Input Estimation for Extended-Release Formulations Exemplified with Exenatide

Overview

Journal Front Bioeng Biotechnol

Date 2017 May 5

PMID 28470000

Citations 2

Authors

Magnus Tragardh

Michael J Chappell

Johan E Palm

Neil D Evans

David L I Janzen

Peter Gennemark

Affiliations

Soon will be listed here.

Abstract

Estimating the absorption profile of a drug is essential when developing extended-release medications. Such estimates can be obtained by measuring plasma concentrations over time and inferring the absorption from a model of the drug's pharmacokinetics. Of particular interest is to predict the bioavailability-the fraction of the drug that is absorbed and enters the systemic circulation. This paper presents a framework for addressing this class of estimation problems and gives advice on the choice of method. In parametric methods, a model is constructed for the absorption process, which can be difficult when the absorption has a complicated profile. Here, we place emphasis on non-parametric methods that avoid making strong assumptions about the absorption. A modern estimation method that can address very general input-estimation problems has previously been presented. In this method, the absorption profile is modeled as a stochastic process, which is estimated using Markov chain Monte Carlo techniques. The applicability of this method for extended-release formulation development is evaluated by analyzing a dataset of Bydureon, an injectable extended-release suspension formulation of exenatide, a GLP-1 receptor agonist for treating diabetes. This drug is known to have non-linear pharmacokinetics. Its plasma concentration profile exhibits multiple peaks, something that can make parametric modeling challenging, but poses no major difficulties for non-parametric methods. The method is also validated on synthetic data, exploring the effects of sampling and noise on the accuracy of the estimates.

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References

Buse J, Drucker D, Taylor K, Kim T, Walsh B, Hu H . DURATION-1: exenatide once weekly produces sustained glycemic control and weight loss over 52 weeks. Diabetes Care. 2010; 33(6):1255-61. PMC: 2875434. DOI: 10.2337/dc09-1914. View

Buse J, Nauck M, Forst T, Sheu W, Shenouda S, Heilmann C . Exenatide once weekly versus liraglutide once daily in patients with type 2 diabetes (DURATION-6): a randomised, open-label study. Lancet. 2012; 381(9861):117-24. DOI: 10.1016/S0140-6736(12)61267-7. View

Mitragotri S, Burke P, Langer R . Overcoming the challenges in administering biopharmaceuticals: formulation and delivery strategies. Nat Rev Drug Discov. 2014; 13(9):655-72. PMC: 4455970. DOI: 10.1038/nrd4363. View

Schwendeman S, Shah R, Bailey B, Schwendeman A . Injectable controlled release depots for large molecules. J Control Release. 2014; 190:240-53. PMC: 4261190. DOI: 10.1016/j.jconrel.2014.05.057. View

Madden F, Godfrey K, Chappell M, Hovorka R, Bates R . A comparison of six deconvolution techniques. J Pharmacokinet Biopharm. 1996; 24(3):283-99. DOI: 10.1007/BF02353672. View

DeFronzo R, Ratner R, Han J, Kim D, Fineman M, Baron A . Effects of exenatide (exendin-4) on glycemic control and weight over 30 weeks in metformin-treated patients with type 2 diabetes. Diabetes Care. 2005; 28(5):1092-100. DOI: 10.2337/diacare.28.5.1092. View

Verotta D . Concepts, properties, and applications of linear systems to describe distribution, identify input, and control endogenous substances and drugs in biological systems. Crit Rev Biomed Eng. 1996; 24(2-3):73-139. DOI: 10.1615/critrevbiomedeng.v24.i2-3.10. View

Tragardh M, Chappell M, Ahnmark A, Linden D, Evans N, Gennemark P . Input estimation for drug discovery using optimal control and Markov chain Monte Carlo approaches. J Pharmacokinet Pharmacodyn. 2016; 43(2):207-21. PMC: 4791487. DOI: 10.1007/s10928-016-9467-z. View

Pillonetto G, Sparacino G, Cobelli C . Handling non-negativity in deconvolution of physiological signals: a nonlinear stochastic approach. Ann Biomed Eng. 2002; 30(8):1077-87. DOI: 10.1114/1.1510449. View

10.

Johansson C, Gennemark P, Artursson P, Abelo A, Ashton M, Jansson-Lofmark R . Population pharmacokinetic modeling and deconvolution of enantioselective absorption of eflornithine in the rat. J Pharmacokinet Pharmacodyn. 2013; 40(1):117-28. DOI: 10.1007/s10928-012-9293-x. View

11.

Gulati N, Gupta H . Parenteral drug delivery: a review. Recent Pat Drug Deliv Formul. 2011; 5(2):133-45. DOI: 10.2174/187221111795471391. View

12.

Geman S, Geman D . Stochastic relaxation, gibbs distributions, and the bayesian restoration of images. IEEE Trans Pattern Anal Mach Intell. 2012; 6(6):721-41. DOI: 10.1109/tpami.1984.4767596. View

13.

DeYoung M, MacConell L, Sarin V, Trautmann M, Herbert P . Encapsulation of exenatide in poly-(D,L-lactide-co-glycolide) microspheres produced an investigational long-acting once-weekly formulation for type 2 diabetes. Diabetes Technol Ther. 2011; 13(11):1145-54. PMC: 3202891. DOI: 10.1089/dia.2011.0050. View

14.

Cardot J, Davit B . In vitro-in vivo correlations: tricks and traps. AAPS J. 2012; 14(3):491-9. PMC: 3385821. DOI: 10.1208/s12248-012-9359-0. View

15.

Yang W, Pierstorff E . Reservoir-based polymer drug delivery systems. J Lab Autom. 2012; 17(1):50-8. DOI: 10.1177/2211068211428189. View

16.

Chen T, Mager D, Kagan L . Interspecies modeling and prediction of human exenatide pharmacokinetics. Pharm Res. 2012; 30(3):751-60. PMC: 3732180. DOI: 10.1007/s11095-012-0917-z. View

17.

Siepmann J, Peppas N . Modeling of drug release from delivery systems based on hydroxypropyl methylcellulose (HPMC). Adv Drug Deliv Rev. 2001; 48(2-3):139-57. DOI: 10.1016/s0169-409x(01)00112-0. View

18.

Arifin D, Lee L, Wang C . Mathematical modeling and simulation of drug release from microspheres: Implications to drug delivery systems. Adv Drug Deliv Rev. 2006; 58(12-13):1274-325. DOI: 10.1016/j.addr.2006.09.007. View

19.

Buse J, Henry R, Han J, Kim D, Fineman M, Baron A . Effects of exenatide (exendin-4) on glycemic control over 30 weeks in sulfonylurea-treated patients with type 2 diabetes. Diabetes Care. 2004; 27(11):2628-35. DOI: 10.2337/diacare.27.11.2628. View

20.

Ford Versypt A, Pack D, Braatz R . Mathematical modeling of drug delivery from autocatalytically degradable PLGA microspheres--a review. J Control Release. 2012; 165(1):29-37. PMC: 3518665. DOI: 10.1016/j.jconrel.2012.10.015. View