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Fang, L., Gong, Y., Hooker, A., Lukacova, V., Rostami-Hodjegan, A., Sale, M., . . . Zhao, L. (2024). The Role of Model Master Files for Sharing, Acceptance, and Communication with FDA. AAPS Journal, 26(2), Article ID 28.
Open this publication in new window or tab >>The Role of Model Master Files for Sharing, Acceptance, and Communication with FDA
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2024 (English)In: AAPS Journal, E-ISSN 1550-7416, Vol. 26, no 2, article id 28Article in journal (Refereed) Published
Abstract [en]

With the evolving role of Model Integrated Evidence (MIE) in generic drug development and regulatory applications, the need for improving Model Sharing, Acceptance, and Communication with the FDA is warranted. Model Master File (MMF) refers to a quantitative model or a modeling platform that has undergone sufficient model Verification & Validation to be recognized as sharable intellectual property that is acceptable for regulatory purposes. MMF provides a framework for regulatorily acceptable modeling practice, which can be used with confidence to support MIE by both the industry and the U.S. Food and Drug Administration (FDA). In 2022, the FDA and the Center for Research on Complex Generics (CRCG) hosted a virtual public workshop to discuss the best practices for utilizing modeling approaches to support generic product development. This report summarizes the presentations and panel discussions of the workshop symposium entitled "Model Sharing, Acceptance, and Communication with the FDA". The symposium and this report serve as a kick-off discussion for further utilities of MMF and best practices of utilizing MMF in drug development and regulatory submissions. The potential advantages of MMFs have garnered acknowledgment from model developers, industries, and the FDA throughout the workshop. To foster a unified comprehension of MMFs and establish best practices for their application, further dialogue and cooperation among stakeholders are imperative. To this end, a subsequent workshop is scheduled for May 2-3, 2024, in Rockville, Maryland, aiming to delve into the practical facets and best practices of MMFs pertinent to regulatory submissions involving modeling and simulation methodologies.

Place, publisher, year, edition, pages
Springer, 2024
National Category
Computer Systems
Identifiers
urn:nbn:se:uu:diva-525068 (URN)10.1208/s12248-024-00897-8 (DOI)001172379500001 ()38413548 (PubMedID)
Available from: 2024-03-21 Created: 2024-03-21 Last updated: 2024-03-21Bibliographically approved
Gong, Y., Zhang, P., Yoon, M., Zhu, H., Kohojkar, A., Hooker, A., . . . Zhao, L. (2023). Establishing the suitability of model-integrated evidence to demonstrate bioequivalence for long-acting injectable and implantable drug products: Summary of workshop. CPT: Pharmacometrics and Systems Pharmacology (PSP), 12(5), 624-630
Open this publication in new window or tab >>Establishing the suitability of model-integrated evidence to demonstrate bioequivalence for long-acting injectable and implantable drug products: Summary of workshop
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2023 (English)In: CPT: Pharmacometrics and Systems Pharmacology (PSP), E-ISSN 2163-8306, Vol. 12, no 5, p. 624-630Article, review/survey (Refereed) Published
Abstract [en]

On November 30, 2021, the US Food and Drug administration (FDA) and the Center for Research on Complex Generics (CRCG) hosted a virtual public workshop titled "Establishing the Suitability of Model-Integrated Evidence (MIE) to Demonstrate Bioequivalence for Long-Acting Injectable and Implantable (LAI) Drug Products. " This workshop brought relevant parties from the industry, academia, and the FDA in the field of modeling and simulation to explore, identify, and recommend best practices on utilizing MIE for bioequivalence (BE) assessment of LAI products. This report summerized presentations and panel discussions for topics including challenges and opportunities in development and assessment of generic LAI products, current status of utilizing MIE, recent research progress of utilizing MIE in generic LAI products, alternative designs for BE studies of LAI products, and model validation/verification strategies associated with different types of MIE approaches.

Place, publisher, year, edition, pages
John Wiley & Sons, 2023
National Category
Pharmacology and Toxicology
Identifiers
urn:nbn:se:uu:diva-513071 (URN)10.1002/psp4.12931 (DOI)000934972700001 ()36710372 (PubMedID)
Available from: 2023-10-04 Created: 2023-10-04 Last updated: 2023-10-04Bibliographically approved
Chasseloup, E., Hooker, A. & Karlsson, M. (2023). Generation and application of avatars in pharmacometric modelling. Journal of Pharmacokinetics and Pharmacodynamics, 50, 411-423
Open this publication in new window or tab >>Generation and application of avatars in pharmacometric modelling
2023 (English)In: Journal of Pharmacokinetics and Pharmacodynamics, ISSN 1567-567X, E-ISSN 1573-8744, Vol. 50, p. 411-423Article in journal (Refereed) Published
National Category
Pharmaceutical Sciences
Identifiers
urn:nbn:se:uu:diva-486296 (URN)10.1007/s10928-023-09873-9 (DOI)
Available from: 2022-10-06 Created: 2022-10-06 Last updated: 2024-04-09Bibliographically approved
Verbeeck, J., Geroldinger, M., Thiel, K., Hooker, A. C., Ueckert, S., Karlsson, M., . . . Zimmermann, G. (2023). How to Analyze Continuous and Discrete Repeated Measures in Small-Sample Cross-Over Trials?. Biometrics, 79(4), 3998-4011
Open this publication in new window or tab >>How to Analyze Continuous and Discrete Repeated Measures in Small-Sample Cross-Over Trials?
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2023 (English)In: Biometrics, ISSN 0006-341X, E-ISSN 1541-0420, Vol. 79, no 4, p. 3998-4011Article in journal (Refereed) Published
Abstract [en]

To optimize the use of data from a small number of subjects in rare disease trials, an at first sight advantageous design is the repeated measures cross-over design. However, it is unclear how these within-treatment period and within-subject clustered data are best analyzed in small-sample trials. In a real-data simulation study based upon a recent epidermolysis bullosa simplex trial using this design, we compare non-parametric marginal models, generalized pairwise comparison models, GEE-type models and parametric model averaging for both repeated binary and count data. The recommendation of which methodology to use in rare disease trials with a repeated measures cross-over design depends on the type of outcome and the number of time points the treatment has an effect on. The non-parametric marginal model testing the treatment-time-interaction effect is suitable for detecting between group differences in the shapes of the longitudinal profiles. For binary outcomes with the treatment effect on a single time point, the parametric model averaging method is recommended, while in the other cases the unmatched generalized pairwise comparison methodology is recommended. Both provide an easily interpretable effect size measure, and do not require exclusion of periods or subjects due to incompleteness.

Place, publisher, year, edition, pages
Oxford University Press, 2023
Keywords
Barnard test, cross-over, epidermolysis bullosa simplex, GEE, generalized pairwise comparison, model averaging, non-parametric marginal model, rare diseases, repeated measures
National Category
Pharmaceutical Sciences Probability Theory and Statistics
Research subject
Pharmaceutical Science; Statistics
Identifiers
urn:nbn:se:uu:diva-526358 (URN)10.1111/biom.13920 (DOI)001049312800001 ()37587671 (PubMedID)
Funder
EU, Horizon 2020, 825575
Available from: 2024-04-09 Created: 2024-04-09 Last updated: 2024-04-17Bibliographically approved
Geroldinger, M., Verbeeck, J., Hooker, A. C., Thiel, K. E., Molenberghs, G., Nyberg, J., . . . Zimmermann, G. (2023). Statistical recommendations for count, binary, and ordinal data in rare disease cross-over trials. Orphanet Journal of Rare Diseases, 18(1), Article ID 391.
Open this publication in new window or tab >>Statistical recommendations for count, binary, and ordinal data in rare disease cross-over trials
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2023 (English)In: Orphanet Journal of Rare Diseases, E-ISSN 1750-1172, Vol. 18, no 1, article id 391Article in journal (Refereed) Published
Abstract [en]

Background

Recommendations for statistical methods in rare disease trials are scarce, especially for cross-over designs. As a result various state-of-the-art methodologies were compared as neutrally as possible using an illustrative data set from epidermolysis bullosa research to build recommendations for count, binary, and ordinal outcome variables. For this purpose, parametric (model averaging), semiparametric (generalized estimating equations type [GEE-like]) and nonparametric (generalized pairwise comparisons [GPC] and a marginal model implemented in the R package nparLD) methods were chosen by an international consortium of statisticians.

Results

It was found that there is no uniformly best method for the aforementioned types of outcome variables, but in particular situations, there are methods that perform better than others. Especially if maximizing power is the primary goal, the prioritized unmatched GPC method was able to achieve particularly good results, besides being appropriate for prioritizing clinically relevant time points. Model averaging led to favorable results in some scenarios especially within the binary outcome setting and, like the GEE-like semiparametric method, also allows for considering period and carry-over effects properly. Inference based on the nonparametric marginal model was able to achieve high power, especially in the ordinal outcome scenario, despite small sample sizes due to separate testing of treatment periods, and is suitable when longitudinal and interaction effects have to be considered.

Conclusion

Overall, a balance has to be found between achieving high power, accounting for cross-over, period, or carry-over effects, and prioritizing clinically relevant time points.

Place, publisher, year, edition, pages
BioMed Central (BMC), 2023
Keywords
Cross-over, Epidermolysis bullosa simplex, Generalized pairwise comparison (GPC), Guidance, Model averaging, NparLD, GEE-like semiparametric model, Rare Diseases, Recommendation, Repeated measures, Small sample size
National Category
Probability Theory and Statistics
Identifiers
urn:nbn:se:uu:diva-519557 (URN)10.1186/s13023-023-02990-1 (DOI)001127464500001 ()38115074 (PubMedID)
Funder
EU, Horizon 2020, 20204-WISS/225/197-2019EU, Horizon 2020, 20102-F1901166-KZP
Available from: 2024-01-09 Created: 2024-01-09 Last updated: 2024-03-14Bibliographically approved
Lyauk, Y. K., Jonker, D. M., Hooker, A., Lund, T. M. & Karlsson, M. (2021). Bounded Integer Modeling of Symptom Scales Specific to Lower Urinary Tract Symptoms Secondary to Benign Prostatic Hyperplasia. AAPS Journal, 23(2), Article ID 33.
Open this publication in new window or tab >>Bounded Integer Modeling of Symptom Scales Specific to Lower Urinary Tract Symptoms Secondary to Benign Prostatic Hyperplasia
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2021 (English)In: AAPS Journal, E-ISSN 1550-7416, Vol. 23, no 2, article id 33Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
SpringerSPRINGER, 2021
Keywords
BPH, BPH impact index, International Prostate Symptom Score, LUTS, Quality of life
National Category
Urology and Nephrology
Identifiers
urn:nbn:se:uu:diva-439825 (URN)10.1208/s12248-021-00568-y (DOI)000621828000001 ()33630188 (PubMedID)
Funder
Swedish Research Council, 2018-03317
Available from: 2021-04-12 Created: 2021-04-12 Last updated: 2024-01-15Bibliographically approved
Kim, S., Hooker, A., Shi, Y., Kim, G. H. & Wong, W. K. (2021). Metaheuristics for pharmacometrics. CPT: Pharmacometrics and Systems Pharmacology (PSP), 10(11), 1297-1309
Open this publication in new window or tab >>Metaheuristics for pharmacometrics
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2021 (English)In: CPT: Pharmacometrics and Systems Pharmacology (PSP), E-ISSN 2163-8306, Vol. 10, no 11, p. 1297-1309Article, review/survey (Refereed) Published
Abstract [en]

Metaheuristics is a powerful optimization tool that is increasingly used across disciplines to tackle general purpose optimization problems. Nature-inspired metaheuristic algorithms is a subclass of metaheuristic algorithms and have been shown to be particularly flexible and useful in solving complicated optimization problems in computer science and engineering. A common practice with metaheuristics is to hybridize it with another suitably chosen algorithm for enhanced performance. This paper reviews metaheuristic algorithms and demonstrates some of its utility in tackling pharmacometric problems. Specifically, we provide three applications using one of its most celebrated members, particle swarm optimization (PSO), and show that PSO can effectively estimate parameters in complicated nonlinear mixed-effects models and to gain insights into statistical identifiability issues in a complex compartment model. In the third application, we demonstrate how to hybridize PSO with sparse grid, which is an often-used technique to evaluate high dimensional integrals, to search for D-efficient designs for estimating parameters in nonlinear mixed-effects models with a count outcome. We also show the proposed hybrid algorithm outperforms its competitors when sparse grid is replaced by its competitor, adaptive gaussian quadrature to approximate the integral, or when PSO is replaced by three notable nature-inspired metaheuristic algorithms.

Place, publisher, year, edition, pages
John Wiley & SonsWiley, 2021
National Category
Computer Sciences
Identifiers
urn:nbn:se:uu:diva-470248 (URN)10.1002/psp4.12714 (DOI)000709888600001 ()34562342 (PubMedID)
Available from: 2022-03-25 Created: 2022-03-25 Last updated: 2024-01-15Bibliographically approved
Sharan, S., Fang, L., Lukacova, V., Chen, X., Hooker, A. C. & Karlsson, M. O. (2021). Model-Informed Drug Development for Long-Acting Injectable Products: Summary of American College of Clinical Pharmacology Symposium. Clinical Pharmacology in Drug Development, 10(3), 220-228
Open this publication in new window or tab >>Model-Informed Drug Development for Long-Acting Injectable Products: Summary of American College of Clinical Pharmacology Symposium
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2021 (English)In: Clinical Pharmacology in Drug Development, ISSN 2160-763X, E-ISSN 2160-7648, Vol. 10, no 3, p. 220-228Article in journal, Editorial material (Other academic) Published
Place, publisher, year, edition, pages
The American College of Clinical PharmacologyThe American College of Clinical Pharmacology, 2021
Keywords
MIDD, Model-Informed Drug Development, LAI, Long Acting Injectable, PBPK, Quantitative Clinical Pharmacology, QCP, Generic Drug, Bioequivalence, Modeling and Simulation
National Category
Pharmacology and Toxicology
Identifiers
urn:nbn:se:uu:diva-438799 (URN)10.1002/cpdd.928 (DOI)000620815300001 ()33624456 (PubMedID)
Available from: 2021-03-30 Created: 2021-03-30 Last updated: 2024-01-15Bibliographically approved
Ryeznik, Y., Sverdlov, O., Svensson, E., Montepiedra, G., Hooker, A. & Wong, W. K. (2021). Pharmacometrics meets statistics-A synergy for modern drug development. CPT: Pharmacometrics and Systems Pharmacology (PSP), 10(10), 1134-1149
Open this publication in new window or tab >>Pharmacometrics meets statistics-A synergy for modern drug development
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2021 (English)In: CPT: Pharmacometrics and Systems Pharmacology (PSP), E-ISSN 2163-8306, Vol. 10, no 10, p. 1134-1149Article in journal (Refereed) Published
Abstract [en]

Modern drug development problems are very complex and require integration of various scientific fields. Traditionally, statistical methods have been the primary tool for design and analysis of clinical trials. Increasingly, pharmacometric approaches using physiology-based drug and disease models are applied in this context. In this paper, we show that statistics and pharmacometrics have more in common than what keeps them apart, and collectively, the synergy from these two quantitative disciplines can provide greater advances in clinical research and development, resulting in novel and more effective medicines to patients with medical need.

Place, publisher, year, edition, pages
John Wiley & SonsWiley, 2021
Keywords
Collaboration, integration of fields, model-based adaptive optimal designs, model-informed drug development, problem solving
National Category
Pharmaceutical Sciences
Identifiers
urn:nbn:se:uu:diva-469832 (URN)10.1002/psp4.12696 (DOI)000686277000001 ()34318621 (PubMedID)
Available from: 2022-03-18 Created: 2022-03-18 Last updated: 2024-01-15Bibliographically approved
Lee, J., Gong, Y., Bhoopathy, S., DiLiberti, C. E., Hooker, A., Rostami-Hodjegan, A., . . . Zhao, L. (2021). Public Workshop Summary Report on Fiscal Year 2021 Generic Drug Regulatory Science Initiatives: Data Analysis and Model-Based Bioequivalence.. Clinical Pharmacology and Therapeutics, 10(5), 1190-1195
Open this publication in new window or tab >>Public Workshop Summary Report on Fiscal Year 2021 Generic Drug Regulatory Science Initiatives: Data Analysis and Model-Based Bioequivalence.
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2021 (English)In: Clinical Pharmacology and Therapeutics, ISSN 0009-9236, E-ISSN 1532-6535, Vol. 10, no 5, p. 1190-1195Article in journal (Refereed) Published
Abstract [en]

On May 4, 2020, the US Food and Drug Administration (FDA) hosted an online public workshop titled "FY 2020 Generic Drug Regulatory Science Initiatives Public Workshop" to provide an overview of the status of the science and research priorities and to solicit input on the development of Generic Drug User Fee Amendments fiscal year 2021 priorities. This report summarizes the podium presentations and the outcome of discussions along with innovative ways to overcome challenges and significant opportunities related to model-based approaches in bioequivalence assessment for breakout session 4 titled, "Data analysis and model-based bioequivalence (BE)." This session focused on the application of model-based approaches in the generic drug development, with a vision of accelerating regulatory decision making for abbreviated new drug application assessments. The session included both podium presentations and panel discussions with three topics of interest: (i) in vitro study evaluation methods and their clinical relevance, (ii) challenges in model-based BE, (iii) emerging expertise and tools in implementing new BE approaches.

Place, publisher, year, edition, pages
John Wiley & Sons, 2021
National Category
Pharmacology and Toxicology
Identifiers
urn:nbn:se:uu:diva-431954 (URN)10.1002/cpt.2120 (DOI)000603129200001 ()33236362 (PubMedID)
Available from: 2021-01-15 Created: 2021-01-15 Last updated: 2024-01-15Bibliographically approved
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Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-2676-5912

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