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Chan P, Peskov K, Song X. Applications of Model-Based Meta-Analysis in Drug Development. Pharm Res 2022; 39:1761-1777. [PMID: 35174432 PMCID: PMC9314311 DOI: 10.1007/s11095-022-03201-5] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Accepted: 02/11/2022] [Indexed: 12/13/2022]
Abstract
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.
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Affiliation(s)
- Phyllis Chan
- Clinical Pharmacology, Genentech, 1 DNA Way, South San Francisco, CA, 94080, USA.
| | - Kirill Peskov
- M&S Decisions LLC, Moscow, Russia
- Sechenov First Moscow State Medical University, Moscow, Russia
- STU 'Sirius', Sochi, Russia
| | - Xuyang Song
- Clinical Pharmacology and Quantitative Pharmacology, AstraZeneca, 1 Medimmune Way, Gaithersburg, MD, 20878, USA
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Hardy WAS, Hughes DA. Methods for Extrapolating Survival Analyses for the Economic Evaluation of Advanced Therapy Medicinal Products. Hum Gene Ther 2022; 33:845-856. [PMID: 35435758 DOI: 10.1089/hum.2022.056] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
There are two significant challenges for analysts conducting economic evaluations of advanced therapy medicinal products (ATMPs): (i) estimating long-term treatment effects in the absence of mature clinical data, and (ii) capturing potentially complex hazard functions. This review identifies and critiques a variety of methods that can be used to overcome these challenges. The narrative review is informed by a rapid literature review of methods used for the extrapolation of survival analyses in the economic evaluation of ATMPs. There are several methods that are more suitable than traditional parametric survival modelling approaches for capturing complex hazard functions, including, cure-mixture models and restricted cubic spline models. In the absence of mature clinical data, analysts may augment clinical trial data with data from other sources to aid extrapolation, however, the relative merits of employing methods for including data from different sources is not well understood. Given the high and potentially irrecoverable costs of making incorrect decisions concerning the reimbursement or commissioning of ATMPs, it is important that economic evaluations are correctly specified, and that both parameter and structural uncertainty associated with survival extrapolations are considered. Value of information analyses allow for this uncertainty to be expressed explicitly, and in monetary terms.
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Affiliation(s)
- Will A S Hardy
- Bangor University College of Health and Behavioural Sciences, 151667, Centre for Health Economics and Medicines Evaluation, Bangor, Gwynedd, United Kingdom of Great Britain and Northern Ireland;
| | - Dyfrig A Hughes
- Bangor University College of Health and Behavioural Sciences, 151667, Centre for Health Economics and Medicines Evaluation, School of Medical and Health Sciences, Ardudwy, Normal Site, Holyhead Road, Bangor, Gwynedd, United Kingdom of Great Britain and Northern Ireland, LL57 2PZ;
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Hill-McManus D, Hughes DA. Combining Model-Based Clinical Trial Simulation, Pharmacoeconomics, and Value of Information to Optimize Trial Design. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2020; 10:75-83. [PMID: 33314752 PMCID: PMC7825194 DOI: 10.1002/psp4.12579] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/05/2020] [Accepted: 10/20/2020] [Indexed: 11/25/2022]
Abstract
The Bayesian decision‐analytic approach to trial design uses prior distributions for treatment effects, updated with likelihoods for proposed trial data. Prior distributions for treatment effects based on previous trial results risks sample selection bias and difficulties when a proposed trial differs in terms of patient characteristics, medication adherence, or treatment doses and regimens. The aim of this study was to demonstrate the utility of using pharmacometric‐based clinical trial simulation (CTS) to generate prior distributions for use in Bayesian decision‐theoretic trial design. The methods consisted of four principal stages: a CTS to predict the distribution of treatment response for a range of trial designs; Bayesian updating for a proposed sample size; a pharmacoeconomic model to represent the perspective of a reimbursement authority in which price is contingent on trial outcome; and a model of the pharmaceutical company return on investment linking drug prices to sales revenue. We used a case study of febuxostat versus allopurinol for the treatment of hyperuricemia in patients with gout. Trial design scenarios studied included alternative treatment doses, inclusion criteria, input uncertainty, and sample size. Optimal trial sample sizes varied depending on the uncertainty of model inputs, trial inclusion criteria, and treatment doses. This interdisciplinary framework for trial design and sample size calculation may have value in supporting decisions during later phases of drug development and in identifying costly sources of uncertainty, and thus inform future research and development strategies.
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Affiliation(s)
- Daniel Hill-McManus
- Centre for Health Economics and Medicines Evaluation, Bangor University, Bangor, UK
| | - Dyfrig A Hughes
- Centre for Health Economics and Medicines Evaluation, Bangor University, Bangor, UK
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Hill-McManus D, Marshall S, Liu J, Willke RJ, Hughes DA. Linked Pharmacometric-Pharmacoeconomic Modeling and Simulation in Clinical Drug Development. Clin Pharmacol Ther 2020; 110:49-63. [PMID: 32936931 DOI: 10.1002/cpt.2051] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2020] [Accepted: 08/24/2020] [Indexed: 12/16/2022]
Abstract
Market access and pricing of pharmaceuticals are increasingly contingent on the ability to demonstrate comparative effectiveness and cost-effectiveness. As such, it is widely recognized that predictions of the economic potential of drug candidates in development could inform decisions across the product life cycle. This may be challenging when safety and efficacy profiles in terms of the relevant clinical outcomes are unknown or highly uncertain early in product development. Linking pharmacometrics and pharmacoeconomics, such that outputs from pharmacometric models serve as inputs to pharmacoeconomic models, may provide a framework for extrapolating from early-phase studies to predict economic outcomes and characterize decision uncertainty. This article reviews the published studies that have implemented this methodology and used simulation to inform drug development decisions and/or to optimize the use of drug treatments. Some of the key practical issues involved in linking pharmacometrics and pharmacoeconomics, including the choice of final outcome measures, methods of incorporating evidence on comparator treatments, approaches to handling multiple intermediate end points, approaches to quantifying uncertainty, and issues of model validation are also discussed. Finally, we have considered the potential barriers that may have limited the adoption of this methodology and suggest that closer alignment between the disciplines of clinical pharmacology, pharmacometrics, and pharmacoeconomics, may help to realize the potential benefits associated with linked pharmacometric-pharmacoeconomic modeling and simulation.
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Affiliation(s)
- Daniel Hill-McManus
- Centre for Health Economics and Medicines Evaluation, Bangor University, Bangor, UK
| | | | - Jing Liu
- Clinical Pharmacology, Pfizer Inc, Groton, Connecticut, USA
| | | | - Dyfrig A Hughes
- Centre for Health Economics and Medicines Evaluation, Bangor University, Bangor, UK
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Srinivasan M, White A, Chaturvedula A, Vozmediano V, Schmidt S, Plouffe L, Wingate LT. Incorporating Pharmacometrics into Pharmacoeconomic Models: Applications from Drug Development. PHARMACOECONOMICS 2020; 38:1031-1042. [PMID: 32734572 PMCID: PMC7578131 DOI: 10.1007/s40273-020-00944-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/09/2023]
Abstract
Pharmacometrics is the science of quantifying the relationship between the pharmacokinetics and pharmacodynamics of drugs in combination with disease models and trial information to aid in drug development and dosing optimization for clinical practice. Considering the variability in the dose-concentration-effect relationship of drugs, an opportunity exists in linking pharmacokinetic and pharmacodynamic model-based estimates with pharmacoeconomic models. This link may provide early estimates of the cost effectiveness of drug therapies, thus informing late-stage drug development, pricing, and reimbursement decisions. Published case studies have demonstrated how integrated pharmacokinetic-pharmacodynamic-pharmacoeconomic models can complement traditional pharmacoeconomic analyses by identifying the impact of specific patient sub-groups, dose, dosing schedules, and adherence on the cost effectiveness of drugs, thus providing a mechanistic basis to predict the economic value of new drugs. Greater collaboration between the pharmacoeconomics and pharmacometrics community can enable methodological improvements in pharmacokinetic-pharmacodynamic-pharmacoeconomic models to support drug development.
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Affiliation(s)
- Meenakshi Srinivasan
- University of North Texas System College of Pharmacy, 3500 Camp Bowie Blvd, Fort Worth, TX, 76107, USA
| | - Annesha White
- University of North Texas System College of Pharmacy, 3500 Camp Bowie Blvd, Fort Worth, TX, 76107, USA.
| | - Ayyappa Chaturvedula
- University of North Texas System College of Pharmacy, 3500 Camp Bowie Blvd, Fort Worth, TX, 76107, USA
| | - Valvanera Vozmediano
- Center for Pharmacometrics and Systems Pharmacology, College of Pharmacy, University of Florida, Orlando, FL, USA
| | - Stephan Schmidt
- Center for Pharmacometrics and Systems Pharmacology, College of Pharmacy, University of Florida, Orlando, FL, USA
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Upreti VV, Venkatakrishnan K. Model‐Based Meta‐Analysis: Optimizing Research, Development, and Utilization of Therapeutics Using the Totality of Evidence. Clin Pharmacol Ther 2019; 106:981-992. [DOI: 10.1002/cpt.1462] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2018] [Accepted: 03/21/2019] [Indexed: 12/29/2022]
Affiliation(s)
- Vijay V. Upreti
- Clinical Pharmacology Modeling and SimulationAmgen Inc. South San Francisco California USA
| | - Karthik Venkatakrishnan
- Quantitative Clinical PharmacologyTakeda Pharmaceuticals International Co. Cambridge Massachusetts USA
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Hill‐McManus D, Marshall S, Soto E, Hughes DA. Integration of Pharmacometrics and Pharmacoeconomics to Quantify the Value of Improved Forgiveness to Nonadherence: A Case Study of Novel Xanthine Oxidase Inhibitors for Gout. Clin Pharmacol Ther 2019; 106:652-660. [DOI: 10.1002/cpt.1454] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2019] [Accepted: 03/04/2019] [Indexed: 12/13/2022]
Affiliation(s)
- Daniel Hill‐McManus
- Centre for Health Economics and Medicines Evaluation Bangor University Bangor UK
| | | | | | - Dyfrig A. Hughes
- Centre for Health Economics and Medicines Evaluation Bangor University Bangor UK
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Germovsek E, Ambery C, Yang S, Beerahee M, Karlsson MO, Plan EL. A Novel Method for Analysing Frequent Observations from Questionnaires in Order to Model Patient-Reported Outcomes: Application to EXACT® Daily Diary Data from COPD Patients. AAPS JOURNAL 2019; 21:60. [PMID: 31028495 PMCID: PMC6486532 DOI: 10.1208/s12248-019-0319-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/20/2018] [Accepted: 03/08/2019] [Indexed: 12/22/2022]
Abstract
Chronic obstructive pulmonary disease (COPD) is a progressive lung disease with approximately 174 million cases worldwide. Electronic questionnaires are increasingly used for collecting patient-reported-outcome (PRO) data about disease symptoms. Our aim was to leverage PRO data, collected to record COPD disease symptoms, in a general modelling framework to enable interpretation of PRO observations in relation to disease progression and potential to predict exacerbations. The data were collected daily over a year, in a prospective, observational study. The e-questionnaire, the EXAcerbations of COPD Tool (EXACT®) included 14 items (i.e. questions) with 4 or 5 ordered categorical response options. An item response theory (IRT) model was used to relate the responses from each item to the underlying latent variable (which we refer to as disease severity), and on each item level, Markov models (MM) with 4 or 5 categories were applied to describe the dependence between consecutive observations. Minimal continuous time MMs were used and parameterised using ordinary differential equations. One hundred twenty-seven COPD patients were included (median age 67 years, 54% male, 39% current smokers), providing approximately 40,000 observations per EXACT® item. The final model suggested that, with time, patients more often reported the same scores as the previous day, i.e. the scores were more stable. The modelled COPD disease severity change over time varied markedly between subjects, but was small in the typical individual. This is the first IRT model with Markovian properties; our analysis proved them necessary for predicting symptom-defined exacerbations.
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Affiliation(s)
- Eva Germovsek
- Department of Pharmaceutical Biosciences, Uppsala University, Box 591, SE-751 24, Uppsala, Sweden
| | - Claire Ambery
- Clinical Pharmacology Modelling and Simulation, GlaxoSmithKline, London, UK
| | - Shuying Yang
- Clinical Pharmacology Modelling and Simulation, GlaxoSmithKline, London, UK
| | - Misba Beerahee
- Clinical Pharmacology Modelling and Simulation, GlaxoSmithKline, London, UK
| | - Mats O Karlsson
- Department of Pharmaceutical Biosciences, Uppsala University, Box 591, SE-751 24, Uppsala, Sweden
| | - Elodie L Plan
- Department of Pharmaceutical Biosciences, Uppsala University, Box 591, SE-751 24, Uppsala, Sweden.
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Hill-McManus D, Marshall S, Soto E, Lane S, Hughes D. Impact of Non-Adherence and Flare Resolution on the Cost-Effectiveness of Treatments for Gout: Application of a Linked Pharmacometric/Pharmacoeconomic Model. VALUE IN HEALTH : THE JOURNAL OF THE INTERNATIONAL SOCIETY FOR PHARMACOECONOMICS AND OUTCOMES RESEARCH 2018; 21:1373-1381. [PMID: 30502780 DOI: 10.1016/j.jval.2018.06.002] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/15/2018] [Revised: 05/02/2018] [Accepted: 06/04/2018] [Indexed: 05/24/2023]
Abstract
BACKGROUND Dual urate-lowering therapy (ULT) with lesinurad in combination with either allopurinol or febuxostat is an option for patients with gout unsuccessfully treated on either monotherapy. Treatment failure is often a result of poor medication adherence. Imperfect adherence in clinical trials may lead to biased estimates of treatment effect and confound the results of cost-effectiveness analyses. OBJECTIVES To estimate the impact of varying medication adherence on the cost effectiveness of lesinurad dual therapy and estimate the value-based price of lesinurad at which the incremental cost-effectiveness ratio is equal to £20,000 per quality-adjusted life-year (QALY). METHODS Treatment effect was simulated using published pharmacokinetic-pharmacodynamic models and scenarios representing adherence in clinical trials, routine practice, and perfect use. The subsequent cost and health impacts, over the lifetime of a patient cohort, were estimated using a bespoke pharmacoeconomic model. RESULTS The base-case incremental cost-effectiveness ratios comparing lesinurad dual ULT with monotherapy ranged from £39,184 to £78,350/QALY gained using allopurinol and £31,901 to £124,212/QALY gained using febuxostat, depending on the assumed medication adherence. Results assuming perfect medication adherence imply a per-quarter value-based price of lesinurad of £45.14 when used in dual ULT compared with allopurinol alone and £57.75 compared with febuxostat alone, falling to £25.41 and £3.49, respectively, in simulations of worsening medication adherence. CONCLUSIONS The estimated value-based prices of lesinurad only exceeded that which has been proposed in the United Kingdom when assuming both perfect drug adherence and the eradication of gout flares in sustained treatment responders.
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Affiliation(s)
- Daniel Hill-McManus
- Centre for Health Economics and Medicines Evaluation, Bangor University, Bangor, UK
| | | | - Elena Soto
- Pharmacometrics, Pfizer Ltd., Sandwich, UK
| | - Steven Lane
- Department of Biostatistics, University of Liverpool, Liverpool, UK
| | - Dyfrig Hughes
- Centre for Health Economics and Medicines Evaluation, Bangor University, Bangor, UK.
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Swift B, Jain L, White C, Chandrasekaran V, Bhandari A, Hughes DA, Jadhav PR. Innovation at the Intersection of Clinical Trials and Real-World Data Science to Advance Patient Care. Clin Transl Sci 2018; 11:450-460. [PMID: 29768712 PMCID: PMC6132367 DOI: 10.1111/cts.12559] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2017] [Accepted: 03/29/2018] [Indexed: 02/01/2023] Open
Abstract
While efficacy and safety data collected from randomized clinical trials are the evidentiary standard for determining market authorization, this alone may no longer be sufficient to address the needs of key stakeholders (regulators, providers, and payers) and guarantee long-term success of pharmaceutical products. There is a heightened interest from stakeholders on understanding the use of real-world evidence (RWE) to substantiate benefit-risk assessment and support the value of a new drug. This review provides an overview of real-world data (RWD) and related advances in the regulatory framework, and discusses their impact on clinical research and development. A framework for linking drug development decisions with the value proposition of the drug, utilizing pharmacokinetic-pharmacodynamic-pharmacoeconomic models, is introduced. The summary presented here is based on the presentations and discussion at the symposium entitled Innovation at the Intersection of Clinical Trials and Real-World Data to Advance Patient Care at the American Society for Clinical Pharmacology and Therapeutics (ASCPT) 2017 Annual Meeting.
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Affiliation(s)
| | - Lokesh Jain
- Quantitative Pharmacology and PharmacometricsMerck & Co., Inc.RahwayNew JerseyUSA
| | - Craig White
- Harvard PhD program in Health PolicyCambridgeMassachusettsUSA
| | - Vasu Chandrasekaran
- Center for Observational and Real World EvidenceMerck & Co., Inc.BostonMassachusettsUSA
| | - Aman Bhandari
- Center for Observational and Real World EvidenceMerck & Co., Inc.BostonMassachusettsUSA
| | - Dyfrig A. Hughes
- Centre for Health Economics and Medicines EvaluationBangor UniversityBangorGwyneddUK
| | - Pravin R. Jadhav
- Corporate ProjectsResearch & Development (R&D) InnovationOtsuka Pharmaceutical Development and Commercialization (OPDC)PrincetonNew JerseyUSA
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