Comparison of Alternative Population Modeling Approaches for Implementing a Level A IVIVC and for Assessing the Time-Scaling Factor Using Deconvolution and Convolution-Based Methods

A convolution-based in vitro-in vivo correlation model for methylphenidate hydrochloride delayed-release and extended-release capsule

Delayed-release and extended-release methylphenidate hydrochloride (JORNAY PM®) is a novel capsule formulation of methylphenidate hydrochloride, used to treat attention deficit hyperactivity disorder in patients 6 years and older. In this paper, we develop a Level A in vitro-in vivo correlation (IVIVC) model for extended-release methylphenidate hydrochloride to support post-approval manufacturing changes by evaluating a point-to-point correlation between the fraction of drug dissolved in vitro and the fraction of drug absorbed in vivo. Dissolution data from an in vitro study of three different release formulations: fast, medium, and slow, and pharmacokinetic data from two in vivo studies were used to develop an IVIVC model using a convolution-based approach. The time-course of the drug concentration resulting from an arbitrary dose was considered as a function of the in vivo drug absorption and the disposition and elimination processes defined by the unit impulse response function using the convolution integral. An IVIVC was incorporated in the model due to the temporal difference seen in the scatterplots of the estimated fraction of drug absorbed in vivo and the fraction of drug dissolved in vitro and Levy plots. Finally, the IVIVC model was subjected to evaluation of internal predictability. This IVIVC model can be used to predict in vivo profiles for different in vitro profiles of extended-release methylphenidate hydrochloride.

Revisiting the in-vitro and in-vivo considerations for in-silico modelling of complex injectable drug products

Complex injectable drug products (CIDPs) have often been developed to modulate the pharmacokinetics along with efficacy for therapeutic agents used for remediation of chronic disorders. The effective development of CIDPs has exhibited complex kinetics associated with multiphasic drug release from the prepared formulations. Consequently, predictability of pharmacokinetic modelling for such CIDPs has been difficult and there is need for advanced complex computational models for the establishment of accurate prediction models for in-vitro-in-vivo correlation (IVIVC). The computational modelling aims at supplementing the existing knowledge with mathematical equations to develop formulation strategies for generation of predictable and discriminatory IVIVC. Such an approach would help in reduction of the burden of effect of hidden factors on preclinical to clinical translations. Computational tools like physiologically based pharmacokinetics (PBPK) modelling have combined physicochemical and physiological properties along with IVIVC characteristics of clinically used formulations. Such techniques have helped in prediction and understanding of variability in pharmacodynamic parameters of potential generic products to clinically used formulations like Doxil®, Ambisome®, Abraxane® in healthy and diseased population using mathematical equations. The current review highlights the important formulation characteristics, in-vitro, preclinical in-vivo aspects which need to be considered while developing a stimulatory predictive PBPK model in establishment of an IVIVC and in-vitro-in-vivo relationship (IVIVR).

Best Practices for Integration of Dissolution Data into Physiologically Based Biopharmaceutics Models (PBBM): A Biopharmaceutics Modeling Scientist Perspective

Dissolution is considered as a critical input into physiologically based biopharmaceutics models (PBBM) as it governs in vivo exposure. Despite many workshops, initiatives by academia, industry, and regulatory, wider practices are followed for dissolution data input into PBBM models. Due to variety of options available for dissolution data input into PBBM models, it is important to understand pros, cons, and best practices while using specific dissolution model. This present article attempts to summarize current understanding of various dissolution models and data inputs in PBBM software's and aims to discuss practical challenges and ways to overcome such scenarios. Different approaches to incorporate dissolution data for immediate, modified, and delayed release formulations are discussed in detail. Common challenges faced during fitting of z-factor are discussed along with novel approach of dissolution data incorporation using P-PSD model. Ways to incorporate dissolution data for MR formulations using Weibull and IVIVR approaches were portrayed with examples. Strategies to incorporate dissolution data for DR formulations was depicted along with practical aspects. Approaches to generate virtual dissolution profiles, using Weibull function, DDDPlus, and time scaling for defining dissolution safe space, and strategies to generate virtual dissolution profiles for justifying single and multiple dissolution specifications were discussed. Finally, novel ways to integrate dissolution data for complex products such as liposomes, data from complex dissolution systems, importance of precipitation, and bio-predictive ability of QC media for evaluation of CBA's impact were discussed. Overall, this article aims to provide an easy guide for biopharmaceutics modeling scientist to integrate dissolution data effectively into PBBM models.

Convolution-based approach for modeling the paliperidone extended release and Long-Acting Injectable (LAI) PK of once-, and three-monthly products administration and for optimizing the development of new LAI products

The aim of this paper was to develop a convolution-based modeling approach for describing the paliperidone PK resulting from the administration of extended-release once-a-day oral dose, and once- and three monthly long-acting injectable products and to compare the performances of this approach to the traditional modeling strategy. The results of the analyses indicated that the traditional and convolution-based models showed comparable performances in the characterization of the paliperidone PK. However, the convolution-based approach showed several appealing features that justify the choice of this modeling as a preferred tool for modeling Long Acting Injectable (LAI) products and for deploying an effective model-informed drug development process. In particular, the convolution-based modeling can (a) facilitate the development of in vitro/in vivo correlation, (b) be used to identify formulations with optimal in vivo release properties, and (c) be used for optimizing the clinical benefit of a treatment by supporting the implementation of integrated models connecting in vitro and in vivo drug release, in vivo drug release to PK, and PK to PD and biomarker endpoints. A case study was presented to illustrate the benefits and the flexibility of the convolution-based modeling outcomes. The model was used to evaluate the in vivo drug release properties associated with a hypothetical once-a-year administration of a LAI product with the assumption that the expected paliperidone exposure during a 3-year treatment overlays the exposure expected after repeated administrations of a 3-month LAI product.

Elucidating the Particle Size Effect of Andrographolide Suspensions on Their IVIVC Performance in Oral Absorption

The study aimed to explore the size effect on the in vitro-in vivo correlation (IVIVC) in the oral absorption of andrographolide nanosuspensions (Ag-NS). Ag-NS with controllable particle sizes were prepared by ultrasonic dispersion method, and the formulation and process parameters were optimized through single factor experiments using mean particle size, polydispersity index, and stability as evaluation indicators. The morphology of Ag-NS was observed by scanning electron microscopy (SEM), and the crystalline state of the nanosuspensions was characterized by X-ray powder diffraction (XRPD) and differential scanning calorimetry (DSC). The dissolution tests were carried out with the paddle method in two different mediums simulating the pH conditions in intestinal fluid pH 6.8 and gastric fluid (pH 1.2), respectively. The pharmacokinetic behaviors were investigated in rats after oral administration, and a deconvolution approach was introduced to determine the correlation between in vitro dissolution and in vivo absorption (IVIVC). The formulation with the use of lecithin and PEG-800 as stabilizers showed its potential in the size-controllable preparation of Ag-NS. Via altering the ultrasonication amplitude and time, three Ag-NS suspensions with particle sizes of particle size, i.e., Ag-NS 250 (244.3 ± 0.4 nm), Ag-NS 450 (464.3 ± 32.2 nm), Ag-NS 1000 (1015 ± 36.1 nm) were prepared. Their morphological and crystal characteristics did not change during the size reduction process, but both of their in vitro dissolution and in vivo absorption were improved. Relatively better IVIVC performance was observed with the in vitro dissolution data at pH 6.8 (r > 0.9). With the reduction of particle size, the in vivo absorption fraction was more closed to the level of the in vitro dissolution. In conclusion, the decrease in particle size would improve the dissolution and absorption of Ag-NS, and also affect their IVIVC performance. The study would facilitate the design and quality control of Ag-NS in terms of particle size and dissolution specifications.

Population pharmacokinetics and IVIVC for mesalazine enteric-coated tablets

Although in-vivo bioequivalence (BE) study serves as a golden standard for establishing interchangeability of oral dosage forms, it remains challenging for products with high inter-subject variability such as mesalazine enteric-coated tablet to fulfil the BE criteria set by regulatory authorities. Mesalazine, as a BCS class IV drug, targets to be delivered to distal ileum or colon with a pH-sensitive polymer coating for the remission of ulcerative colitis. Through population pharmacokinetic (PK) analysis and in-vitro in-vivo correlation (IVIVC) modeling on the dissolution and BE data of a generic enteric-coated product (EM) and its reference Salofalk® 250 mg tablet (SM), we for the first time revealed the underlying mechanism of the high inter-subject variability for such delayed-release formulation. It was also noted that the in-vivo start time of absorption (T_s) for EM and SM was positively correlated with their in-vitro lag time (T_lag) under the USP three-stage dissolution condition and reversely correlated with their in-vivo bioavailability. The varied oral bioavailability of mesalazine enteric-coated tablet was mainly due to the varied N-acetyltransferase activities along GI tract. Although such extensive intestinal first-pass metabolism with large individual differences led to a significant variation of mesalazine C_max (coefficient of variation: 60-63.5%) and AUC_0-t (coefficient of variation: 37.5-46.9%), the corresponding variations in the total absorbed mesalazine (mesalazine and its metabolite N-acetyl mesalazine) were significantly reduced by 12 to 45%. Since the BE purpose for mesalazine enteric-coated tablet focused on their comparable safety profiles, total absorbed mesalazine was recommended to be adopted for the development of the IVIVC model and BE evaluation for EM. All in all, our model-based approach has not only successfully identified the key factors that affect the BE of EM to guide its further formulation optimization, but also demonstrated the indispensable role of modeling in the development of generic pharmaceutical product at its early stages.

Inferring pulmonary exposure based on clinical PK data: accuracy and precision of model-based deconvolution methods

Determining and understanding the target-site exposure in clinical studies remains challenging. This is especially true for oral drug inhalation for local treatment, where the target-site is identical to the site of drug absorption, i.e., the lungs. Modeling and simulation based on clinical pharmacokinetic (PK) data may be a valid approach to infer the pulmonary fate of orally inhaled drugs, even without local measurements. In this work, a simulation-estimation study was systematically applied to investigate five published model structures for pulmonary drug absorption. First, these models were compared for structural identifiability and how choosing an inadequate model impacts the inference on pulmonary exposure. Second, in the context of the population approach both sequential and simultaneous parameter estimation methods after intravenous administration and oral inhalation were evaluated with typically applied models. With an adequate model structure and a well-characterized systemic PK after intravenous dosing, the error in inferring pulmonary exposure and retention times was less than twofold in the majority of evaluations. Whether a sequential or simultaneous parameter estimation was applied did not affect the inferred pulmonary PK to a relevant degree. One scenario in the population PK analysis demonstrated biased pulmonary exposure metrics caused by inadequate estimation of systemic PK parameters. Overall, it was demonstrated that empirical modeling of intravenous and inhalation PK datasets provided robust estimates regarding accuracy and bias for the pulmonary exposure and pulmonary retention, even in presence of the high variability after drug inhalation.

Population Pharmacokinetic Modelling and Simulation to Determine the Optimal Dose of Nanoparticulated Sorafenib to the Reference Sorafenib

Sorafenib, an oral multikinase inhibitor, exhibits a highly variable absorption profile due to enterohepatic reabsorption and poor solubility. SYO-1644 improved the solubility of sorafenib by nanoparticulation technology leading to enhanced bioavailability. To evaluate the pharmacokinetically equivalent dose of SYO-1644 to the reference Nexavar^® 200 mg, a randomized, open-label, replicated two-period study was conducted in healthy volunteers. A total of 32 subjects orally received a single dose of the following assigned treatment under a fasted state in the first period and repeated once more in the second period with a two-week washout: SYO-1644 100, 150 and 200 mg and Nexavar^® 200 mg. Pharmacokinetic (PK) samples were collected up to 168 h post-dose. The PK profile was evaluated by both non-compartmental analysis and population PK method. With the final model, 2 × 2 crossover trial scenarios with Nexavar^® 200 mg and each dose of SYO-1644 ranging from 100 to 150 mg were repeated 500 times by Monte Carlo simulation, and the proportion of bioequivalence achievement was assessed. Transit absorption compartments, followed by a one-compartment model with first-order elimination and enterohepatic reabsorption components were selected as the final model. The simulation results demonstrated that the SYO-1644 dose between 120 and 125 mg could yielded the highest proportion of bioequivalence.

Development of an In Vivo Predictive Dissolution Methodology of Topiroxostat Immediate-Release Tablet Using In Silico Simulation

The main objective of this study was to develop an in vivo predictive dissolution (IVPD) model for topiroxostat immediate-release (IR) formulation by the combination of mechanistic absorption model (MAM) deconvolution method with time shifting factor (TSF) adjustment. The in vitro dissolution profiles in different biorelevant dissolution media containing different concentrations of sodium lauryl sulfate (SLS) were obtained from dissolution testing with the paddle method of the US Pharmacopeia, while the human pharmacokinetic profile was taken from the published experimental results. The GastroPlus™ software was used to observe the linear relationship between in vitro drug dissolution and in vivo absorption. The pharmacokinetic profile of topiroxostat IR tablet was first deconvoluted through the MAM method to obtain the fraction absorbed in vivo. Next, Levy plot was constructed to estimate the TSF, and the time scale for both processes of dissolution and absorption was then adjusted to be superimposable. The IVPD modelling was subsequently established with data between in vitro dissolution profiles and fraction absorbed in vivo. Finally, the dissolution profiles of topiroxostat IR tablet were translated into a pharmacokinetic curve in terms of convolution method. The comparison between translated and observed pharmacokinetic data will validate the performance of the developed IVPD model. This new linear IVPD model with high predictive power for the tablet can predict the in vivo pharmacokinetic differences through in vitro dissolution data, and it can be utilized as a risk-control tool for the formulation development of the topiroxostat IR tablet and the quality control of product batches.

Screening of Bioequivalent Extended-Release Formulations for Metformin by Principal Component Analysis and Convolution-Based IVIVC Approach

Bioequivalence (BE) is usually hard to achieve for extended-release (ER) dosage form products due to not only its complicated formulation but also to the BCS classification of the investigated drugs. Considering the difficulties in establishing full-scale IVIVC and limited in vivo pharmacokinetics data in the early stage of formulation development, we have selected BCS III drug metformin as a model drug to demonstrate a novel approach for the selection of BE formulations. Firstly, dissolution tests in both standard and biorelevant media were performed followed by identification of the most similar formulation WM to the reference product (GXR) based on principal component analysis (PCA) of the dissolution data. Then, we developed an IVIVC model using the reported GXR pharmacokinetics profiles via a convolution-based approach. Based on our established IVIVC and in vitro dissolution profiles of generic metformin ER products, we were able to predict their in vivo pharmacokinetic profiles and quantitatively compare the differences in AUC and C_max to ensure the correct selection of BE product. Finally, the selection of WM as the BE formulation of GXR was confirmed with a pilot BE study in healthy volunteers under fasting state. Moreover, the in vivo data from the fed state study were further integrated into our IVIVC model to identify FeSSIF-V2 as the biorelevant media for WM. Our novel integrative approach of PCA with a convolution-based IVIVC was successfully adopted for the screening of the BE metformin ER formulation and such an approach could be further utilized for the effective selection of BE formulation for other drugs/formulations with complex in vivo absorption processes.