PREDICTION OF SULFAMETHOXAZOLE AND TRIMETHOPRIM PLASMA LEVELS FROM TABLETS AND DISSOLUTION MEDIA OF PHYSIOLOGICAL RELEVANCE

Authors

  • JOSE MANUEL RIOS-RODRIGUEZ Departamento Sistemas Biológicos, Universidad Autónoma Metropolitana-Xochimilco, Mexico City, Mexico https://orcid.org/0009-0000-3993-2954
  • FELIPE DINO REYES-RAMIREZ Departamento Sistemas Biológicos, Universidad Autónoma Metropolitana-Xochimilco, Mexico City, Mexico https://orcid.org/0009-0009-6876-9223
  • JUAN CARLOS RUIZ-SEGURA Departamento Sistemas Biológicos, Universidad Autónoma Metropolitana-Xochimilco, Mexico City, Mexico
  • JOSE RAUL MEDINA-LOPEZ Departamento Sistemas Biológicos, Universidad Autónoma Metropolitana-Xochimilco, Mexico City, Mexico https://orcid.org/0000-0002-4159-8403

DOI:

https://doi.org/10.22159/ijap.2024v16i3.50409

Keywords:

Convolution, Fixed-dose formulations, Sulfamethoxazole, Trimethoprim, Prediction error

Abstract

Objective: To estimate plasma concentrations-time profiles of Sulfamethoxazole (SMZ) and Trimethoprim (TMP) from fixed-dose combination formulations through in vitro data of dissolution media of physiological relevance and a convolution model.

Methods: Dissolution profiles of SMZ/TMP tablets (400/80 mg) were obtained with USP paddle apparatus at 100 rpm and 900 ml of 0.1 N HCl, pH 4.5 acetate buffer, and pH 6.8 phosphate buffer. The reference drug product and two generic formulations were tested. Drugs were quantified by a derivative method. Dissolution profiles were compared with model-dependent and -independent methods. SMZ/TMP plasma levels were simulated with dissolution data and published in vivo information. Percent of prediction error (PE) for peak plasma concentration (Cmax) and area under the curve from zero time to infinity (AUC0-inf) at each condition were calculated.

Results: In all used conditions similar dissolution profiles were found excepting for TMP at pH 1.2 (f2<50). The in vitro release performance for reference and generic formulations was explained by the Weibull function only for SMZ at pH 6.8 and TMP at pH 4.5. Values of PE>19% for both generic formulations were found with TMP at pH 1.2.

Conclusion: Significant differences in TMP dissolution profiles of generic formulations at pH 1.2 reflect the subsequent differences found in predicted Cmax and AUC0-inf.

Downloads

Download data is not yet available.

References

Master PA, O’Bryan TA, Zurlo J, Miller DQ, Joshi N. Trimethoprim-sulfamethoxazole revisited. Arch Intern Med. 2003;163(4):402-10. doi: 10.1001/archinte.163.4.402

Lindenberg M, Kopp S, Dressman JB. Classification of orally administered drugs on the World Health Organization Model list of essential medicines according to the biopharmaceutics classification system. Eur J Pharm Biopharm. 2004;58(2):265-78. doi: 10.1016/j.ejpb.2004.03.001

United States Pharmacopeia. USP 44. NF 39. The United States of America Pharmacopeial Convention. Rockville, MD: Inc; 2021.

Uddin R, Saffoon N, Sutradhar B. Dissolution and dissolution apparatus: a review. Int J Curr Biomed Pharm Res. 2011;1(4):201-7.

Fuerte V, Maldonado M, Rees GD. The multicomponent automated dissolution system: an alternative in the development and pharmaceutical analysis of generic polydrugs. J Pharm Biomed Anal. 1999;21(2):267-72. doi: 10.1016/s0731-7085(99)00122-3.

Medina R, Mirando M, Hurtado M, Domínguez-Ramírez AM, Reyes O, Ruiz-Segura JC. In vitro evaluation of trimethoprim and sulfamethoxazole from fixed-dose combination generic drugs using spectrophotometry: comparison of flow-through cell and USP paddle methods. Trop J Pharm Res. 2015;14(11):2061-9. doi: 10.4314/tjpr.v14i11.16

Hassan HA, Charoo NA, Ali AA, Alkhatem SS. Establishment of a bioequivalence-indicating dissolution specification for candesartan cilexetil tablets using a convolution model. Dissolut Technol. 2015;22(1):36-43. doi: 10.14227/DT220115P36

Rastogi V, Yadav P, Lal N, Rastogi P, Singh BK, Verma N, Verma A. Mathematical prediction of pharmacokinetic parameters-an- in-vitro approach for investigating pharmaceutical products for IVIVC. Future J Pharm Sci. 2018;4(2):175-84. doi: 10.1016/j.fjps.2018.03.001

Listado actualizado de Medicamentos de Referencia 2023/01. Cofepris. Mexico. Available from: https://www.gob.mx/cms/uploads/attachment/file/869172/LMR_2023-02_actualizaci_n_18_octubre_2023.pdf [Last accessed on 18 Jan 2024]

Calaça GN, Pessoa CA, Wohnrath K, Nagata N. Simultaneous determination of sulfamethoxazole and trimethoprim in pharmaceutical formulations by square wave voltammetry. Int J Pharm Pharm Sci. 2014;6(9):438-42.

Swetha G, Kumar KP, Sirisha K. New validated method developmet for the estimation of sulfamethoxazole and trimethoprim in bulk form by visible spectroscopy. Int J Pharm Pharm Sci. 2018;10(12):50-7. doi: 10.22159/ijpps.2018v10i12.26650

Muchlisyam, Pardede TR, Satiawan R. Determination of simultaneous sulfamethoxazole and trimethoprim by ultraviolet spectrophotometry with mean centering of ratio spectra. Asian J Pharm Clin Res. 2018;11(1):61-9. doi: 10.22159/ajpcr.2018.v11s1.26569

Medina JR, Miranda M, Hurtado M, Domínguez-Ramírez AM, Ruiz-Segura JC. Simultaneous determination of trimethoprim and sulfamethoxazole in immediate-release oral dosage forms by first-order derivative spectroscopy: application to dissolution studies. Int J Pharm Pharm Sci. 2013;5(4):505-10.

Zhang Y, Huo M, Zhou J, Zou A, Li W, Yao C, Xie S. DDSolver: an add-in program for modeling and comparison of drug dissolution profiles. AAPS J. 2010;12(3):263-71. doi: 10.1208/s12248-010-9185-1

Yuksel N, Kanik AE, Baykara T. Comparison of in vitro dissolution profiles by ANOVA-based, model-dependent and -independent methods. Int J Pharm. 2000;209(1‒2):57-67. doi: 10.1016/s0378-5173(00)00554-8

Hassan HA, Charoo NA, Ali AA, Alkhatem SS. Establishment of a bioequivalence-indicating dissolution specification for candesartan cilexetil tablets using a convolution model. Dissolut Technol. 2015;36-43. doi: 10.14227/DT220115P36

Amini H, Ahmadiani A. Rapid and simultaneous determination of sulfamethoxazole and trimethoprim in human plasma by high-performance liquid chromatography. J Pharm Biomed Anal. 2007;43(3):1146-50. doi: 10.1016/j.jpba.2006.09.004

Stevens RC, Rodman JH. Pharmacokinetics of antimicrobial therapy. Seminar in Pediatric Infectious Diseases. 1998;9(4):273-80. doi: 10.1016/S1045-1870(98)80016-2

Cardot JM, Lukas JC, Muniz P. Time scaling for in vitro-in vivo correlation: the inverse release function (IRF) approach. AAPS J. 2018;20(6):95. doi: 10.1208/s12248-018-0250-5

Zhang Y, Huo M, Zhou J, Xie S. PKSolver: an add-in program for pharmacokinetic and pharmacodynamic data analysis in Microsoft Excel. Comput Methods Programs Biomed. 2010;99(3):306-14. doi: 10.1016/j.cmpb.2010.01.007

Food and Drug Administration. Guidance for Industry: extended release oral dosage forms: Development, evaluation, and application of in vitro/in vivo correlations; 1997. Available from: https://www.fda.gov/media/70939/download [Last accessed on 18 Jan 2024]

Bendas ER. Two different approaches for the prediction of in vivo plasma concentration–time profile from in vitro release data of once daily formulations of diltiazem hydrochloride. Arch Pharm Res. 2009;32:(9):1317-29. doi: 10.1007/s12272-009-1918-2

El-Masry SM, Helmy SA. Hydrogel-based matrices for controlled drug delivery of etamsylate: prediction of in-vivo plasma profiles. Saudi Pharm J. 2020;28(12):1704-18. doi: 10.1016/j.jsps.2020.10.016

Food and Drug Administration. Guidance for Industry: waiver on in vivo bioavailability and bioequivalence studies for immediate-release solid oral dosage forms based on a biopharmaceutics classification system; 2017. Available from: https://collections.nlm.nih.gov/catalog/nlm:nlmuid-101720038-pdf. [Last accessed on 18 Jan 2024]

Demirtürk E, Öner L. In vitro-in vivo correlations. FABAD J Pharm Sci. 2003;28:215-24.

Nainar S, Rajiah K, Angamuthu S, Prabakaran D, Kasibhatta R. Biopharmaceutical classification system in in-vitro/in-vivo correlation: concept and development strategies in drug delivery. Trop J Pharm Res. 2012;11(2):319-29. doi: 10.4314/tjpr.v11i2.20

Cardot JM, Garrait G, Beyssac E. Use of IVIVC to optimize generic development. Dissolut Technol. 2015;22(2):44-8. doi: 10.142227/DT220215P44

Marroum P. Role of in vitro-in vivo correlations in drug development. Dissolut Technol. 2015;22(2):50-6. doi: 10.14227/DT220215P50

Mitra A, Wu Y. Challenges and opportunities in achieving bioequivalence for fixed-dose combination products. AAPS J. 2012;14(3):646-55. doi: 10.1208/s12248-012-9378-x

Sowmya C, Abrar Ahmed H, Suriya Prakaash KK. Virtual bioequivalence in pharmaceuticals: current status and future prospects. Int J App Pharm. 2023;15(5):1-9. doi:10.22159/ijap.2023v15i5.48589

Xu J, Zhang L, Shao X. Applications of bio-predictive dissolution tools for the development of solid oral dosage forms: current industry experience. Drug Dev Ind Pharm. 2022;48(3):79-97. doi: 10.1080/03639045.2022.2098315

Published

11-03-2024

How to Cite

RIOS-RODRIGUEZ, J. M., REYES-RAMIREZ, F. D., RUIZ-SEGURA, J. C., & MEDINA-LOPEZ, J. R. (2024). PREDICTION OF SULFAMETHOXAZOLE AND TRIMETHOPRIM PLASMA LEVELS FROM TABLETS AND DISSOLUTION MEDIA OF PHYSIOLOGICAL RELEVANCE. International Journal of Applied Pharmaceutics, 16(3). https://doi.org/10.22159/ijap.2024v16i3.50409

Issue

Section

Original Article(s)

Most read articles by the same author(s)

1 2 > >>