Parameterization of Physiologically Based Biopharmaceutics Models: Workshop Summary Report.

This Article shares the proceedings from the August 29th, 2023 (day 1) workshop "Physiologically Based Biopharmaceutics Modeling (PBBM) Best Practices for Drug Product Quality: Regulatory and Industry Perspectives". The focus of the day was on model parametrization; regulatory authorities from Canada, the USA, Sweden, Belgium, and Norway presented their views on PBBM case studies submitted by industry members of the IQ consortium. The presentations shared key questions raised by regulators during the mock exercise, regarding the PBBM input parameters and their justification. These presentations also shed light on the regulatory assessment processes, content, and format requirements for future PBBM regulatory submissions. In addition, the day 1 breakout presentations and discussions gave the opportunity to share best practices around key questions faced by scientists when parametrizing PBBMs. Key questions included measurement and integration of drug substance solubility for crystalline vs amorphous drugs; impact of excipients on apparent drug solubility/supersaturation; modeling of acid-base reactions at the surface of the dissolving drug; choice of dissolution methods according to the formulation and drug properties with a view to predict the in vivo performance; mechanistic modeling of in vitro product dissolution data to predict in vivo dissolution for various patient populations/species; best practices for characterization of drug precipitation from simple or complex formulations and integration of the data in PBBM; incorporation of drug permeability into PBBM for various routes of uptake and prediction of permeability along the GI tract.

Rerouting cardiovascular management following gastric bypass surgery: Dose optimization of carvedilol using population-based analysis.

AIMS: A population-based pharmacokinetic (PK) modeling approach (PopPK) was used to investigate the impact of Roux-en-Y gastric bypass (RYGB) on the PK of (R)- and (S)-carvedilol. We aimed to optimize carvedilol dosing for these patients utilizing a pharmacokinetic/pharmacodynamic (PK/PD) link model. METHODS: PopPK models were developed utilizing data from 52 subjects, including nonobese, obese, and post- RYGB patients who received rac- carvedilol orally. Covariate analysis included anthropometric and laboratory data, history of RYGB surgery, CYP2D6 and CYP3A4 in vivo activity, and relative intestinal abundance of major drug- metabolizing enzymes and transporters. A direct effect inhibitory Emax pharmacodynamic model was linked to the PK model of (S)- carvedilol to simulate the changes in exercise- induced heart rate. RESULTS: A 2-compartmental model with linear elimination and parallel first-order absorptions best described (S)-carvedilol PK. RYGB led to a twofold reduction in relative oral bioavailability compared to nonoperated subjects, along with delayed absorption of both enantiomers. The intestinal ABCC2 mRNA expression increases the time to reach the maximum plasma concentration. The reduced exposure (AUC) of (S)-carvedilol post-RYGB corresponded to a 33% decrease in the predicted area under the effect curve (AUEC) for the 24-hour ß-blocker response. Simulation results suggested that a 50-mg daily dose in post-RYGB patients achieved comparable AUC and AUEC to 25-mg dose in nonoperated subjects. CONCLUSION: Integrated PK/PD modeling indicated that standard dosage regimens for nonoperated subjects do not provide equivalent ß-blocking activity in RYGB patients. This study highlights the importance of personalized dosing strategies to attain desired therapeutic outcomes in this patient cohort.

Impact of obesity and roux-en-Y gastric bypass on the pharmacokinetics of (R)- and (S)-omeprazole and intragastric pH.

This study employed physiologically-based pharmacokinetic-pharmacodynamics (PBPK/PD) modeling to predict the effect of obesity and gastric bypass surgery on the pharmacokinetics and intragastric pH following omeprazole treatment. The simulated plasma concentrations closely matched the observed data from non-obese, morbidly obese, and post-gastric bypass populations. Obesity significantly reduces CYP3A4 and CYP2C19 activities, as reflected by the metabolic ratio [omeprazole sulphone]/[omeprazole] and [5-hydroxy-omeprazole]/[omeprazole]. The morbidly obese model accounted for the down-regulation of CYP2C19 and CYP3A4 to recapitulate the observed data. Sensitivity analysis showed that intestinal CYP3A4, gastric pH, small intestine bypass, and the delay in bile release do not have a major influence on omeprazole exposure. Hepatic CYP3A4 had a significant impact on the AUC of (S)-omeprazole, while hepatic CYP2C19 affected both (R)- and (S)-omeprazole AUC. After gastric bypass surgery, the activity of CYP3A4 and CYP2C19 is restored. The PBPK model was linked to a mechanism-based PD model to assess the effect of omeprazole on intragastric pH. Following 40 mg omeprazole, the mean intragastric pH was 4.3, 4.6, and 6.6 in non-obese, obese, and post-gastric bypass populations, and the daily time with pH >4 was 14.7, 16.4, and 24 h. Our PBPK/PD approach provides a comprehensive understating of the impact of obesity and weight loss on CYP3A4 and CYP2C19 activity and omeprazole pharmacokinetics. Given that simulated intragastric pH is relatively high in post-RYGB patients, irrespective of the dose of omeprazole, additional clinical outcomes are imperative to assess the effect of proton pump inhibitor in preventing marginal ulcers in this population.

Recent advances in cell-based in vitro models for predicting drug permeability across brain, intestinal, and pulmonary barriers.

INTRODUCTION: Recent years have witnessed remarkable progress in the development of cell-based in vitro models aimed at predicting drug permeability, particularly focusing on replicating the barrier properties of the blood-brain barrier (BBB), intestinal epithelium, and lung epithelium. AREA COVERED: This review provides an overview of 2D in vitro platforms, including monocultures and co-culture systems, highlighting their respective advantages and limitations. Additionally, it discusses tools and techniques utilized to overcome these limitations, paving the way for more accurate predictions of drug permeability. Furthermore, this review delves into emerging technologies, particularly microphysiological systems (MPS), encompassing static platforms such as organoids and dynamic platforms like microfluidic devices. Literature searches were performed using PubMed and Google Scholar. We focus on key terms such as in vitro permeability models, MPS, organoids, intestine, BBB, and lungs. EXPERT OPINION: The potential of these MPS to mimic physiological conditions more closely offers promising avenues for drug permeability assessment. However, transitioning these advanced models from bench to industry requires rigorous validation against regulatory standards. Thus, there is a pressing need to validate MPS to industry and regulatory agency standards to exploit their potential in drug permeability prediction fully. This review underscores the importance of such validation processes to facilitate the translation of these innovative technologies into routine pharmaceutical practice.

Biowaiver Monograph for Immediate-Release Solid Oral Dosage Forms: Fexofenadine.

In this monograph, the potential use of methods based on the Biopharmaceutics Classification System (BCS) framework to evaluate the bioequivalence of solid immediate-release (IR) oral dosage forms containing fexofenadine hydrochloride as a substitute for a pharmacokinetic study in human volunteers is investigated. We assessed the solubility, permeability, dissolution, pharmacokinetics, pharmacodynamics, therapeutic index, bioavailability, drug-excipient interaction, and other properties using BCS recommendations from the ICH, FDA and EMA. The findings unequivocally support fexofenadine's classification to BCS Class IV as it is neither highly soluble nor highly permeable. Further impeding the approval of generic equivalents through the BCS-biowaiver pathway is the reference product's inability to release ≥ 85 % of the drug substance within 30 min in pH 1.2 and pH 4.5 media. According to ICH rules, BCS class IV drugs do not qualify for waiving clinical bioequivalence studies based on the BCS, even though fexofenadine has behaved more like a BCS class I/III than a class IV molecule in pharmacokinetic studies to date and has a wide therapeutic index.

Applying Physiologically Based Pharmacokinetic Modeling to Interpret Carbamazepine's Nonlinear Pharmacokinetics and Its Induction Potential on Cytochrome P450 3A4 and Cytochrome P450 2C9 Enzymes.

Carbamazepine (CBZ) is commonly prescribed for epilepsy and frequently used in polypharmacy. However, concerns arise regarding its ability to induce the metabolism of other drugs, including itself, potentially leading to the undertreatment of co-administered drugs. Additionally, CBZ exhibits nonlinear pharmacokinetics (PK), but the root causes have not been fully studied. This study aims to investigate the mechanisms behind CBZ's nonlinear PK and its induction potential on CYP3A4 and CYP2C9 enzymes. To achieve this, we developed and validated a physiologically based pharmacokinetic (PBPK) parent-metabolite model of CBZ and its active metabolite Carbamazepine-10,11-epoxide in GastroPlus®. The model was utilized for Drug-Drug Interaction (DDI) prediction with CYP3A4 and CYP2C9 victim drugs and to further explore the underlying mechanisms behind CBZ's nonlinear PK. The model accurately recapitulated CBZ plasma PK. Good DDI performance was demonstrated by the prediction of CBZ DDIs with quinidine, dolutegravir, phenytoin, and tolbutamide; however, with midazolam, the predicted/observed DDI AUClast ratio was 0.49 (slightly outside of the two-fold range). CBZ's nonlinear PK can be attributed to its nonlinear metabolism caused by autoinduction, as well as nonlinear absorption due to poor solubility. In further applications, the model can help understand DDI potential when CBZ serves as a CYP3A4 and CYP2C9 inducer.

Relative Performance of Volume of Distribution Prediction Methods for Lipophilic Drugs with Uncertainty in LogP Value.

PURPOSE: The goal was to assess, for lipophilic drugs, the impact of logP on human volume of distribution at steady-state (VDss) predictions, including intermediate fut and Kp values, from six methods: Oie-Tozer, Rodgers-Rowland (tissue-specific Kp and only muscle Kp), GastroPlus, Korzekwa-Nagar, and TCM-New. METHOD: A sensitivity analysis with focus on logP was conducted by keeping pKa and fup constant for each of four drugs, while varying logP. VDss was also calculated for the specific literature logP values. Error prediction analysis was conducted by analyzing prediction errors by source of logP values, drug, and overall values. RESULTS: The Rodgers-Rowland methods were highly sensitive to logP values, followed by GastroPlus and Korzekwa-Nagar. The Oie-Tozer and TCM-New methods were only modestly sensitive to logP. Hence, the relative performance of these methods depended upon the source of logP value. As logP values increased, TCM-New and Oie-Tozer were the most accurate methods. TCM-New was the only method that was accurate regardless of logP value source. Oie-Tozer provided accurate predictions for griseofulvin, posaconazole, and isavuconazole; GastroPlus for itraconazole and isavuconazole; Korzekwa-Nagar for posaconazole; and TCM-New for griseofulvin, posaconazole, and isavuconazole. Both Rodgers-Rowland methods provided inaccurate predictions due to the overprediction of VDss. CONCLUSIONS: TCM-New was the most accurate prediction of human VDss across four drugs and three logP sources, followed by Oie-Tozer. TCM-New showed to be the best method for VDss prediction of highly lipophilic drugs, suggesting BPR as a favorable surrogate for drug partitioning in the tissues, and which avoids the use of fup.

In Vitro Lipolysis Model to Predict Food Effect of Poorly Water-Soluble Drugs Itraconazole, Rivaroxaban, and Ritonavir.

It is desirable to predict positive food effect of oral formulations due to food mediated dissolution enhancement of lipophilic drugs. The objective was to assess the ability of in vitro lipolysis to anticipate a positive food effect. Tested formulations included rivaroxaban and itraconazole, where some formulations, but not all, exhibit a positive food effect in vivo in humans. Amorphous solid dispersion formulations of ritonavir, which exhibit a negative food effect in vivo in humans, were also studied. Fe-lipolysis and Fa-lipolysis media representing fed and fasted intestinal conditions were used. Results show frequent agreement between in vitro lipolysis predictions and in vivo human outcomes. For rivaroxaban, food effect of unformulated active pharmaceutical ingredient (API) and products were correctly predicted where 2.5 mg and 10 mg strengths did not show any food effect; however, 20 mg did show a positive food effect. For itraconazole, all four products were correctly predicted, with Sporanox, Sempera, and generic capsules having a food effect, but Tolsura not having a positive food effect. For ritonavir, lipolysis predicted a positive food effect for API and Norvir tablet and powder, but Norvir products have negative food effect in vivo in humans. Overall, the lipolysis model showed favorable predictability and merits additional evaluation.

Mechanistic Modeling of In Vitro Biopharmaceutic Data for a Weak Acid Drug: A Pathway Towards Deriving Fundamental Parameters for Physiologically Based Biopharmaceutic Modeling.

Mechanistic modeling of in vitro experiments using metabolic enzyme systems enables the extrapolation of metabolic clearance for in vitro-in vivo predictions. This is particularly important for successful clearance predictions using physiologically based pharmacokinetic (PBPK) modeling. The concept of mechanistic modeling can also be extended to biopharmaceutics, where in vitro data is used to predict the in vivo pharmacokinetic profile of the drug. This approach further allows for the identification of parameters that are critical for oral drug absorption in vivo. However, the routine use of this analysis approach has been hindered by the lack of an integrated analysis workflow. The objective of this tutorial is to (1) review processes and parameters contributing to oral drug absorption in increasing levels of complexity, (2) outline a general physiologically based biopharmaceutic modeling workflow for weak acids, and (3) illustrate the outlined concepts via an ibuprofen (i.e., a weak, poorly soluble acid) case example in order to provide practical guidance on how to integrate biopharmaceutic and physiological data to better understand oral drug absorption. In the future, we plan to explore the usefulness of this tutorial/roadmap to inform the development of PBPK models for BCS 2 weak bases, by expanding the stepwise modeling approach to accommodate more intricate scenarios, including the presence of diprotic basic compounds and acidifying agents within the formulation.

Comparative Study of Basement-membrane Matrices for Human Stem Cell Maintenance and Intestinal Organoid Generation.

Extracellular matrix (ECM) plays a critical role in cell behavior and development. Organoids generated from human induced pluripotent stem cells (hiPSCs) are in the spotlight of many research areas. However, the lack of physiological cues in classical cell culture materials hinders efficient iPSC differentiation. Incorporating commercially available ECM into stem cell culture provides physical and chemical cues beneficial for cell maintenance. Animal-derived commercially available basement membrane products are composed of ECM proteins and growth factors that support cell maintenance. Since the ECM holds tissue-specific properties that can modulate cell fate, xeno-free matrices are used to stream up translation to clinical studies. While commercially available matrices are widely used in hiPSC and organoid work, the equivalency of these matrices has not been evaluated yet. Here, a comparative study of hiPSC maintenance and human intestinal organoids (hIO) generation in four different matrices: Matrigel (Matrix 1-AB), Geltrex (Matrix 2-AB), Cultrex (Matrix 3-AB), and VitroGel (Matrix 4-XF) was conducted. Although the colonies lacked a perfectly round shape, there was minimal spontaneous differentiation, with over 85% of the cells expressing the stem cell marker SSEA-4. Matrix 4-XF led to the formation of 3D round clumps. Also, increasing the concentration of supplement and growth factors in the media used to make the Matrix 4-XF hydrogel solution improved hiPSC expression of SSEA-4 by 1.3-fold. Differentiation of Matrix 2-AB -maintained hiPSC led to fewer spheroid releases during the mid-/hindgut stage compared to the other animal-derived basement membranes. Compared to others, the xeno-free organoid matrix (Matrix 4-O3) leads to larger and more mature hIO, suggesting that the physical properties of xeno-free hydrogels can be harnessed to optimize organoid generation. Altogether, the results suggest that variations in the composition of different matrices affect stages of IO differentiation. This study raises awareness about the differences in commercially available matrices and provides a guide for matrix optimization during iPSC and IO work.

Exploring the impact of CYP2D6 and UGT2B7 gene-drug interactions, and CYP-mediated DDI on oxycodone and oxymorphone pharmacokinetics using physiologically-based pharmacokinetic modeling and simulation.

Oxycodone is one of the most commonly used opioids to treat moderate to severe pain. It is metabolized mainly by CYP3A4 and CYP2D6, while only a small fraction of the dose is excreted unchanged into the urine. Oxymorphone, the metabolite primarily formed by CYP2D6, has a 40- to 60-fold higher mu-opioid receptor affinity than the parent compound. While CYP2D6-mediated gene-drug-interactions (GDIs) and drug-drug interactions (DDIs) are well-studied, they only account for a portion of the variability in oxycodone and oxymorphone exposure. The combined impact of CYP2D6-mediated GDIs and DDIs, CYP3A4-mediated DDIs, and UGT2B7 GDIs is not fully understood yet and hard to study in head-to-head clinical trials given the relatively large number of scenarios. Instead, we propose the use of a physiologically-based pharmacokinetic model that integrates available information on oxycodone's metabolism to characterize and predict the impact of DDIs and GDIs on the exposure of oxycodone and its major, pharmacologically-active metabolite oxymorphone. To this end, we first developed and verified a PBPK model for oxycodone and its metabolites using published clinical data. The verified model was then applied to determine the dose-exposure relationship of oxycodone and oxymorphone stratified by CYP2D6 and UGT2B7 phenotypes respectively, and administered perpetrators of CYP-based drug interactions. Our simulations demonstrate that the combination of CYP2D6 UM and a UGT2B7Y (268) mutation may lead to a 2.3-fold increase in oxymorphone exposure compared to individuals who are phenotyped as CYP2D6 NM / UGT2B7 NM. The extent of oxymorphone exposure increases up to 3.2-fold in individuals concurrently taking CYP3A4 inhibitors, such as ketoconazole. Inhibition of the CYP3A4 pathway results in a relative increase in the partial metabolic clearance of oxycodone to oxymorphone. Oxymorphone is impacted to a higher extent by GDIs and DDIs than oxycodone. We predict oxymorphone exposure to be highest in CYP2D6 UMs/UGT2B7 PMs in the presence of ketoconazole (strong CYP3A4 index inhibitor) and lowest in CYP2D6 PMs/UGT2B7 NMs in the presence of rifampicin (strong CYP3A4 index inducer) covering a 55-fold exposure range.

Adult and pediatric physiologically-based biopharmaceutics modeling to explain lamotrigine immediate release absorption process.

Physiologically-based biopharmaceutics modeling (PBBM) has potential to accelerate the development of new drug and formulations. An important application of PBBM is for special populations such as pediatrics that have pharmacokinetics dependent on the maturation process. Lamotrigine (LTG) is a Biopharmaceutics Classification System (BCS) II drug and is widely prescribed. Therefore, the goal of this study was to assess the biopharmaceutics risk of the low-soluble drug LTG when the ontogeny on gastrointestinal tract (GIT) physiological parameters are considered. An oral physiologically-based pharmacokinetic model and a PBBM were developed and verified using GastroPlus™ software for both adults and children (2-12 years old, 12-52 kg). The biopharmaceutics properties and GIT physiological parameters were evaluated by sensitivity analysis. High doses were simulated assuming a worst case scenario, that is, the dose of 200 mg for adults and 5 mg/kg (up to the maximum of 200 mg) for 2-year-old children. Although several authors have suggested that ontogeny may have an effect on gastrointestinal fluid volume, our study found no evidence of interference between fluid and dose volumes with in vivo dissolution of LTG. The most impactful parameter was found to be the gastric transit time. Therefore, the hypothesis is developed to examine whether LTG exhibits characteristics of a BCS II classification in vitro while showing BCS I-like behavior in vivo. This hypothesis could act as a base for conducting novel studies on model-informed precision dosing, tailored to specific populations and clinical conditions. In addition, it could be instrumental in assessing the influence of various release profiles on in vivo performance for both adult and pediatric populations.

Biowaiver Monograph for Immediate-Release Solid Oral Dosage Forms: Isavuconazonium Sulfate.

A Biopharmaceutics Classification System (BCS)-based biowaiver monograph is presented for isavuconazonium sulfate. A BCS-based biowaiver is a regulatory option to substitute appropriate in vitro data for in vivo bioequivalence studies. Isavuconazonium sulfate is the prodrug of isavuconazole, a broad-spectrum azole antifungal indicated for invasive fungal infections. While the prodrug can be classified as a BCS Class III drug with high solubility but low permeability, the parent drug can be classified as a BCS Class II drug with low solubility but high permeability. Interestingly, the in vivo behavior of both is additive and leads isavuconazonium sulfate to act like a BCS class I drug substance after oral administration. In this work, experimental solubility and dissolution data were evaluated and compared with available literature data to investigate whether it is feasible to approve immediate release solid oral dosage forms containing isavuconazonium sulfate according to official guidance from the FDA, EMA and/or ICH. The risks associated with waiving a prodrug according to the BCS-based biowaiver guidelines are reviewed and discussed, noting that current regulations are quite restrictive on this point. Further, results show high solubility but instability of isavuconazonium sulfate in aqueous media. Although experiments on the dissolution of the capsule contents confirmed 'very rapid' dissolution of the active pharmaceutical ingredient (API) isavuconazonium sulfate, its release from the commercial marketed capsule formulation Cresemba is limited by the choice of capsule shell material, providing an additional impediment to approval of generic versions via the BCS-Biowaiver approach.

Challenges in Permeability Assessment for Oral Drug Product Development.

Drug permeation across the intestinal epithelium is a prerequisite for successful oral drug delivery. The increased interest in oral administration of peptides, as well as poorly soluble and poorly permeable compounds such as drugs for targeted protein degradation, have made permeability a key parameter in oral drug product development. This review describes the various in vitro, in silico and in vivo methodologies that are applied to determine drug permeability in the human gastrointestinal tract and identifies how they are applied in the different stages of drug development. The various methods used to predict, estimate or measure permeability values, ranging from in silico and in vitro methods all the way to studies in animals and humans, are discussed with regard to their advantages, limitations and applications. A special focus is put on novel techniques such as computational approaches, gut-on-chip models and human tissue-based models, where significant progress has been made in the last few years. In addition, the impact of permeability estimations on PK predictions in PBPK modeling, the degree to which excipients can affect drug permeability in clinical studies and the requirements for colonic drug absorption are addressed.

Comprehensive Physiologically Based Pharmacokinetic Model to Assess Drug-Drug Interactions of Phenytoin.

Regulatory agencies worldwide expect that clinical pharmacokinetic drug-drug interactions (DDIs) between an investigational new drug and other drugs should be conducted during drug development as part of an adequate assessment of the drug's safety and efficacy. However, it is neither time nor cost efficient to test all possible DDI scenarios clinically. Phenytoin is classified by the Food and Drug Administration as a strong clinical index inducer of CYP3A4, and a moderate sensitive substrate of CYP2C9. A physiologically based pharmacokinetic (PBPK) platform model was developed using GastroPlus® to assess DDIs with phenytoin acting as the victim (CYP2C9, CYP2C19) or perpetrator (CYP3A4). Pharmacokinetic data were obtained from 15 different studies in healthy subjects. The PBPK model of phenytoin explains the contribution of CYP2C9 and CYP2C19 to the formation of 5-(4'-hydroxyphenyl)-5-phenylhydantoin. Furthermore, it accurately recapitulated phenytoin exposure after single and multiple intravenous and oral doses/formulations ranging from 248 to 900 mg, the dose-dependent nonlinearity and the magnitude of the effect of food on phenytoin pharmacokinetics. Once developed and verified, the model was used to characterize and predict phenytoin DDIs with fluconazole, omeprazole and itraconazole, i.e., simulated/observed DDI AUC ratio ranging from 0.89 to 1.25. This study supports the utility of the PBPK approach in informing drug development.

A robust, viable, and resource sparing HPLC-based logP method applied to common drugs.

Reliable, experimentally determined partition coefficient P (logP) for most drugs are often unavailable in the literature. Many values are from in silico predictions and may not accurately reflect drug lipophilicity. In this study, a robust, viable, and resource sparing method to measure logP was developed using reverse phase high performance liquid chromatography (RP-HPLC). The logP of twelve common drugs was measured using calibration curves at pH 6 and 9 that were created using reference standards with well-established logP. The HPLC method reported here can be used for high throughput estimation of logP of commonly used drugs. LogP values here showed general agreement with the other few HPLC-based literature logP values available. Additionally, the HPLC-based logP values found here agreed partially with literature logP values found using other methodologies (±10%). However, there was no strong agreement since there are few experimentally determined literature logP values. This paper shows a facile method to estimate logP without using octanol or computational approaches. This method has excellent promise to provide reliable logP values of commonly used drugs available in literature. A larger pool of reliable logP values of commonly drugs has promise to improve quality of medicinal chemistry and pharmacokinetic (PK) models.

Model-Based Analysis of In Vivo Release Data of Levonorgestrel Implants: Projecting Long-Term Systemic Exposure.

Levonorgestrel (LNG) is a progestin used in many contraceptive formulations, including subcutaneous implants. There is an unmet need for developing long-acting formulations for LNG. To develop long-acting formulations, release functions need to be investigated for LNG implant. Therefore, a release model was developed and integrated into an LNG physiologically-based pharmacokinetic (PBPK) model. Utilizing a previously developed LNG PBPK model, subcutaneous administration of 150 mg LNG was implemented into the modeling framework. To mimic LNG release, ten functions incorporating formulation-specific mechanisms were explored. Release kinetic parameters and bioavailability were optimized using Jadelle® clinical trial data (n = 321) and verified using two additional clinical trials (n = 216). The First-order release and Biexponential release models showed the best fit with observed data, the adjusted R-squared (R2) value is 0.9170. The maximum released amount is approximately 50% of the loaded dose and the release rate is 0.0009 per day. The Biexponential model also showed good agreement with the data (adjusted R2 = 0.9113). Both models could recapitulate observed plasma concentrations after integration into the PBPK simulations. First-order and Biexponential release functionality may be useful in modeling subcutaneous LNG implants. The developed model captures central tendency of the observed data as well as variability of release kinetics. Future work focuses on incorporating various clinical scenarios into model simulations, including drug-drug interactions and a range of BMIs.