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Sungzoon Cho - One of the best experts on this subject based on the ideXlab platform.
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Pattern selection for support vector regression based Response Modeling
Expert Systems with Applications, 2012Co-Authors: Dongil Kim, Sungzoon ChoAbstract:Highlights? A pattern selection method called Expected Margin based Pattern Selection (EMPS) is proposed. ? EMPS reduces the training complexities of SVR for use as a Response Modeling dataset. ? The experimental results involving one real-world marketing dataset showed that EMPS improved SVR efficiency for Response Modeling. Two-stage Response Modeling, identifying respondents and then ranking them according to their expected profit, was proposed in order to increase the profit of direct marketing. For the second stage of two-stage Response Modeling, support vector regression (SVR) has been successfully employed due to its great generalization performances. However, the training complexities of SVR have made it difficult to apply to Response Modeling based on the large amount of data. In this paper, we propose a pattern selection method called Expected Margin based Pattern Selection (EMPS) to reduce the training complexities of SVR for use as a Response Modeling dataset with high dimensionality and high nonlinearity. EMPS estimates the expected margin for all training patterns and selects patterns which are likely to become support vectors. The experimental results involving 20 benchmark datasets and one real-world marketing dataset showed that EMPS improved SVR efficiency for Response Modeling.
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Improved Response Modeling based on clustering, under-sampling, and ensemble
Expert Systems with Applications, 2012Co-Authors: Pilsung Kang, Sungzoon Cho, Douglas L. MaclachlanAbstract:The purpose of Response Modeling for direct marketing is to identify those customers who are likely to purchase a campaigned product, based upon customers' behavioral history and other information available. Contrary to mass marketing strategy, well-developed Response models used for targeting specific customers can contribute profits to firms by not only increasing revenues, but also lowering marketing costs. Endemic in customer data used for Response Modeling is a class imbalance problem: the proportion of respondents is small relative to non-respondents. In this paper, we propose a novel data balancing method based on clustering, under-sampling, and ensemble to deal with the class imbalance problem, and thus improve Response models. Using publicly available Response Modeling data sets, we compared the proposed method with other data balancing methods in terms of prediction accuracy and profitability. To investigate the usability of the proposed algorithm, we also employed various prediction algorithms when building the Response models. Based on the Response rate and profit analysis, we found that our proposed method (1) improved the Response model by increasing Response rate as well as reducing performance variation, and (2) increased total profit by significantly boosting revenue.
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Semi-Supervised Response Modeling
Journal of Interactive Marketing, 2010Co-Authors: Hyoung-joo Lee, Sungzoon Cho, Hyunjung Shin, Seong-seob Hwang, Douglas L. MaclachlanAbstract:Response Modeling is concerned with identifying potential customers who are likely to purchase a promoted product, based on customers' demographic and behavioral data. Constructing a Response model requires a preliminary campaign result database. Customers who responded to the campaign are labeled as respondents while those who did not are labeled as non-respondents. Those customers who were not chosen for the preliminary campaign do not have labels, and thus are called unlabeled. Then, using only those labeled customer data, a classification model is built in the supervised learning framework to predict all existing customers. However, often in Response Modeling, only a small part of customers are labeled, and thus available for model building, while a large number of unlabeled data may give valuable information. As a method to exploit the unlabeled data, we introduce semi-supervised learning to the interactive marketing community. A case study on the CoIL Challenge 2000 and the Direct Marketing Educational Foundation data sets shows that the transductive support vector machine, one of widely used semi-supervised models, can identify more respondents than conventional supervised models, especially when a small number of data are labeled. Semi-supervised learning is a viable alternative and merits further investigation.
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Response Modeling with support vector regression
Expert Systems with Applications, 2008Co-Authors: Dongil Kim, Lee Hyoungjoo, Sungzoon ChoAbstract:Response Modeling has become a key factor to direct marketing. In general, there are two stages in Response Modeling. The first stage is to identify respondents from a customer database while the second stage is to estimate purchase amounts of the respondents. This paper focuses on the second stage where a regression, not a classification, problem is solved. Recently, several non-linear models based on machine learning such as support vector machines (SVM) have been applied to Response Modeling. However, there is a major difficulty. A typical training dataset for Response Modeling is so large that Modeling takes very long, or, even worse, Modeling may be impossible. Therefore, sampling methods have been usually employed in practice. However a sampled dataset usually leads to lower accuracy. In this paper, we employed an @e-tube based sampling for support vector regression (SVR) which leads to better accuracy than the random sampling method.
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Response Modeling with support vector machines
Expert Systems with Applications, 2006Co-Authors: Hyunjung Shin, Sungzoon ChoAbstract:Support Vector Machine (SVM) employs Structural Risk Minimization (SRM) principle to generalize better than conventional machine learning methods employing the traditional Empirical Risk Minimization (ERM) principle. When applying SVM to Response Modeling in direct marketing, however, one has to deal with the practical difficulties: large training data, class imbalance and scoring from binary SVM output. For the first difficulty, we propose a way to alleviate or solve it through a novel informative sampling. For the latter two difficulties, we provide guidelines within SVM framework so that one can readily use the paper as a quick reference for SVM Response Modeling: use of different costs for different classes and use of distance to decision boundary, respectively. This paper also provides various evaluation measures for Response models in terms of accuracies, lift chart analysis, and computational efficiency.
Honghui Zhou - One of the best experts on this subject based on the ideXlab platform.
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Modeling near-continuous clinical endpoint as categorical: application to longitudinal exposure–Response Modeling of Mayo scores for golimumab in patients with ulcerative colitis
Journal of Pharmacokinetics and Pharmacodynamics, 2018Co-Authors: Chuanpu Hu, Omoniyi J Adedokun, Amarnath Sharma, Liping Zhang, Honghui ZhouAbstract: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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exposure Response Modeling analyses for sirukumab a human monoclonal antibody targeting interleukin 6 in patients with moderately to severely active rheumatoid arthritis
The Journal of Clinical Pharmacology, 2018Co-Authors: Yan Xu, Yanli Zhuang, Chuanpu Hu, Amarnath Sharma, Zhenhua Xu, Honghui ZhouAbstract:To characterize the dose-exposure-Response relationship of sirukumab, an anti-interleukin 6 human monoclonal antibody, in the treatment of moderately to severely active rheumatoid arthritis (RA), we conducted exposure-Response (E-R) Modeling analyses based on data from two pivotal phase 3 placebo-controlled trials of sirukumab in patients with RA who were inadequate responders to nonbiologic disease-modifying antirheumatic drugs or anti-tumor necrosis factor α agents. A total of 2176 patients were included for the analyses and received subcutaneous administration of either placebo or sirukumab 50 mg every 4 weeks or 100 mg every 2 weeks. The clinical endpoints were 20%, 50%, and 70% improvement in the American College of Rheumatology Response criteria (ie, ACR20, ACR50, and ACR70), and 28-joint Disease Activity Index Score (DAS28) using C-reactive protein. To provide a thorough assessment of the sirukumab E-R relationship, 2 pharmacokinetic/pharmacodynamic Modeling approaches were implemented, including joint longitudinal Modeling (ie, indirect Response Modeling of the time course of the 2 clinical endpoints) and landmark analyses (ie, direct linking of selected pharmacokinetic parameters to Response at week 16 or 24). Results from both Modeling analyses were generally consistent, and collectively suggested that the sirukumab subcutaneous dose of 50 mg every 4 weeks would produce near-maximal efficacy. No covariates identified in the E-R Modeling analyses would have a significant impact on dose-Response. Despite body weight and comorbid diabetes having significant effect on sirukumab exposure, simulations suggested that their effect on efficacy was small. Our work provides a comprehensive evaluation of sirukumab E-R to support dose recommendations in patients with RA.
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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
Journal of Pharmacokinetics and Pharmacodynamics, 2018Co-Authors: Chuanpu Hu, Yanli Zhuang, Amarnath Sharma, Zhenhua Xu, Yan Xu, Liping Zhang, Honghui ZhouAbstract:Exposure–Response Modeling is important to optimize dose and dosing regimen in clinical drug development. The joint 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 presents the results of joint Modeling of continuous and ordered categorical endpoints in the latent variable IDR Modeling framework through the sharing of model parameters, with an application to the exposure–Response Modeling of sirukumab. Sirukumab is a human anti- interleukin-6 (IL-6) monoclonal antibody that binds soluble human IL-6 thus blocking IL-6 signaling, which plays a major role in the pathophysiology of rheumatoid arthritis (RA). A phase 2 clinical trial was conducted in patients with active RA despite methotrexate therapy, who received subcutaneous (SC) administration of either placebo or sirukumab of 25, 50 or 100 mg every 4 weeks (q4w) or 100 mg every 2 weeks (q2w). Major efficacy endpoints were the 20, 50, and 70% improvement in the American College of Rheumatology (ACR20, ACR50, and ACR70) disease severity criteria, and the 28-joint disease activity score using C-reactive protein (DAS28). The ACR endpoints were treated as ordered categorical and DAS28 as continuous. The results showed that, compared with the common approach of separately Modeling the endpoints, the joint model could describe the observed data better with fewer parameters through the sharing of random effects, and thus more precisely characterize the dose–Response relationship. The implications on future dose and dosing regimen optimization are discussed in contrast with those from landmark analysis.
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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
Journal of Pharmacokinetics and Pharmacodynamics, 2018Co-Authors: Chuanpu Hu, Yanli Zhuang, Amarnath Sharma, Zhenhua Xu, Yan Xu, Liping Zhang, Honghui ZhouAbstract:Exposure-Response Modeling is important to optimize dose and dosing regimen in clinical drug development. The joint 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 presents the results of joint Modeling of continuous and ordered categorical endpoints in the latent variable IDR Modeling framework through the sharing of model parameters, with an application to the exposure-Response Modeling of sirukumab. Sirukumab is a human anti- interleukin-6 (IL-6) monoclonal antibody that binds soluble human IL-6 thus blocking IL-6 signaling, which plays a major role in the pathophysiology of rheumatoid arthritis (RA). A phase 2 clinical trial was conducted in patients with active RA despite methotrexate therapy, who received subcutaneous (SC) administration of either placebo or sirukumab of 25, 50 or 100 mg every 4 weeks (q4w) or 100 mg every 2 weeks (q2w). Major efficacy endpoints were the 20, 50, and 70% improvement in the American College of Rheumatology (ACR20, ACR50, and ACR70) disease severity criteria, and the 28-joint disease activity score using C-reactive protein (DAS28). The ACR endpoints were treated as ordered categorical and DAS28 as continuous. The results showed that, compared with the common approach of separately Modeling the endpoints, the joint model could describe the observed data better with fewer parameters through the sharing of random effects, and thus more precisely characterize the dose-Response relationship. The implications on future dose and dosing regimen optimization are discussed in contrast with those from landmark analysis.
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Exposure‐Response Modeling Analyses for Sirukumab, a Human Monoclonal Antibody Targeting Interleukin 6, in Patients With Moderately to Severely Active Rheumatoid Arthritis
The Journal of Clinical Pharmacology, 2018Co-Authors: Yan Xu, Yanli Zhuang, Chuanpu Hu, Amarnath Sharma, Zhenhua Xu, Honghui ZhouAbstract:To characterize the dose-exposure-Response relationship of sirukumab, an anti-interleukin 6 human monoclonal antibody, in the treatment of moderately to severely active rheumatoid arthritis (RA), we conducted exposure-Response (E-R) Modeling analyses based on data from two pivotal phase 3 placebo-controlled trials of sirukumab in patients with RA who were inadequate responders to nonbiologic disease-modifying antirheumatic drugs or anti-tumor necrosis factor α agents. A total of 2176 patients were included for the analyses and received subcutaneous administration of either placebo or sirukumab 50 mg every 4 weeks or 100 mg every 2 weeks. The clinical endpoints were 20%, 50%, and 70% improvement in the American College of Rheumatology Response criteria (ie, ACR20, ACR50, and ACR70), and 28-joint Disease Activity Index Score (DAS28) using C-reactive protein. To provide a thorough assessment of the sirukumab E-R relationship, 2 pharmacokinetic/pharmacodynamic Modeling approaches were implemented, including joint longitudinal Modeling (ie, indirect Response Modeling of the time course of the 2 clinical endpoints) and landmark analyses (ie, direct linking of selected pharmacokinetic parameters to Response at week 16 or 24). Results from both Modeling analyses were generally consistent, and collectively suggested that the sirukumab subcutaneous dose of 50 mg every 4 weeks would produce near-maximal efficacy. No covariates identified in the E-R Modeling analyses would have a significant impact on dose-Response. Despite body weight and comorbid diabetes having significant effect on sirukumab exposure, simulations suggested that their effect on efficacy was small. Our work provides a comprehensive evaluation of sirukumab E-R to support dose recommendations in patients with RA.
Dongil Kim - One of the best experts on this subject based on the ideXlab platform.
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Pattern selection for support vector regression based Response Modeling
Expert Systems with Applications, 2012Co-Authors: Dongil Kim, Sungzoon ChoAbstract:Highlights? A pattern selection method called Expected Margin based Pattern Selection (EMPS) is proposed. ? EMPS reduces the training complexities of SVR for use as a Response Modeling dataset. ? The experimental results involving one real-world marketing dataset showed that EMPS improved SVR efficiency for Response Modeling. Two-stage Response Modeling, identifying respondents and then ranking them according to their expected profit, was proposed in order to increase the profit of direct marketing. For the second stage of two-stage Response Modeling, support vector regression (SVR) has been successfully employed due to its great generalization performances. However, the training complexities of SVR have made it difficult to apply to Response Modeling based on the large amount of data. In this paper, we propose a pattern selection method called Expected Margin based Pattern Selection (EMPS) to reduce the training complexities of SVR for use as a Response Modeling dataset with high dimensionality and high nonlinearity. EMPS estimates the expected margin for all training patterns and selects patterns which are likely to become support vectors. The experimental results involving 20 benchmark datasets and one real-world marketing dataset showed that EMPS improved SVR efficiency for Response Modeling.
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Response Modeling with support vector regression
Expert Systems with Applications, 2008Co-Authors: Dongil Kim, Lee Hyoungjoo, Sungzoon ChoAbstract:Response Modeling has become a key factor to direct marketing. In general, there are two stages in Response Modeling. The first stage is to identify respondents from a customer database while the second stage is to estimate purchase amounts of the respondents. This paper focuses on the second stage where a regression, not a classification, problem is solved. Recently, several non-linear models based on machine learning such as support vector machines (SVM) have been applied to Response Modeling. However, there is a major difficulty. A typical training dataset for Response Modeling is so large that Modeling takes very long, or, even worse, Modeling may be impossible. Therefore, sampling methods have been usually employed in practice. However a sampled dataset usually leads to lower accuracy. In this paper, we employed an @e-tube based sampling for support vector regression (SVR) which leads to better accuracy than the random sampling method.
Anne Chatton - One of the best experts on this subject based on the ideXlab platform.
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game addiction scale assessment through a nationally representative sample of young adult men item Response theory graded Response Modeling
Journal of Medical Internet Research, 2018Co-Authors: Yasser Khazaal, Gabriel Thorens, Daniele Fabio Zullino, Sophia Achab, Kyrre Breivik, Joel Billieux, Gerhard Gmel, Anne ChattonAbstract:Background The 7-item Game Addiction Scale (GAS) has been validated under standard confirmatory factor analysis and exhibits good psychometric properties. Whether this scale satisfies the necessary conditions for consideration by item Response theory (IRT) Modeling remains unknown. However, the Diagnostic and Statistical Manual of Mental Disorders, 5th Edition (DSM-5) recently proposed criteria, in its section 3, to define internet gaming disorder (IGD) to promote research on this possible condition.
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game addiction scale assessment through a nationally representative sample of young adult men item Response theory graded Response Modeling
Journal of Medical Internet Research, 2018Co-Authors: Yasser Khazaal, Gabriel Thorens, Daniele Fabio Zullino, Sophia Achab, Kyrre Breivik, Joel Billieux, Gerhard Gmel, Anne ChattonAbstract:BACKGROUND The 7-item Game Addiction Scale (GAS) has been validated under standard confirmatory factor analysis and exhibits good psychometric properties. Whether this scale satisfies the necessary conditions for consideration by item Response theory (IRT) Modeling remains unknown. However, the Diagnostic and Statistical Manual of Mental Disorders, 5th Edition (DSM-5) recently proposed criteria, in its section 3, to define internet gaming disorder (IGD) to promote research on this possible condition. OBJECTIVE The objective of our study was to (1) analyze GAS in the context of IRT (graded-Response) Modeling; (2) investigate differential item functioning (DIF), a feature of IRT Modeling, in 2 subsamples; and (3) contribute to the ongoing (IGD) debate related to the validity of the DSM-5 criteria using GAS items as a proxy. METHODS We assessed 2 large representative samples of Swiss men (3320 French-speaking and 2670 German-speaking) with GAS. RESULTS All items comprised high discrimination parameters. GAS items such as relapse, conflict, withdrawal, and problems (loss of interests) were endorsed more frequently in more severe IGD stages, whereas items related to tolerance, salience (preoccupation), and mood modification (escape) were endorsed more widely among participants (including in less severe IGD stages). Several DIF effects were found but were classified as negligible. CONCLUSIONS The results of the analyses partly support the relevance of using IRT to further establish the psychometric properties of the GAS items. This study contributes to testing the validity of the IGD criteria, although cautious generalization of our findings is required with GAS being only a proxy of the IGD criteria.
Chuanpu Hu - One of the best experts on this subject based on the ideXlab platform.
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Modeling near-continuous clinical endpoint as categorical: application to longitudinal exposure–Response Modeling of Mayo scores for golimumab in patients with ulcerative colitis
Journal of Pharmacokinetics and Pharmacodynamics, 2018Co-Authors: Chuanpu Hu, Omoniyi J Adedokun, Amarnath Sharma, Liping Zhang, Honghui ZhouAbstract: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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exposure Response Modeling analyses for sirukumab a human monoclonal antibody targeting interleukin 6 in patients with moderately to severely active rheumatoid arthritis
The Journal of Clinical Pharmacology, 2018Co-Authors: Yan Xu, Yanli Zhuang, Chuanpu Hu, Amarnath Sharma, Zhenhua Xu, Honghui ZhouAbstract:To characterize the dose-exposure-Response relationship of sirukumab, an anti-interleukin 6 human monoclonal antibody, in the treatment of moderately to severely active rheumatoid arthritis (RA), we conducted exposure-Response (E-R) Modeling analyses based on data from two pivotal phase 3 placebo-controlled trials of sirukumab in patients with RA who were inadequate responders to nonbiologic disease-modifying antirheumatic drugs or anti-tumor necrosis factor α agents. A total of 2176 patients were included for the analyses and received subcutaneous administration of either placebo or sirukumab 50 mg every 4 weeks or 100 mg every 2 weeks. The clinical endpoints were 20%, 50%, and 70% improvement in the American College of Rheumatology Response criteria (ie, ACR20, ACR50, and ACR70), and 28-joint Disease Activity Index Score (DAS28) using C-reactive protein. To provide a thorough assessment of the sirukumab E-R relationship, 2 pharmacokinetic/pharmacodynamic Modeling approaches were implemented, including joint longitudinal Modeling (ie, indirect Response Modeling of the time course of the 2 clinical endpoints) and landmark analyses (ie, direct linking of selected pharmacokinetic parameters to Response at week 16 or 24). Results from both Modeling analyses were generally consistent, and collectively suggested that the sirukumab subcutaneous dose of 50 mg every 4 weeks would produce near-maximal efficacy. No covariates identified in the E-R Modeling analyses would have a significant impact on dose-Response. Despite body weight and comorbid diabetes having significant effect on sirukumab exposure, simulations suggested that their effect on efficacy was small. Our work provides a comprehensive evaluation of sirukumab E-R to support dose recommendations in patients with RA.
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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
Journal of Pharmacokinetics and Pharmacodynamics, 2018Co-Authors: Chuanpu Hu, Yanli Zhuang, Amarnath Sharma, Zhenhua Xu, Yan Xu, Liping Zhang, Honghui ZhouAbstract:Exposure–Response Modeling is important to optimize dose and dosing regimen in clinical drug development. The joint 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 presents the results of joint Modeling of continuous and ordered categorical endpoints in the latent variable IDR Modeling framework through the sharing of model parameters, with an application to the exposure–Response Modeling of sirukumab. Sirukumab is a human anti- interleukin-6 (IL-6) monoclonal antibody that binds soluble human IL-6 thus blocking IL-6 signaling, which plays a major role in the pathophysiology of rheumatoid arthritis (RA). A phase 2 clinical trial was conducted in patients with active RA despite methotrexate therapy, who received subcutaneous (SC) administration of either placebo or sirukumab of 25, 50 or 100 mg every 4 weeks (q4w) or 100 mg every 2 weeks (q2w). Major efficacy endpoints were the 20, 50, and 70% improvement in the American College of Rheumatology (ACR20, ACR50, and ACR70) disease severity criteria, and the 28-joint disease activity score using C-reactive protein (DAS28). The ACR endpoints were treated as ordered categorical and DAS28 as continuous. The results showed that, compared with the common approach of separately Modeling the endpoints, the joint model could describe the observed data better with fewer parameters through the sharing of random effects, and thus more precisely characterize the dose–Response relationship. The implications on future dose and dosing regimen optimization are discussed in contrast with those from landmark analysis.
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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
Journal of Pharmacokinetics and Pharmacodynamics, 2018Co-Authors: Chuanpu Hu, Yanli Zhuang, Amarnath Sharma, Zhenhua Xu, Yan Xu, Liping Zhang, Honghui ZhouAbstract:Exposure-Response Modeling is important to optimize dose and dosing regimen in clinical drug development. The joint 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 presents the results of joint Modeling of continuous and ordered categorical endpoints in the latent variable IDR Modeling framework through the sharing of model parameters, with an application to the exposure-Response Modeling of sirukumab. Sirukumab is a human anti- interleukin-6 (IL-6) monoclonal antibody that binds soluble human IL-6 thus blocking IL-6 signaling, which plays a major role in the pathophysiology of rheumatoid arthritis (RA). A phase 2 clinical trial was conducted in patients with active RA despite methotrexate therapy, who received subcutaneous (SC) administration of either placebo or sirukumab of 25, 50 or 100 mg every 4 weeks (q4w) or 100 mg every 2 weeks (q2w). Major efficacy endpoints were the 20, 50, and 70% improvement in the American College of Rheumatology (ACR20, ACR50, and ACR70) disease severity criteria, and the 28-joint disease activity score using C-reactive protein (DAS28). The ACR endpoints were treated as ordered categorical and DAS28 as continuous. The results showed that, compared with the common approach of separately Modeling the endpoints, the joint model could describe the observed data better with fewer parameters through the sharing of random effects, and thus more precisely characterize the dose-Response relationship. The implications on future dose and dosing regimen optimization are discussed in contrast with those from landmark analysis.
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Exposure‐Response Modeling Analyses for Sirukumab, a Human Monoclonal Antibody Targeting Interleukin 6, in Patients With Moderately to Severely Active Rheumatoid Arthritis
The Journal of Clinical Pharmacology, 2018Co-Authors: Yan Xu, Yanli Zhuang, Chuanpu Hu, Amarnath Sharma, Zhenhua Xu, Honghui ZhouAbstract:To characterize the dose-exposure-Response relationship of sirukumab, an anti-interleukin 6 human monoclonal antibody, in the treatment of moderately to severely active rheumatoid arthritis (RA), we conducted exposure-Response (E-R) Modeling analyses based on data from two pivotal phase 3 placebo-controlled trials of sirukumab in patients with RA who were inadequate responders to nonbiologic disease-modifying antirheumatic drugs or anti-tumor necrosis factor α agents. A total of 2176 patients were included for the analyses and received subcutaneous administration of either placebo or sirukumab 50 mg every 4 weeks or 100 mg every 2 weeks. The clinical endpoints were 20%, 50%, and 70% improvement in the American College of Rheumatology Response criteria (ie, ACR20, ACR50, and ACR70), and 28-joint Disease Activity Index Score (DAS28) using C-reactive protein. To provide a thorough assessment of the sirukumab E-R relationship, 2 pharmacokinetic/pharmacodynamic Modeling approaches were implemented, including joint longitudinal Modeling (ie, indirect Response Modeling of the time course of the 2 clinical endpoints) and landmark analyses (ie, direct linking of selected pharmacokinetic parameters to Response at week 16 or 24). Results from both Modeling analyses were generally consistent, and collectively suggested that the sirukumab subcutaneous dose of 50 mg every 4 weeks would produce near-maximal efficacy. No covariates identified in the E-R Modeling analyses would have a significant impact on dose-Response. Despite body weight and comorbid diabetes having significant effect on sirukumab exposure, simulations suggested that their effect on efficacy was small. Our work provides a comprehensive evaluation of sirukumab E-R to support dose recommendations in patients with RA.
