Uloga fiziološki-zasnovanog farmakokinetičkog/biofarmaceutskog modelovanja u razvoju farmaceutskih preparata

  • Sandra Cvijić University of Belgrade-Faculty of Pharmacy, Katedra za tehnologiju i kozmetologiju
  • Jelisaveta Ignjatović Univerzitet u Beogradu-Farmaceutski fakultet, Katedra za farmaceutsku tehnologiju i kozmetologiju
  • Jelena Parojčić Univerzitet u Beogradu-Farmaceutski fakultet, Katedra za farmaceutsku tehnologiju i kozmetologiju
  • Svetlana Ibrić Univerzitet u Beogradu-Farmaceutski fakultet, Katedra za farmaceutsku tehnologiju i kozmetologiju
DOI: https://doi.org/10.5937/arhfarm71-32479
Ključne reči: fiziološki zasnovano farmakokinetičko modelovanje (PBPK), fiziološki zasnovano biofarmaceutsko modelovanje (PBBM), model zasnovan razvoj lekova (MIDD), bioperformanse lekova

Sažetak


Računarski podržano (in silico) modelovanje se danas koristi u različitim oblastima farmaceutskih nauka, sa širokom paletom primene. Kao jedan od in silico alata, fiziološki zasnovano farmakokinetičko/biofarmaceutsko modelovanje (PBPK/PBBM) se pokazalo posebno korisnim u razvoju farmaceutskih preparata. Strategije zasnovane na PBPK/PBBM modelovanju se poslednjih godina sve više razmatraju, što se vidi iz izveštaja farmaceutskih kompanija i velikog broja publikovanih istraživačkih i revijalnih radova na ovu temu. Takođe, vodeće regulatorne agencije su nedavno izdale vodiče koji se odnose na primenu PBPK modelovanja u razvoju farmaceutskih preparata. In silico PBPK modelovanje je primenjivo za različite puteve primene leka (peroralni, (intra)oralni, parenteralni, inhalacioni, okularni, dermalni itd), mada se najveći broj primera iz literature odnosi na modelovanje bioperformansi peroralno primenjenih lekova. Kako bi olakšale primenu PBPK modelovanja, nekoliko kompanija je razvilo komercijalno dostupne programske pakete, kao što su GastroPlus™, Simcyp™ PBPK Simulator i PK-Sim®. U ovom radu su istaknute različite mogućnosti primene PBPK/PBBM modelovanja, uključujući osnovne principe, prednosti i ograničenja. Takođe, prikazani su odgovarajući primeri koji opisuju praktičnu primenu modelovanja i simulacija u različitim fazama razvoja leka.

Reference

Wang Y, Zhu H, Madabushi R, Liu Q, Huang SM, Zineh I. Model-informed drug development: current US regulatory practice and future considerations. Clin Pharmacol Ther. 2019;105(4):899-911.

Reddy MB, Clewell III HJ, Lave T, Andersen EM. Physiologically based pharmacokinetic modeling: a tool for understanding ADMET properties and extrapolating to human. New Insights into Toxic Drug Test. 2013; p 197-217.

Bermejo M, Hens B, Dickens J, Mudie D, Paixão P, Tsume Y, et al. A mechanistic physiologically-based biopharmaceutics modeling (PBBM) approach to assess the in vivo performance of an orally administered drug product: From IVIVC to IVIVP. Pharmaceutics. 2020;12(1):74.

Grbic S, Parojcic J, Djuric Z. Computer-aided biopharmaceutical characterization: gastrointestinal absorption simulation. Computer-aided applications in pharmaceutical technology. Woodhead Publishing Limited; 2013; p 177-232.

Kostewicz ES, Aarons L, Bergstrand M, Bolger MB, Galetin A, Hatley O, et al. PBPK models for the prediction of in vivo performance of oral dosage forms. Eur J Pharm Sci. 2014;57(1):300-21.

Hartmanshenn C, Scherholz M, Androulakis IP. Physiologically-based pharmacokinetic models: approaches for enabling personalized medicine. J Pharmacokinet Pharmacodyn. 2016;43(5):481-504.

Plusquellec Y, Efthymiopoulos C, Duthil P, Houin G. A pharmacokinetic model for multiple sites discontinuous gastrointestinal absorption. Med Eng Phys. 1999;21(8):525-32.

Zhang X, Quinney SK, Gorski JC, Jones DR, Hall SD. Semiphysiologically based pharmacokinetic models for the inhibition of midazolam clearance by diltiazem and its major metabolite. Drug Metab Dispos. 2009;37(8):1587-97.

Kambayashi A, Dressman JB. An in vitro-in silico-in vivo approach to predicting the oral pharmacokinetic profile of salts of weak acids: Case example dantrolene. Eur J Pharm Biopharm. 2013;84(1):200-7.

Scotcher D, Jones C, Rostami-Hodjegan A, Galetin A. Novel minimal physiologically-based model for the prediction of passive tubular reabsorption and renal excretion clearance. Eur J Pharm Sci. 2016;94:59-71.

Patel S, Zhu W, Xia B, Sharma N, Hermans A, Ehrick JD, et al. Integration of precipitation kinetics from an in vitro, multicompartment transfer system and mechanistic oral absorption modeling for pharmacokinetic prediction of weakly basic drugs. J Pharm Sci. 2019;108(1):574-83.

Agoram B, Woltosz WS, Bolger MB. Predicting the impact of physiological and biochemical processes on oral drug bioavailability. Adv Drug Deliv Rev. 2001;50:S41-67.

Lin L, Wong H. Predicting oral drug absorption: Mini review on physiologically-based pharmacokinetic models. Pharmaceutics. 2017;9(4):41.

Theil FP, Guentert TW, Haddad S, Poulin P. Utility of physiologically based pharmacokinetic models to drug development and rational drug discovery candidate selection. Toxicol Lett. 2003;138(1–2):29-49.

Jones HM, Rowland-Yeo K. Basic concepts in physiologically based pharmacokinetic modeling in drug discovery and development. CPT Pharmacometrics Syst Pharmacol. 2013;2(8):1-12.

Xia B, Yang Z, Zhou H, Lukacova V, Zhu W, Milewski M, et al. Development of a novel oral cavity compartmental absorption and transit model for sublingual administration: illustration with zolpidem. AAPS J. 2015;17(3):631-42.

Szabó P, Daróczi TB, Tóth G, Zelkó R. In vitro and in silico investigation of electrospun terbinafine hydrochloride-loaded buccal nanofibrous sheets. J Pharm Biomed Anal. 2016;131:156-9.

Chen F, Liu H, Wang B, Yang L, Cai W, Jiao Z, et al. Physiologically based pharmacokinetic modeling to understand the absorption of risperidone orodispersible film. Front Pharmacol. 2020;10:1-10.

Drašković M, Turković E, Vasiljević I, Trifković K, Cvijić S, Vasiljević D, et al. Comprehensive evaluation of formulation factors affecting critical quality attributes of casted orally disintegrating films. J Drug Deliv Sci Technol. 2020;56:101614.

Kurcubic I, Cvijic S, Filipcev B, Ignjatovic J, Ibric S, Djuris J. Development of propranolol hydrochloride bilayer mucoadhesive buccal tablets supported by in silico physiologically-based modeling. React Funct Polym. 2020;151:104587.

Jovanović M, Tomić N, Cvijić S, Stojanović D, Ibrić S, Uskoković P. Mucoadhesive gelatin buccal films with propranolol hydrochloride: Evaluation of mechanical, mucoadhesive, and biopharmaceutical properties. Pharmaceutics. 2021;13(2):1-19.

Santos J, Lobato L, Vale N. Clinical pharmacokinetic study of latrepirdine via in silico sublingual administration. Silico Pharmacol. 2021;9(1):29.

Wu S, Zellnitz S, Mercuri A, Salar-Behzadi S, Bresciani M, Fröhlich E. An in vitro and in silico study of the impact of engineered surface modifications on drug detachment from model carriers. Int J Pharm. 2016;513(1-2):109-17.

Bäckman P, Tehler U, Olsson B. Predicting exposure after oral inhalation of the selective glucocorticoid receptor modulator, AZD5423, based on dose, deposition pattern, and mechanistic modeling of pulmonary disposition. J Aerosol Med Pulm Drug Deliv. 2017;30(2):108-17.

Salar-Behzadi S, Wu S, Mercuri A, Meindl C, Stranzinger S, Fröhlich E. Effect of the pulmonary deposition and in vitro permeability on the prediction of plasma levels of inhaled budesonide formulation. Int J Pharm. 2017;532(1):337-44.

Vulović A, Šušteršič T, Cvijić S, Ibrić S, Filipović N. Coupled in silico platform: Computational fluid dynamics (CFD) and physiologically-based pharmacokinetic (PBPK) modelling. Eur J Pharm Sci. 2018;113:171-84.

Shi C, Ignjatović J, Liu T, Han M, Cun D, Đuriš J, et al. In vitro - in vivo - in silico approach in the development of inhaled drug products: Nanocrystal-based formulations with budesonide as a model drug. Asian J Pharm Sci. 2021; doi: 10.1016/j.ajps.2020.12.001.

Chaudhuri SR, Lukacova V, Woltosz WS. Application of a respiratory PBPK model for predicting deposition and disposition following inhaled administration of morphine. AAPS Annu Meet Expo. 2010;93534. Available from: https://www.simulations-plus.com/assets/Chaudhuri-Application_Respiratory_PBPK_Model_Predicting_Deposition_Disposition_following_Inhaled_Administration_Morphine-AAPS-2010.pdf.

Mathias NR, Crison J. The use of modeling tools to drive efficient oral product design. AAPS J. 2012;14(3):591-600.

Wei H, Dalton C, Di Maso M, Kanfer I, Löbenberg R. Physicochemical characterization of five glyburide powders: A BCS based approach to predict oral absorption. Eur J Pharm Biopharm. 2008;69(3):1046-56.

Kesisoglou F, Wu Y. Understanding the effect of API properties on bioavailability through absorption modeling. AAPS J. 2008;10(4):516-25.

Melillo N, Aarons L, Magni P, Darwich AS. Variance based global sensitivity analysis of physiologically based pharmacokinetic absorption models for BCS I–IV drugs. J Pharmacokinet Pharmacodyn. 2019;46(1):27-42.

Johnson TN, Zhou D, Bui KH. Development of physiologically based pharmacokinetic model to evaluate the relative systemic exposure to quetiapine after administration of IR and XR formulations to adults, children and adolescents. Biopharm Drug Dispos. 2014;35(6):341-52.

Vaidhyanathan S, Wang X, Crison J, Varia S, Gao JZH, Saxena A, et al. Bioequivalence comparison of pediatric dasatinib formulations and elucidation of absorption mechanisms through integrated PBPK modeling. J Pharm Sci. 2019;108(1):741-9.

Prado-Velasco M, Borobia A, Carcas-Sansuan A. Predictive engines based on pharmacokinetics modelling for tacrolimus personalized dosage in paediatric renal transplant patients. Sci Rep. 2020;10(1):1-18.

Marsousi N, Desmeules JA, Rudaz S, Daali Y. Usefulness of PBPK modeling in incorporation of clinical conditions in personalized medicine. J Pharm Sci. 2017;106(9):2380-91.

Chetty M, Johnson TN, Polak S, Salem F, Doki K, Rostami-Hodjegan A. Physiologically based pharmacokinetic modelling to guide drug delivery in older people. Adv Drug Deliv Rev. 2018;135:85-96.

Rashid M, Sarfraz M, Arfat M, Hussain A, Abbas N, Hussain K, et al. Prediction of pharmacokinetic parameters and dose of pregabalin gastroretentive formulation in healthy adults, healthy pediatrics and renal-impaired geriatrics. J Drug Deliv Sci Technol. 2021;63:102548.

Franchetti Y, Nolin TD. Dose optimization in kidney disease: Opportunities for PBPK modeling and simulation. J Clin Pharmacol. 2020;60(S1):S36-51.

Kalam MN, Rasool MF, Alqahtani F, Imran I, Rehman AU, Ahmed N. Development and evaluation of a physiologically based pharmacokinetic drug-disease model of propranolol for suggesting model informed dosing in liver cirrhosis patients. Drug Des Devel Ther. 2021;15:1195-211.

Rasool MF, Ali S, Khalid S, Khalid R, Majeed A, Imran I, et al. Development and evaluation of physiologically based pharmacokinetic drug-disease models for predicting captopril pharmacokinetics in chronic diseases. Sci Rep. 2021;11(1):1-16.

Ćetković Z, Cvijić S, Vasiljević D. In vitro/in silico approach in the development of simvastatin-loaded self-microemulsifying drug delivery systems. Drug Dev Ind Pharm. 2018;44(5):849-60.

Ćetković Z, Cvijić S, Vasiljević D. Formulation and characterization of novel lipid-based drug delivery systems containing polymethacrylate polymers as solid carriers for sustained release of simvastatin. J Drug Deliv Sci Technol. 2019;53: 101222.

Medarević D, Cvijić S, Dobričić V, Mitrić M, Djuriš J, Ibrić S. Assessing the potential of solid dispersions to improve dissolution rate and bioavailability of valsartan: In vitro-in silico approach. Eur J Pharm Sci. 2018;124:188-98.

Cvijic S, Ibric S, Parojcic J, Djuris J. An in vitro - in silico approach for the formulation and characterization of ranitidine gastroretentive delivery systems. J Drug Deliv Sci Technol. 2018;45:1-10.

Yuan D, He H, Wu Y, Fan J, Cao Y. Physiologically based pharmacokinetic modeling of nanoparticles. J Pharm Sci. 2019;108(1):58-72.

Kesisoglou F, Xia B, Agrawal NGB. Comparison of deconvolution-based and absorption modeling IVIVC for extended release formulations of a BCS III drug development candidate. AAPS J. 2015;17(6):1492-500.

Ghate VM, Chaudhari P, Lewis SA. Physiologically based pharmacokinetic (PBPK) modelling for in vitro-in vivo extrapolation: Emphasis on the use of dissolution data. Dissolution Technol. 2019;26(3):18-27.

Stillhart C, Pepin X, Tistaert C, Good D, Van Den Bergh A, Parrott N, et al. PBPK absorption modeling: Establishing the in vitro–in vivo link-industry perspective. AAPS J. 2019;21(2):1-13.

Li X, Yang Y, Zhang Y, Wu C, Jiang Q, Wang W, et al. Justification of biowaiver and dissolution rate specifications for piroxicam immediate release products based on physiologically based pharmacokinetic modeling: An in-Depth analysis. Mol Pharm. 2019;16(9):3780-90.

Al-Tabakha MM, Alomar MJ. In vitro dissolution and in silico modeling shortcuts in bioequivalence testing. Pharmaceutics. 2020;12(1):1-16.

Wu F, Cristofoletti R, Zhao L, Rostami-Hodjegan A. Scientific considerations to move towards biowaiver for biopharmaceutical classification system class III drugs: How modeling and simulation can help. Biopharm Drug Dispos. 2021;42(4):118-27.

Zhang X, Lionberger RA, Davit BM, Yu LX. Utility of physiologically based absorption modeling in implementing quality by design in drug development. AAPS J. 2011;13(1):59-71.

Jereb R, Opara J, Legen I, Petek B, Bajc A, Žakelj S, et al. PBPK absorption modeling of food effect and bioequivalence in fed state for two formulations with crystalline and amorphous forms of BCS 2 class drug in generic drug development. AAPS PharmSciTech. 2019;20(2):1-10.

Jereb R, Kristl A, Mitra A. Prediction of fasted and fed bioequivalence for immediate release drug products using physiologically based biopharmaceutics modeling (PBBM). Eur J Pharm Sci. 2020;155:105554.

Kesisoglou F. Can PBPK modeling streamline food effect assessments? J Clin Pharmacol. 2020;60(S1):S98-104.

Le Merdy M, Fan J, Bolger MB, Lukacova V, Spires J, Tsakalozou E, et al. Application of mechanistic ocular absorption modeling and simulation to understand the impact of formulation properties on ophthalmic bioavailability in rabbits: a case study using dexamethasone suspension. AAPS J. 2019;21(4):1-11.

Le Merdy M, Tan ML, Babiskin A, Zhao L. Physiologically based pharmacokinetic model to support ophthalmic suspension product development. AAPS J. 2020;22(2):1-10.

de Melo Fonseca A, Araújo CD, da Silva JH, da Silva Honório T, Nasciutti LE, Cabral LM, et al. Development of transdermal based hydrogel formulations of vinorelbine with an evaluation of their in vitro profiles and activity against melanoma cells and in silico prediction of drug absorption. J Drug Deliv Sci Technol. 2021;63:102449.

Al-Akayleh F, Adwan S, Khanfer M, Idkaidek N, Al-Remawi M. A Novel eutectic-based transdermal delivery system for risperidone. AAPS PharmSciTech. 2021;22(1):1-11.

Mahdi WA, Hussain A, Altamimi MA, Alshehri S, Bukhari SI, Ahsan MN. Experimental solubility, thermodynamic/computational validations, and GastroPlus-based in silico prediction for subcutaneous delivery of rifampicin. AAPS PharmSciTech. 2021;22(3):116.

Sharan S, Fang L, Lukacova V, Chen X, Hooker AC, Karlsson MO. Model-informed drug development for long-acting injectable products: summary of American College of Clinical Pharmacology Symposium. Clin Pharmacol Drug Dev. 2021;10(3):220-8.

European Medicines Agency. Guideline on the reporting of physiologically based pharmacokinetic (PBPK) modelling and simulation. 2018. Available from: https://www.ema.europa.eu/en/documents/scientific-guideline/guideline-reporting-physiologically-based-pharmacokinetic-pbpk-modelling-simulation_en.pdf.

FDA. Physiologically Based Pharmacokinetic Analyses — Format and Content. Guidance. 2018. Available from: https://www.fda.gov/files/drugs/published/Physiologically-Based-Pharmacokinetic-Analyses-%E2%80%94-Format-and-Content-Guidance-for-Industry.pdf.

FDA. The Use of Physiologically Based Pharmacokinetic Analyses-Biopharmaceutics Applications for Oral Drug Product Development, Manufacturing Changes, and Controls Guidance for Industry Draft Guidance. 2020. Available from: https://www.fda.gov/media/142500/download.

European Medical Agency. Activity report of the Modelling and simulation working group (MSWG). 2016. Available from: https://www.ema.europa.eu/en/documents/report/2016-activity-report-modelling-simulation-working-group-mswg_en.pdf.

Luzon E, Blake K, Cole S, Nordmark A, Versantvoort C, Berglund EG. Physiologically based pharmacokinetic modeling in regulatory decision‐making at the European Medicines Agency. Clin Pharmacol Ther. 2017;102(1):98-105.

Grimstein M, Yang Y, Zhang X, Grillo J, Huang SM, Zineh I, et al. Physiologically based pharmacokinetic modeling in regulatory science: An update from the U.S. Food and Drug Administration’s Office of Clinical Pharmacology. J Pharm Sci. 2019;108(1):21-5.

Wang Y. PBPK Current Status and Challenges: A Regulatory Perspective. 2019. Available from: https://www.fda.gov/media/134099/download.

Cvijić S. In Vitro/In Vivo Correlation for Transporters. Hock FJ, Gralinski MR, editors. Drug Discovery and Evaluation: Methods in Clinical Pharmacology. Springer International Publishing; 2019; p. 1-31.

Tsume Y, Mudie DM, Langguth P, Amidon GE, Amidon GL. The Biopharmaceutics Classification System: Subclasses for in vivo predictive dissolution (IPD) methodology and IVIVC. Eur J Pharm Sci. 2014;57(1):152-63.

Lennernäs H, Lindahl A, Van Peer A, Ollier C, Flanagan T, Lionberger R, et al. In vivo predictive dissolution (IPD) and biopharmaceutical modeling and simulation: Future use of modern approaches and methodologies in a regulatory context. Mol Pharm. 2017;14(4):1307-14.

Butler J, Hens B, Vertzoni M, Brouwers J, Berben P, Dressman J, et al. In vitro models for the prediction of in vivo performance of oral dosage forms: Recent progress from partnership through the IMI OrBiTo collaboration. Eur J Pharm Biopharm. 2019;136:70-83.

Koziolek M, Garbacz G, Neumann M, Weitschies W. Simulating the postprandial stomach: Physiological considerations for dissolution and release testing. Mol Pharm. 2013;10(5):1610-22.

Hens B, Talattof A, Paixão P, Bermejo M, Tsume Y, Löbenberg R, et al. Measuring the impact of gastrointestinal variables on the systemic outcome of two suspensions of posaconazole by a PBPK model. AAPS J. 2018;20(3):1-14.

Sutch BT, Romero RM, Neamati N, Haworth IS. Integrated teaching of structure-based drug design and biopharmaceutics: A computer-based approach. J Chem Educ. 2012;89(1):45-51.

Romero RM, Bolger MB, Morningstar-Kywi N, Haworth IS. Teaching of biopharmaceutics in a drug design course: Use of GastroPlus as educational software. J Chem Educ. 2020;97(8):2212-20.

Objavljeno
2021/08/27
Broj časopisa
Vol. 71 Br. Notebook 4 (2021): Arhiv za farmaciju
Rubrika
Pregledni (Revijalni) rad
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