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Spiliotopoulos D, Koelbert C, Audebert M, Barisch I, Bellet D, Constans M, Czich A, Finot F, Gervais V, Khoury L, Kirchnawy C, Kitamoto S, Le Tesson A, Malesic L, Matsuyama R, Mayrhofer E, Mouche I, Preikschat B, Prielinger L, Rainer B, Roblin C, Wäse K. Assessment of the performance of the Ames MPF™ assay: A multicenter collaborative study with six coded chemicals. MUTATION RESEARCH. GENETIC TOXICOLOGY AND ENVIRONMENTAL MUTAGENESIS 2024; 893:503718. [PMID: 38272629 DOI: 10.1016/j.mrgentox.2023.503718] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Revised: 11/06/2023] [Accepted: 11/19/2023] [Indexed: 01/27/2024]
Abstract
The Ames MPF™ is a miniaturized, microplate fluctuation format of the Ames test. It is a standardized, commercially available product which can be used to assess mutagenicity in Salmonella and E. coli strains in 384-well plates using a color change-based readout. Several peer-reviewed comparisons of the Ames MPF™ to the Ames test in Petri dishes confirmed its suitability to evaluate the mutagenic potential of a variety of test items. An international multicenter study involving seven laboratories tested six coded chemicals with this assay using five bacterial strains, as recommended by the OECD test guideline 471. The data generated by the participating laboratories was in excellent agreement (93%), and the similarity of their dose response curves, as analyzed with sophisticated statistical approaches further confirmed the suitability of the Ames MPF™ assay as an alternative to the Ames test on agar plates, but with advantages with respect to significantly reduced amount of test substance and S9 requirements, speed, hands-on time and, potentially automation.
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Affiliation(s)
| | | | - Marc Audebert
- PrediTox, 1 place Pierre Potier, 31100 Toulouse, France; INRAE UMR1331 Toxalim, 180 chemin de Tournefeuille, 31300 Toulouse, France
| | - Ilona Barisch
- Genetic Toxicology, Preclinical Safety, Sanofi-Aventis Deutschland GmbH, 65926 Frankfurt am Main, Germany
| | - Deborah Bellet
- GenEvolutioN, 2, 8 Rue de Rouen, 78440 Porcheville, France
| | | | - Andreas Czich
- Genetic Toxicology, Preclinical Safety, Sanofi-Aventis Deutschland GmbH, 65926 Frankfurt am Main, Germany
| | - Francis Finot
- GenEvolutioN, 2, 8 Rue de Rouen, 78440 Porcheville, France
| | - Véronique Gervais
- Servier Group, Non-Clinical Safety Department, F-45403 Orléans-Gidy, France
| | - Laure Khoury
- PrediTox, 1 place Pierre Potier, 31100 Toulouse, France
| | - Christian Kirchnawy
- OFI, Austrian Research Institute for Chemistry and Technology, Department for Microbiology and Cell Culture, Franz-Grill Straße 5, Objekt 213, 1030 Vienna, Austria
| | - Sachiko Kitamoto
- Environmental Health Science Laboratory, Sumitomo Chemical Co., Ltd., 1-98, Kasugadenaka 3-chome, konohana-ku, Osaka, Japan
| | - Audrey Le Tesson
- Servier Group, Non-Clinical Safety Department, F-45403 Orléans-Gidy, France
| | - Laure Malesic
- GenEvolutioN, 2, 8 Rue de Rouen, 78440 Porcheville, France
| | - Ryoko Matsuyama
- Environmental Health Science Laboratory, Sumitomo Chemical Co., Ltd., 1-98, Kasugadenaka 3-chome, konohana-ku, Osaka, Japan
| | - Elisa Mayrhofer
- OFI, Austrian Research Institute for Chemistry and Technology, Department for Microbiology and Cell Culture, Franz-Grill Straße 5, Objekt 213, 1030 Vienna, Austria
| | | | - Birgit Preikschat
- Genetic Toxicology, Preclinical Safety, Sanofi-Aventis Deutschland GmbH, 65926 Frankfurt am Main, Germany
| | - Lukas Prielinger
- Department of Applied Life Sciences, University of Applied Sciences, FH Campus Wien, Favoritenstraße 222, 1100 Vienna, Austria
| | - Bernhard Rainer
- Department of Applied Life Sciences, University of Applied Sciences, FH Campus Wien, Favoritenstraße 222, 1100 Vienna, Austria
| | - Clémence Roblin
- Servier Group, Non-Clinical Safety Department, F-45403 Orléans-Gidy, France
| | - Kerstin Wäse
- Genetic Toxicology, Preclinical Safety, Sanofi-Aventis Deutschland GmbH, 65926 Frankfurt am Main, Germany
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Sun W, Schuirmann D, Grosser S. Qualitative versus Quantitative Treatment-by-Subgroup Interaction in Equivalence Studies with Multiple Subgroups. Stat Biopharm Res 2022. [DOI: 10.1080/19466315.2022.2123385] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Affiliation(s)
- Wanjie Sun
- FDA/CDER/Office of Translational Science/Office of Biostatistics/DBVIII, Silver Spring, MD
| | - Don Schuirmann
- FDA/CDER/Office of Translational Science/Office of Biostatistics/DBVIII, Silver Spring, MD
| | - Stella Grosser
- FDA/CDER/Office of Translational Science/Office of Biostatistics/DBVIII, Silver Spring, MD
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Oulhaj A, El Ghouch A, Holman RR. Testing for qualitative heterogeneity: An application to composite endpoints in survival analysis. Stat Methods Med Res 2017; 28:151-169. [PMID: 28670972 DOI: 10.1177/0962280217717761] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Composite endpoints are frequently used in clinical outcome trials to provide more endpoints, thereby increasing statistical power. A key requirement for a composite endpoint to be meaningful is the absence of the so-called qualitative heterogeneity to ensure a valid overall interpretation of any treatment effect identified. Qualitative heterogeneity occurs when individual components of a composite endpoint exhibit differences in the direction of a treatment effect. In this paper, we develop a general statistical method to test for qualitative heterogeneity, that is to test whether a given set of parameters share the same sign. This method is based on the intersection-union principle and, provided that the sample size is large, is valid whatever the model used for parameters estimation. We propose two versions of our testing procedure, one based on a random sampling from a Gaussian distribution and another version based on bootstrapping. Our work covers both the case of completely observed data and the case where some observations are censored which is an important issue in many clinical trials. We evaluated the size and power of our proposed tests by carrying out some extensive Monte Carlo simulations in the case of multivariate time to event data. The simulations were designed under a variety of conditions on dimensionality, censoring rate, sample size and correlation structure. Our testing procedure showed very good performances in terms of statistical power and type I error. The proposed test was applied to a data set from a single-center, randomized, double-blind controlled trial in the area of Alzheimer's disease.
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Affiliation(s)
- Abderrahim Oulhaj
- 1 Institute of public health, College of Medicine & Health Sciences, United Arab Emirates University (UAEU), United Arab Emirates
| | - Anouar El Ghouch
- 2 Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA), Université catholique de Louvain, Louvain-la-Neuve, Belgium
| | - Rury R Holman
- 3 Diabetes Trial Unit (DTU), University of Oxford, Oxford, UK
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Wang W, Jiang Z, Qiu J, Xia J, Guo X. A nested group sequential framework for regional evaluation in global drug development program. J Biopharm Stat 2017; 27:945-962. [PMID: 28323515 DOI: 10.1080/10543406.2017.1293079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
The primary objective of a multiregional clinical trial (MRCT) is to assess the efficacy of all participating regions and evaluate the probability of applying the overall results to a specific region. The consistency assessment of the target region with the overall results is the most common way of evaluating the efficacy in a specific region. Recently, Huang et al. (2012) proposed an additional trial in the target region to an MRCT to evaluate the efficacy in the target ethnic (TE) population under the framework of simultaneous global drug development program (SGDDP). However, the operating characteristics of this statistical framework were not well considered. Therefore, a nested group sequential program for regional efficacy evaluation is proposed in this paper. It is an extension of Huang's SGDDP framework and allows interim analysis after MRCT and in the course of local clinical trial (LCT) phase. It is able to well control the family-wise type I error in the program level and enhances the flexibility of the program. In LCT sample size estimation, we introduce virtual trial, which is transformed from the original program by using discounting factor, and an iteration method is employed to calculate the sample size and stopping boundaries of interim analyses. The proposed sample size estimation method is validated in the simulations and the effect of varied weight, effect size of TE population, and design setting is explored. Examples with normal end point, binary end point, and survival end point are shown to illustrate the application of the proposed nested group sequential program.
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Affiliation(s)
- William Wang
- a Biostatistics and Research Decision Science , Merck Research Laboratory, Merck & Co., Inc ., Beijing , China
| | - Zhiwei Jiang
- a Biostatistics and Research Decision Science , Merck Research Laboratory, Merck & Co., Inc ., Beijing , China.,b Department of Health Statistics , School of Preventive Medicine, Fourth Military Medical University , Xi'an , Shaanxi , China
| | - Jingjun Qiu
- a Biostatistics and Research Decision Science , Merck Research Laboratory, Merck & Co., Inc ., Beijing , China.,b Department of Health Statistics , School of Preventive Medicine, Fourth Military Medical University , Xi'an , Shaanxi , China
| | - Jielai Xia
- b Department of Health Statistics , School of Preventive Medicine, Fourth Military Medical University , Xi'an , Shaanxi , China
| | - Xiang Guo
- a Biostatistics and Research Decision Science , Merck Research Laboratory, Merck & Co., Inc ., Beijing , China
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Tanniou J, van der Tweel I, Teerenstra S, Roes KCB. Subgroup analyses in confirmatory clinical trials: time to be specific about their purposes. BMC Med Res Methodol 2016; 16:20. [PMID: 26891992 PMCID: PMC4757983 DOI: 10.1186/s12874-016-0122-6] [Citation(s) in RCA: 46] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2016] [Accepted: 02/09/2016] [Indexed: 11/26/2022] Open
Abstract
Background It is well recognized that treatment effects may not be homogeneous across the study population. Subgroup analyses constitute a fundamental step in the assessment of evidence from confirmatory (Phase III) clinical trials, where conclusions for the overall study population might not hold. Subgroup analyses can have different and distinct purposes, requiring specific design and analysis solutions. It is relevant to evaluate methodological developments in subgroup analyses against these purposes to guide health care professionals and regulators as well as to identify gaps in current methodology. Methods We defined four purposes for subgroup analyses: (1) Investigate the consistency of treatment effects across subgroups of clinical importance, (2) Explore the treatment effect across different subgroups within an overall non-significant trial, (3) Evaluate safety profiles limited to one or a few subgroup(s), (4) Establish efficacy in the targeted subgroup when included in a confirmatory testing strategy of a single trial. We reviewed the methodology in line with this “purpose-based” framework. The review covered papers published between January 2005 and April 2015 and aimed to classify them in none, one or more of the aforementioned purposes. Results In total 1857 potentially eligible papers were identified. Forty-eight papers were selected and 20 additional relevant papers were identified from their references, leading to 68 papers in total. Nineteen were dedicated to purpose 1, 16 to purpose 4, one to purpose 2 and none to purpose 3. Seven papers were dedicated to more than one purpose, the 25 remaining could not be classified unambiguously. Purposes of the methods were often not specifically indicated, methods for subgroup analysis for safety purposes were almost absent and a multitude of diverse methods were developed for purpose (1). Conclusions It is important that researchers developing methodology for subgroup analysis explicitly clarify the objectives of their methods in terms that can be understood from a patient’s, health care provider’s and/or regulator’s perspective. A clear operational definition for consistency of treatment effects across subgroups is lacking, but is needed to improve the usability of subgroup analyses in this setting. Finally, methods to particularly explore benefit-risk systematically across subgroups need more research. Electronic supplementary material The online version of this article (doi:10.1186/s12874-016-0122-6) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Julien Tanniou
- Julius Center for Health Sciences and Primary Care, UMC Utrecht, Universiteitsweg 100, 3584 CG, Utrecht, The Netherlands. .,College ter Beoordeling van Geneesmiddelen, Dutch Medicines Evaluation Board, Graadt van Roggenweg 500, 3531 AH, Utrecht, The Netherlands.
| | - Ingeborg van der Tweel
- Julius Center for Health Sciences and Primary Care, UMC Utrecht, Universiteitsweg 100, 3584 CG, Utrecht, The Netherlands.
| | - Steven Teerenstra
- College ter Beoordeling van Geneesmiddelen, Dutch Medicines Evaluation Board, Graadt van Roggenweg 500, 3531 AH, Utrecht, The Netherlands. .,Department of Health Evidence, Section Biostatistics, Radboud University Medical Centre, Geert Grooteplein 21, 6525 GA, Nijmegen, The Netherlands.
| | - Kit C B Roes
- Julius Center for Health Sciences and Primary Care, UMC Utrecht, Universiteitsweg 100, 3584 CG, Utrecht, The Netherlands. .,College ter Beoordeling van Geneesmiddelen, Dutch Medicines Evaluation Board, Graadt van Roggenweg 500, 3531 AH, Utrecht, The Netherlands.
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Kitsche A. Detecting qualitative interactions in clinical trials with binary responses. Pharm Stat 2014; 13:309-15. [PMID: 25049176 DOI: 10.1002/pst.1632] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2013] [Revised: 03/21/2014] [Accepted: 06/23/2014] [Indexed: 11/07/2022]
Abstract
This study considers the detection of treatment-by-subset interactions in a stratified, randomised clinical trial with a binary-response variable. The focus lies on the detection of qualitative interactions. In addition, the presented method is useful more generally, as it can assess the inconsistency of the treatment effects among strata by using an a priori-defined inconsistency margin. The methodology presented is based on the construction of ratios of treatment effects. In addition to multiplicity-adjusted p-values, simultaneous confidence intervals are recommended to use in detecting the source and the amount of a potential qualitative interaction. The proposed method is demonstrated on a multi-regional trial using the open-source statistical software R.
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Affiliation(s)
- Andreas Kitsche
- Institut für Biostatistik, Leibniz Universität Hannover, Herrenhäuser Straße 2, Hannover, Germany
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