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Dissolution Profiles Comparison Using Conventional and Bias Corrected and Accelerated f2 Bootstrap Approaches with Different Software’s: Impact of Variability, Sample Size and Number of Bootstraps

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Abstract

Dissolution profiles comparison is an important element in order to support biowaivers, scale-up and post approval changes and site transfers. Highly variable dissolution can possess significant challenges for comparison and f2 bootstrap approach can be utilized in such cases. However, availability of different types of f2 and confidence intervals (CI) methods indicates necessity to understand each type of calculation thoroughly. Among all approaches, bias corrected and accelerated (BCa) can be an attractive choice as it corrects the bias and skewness of the distribution. In this manuscript, we have performed comparison of highly variable dissolution data using various software’s by adopting percentile and BCa CI approaches. Diverse data with different variability’s, number of samples and bootstraps were evaluated with JMP, DDSolver, R-software, SAS and PhEq. While all software’s yielded similar observed f2 values, differences in lower percentile CI was observed. BCa with R-software and JMP provided superior lower percentile as compared to other computations. Expected f2 recommended by EMA has resulted as stringent criteria as compared to estimated f2. No impact of number of bootstraps on similarity analysis was observed whereas number of samples increased chance of acceptance. Variability has impacted similarity outcome with estimated f2 but chance of acceptance enhanced with BCa approach. Further, freely available R-software can be of attractive choice due to computation of various types of f2, percentile and BCa intervals. Overall, this work can enable regulatory submissions to enhance probability of similarity through appropriate selection of number of samples, technique based on variability of dissolution data.

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Data Availability

The authors would like to indicate that data supporting the findings of this work are available within the article.

Abbreviations

BCa:

Bias corrected and accelerated bootstrap interval

BC-f2:

Bias corrected f2

BCS:

Biopharmaceutical classification system

CI:

Confidence interval

EMA:

European medicines agency

Est f2:

Estimated f2

Exp f2:

Expected f2

F2:

Similarity factor

FDA:

Food and Drug Administration

GUI:

Graphic user interface

MSD:

Multivariate statistical distance

Obs f2:

Observed f2

QQ plot:

Quantile-quantile plot

%RSD:

% Relative standard deviation

SAS:

Statistical Analysis System

USFDA:

United states Food and Drug Administration

VC exp f2:

Variance corrected expected f2

VBC f2:

Variance- and bias-corrected f2

References

  1. Gray VA. Power of the dissolution test in distinguishing a change in dosage form critical quality attributes. AAPS PharmSciTech. 2018;19:3328–32. https://doi.org/10.1208/s12249-018-1197-7.

    Article  PubMed  Google Scholar 

  2. Shah VP. Dissolution: a quality control test vs. a bioequivalence Test. Dissolution Technol. 2021. https://doi.org/10.14227/DT080401P6.

  3. Cardot JM, Garcia-Arieta A, Paixao P, Tasevska I, Davit B. Implementing the additional strength biowaiver for generics: EMA recommended approaches and challenges for a US-FDA submission. Eur J Pharm Sci. 2018;111:399–408. https://doi.org/10.1016/j.ejps.2017.10.013.

    Article  CAS  PubMed  Google Scholar 

  4. USFDA Guidance for industry. 2021. Bioequivalence Studies With Pharmacokinetic Endpoints for Drugs Submitted Under an ANDA. https://www.fda.gov/media/87219/download. Accessed 30 Sep 2023.

  5. EMA Guideline on investigation of bioequivalence. 2010. https://www.ema.europa.eu/en/documents/scientific-guideline/guideline-investigation-bioequivalence-rev1_en.pdf. Accessed 30 Sep 2023.

  6. USFDA, ICH M9. Guidance for industry. 2021. M9 Biopharmaceutics Classification System-Based Biowaivers. https://www.fda.gov/regulatory-information/search-fda-guidance-documents/m9-biopharmaceutics-classification-system-based-biowaivers. Accessed 30 Sep 2023.

  7. Kollipara S, Ahmed T, Bhattiprolu AK, Chachad S. In vitro and In silico biopharmaceutic regulatory guidelines for generic bioequivalence for oral products: comparison among various regulatory agencies. Biopharm Drug Dispos. 2021;42(7):297–318. https://doi.org/10.1002/bdd.2292.

    Article  CAS  PubMed  Google Scholar 

  8. USFDA, Guidance for industry, 1997. Dissolution Testing of Immediate Release Solid Oral Dosage Forms. https://www.fda.gov/media/70936/download. Accessed 30 Sep 2023.

  9. Cardot JM, Roudier B, Schütz H. Dissolution comparisons using a Multivariate Statistical Distance (MSD) test and a comparison of various approaches for calculating the measurements of dissolution profile comparison. AAPS J. 2017;19(4):1091–101. https://doi.org/10.1208/s12248-017-0063-y.

    Article  CAS  PubMed  Google Scholar 

  10. Hoffelder T. Equivalence analyses of dissolution profiles with the Mahalanobis distance. Biom J. 2019;61(5):1120–37. https://doi.org/10.1002/bimj.201700257.

    Article  MathSciNet  PubMed  Google Scholar 

  11. Sathe PM, Tsong Y, Shah VP. In-vitro dissolution profile comparison: statistics and analysis, model dependent approach. Pharm Res. 1996;3:1799–1803. https://doi.org/10.1023/A:1016020822093.

  12. Usta DY, Incecayir T. Modeling of in vitro dissolution profiles of carvedilol immediate-release tablets in different dissolution media. AAPS PharmSciTech. 2022;23:201. https://doi.org/10.1208/s12249-022-02355-0.

    Article  CAS  PubMed  Google Scholar 

  13. Kollipara S, Boddu R, Ahmed T, Chachad S. Simplified model-dependent and model-independent approaches for dissolution profile comparison for oral products: regulatory perspective for generic product development. AAPS PharmSciTech. 2022;23(1):53. https://doi.org/10.1208/s12249-021-02203-7.

    Article  PubMed  Google Scholar 

  14. EMA 2018. Question and answer on the adequacy of the Mahalanobis distance to assess the comparability of drug dissolution profiles. https://www.ema.europa.eu/en/documents/scientific-guideline/question-answer-adequacy-mahalanobis-distance-assess-comparability-drug-dissolution-profiles_en.pdf. Accessed 30 Sep 2023.

  15. Muselik J, Komersova A, Kubova K, Matzick K, Skalicka B. A Critical overview of FDA and EMA statistical methods to compare in vitro drug dissolution profiles of pharmaceutical products. Pharmaceutics. 2021;13(10):1703. https://doi.org/10.3390/pharmaceutics13101703.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Shah VP, Tsong Y, Sathe P, Liu JP. In vitro dissolution profile comparison–statistics and analysis of the similarity factor, f2. Pharm Res. 1998;15(6):889–96. https://doi.org/10.1023/a:1011976615750.

    Article  CAS  PubMed  Google Scholar 

  17. Paixao P, Gouvenia LF, Silva N, Morais JAG. Evaluation of dissolution profile similarity – comparison between the f2, the multivariate statistical distance and the f2 bootstraps methods. Eur J Pharm Biopharm. 2017;112:67–74. https://doi.org/10.1016/j.ejpb.2016.10.026.

    Article  CAS  PubMed  Google Scholar 

  18. Stevens RE, Gray V, Dorantes A, Gold L, Pham L. Scientific and regulatory standards for assessing product performance using the similarity factor, f2. AAPS J. 2015;17(2):301–6. https://doi.org/10.1208/s12248-015-9723-y.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Efron B. Better bootstrap confidence intervals. J Am Stat Assoc. 1987;82(397):171–85. https://doi.org/10.2307/2289144.

    Article  MathSciNet  Google Scholar 

  20. SAS Blogs. The bias-corrected and accelerated (BCa) bootstrap interval. https://blogs.sas.com/content/iml/2017/07/12/bootstrap-BCa-interval.html. Accessed 30 Sep 2023.

  21. Noce L, Gwaza L, Mangas-Sanjuan V, Garcia-Arieta A. Comparison of free software platforms for the calculation of the 90% confidence interval of f2 similarity factor by bootstrap analysis. Eur J Pharm Sci. 2020;15(146): 105259. https://doi.org/10.1016/j.ejps.2020.105259.

    Article  CAS  Google Scholar 

  22. Xu Z, Merino-Sanjuan M, Mangas-Sanjuan V, García-Arieta A. Estimators and confidence intervals of f2 using bootstrap methodology for the comparison of dissolution profiles. Comput Methods Programs Biomed. 2021;212: 106449. https://doi.org/10.1016/j.cmpb.2021.106449.

    Article  PubMed  Google Scholar 

  23. Zhang Y, Huo M, Zhou J, et al. DDSolver: an add-in program for modeling and comparison of drug dissolution profiles. AAPS J. 2010;12:263–71. https://doi.org/10.1208/s12248-010-9185-1.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Mendyk A, Paclawski A, Szlek J, Jachowicz R. PhEq_bootstrap: open-source software for the simulation of f2 distribution in cases of large variability in dissolution profiles. Dissolution Technol. 2013. https://doi.org/10.14227/DT200113P13.

  25. Qazi S, Samuel NKP, Venkatachalam TK, Uckun FM. Evaluating dissolution profiles of an anti-HIV agent using ANOVA and non-linear regression models in JMP software. Int J Pharm. 2003;252(1–2):27–39. https://doi.org/10.1016/S0378-5173(02)00603-8.

    Article  CAS  PubMed  Google Scholar 

  26. R package bootf2. 2022. https://cran.radicaldevelop.com/web/packages/bootf2/bootf2.pdf. Accessed 30 Sep 2023.

  27. Ahmed T, Kollipara S, Boddu R, Bhattiprolu A. Biopharmaceutics risk assessment-connecting critical bioavailability attributes with in vitro, in vivo properties and physiologically based biopharmaceutics modeling to enable generic regulatory submissions. AAPS J. 2023;25(5):77. https://doi.org/10.1208/s12248-023-00837-y.

    Article  PubMed  Google Scholar 

  28. LeBond D, Altan S, Novick S, Peterson J, Shen Y Yang H. In vitro dissolution curve comparisons: a critique of current practice. Dissolution Technol. 2016. https://doi.org/10.14227/DT230116P14.

  29. Bhattiprolu AK, Kollipara S, Boddu R, Ahmed T, Chachad S. Utility of physiologically based biopharmaceutics modeling (pbbm) in regulatory perspective: application to supersede f2, enabling biowaivers & creation of dissolution safe space. J Pharm Sci. 2022;111(12):3397–410. https://doi.org/10.1016/j.xphs.2022.09.003.

    Article  CAS  PubMed  Google Scholar 

  30. Jaiswal S, Ahmed T, Kollipara S, Bhargava M, Chachad S. Development, validation and application of physiologically based biopharmaceutics model to justify the change in dissolution specifications for DRL ABC extended release tablets. Drug Dev Ind Pharm. 2021;47(5):778–89. https://doi.org/10.1080/03639045.2021.1934870.

    Article  CAS  PubMed  Google Scholar 

  31. Yuvaneshwari K, Kollipara S, Ahmed T, Chachad S. Applications of PBPK/PBBM modeling in generic product development: an industry perspective. J Drug Del Sci Technol. 2022;69: 103152. https://doi.org/10.1016/j.jddst.2022.103152.

    Article  CAS  Google Scholar 

  32. Boddu R, Kollipara S, Vijaywargi G, Ahmed T. Power of integrating PBPK with PBBM (PBPK-BM): a single model predicting food effect, gender impact, drug-drug interactions and bioequivalence in fasting & fed conditions. Xenobiotica. 2023;53(4):260–78. https://doi.org/10.1080/00498254.2023.2238048.

    Article  CAS  PubMed  Google Scholar 

  33. Mangas-Sanjuan V, Colon-Useche S, Gonzalez-Alvarez I, Bermejo M, Garcia-Arieta A. Assessment of the regulatory methods for the comparison of highly variable dissolution profiles. AAPS J. 2016;18(6):1550–61. https://doi.org/10.1208/s12248-016-9971-5.

    Article  PubMed  Google Scholar 

  34. Islam MM, Begum M. Bootstrap confidence intervals for dissolution similarity factor f 2. Biom Biostat Int J. 2018;7(5):397–403. https://doi.org/10.15406/bbij.2018.07.00237.

  35. Costa P, Manuel J, Lobo S. Modeling and comparison of dissolution profiles. Eur J Pharm Sci. 2001;13(2):123–33. https://doi.org/10.1016/S0928-0987(01)00095-1.

    Article  CAS  PubMed  Google Scholar 

  36. Wang Y, Snee RD, Keyvan G, Muzzio FJ. Statistical comparison of dissolution profiles. Drug Dev Ind Pharm. 2016;42(5):796–807. https://doi.org/10.3109/03639045.2015.1078349.

    Article  CAS  PubMed  Google Scholar 

  37. Hoffelder T, Leblond D, Van Alstine L, Diaz DA, Suarez-Sharp S, Witkowski K. et al. Dissolution profile similarity analyses – statistical principles, methods and considerations. AAPS J. 2022;24:54. https://doi.org/10.1208/s12248-022-00697-y.

  38. Freitag G. Guidelines on dissolution profile comparison. Drug Inf J. 2001;35(3):865–74. https://doi.org/10.1177/009286150103500325.

    Article  Google Scholar 

  39. Diaz DA, Colgan ST, Langer CS, Bandi NT, Likar MD, Alstine LV. Dissolution similarity requirements: how similar or dissimilar are the global regulatory expectations? AAPS J. 2016;18:15–22. https://doi.org/10.1208/s12248-015-9830-9.

    Article  PubMed  Google Scholar 

  40. Horkovics-Kovats S. Comparison of dissolution time profiles: No similarity but where is the difference? Eur J Pharm Sci. 2018;121:9–15. https://doi.org/10.1016/j.ejps.2018.05.017.

    Article  CAS  PubMed  Google Scholar 

  41. Suarez-Sharp S, Abend A, Hoffelder T, Hoffelder T, Leblond D, Delvadia P. In vitro dissolution profiles similarity assessment in support of drug product quality: what, how, when—workshop summary report. AAPS J. 2020;22:74. https://doi.org/10.1208/s12248-020-00458-9.

    Article  PubMed  Google Scholar 

  42. Abend AM, Zhang L, Fredro-Kumbaradzi E, Hoffelder T, Choen MJ, Anand O, et al. Current approaches for dissolution similarity assessment, requirements, and global expectations. AAPS J. 2022;24:50. https://doi.org/10.1208/s12248-022-00691-4.

    Article  PubMed  Google Scholar 

  43. Abend AM, Hoffelder T, Cohen MJ, Alstine LV, Diaz DA, Fredro-Kumbaradzi E, et al. Dissolution profile similarity assessment—best practices, decision trees and global harmonization. AAPS J. 2023;25:44. https://doi.org/10.1208/s12248-023-00795-5.

    Article  PubMed  Google Scholar 

  44. Srebro J, Abend A, Dorożyński P, Fotaki N, Garbacz G, Gray VA et al. Report on the virtual workshop: a quest for biowaiver, including next generation dissolution characterization and modeling. Dissolution Technol. 2023. https://doi.org/10.14227/DT300223P100.

  45. Liu S, Cai X, Shen M, Tsong T. In vitro dissolution profile comparison using bootstrap bias corrected similarity factor, f2. J Biopharm Stat. 2023;29:1–12. https://doi.org/10.1080/10543406.2023.2171429.

    Article  CAS  Google Scholar 

  46. Novick S, Shen Y, Yang H, Peterson J, LeBlond D, Altan S. Dissolution Curve Comparisons Through the F2 Parameter, a Bayesian Extension of the f2 Statistic. J Biopharm Stat. 2015;25(2): https://doi.org/10.1080/10543406.2014.971175.

  47. Xu Z, Merino-Sanjuan M, Mangas-Sanjuan V, García-Arieta A. Estimators and confidence intervals of f2 using bootstrap methodology for the comparison of dissolution profiles. Comput Methods Programs Biomed. 2021;212. https://doi.org/10.1016/j.cmpb.2021.106449.

  48. Kaity S, Sah SK, Karanwad T, Banerjee S. Bootstrap statistics and its application in disintegration and dissolution data analysis. Mol Pharmaceutics. 2023;20(8):3791–803. https://doi.org/10.1021/acs.molpharmaceut.3c00222.

    Article  CAS  Google Scholar 

  49. Solis-Cruz B, Hernandez-Patlan D, Hipólito EAM, Tellez-Isaias G, Pineda AA, López-Arellano R. Discriminative dissolution method using the open-loop configuration of the USP IV apparatus to compare dissolution profiles of metoprolol tartrate immediate-release tablets: use of kinetic parameters. Pharmaceutics. 2023;15(9):2191. https://doi.org/10.3390/pharmaceutics15092191.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Martinez MN, Zhao Z. A simple approach for comparing the in vitro dissolution profiles of highly variable drug products: a proposal. AAPS J. 2018;20:78. https://doi.org/10.1208/s12248-018-0238-1.

    Article  CAS  PubMed  Google Scholar 

  51. USFDA NDA 204412. Clinical Pharmacology and Biopharmaceutics Reviews for Mesalamine Delzicol capsules. 2013. https://www.accessdata.fda.gov/drugsatfda_docs/nda/2013/204412Orig1s000ClinPharmR.pdf. Accessed 30 Sep 2023.

  52. EMA, Clinical pharmacology and pharmacokinetics: questions and answers. https://www.ema.europa.eu/en/human-regulatory/research-development/scientific-guidelines/clinical-pharmacology-pharmacokinetics/clinical-pharmacology-pharmacokinetics-questions-answers. Accessed 30 Sep 2023.

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Acknowledgements

The authors would like to thank Dr. Reddy’s Laboratories Ltd. for providing opportunity to publish this manuscript.

Funding

This article has been sponsored by Dr. Reddy’s Laboratories Ltd.

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Authors and Affiliations

  1. Biopharmaceutics Group, Global Clinical Management, Dr. Reddy’s Laboratories Ltd., Integrated Product Development Organization (IPDO), Bachupally, Medchal Malkajgiri District, Hyderabad, 500 090, Telangana, India

    Rajkumar Boddu, Sivacharan Kollipara, Adithya Karthik Bhattiprolu, Ashima Bhatia & Tausif Ahmed

  2. Digital and Process Excellence, Dr. Reddy’s Laboratories Ltd., Integrated Product Development Organization (IPDO), Bachupally, Medchal Malkajgiri District, Hyderabad, 500 090, Telangana, India

    Karthik Parsa

  3. Biostatistics & Data Management, Global Clinical Management, Dr. Reddy’s Laboratories Ltd., Integrated Product Development Organization (IPDO), Bachupally, Medchal Malkajgiri District, Hyderabad, 500 090, Telangana, India

    Sanketh Kumar Chakilam & Krishna Reddy Daka

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Contributions

Rajkumar Boddu – conceptualization, methodology, visualization, writing – original draft, writing – review and editing; Sivacharan Kollipara – conceptualization, methodology, visualization, writing – original draft, writing – review and editing; Adithya Karthik Bhattiprolu – conceptualization, writing – original draft; Karthik Parsa – conceptualization, methodology, visualization, writing – original draft; Sanketh Kumar Chakilam – conceptualization, methodology, visualization; Daka Krishna Reddy – conceptualization, methodology, visualization; Ashima Bhatia – writing – review and editing, supervision; Tausif Ahmed – conceptualization, methodology, visualization, writing – review and editing, supervision.

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Correspondence to Tausif Ahmed.

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Boddu, R., Kollipara, S., Bhattiprolu, A.K. et al. Dissolution Profiles Comparison Using Conventional and Bias Corrected and Accelerated f2 Bootstrap Approaches with Different Software’s: Impact of Variability, Sample Size and Number of Bootstraps. AAPS PharmSciTech 25, 5 (2024). https://doi.org/10.1208/s12249-023-02710-9

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