RESUMEN
By applying nonlinear mixed-effect (NLME) models, model-integrated evidence (MIE) approaches are able to analyze bioequivalence (BE) data with pharmacokinetic end points that have sparse sampling, which is problematic for non-compartmental analysis (NCA). However, MIE approaches may suffer from inflation of type I error due to underestimation of parameter uncertainty and to the assumption of asymptotic normality. In this study, we developed a MIE BE analysis method that is based on a pre-defined model and consists of several steps including model fitting, uncertainty assessment, simulation, and BE determination. The presented MIE approach has several improvements compared with the previously reported model-integrated methods: (1) treatment, sequence, and period effects are only added to absorption parameters (such as relative bioavailability and rate of absorption) instead of all PK parameters; (2) a simulation step is performed to generate confidence intervals of the pharmacokinetic metrics for BE assessment; and (3) in an effort to maintain type I error, two more advanced parameter uncertainty evaluation approaches are explored, a nonparametric (case resampling) bootstrap, and sampling importance resampling (SIR). To evaluate the developed method and compare the uncertainty assessment methods, simulation experiments were performed for BE studies using a two-way crossover design with different amounts of information (sparse to rich designs) and levels of variability. Based on the simulation results, the method using SIR for parameter uncertainty quantification controls type I error at the nominal level of 0.05 (i.e., the significance level set for BE evaluation) even for studies with small sample size and/or sparse sampling. As expected, our MIE approach for BE assessment exhibited higher power than the NCA-based method, especially as the data becomes sparser and/or more variable.
RESUMEN
Conventional approaches for establishing bioequivalence (BE) between test and reference formulations using non-compartmental analysis (NCA) may demonstrate low power in pharmacokinetic (PK) studies with sparse sampling. In this case, model-integrated evidence (MIE) approaches for BE assessment have been shown to increase power, but may suffer from selection bias problems if models are built on the same data used for BE assessment. This work presents model averaging methods for BE evaluation and compares the power and type I error of these methods to conventional BE approaches for simulated studies of oral and ophthalmic formulations. Two model averaging methods were examined: bootstrap model selection and weight-based model averaging with parameter uncertainty from three different sources, either from a sandwich covariance matrix, a bootstrap, or from sampling importance resampling (SIR). The proposed approaches increased power compared with conventional NCA-based BE approaches, especially for the ophthalmic formulation scenarios, and were simultaneously able to adequately control type I error. In the rich sampling scenario considered for oral formulation, the weight-based model averaging method with SIR uncertainty provided controlled type I error, that was closest to the target of 5%. In sparse-sampling designs, especially the single sample ophthalmic scenarios, the type I error was best controlled by the bootstrap model selection method.
RESUMEN
Conventional pharmacokinetic (PK) bioequivalence (BE) studies aim to compare the rate and extent of drug absorption from a test (T) and reference (R) product using non-compartmental analysis (NCA) and the two one-sided test (TOST). Recently published regulatory guidance recommends alternative model-based (MB) approaches for BE assessment when NCA is challenging, as for long-acting injectables and products which require sparse PK sampling. However, our previous research on MB-TOST approaches showed that model misspecification can lead to inflated type I error. The objective of this research was to compare the performance of model selection (MS) on R product arm data and model averaging (MA) from a pool of candidate structural PK models in MBBE studies with sparse sampling. Our simulation study was inspired by a real case BE study using a two-way crossover design. PK data were simulated using three structural models under the null hypothesis and one model under the alternative hypothesis. MB-TOST was applied either using each of the five candidate models or following MS and MA with or without the simulated model in the pool. Assuming T and R have the same PK model, our simulation shows that following MS and MA, MB-TOST controls type I error rates at or below 0.05 and attains similar or even higher power than when using the simulated model. Thus, we propose to use MS prior to MB-TOST for BE studies with sparse PK sampling and to consider MA when candidate models have similar Akaike information criterion.
Asunto(s)
Simulación por Computador , Estudios Cruzados , Modelos Estadísticos , Equivalencia Terapéutica , Humanos , FarmacocinéticaAsunto(s)
Acarbosa , Hipoglucemiantes , Equivalencia Terapéutica , Humanos , Acarbosa/farmacocinética , Acarbosa/administración & dosificación , Acarbosa/farmacología , Hipoglucemiantes/farmacocinética , Hipoglucemiantes/administración & dosificación , Hipoglucemiantes/uso terapéutico , Inhibidores de Glicósido Hidrolasas/farmacocinética , Inhibidores de Glicósido Hidrolasas/administración & dosificación , Inhibidores de Glicósido Hidrolasas/farmacologíaRESUMEN
The 2023 Generic Drug Science and Research Initiative Public Workshop organized by the U.S. Food and Drug Administration (FDA) discussed the research needs to improve and enhance bioequivalence (BE) approaches for generic drug development. FDA takes such research needs and panel discussions into account to develop its Generic Drug User Fee Amendments III (GDUFA III) Science and Research Initiatives specific to generics. During the five workshop sessions, presentations and panel discussions focused on identifying and addressing scientific gaps and research needs related to nitrosamine impurity issues, BE assessment for oral products, innovative BE approaches for long-acting injectable products, alternative BE approaches for orally inhaled products, and advanced BE methods for topical products. Specifically, this report highlights the discussions on how to improve BE assessment for developing generic drug products based on research priorities for leveraging quantitative methods and modeling, as well as artificial intelligence/machine learning (AI/ML).
Asunto(s)
Inteligencia Artificial , Medicamentos Genéricos , Estados Unidos , Equivalencia Terapéutica , Desarrollo de Medicamentos , United States Food and Drug AdministrationRESUMEN
Advair Diskus is an essential treatment for asthma and chronic obstructive pulmonary disease. It is a dry powder inhaler with a combination of fluticasone propionate (FP) and salmeterol xinafoate (SX). However, the pharmacokinetics (PK) batch-to-batch variability of the reference-listed drug (RLD) hindered its generic product development. This work developed the PK models for inhaled FP and SX that could represent potential batch variability. Two batches each of the reference and the test product (R1, R2, T1, T2) of Advair Diskus (100 µg FP/50 µg SX inhalation) were administered to 60 healthy subjects in a 4-period, 4-sequence crossover study. The failure of the bioequivalence (BE) between R1 and R2 confirmed the high between-batch variability of the RLD. Non-linear mixed effect modeling was used to estimate the population mean PK parameters for each batch. For FP, a 2-compartment model with a sequential dual zero-order absorption best described the PK profile. For SX, a 2-compartment model with a first-order absorption model best fit the data. Both models were able to capture the plasma concentration, the maximum concentration, and the total exposure (AUCinf) adequately for each batch, which could be used to simulate the BE study in the future. In vitro properties were also measured for each batch, and the batch with a higher fraction of the fine particle (diameter < 1 µm, < 2 µm) had a higher AUCinf. This positive correlation for both FP and SX could potentially assist the batch selection for the PK BE study.
Asunto(s)
Broncodilatadores , Estudios Cruzados , Inhaladores de Polvo Seco , Combinación Fluticasona-Salmeterol , Modelos Biológicos , Equivalencia Terapéutica , Humanos , Administración por Inhalación , Masculino , Adulto , Combinación Fluticasona-Salmeterol/farmacocinética , Combinación Fluticasona-Salmeterol/administración & dosificación , Adulto Joven , Broncodilatadores/farmacocinética , Broncodilatadores/administración & dosificación , Broncodilatadores/sangre , Femenino , Persona de Mediana Edad , Fluticasona/farmacocinética , Fluticasona/administración & dosificación , Xinafoato de Salmeterol/farmacocinética , Xinafoato de Salmeterol/administración & dosificación , Voluntarios SanosRESUMEN
With the evolving role of Model Integrated Evidence (MIE) in generic drug development and regulatory applications, the need for improving Model Sharing, Acceptance, and Communication with the FDA is warranted. Model Master File (MMF) refers to a quantitative model or a modeling platform that has undergone sufficient model Verification & Validation to be recognized as sharable intellectual property that is acceptable for regulatory purposes. MMF provides a framework for regulatorily acceptable modeling practice, which can be used with confidence to support MIE by both the industry and the U.S. Food and Drug Administration (FDA). In 2022, the FDA and the Center for Research on Complex Generics (CRCG) hosted a virtual public workshop to discuss the best practices for utilizing modeling approaches to support generic product development. This report summarizes the presentations and panel discussions of the workshop symposium entitled "Model Sharing, Acceptance, and Communication with the FDA". The symposium and this report serve as a kick-off discussion for further utilities of MMF and best practices of utilizing MMF in drug development and regulatory submissions. The potential advantages of MMFs have garnered acknowledgment from model developers, industries, and the FDA throughout the workshop. To foster a unified comprehension of MMFs and establish best practices for their application, further dialogue and cooperation among stakeholders are imperative. To this end, a subsequent workshop is scheduled for May 2-3, 2024, in Rockville, Maryland, aiming to delve into the practical facets and best practices of MMFs pertinent to regulatory submissions involving modeling and simulation methodologies.
Asunto(s)
Comunicación , Desarrollo de Medicamentos , Estados Unidos , United States Food and Drug Administration , Simulación por Computador , Medicamentos GenéricosRESUMEN
This report summarizes relevant insights and discussions from a 2022 FDA public workshop titled Best Practices for Utilizing Modeling Approaches to Support Generic Product Development which illustrated how model-integrated evidence has been used and can be leveraged further to inform generic drug product development and regulatory decisions during the assessment of generic drug applications submitted to the FDA. The workshop attendees discussed that model-integrated evidence (MIE) approaches for generics are being applied in the space of long-acting injectable (LAI) products to develop shorter and more cost-effective alternative study designs for LAI products. Modeling and simulation approaches are utilized to support virtual BE assessments at the site of action for locally acting drug products and to assess the impact of food on BE assessments for oral dosage forms. The factors contributing to the success of the model-informed drug development program under PDUFA VI were discussed. The generic drug industry shared that decisions on formulation candidate/formulation variant selection, on pilot in vivo bioavailability studies, and on alternative study designs for BE assessment are informed by modeling and simulation approaches. There was agreement that interactions between the regulatory agencies and the industry are desirable because they improve the industry's understanding of scientific and other regulatory considerations on implementing modeling and simulation approaches in drug development and regulatory submissions.
Asunto(s)
Desarrollo de Medicamentos , Medicamentos Genéricos , Disponibilidad Biológica , Simulación por Computador , Industria FarmacéuticaRESUMEN
Proton pump inhibitors (PPIs) can affect the release of drugs from their dosage forms in vivo by elevating the gastric pH. Our recent clinical study has demonstrated that drug-drug interactions (DDIs) exist between a PPI, omeprazole, and nifedipine extended-release formulations, where systemic exposure of nifedipine was increased in subjects after multiple-dose pretreatment of omeprazole. However, the mechanism of the observed DDIs between omeprazole and nifedipine has not been well-understood, as the DDI may also be mediated through CYP3A4 enzyme inhibition in addition to the elevated gastric pH caused by omeprazole. This study used physiologically-based pharmacokinetic (PBPK) modeling and simulations to investigate the underlying mechanism of these complex DDIs. A formulation exhibiting differences in in vitro dissolution across physiological pH range and another formulation where pH does not impact dissolution appreciably (e.g., an osmotic pump) were chosen to characterize the potential impact of pH. The PBPK models incorporated two-stage in vitro release profiles via US Pharmacopeia 2 apparatus. PBPK simulations suggest that the elevated gastric pH following multiple-dose administration of omeprazole has a minimal effect on nifedipine pharmacokinetics (PKs), whereas CYP3A4-mediated DDI is likely the main driver to the observed change of nifedipine PKs in the presence of omeprazole. Compared to the osmotic formulation, the slightly increased exposure of nifedipine can be accounted for by the enhanced drug release in the pH-dependent formulation. The reported model-based approach may be useful in DDI risk assessments, product formulation designs, and bioequivalence evaluations.
Asunto(s)
Nifedipino , Omeprazol , Humanos , Nifedipino/química , Nifedipino/farmacocinética , Omeprazol/farmacología , Citocromo P-450 CYP3A/metabolismo , Interacciones Farmacológicas , Liberación de Fármacos , Administración OralRESUMEN
This study investigated the impact of gastro-intestinal fluid volume and bile salt (BS) concentration on the dissolution of carbamazepine (CBZ) immediate release (IR) 100 mg tablets and to integrate these in vitro biorelevant dissolution profiles into physiologically based pharmacokinetic modelling (PBPK) in pediatric and adult populations to determine the biopredictive dissolution profile. Dissolution profiles of CBZ IR tablets (100 mg) were generated in 50-900 mL biorelevant adult fasted state simulated gastric and intestinal fluid (Ad-FaSSGF and Ad-FaSSIF), also in three alternative compositions of biorelevant pediatric FaSSGF and FaSSIF medias at 200 mL. This study found that CBZ dissolution was poorly sensitive to changes in the composition of the biorelevant media, where dissimilar dissolution (F2 = 46.2) was only observed when the BS concentration was changed from 3000 to 89 µM (Ad-FaSSIF vs Ped-FaSSIF 50% 14 BS). PBPK modeling demonstrated the most predictive dissolution volume and media composition to forecast the PK was 500 mL of Ad-FaSSGF/Ad-FaSSIF media for adults and 200 mL Ped-FaSSGF/FaSSIF media for pediatrics. A virtual bioequivalence simulation was conducted by using Ad-FaSSGF and/or Ad-FaSSIF 500 mL or Ped-FaSSGF and/or Ped-FaSSIF 200 mL dissolution data for CBZ 100 mg (reference and generic test) IR product. The CBZ PBPK models showed bioequivalence of the product. This study demonstrates that the integration of biorelevant dissolution data can predict the PK profile of a poorly soluble drug in both populations. Further work using more pediatric drug products is needed to verify biorelevant dissolution data to predict the in vivo performance in pediatrics.
Asunto(s)
Benzodiazepinas , Medicamentos Genéricos , Humanos , Niño , Adulto , Solubilidad , Equivalencia Terapéutica , Ácidos y Sales Biliares , CarbamazepinaRESUMEN
In a traditional pharmacokinetic (PK) bioequivalence (BE) study, a two-way crossover study is conducted, PK parameters (namely the area under the time-concentration curve [AUC] and the maximal concentration [ C max ]) are obtained by noncompartmental analysis (NCA), and the BE analysis is performed using the two one-sided test (TOST) method. For ophthalmic drugs, however, only one sample of aqueous humor, in one eye, per eye can be obtained in each patient, which precludes the traditional BE analysis. To circumvent this issue, the U.S. Food and Drug Administration (FDA) has proposed an approach coupling NCA with either parametric or nonparametric bootstrap (NCA bootstrap). The model-based TOST (MB-TOST) has previously been proposed and evaluated successfully for various settings of sparse PK BE studies. In this paper, we evaluate, via simulations, MB-TOST in the specific setting of single sample PK BE study and compare its performance to NCA bootstrap. We performed BE study simulations using a published PK model and parameter values and evaluated multiple scenarios, including study design (parallel or crossover), sampling times (5 or 10 spread across the dosing interval), and geometric mean ratio (of 0.8, 0.9, 1, and 1.25). Using the simulated structural PK model, MB-TOST performed similarly to NCA bootstrap for AUC. For C max , the latter tended to be conservative and less powerful. Our research suggests that MB-TOST may be considered as an alternative BE approach for single sample PK studies, provided that the PK model is correctly specified and the test drug has the same structural model as the reference drug.
Asunto(s)
Equivalencia Terapéutica , Humanos , Estudios Cruzados , Área Bajo la CurvaRESUMEN
This workshop report summarizes the presentations and panel discussion related to the use of physiologically based pharmacokinetic (PBPK) modeling approaches for food effect assessment, collected from Session 2 of Day 2 of the workshop titled "Regulatory Utility of Mechanistic Modeling to Support Alternative Bioequivalence Approaches." The US Food and Drug Administration in collaboration with the Center for Research on Complex Generics organized this workshop where this particular session titled "Oral PBPK for Evaluating the Impact of Food on BE" presented successful cases of PBPK modeling approaches for food effect assessment. Recently, PBPK modeling has started to gain popularity among academia, industries, and regulatory agencies for its potential utility during bioavailability (BA) and/or bioequivalence (BE) studies of new and generic drug products to assess the impact of food on BA/BE. Considering the promises of PBPK modeling in generic drug development, the aim of this workshop session was to facilitate knowledge sharing among academia, industries, and regulatory agencies to understand the knowledge gap and guide the path forward. This report collects and summarizes the information presented and discussed during this session to disseminate the information into a broader audience for further advancement in this area.
Asunto(s)
Modelos Biológicos , Informe de Investigación , Humanos , Equivalencia Terapéutica , Disponibilidad Biológica , Desarrollo de Medicamentos , Medicamentos GenéricosRESUMEN
On September 30 and October 1, 2021, the US Food and Drug Administration (FDA) and the Center for Research on Complex Generics cosponsored a live virtual workshop titled "Regulatory Utility of Mechanistic Modeling to Support Alternative Bioequivalence Approaches." The overall aims of the workshop included (i) engaging the generic drug industry and other involved stakeholders regarding how mechanistic modeling and simulation can support their product development and regulatory submissions; (ii) sharing the current state of mechanistic modeling for bioequivalence (BE) assessment through case studies; (iii) establishing a consensus on best practices for using mechanistic modeling approaches, such as physiologically based pharmacokinetic modeling and computational fluid dynamics modeling, for BE assessment; and (iv) introducing the concept of a Model Master File to improve model sharing between model developers, industry, and the FDA. More than 1500 people registered for the workshop. Based on a postworkshop survey, the majority of participants reported that their fundamental scientific understanding of mechanistic models was enhanced, there was greater consensus on model validation and verification, and regulatory expectations for mechanistic modeling submitted in abbreviated new drug applications were clarified by the workshop.
Asunto(s)
Medicamentos Genéricos , Estados Unidos , Humanos , Equivalencia Terapéutica , Medicamentos Genéricos/farmacocinética , Simulación por Computador , United States Food and Drug AdministrationRESUMEN
On November 30, 2021, the US Food and Drug administration (FDA) and the Center for Research on Complex Generics (CRCG) hosted a virtual public workshop titled "Establishing the Suitability of Model-Integrated Evidence (MIE) to Demonstrate Bioequivalence for Long-Acting Injectable and Implantable (LAI) Drug Products." This workshop brought relevant parties from the industry, academia, and the FDA in the field of modeling and simulation to explore, identify, and recommend best practices on utilizing MIE for bioequivalence (BE) assessment of LAI products. This report summerized presentations and panel discussions for topics including challenges and opportunities in development and assessment of generic LAI products, current status of utilizing MIE, recent research progress of utilizing MIE in generic LAI products, alternative designs for BE studies of LAI products, and model validation/verification strategies associated with different types of MIE approaches.
Asunto(s)
Medicamentos Genéricos , Estados Unidos , Humanos , Equivalencia Terapéutica , United States Food and Drug Administration , Simulación por ComputadorRESUMEN
OBJECTIVE: Lamotrigine is a commonly prescribed antiepileptic drug. U.S. Food and Drug Administration (FDA)-funded clinical studies have demonstrated bioequivalence (BE) for generic lamotrigine immediate-release (IR) products in epilepsy patients with generic substitution. To address the potential concerns about the risk of generic-brand substitution of lamotrigine extended-release (ER) products, considering the complexity of controlled release systems and pharmacokinetic variations associated with possible within-subject variability (WSV), this prospective study assessed (1) BE of generic and brand lamotrigine ER products in a fully replicated BE study design in healthy subjects and (2) whether such fully replicated study design and WSV data can better support the approval of generic lamotrigine ER products. METHODS: This open-label, single-dose, two-treatment, four-period, two-sequence, fully replicated crossover BE study compared generic lamotrigine ER tablet to brand Lamictal XR (200 mg) in 30 healthy subjects under fed conditions. Pharmacokinetics (PK) profiles were generated based on intensive blood sampling up to 144 h. RESULTS: The two products showed comparable peak plasma concentration (Cmax ), area under the concentration-time curve (AUC) from time zero to the last measurable time point (AUC0-t ) and AUC extrapolated to infinity (AUC0-inf ), whereas median time to Cmax (Tmax ) values differed, that is, 10 h for generic and 22 h for brand products, respectively. WSVs for PK metrics were small (~8% of Cmax and ~6% of AUC) and similar between these two products. PK simulation predicted equivalent PK measurements of both products at steady state and after brand-to-generic switch, except the first day upon switching. No serious adverse events were reported. SIGNIFICANCE: The generic lamotrigine ER tablet product demonstrates BE to the brand product in a fully replicated BE study design with healthy subjects, supporting the adequacy of the two-way crossover study design to demonstrate BE and generic-brand substitution of lamotrigine ER products.
Asunto(s)
Anticonvulsivantes , Medicamentos Genéricos , Humanos , Anticonvulsivantes/efectos adversos , Área Bajo la Curva , Estudios Cruzados , Medicamentos Genéricos/farmacocinética , Lamotrigina , Estudios Prospectivos , Comprimidos , Equivalencia TerapéuticaRESUMEN
This report summarizes the proceedings for day 2 sessions 1 and 3 of the 2-day public workshop entitled "Regulatory Utility of Mechanistic Modeling to Support Alternative Bioequivalence Approaches," a jointly sponsored workshop by the US Food and Drug Administration (FDA) and the Center for Research on Complex Generics (CRCG). The aims of this workshop were: (1) to discuss how mechanistic modeling, including physiologically-based pharmacokinetic (PBPK) modeling and simulation, can support product development, and regulatory submissions; (2) to share the current state of mechanistic modeling for bioequivalence (BE) assessment through case studies; (3) to establish a consensus on best practices for using PBPK modeling for BE assessment to help drive further investment by the generic drug industry into mechanistic modeling and simulation; and (4) to introduce the concept of a Model Master File to improve model-sharing. The theme of day 2 covered PBPK absorption model for oral products as an alternative BE approach and a tool for supporting risk assessment and biowaiver (session 1), oral PBPK for evaluating the impact of food on BE (session 2), successful cases, and challenges for oral PBPK (session 3). This report summarizes the topics of the presentations of day 2 sessions 1 and session 3 from FDA, academia, and pharmaceutical industry, including the current status of oral PBPK, case examples as well as the challenges and opportunities in this area. In addition, panel discussions on the utility of oral PBPK in both new drugs and generic drugs from regulatory and industry perspective are also summarized.
Asunto(s)
Modelos Biológicos , Informe de Investigación , Humanos , Equivalencia Terapéutica , Simulación por ComputadorRESUMEN
The development of a generic drug product involves demonstrating that there is no significant difference in the rate and extent to which the active ingredient becomes available at the site of action, relative to the reference listed drug product. This remains challenging for many locally acting topical dermatological products because measuring the concentration of the active ingredient at the site of action in the skin may not be straightforward, and, in most instances, there are no established relationships between skin and plasma pharmacokinetic profiles. In recent years, the Office of Generic Drugs of the US Food and Drug Administration (FDA) established scientific research programs with the goal of enhancing patient access to high quality, affordable topical dermatological generics. A key strategy of these research programs was to leverage modeling and simulation methodologies that accelerate the development of these generics by facilitating alternative bioequivalence approaches for dermatological drug products. This report summarizes relevant insights and discussions from a 2021 FDA public workshop titled "Regulatory Utility of Mechanistic Modeling to Support Alternative Bioequivalence Approaches," which illustrated how mechanistic modeling and simulation approaches can be utilized (and have been used) to inform generic drug product development and regulatory decisions during the assessment of generic drug applications submitted to the FDA.
Asunto(s)
Medicamentos Genéricos , Informe de Investigación , Humanos , Preparaciones Farmacéuticas , Piel , Equivalencia TerapéuticaRESUMEN
This article evaluates the performance of pharmacokinetic (PK) equivalence testing between two formulations of a drug through the Two-One Sided Tests (TOST) by a model-based approach (MB-TOST), as an alternative to the classical non-compartmental approach (NCA-TOST), for a sparse design with a few time points per subject. We focused on the impact of model misspecification and the relevance of model selection for the reference data. We first analysed PK data from phase I studies of gantenerumab, a monoclonal antibody for the treatment of Alzheimer's disease. Using the original rich sample data, we compared MB-TOST to NCA-TOST for validation. Then, the analysis was repeated on a sparse subset of the original data with MB-TOST. This analysis inspired a simulation study with rich and sparse designs. With rich designs, we compared NCA-TOST and MB-TOST in terms of type I error and study power. With both designs, we explored the impact of misspecifying the model on the performance of MB-TOST and adding a model selection step. Using the observed data, the results of both approaches were in general concordance. MB-TOST results were robust with sparse designs when the underlying PK structural model was correctly specified. Using the simulated data with a rich design, the type I error of NCA-TOST was close to the nominal level. When using the simulated model, the type I error of MB-TOST was controlled on rich and sparse designs, but using a misspecified model led to inflated type I errors. Adding a model selection step on the reference data reduced the inflation. MB-TOST appears as a robust alternative to NCA-TOST, provided that the PK model is correctly specified and the test drug has the same PK structural model as the reference drug.