Bioequivalence risk assessment of oral formulations containing racemic ibuprofen through a chiral physiologically based pharmacokinetic model of ibuprofen enantiomers.

The characterization of the time course of ibuprofen enantiomers can be useful in the selection of the most sensitive analyte in bioequivalence studies. Physiologically based pharmacokinetic (PBPK) modelling and simulation represents the most efficient methodology to virtually assess bioequivalence outcomes. In this work, we aim to develop and verify a PBPK model for ibuprofen enantiomers administered as a racemic mixture with different immediate release dosage forms to anticipate bioequivalence outcomes based on different particle size distributions. A PBPK model incorporating stereoselectivity and non-linearity in plasma protein binding and metabolism as well as R-to-S unidirectional inversion has been developed in Simcyp®. A dataset composed of 11 Phase I clinical trials with 54 scenarios (27 per enantiomer) and 14,452 observations (7129 for R-ibuprofen and 7323 for S-ibuprofen) was used. Prediction errors for AUC0-t and Cmax for both enantiomers fell within the 0.8-1.25 range in 50/54 (93 %) and 42/54 (78 %) of scenarios, respectively. Outstanding model performance, with 10/10 (100 %) of Cmax and 9/10 (90 %) of AUC0-t within the 0.9-1.1 range, was demonstrated for oral suspensions, which strongly supported its use for bioequivalence risk assessment. The deterministic bioequivalence risk assessment has revealed R-ibuprofen as the most sensitive analyte to detect differences in particle size distribution for oral suspensions containing 400 mg of racemic ibuprofen, suggesting that achiral bioanalytical methods would increase type II error and declare non-bioequivalence for formulations that are bioequivalent for the eutomer.

Towards precision medicine of long-acting aripiprazole through population pharmacokinetic modelling.

Population pharmacokinetic (popPK) models constitute a valuable tool for characterizing the pharmacokinetic properties of once-monthly long-acting injectable aripiprazole (LAI aripiprazole) and quantifying the sources of variability in drug exposure. Our aim is to develop a popPK model of both aripiprazole and its metabolite dehydro-aripiprazole in patients treated with LAI aripiprazole, and to personalize the dosing regimen of aripiprazole across different sub-groups of patients. This is a prospective study investigating the pharmacokinetics of LAI aripiprazole. A total of 93 patients were included, 21 for model development and 71 for external model evaluation. A one-compartment model with linear absorption and elimination adequately described both aripiprazole and dehydro-aripiprazole concentrations. The weight of the patients has been shown to be the factor that most influences the absorption. However, the metabolizing phenotype for CYP2D6 and the concomitant treatment with strong inhibitors of this cytochrome have been shown to be the covariates that most influence total drug exposure. This is the first popPK model developed for LAI aripiprazole that includes aripiprazole and its main active metabolite, dehydroaripiprazole. It provides a personalized dosage recommendation that maximizes the probability of achieving optimal therapeutic concentrations and minimizes the difficulties associated with trial-and-error therapeutic strategies carried out in clinical practice.

Integrating Mechanistic and Toxicokinetic Information in Predictive Models of Cholestasis.

Drug development involves the thorough assessment of the candidate's safety and efficacy. In silico toxicology (IST) methods can contribute to the assessment, complementing in vitro and in vivo experimental methods, since they have many advantages in terms of cost and time. Also, they are less demanding concerning the requirements of product and experimental animals. One of these methods, Quantitative Structure-Activity Relationships (QSAR), has been proven successful in predicting simple toxicity end points but has more difficulties in predicting end points involving more complex phenomena. We hypothesize that QSAR models can produce better predictions of these end points by combining multiple QSAR models describing simpler biological phenomena and incorporating pharmacokinetic (PK) information, using quantitative in vitro to in vivo extrapolation (QIVIVE) models. In this study, we applied our methodology to the prediction of cholestasis and compared it with direct QSAR models. Our results show a clear increase in sensitivity. The predictive quality of the models was further assessed to mimic realistic conditions where the query compounds show low similarity with the training series. Again, our methodology shows clear advantages over direct QSAR models in these situations. We conclude that the proposed methodology could improve existing methodologies and could be suitable for being applied to other toxicity end points.

New insights into the role of VKORC1 polymorphisms for optimal warfarin dose selection in Caribbean Hispanic patients through an external validation of a population PK/PD model.

Warfarin, an oral anticoagulant, has been used for decades to prevent thromboembolic events. The complex interplay between CYP2C9 and VKORC1 genotypes on warfarin PK and PD properties is not fully understood in special sub-groups of patients. This study aimed to externally validate a population pharmacokinetic/pharmacodynamic (PK/PD) model for the effect of warfarin on international normalized ratio (INR) and to evaluate optimal dosing strategies based on the selected covariates in Caribbean Hispanic patients. INR, and CYP2C9 and VKORC1 genotypes from 138 patients were used to develop a population PK/PD model in NONMEM. The structural definition of a previously published PD model for INR was implemented. A numerical evaluation of the parameter-covariate relationship was performed. Simulations were conducted to determine optimal dosing strategies for each genotype combinations, focusing on achieving therapeutic INR levels. Findings revealed elevated IC50 for G/G, G/A, and A/A VKORC1 haplotypes (11.76, 10.49, and 9.22 mg/L, respectively), in this population compared to previous reports. The model-guided dosing analysis recommended daily warfarin doses of 3-5 mg for most genotypes to maintain desired INR levels, although subjects with combination of CYP2C9 and VKORC1 genotypes * 2/* 2-, * 2/* 3- and * 2/* 5-A/A would require only 1 mg daily. This research underscores the potential of population PK/PD modeling to inform personalized warfarin dosing in populations typically underrepresented in clinical studies, potentially leading to improved treatment outcomes and patient safety. By integrating genetic factors and clinical data, this approach could pave the way for more effective and tailored anticoagulation therapy in diverse patient groups.

Usage of model combination in computational toxicology.

New Approach Methodologies (NAMs) have ushered in a new era in the field of toxicology, aiming to replace animal testing. However, despite these advancements, they are not exempt from the inherent complexities associated with the study's endpoint. In this review, we have identified three major groups of complexities: mechanistic, chemical space, and methodological. The mechanistic complexity arises from interconnected biological processes within a network that are challenging to model in a single step. In the second group, chemical space complexity exhibits significant dissimilarity between compounds in the training and test series. The third group encompasses algorithmic and molecular descriptor limitations and typical class imbalance problems. To address these complexities, this work provides a guide to the usage of a combination of predictive Quantitative Structure-Activity Relationship (QSAR) models, known as metamodels. This combination of low-level models (LLMs) enables a more precise approach to the problem by focusing on different sub-mechanisms or sub-processes. For mechanistic complexity, multiple Molecular Initiating Events (MIEs) or levels of information are combined to form a mechanistic-based metamodel. Regarding the complexity arising from chemical space, two types of approaches were reviewed to construct a fragment-based chemical space metamodel: those with and without structure sharing. Metamodels with structure sharing utilize unsupervised strategies to identify data patterns and build low-level models for each cluster, which are then combined. For situations without structure sharing due to pharmaceutical industry intellectual property, the use of prediction sharing, and federated learning approaches have been reviewed. Lastly, to tackle methodological complexity, various algorithms are combined to overcome their limitations, diverse descriptors are employed to enhance problem definition and balanced dataset combinations are used to address class imbalance issues (methodological-based metamodels). Remarkably, metamodels consistently outperformed classical QSAR models across all cases, highlighting the importance of alternatives to classical QSAR models when faced with such complexities.

Mechanistic characterization of oscillatory patterns in unperturbed tumor growth dynamics: The interplay between cancer cells and components of tumor microenvironment.

Mathematical modeling of unperturbed and perturbed tumor growth dynamics (TGD) in preclinical experiments provides an opportunity to establish translational frameworks. The most commonly used unperturbed tumor growth models (i.e. linear, exponential, Gompertz and Simeoni) describe a monotonic increase and although they capture the mean trend of the data reasonably well, systematic model misspecifications can be identified. This represents an opportunity to investigate possible underlying mechanisms controlling tumor growth dynamics through a mathematical framework. The overall goal of this work is to develop a data-driven semi-mechanistic model describing non-monotonic tumor growth in untreated mice. For this purpose, longitudinal tumor volume profiles from different tumor types and cell lines were pooled together and analyzed using the population approach. After characterizing the oscillatory patterns (oscillator half-periods between 8-11 days) and confirming that they were systematically observed across the different preclinical experiments available (p<10-9), a tumor growth model was built including the interplay between resources (i.e. oxygen or nutrients), angiogenesis and cancer cells. The new structure, in addition to improving the model diagnostic compared to the previously used tumor growth models (i.e. AIC reduction of 71.48 and absence of autocorrelation in the residuals (p>0.05)), allows the evaluation of the different oncologic treatments in a mechanistic way. Drug effects can potentially, be included in relevant processes taking place during tumor growth. In brief, the new model, in addition to describing non-monotonic tumor growth and the interaction between biological factors of the tumor microenvironment, can be used to explore different drug scenarios in monotherapy or combination during preclinical drug development.

Application of population physiologically based pharmacokinetic modelling to optimize target expression and clearance mechanisms of therapeutic monoclonal antibodies.

AIMS: To use population physiologically based pharmacokinetic (PopPBPK) modelling to optimize target expression, kinetics and clearance of HER1/2 directed therapeutic monoclonal antibodies (mAbs). Thus, to propose a general workflow of PopPBPK modelling and its application in clinical pharmacology. METHODS: Full PBPK model of pertuzumab (PTZ) was developed in patient population using Simcyp V21R1 incorporating mechanistic targeted-mediated drug disposition process by fitting known clinical PK and sparse receptor proteomics data to optimize target expression and kinetics of HER2 receptor. Trastuzumab (TTZ) PBPK modelling was used to validate the optimized HER2 target. Additionally, the simulator was also used to develop a full PBPK model for the HER1-directed mAb cetuximab (CTX) to assess the underlying targeted-mediated drug disposition-independent elimination mechanisms. RESULTS: HER2 final parameterisation coming from the PBPK modelling of PTZ was successfully cross validated through PBPK modelling of TTZ with average fold error (AFE), absolute AFE and percent prediction error values for area under the concentration-time curve (AUC) and maximum plasma concentration (Cmax ) of 1.13, 1.16 and 16, and 1.01, 1.07 and 7, respectively. CTX PBPK model performance was validated after the incorporation of an additional systemic clearance of 0.033 L/h as AFE and absolute AFE showed an acceptable predictive power of AUC and Cmax with percent prediction error of 13% for AUC and 10% for Cmax . CONCLUSIONS: Optimisation of both system and drug related parameters were performed through PBPK modelling to improve model performance of therapeutic mAbs (PTZ, TTZ and CTX). General workflow was proposed to develop and apply PopPBPK to support clinical development of mAbs targeting same receptor.

Alternative Pharmacokinetic Metrics in Single-Dose Studies to Ensure Bioequivalence of Prolonged-Release Products at Steady State-A Case Study.

(1) Background: this article investigates which PK metrics in a single-dose study (concentration at the end of posology interval, Cτ, partial areas under the curve, pAUCs, or half-value duration, HVD) are more sensitive and less variable for predicting the failure of a prolonged-release product at steady-state that was the bioequivalent for Cmax, AUC0-t and AUC0-inf, in the single-dose study; (2) Methods: a cross-over study was performed in 36 subjects receiving desvenlafaxine 100 mg prolonged-release tablets. Conventional (Cmax, AUC0-t and AUC0-inf) and additional (Cτ, pAUCs and HVD) PK metrics were considered after single-dose conditions. Predicted PK metrics at steady state (AUC0-τ, Cmax,ss, and Cτ,ss) were derived using a population PK model approach; (3) Results: the existing differences in the shape of the concentration-time curves precluded to show equivalence for Cτ,ss in the simulated study at steady state. This failure to show equivalence at steady state was predicted by Cτ, pAUCs and HVD in the single-dose study. Cτ was the most sensitive metric for detecting the different shape, with a lower intra-subject variability than HVD; (4) Conclusions: conventional PK metrics for single-dose studies (Cmax, AUC0-t and AUC0-inf) are not enough to guarantee bioequivalence at steady state for prolonged-release products.

Regulatory Requirements for the Development of Second-Entry Semisolid Topical Products in the European Union.

The development of second-entry topical products is hampered by several factors. The excipient composition should be similar to the reference product because excipients may also contribute to efficacy. Conventional pharmacokinetic bioequivalence studies were not considered acceptable because drug concentrations are measured downstream after the site of action. There was no agreed methodology to characterize the microstructure of semisolids, and waivers of therapeutic equivalence studies with clinical endpoints were not possible. Only the vasoconstrictor assay for corticosteroids was accepted as a surrogate. This paper describes the implementation of the European Union's stepwise approach for locally acting products to cutaneous products, discusses the equivalence requirements of the EMA Draft Guideline on the Quality and Equivalence of Topical Products, and compares them with the US Food and Drug Administration recommendations. Step 1 includes the possibility of waivers for simple formulations based on in vitro data only (Q1 + Q2 + Q3 + IVRT). Step 2 includes step 1 requirements plus a kinetic study (TS/IVPT/PKBE) to compare the local availability of complex formulations. Step 3 refers to clinical studies with pharmacodynamic/clinical endpoints. As excipients may affect the local tolerability and efficacy of the products, the similarity of excipient composition is required in all steps, except where clinical endpoints are compared.

Population Pharmacokinetic/Pharmacodynamic Modelling of Daptomycin for Schedule Optimization in Patients with Renal Impairment.

The aims of this study are (i) to develop a population pharmacokinetic/pharmacodynamic model of daptomycin in patients with normal and impaired renal function, and (ii) to establish the optimal dose recommendation of daptomycin in clinical practice. Several structural PK models including linear and non-linear binding kinetics were evaluated. Monte Carlo simulations were conducted with a fixed combination of creatinine clearance (30-90 mL/min/1.73 m2) and body weight (50-100 kg). The final dataset included 46 patients and 157 daptomycin observations. A two-compartment model with first-order peripheral distribution and elimination kinetics assuming non-linear protein-binding kinetics was selected. The bactericidal effect for Gram+ strains with MIC ≤ 0.5 mg/L could be achieved with 5-12 mg/kg daily daptomycin based on body weight and renal function. The administration of 10-17 mg/kg q48 h daptomycin allows to achieve bactericidal effect for Gram+ strains with MIC ≤ 1 mg/L. Four PK samples were selected as the optimal sampling strategy for an accurate AUC estimation. A quantitative framework has served to characterize the non-linear binding kinetics of daptomycin in patients with normal and impaired renal function. The impact of different dosing regimens on the efficacy and safety outcomes of daptomycin treatment based on the unbound exposure of daptomycin and individual patient characteristics has been evaluated.

A physiologically based pharmacokinetic model for open acid and lactone forms of atorvastatin and metabolites to assess the drug-gene interaction with SLCO1B1 polymorphisms.

Atorvastatin is the most prescribed 3-hydroxy-3-methylglutaryl coenzyme A reductase inhibitor used to lower cardiovascular risk and constitutes one of the best-selling drugs world-wide. Several physiologically based pharmacokinetic (PBPK) models have been developed to assess its non-straightforward pharmacokinetics (PK) as well as that of its metabolites and have been only applied to assess drug-drug interactions (DDI). Here we present a full PBPK model for atorvastatin and its metabolites able to predict within a 2-fold error their PK after the administration of a solid oral dosage form containing the calcium salt of atorvastatin in single and multiple dosing schedules at 20, 40, and 80 mg and 10 mg dose levels, respectively. Internal validation with data from Phase 1 clinical trials as well as external validation in predicting clinically relevant DDIs consolidated model structure and parameterization. The model has been used to quantitatively assess the drug-gene interaction (DGI) between SLCO1B1 polymorphisms and atorvastatin exposure and revealed that patients with a reduced activity in hepatic uptake of atorvastatin are at increased risk of suffering muscle discomfort because of a 30% lower clearance (p < 0.01), leading to a 40% and 33% higher (p < 0.05) atorvastatin AUC and Cmax, respectively. These findings could explain the reported hazard ratio of 1.4 (95% CI: 1.1-1.7, p = 0.02) for suffering statin-induced myopathies and the treatment discontinuation among these patients (odds ratio 1.67, p = 0.0001) observed in the context of routine clinical care.

A Review of Population Pharmacokinetic Analyses of Linezolid.

In recent years, many studies on population pharmacokinetics of linezolid have been conducted. This comprehensive review aimed to summarize population pharmacokinetic models of linezolid, by focusing on dosage optimization to maximize the probability of attaining a certain pharmacokinetic-pharmacodynamic parameter in special populations. We searched the PubMed and EMBASE databases for population pharmacokinetic analyses of linezolid using a parametric non-linear mixed-effect approach, including both observational and prospective trials. Of the 32 studies, 26 were performed in adults, four in children, and one in both adults and children. High between-subject variability was determined in the majority of the models, which was in line with the variability of linezolid concentrations previously detected in observational studies. Some studies found that patients with renal impairment, hepatic failure, advanced age, or low body weight had higher exposure and adverse reactions rates. In contrast, lower concentrations and therapeutic failure were associated with obese patients, young patients, and patients who had undergone renal replacement techniques. In critically ill patients, the inter-individual and intra-individual variability was even greater, suggesting that this population is at an even higher risk of underexposure and overexposure. Therapeutic drug monitoring may be warranted in a large proportion of patients given that the Monte Carlo simulations demonstrated that the one-size-fits-all labeled dosing of 600 mg every 12 h could lead to toxicity or therapeutic failure for high values of the minimum inhibitory concentration of the target pathogen. Further research on covariates, including renal function, hepatic function, and drug-drug interactions related to P-glycoprotein could help to explain variability and improve linezolid dosing regimens.

Pharmacometric characterization of entero-hepatic circulation processes of orally administered formulations of amiodarone under complex binding kinetics.

AIMS: The aims of this work are (i) to characterize the absorption properties of orally administered formulations at different dose levels, and (ii) to evaluate the impact of entero-hepatic circulation on the time-course of amiodarone (AM) in rats in order to optimize the development of new oral (OR) formulations. METHODS: Intravenous (IV) formulation consisted on a solution of a commercial injectable of AM chlorhydrate. OR formulations included the IV commercial formulation (Trangorex®) (Solution I), an aqueous supramicellar solution of AM chlorhydrate with Polysorbate at 5% (Solution II) and a suspension from Trangorex® tablets (Tablet). Data from 96 male Wistar rats, including 985 AM observations, were analyzed using NONMEM v7.4. RESULTS: The population pharmacokinetic (PK) model assumes linear absorption processes, showing ka of AM from Solution II (Polysorbate 80, 5%) and Solution I increased by 2.5- and 1.62-fold compared to Tablet formulation. OR bioavailability of AM from Tablet, Solution I and Solution II was 37%, 40%, and 50%, respectively. The structural model of AM disposition was adapted from a previously population PK model and expanded by incorporating entero-hepatic reabsorption (EHR) processes, which estimated a 12.3% biliary excretion of AM and complete re-absorption from lumen. CONCLUSIONS: The current population PK model of AM demonstrated the absorption rate enhancement when AM is formulated with supramicellar concentrations of Polysorbate 80. The study design allowed to characterize the EHR of AM and its contribution in the overall AM disposition.

Developing Clinically Relevant Dissolution Specifications (CRDSs) for Oral Drug Products: Virtual Webinar Series.

A webinar series that was organised by the Academy of Pharmaceutical Sciences Biopharmaceutics focus group in 2021 focused on the challenges of developing clinically relevant dissolution specifications (CRDSs) for oral drug products. Industrial scientists, together with regulatory and academic scientists, came together through a series of six webinars, to discuss progress in the field, emerging trends, and areas for continued collaboration and harmonisation. Each webinar also hosted a Q&A session where participants could discuss the shared topic and information. Although it was clear from the presentations and Q&A sessions that we continue to make progress in the field of CRDSs and the utility/success of PBBM, there is also a need to continue the momentum and dialogue between the industry and regulators. Five key areas were identified which require further discussion and harmonisation.

Impact of Pharmacokinetic and Pharmacodynamic Properties of Monoclonal Antibodies in the Management of Psoriasis.

The treatment of psoriasis has been revolutionized by the emergence of biological therapies. Monoclonal antibodies (mAb) generally have complex pharmacokinetic (PK) properties with nonlinear distribution and elimination. In recent years, several population pharmacokinetic/pharmacodynamic (PK/PD) models capable of describing different types of mAb have been published. This study aims to summarize the findings of a literature search about population PK/PD modeling and therapeutic drug monitoring (TDM) of mAb in psoriasis. A total of 22 articles corresponding to population PK/PD models of tumor necrosis factor (TNF)-α inhibitors (adalimumab and golimumab), interleukin (IL)-23 inhibitors (guselkumab, tildrakizumab, and risankizumab), IL-23/IL-12 inhibitor (ustekinumab), and IL-17 inhibitors (secukinumab, ixekizumab, and brodalumab) were collected. A summary of the clinical trials conducted so far in psoriasis was included, together with the current structural population PK and PD models. The most significant and clinical covariates were body weight (BW) and the presence of immunogenicity on clearance (CL). The lack of consensus on PK/PD relationships has prevented establishing an adequate dosage and, therefore, accentuates the need for TDM in psoriasis.

Quantitative assessment of the exposure-efficacy relationship of glucocerebrosidase using Markovian elements in Gaucher patients treated with enzyme replacement therapy.

AIMS: The aims of this study are (i) to develop a population pharmacokinetic model of enzyme activity in Gaucher-type 1 (GD1) patients after intravenous administration of enzyme replacement therapy (ERT), and (ii) to establish an exposure-efficacy relationship for bone marrow infiltration to propose dose adjustments according to patient covariate values. METHODS: A prospective follow-up, semi-experimental multi-centre study was conducted in four hospitals to evaluate the pharmacokinetics, efficacy and safety of ERT in GD1 patients. Twenty-five individuals with 266 glucocerebrosidase (GCase) observations in plasma and leukocytes and 14 individuals with 68 Spanish magnetic resonance imaging (S-MRI) observations were enrolled. RESULTS: A two concatenated compartment model with zero-order endogenous production and first-order distribution (CL1 = 3.85 × 10-1 L/d) and elimination (CL2 = 1.25 L/d) allowed GCase observations in plasma and leukocytes to be described, respectively. An exponential time dependency (kT = 6.14 × 10-1 d-1 ) effect on CL1 was incorporated. The final exposure-efficacy model was a longitudinal logistic regression model with a first-order Markov element. An Emax function (EC50 = 15.73 U/L and Emax = 2.33) linked steady-state concentrations of GCase in leukocytes to the probability of transition across the different S-MRI stages. CONCLUSION: A population pharmacokinetic model successfully characterized the leukocyte activity-time profiles of GCase following intravenous administration of ERT in GD1 patients together with an exposure-efficacy relationship in bone marrow using Markovian elements. The information obtained from this study could be of high clinical relevance in individualization of ERT in GD1 patients, as this could lead to anticipative decision-making regarding clinical response in bone and optimal dosing strategy.

Semi-Mechanistic Model for the Antitumor Response of a Combination Cocktail of Immuno-Modulators in Non-Inflamed (Cold) Tumors.

Immune checkpoint inhibitors, administered as single agents, have demonstrated clinical efficacy. However, when treating cold tumors, different combination strategies are needed. This work aims to develop a semi-mechanistic model describing the antitumor efficacy of immunotherapy combinations in cold tumors. Tumor size of mice treated with TC-1/A9 non-inflamed tumors and the drug effects of an antigen, a toll-like receptor-3 agonist (PIC), and an immune checkpoint inhibitor (anti-programmed cell death 1 antibody) were modeled using Monolix and following a middle-out strategy. Tumor growth was best characterized by an exponential model with an estimated initial tumor size of 19.5 mm3 and a doubling time of 3.6 days. In the treatment groups, contrary to the lack of response observed in monotherapy, combinations including the antigen were able to induce an antitumor response. The final model successfully captured the 23% increase in the probability of cure from bi-therapy to triple-therapy. Moreover, our work supports that CD8+ T lymphocytes and resistance mechanisms are strongly related to the clinical outcome. The activation of antigen-presenting cells might be needed to achieve an antitumor response in reduced immunogenic tumors when combined with other immunotherapies. These models can be used as a platform to evaluate different immuno-oncology combinations in preclinical and clinical scenarios.

Estimators and confidence intervals of f2 using bootstrap methodology for the comparison of dissolution profiles.

BACKGROUND AND OBJECTIVES: The most widely used method to compare dissolution profiles is the similarity factor f2. When this method is not applicable, the confidence interval of f2 using bootstrap methodology has been recommended instead. As neither details of the estimator nor the types of confidence intervals are described in the guidelines, the suitability of five estimators and fourteen types of confidence intervals were investigated in this study by simulation. METHODS: One million individual dissolution profiles were simulated for the reference and test populations with predefined target population f2 values, where random samples of different sizes were drawn without replacement. From each pair of random samples, five f2 estimators were calculated, and fourteen types of confidence intervals were obtained using 5000 bootstrap samples. The whole process was repeated 10000 times and the percentage of the similarity conclusions was measured. In addition, the uncertainty associated with the current practice of using f^2 point estimate alone for the statistical inference was evaluated. RESULTS: When combined with different types of confidence intervals, the estimated f2 (f^2), the bias-corrected f2 (f^2,bc), and the variance- and bias-corrected f2 (f^2,vcbc) are not suitable estimators due to higher-than-acceptable type I errors. The estimator f^2,exp, calculated based on the mathematical expectation of f^2, and f^2,vcexp, the variance-corrected f^2,exp, showed acceptable type I errors when combined with any of the ten percentile intervals. However, they have the drawback of low power, which might be addressed by increasing the sample size. To properly control the type I error, samples with at least 12 units should be used. CONCLUSION: The best combinations of estimator and type of confidence interval are f^2,exp and f^2,vcexp combined with any of the ten types of percentile intervals. When the sample f2 value is close to 50, the use of the confidence interval of f2 is recommended even when the variability of the dissolution profiles is low and the prerequisites defined in the regulatory guidelines for using the conventional f2 method are fulfilled in order to control the type I error rate.