28 in the control arms. Clinical trial simulations using the present MBMA indicated that abatacept, certolizumab, etanercept, tocilizumab, and tofacitinib would be expected to have a greater than 70% probability of showing a statistically significant difference vs. control at Week 6 with a sample size of ~ 30 patients per arm. In future RA clinical trials, an interim analysis conducted as early as 6 weeks after treatment initiation, with relatively small sample sizes, should be sufficient to detect the ΔDAS28 treatment effect vs. placebo.

Analysis of time course and dose effect of tacrolimus on proteinuria in lupus nephritis patients

WHAT IS KNOWN AND OBJECTIVES

Tacrolimus is used to treat patients with lupus nephritis; however, its time course and dose effect on proteinuria in lupus nephritis patients remain unknown. The purpose of this study was to determine the time course and dose effect of tacrolimus on proteinuria in lupus nephritis patients via model-based meta-analysis (MBMA).

METHODS

PubMed, Web of Science, Cochrane Library and ClinicalTrials.gov databases were systematically searched for information on the efficacy of tacrolimus against proteinuria in lupus nephritis patients. Useful data were extracted to build a model for the population studied using a non-linear mixed-effect model (NONMEM). This model was applied to simulate time course of tacrolimus on proteinuria using Monte Carlo simulations.

RESULTS

Ten clinical studies that recruited 222 patients with lupus nephritis were included. Based on various diagnostic plots, we found that the established model described the observed data reasonably well. In addition, the typical E_max and ET₅₀ of tacrolimus for 24-hour proteinuria in lupus nephritis patients were -5.88 g and 0.37 months, respectively. The baseline value of 24-hour proteinuria affected E_max . No significant dose-response relationship was observed in the range of tacrolimus concentration used in the present study (3-10 ng/mL), indicating that the effect of tacrolimus on proteinuria depends on effective concentration range and not the dose. However, the time course relationship was obvious; the efficacy of tacrolimus increased over time, reaching a plateau (80% E_max ) at approximately 1.48 months from the beginning of treatment.

WHAT IS NEW AND CONCLUSION

When the concentration range of tacrolimus is maintained at 3-10 ng/mL, at least 1.48 months of treatment is required to achieve a better outcome with regard to proteinuria in lupus nephritis patients.

Model-based Meta-analysis to Compare Primary Efficacy-endpoint, Efficacy-time Course, Safety, and Tolerability of Opioids Used in the Management of Osteoarthritic Pain in Humans

BACKGROUND

Despite recent therapeutic advances, osteoarthritis continues to be a challenging health problem, especially in the elderly population. Opioids, which are potent analgesics, have shown an extraordinary ability to reduce intense pain in many osteoarthritic clinical trials; however, there is an increased need for a study to integrate the reported outcomes and utilize them to achieve a better understanding. Herein, efficacy and safety aspects of opioids used to manage osteoarthritic pain were assessed and compared using a model-based meta-analysis (MBMA).

METHODS

To perform the analysis, a comprehensive database consisting of pain relief compounds with information on summary-level of efficacy over time, adverse events and dropout rates was compiled from multiple sources. MBMA was conducted using a nonlinear mixed-effects modeling approach.

RESULTS

The results of primary efficacy endpoint analysis indicated that the doses of oxycodone, oxymorphone, and tramadol required to produce 50% of the maximum effect were 47, 84, and 247 mg per day, respectively. Efficacytime course analysis showed that opioids had rapid time to efficacy onset, suggesting potentially powerful painrelieving effects. It was also found that gastrointestinal adverse events were the most opioid-associated and dosedependent adverse effects. In addition, the analysis revealed that opioids were well-tolerated at low to moderate doses.

CONCLUSION

This MBMA provides clinically meaningful insights into the efficacy and safety profiles of oxycodone, oxymorphone, and tramadol. Resultantly, the presented framework analysis can have an impact in the clinic on drug development where it can guide: the optimization of doses of opioids required to manage osteoarthritic pain; the making of precise key decisions for the positioning of new drugs, and; the design of more efficient trials.

Model-Informed Drug Development for Ixazomib, an Oral Proteasome Inhibitor

Model‐informed drug development (MIDD) was central to the development of the oral proteasome inhibitor ixazomib, facilitating internal decisions (switch from body surface area (BSA)‐based to fixed dosing, inclusive phase III trials, portfolio prioritization of ixazomib‐based combinations, phase III dose for maintenance treatment), regulatory review (model‐informed QT analysis, benefit–risk of 4 mg dose), and product labeling (absolute bioavailability and intrinsic/extrinsic factors). This review discusses the impact of MIDD in enabling patient‐centric therapeutic optimization during the development of ixazomib.