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Llanos-Paez C, Ambery C, Yang S, Beerahee M, Plan EL, Karlsson MO. Joint longitudinal model-based meta-analysis of FEV 1 and exacerbation rate in randomized COPD trials. J Pharmacokinet Pharmacodyn 2023:10.1007/s10928-023-09853-z. [PMID: 36947282 PMCID: PMC10374752 DOI: 10.1007/s10928-023-09853-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Accepted: 02/20/2023] [Indexed: 03/23/2023]
Abstract
Model-based meta-analysis (MBMA) is an approach that integrates relevant summary level data from heterogeneously designed randomized controlled trials (RCTs). This study not only evaluated the predictability of a published MBMA for forced expiratory volume in one second (FEV1) and its link to annual exacerbation rate in patients with chronic obstructive pulmonary disease (COPD) but also included data from new RCTs. A comparative effectiveness analysis across all drugs was also performed. Aggregated level data were collected from RCTs published between July 2013 and November 2020 (n = 132 references comprising 156 studies) and combined with data used in the legacy MBMA (published RCTs up to July 2013 - n = 142). The augmented data (n = 298) were used to evaluate the predictive performance of the published MBMA using goodness-of-fit plots for assessment. Furthermore, the model was extended including drugs that were not available before July 2013, estimating a new set of parameters. The legacy MBMA model predicted the post-2013 FEV1 data well, and new estimated parameters were similar to those of drugs in the same class. However, the exacerbation model overpredicted the post-2013 mean annual exacerbation rate data. Inclusion of year when the study started on the pre-treatment placebo rate improved the model predictive performance perhaps explaining potential improvements in the disease management over time. The addition of new data to the legacy COPD MBMA enabled a more robust model with increased predictability performance for both endpoints FEV1 and mean annual exacerbation rate.
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Affiliation(s)
| | - Claire Ambery
- Clinical Pharmacology Modelling and Simulation, GSK, London, UK
| | - Shuying Yang
- Clinical Pharmacology Modelling and Simulation, GSK, London, UK
| | - Misba Beerahee
- Clinical Pharmacology Modelling and Simulation, GSK, London, UK
| | - Elodie L Plan
- Department of Pharmacy, Uppsala University, Uppsala, Sweden
| | - Mats O Karlsson
- Department of Pharmacy, Uppsala University, Uppsala, Sweden.
- Department of Pharmacy, Uppsala University, BMC, Box 580, 751 23, Uppsala, Sweden.
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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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Lyu S, Ding R, Yang S, Chen W, Rao Y, OuYang H, Liu P, Feng Y. Establishment of a clinical diagnostic model for gouty arthritis based on the serum biochemical profile: A case-control study. Medicine (Baltimore) 2021; 100:e25542. [PMID: 33879701 PMCID: PMC8078334 DOI: 10.1097/md.0000000000025542] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Accepted: 03/24/2021] [Indexed: 01/04/2023] Open
Abstract
The disease progression of gouty arthritis (GA) is relatively clear, with the 4 stages of hyperuricemia (HUA), acute gouty arthritis (AGA), gouty arthritis during the intermittent period (GIP), and chronic gouty arthritis (CGA). This paper attempts to construct a clinical diagnostic model based on blood routine test data, in order to avoid the need for bursa fluid examination and other tedious steps, and at the same time to predict the development direction of GA.Serum samples from 579 subjects were collected within 3 years in this study and were divided into a training set (n = 379) and validation set (n = 200). After a series of multivariate statistical analyses, the serum biochemical profile was obtained, which could effectively distinguish different stages of GA. A clinical diagnosis model based on the biochemical index of the training set was established to maximize the probability of the stage as a diagnosis, and the serum biochemical data from 200 patients were used for validation.The total area under the curve (AUC) of the clinical diagnostic model was 0.9534, and the AUCs of the 5 models were 0.9814 (Control), 0.9288 (HUA), 0.9752 (AGA), 0.9056 (GIP), and 0.9759 (CGA). The kappa coefficient of the clinical diagnostic model was 0.80.This clinical diagnostic model could be applied clinically and in research to improve the accuracy of the identification of the different stages of GA. Meanwhile, the serum biochemical profile revealed by this study could be used to assist the clinical diagnosis and prediction of GA.
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Affiliation(s)
- Shang Lyu
- National Pharmaceutical Engineering Center for Solid Preparation in Chinese Herbal Medicine, Jiangxi University of Traditional Chinese Medicine
| | - Ruowen Ding
- National Pharmaceutical Engineering Center for Solid Preparation in Chinese Herbal Medicine, Jiangxi University of Traditional Chinese Medicine
| | - Shilin Yang
- National Pharmaceutical Engineering Center for Solid Preparation in Chinese Herbal Medicine, Jiangxi University of Traditional Chinese Medicine
| | - Wanyuan Chen
- Department of Pathology, Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, Hangzhou
| | - Yi Rao
- National Pharmaceutical Engineering Center for Solid Preparation in Chinese Herbal Medicine, Jiangxi University of Traditional Chinese Medicine
| | - Hui OuYang
- National Pharmaceutical Engineering Center for Solid Preparation in Chinese Herbal Medicine, Jiangxi University of Traditional Chinese Medicine
- State Key Laboratory of Innovative Drug and Efficient Energy-Saving Pharmaceutical Equipment, Nanchang, China
| | - Peng Liu
- National Pharmaceutical Engineering Center for Solid Preparation in Chinese Herbal Medicine, Jiangxi University of Traditional Chinese Medicine
| | - Yulin Feng
- National Pharmaceutical Engineering Center for Solid Preparation in Chinese Herbal Medicine, Jiangxi University of Traditional Chinese Medicine
- State Key Laboratory of Innovative Drug and Efficient Energy-Saving Pharmaceutical Equipment, Nanchang, China
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Egnell AC, Johansson S, Chen C, Berges A. Clinical Pharmacology Modeling and Simulation in Drug Development. SYSTEMS MEDICINE 2021. [DOI: 10.1016/b978-0-12-801238-3.11546-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
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Leil TA, Lu Y, Bouillon-Pichault M, Wong R, Nowak M. Model-Based Meta-Analysis Compares DAS28 Rheumatoid Arthritis Treatment Effects and Suggests an Expedited Trial Design for Early Clinical Development. Clin Pharmacol Ther 2020; 109:517-527. [PMID: 32860421 PMCID: PMC7894503 DOI: 10.1002/cpt.2023] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2020] [Accepted: 08/11/2020] [Indexed: 12/25/2022]
Abstract
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.
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Affiliation(s)
- Tarek A Leil
- Quantitative Clinical Pharmacology, Bristol Myers Squibb, Princeton, New Jersey, USA
| | - Yasong Lu
- Quantitative Clinical Pharmacology, Bristol Myers Squibb, Princeton, New Jersey, USA
| | | | - Robert Wong
- Innovative Medicines Development, Bristol Myers Squibb, Princeton, New Jersey, USA
| | - Miroslawa Nowak
- Innovative Medicines Development, Bristol Myers Squibb, Princeton, New Jersey, USA
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Chan P, Yu J, Chinn L, Prohn M, Huisman J, Matzuka B, Hanley W, Tuckwell K, Quartino A. Population Pharmacokinetics, Efficacy Exposure-response Analysis, and Model-based Meta-analysis of Fenebrutinib in Subjects with Rheumatoid Arthritis [corrected]. Pharm Res 2020; 37:25. [PMID: 31907670 PMCID: PMC6944649 DOI: 10.1007/s11095-019-2752-y] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2019] [Accepted: 12/18/2019] [Indexed: 01/08/2023]
Abstract
Purpose Fenebrutinib (GDC-0853), a Bruton’s tyrosine kinase (BTK) inhibitor was investigated in a Phase 2 clinical trial in patients with rheumatoid arthritis (RA). Our aim was to apply a model-informed drug development (MIDD) approach to examine the totality of available clinical efficacy data. Methods Population pharmacokinetics (popPK) modeling, exposure-response (E-R) analysis, and model-based meta-analysis (MBMA) of fenebrutinib were performed based on the Phase 2 data. Results PopPK of fenebrutinib after oral administration was described using a 3-compartment model with linear elimination and a flexible absorption transit compartment model. Healthy subjects had a 52% higher apparent clearance than patients. E-R analyses based on longitudinal ACR20, ACR50, and ACR70 and DAS28 (CRP) data modeled fenebrutinib effect with an Emax function, and an efficacy plateau was achieved within the exposure range obtained in the Phase 2 clinical trial. Based on literature data, a summary-level clinical efficacy database was constructed, and MBMA determined ACR20, ACR50, and ACR70 responder rates in the placebo and adalimumab arms of the Phase 2 clinical trial were found to be consistent with historical data for these treatments. Conclusions Our multi-pronged approach applied MIDD to maximize knowledge extraction of efficacy data and enabled robust interpretation from a Phase 2 clinical trial. Electronic supplementary material The online version of this article (10.1007/s11095-019-2752-y) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Phyllis Chan
- Clinical Pharmacology, Genentech Inc., South San Francisco, California, USA.
| | - Jiajie Yu
- Clinical Pharmacology, Genentech Inc., South San Francisco, California, USA
| | - Leslie Chinn
- Clinical Pharmacology, Genentech Inc., South San Francisco, California, USA
| | | | | | | | - William Hanley
- Former Genentech employee, currently of Seattle Genetics, South San Francisco, California, USA
| | - Katie Tuckwell
- Clinical Sciences, Early Clinical Development, Genentech, South San Francisco, California, USA
| | - Angelica Quartino
- Clinical Pharmacology, Genentech Inc., South San Francisco, California, 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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Archer R, Hock E, Hamilton J, Stevens J, Essat M, Poku E, Clowes M, Pandor A, Stevenson M. Assessing prognosis and prediction of treatment response in early rheumatoid arthritis: systematic reviews. Health Technol Assess 2019; 22:1-294. [PMID: 30501821 DOI: 10.3310/hta22660] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND Rheumatoid arthritis (RA) is a chronic, debilitating disease associated with reduced quality of life and substantial costs. It is unclear which tests and assessment tools allow the best assessment of prognosis in people with early RA and whether or not variables predict the response of patients to different drug treatments. OBJECTIVE To systematically review evidence on the use of selected tests and assessment tools in patients with early RA (1) in the evaluation of a prognosis (review 1) and (2) as predictive markers of treatment response (review 2). DATA SOURCES Electronic databases (e.g. MEDLINE, EMBASE, The Cochrane Library, Web of Science Conference Proceedings; searched to September 2016), registers, key websites, hand-searching of reference lists of included studies and key systematic reviews and contact with experts. STUDY SELECTION Review 1 - primary studies on the development, external validation and impact of clinical prediction models for selected outcomes in adult early RA patients. Review 2 - primary studies on the interaction between selected baseline covariates and treatment (conventional and biological disease-modifying antirheumatic drugs) on salient outcomes in adult early RA patients. RESULTS Review 1 - 22 model development studies and one combined model development/external validation study reporting 39 clinical prediction models were included. Five external validation studies evaluating eight clinical prediction models for radiographic joint damage were also included. c-statistics from internal validation ranged from 0.63 to 0.87 for radiographic progression (different definitions, six studies) and 0.78 to 0.82 for the Health Assessment Questionnaire (HAQ). Predictive performance in external validations varied considerably. Three models [(1) Active controlled Study of Patients receiving Infliximab for the treatment of Rheumatoid arthritis of Early onset (ASPIRE) C-reactive protein (ASPIRE CRP), (2) ASPIRE erythrocyte sedimentation rate (ASPIRE ESR) and (3) Behandelings Strategie (BeSt)] were externally validated using the same outcome definition in more than one population. Results of the random-effects meta-analysis suggested substantial uncertainty in the expected predictive performance of models in a new sample of patients. Review 2 - 12 studies were identified. Covariates examined included anti-citrullinated protein/peptide anti-body (ACPA) status, smoking status, erosions, rheumatoid factor status, C-reactive protein level, erythrocyte sedimentation rate, swollen joint count (SJC), body mass index and vascularity of synovium on power Doppler ultrasound (PDUS). Outcomes examined included erosions/radiographic progression, disease activity, physical function and Disease Activity Score-28 remission. There was statistical evidence to suggest that ACPA status, SJC and PDUS status at baseline may be treatment effect modifiers, but not necessarily that they are prognostic of response for all treatments. Most of the results were subject to considerable uncertainty and were not statistically significant. LIMITATIONS The meta-analysis in review 1 was limited by the availability of only a small number of external validation studies. Studies rarely investigated the interaction between predictors and treatment. SUGGESTED RESEARCH PRIORITIES Collaborative research (including the use of individual participant data) is needed to further develop and externally validate the clinical prediction models. The clinical prediction models should be validated with respect to individual treatments. Future assessments of treatment by covariate interactions should follow good statistical practice. CONCLUSIONS Review 1 - uncertainty remains over the optimal prediction model(s) for use in clinical practice. Review 2 - in general, there was insufficient evidence that the effect of treatment depended on baseline characteristics. STUDY REGISTRATION This study is registered as PROSPERO CRD42016042402. FUNDING The National Institute for Health Research Health Technology Assessment programme.
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Affiliation(s)
- Rachel Archer
- School of Health and Related Research (ScHARR), University of Sheffield, Sheffield, UK
| | - Emma Hock
- School of Health and Related Research (ScHARR), University of Sheffield, Sheffield, UK
| | - Jean Hamilton
- School of Health and Related Research (ScHARR), University of Sheffield, Sheffield, UK
| | - John Stevens
- School of Health and Related Research (ScHARR), University of Sheffield, Sheffield, UK
| | - Munira Essat
- School of Health and Related Research (ScHARR), University of Sheffield, Sheffield, UK
| | - Edith Poku
- School of Health and Related Research (ScHARR), University of Sheffield, Sheffield, UK
| | - Mark Clowes
- School of Health and Related Research (ScHARR), University of Sheffield, Sheffield, UK
| | - Abdullah Pandor
- School of Health and Related Research (ScHARR), University of Sheffield, Sheffield, UK
| | - Matt Stevenson
- School of Health and Related Research (ScHARR), University of Sheffield, Sheffield, UK
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Boucher M, Bennetts M. Many Flavors of Model-Based Meta-Analysis: Part II - Modeling Summary Level Longitudinal Responses. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2018; 7:288-297. [PMID: 29569841 PMCID: PMC5980518 DOI: 10.1002/psp4.12299] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/06/2017] [Revised: 03/16/2018] [Accepted: 03/19/2018] [Indexed: 12/22/2022]
Abstract
Meta-analyses typically assess comparative treatment response for an end point at specific timepoints across studies. However, during drug development, it is often of interest to understand the response time-course of competitor compounds for a variety of purposes. Examples of such application include informing study design and characterizing the onset, maintenance, and offset of action. This tutorial acts as a "points for consideration" document, reviews relevant literature, and fits a longitudinal model to an example dataset.
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Affiliation(s)
- Martin Boucher
- Department of Pharmacometrics, Pfizer Ltd, Sandwich, Kent, UK
| | - Meg Bennetts
- Department of Pharmacometrics, Pfizer Ltd, Sandwich, Kent, UK
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Teng Z, Gupta N, Hua Z, Liu G, Samnotra V, Venkatakrishnan K, Labotka R. Model-Based Meta-Analysis for Multiple Myeloma: A Quantitative Drug-Independent Framework for Efficient Decisions in Oncology Drug Development. Clin Transl Sci 2017; 11:218-225. [PMID: 29168990 PMCID: PMC5867027 DOI: 10.1111/cts.12524] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2017] [Accepted: 10/23/2017] [Indexed: 12/24/2022] Open
Abstract
The failure rate for phase III trials in oncology is high; quantitative predictive approaches are needed. We developed a model‐based meta‐analysis (MBMA) framework to predict progression‐free survival (PFS) from overall response rates (ORR) in relapsed/refractory multiple myeloma (RRMM), using data from seven phase III trials. A Bayesian analysis was used to predict the probability of technical success (PTS) for achieving desired phase III PFS targets based on phase II ORR data. The model demonstrated a strongly correlated (R2 = 0.84) linear relationship between ORR and median PFS. As a representative application of the framework, MBMA predicted that an ORR of ∼66% would be needed in a phase II study of 50 patients to achieve a target median PFS of 13.5 months in a phase III study. This model can be used to help estimate PTS to achieve gold‐standard targets in a target product profile, thereby enabling objectively informed decision‐making.
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Affiliation(s)
- Zhaoyang Teng
- Millennium Pharmaceuticals, Inc., a wholly owned subsidiary of Takeda Pharmaceutical Company Limited, Cambridge, Massachusetts, USA
| | - Neeraj Gupta
- Millennium Pharmaceuticals, Inc., a wholly owned subsidiary of Takeda Pharmaceutical Company Limited, Cambridge, Massachusetts, USA
| | - Zhaowei Hua
- Millennium Pharmaceuticals, Inc., a wholly owned subsidiary of Takeda Pharmaceutical Company Limited, Cambridge, Massachusetts, USA
| | - Guohui Liu
- Millennium Pharmaceuticals, Inc., a wholly owned subsidiary of Takeda Pharmaceutical Company Limited, Cambridge, Massachusetts, USA
| | - Vivek Samnotra
- Millennium Pharmaceuticals, Inc., a wholly owned subsidiary of Takeda Pharmaceutical Company Limited, Cambridge, Massachusetts, USA
| | - Karthik Venkatakrishnan
- Millennium Pharmaceuticals, Inc., a wholly owned subsidiary of Takeda Pharmaceutical Company Limited, Cambridge, Massachusetts, USA
| | - Richard Labotka
- Millennium Pharmaceuticals, Inc., a wholly owned subsidiary of Takeda Pharmaceutical Company Limited, Cambridge, Massachusetts, USA
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Chan P, Bax L, Chen C, Zhang N, Huang S, Soares H, Rosen G, AbuTarif M. Model-based Meta-Analysis on the Efficacy of Pharmacological Treatments for Idiopathic Pulmonary Fibrosis. CPT Pharmacometrics Syst Pharmacol 2017; 6:695-704. [PMID: 28699195 PMCID: PMC5658284 DOI: 10.1002/psp4.12227] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2017] [Revised: 04/18/2017] [Accepted: 06/29/2017] [Indexed: 11/08/2022] Open
Abstract
Recently, the US Food and Drug Administration (FDA) approved the first two drugs (pirfenidone and nintedanib) indicated for the treatment of idiopathic pulmonary fibrosis (IPF). The purpose of this analysis was to leverage publicly available data to quantify comparative efficacy of compounds that are approved or in development. An analysis-ready database was developed, and the analysis dataset is composed of summary-level data from 43 arms in 20 trials, with treatment durations ranging from 8-104 weeks. A hierarchical multivariable regression model with nonparametric placebo estimation was used to fit the longitudinal profile of change from baseline of percent predicted forced vital capacity (%predicted FVC) data. Pirfenidone and nintedanib were the only drugs identified to have significant estimated positive treatment effects. Model simulations were performed to further evaluate the covariate and time course of treatment effects on longitudinal change from baseline %predicted FVC to inform future trial designs and support decision making.
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Affiliation(s)
| | - Leon Bax
- Quantitative SolutionsMenlo ParkCaliforniaUSA
| | | | - Nancy Zhang
- Quantitative SolutionsMenlo ParkCaliforniaUSA
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Liu D, Zhang Y, Jiang J, Choi J, Li X, Zhu D, Xiao D, Ding Y, Fan H, Chen L, Hu P. Translational Modeling and Simulation in Supporting Early-Phase Clinical Development of New Drug: A Learn–Research–Confirm Process. Clin Pharmacokinet 2016; 56:925-939. [PMID: 28000102 DOI: 10.1007/s40262-016-0484-2] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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13
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Mawdsley D, Bennetts M, Dias S, Boucher M, Welton NJ. Model-Based Network Meta-Analysis: A Framework for Evidence Synthesis of Clinical Trial Data. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2016; 5:393-401. [PMID: 27479782 PMCID: PMC4999602 DOI: 10.1002/psp4.12091] [Citation(s) in RCA: 47] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/01/2016] [Revised: 05/30/2016] [Accepted: 06/06/2016] [Indexed: 12/13/2022]
Abstract
Model-based meta-analysis (MBMA) is increasingly used in drug development to inform decision-making and future trial designs, through the use of complex dose and/or time course models. Network meta-analysis (NMA) is increasingly being used by reimbursement agencies to estimate a set of coherent relative treatment effects for multiple treatments that respect the randomization within the trials. However, NMAs typically either consider different doses completely independently or lump them together, with few examples of models for dose. We propose a framework, model-based network meta-analysis (MBNMA), that combines both approaches, that respects randomization, and allows estimation and prediction for multiple agents and a range of doses, using plausible physiological dose-response models. We illustrate our approach with an example comparing the efficacies of triptans for migraine relief. This uses a binary endpoint, although we note that the model can be easily modified for other outcome types.
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Affiliation(s)
- D Mawdsley
- School of Social and Community Medicine, University of Bristol, Bristol, United Kingdom
| | - M Bennetts
- Pharmacometrics Group, Pfizer Ltd, Sandwich, Kent, United Kingdom
| | - S Dias
- School of Social and Community Medicine, University of Bristol, Bristol, United Kingdom
| | - M Boucher
- Pharmacometrics Group, Pfizer Ltd, Sandwich, Kent, United Kingdom
| | - N J Welton
- School of Social and Community Medicine, University of Bristol, Bristol, United Kingdom
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