Literature DB >> 36070049

Transforming Evidence Generation for Drug Label Changes: A Case Study.

Lane Desborough1, Karen Jaffe2, Joseph Hanna2, Johanna Ulloa2, Kevin Kaiserman2.   

Abstract

Computer Modeling and Simulation (CM&S) provides the opportunity to drastically reduce clinical trial patient burden and advance regulatory decision making. At the suggestion of the US Food and Drug Administration (FDA), MannKind Corporation and Nudge BG submitted an application to the FDA Model-Informed Drug Development (MIDD) pilot program to support a label change for the initial dose of Afrezza® (insulin human), a novel inhalable insulin with a rapid pharmacokinetic and pharmacodynamic profile. The MIDD pilot program demonstrates the FDA's commitment to advancing regulatory science through the adoption of evidence generated by CM&S. A simulation framework was developed based on empirical data. It was used to generate evidence to support the label change. Briefing packages and presentations were prepared for two meetings with the FDA, over a period of four months. The model was thoroughly characterized, determined to be low risk for the question of interest, and submitted along with additional clinical evidence for validation. The FDA found the simulation framework to be helpful in providing insights into the question of interest and provides reasonable glycemic outcome predictions. At the conclusion of the MIDD paired meetings, FDA personnel from the Center for Drug Evaluation and Research review team accepted the simulation and requested additional, traditional clinical evidence to support the proposed label change. In the post-meeting comments, the FDA invited MannKind to submit a proposal for a data package including the CM&S evidence in a Type C meeting for further discussion on the label change. This MIDD pilot experience suggests that CM&S is a credible method for evidence generation. Collaboration between sponsor organizations as well as all stakeholders in the FDA, including proponents of CM&S, can further support regulatory decision-making. The learnings from early participants will allow the program to reach its full potential and thereby ultimately benefit patients, sponsors, and FDA.
© 2022. The Author(s) under exclusive licence to Biomedical Engineering Society.

Entities:  

Keywords:  Afrezza; Computer modeling and simulation; Diabetes; FDA; Inhaled insulin; Insulin; Model-informed drug development

Year:  2022        PMID: 36070049     DOI: 10.1007/s10439-022-03062-4

Source DB:  PubMed          Journal:  Ann Biomed Eng        ISSN: 0090-6964            Impact factor:   4.219


  5 in total

1.  DREAM5: An open-label, randomized, cross-over study to evaluate the safety and efficacy of day and night closed-loop control by comparing the MD-Logic automated insulin delivery system to sensor augmented pump therapy in patients with type 1 diabetes at home.

Authors:  Torben Biester; Judith Nir; Kerstin Remus; Alon Farfel; Ido Muller; Sarah Biester; Eran Atlas; Klemen Dovc; Nataša Bratina; Olga Kordonouri; Tadej Battelino; Moshe Philip; Thomas Danne; Revital Nimri
Journal:  Diabetes Obes Metab       Date:  2018-12-21       Impact factor: 6.577

2.  AUTONOMY: the first randomized trial comparing two patient-driven approaches to initiate and titrate prandial insulin lispro in type 2 diabetes.

Authors:  Steve V Edelman; Rong Liu; Jennal Johnson; Leonard C Glass
Journal:  Diabetes Care       Date:  2014-04-17       Impact factor: 19.112

3.  REPLACE-BG: A Randomized Trial Comparing Continuous Glucose Monitoring With and Without Routine Blood Glucose Monitoring in Adults With Well-Controlled Type 1 Diabetes.

Authors:  Grazia Aleppo; Katrina J Ruedy; Tonya D Riddlesworth; Davida F Kruger; Anne L Peters; Irl Hirsch; Richard M Bergenstal; Elena Toschi; Andrew J Ahmann; Viral N Shah; Michael R Rickels; Bruce W Bode; Athena Philis-Tsimikas; Rodica Pop-Busui; Henry Rodriguez; Emily Eyth; Anuj Bhargava; Craig Kollman; Roy W Beck
Journal:  Diabetes Care       Date:  2017-02-16       Impact factor: 19.112

Review 4.  Rethinking the Viability and Utility of Inhaled Insulin in Clinical Practice.

Authors:  Lutz Heinemann; Christopher G Parkin
Journal:  J Diabetes Res       Date:  2018-03-07       Impact factor: 4.011

Review 5.  Clinical Targets for Continuous Glucose Monitoring Data Interpretation: Recommendations From the International Consensus on Time in Range.

Authors:  Tadej Battelino; Thomas Danne; Richard M Bergenstal; Stephanie A Amiel; Roy Beck; Torben Biester; Emanuele Bosi; Bruce A Buckingham; William T Cefalu; Kelly L Close; Claudio Cobelli; Eyal Dassau; J Hans DeVries; Kim C Donaghue; Klemen Dovc; Francis J Doyle; Satish Garg; George Grunberger; Simon Heller; Lutz Heinemann; Irl B Hirsch; Roman Hovorka; Weiping Jia; Olga Kordonouri; Boris Kovatchev; Aaron Kowalski; Lori Laffel; Brian Levine; Alexander Mayorov; Chantal Mathieu; Helen R Murphy; Revital Nimri; Kirsten Nørgaard; Christopher G Parkin; Eric Renard; David Rodbard; Banshi Saboo; Desmond Schatz; Keaton Stoner; Tatsuiko Urakami; Stuart A Weinzimer; Moshe Phillip
Journal:  Diabetes Care       Date:  2019-06-08       Impact factor: 19.112

  5 in total

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