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Wojciechowski J, Mukherjee A, Banfield C, Nicholas T. Model-Informed Assessment of Probability of Phase 3 Success for Ritlecitinib in Patients with Moderate-to-Severe Ulcerative Colitis. Clin Pharmacol Ther 2024; 116:724-735. [PMID: 38627914 DOI: 10.1002/cpt.3251] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Accepted: 03/02/2024] [Indexed: 08/22/2024]
Abstract
Ritlecitinib, an oral Janus kinase 3/tyrosine kinase expressed in hepatocellular carcinoma family inhibitor, was evaluated in patients with ulcerative colitis (UC) in a phase 2b trial. Model-informed drug development strategies were applied to bridge observations from phase 2b to predictions for a proposed phase 3 study design to assess the probability of achieving the target efficacy outcome. A longitudinal exposure-response model of the time course of the 4 Mayo subscores (rectal bleeding, stool frequency, physician's global assessment, and endoscopic subscore) in patients with UC receiving placebo or ritlecitinib was developed using population modeling approaches and an item response theory framework. The quantitative relationships between the 4 Mayo subscores accommodated the prediction of composite endpoints such as total Mayo score and partial Mayo score (key endpoints from phase 2b), and modified clinical remission and endoscopic remission (proposed phase 3 endpoints). Clinical trial simulations using the final model assessed the probability of candidate ritlecitinib dosing regimens (including those tested in phase 2b and alternative) and phase 3 study designs for achieving target efficacy outcomes benchmarked against an approved treatment for moderate-to-severe UC. The probabilities of achieving target modified clinical remission and endoscopic improvement outcomes at both weeks 8 and 52 for ritlecitinib 100 mg once daily was 74.8%. Model-based assessment mitigated some of the risk associated with proceeding to pivotal phase 3 trials with dosing regimens of which there was limited clinical experience.
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2
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Tsamandouras N, Qiu R, Hughes JH, Sweeney K, Prybylski JP, Banfield C, Nicholas T. Employing zero-inflated beta distribution in an exposure-response analysis of TYK2/JAK1 inhibitor brepocitinib in patients with plaque psoriasis. J Pharmacokinet Pharmacodyn 2024; 51:265-277. [PMID: 38431923 PMCID: PMC11136736 DOI: 10.1007/s10928-024-09901-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Accepted: 01/08/2024] [Indexed: 03/05/2024]
Abstract
Brepocitinib is an oral selective dual TYK2/JAK1 inhibitor and based on its cytokine inhibition profile is expected to provide therapeutic benefit in the treatment of plaque psoriasis. Efficacy data from a completed Phase 2a study in patients with moderate-to-severe plaque psoriasis were utilized to develop a population exposure-response model that can be employed to inform dose selection decisions for further clinical development. A modeling approach that employs the zero-inflated beta distribution was used to account for the bounded nature and distributional characteristics of the Psoriasis Area and Severity Index (PASI) score data. The developed exposure-response model provided an adequate description of the observed PASI scores across all the treatment arms tested and across both the induction and maintenance dosing periods of the study. In addition, the developed model exhibited a good predictive capacity with regard to the derived responder metrics (e.g., 75%/90%/100% improvement in PASI score [PASI75/90/100]). Clinical trial simulations indicated that the induction/maintenance dosing paradigm explored in this study does not offer any advantages from an efficacy perspective and that doses of 10, 30, and 60 mg once-daily may be suitable candidates for clinical evaluation in subsequent Phase 2b studies.
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Affiliation(s)
- Nikolaos Tsamandouras
- Clinical Pharmacology, Early Clinical Development, Worldwide Research, Development and Medical, Pfizer, Cambridge, MA, USA.
| | - Ruolun Qiu
- Clinical Pharmacology, Early Clinical Development, Worldwide Research, Development and Medical, Pfizer, Cambridge, MA, USA
| | - Jim H Hughes
- Clinical Pharmacology, Global Product Development, Pfizer, Groton, CT, USA
| | - Kevin Sweeney
- Clinical Pharmacology, Global Product Development, Pfizer, Groton, CT, USA
| | - John P Prybylski
- Clinical Pharmacology, Global Product Development, Pfizer, Groton, CT, USA
| | - Christopher Banfield
- Clinical Pharmacology, Early Clinical Development, Worldwide Research, Development and Medical, Pfizer, Cambridge, MA, USA
| | - Timothy Nicholas
- Clinical Pharmacology, Global Product Development, Pfizer, Groton, CT, USA
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Barrett JS, Nicholas T, Azer K, Corrigan BW. Role of Disease Progression Models in Drug Development. Pharm Res 2022; 39:1803-1815. [PMID: 35411507 PMCID: PMC9000925 DOI: 10.1007/s11095-022-03257-3] [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: 03/01/2022] [Accepted: 04/05/2022] [Indexed: 12/11/2022]
Abstract
The use of Disease progression models (DPMs) in Drug Development has been widely adopted across therapeutic areas as a method for integrating previously obtained disease knowledge to elucidate the impact of novel therapeutics or vaccines on disease course, thus quantifying the potential clinical benefit at different stages of drug development programs. This paper provides a brief overview of DPMs and the evolution in data types, analytic methods, and applications that have occurred in their use by Quantitive Clinical Pharmacologists. It also provides examples of how these models have informed decisions and clinical trial design across several therapeutic areas and at various stages of development. It briefly describes potential new applications of DPMs utilizing emerging data sources, and utilizing new analytic techniques, and discuss new challenges faced such as requiring description of multiple endpoints, rapid model development, application of machine learning-based analytics, and use of high dimensional and real-world data. Considerations for the continued evolution future of DPMs to serve as community-maintained expert systems are also provided.
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Affiliation(s)
- Jeffrey S. Barrett
- Rare Disease Cures Accelerator Data Analytics Platform, Critical Path Institute, Tuscon, AZ 85718 USA
| | - Tim Nicholas
- Global Product Development, Pfizer Inc, 445 Eastern Point Rd, Groton, CT 06340 USA
| | - Karim Azer
- Axcella Therapeutics, 840 Memorial Drive, Cambridge, MA 02139 USA
| | - Brian W. Corrigan
- Global Product Development, Pfizer Inc, 445 Eastern Point Rd, Groton, CT 06340 USA
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Lyauk YK, Jonker DM, Hooker AC, Lund TM, Karlsson MO. Bounded Integer Modeling of Symptom Scales Specific to Lower Urinary Tract Symptoms Secondary to Benign Prostatic Hyperplasia. AAPS JOURNAL 2021; 23:33. [PMID: 33630188 PMCID: PMC7906927 DOI: 10.1208/s12248-021-00568-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/21/2020] [Accepted: 02/04/2021] [Indexed: 11/30/2022]
Abstract
The International Prostate Symptom Score (IPSS), the quality of life (QoL) score, and the benign prostatic hyperplasia impact index (BII) are three different scales commonly used to assess the severity of lower urinary tract symptoms associated with benign prostatic hyperplasia (BPH-LUTS). Based on a phase II clinical trial including 403 patients with moderate to severe BPH-LUTS, the objectives of this study were to (i) develop traditional pharmacometric and bounded integer (BI) models for the IPSS, QoL score, and BII endpoints, respectively; (ii) compare the power and type I error in detecting drug effects of BI modeling with traditional methods through simulation; and (iii) obtain quantitative translation between scores on the three abovementioned scales using a BI modeling framework. All developed models described the data adequately. Pharmacometric modeling using a continuous variable (CV) approach was overall found to be the most robust in terms of type I error and power to detect a drug effect. In most cases, BI modeling showed similar performance to the CV approach, yet severely inflated type I error was generally observed when inter-individual variability (IIV) was incorporated in the BI variance function (g()). BI modeling without IIV in g() showed greater type I error control compared to the ordered categorical approach. Lastly, a multiple-scale BI model was developed and estimated the relationship between scores on the three BPH-LUTS scales with overall low uncertainty. The current study yields greater understanding of the operating characteristics of the novel BI modeling approach and highlights areas potentially requiring further improvement.
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Affiliation(s)
- Yassine Kamal Lyauk
- Translational Medicine, Ferring Pharmaceuticals A/S, Kay Fiskers Plads, 11, Copenhagen, Denmark. .,Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark. .,Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden.
| | - Daniël M Jonker
- Translational Medicine, Ferring Pharmaceuticals A/S, Kay Fiskers Plads, 11, Copenhagen, Denmark
| | - Andrew C Hooker
- Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden
| | - Trine Meldgaard Lund
- Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark
| | - Mats O Karlsson
- Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden
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Ueckert S, Karlsson MO. Improved numerical stability for the bounded integer model. J Pharmacokinet Pharmacodyn 2020; 48:241-251. [PMID: 33242184 PMCID: PMC8060183 DOI: 10.1007/s10928-020-09727-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2020] [Accepted: 11/05/2020] [Indexed: 12/01/2022]
Abstract
This article highlights some numerical challenges when implementing the bounded integer model for composite score modeling and suggests an improved implementation. The improvement is based on an approximation of the logarithm of the error function. After presenting the derivation of the improved implementation, the article compares the performance of the algorithm to a naive implementation of the log-likelihood using both simulations and a real data example. In the simulation setting, the improved algorithm yielded more precise and less biased parameter estimates when the within-subject variability was small and estimation was performed using the Laplace algorithm. The estimation results did not differ between implementations when the SAEM algorithm was used. For the real data example, bootstrap results differed between implementations with the improved implementation producing identical or better objective function values. Based on the findings in this article, the improved implementation is suggested as the new default log-likelihood implementation for the bounded integer model.
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Germovsek E, Hansson A, Karlsson MO, Westin Å, Soons PA, Vermeulen A, Kjellsson MC. A Time-to-Event Model Relating Integrated Craving to Risk of Smoking Relapse Across Different Nicotine Replacement Therapy Formulations. Clin Pharmacol Ther 2020; 109:416-423. [PMID: 32734606 DOI: 10.1002/cpt.2000] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2020] [Accepted: 07/10/2020] [Indexed: 11/07/2022]
Abstract
Smoking increases the risk of cancer and other diseases, causing an estimated 7 million deaths per year. Nicotine replacement therapy (NRT) reduces craving for smoking, therefore, increasing an individual's probability to remain abstinent. In this work, we for the first time quantitatively described the relationship between craving and smoking abstinence, using retrospectively collected data from 19 studies, including 3 NRT formulations (inhaler, mouth spray, and patch) and a combination of inhaler and patch. Smokers motivated to quit were included in the NRT or placebo arms. Integrated craving (i.e., craving over a period of time) was assessed with 4-category, 5-category, or 100-mm visual analogue scale. The bounded integer model was used to assess latent craving from all scales. A time-to-event model linked predicted integrated craving to the hazard of smoking relapse. Available data included 9,323 adult subjects, observed for 3 weeks up to 2 years. At the study end, 9% (11% for NRT and 5% for placebo), on average, remained abstinent according to the protocol definition. A Gompertz-Makeham hazard best described the data, with a hazard of smoking relapse decreasing over time. Latent integrated craving was positively related to the hazard of smoking relapse, through a sigmoidal maximum effect function. For the same craving, being on NRT was found to reduce the hazard of relapse by an additional 30% compared with placebo. This work confirmed that low craving is associated with a high probability of remaining smoking abstinent and that NRT, in addition to reducing craving, increases the probability of remaining smoking abstinent.
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Affiliation(s)
- Eva Germovsek
- Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden
| | | | - Mats O Karlsson
- Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden
| | | | - Paul A Soons
- Janssen R&D - A Division of Janssen Pharmaceutica NV, Beerse, Belgium
| | - An Vermeulen
- Janssen R&D - A Division of Janssen Pharmaceutica NV, Beerse, Belgium
| | - Maria C Kjellsson
- Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden
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Buri M, Curt A, Steeves J, Hothorn T. Baseline-adjusted proportional odds models for the quantification of treatment effects in trials with ordinal sum score outcomes. BMC Med Res Methodol 2020; 20:104. [PMID: 32375705 PMCID: PMC7204322 DOI: 10.1186/s12874-020-00984-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2019] [Accepted: 04/20/2020] [Indexed: 11/10/2022] Open
Abstract
Background Sum scores of ordinal outcomes are common in randomized clinical trials. The approaches routinely employed for assessing treatment effects, such as t-tests or Wilcoxon tests, are not particularly powerful in detecting changes in relevant parameters or lack the ability to incorporate baseline information. Hence, tailored statistical methods are needed for the analysis of ordinal outcomes in clinical research. Methods We propose baseline-adjusted proportional odds logistic regression models to overcome previous limitations in the analysis of ordinal outcomes in randomized clinical trials. For the validation of our method, we focus on common ordinal sum score outcomes of neurological clinical trials such as the upper extremity motor score, the spinal cord independence measure, and the self-care subscore of the latter. We compare the statistical power of our models to other conventional approaches in a large simulation study of two-arm randomized clinical trials based on data from the European Multicenter Study about Spinal Cord Injury (EMSCI, ClinicalTrials.gov Identifier: NCT01571531). We also use the new method as an alternative analysis of the historical Sygen®clinical trial. Results The simulation study of all postulated trial settings demonstrated that the statistical power of the novel method was greater than that of conventional methods. Baseline adjustments were more suited for the analysis of the upper extremity motor score compared to the spinal cord independence measure and its self-care subscore. Conclusions The proposed baseline-adjusted proportional odds models allow the global treatment effect to be directly interpreted. This clear interpretation, the superior statistical power compared to the conventional analysis approaches, and the availability of open-source software support the application of this novel method for the analysis of ordinal outcomes of future clinical trials.
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Affiliation(s)
- Muriel Buri
- Department of Biostatistics, Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Hirschengraben 84, Zurich, CH-8001, Switzerland
| | - Armin Curt
- University Hospital Balgrist, Spinal Cord Injury Center, Forchstrasse 340, Zurich, CH-8008, Switzerland
| | - John Steeves
- International Collaboration On Repair Discoveries (ICORD), University of British Columbia, Vancouver/Kelowna, Canada
| | - Torsten Hothorn
- Department of Biostatistics, Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Hirschengraben 84, Zurich, CH-8001, Switzerland.
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Germovsek E, Hansson A, Kjellsson MC, Perez Ruixo JJ, Westin Å, Soons PA, Vermeulen A, Karlsson MO. Relating Nicotine Plasma Concentration to Momentary Craving Across Four Nicotine Replacement Therapy Formulations. Clin Pharmacol Ther 2019; 107:238-245. [PMID: 31355455 DOI: 10.1002/cpt.1595] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2019] [Accepted: 07/15/2019] [Indexed: 01/05/2023]
Abstract
Tobacco use is a major health concern. To assist smoking cessation, nicotine replacement therapy (NRT) is used to reduce nicotine craving. We quantitatively described the relationship between nicotine pharmacokinetics (PKs) from NRTs and momentary craving, linking two different pharmacodynamic (PD) scales for measuring craving. The dataset comprised retrospective data from 17 clinical studies and included 1,077 adult smokers with 39,802 craving observations from four formulations: lozenge, gum, mouth spray, and patch. A PK/PD model was developed that linked individual predicted nicotine concentrations with the categorical and visual analogue PD scales through a joint bounded integer model. A maximum effect model, accounting for acute tolerance development, successfully related nicotine concentrations to momentary craving. Results showed that all formulations were similarly effective in reducing craving, albeit with a fourfold lower potency for the patch. Women were found to have a higher maximal effect of nicotine to reduce craving, compared with men.
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Affiliation(s)
- Eva Germovsek
- Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden
| | | | - Maria C Kjellsson
- Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden
| | | | | | - Paul A Soons
- Janssen R&D, a division of Janssen Pharmaceutica NV, Beerse, Belgium
| | - An Vermeulen
- Janssen R&D, a division of Janssen Pharmaceutica NV, Beerse, Belgium
| | - Mats O Karlsson
- Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden
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9
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Hu C. On the Comparison of Methods in Analyzing Bounded Outcome Score Data. AAPS JOURNAL 2019; 21:102. [PMID: 31451952 DOI: 10.1208/s12248-019-0370-6] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/01/2019] [Accepted: 08/01/2019] [Indexed: 11/30/2022]
Abstract
Clinical trial endpoints often take the form of bounded outcome scores (BOS) which report a discrete set of values on a finite range. Conceptually such endpoints are ordered categorical in nature, but in practice they are often analyzed as continuous variables, which may result in data range violations and difficulties to handle data skewness. Analysis methods dedicated for BOS data have been proposed; however, much confusion exists among pharmacometricians on how to compare the possible methods. This commentary reviews the main methods used in pharmacometrics applications and discusses their theoretical and practical comparisons. The expected performance of some conceptually appealing methods in different situations is discussed, and a guideline is provided on selecting analysis methods in practice.
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Affiliation(s)
- Chuanpu Hu
- Clinical Pharmacology and Pharmacometrics, Janssen Research & Development, LLC, 1400 McKean Road, PO Box 776, Spring House, Pennsylvania, 19477, USA.
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Wellhagen GJ, Kjellsson MC, Karlsson MO. A Bounded Integer Model for Rating and Composite Scale Data. AAPS J 2019; 21:74. [PMID: 31172350 PMCID: PMC6554249 DOI: 10.1208/s12248-019-0343-9] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2018] [Accepted: 05/24/2019] [Indexed: 01/27/2023] Open
Abstract
Rating and composite scales are commonly used to assess treatment efficacy. The two main strategies for modelling such endpoints are to treat them as a continuous or an ordered categorical variable (CV or OC). Both strategies have disadvantages, including making assumptions that violate the integer nature of the data (CV) and requiring many parameters for scales with many response categories (OC). We present a method, called the bounded integer (BI) model, which utilises the probit function with fixed cut-offs to estimate the probability of a certain score through a latent variable. This method was successfully implemented to describe six data sets from four different therapeutic areas: Parkinson's disease, Alzheimer's disease, schizophrenia, and neuropathic pain. Five scales were investigated, ranging from 11 to 181 categories. The fit (likelihood) was better for the BI model than for corresponding OC or CV models (ΔAIC range 11-1555) in all cases but one (∆AIC - 63), while the number of parameters was the same or lower. Markovian elements were successfully implemented within the method. The performance in external validation, assessed through cross-validation, was also in favour of the new model (ΔOFV range 22-1694) except in one case (∆OFV - 70). A residual for diagnostic purposes is discussed. This study shows that the BI model respects the integer nature of data and is parsimonious in terms of number of estimated parameters.
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Affiliation(s)
- Gustaf J Wellhagen
- Pharmacometrics Research Group, Department of Pharmaceutical Biosciences, Uppsala University, Box 591, 751 24, Uppsala, Sweden
| | - Maria C Kjellsson
- Pharmacometrics Research Group, Department of Pharmaceutical Biosciences, Uppsala University, Box 591, 751 24, Uppsala, Sweden
| | - Mats O Karlsson
- Pharmacometrics Research Group, Department of Pharmaceutical Biosciences, Uppsala University, Box 591, 751 24, Uppsala, Sweden.
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Hu C, Adedokun OJ, Zhang L, Sharma A, Zhou H. Modeling near-continuous clinical endpoint as categorical: application to longitudinal exposure-response modeling of Mayo scores for golimumab in patients with ulcerative colitis. J Pharmacokinet Pharmacodyn 2018; 45:803-816. [PMID: 30377888 DOI: 10.1007/s10928-018-9610-0] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2018] [Accepted: 10/20/2018] [Indexed: 02/08/2023]
Abstract
Accurate characterization of exposure-response relationship of clinical endpoints is important in drug development to identify optimal dose regimens. Endpoints with ≥ 10 ordered categories are typically analyzed as continuous. This manuscript aims to show circumstances where it is advantageous to analyze such data as ordered categorical. The results of continuous and categorical analyses are compared in a latent-variable based Indirect Response modeling framework for the longitudinal modeling of Mayo scores, ranging from 0 to 12, which is commonly used as a composite endpoint to measure the severity of ulcerative colitis (UC). Exposure response modeling of Mayo scores is complicated by the fact that studies typically include induction and maintenance phases with re-randomizations and other response-driven dose adjustments. The challenges are illustrated in this work by analyzing data collected from 3 phase II/III trials of golimumab in patients with moderate-to-severe UC. Visual predictive check was used for model evaluations. The ordered categorical approach is shown to be accurate and robust compared to the continuous approach. In addition, a disease progression model with an empirical bi-phasic rate of onset was found to be superior to the commonly used placebo model with one onset rate. An application of this modeling approach in guiding potential dose-adjustment was illustrated.
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Affiliation(s)
- Chuanpu Hu
- Global Clinical Pharmacology, Janssen Research & Development, LLC, PO Box 776, 1400 McKean Road, Spring House, PA, 19477, USA.
| | - Omoniyi J Adedokun
- Global Clinical Pharmacology, Janssen Research & Development, LLC, PO Box 776, 1400 McKean Road, Spring House, PA, 19477, USA
| | - Liping Zhang
- Global Clinical Pharmacology, Janssen Research & Development, LLC, PO Box 776, 1400 McKean Road, Spring House, PA, 19477, USA
| | - Amarnath Sharma
- Global Clinical Pharmacology, Janssen Research & Development, LLC, PO Box 776, 1400 McKean Road, Spring House, PA, 19477, USA
| | - Honghui Zhou
- Global Clinical Pharmacology, Janssen Research & Development, LLC, PO Box 776, 1400 McKean Road, Spring House, PA, 19477, USA
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Joint longitudinal model development: application to exposure–response modeling of ACR and DAS scores in rheumatoid arthritis patients treated with sirukumab. J Pharmacokinet Pharmacodyn 2018; 45:679-691. [DOI: 10.1007/s10928-018-9598-5] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2018] [Accepted: 06/25/2018] [Indexed: 12/26/2022]
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13
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A comprehensive evaluation of exposure–response relationships in clinical trials: application to support guselkumab dose selection for patients with psoriasis. J Pharmacokinet Pharmacodyn 2018; 45:523-535. [DOI: 10.1007/s10928-018-9581-1] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2018] [Accepted: 03/08/2018] [Indexed: 10/17/2022]
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Improvement in latent variable indirect response modeling of multiple categorical clinical endpoints: application to modeling of guselkumab treatment effects in psoriatic patients. J Pharmacokinet Pharmacodyn 2017. [PMID: 28634654 DOI: 10.1007/s10928-017-9531-3] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
Exposure-response modeling plays an important role in optimizing dose and dosing regimens during clinical drug development. The modeling of multiple endpoints is made possible in part by recent progress in latent variable indirect response (IDR) modeling for ordered categorical endpoints. This manuscript aims to investigate the level of improvement achievable by jointly modeling two such endpoints in the latent variable IDR modeling framework through the sharing of model parameters. This is illustrated with an application to the exposure-response of guselkumab, a human IgG1 monoclonal antibody in clinical development that blocks IL-23. A Phase 2b study was conducted in 238 patients with psoriasis for which disease severity was assessed using Psoriasis Area and Severity Index (PASI) and Physician's Global Assessment (PGA) scores. A latent variable Type I IDR model was developed to evaluate the therapeutic effect of guselkumab dosing on 75, 90 and 100% improvement of PASI scores from baseline and PGA scores, with placebo effect empirically modeled. The results showed that the joint model is able to describe the observed data better with fewer parameters compared with the common approach of separately modeling the endpoints.
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15
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Improvement in latent variable indirect response joint modeling of a continuous and a categorical clinical endpoint in rheumatoid arthritis. J Pharmacokinet Pharmacodyn 2015; 43:45-54. [PMID: 26553114 DOI: 10.1007/s10928-015-9453-x] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2015] [Accepted: 10/31/2015] [Indexed: 10/22/2022]
Abstract
Improving the quality of exposure-response modeling is important in clinical drug development. The general joint modeling of multiple endpoints is made possible in part by recent progress on the latent variable indirect response (IDR) modeling for ordered categorical endpoints. This manuscript aims to investigate, when modeling a continuous and a categorical clinical endpoint, the level of improvement achievable by joint modeling in the latent variable IDR modeling framework through the sharing of model parameters for the individual endpoints, guided by the appropriate representation of drug and placebo mechanism. This was illustrated with data from two phase III clinical trials of intravenously administered mAb X for the treatment of rheumatoid arthritis, with the 28-joint disease activity score (DAS28) and 20, 50, and 70% improvement in the American College of Rheumatology (ACR20, ACR50, and ACR70) disease severity criteria were used as efficacy endpoints. The joint modeling framework led to a parsimonious final model with reasonable performance, evaluated by visual predictive check. The results showed that, compared with the more common approach of separately modeling the endpoints, it is possible for the joint model to be more parsimonious and yet better describe the individual endpoints. In particular, the joint model may better describe one endpoint through subject-specific random effects that would not have been estimable from data of this endpoint alone.
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16
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Xu XS, Samtani M, Yuan M, Nandy P. Modeling of bounded outcome scores with data on the boundaries: application to disability assessment for dementia scores in Alzheimer's disease. AAPS JOURNAL 2014; 16:1271-81. [PMID: 25165039 DOI: 10.1208/s12248-014-9655-y] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/20/2014] [Accepted: 07/25/2014] [Indexed: 11/30/2022]
Abstract
Mixed-effects beta regression (BR), boundary-inflated beta regression (ZOI), and coarsening model (CO) were investigated for analyzing bounded outcome scores with data at the boundaries in the context of Alzheimer's disease. Monte Carlo simulations were conducted to simulate disability assessment for dementia (DAD) scores using these three models, and each set of simulated data were analyzed by the original simulation model. One thousand trials were simulated, and each trial contained 250 subjects. For each subject, DAD scores were simulated at baseline, 13, 26, 39, 52, 65, and 78 weeks. The simulation-reestimation exercise showed that all the three models could reasonably recover their true parameter values. The bias of the parameter estimates of the ZOI model was generally less than 1%, while the bias of the CO model was mainly within 5%. The bias of the BR model was slightly higher, i.e., less than or in the order of 20%. In the application to real-world DAD data from clinical studies, examination of prediction error and visual predictive check (VPC) plots suggested that both BR and ZOI models had similar predictive performance and described the longitudinal progression of DAD slightly better than the CO model. In conclusion, the investigated three modeling approaches may be sensible choices for bounded outcome scores with data on the edges. Prediction error and VPC plots can be used to identify the model with best predictive performance.
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Affiliation(s)
- Xu Steven Xu
- Model-Based Drug Development, Janssen Research and Development, 920 Route 202, Raritan, New Jersey, USA,
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Mohd Din SH, Molas M, Luime J, Lesaffre E. Longitudinal profiles of bounded outcome scores as predictors for disease activity in rheumatoid arthritis patients: a joint modeling approach. J Appl Stat 2014. [DOI: 10.1080/02664763.2014.882499] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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Latent variable indirect response joint modeling of a continuous and a categorical clinical endpoint. J Pharmacokinet Pharmacodyn 2014; 41:335-49. [PMID: 25038623 DOI: 10.1007/s10928-014-9366-0] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2014] [Accepted: 06/24/2014] [Indexed: 10/25/2022]
Abstract
Informative exposure-response modeling of clinical endpoints is important in drug development. There has been much recent progress in latent variable modeling of ordered categorical endpoints, including the application of indirect response (IDR) models and accounting for residual correlations between multiple categorical endpoints. This manuscript describes a framework of latent-variable-based IDR models that facilitate easy simultaneous modeling of a continuous and a categorical clinical endpoint. The model was applied to data from two phase III clinical trials of subcutaneously administered ustekinumab for the treatment of psoriatic arthritis, where Psoriasis Area and Severity Index scores and 20, 50, and 70 % improvement in the American College of Rheumatology response criteria were used as efficacy endpoints. Visual predictive check and external validation showed reasonable parameter estimation precision and model performance.
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Exposure-response modeling of clinical end points using latent variable indirect response models. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2014; 3:e117. [PMID: 24897307 PMCID: PMC4076802 DOI: 10.1038/psp.2014.15] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/19/2013] [Accepted: 04/07/2014] [Indexed: 12/23/2022]
Abstract
Exposure-response modeling facilitates effective dosing regimen selection in clinical drug development, where the end points are often disease scores and not physiological variables. Appropriate models need to be consistent with pharmacology and identifiable from the time courses of available data. This article describes a general framework of applying mechanism-based models to various types of clinical end points. Placebo and drug model parameterization, interpretation, and assessment are discussed with a focus on the indirect response models.
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Information contributed by meta-analysis in exposure-response modeling: application to phase 2 dose selection of guselkumab in patients with moderate-to-severe psoriasis. J Pharmacokinet Pharmacodyn 2014; 41:239-50. [PMID: 24852042 DOI: 10.1007/s10928-014-9360-6] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2014] [Accepted: 05/07/2014] [Indexed: 10/25/2022]
Abstract
Ustekinumab, a human immunoglobulin G1 kappa (IgG1κ) monoclonal antibody that binds with high affinity to human interleukin (IL)-12 and IL-23, has been approved to treat patients with psoriasis. Guselkumab is a related human IgG1 monoclonal antibody in clinical development which specifically blocks IL-23. The objective of this study was to study the exposure-response relationship of guselkumab to guide dose selection for a Phase 2 study in patients with moderate-to-severe psoriasis. Data were available from a Phase 1 study of 47 healthy subjects and 24 patients with psoriasis who received various doses of guselkumab. Disease severity was assessed using Psoriasis Area and Severity Index (PASI) scores in all studies. Individual pharmacokinetic parameters were derived from population pharmacokinetics modeling for the purpose of exposure-response modeling to guide dosing regimen selection. A population mechanism-based exposure-response model of guselkumab was developed to evaluate the association of guselkumab dosing with PASI scores using a Type I indirect response model, with placebo effect empirically modeled. The model was subsequently updated, first by incorporating data from psoriasis patients who received placebo (n = 765) and from patients actively treated with ustekinumab 45 or 90 mg (n = 1,230) in two ustekinumab Phase 3 trials. Inclusion of this additional ustekinumab data and the consequent contributions to specific model components substantially reduced uncertainties in all model components except for one parameter. Additional sensitivity analyses showed that the dose selection decision was robust to this remaining uncertainty. The described approach underscores the importance of utilizing all available sources of information in dose selection decisions, along with the importance of effective development team interaction.
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Tham LS, Tang CC, Choi SL, Satterwhite JH, Cameron GS, Banerjee S. Population exposure-response model to support dosing evaluation of ixekizumab in patients with chronic plaque psoriasis. J Clin Pharmacol 2014; 54:1117-24. [DOI: 10.1002/jcph.312] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2014] [Accepted: 04/15/2014] [Indexed: 11/08/2022]
Affiliation(s)
- Lai-San Tham
- Lilly-NUS Centre for Clinical Pharmacology; Singapore
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Salinger DH, Endres CJ, Martin DA, Gibbs MA. A semi-mechanistic model to characterize the pharmacokinetics and pharmacodynamics of brodalumab in healthy volunteers and subjects with psoriasis in a first-in-human single ascending dose study. Clin Pharmacol Drug Dev 2014; 3:276-83. [PMID: 27128833 DOI: 10.1002/cpdd.103] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2013] [Accepted: 12/31/2013] [Indexed: 12/14/2022]
Abstract
Pharmacokinetic-pharmacodynamic (PK-PD) modeling can provide a framework for quantitative "learning and confirming" from studies in all phases of drug development. Brodalumab is a human monoclonal antibody (IgG2 ) targeting the IL-17 receptor A that blocks signaling by cytokines thought to play a central role in the pathogenesis of psoriasis (IL-17A, IL-17F, and IL-17A/F). We used semi-mechanistic modeling of single dose, first-in-human data to characterize the exposure-response relationship between brodalumab and the Psoriasis Area and Severity Index (PASI) in a Phase 1 clinical trial. Fifty-seven healthy volunteers and 25 subjects with moderate to severe psoriasis received single intravenous or subcutaneous administration of placebo or brodalumab (7-700 mg). A two-compartment model with parallel linear and nonlinear (Michaelis-Menten) elimination pathways described brodalumab PK. The PK-PASI relationship was characterized by linking a signaling compartment with an indirect response model of psoriatic plaques, where signaling suppressed plaque formation. The concentration of half-maximal inhibition IC50 was 2.86 µg/mL (SE: 50%). The endogenous psoriatic plaque formation rate of 0.862 (SE: 40%) PASI units/day was comparable with literature precedent. Despite the small sample size and single administration data, this semi-mechanistic modeling approach provided a quantitative framework to inform design of dose-ranging Phase 2 studies of brodalumab in psoriasis.
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Yang H, Feng Y, Xu XS. Pharmacokinetic and pharmacodynamic modeling for acute and chronic pain drug assessment. Expert Opin Drug Metab Toxicol 2014; 10:229-48. [DOI: 10.1517/17425255.2014.864636] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
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Mixed-effects beta regression for modeling continuous bounded outcome scores using NONMEM when data are not on the boundaries. J Pharmacokinet Pharmacodyn 2013; 40:537-44. [DOI: 10.1007/s10928-013-9318-0] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2012] [Accepted: 04/20/2013] [Indexed: 10/26/2022]
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Sun W, Laughren TP, Zhu H, Hochhaus G, Wang Y. Development of a placebo effect model combined with a dropout model for bipolar disorder. J Pharmacokinet Pharmacodyn 2013; 40:359-68. [DOI: 10.1007/s10928-013-9305-5] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2013] [Accepted: 02/12/2013] [Indexed: 12/17/2022]
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Hu C, Xu Z, Mendelsohn AM, Zhou H. Latent variable indirect response modeling of categorical endpoints representing change from baseline. J Pharmacokinet Pharmacodyn 2012; 40:81-91. [DOI: 10.1007/s10928-012-9288-7] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2012] [Accepted: 12/11/2012] [Indexed: 11/30/2022]
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Plan EL, Elshoff JP, Stockis A, Sargentini-Maier ML, Karlsson MO. Likert Pain Score Modeling: A Markov Integer Model and an Autoregressive Continuous Model. Clin Pharmacol Ther 2012; 91:820-8. [DOI: 10.1038/clpt.2011.301] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
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