İLAÇLARDA SANAL BİYOEŞDEĞERLİK UYGULAMALARI

Öz

Amaç: Sanal biyoeşdeğerlik çalışmaları hem yeni ilaçların hem de jenerik ilaçların geliştirme süreçlerini kolaylaştırma ve optimize etmede kritik rol oynamaktadır. Bu yaklaşım, ilaçların insan vücudundaki davranışlarını taklit etmek ve kestirebilmek için matematiksel hesaplamalara dayanmaktadır. Sanal biyoeşdeğerlik çalışmaları ile in vitro, in siliko ve in vivo veriler kullanılarak, test ve referans formülasyonlar arasındaki farmakokinetik ve klinik performans değerlendirebilir. Bu modeller, ilaçların vücutta nasıl dağıldığını, metabolize olduğunu ve atıldığını daha duyarlı bir şekilde tahmin edebilir. Bu sayede ilaçların etkilerinin kestirilebilmesi ve dozun optimize edilmesine olanak sağlar. Sonuç ve Tartışma: Sanal biyoeşdeğerlik çalışmalarının yasal düzenlemelerdeki yeri henüz tam olarak belirlenememiştir, bu nedenle ilaçla ilgili yasal otoriteler, ilaç endüstrisi, üniversiteler ve araştırma kuruluşlarının iş birliği yapması oldukça önemlidir. Özellikle ağız yolu ve diğer uygulama yolları ile kullanılan sistemik etki gösteren ilaçların, fizyolojik temelli farmakokinetik ve biyofarmasötik modelleme çalışmalarının çerçevesinin belirlenmesi, in vivo klinik çalışmalardan muafiyetin ve optimizasyonunun desteklenmesi için sanal biyoeşdeğerlik çalışmaları önemlidir. Sanal biyoeşdeğerlik çalışmaları, ilaç geliştirme süreçlerini iyileştirmek, süreyi kısaltmak ve maliyetleri düşürmek için önemli bir araç olabilir, ancak bu alandaki ilerlemelerin devam etmesi ve bu yöntemlerin ilaçla ilgili yasal düzenleme süreçlerine daha fazla entegre edilmesi gerekmektedir.

Anahtar Kelimeler

• Biyomuafiyet
• fizyolojik temelli farmakokinetik modelleme
• güvenli alan
• in siliko
• sanal biyoeşdeğerlik
• simülasyon
• yapay zekâ

VIRTUAL BIOEQUIVALENCE APPLICATIONS IN DRUGS

Öz

Objective: Virtual bioequivalence studies play a critical role in facilitating and optimizing drug development processes of new drugs and generic drugs. This approach relies on mathematical calculations to mimic and predict the behavior of drugs in the human body. Virtual bioequivalence studies can assess the pharmacokinetic and clinical performance between test and reference formulations by utilizing in vitro, in silico and in vivo data. This enables the prediction of drug effects and optimization of dosage. Result and Discussion: The regulatory position of virtual bioequivalence studies has not yet been fully determined, making collaboration among regulatory authorities, the pharmaceutical industry, universities, and research institutions crucial. Particularly for drugs administered orally or through other systemic routes, determining the framework of physiologically-based pharmacokinetic and biopharmaceutical modeling studies through virtual bioequivalence is important to support exemptions and optimization from in vivo clinical trials. Virtual bioequivalence studies can be a significant tool in improving drug development processes, reducing time, and cutting costs. However, continued progress in this field and further integration of these methods into drug-related regulatory processes are necessary.

Anahtar Kelimeler

• Artificial intelligence
• biowaiver
• physiologically based pharmacokinetic modeling
• in silico
• safe space
• simulation
• virtual bioequivalence

Kaynakça

1. 1. Davit, B., Braddy, A.C., Conner, D.P., Yu, L.X. (2013). International guidelines for bioequivalence of systemically available orally administered generic drug products: A survey of similarities and differences. The AAPS Journal, 15(4), 974-990. [CrossRef]
2. 2. Doki, K., Darwich, A.S., Patel, N., Rostami-Hodjegan, A. (2017). Virtual bioequivalence for achlorhydric subjects: The use of PBPK modelling to assess the formulation-dependent effect of achlorhydria. European Journal of Pharmaceutical Sciences, 109, 111-120. [CrossRef]
3. 3. Amidon, G.L., Lennernäs, H., Shah, V.P., Crison, J.R. (1995). A theoretical basis for a biopharmaceutic drug classification: the correlation of in vitro drug product dissolution and in vivo bioavailability. Pharmaceutical Research, 12(3), 413-420. [CrossRef]
4. 4. WHO Technical Report Series, Annex 6: Multisource (generic) pharmaceutical products: Guidelines on registration requirements to establish interchangeability. (2017). Erişim adresi: https://www.who. int/docs/default-source/medicines/norms-and-standards/guidelines/regulatory-standards/trs1003-annex6-who-multisource-pharmaceutical-products-interchangeability.pdf. Erişim tarihi:15.03.2024.
5. 5. ICH M9 guideline on biopharmaceutics classification system-based biowaivers (EMA). (2024). Erişim adresi: https://www.ema.europa.eu/en/documents/scientific-guideline/ich-m9-biopharmaceutics-classifica tion-system-based-biowaivers-step-5_en.pdf. Erişim tarihi:15.03.2024.
6. 6. FDA, CDER. (1995). SUPAC-IR: Immediate-Release Solid Oral Dosage Forms: Scale-Up and Post-Approval Changes: Chemistry, Manufacturing and Controls, In Vitro Dissolution Testing, and In Vivo Bioequivalence Documentation. Erişim adresi: https://www.fda.gov/regulatory-information/search-fda-guidance-documents/supac-ir-immediate-release-solid-oral-dosage-forms-scale-and-post-approval-changes-chemistry. Erişim tarihi:15.03.2024.
7. 7. Abend, A., Heimbach, T., Cohen, M., Kesisoglou, F., Pepin, X., Suarez-Sharp, S. (2018). Dissolution and translational modeling strategies enabling patient-centric drug product development: The M-CERSI workshop summary report. The AAPS Journal, 20(3), 60. [CrossRef]
8. 8. FDA, CDER. (2020). Guidance for industry, the use of physiologically based pharmacokinetic analyses-biopharmaceutics applications for oral drug product development, Manufacturing changes and controls. Erişim adresi: https://www.fda.gov/regulatory-information/search-fda-guidance-documents/use-physiologically-based-pharmacokinetic-analyses-biopharmaceutics-applications-oral-drug-product. Erişim tarihi:15.03.2024.

9. 9. Pepin, X.J.H., Parrott, N., Dressman, J., Delvadia, P., Mitra, A., Zhang, X., Babiskin, A., Kolhatkar, V., Suarez-Sharp, S. (2021). Current state and future expectations of Ttanslational modeling strategies to support drug product development, manufacturing changes and controls: A workshop summary report. Journal of Pharmaceutical Sciences, 110(2), 555-566. [CrossRef]
10. 10. Anand, O., Pepin, X.J.H., Kolhatkar, V., Seo, P. (2022). The Use of physiologically based pharmacokinetic analyses-in biopharmaceutics applications-regulatory and industry perspectives. Pharmaceutical Research, 39(8), 1681-1700. [CrossRef]
11. 11. Babiskin, A., Wu, F., Mousa, Y., Tan, M.L., Tsakalozou, E., Walenga, R.L., Yoon, M., Raney, S.G., Polli, J.E., Schwendeman, A., Krishnan, V., Fang, L., Zhao, L. (2023). Regulatory utility of mechanistic modeling to support alternative bioequivalence approaches: A workshop overview. CPT: Pharmacometrics & Systems Pharmacology, 12(5), 619-623. [CrossRef]
12. 12. Zhao, P., Zhang, L., Grillo, J., Liu, Q., Bullock, J., Moon, Y., Song, P., Brar, S., Madabushi, R., Wu, T., Booth, B., Rahman, N., Reynolds, K., Gil Berglund, E., Lesko, L., Huang, S.M. (2011). Applications of physiologically based pharmacokinetic (PBPK) modeling and simulation during regulatory review. Clinical Pharmacology & Therapeutics, 89(2), 259-267. [CrossRef]
13. 13. Walenga, R.L., Babiskin, A.H., Zhao, L. (2019). In silico methods for development of generic drug-device combination orally inhaled drug products. CPT Pharmacometrics System Pharmacology, 8(6), 359-370. [CrossRef]
14. 14. Hens, B., Sinko, P.D., Job, N., Dean, M., Al-Gousous, J., Salehi, N., Ziff, R.M., Tsume, Y., Bermejo, M., Paixão, P., Brasseur, J.G., Yu, A., Talattof, A., Benninghoff, G., Langguth, P., Lennernäs, H., Hasler, W. L., Marciani, L., Dickens, J., Shedden K., Sun D., Amidon G.E. Amidon, G.L. (2018). Formulation predictive dissolution (fPD) testing to advance oral drug product development: An introduction to the US FDA funded '21st Century BA/BE' project. International Journal of Pharmaceutics, 548(1), 120-127. [CrossRef]
15. 15. Thakore, S.D., Sirvi, A., Joshi, V.C., Panigrahi, S.S., Manna, A., Singh, R., Sangamwar, A.T., Bansal, A.K. (2021). Biorelevant dissolution testing and physiologically based absorption modeling to predict in vivo performance of supersaturating drug delivery systems. International Journal of Pharmaceutics, 607, 120958. [CrossRef]
16. 16. Parrott, N., Lavé, T. (2008). Applications of physiologically based absorption models in drug discovery and development. Molecular Pharmaceutics, 5, 760-775. [CrossRef]
17. 17. FDA. (2017). FY2015 Regulatory Science Research Report: Physiologically-Based Absorption and Pharmacokinetic Models. Erişim adresi: https://www.fda.gov/industry/generic-drug-user-fee-amendments/fy2015-regulatory-science-research-report-physiologically-based-absorption-and-pharmacokinetic. Erişim tarihi:07.05.2024.
18. 18. Polak, S., (2021). Demonstrating Virtual Bioequivalence (VBE) using the Simcyp Simulator™ Erişim adresi: https://www.certara.com/white-paper/demonstrating-virtual-bioequivalence-vbe-using-the--simulator/. Erişim tarihi:05.03.2024.
19. 19. Zhang, F., Jia, R., Gao, H., Wu, X., Liu, B., Wang, H. (2021). In silico modeling and simulation to guide bioequivalence testing for oral drugs in a virtual population. Clinical Pharmacokinetics, 60(11), 1373-1385. [CrossRef]
20. 20. FDA, CDER. (2021). Bioequivalence studies with pharmacokinetic endpoints for drugs submitted under an ANDA guidance for industry. Erişim adresi: https://www.fda.gov/media/87219/download. Erişim tarihi:15.03.2024.
21. 21. Jamei, M., Abrahamsson, B., Brown, J., Bevernage, J., Bolger, M.B., Heimbach, T., Karlsson, E., Kotzagiorgis, E., Lindahl, A., McAllister, M., Mullin, J.M., Pepin, X., Tistaert, C., Turner, D.B., Kesisoglou, F. (2020). Current status and future opportunities for incorporation of dissolution data in PBPK modeling for pharmaceutical development and regulatory applications: OrBiTo consortium commentary. European Journal of Pharmaceutics and Biopharmaceutics, 155, 55-68. [CrossRef]
22. 22. Ni, Z., Talattof, A., Fan, J., Tsakalozou, E., Sharan, S., Sun, D., Wen, H., Zhao, L., Zhang, X. (2017). Physiologically based pharmacokinetic and absorption modeling for osmotic pump products. The AAPS Journal, 19(4), 1045-1053. [CrossRef]
23. 23. FDA, CDER. (1997). Guidance for industry extended release oral dosage forms: Development, evaluation, and application of in vitro/in vivo correlations. Erişim adresi: https://www.fda.gov/media/70939/download . Erişim tarihi:15.03.2024.
24. 24. FDA, CDER. (2022). Bioavailability studies submitted in NDAs or INDs-General considerationsguidance for industry. Erişim adresi: https://www.fda.gov/media/121311/download. Erişim tarihi:15.03.2024.
25. 25. González-García, I., Mangas-Sanjuán, V., Merino-Sanjuán, M., Bermejo, M. (2015). In vitro-in vivo correlations: general concepts, methodologies and regulatory applications. Drug Development and Industrial Pharmacy 41(12), 1935-1947. [CrossRef]
26. 26. Kato, T., Nakagawa, H., Mikkaichi, T., Miyano, T., Matsumoto, Y., Ando, S. (2020). Establishment of a clinically relevant specification for dissolution testing using physiologically based pharmacokinetic (PBPK) modeling approaches. European Journal of Pharmaceutics and Biopharmaceutics, 151, 45-52. [CrossRef]
27. 27. Kostewicz, E.S., Aarons, L., Bergstrand, M., Bolger, M B., Galetin, A., Hatley, O., Jamei, M., Lloyd, R., Pepin, X., Rostami-Hodjegan, A., Sjögren, E., Tannergren, C., Turner, D.B., Wagner, C., Weitschies, W., Dressman, J. (2014). PBPK models for the prediction of in vivo performance of oral dosage forms. European Journal of Pharmaceutical Sciences, 57, 300-321. [CrossRef]
28. 28. Heimbach, T., Suarez-Sharp, S., Kakhi, M., Holmstock, N., Olivares-Morales, A., Pepin, X., Sjögren, E., Tsakalozou, E., Seo, P., Li, M., Zhang, X., Lin, H.P., Montague, T., Mitra, A., Morris, D., Patel, N., Kesisoglou, F. (2019). Dissolution and translational modeling strategies toward establishing an in vitro-in vivo link-a workshop summary report. The AAPS Journal, 21(2), 29. [CrossRef]
29. 29. Wang, X., Wu, J., Ye, H., Zhao, X., Zhu, S. (2024). Research landscape of physiologically based pharmacokinetic model utilization in different fields: A bibliometric analysis (1999-2023). Pharmaceutical Research, 41, 609-622. [CrossRef]
30. 30. Zhuang, X., Lu, C. (2016). PBPK modeling and simulation in drug research and development. Acta Pharmaceutica Sinica B, 6(5), 430-440. [CrossRef]
31. 31. Grimstein, M., Yang, Y., Zhang, X., Grillo, J., Huang, S.M., Zineh, I., Wang, Y. (2019). Physiologically based pharmacokinetic modeling in regulatory science: An update from the U.S. food and drug administration’s office of clinical pharmacology. Journal of Pharmaceutical Sciences, 108(1), 21-25. [CrossRef]
32. 32. Wagner, C., Zhao, P., Pan, Y., Hsu, V., Grillo, J., Huang, S., Sinha, V. (2015). Application of physiologically based pharmacokinetic (PBPK) modeling to support sose Selection: Report of an FDA public workshop on PBPK. CPT: Pharmacometrics & Systems Pharmacology, 4(4), 226-230. [CrossRef]
33. 33. Zhao, P. (2017). Report from the EMA workshop on qualification and reporting of physiologically based pharmacokinetic (PBPK) modeling and simulation. CPT: Pharmacometrics & Systems Pharmacology, 6(2), 71-72. [CrossRef]
34. 34. Luzon, E., Blake, K., Cole, S., Nordmark, A., Versantvoort, C., Berglund, E.G. (2017). Physiologically based pharmacokinetic modeling in regulatory decision-making at the European Medicines Agency. Clinical Pharmacology & Therapeutics, 102(1), 98-105. [CrossRef]
35. 35. Mitra, A., Suarez-Sharp, S., Pepin, X.J.H., Flanagan, T., Zhao, Y., Kotzagiorgis, E., Parrott, N., Sharan, S., Tistaert, C., Heimbach, T., Zolnik, B., Sjögren, E., Wu, F., Anand, O., Kakar, S., Li, M., Veerasingham, S., Kijima, S., Lima Santos, G.M., Ning, B., Raines, K., Rullo, G., Mandula, H., Delvadia, P., Dressman, J., Dickinson, P.A., Babiskin, A. (2021). Applications of physiologically based biopharmaceutics modeling (PBBM) to support drug product quality: A workshop summary report. Journal of Pharmaceutical Sciences, 110(2), 594-609. [CrossRef]
36. 36. EMA. (2019). Guideline on the reporting of physiologically based pharmacokinetic (PBPK) modelling and simulation. Erişim adresi: https://www.ema.europa.eu/en/documents/scientific-guideline/guideline-report ing-physiologically-based-pharmacokinetic-pbpk-modelling-and-simulation_en.pdf. Erişim tarihi: 15.03.2024.
37. 37. FDA, CDER. (2018). Guidance for industry, physiologically based pharmacokinetic analyses-format and content. Erişim adresi: https://www.fda.gov/media/101469/download. Erişim tarihi:15.03.2024.
38. 38. Yuvaneshwari K., Kollipara, S., Ahmed, T., Chachad, S. (2022). Applications of PBPK/PBBM modeling in generic product development: An industry perspective. Journal of Drug Delivery Science and Technology, 69, 103152. [CrossRef]
39. 39. Sowmay, C., Ahmed, A., Kannan, S.P. (2023). Virtual bioequıvalence in Pharmaceuticals: Current status and future prospects. International Journal of Applied Pharmaceutics, 15(5), 1-9. [CrossRef]
40. 40. Kambayashi, A., Dressman, J.B. (2022). Towards virtual bioequivalence studies for oral dosage forms containing poorly water-soluble drugs: A physiologically based biopharmaceutics modeling (PBBM) approach. Journal of Pharmaceutical Sciences, 111(1), 135-145. [CrossRef]
41. 41. Gao, D., Wang, G., Wu, H., Wu, J., Zhao, X. (2023). Prediction for plasma trough concentration and optimal dosing of imatinib under multiple clinical situations using physiologically based pharmacokinetic modeling. ACS Omega, 8(15), 13741-13753. [CrossRef]
42. 42. Li, X., Yang, Y., Zhang, Y., Wu, C., Jiang, Q., Wang, W., Li, H., Li, J., Luo, C., Wu, W., Wang, Y., Zhang, T. (2019). Justification of biowaiver and dissolution rate specifications for piroxicam immediate release products based on physiologically based pharmacokinetic modeling: An in-depth analysis. Molecular Pharmaceutics, 16(9), 3780-3790. [CrossRef]
43. 43. Kesisoglou, F., Mitra, A. (2015). Application of absorption modeling in rational design of drug product under quality-by-design paradigm. The AAPS Journal, 17(5), 1224-1236. [CrossRef]
44. 44. Tsakalozou, E., Alam, K., Babiskin, A., Zhao, L. (2022). Physiologically-based pharmacokinetic modeling to support determination of bioequivalence for Ddrmatological drug products: Scientific and regulatory considerations. Clinical Pharmacology & Therapeutics, 111(5), 1036-1049. [CrossRef]
45. 45. Tistaert, C., Heimbach, T., Xia, B., Parrott, N., Samant, T S., Kesisoglou, F. (2019). Food effect projections via physiologically based pharmacokinetic modeling: Predictive case studies. Journal of Pharmaceutical Sciences, 108(1), 592-602. [CrossRef]
46. 46. Rebeka, J., Jerneja, O., Igor, L., Boštjan, P., Aleksander, B., Simon, Ž., Albin, K. (2019). 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, 20(2), 59. [CrossRef]
47. 47. Jereb, R., Kristl, A., Mitra, A. (2020). Prediction of fasted and fed bioequivalence for immediate release drug products using physiologically based biopharmaceutics modeling (PBBM). European Journal of Pharmaceutical Sciences, 155, 105554. [CrossRef]
48. 48. Li, A., Yeo, K., Welty, D., Rong, H. (2018). Development of guanfacine extended-release dosing strategies in children and adolescents with ADHD using a physiologically based pharmacokinetic model to predict drug-drug interactions with moderate CYP3A4 inhibitors or inducers. Pediatric Drugs, 20(2), 181-194. [CrossRef]
49. 49. Demir, H., Arica-Yegin, B., Oner, L. (2018). Application of an artificial neural network to predict dissolution data and determine the combined effect of pH and surfactant addition on the solubility and dissolution of the weak acid drug etodolac. Journal of Drug Delivery Science and Technology, 47, 215-222. [CrossRef]
50. 50. Wu, D., Sanghavi, M., Kollipara, S., Ahmed, T., Saini, A.K., Heimbach, T. (2023). Physiologically based pharmacokinetics modeling in biopharmaceutics: Case studies for establishing the bioequivalence safe space for innovator and generic drugs. Pharmaceutical Research, 40(2), 337-357. [CrossRef]
51. 51. Wu, F., Shah, H., Li, M., Duan, P., Zhao, P., Suarez, S., Raines, K., Zhao, Y., Wang, M., Lin, H.P., Duan, J., Yu, L., Seo, P. (2021). Biopharmaceutics applications of physiologically based pharmacokinetic absorption modeling and simulation in regulatory submissions to the U.S. Food and Drug Administration for new drugs. The AAPS Journal, 23(2), 31. [CrossRef]
52. 52. Anand, O. (2021). Clinically relevant dissolution specifications: A biopharmaceutics’ risk based approach: an FDA perspective. The academy of pharmaceutical sciences webinar series. Erişim adresi: https://www.apsgb.co.uk/wp-content/uploads/2021/05/Clinically-Relevant-Dissolution-Specifications-an-FDA-Perspective-__Om-Anand.pdf. Erişim tarihi:15.03.2024.
53. 53. Yang, Z., Sandra, S. (2019). FDA expectations in building a safe space to gain regulatory flexibility based on physiologically based biopharmaceutics modeling (PBBM). 2019 current state and future expectations of translational modeling strategies to support drug product development, manufacturing changes and controls workshop. Erişim adresi: https://cersi.umd.edu/sites/cersi.umd.edu/files/Day%203-1%20Zhao%20Suarez%20LM.pdf. Erişim tarihi:15.03.2024.
54. 54. Suarez-Sharp, S. (2020). Utilization of PBBM/PBPK models for building a safe space and regulatory applications in support of drug product quality. Erişim adresi: https://www.simulations-plus.com/wp-content/uploads/Safe-space_GP-User-group_Suarez_final.pdf. Erişim tarihi:15.03.2024.
55. 55. FDA, CDER. (2003). Guidance for industry- Exposure-response relationships-Study design, data analysis, and regulatory applications. Erişim adresi: https://www.fda.gov/media/71277/download. Erişim tarihi:15.03.2024.
56. 56. Suarez-Sharp, S., Lindahl, A., Heimbach, T., Rostami-Hodjegan, A., Bolger, M.B., Ray Chaudhuri, S., Hens, B. (2020). Translational modeling strategies for orally administered drug products: Academic, industrial and regulatory perspectives. Pharmaceutical Research 37(6), 95. [CrossRef]
57. 57. Paraiso, R.L.M., Rose, R.H., Fotaki, N., McAllister, M., Dressman, J.B. (2020). The use of PBPK/PD to establish clinically relevant dissolution specifications for zolpidem immediate release tablets. European Journal of Pharmaceutical Sciences, 155, 105534. [CrossRef]
58. 58. Al-Tabakha, M.M., Alomar, M.J. (2020). In vitro dissolution and in silico modeling shortcuts in bioequivalence testing. Pharmaceutics, 12(1), 45. [CrossRef]
59. 59. Pawestri, S. (2023). Application in silico modeling simulation in bioequivalence studies: A review. Journal of Food and Pharmaceutical Sciences, 763-769. [CrossRef]
60. 60. Zhao, L., Seo, P., Lionberger, R. (2019). Current scientific considerations to verify physiologically-based pharmacokinetic models and their implications for locally acting products. CPT: Pharmacometrics & Systems Pharmacology, 8(6), 347-351. [CrossRef]
61. 61. Andreas, C.J., Rosenberger, J., Butler, J., Augustijns, P., McAllister, M., Abrahamsson, B., Dressman, J. (2018). Introduction to the OrBiTo decision tree to select the most appropriate in vitro methodology for release testing of solid oral dosage forms during development. European Journal of Pharmaceutics and Biopharmaceutics, 130, 207-213. [CrossRef]
62. 62. Lei, Z. (2018). FDA research update on the FY18 initiatives FY2018 generic drug regulatory science initiatives public workshop. Erişim adresi: https://www.fda.gov/media/113597/download. Erişim tarihi:15.03.2024.
63. 63. Loisios-Konstantinidis, I., Hens, B., Mitra, A., Kim, S., Chiann, C., Cristofoletti, R. (2020). Using physiologically based pharmacokinetic modeling to assess the risks of failing bioequivalence criteria: a tale of two ibuprofen products. The AAPS Journal, 22(5), 113. [CrossRef]
64. 64. Bego, M., Patel, N., Cristofoletti, R. (2022). Proof of concept in assignment of within-subject variability during virtual bioequivalence studies: Propagation of intra-subject variation in gastrointestinal physiology using physiologically based pharmacokinetic modeling. The AAPS Journal, 24, 21. [CrossRef]
65. 65. Wendling, T., Tsamandouras, N., Dumitras, S., Pigeolet, E., Ogungbenro, K., Aarons, L. (2016). Reduction of a whole-body physiologically based pharmacokinetic model to stabilise the bayesian analysis of clinical data. The AAPS Journal, 18(1), 196-209. [CrossRef]
66. 66. Yoon, M., Babiskin, A., Hu, M., Wu, F., Raney, S.G., Fang, L., Zhao, L. (2023). Increasing impact of quantitative methods and modeling in establishment of bioequivalence and characterization of drug delivery. CPT: Pharmacometrics & Systems Pharmacology, 12(5), 552-555. [CrossRef]
67. 67. Laisney, M., Heimbach, T., Mueller-Zsigmondy, M., Blumenstein, L., Costa, R., Ji, Y. (2022). Physiologically based biopharmaceutics modeling to demonstrate virtual bioequivalence and bioequivalence safe-space for ribociclib which has permeation rate-controlled absorption. Journal of Pharmaceutical Sciences, 111(1), 274-284. [CrossRef]