In Silico Modeling and Simulation to Guide Bioequivalence Testing for Oral Drugs in a Virtual Population

Physiologically-Based Biopharmaceutics Modeling for Ibuprofen: Identifying Key Formulation Parameter and Virtual Bioequivalence Assessment

Background: Physiologically based pharmacokinetic (PBPK) modeling for biopharmaceutics applications (i.e., physiologically based biopharmaceutics modeling (PBBM)) enables mechanistic modeling from dissolution to absorption and disposition, facilitating the prediction of bioequivalence (BE) outcomes and the delimitation of the safe space. This study aims to identify the product-related parameter driving ibuprofen dissolution to upgrade an existing PBPK model, so that an in vitro safe space and virtual BE (VBE) predictions of IR ibuprofen tablets can be performed. Methods: Cmax within- and between-subject variabilities of a previous PBPK model were optimized after identifying crucial physiological parameters for ibuprofen absorption and disposition. In vitro data modeling was performed to estimate the value of the parameter driving ibuprofen dissolution. A safe space was defined for this parameter and the sample size to declare BE was calculated. Finally, VBE simulations were performed to explore the effect of sample size as well as number of trial replicates and runs. Results: Cmax variability was adequately predicted after changing Vss and MRT in stomach and small intestine CV (%) to 10 and 150%, respectively. Particle surface pH was identified as the dissolution key parameter for ibuprofen. A safe space for test product surface pH values of 5.64-6.40 was defined in order to achieve a 90%CI for the Cmax ratio within the 80-125% range when the reference product surface pH is 6.02. R-ibuprofen was identified as the most discriminative enantiomer. VBE studies with 24 individuals showed BE outcomes that are sensitive to the number of trial replicates and runs. Conclusions: Ibuprofen particle surface pH has been identified as the in vitro parameter governing dissolution in maleate buffer 7 mM with HCl pH 2.0 pretreatment, allowing to establish an in vitro safe space useful for calculating sample sizes and to evaluate the BE success rate through PBBM/PBPK model-informed VBE simulations.

Establishment of Biopredictive Dissolution and Bioequivalence Safe Space Using the Physiologically Based Biopharmaceutics Modeling for Tacrolimus Extended-Release Capsules

A slight variation in in vivo exposure for tacrolimus extended-release (ER) capsules, which have a narrow therapeutic index (NTI), significantly affects the pharmacodynamics of the drug. Generic drug bioequivalence (BE) standards are stricter, necessitating accurate assessment of the rate and extent of drug release. Therefore, an in vitro dissolution method with high in vivo predictive power is crucial for developing generic drugs. In this study, physiologically based biopharmaceutics modeling (PBBM) for 5 mg tacrolimus ER capsules was developed and validated. The reference and non-BE test formulations were assessed using the Flow-Through Cell apparatus (USP IV) with biorelevant media to establish a biopredictive dissolution method. Using PBBM, virtual bioequivalence trials with virtual batches were conducted to propose a BE safe space. These criteria can identify formulations that pass the internal quality control test but are likely non-BE. This study highlights the benefits of developing biopredictive dissolution methods that are based on biorelevant dissolution. The PBBM, constructed by integrating various drug parameters, combined with the developed biopredictive dissolution methods, is a convenient approach for BE evaluation of NTI drugs and a practical tool for developing new drugs.

Physiologically-based pharmacokinetic model of in vitro porcine ear skin permeation for drug delivery research

In silico techniques, such as physiologically based pharmacokinetic modeling (PBKP), are recently gaining importance. Computational methods in drug discovery and development and the generic drugs industry enhance research effectiveness by saving time and money and avoiding ethical issues. One key advantage is the ability to conduct toxicology studies without risking harm to living beings. This study aimed to repurpose the multi-phase multi-layer mechanistic dermal absorption (MPML MechDermA) PBPK model for simulation permeation through porcine ear skin under in vitro conditions. The work was divided into four steps: (1) the development of a pig ear skin model based on a previously collected dataset; (2) testing the model's ability to discriminate permeation between pig ear, human abdomen, and human back skin; (3) development of a caffeine permeation model; and (4) testing the caffeine model's performance against in vitro generated data sourced from the scientific literature. Data from 31 manuscripts were used for the development of the pig skin model. Based on these data, values specific to pig skin were found for 22 parameters of the MPML MechDermA model. The model was able to discriminate permeation between pig and human skin. A caffeine model was developed and used to simulate seven experiments identified in the literature. The model's performance was assessed by comparing simulated to observed results. Based on a visual check, all simulations were considered acceptable, whereas three out of seven experiments met the twofold difference criterion. The variability of the experimental data was considered the biggest challenge for reliable model assessment.

A Bayesian framework for virtual comparative trials and bioequivalence assessments

Introduction

In virtual bioequivalence (VBE) assessments, pharmacokinetic models informed with in vitro data and verified with small clinical trials' data are used to simulate otherwise unfeasibly large trials. Simulated VBE trials are assessed in a frequentist framework as if they were real despite the unlimited number of virtual subjects they can use. This may adequately control consumer risk but imposes unnecessary risks on producers. We propose a fully Bayesian model-integrated VBE assessment framework that circumvents these limitations.

Methods

We illustrate our approach with a case study on a hypothetical paliperidone palmitate (PP) generic long-acting injectable suspension formulation using a validated population pharmacokinetic model published for the reference formulation. BE testing, study power, type I and type II error analyses or their Bayesian equivalents, and safe-space analyses are demonstrated.

Results

The fully Bayesian workflow is more precise than the frequentist workflow. Decisions about bioequivalence and safe space analyses in the two workflows can differ markedly because the Bayesian analyses are more accurate.

Discussion

A Bayesian framework can adequately control consumer risk and minimize producer risk . It rewards data gathering and model integration to make the best use of prior information. The frequentist approach is less precise but faster to compute, and it can still be used as a first step to narrow down the parameter space to explore in safe-space analyses.

Predicting Drug-Drug Interactions Involving Rifampicin Using a Semi-mechanistic Hepatic Compartmental Model

AIMS

To develop a semi-mechanistic hepatic compartmental model to predict the effects of rifampicin, a known inducer of CYP3A4 enzyme, on the metabolism of five drugs, in the hope of informing dose adjustments to avoid potential drug-drug interactions.

METHODS

A search was conducted for DDI studies on the interactions between rifampicin and CYP substrates that met specific criteria, including the availability of plasma concentration-time profiles, physical and absorption parameters, pharmacokinetic parameters, and the use of healthy subjects at therapeutic doses. The semi-mechanistic model utilized in this study was improved from its predecessors, incorporating additional parameters such as population data (specifically for Chinese and Caucasians), virtual individuals, gender distribution, age range, dosing time points, and coefficients of variation.

RESULTS

Optimal parameters were identified for our semi-mechanistic model by validating it with clinical data, resulting in a maximum difference of approximately 2-fold between simulated and observed values. PK data of healthy subjects were used for most CYP3A4 substrates, except for gilteritinib, which showed no significant difference between patients and healthy subjects. Dose adjustment of gilteritinib co-administered with rifampicin required a 3-fold increase of the initial dose, while other substrates were further tuned to achieve the desired drug exposure.

CONCLUSIONS

The pharmacokinetic parameters AUCR and CmaxR of drugs metabolized by CYP3A4, when influenced by Rifampicin, were predicted by the semi-mechanistic model to be approximately twice the empirically observed values, which suggests that the semi-mechanistic model was able to reasonably simulate the effect. The doses of four drugs adjusted via simulation to reduce rifampicin interaction.

Predicting pharmacodynamic effects through early drug discovery with artificial intelligence-physiologically based pharmacokinetic (AI-PBPK) modelling

A mechanism-based pharmacokinetic/pharmacodynamic (PK/PD) model links the concentration-time profile of a drug with its therapeutic effects based on the underlying biological or physiological processes. Clinical endpoints play a pivotal role in drug development. Despite the substantial time and effort invested in screening drugs for favourable pharmacokinetic (PK) properties, they may not consistently yield optimal clinical outcomes. Furthermore, in the virtual compound screening phase, researchers cannot observe clinical outcomes in humans directly. These uncertainties prolong the process of drug development. As incorporation of Artificial Intelligence (AI) into the physiologically based pharmacokinetic/pharmacodynamic (PBPK) model can assist in forecasting pharmacodynamic (PD) effects within the human body, we introduce a methodology for utilizing the AI-PBPK platform to predict the PK and PD outcomes of target compounds in the early drug discovery stage. In this integrated platform, machine learning is used to predict the parameters for the model, and the mechanism-based PD model is used to predict the PD outcome through the PK results. This platform enables researchers to align the PK profile of a drug with desired PD effects at the early drug discovery stage. Case studies are presented to assess and compare five potassium-competitive acid blocker (P-CAB) compounds, after calibration and verification using vonoprazan and revaprazan.

Conjunction of semi-mechanistic in vitro-in vivo modeling and population pharmacokinetics as a tool for virtual bioequivalence analysis - a case study for a BCS class II drug

Virtual bioequivalence trial (VBE) simulations based on (semi)mechanistic in vitro-in vivo (IVIV) modeling have gained a huge interest in the pharmaceutical industry. Sophisticated commercially available software allows modeling variable drug fates in the gastrointestinal tract (GIT). Surprisingly, the between-subject and inter-occasion variability (IOV) of the distribution volumes and clearances are ignored or simplified, despite substantially contributing to varied plasma drug concentrations. The paper describes a novel approach for IVIV-based VBE by using population pharmacokinetics (popPK). The data from two bioequivalence trials with a poorly soluble BCS class II drug were analyzed retrospectively. In the first trial, the test drug product (biobatch 1) did not meet the bioequivalence criteria, but after a reformulation, the second trial succeeded (biobatch 2). The popPK model was developed in the Monolix software (Lixoft SAS, Simulation Plus) based on the originator's plasma concentrations. The modified Noyes-Whitney model was fitted to the results of discriminative biorelevant dissolution tests of the two biobatches and seven other reformulations. Then, the IVIV model was constructed by joining the popPK model with fixed drug disposition parameters, the drug dissolution model, and mechanistic approximation of the GIT transit. It was used to simulate the drug concentrations at different IOV levels of the primary pharmacokinetic parameters and perform the VBE. Estimated VBE success rates for both biobatches well reflected the outcomes of the bioequivalence trials. The predicted 90% confidence intervals for the area under the time-concentration curves were comparable with the observed values, and the 10% IOV allowed the closest approximation to the clinical results. Simulations confirmed that a significantly lower maximum drug concentration for biobatch 1 was responsible for the first clinical trial's failure. In conclusion, the proposed workflow might aid formulation screening in generic drug development.

Predicting the Pharmacokinetics of Orally Administered Drugs across BCS Classes 1-4 by Virtual Bioequivalence Model

To evaluate the influence of solubility and permeability on the pharmacokinetic prediction performance of orally administered drugs using avirtual bioequivalence (VBE) model, a total of 23 orally administered drugs covering Biopharmaceutics Classification System (BCS) classes 1-4 were selected. A VBE model (i.e., a physiologically based pharmacokinetic model integrated with dissolution data) based on a B²O simulator was applied for pharmacokinetic (PK) prediction in a virtual population. Parameter sensitivity analysis was used for input parameter selection. The predictive performances of PK parameters (i.e., AUC_0-t, C_max, and T_max), PK profiles, and bioequivalence (BE) results were evaluated using the twofold error, average fold error (AFE), absolute average fold error (AAFE), and BE reassessment metrics. All models successfully simulated the mean PK profiles, with AAFE < 2 and AFE ranging from 0.58 to 1.66. As for the PK parameters, except for the time of peak concentration, T_max, of isosorbide mononitrate, other simulated PK parameters were all within a twofold error. The simulated PK behaviors were comparable to the observed ones, both for test (T) and reference (R) products, and the simulated T/R arithmetic mean ratios were all within 0.88-1.16 of the observed values. These four evaluation metrics were distributed equally among BCS class 1-4 drugs. The VBE model showed powerful performance to predict the PK behavior of orally administered drugs with various combinations of solubility and permeability, irrespective of the BCS category.

Model-Informed drug development of gastroretentive release systems for sildenafil citrate

Gastroretentive drug delivery systems (GRDDS) are modified-release dosage forms designed to prolong their residence time in the upper gastrointestinal tract, where some drugs are preferentially absorbed, and increase the drug bioavailability. This work aimed the development of a novel GRDDS containing 60 mg of sildenafil citrate, and the evaluation of the feasibility of the proposed formulation for use in the treatment of pulmonary arterial hypertension (PAH), for once a day administration, by using in silico pharmacokinetic (PK) modeling and simulations using GastroPlus^TM. The Model-Informed Drug Development (MIDD) approach was used in formulation design and pharmacokinetic exposure prospecting. A 2² factorial design with a central point was used for optimization of the formulation, which was produced by direct compression and characterized by some tests, including buoyancy test, assay, impurities, and in vitro dissolution. A compartmental PK model was built using the GatroPlus^TM software for virtual bioequivalence of the proposed formulations in comparison with the defined target release profile provided by an immediate release (IR) tablet formulation containing 20 mg of sildenafil administered three times a day (TID). The results of the factorial design showed a direct correlation between the dissolution rate and the amount of hydroxypropyl methyl cellulose (HPMC) in the formulations. By comparing the PK parameters predicted by the virtual bioequivalence, the formulations F1, F2, F3 and F5 failed on bioequivalence. The F4 showed bioequivalence to the reference and was considered the viable formulation to substitute the IR. Thus, GRDDS could be a promising alternative for controlling the release of drugs with a pH-dependent solubility and narrow absorption window, specifically in the gastric environment, and an interesting way to reduce dose frequency and increase the drug bioavailability. The MIDD approach increases the level of information about the pharmaceutical product and guide the drug development for more assertive ways.

A Novel Approach to Justify Dissolution Differences in an Extended Release Drug Product Using Physiologically Based Biopharmaceutics Modeling and Simulation

Dr Reddy's Laboratories Ltd. developed generic version of XYZ extended release tablets (ER) and achieved bioequivalence as per criteria mentioned by USFDA in both fasting and fed conditions for higher strength formulation (1200 mg). However, on comparison of multimedia dissolution profiles in pH 4.5 acetate media, the f2 similarity value was <50. The lower strength formulation (600 mg) demonstrated faster dissolution profile. This was identified as strength-dependent sink condition difference and in vitro multiunit dissolution studies were used to justify sink differences between the higher and lower strengths. Additionally, a Physiologically Based Biopharmaceutics Model (PBBM) was developed using GastroPlus^TM. The validity of this model was established using in-house human pharmacokinetic data. Further, this model was used to justify the insignificant in vivo impact of the faster dissolution profile for the lower strength formulation. This work provides a novel and less explored approach that can be used to obtain biowaiver for lower strength formulations when the standard biowaiver criteria cannot be met. This work also demonstrates the usefulness of PBBM to justify dissolution dissimilarity between dose proportional formulations and to evaluate its biopharmaceutics risk without the need for actual in vivo studies.