Title: Development of a Compartmental Advanced Physiologically based Dissolution, Absorption and Transit (CA-PhysDAT) model in PhysPK
Marina Cuquerella-Gilabert(1,3), Javier Reig-López(1,3), Jenifer Serna (2), Almudena Rueda-Ferreiro(2), Matilde Merino-Sanjuan (1,3), Sergio Sánchez-Herrero (2), Victor Mangas-Sanjuan (1,3)
(1) Department of Pharmacy and Pharmaceutical Technology and Parasitology, School of Pharmacy, University of Valencia, Valencia, Spain; (2) Simulation Department, Empresarios Agrupados Internacional S.A., Madrid, Spain; (3) Interuniversity Research Institute for Molecular Recognition and Technological Development, 46100 Burjassot, Valencia, Spain.
Introduction: Oral drug absorption is a complex process depending on the interplay between both compound characteristics and physiological and dynamic properties of gastrointestinal tract (GI) [1]. Modeling and simulation methods are the key for testing pharmacokinetic, pharmacodynamic and biopharmaceutic properties of drugs in the most efficient way and has been recognised by the main regulatory authorities [2,3]. Nowadays, different PBPK platforms are available in the market: the so-called “designed” and “open” software. However, the development of a PBPK platform able to integrate complex LADME processes together with compartmental elements with a multilevel and acausal object-oriented methodology represents a scientific area of great interest. [3,4]
Objectives: The main aims of this project are: (i) to implement in the platform PhysPK the foundations of a Compartmental Advanced Physiologically based Dissolution, Absorption and Transit (CA-PhysDAT) model within a population-based mechanistic modeling framework, focusing in the fundamentals of physicochemical and biological processes that underlies the dissolution, precipitation, pH effect, absorption, plasma protein binding and transit processes through the GI tract and (ii) to assess the predictive reliability of the early stage CA-PhysDAT model versus in vivo, in vitro and in silico published data.
Methods: A physiologically based pharmacokinetic (PBPK) model was developed including solid, undissolved and dissolved thermodynamic states of the drug. Nine compartments (stomach, duodenum, jejunum 1-2, ileum 1-3, caecum and colon) represented the physiological segments of the GI. First-order transit kinetics through the GI tract was assumed for the solid, undissolved and dissolved fractions of the drug. Dissolution processes were described using solubility-independent or solubility-dependent mechanisms and pH effects with the Noyes-Whitney and the Henderson-Hasselbach equations [3]. Linear absorption mechanisms including gradual decrease absorption rate were considered to represent the passive diffusion process [4]. After implementing linear transit and linear absorption, plasma-concentration profiles of each compartment for undissolved, dissolved and absorbed fractions were represented. The model was built using PhysPK® v.2.4.1, a disruptive multi-libraries Modeling & Simulation (M&S) software tool codified using EcosimPro language (EL) [5]. An internal and external validation of the he CA-PhysDAT model was performed. It was internally validated through a simulation-based analysis, where a set of six theoretical scenarios were considered with different solubility, pH, transit time and absorption conditions. For the external validation in silico and in vivo data of GI segments and plasma concentrations were considered. The drugs selected had low hepatic extraction, low protein binding and dissolution-limited absorption and passive diffusion. Different BCS I and II class were included (BCS I: theophylline, dexketoprofen, levetiracetam, pramipexol, citalopram, triazolam and metronidazole; BCS II: ibuprofen, carbamazepine) [6, 7, 8, 9,10,11,12]. Average Fold Error (AFE), Absolute Average Fold Error (AAFE) and Percentage Prediction Error (PPE) for plasma Cmax, Tmax and AUC 0-t were calculated.
Results: Regarding the internal validation, the model properly fits the assumptions made according to the theoretical framework in all the purposed scenarios. The results obtained in the external validation showed that the model was able to predict accurately Cmax (AFE: 1.07; AAFE: 1.19; PPE: 5 ), Tmax (AFE: 0.96; AAFE: 1.25; PPE: 9) and AUC 0-t (AFE: 1.08; AAFE: 1.12; PPE: 12) for the drugs corresponding to BCS I and BCS II (Cmax: AFE:0.91; AAFE: 1.1; PPE: 9; Tmax: AFE: 1.08; AAFE: 0.92; PPE: 11; AUC 0-t: AFE:0.89; AAFE:1.12 ; PPE:10). Predicted/observed ratios were included between 0.8-1.25 for both BCS I and BCS II drugs.
Conclusions: Dissolution, pH effect, precipitation, binding, absorption and intestinal transit were properly implemented and validated in PhysPK setting the basis for a sophisticated mechanistic absorption model. PhysPK biosimulation software is useful for predicting the pharmacokinetic parameters of orally administered drugs since the model showed good descriptive and predictive power of data.
ACKNOWLEDGEMENTS:
This project has been funded with support of the grant for Industrial PhD DIN2021-012249 from the MCIN/AEI/10.13039/501100011033 and from Empresarios Agrupados Internacional.We thank the University of Valencia and Empresarios Agrupados International for their support and continued commitment.
References:
[1] Lin L., Wong H., Predicting Oral Drug Absorption: Mini Review on Physiologically-Based Pharmacokinetic Models. Pharmaceutics, 9(4), 41, 2017.
[2] Shepard T, Scott G, Cole S, Nordmark A, Bouzom F. Physiologically Based Models in Regulatory Submissions: Output From the ABPI/MHRA Forum on Physiologically Based Modeling and Simulation: PBPK Models in Regulatory Submissions. CPT Pharmacometrics Syst Pharmacol. 2015;4(4):221-5.
[3] U.S. Department of Health and Human Services Food and Drug Administration Center for Drug Evaluation and Research (CDER).The Use of Physiologically Based Pharmacokinetic Analyses — Biopharmaceutics Applications for Oral Drug Product Development, Manufacturing Changes, and Controls.Guidance for Industry.2020.
[4] Chetty M. et al., Advanced Drug Delivery Reviews,135,85,2018.
[5] Shekunov B., Montgomery ER., Theoretical Analysis of Drug Dissolution: I. Solubility and Intrinsic Dissolution Rate. J Pharm Sci., 105(9):2685-2697,2016.
[6] Olivares-Morales A, Lennernäs H, Aarons L, Rostami-Hodjegan A. Translating Human Effective Jejunal Intestinal Permeability to Surface-Dependent Intrinsic Permeability: a Pragmatic Method for a More Mechanistic Prediction of Regional Oral Drug Absorption. AAPS J.2015;17(5):1177-92.
[7] Reig-Lopez J. et al., A multilevel object-oriented modelling methodology for physiologically-based pharmacokinetics (PBPK): Evaluation with a semi-mechanistic pharmacokinetic model. Comput Methods Programs Biomed, 189, 2020.
[8] Bermejo M., 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, 17;12(1):74, 2020.
[9] Abduljalil K, Gardner I, Jamei M. Application of a Physiologically Based Pharmacokinetic Approach to Predict Theophylline Pharmacokinetics Using Virtual Non-Pregnant, Pregnant, Fetal, Breast-Feeding, and Neonatal Populations. Front Pediatr. 2022;10:840710.
[10] Zhang S, Fang M, Zhang Q, Li X, Zhang T. Evaluating the bioequivalence of metronidazole tablets and analyzing the effect of in vitro dissolution on in vivo absorption based on PBPK modeling. Drug Dev Ind Pharm. 2019;45(10):1646-53.
[11] Kovacević I, Parojcić J, Homsek I, Tubić-Grozdanis M, Langguth P. Justification of biowaiver for carbamazepine, a low soluble high permeable compound, in solid dosage forms based on IVIVC and gastrointestinal simulation. Mol Pharm. 2009;6(1):40-7.
[12] Wu X, Zhang H, Miah MK, Caritis SN, Venkataramanan R. Physiologically Based Pharmacokinetic Approach Can Successfully Predict Pharmacokinetics of Citalopram in Different Patient Populations. J Clin Pharmacol. 2020;60(4):477-88.
[13] Macente J, Martins F, Bonan R, Caleffi-Marchesini E, Pereira L, Lima P, et al. Model-informed precision dosing of levetiracetam in pediatrics population [Internet]. Preprints; 2021.
[14] Zhang X, Ye X, Hu K, Li W, Li W, Xiao Q, et al. A Physiologically Based Pharmacokinetic Model for Studying the Biowaiver Risk of Biopharmaceutics Classification System Class I Drugs With Rapid Elimination: Dexketoprofen Trometamol Case Study. Front Pharmacol. 2022;13:808456.