Introduction to computational oral absorption simulation

Bioequivalence prediction with small-scale biphasic dissolution and simultaneous dissolution-permeation apparatus-An aripiprazole case study

Both biphasic dissolution and simultaneous dissolution-permeation (D-P) systems have great potential to improve the in vitro-in vivo correlation compared to simple dissolution assays, but the assay conditions, and the evaluation methods still need to be refined in order to effectively use these apparatuses in drug development. Therefore, this comprehensive study aimed to compare the predictive accuracy of small-volume (16-20 mL) D-P system and small-volume (40-80 mL) biphasic dissolution apparatus in bioequivalence prediction of five aripiprazole (ARP) containing marketed drug products. Assay conditions, specifically dose dependence were studied to overcome the limitations of both small-scale systems. In case of biphasic dissolution the in vivo maximum plasma concentration (Cmax) prediction greatly improved with the dose reduction of ARP, while in case of the D-P setup the use of whole tablet gave just as accurate prediction as the scaled dose. With the dose reduction strategy both equipment was able to reach 100 % accuracy in bioequivalence prediction for Cmax ratio. In case of the in vivo area under the curve (AUC) prediction the predictive accuracy for the AUC ratio was not dependent on the dose, and both apparatus had a 100 % accuracy predicting bioequivalence based on AUC results. This paper presents for the first time that not only selected parameters of flux assays (like permeability, initial flux, AUC value) were used as an input parameter of a mechanistic model (gastrointestinal unified theory) to predict absorption rate but the whole in vitro flux profile was used. All fraction absorbed values estimated by Predictor Software fell within the ±15 % acceptance range during the comparison with the in vivo data.

A Refined Thin-Film Model for Drug Dissolution Considering Radial Diffusion - Simulating Powder Dissolution

PURPOSE

We aim to present a refined thin-film model describing the drug particle dissolution considering radial diffusion in spherical boundary layer, and to demonstrate the ability of the model to describe the dissolution behavior of bulk drug powders.

METHODS

The dissolution model introduced in this study was refined from a radial diffusion-based model previously published by our laboratory (So et al. in Pharm Res. 39:907-17, 2022). The refined model was created to simulate the dissolution of bulk powders, and to account for the evolution of particle size and diffusion layer thickness during dissolution. In vitro dissolution testing, using fractionated hydrochlorothiazide powders, was employed to assess the performance of the model.

RESULTS

Overall, there was a good agreement between the experimental dissolution data and the predicted dissolution profiles using the proposed model across all size fractions of hydrochlorothiazide. The model over-predicted the dissolution rate when the particles became smaller. Notably, the classic Nernst-Brunner formalism led to an under-estimation of the dissolution rate. Additionally, calculation based on the equivalent particle size derived from the specific surface area substantially over-predicted the dissolution rate.

CONCLUSION

The study demonstrated the potential of the radial diffusion-based model to describe dissolution of drug powders. In contrast, the classic Nernst-Brunner equation could under-estimate drug dissolution rate, largely due to the underlying assumption of translational diffusion. Moreover, the study indicated that not all surfaces on a drug particle contribute to dissolution. Therefore, relying on the experimentally-determined specific surface area for predicting drug dissolution is not advisable.

Predictive Modelling in pharmacokinetics: from in-silico simulations to personalized medicine

INTRODUCTION

Pharmacokinetic parameters assessment is a critical aspect of drug discovery and development, yet challenges persist due to limited training data. Despite advancements in machine learning and in-silico predictions, scarcity of data hampers accurate prediction of drug candidates' pharmacokinetic properties.

AREAS COVERED

The study highlights current developments in human pharmacokinetic prediction, talks about attempts to apply synthetic approaches for molecular design, and searches several databases, including Scopus, PubMed, Web of Science, and Google Scholar. The article stresses importance of rigorous analysis of machine learning model performance in assessing progress and explores molecular modeling (MM) techniques, descriptors, and mathematical approaches. Transitioning to clinical drug development, article highlights AI (Artificial Intelligence) based computer models optimizing trial design, patient selection, dosing strategies, and biomarker identification. In-silico models, including molecular interactomes and virtual patients, predict drug performance across diverse profiles, underlining the need to align model results with clinical studies for reliability. Specialized training for human specialists in navigating predictive models is deemed critical. Pharmacogenomics, integral to personalized medicine, utilizes predictive modeling to anticipate patient responses, contributing to more efficient healthcare system. Challenges in realizing potential of predictive modeling, including ethical considerations and data privacy concerns, are acknowledged.

EXPERT OPINION

AI models are crucial in drug development, optimizing trials, patient selection, dosing, and biomarker identification and hold promise for streamlining clinical investigations.

Exploring the Use of a Kinetic pH Calculation to Correct the ACAT Model with a Single Stomach Compartment Setting: Impact of Stomach Setting on Food Effect Prediction for Basic Compounds

Advanced compartmental absorption and transit (ACAT) based computational models have become increasingly popular in the industry for predicting oral drug product performance. However, due to its complexity, some compromises have been made in practice, and the stomach is often assigned as a single compartment. Although this assignment worked generally, it may not be sufficient to reflect the complexity of the gastric environment under certain conditions. For example, this setting was found to be less accurate in estimating stomach pH and solubilization of certain drugs under food intake, which leads to a misprediction of the food effect. To overcome the above, we explored the use of a kinetic pH calculation (KpH) for the single-compartment stomach setting. Several drugs have been tested with the KpH approach and compared with the default setting of Gastroplus. In general, the Gastroplus prediction of food effect is greatly improved, suggesting this approach is effective in improving the estimation of physicochemical properties related to food effect for several basic drugs by Gastroplus.

Prediction of Oral Drug Absorption in Rats from In Vitro Data

PURPOSE

In drug discovery, rats are widely used for pharmacological and toxicological studies. We previously reported that a mechanism-based oral absorption model, the gastrointestinal unified theoretical framework (GUT framework), can appropriately predict the fraction of a dose absorbed (Fa) in humans and dogs. However, there are large species differences between humans and rats. The purpose of the present study was to evaluate the predictability of the GUT framework for rat Fa.

METHOD

The Fa values of 20 model drugs (a total of 39 Fa data) were predicted in a bottom-up manner. Based on the literature survey, the bile acid concentration (C_bile) and the intestinal fluid volume were set to 15 mM and 4 mL/kg, respectively, five and two times higher than in humans. LogP, pK_a, molecular weight, intrinsic solubility, bile micelle partition coefficients, and Caco-2 permeability were used as input data.

RESULTS

The Fa values were appropriately predicted for highly soluble drugs (absolute average fold error (AAFE) = 1.65, 18 Fa data) and poorly soluble drugs (AAFE = 1.57, 21 Fa data). When the species difference in C_bile was ignored, Fa was over- and under-predicted for permeability and solubility limited cases, respectively. High C_bile in rats reduces the free fraction of drug molecules available for epithelial membrane permeation while increasing the solubility of poorly soluble drugs.

CONCLUSION

The Fa values in rats were appropriately predicted by the GUT framework. This result would be of great help for a better understanding of species differences and model-informed preclinical formulation development.

Modeling Drug Dissolution in 3-Dimensional Space

PURPOSE

The purpose of the study is to present a mathematical model capable of describing drug particle dissolution in 3-dimensional (3D) space, and to provide experimental model verification. Through this study, we also aim to elaborate limitations of the classic, 1D-based Nernst-Brunner formalism in dissolution modeling.

METHODS

The 3D dissolution model was derived by treating the dissolution of a spherical particle as a diffusion-driven process, and by solving Fick's 2^nd law of diffusion in spherical coordinates using numerical methods. The resulting model was experimentally verified through analyzing the dissolution behavior of single succinic acid particles in un-stirred water droplet under polarized light microscopy, in combination with image segmentation techniques.

RESULTS

A set of working equations was developed to describe drug particle dissolution in 3D space. The predicted dissolution time and profile are in good agreement with the experimental results. The model clearly shows that the concentration gradient within the diffusion layer, in realistic 3D condition, must not be a constant value as implicated in the Nernst-Brunner formalism. The actual concentration profile is a hyperbola, and the concentration gradient at the surface of the particle can be significantly higher than the classic 1D-based dissolution model.

CONCLUSION

The study demonstrates that the classic, 1D-based dissolution models may lead to significant under-estimation of drug dissolution rates. In contrast, modeling dissolution in 3D space yields more reliable results. This study merits further development of comprehensive 3D drug dissolution models, by considering polydispersed particle ensemble and imposing the changes of diffusion layer thickness during dissolution.

Exploring bulk volume, particle size and particle motion definitions to increase the predictive ability of in vitro dissolution simulations

The definition of the local dissolution environment is central to accurate particle dissolution simulation, and is determined by the apparatus and conditions used. In the flow-through apparatus dissolution occurs in the cell, often in a low velocity environment, with the reservoir considered the relevant volume for dissolution kinetics. Dissolution simulations were conducted using a reduced-order model based on the Ranz-Marshall correlation for mass transfer from spherical particles. Using ibuprofen as a model drug, the effect of defining a local volume to simulate dynamic bulk concentration conditions in the flow-through and paddle apparatus was assessed by comparing use of a near particle volume (NPV), extending a distance of one radius from the particle surface, with a flow-through apparatus cell volume or paddle apparatus vessel volume as the relevant instantaneous volume for dissolution. The instantaneous inlet concentration to NPV or cell volume is the reservoir/vessel concentration at that simulation time point, reflecting the continuous input to the cell of more dilute solution from the reservoir (closed system). Additionally, inputting particle size distribution (PSD) instead of a median particle size (MPS) and enabling or disabling particle motion were investigated, in two media (resulting in low and high solubility) and with two fluid velocity conditions in each apparatus. The NPV predicted effects of fluid velocity differences on dissolution in the high solubility medium in the flow-through apparatus, but had no effect on predictive ability in the paddle apparatus. In both apparatuses, simulations were reasonable for the high solubility environment but underpredicted dissolution in the low solubility environment. The PSD option and disabling particle motion increased the predictive ability of the simulations in low solubility media in the flow-through apparatus. The results highlight the necessity to incorporate the local dynamic dissolution conditions in the flow-through apparatus for accurate dissolution simulation, and the challenges of defining an effective particle size for dissolution simulation and of reflecting hydrodynamic complexity in simulating dissolution in the paddle apparatus.

Development of a Novel Gastric Process Simulation Model: The Successful Assessment of Bioequivalence and Bioinequivalence of a Biopharmaceutics Classification System Class II Weak Acid Drug

Bioequivalence has been assessed using in vitro dissolution testing, such as in vivo predictive dissolution methodology. However, the assessment of bioequivalence should be performed carefully, considering the effect of the in vivo environment and according to the properties of the drug. The gastric emptying process is a key factor for the assessment of biopharmaceutics classification system class II (BCS class IIa) drugs with acidic properties since they cannot dissolve in the acidic stomach, but do dissolve in the small intestine (SI). The disintegration of a tablet in the stomach affects the distribution/dissolution in the SI due to the difference in the gastric emptying step, which in turn is a result of the varying formulation of the drugs. In this study, we used the reported dynamic pH change method and a novel gastric process simulation (GPS) model, which can compare the gastric emptying of particular-sized drug particles. The in vitro results were compared to clinical data using bioequivalent and bioinequivalent products of candesartan cilexetil. It was revealed that the dynamic pH change method was inappropriate, whereas the amount of filtered drug in GPS studies with 20 and 50 µm pore size filters could reflect the clinical results of all products. The evaluation of the gastric emptying process of drug particles less than 50 µm enabled us to assess the bioequivalence because they probably caused the difference in the distribution in the SI. This study demonstrated the utility of the GPS model for the assessment of bioequivalence of BCS class IIa drugs.

In-Silico Prediction of Oral Drug Bioavailability: A multi-boluses approach

This work focuses on a new mathematical model able to describe in a simple manner the intestinal physiology, in order to better study drug absorption and bioavailability. The aim of our model is to overcome the limitations of physiological pharmacokinetics models of the literature, introducing a different modelling approach. The core of the new proposed model is a Discrete-Continuous Approach (DCA): a sequence of boluses travels in the investigated portion of the intestine, in counter-current with blood that flows in continuous mode. No empirical equations are implemented in this model. Simulation results show an excellent correlation between the predicted and experimental concentration profile used to validate our model. Our new approach provides a simple tool, with a good reliability, to analyze a very complex phenomenon, using only few parameters.

Biopharmaceutics classification evaluation for paris saponin VII

To study the biopharmaceutics characteristics of paris saponin VII (PSVII). The solubility of PSVII was evaluated by measurement of the equilibrium solubility in different solvents and media. The permeability of PSVII was evaluated by measuring the oil/water partition coefficient (lgP_app) and determining the apparent permeability coefficient (PC_app) on a mono-layer Caco-2 cell model. The effects of p-glycoprotein and multidrug resistance related protein 2 on PSVII transport in mono-layer Caco-2 cell model were further investigated. Finally, the small intestinal absorption of PSVII was investigated in rat. In solvents of different pH, the equilibrium solubility of PSVII was quite low, and the dose number of PSVII was larger than 1. The lgP_app of PSVII was less than 0. The apparent permeability coefficient [PC_app(AP-BL)] of PSVII in mono-layer Caco-2 cell model was less than 14.96 × 10^-6 cm·s^-1, and the efflux ratio of PSVII in mono-layer Caco-2 cell model was less than 1. The transport rate of PSVII in mono-layer Caco-2 cell model was not affected by the inhibitors of p-glycoprotein and multidrug resistance related protein 2. After oral administration, PSVII could be detected in rat intestinal contents, but could not be detected in the small intestinal mucosa. PSVII showed low solubility and permeability, which would result in low oral bioavailability in clinic. PSVII belonged to Class IV compound in biopharmaceutics classification system.

The impact of the intestinal microbiota and the mucosal permeability on three different antibiotic drugs

BackgroundThe totality of bacteria, protozoa, viruses and fungi that lives in the human body is called microbiota. Human microbiota specifically colonizes the skin, the respiratory and urinary tract, the urogenital tract and the gastrointestinal system. This study focuses on the intestinal microbiota to explore the drug-microbiota relationship and, therefore, how the drug bioavailability changes in relation to the microbiota biodiversity to identify more personalized therapies, with the minimum risk of side effects. MethodsTo achieve this goal, we developed a new mathematical model with two compartments, the intestine and the blood, which takes into account the colonic mucosal permeability variation - measured by Ussing chamber system on human colonic mucosal biopsies - and the fecal microbiota composition, determined through microbiota 16S rRNA sequencing analysis. Both of the clinical parameters were evaluated in a group of Irritable Bowel Syndrome patients compared to a group of healthy controls. Key ResultsThe results show that plasma drug concentration increases as bacterial concentration decreases, while it decreases as intestinal length decreases too. ConclusionsThe study provides interesting data since in literature there are not yet mathematical models with these features, in which the importance of intestinal microbiota, the "forgotten organ", is considered both for the subject health state and in the nutrients and drugs metabolism.

Evaluation of Drug Dissolution Rate in Co-amorphous and Co-crystal Binary Drug Delivery Systems by Thermodynamic and Kinetic Methods

In order to better explain and predict the dissolution characteristics of binary drug delivery systems (BDDSs), the dissolution behaviors of co-crystal (CC) and co-amorphous (CA) systems of sacubitril (SCB) and valsartan (VST) were evaluated in vitro and in vivo by thermodynamic and kinetic methods. The CCs of SCB and VST were prepared into a CA state through rotary evaporation. Solid-state properties were systematically evaluated. Herein, based on the results from previous studies of single-phase systems, we used thermodynamic methods to evaluate the increase in drug dissolution rate after BDDSs change from the crystalline to the amorphous state. After comparing the predicted and measured dissolution rate enhancement of the CC and CA systems, this paper attempts to explain the dissolution rate characteristics of the BDDSs. We then evaluated the bioavailability of two BDDSs in beagle dogs to confirm that there was no discrepancy in vivo with the results obtained in vitro. The results exhibited that there is strong intermolecular interaction between SCB and VST and good physical stability for the CA system. Compared with the CC, the bioavailability of SCB and VST in the CA system increased by 313.9% and 130.5%, respectively. The predicted dissolution rate ratio between CC and CA systems and their actual intrinsic dissolution rates differed by only a factor of 2.5, demonstrating the good correlation between the predicted and measured values. In the future, this method could be expanded to a variety of new samples and exciting drug prospects.

Prediction of negative food effect induced by bile micelle binding on oral absorption of hydrophilic cationic drugs

The purpose of the present study was to quantitatively predict the negative food effect induced by bile micelle binding on the oral absorption of hydrophilic cationic drugs. The intrinsic membrane permeability and bile micelle unbound fraction of 12 model drugs (7 tertiary amines, 3 quaternary ammoniums, and 2 neutral drugs) were calculated from the experimental Caco-2 permeability data (P_app) under fasted and fed conditions. From these input data, the fraction of a dose absorbed (Fa) was predicted using the gastrointestinal unified theoretical framework, a mechanism-based oral absorption model. The predicted Fa ratio (fed/fasted) was then compared with the in vivo fed/fasted area under the plasma concentration-time curve ratio (AUCr). The AUCr values of tertiary amines and neutral drugs were appropriately predicted (absolute average fold error (AAFE) = 1.19), whereas those of quaternary ammoniums were markedly underestimated (AAFE = 4.70). The P_app ratio (fed/fasted) predicted AUCr less quantitatively (AAFE = 1.30 for tertiary amines and neutral drugs). The results of the present study would lead to a better understanding of negative food effect on oral drug absorption.

Bottom-Up Physiologically Based Oral Absorption Modeling of Free Weak Base Drugs

In this study, we systematically evaluated "bottom-up" physiologically based oral absorption modeling, focusing on free weak base drugs. The gastrointestinal unified theoretical framework (the GUT framework) was employed as a simple and transparent model. The oral absorption of poorly soluble free weak base drugs is affected by gastric pH. Alternation of bulk and solid surface pH by dissolving drug substances was considered in the model. Simple physicochemical properties such as pKa, the intrinsic solubility, and the bile micelle partition coefficient were used as input parameters. The fraction of a dose absorbed (Fa) in vivo was obtained by reanalyzing the pharmacokinetic data in the literature (15 drugs, a total of 85 Fa data). The AUC ratio with/without a gastric acid-reducing agent (AUCr) was collected from the literature (22 data). When gastric dissolution was neglected, Fa was underestimated (absolute average fold error (AAFE) = 1.85, average fold error (AFE) = 0.64). By considering gastric dissolution, predictability was improved (AAFE = 1.40, AFE = 1.04). AUCr was also appropriately predicted (AAFE = 1.54, AFE = 1.04). The Fa values of several drugs were slightly overestimated (less than 1.7-fold), probably due to neglecting particle growth in the small intestine. This modeling strategy will be of great importance for drug discovery and development.

Prediction Characteristics of Oral Absorption Simulation Software Evaluated Using Structurally Diverse Low-Solubility Drugs

The purpose of the present study was to characterize current biopharmaceutics modeling and simulation software regarding the prediction of the fraction of a dose absorbed (Fa) in humans. As commercial software products, GastroPlus™ and Simcyp® were used. In addition, the gastrointestinal unified theoretical framework, a simple and publicly accessible model, was used as a benchmark. The Fa prediction characteristics for a total of 96 clinical Fa data of 27 model drugs were systematically evaluated using the default settings of each software product. The molecular weight, dissociation constant, octanol-water partition coefficient, solubility in biorelevant media, dose, and particle size of model drugs were used as input data. Although the same input parameters were used, GastroPlus™, Simcyp®, and the gastrointestinal unified theoretical framework showed different Fa prediction characteristics depending on the rate-limiting steps of oral drug absorption. The results of the present study would be of great help for the overall progression of physiologically based absorption models.

Practical Approach to Modeling the Impact of Amorphous Drug Nanoparticles on the Oral Absorption of Poorly Soluble Drugs

Recently published studies have proposed that amorphous drug nanoparticles in gastrointestinal fluids may be beneficial for the absorption of poorly soluble compounds. Nanosized drug particles are known to provide rapid dissolution rates and, in some instances, a slight increase in solubility. However, in recent studies, the differences observed in vivo could not be explained solely by these attributes. Given the high dose and very low aqueous solubility of the study compounds, rapid equilibration to the drug-saturated solubility in gastrointestinal fluid would occur independent of the presence of nanoparticles. Alternatively, it has been proposed that drug nanoparticles (ca. ≤ 200 to 300 nm) may provide a "shuttle" for drug across the unstirred water layer (UWL) adjacent to the intestinal epithelium, particularly for low solubility/lipophilic compounds where absorption may be largely UWL-limited. This transport mechanism would result in a higher unbound drug concentration at the surface of the epithelium for absorption. This study evaluates this mechanism using a simple modification of the effective permeability to account for the effect of drug nanoparticles diffusing across the UWL. The modification can be made using inputs for solubility and nanoparticle size. The permeability modification was evaluated using three published case studies for amorphous formulations of itraconazole, anacetrapib, and enzalutamide, where the formation of amorphous drug nanoparticles upon dissolution resulted in improved drug absorption. Absorption modeling was performed using GastroPlus to assess the impact of the nanomodified permeability method on the accuracy of model prediction compared to in vivo data. Simulation results were compared to those for baseline simulations using an unmodified effective permeability. The results show good agreement using the nanomodified permeability, which described the data better than the standard baseline predictions. The nanomodified permeability method can be a suitable, fit-for-purpose in silico approach for evaluating or predicting oral absorption of poorly soluble, UWL-limited drugs from formulations that produce a significant number of amorphous drug nanoparticles.

Intestinal Permeability and Drug Absorption: Predictive Experimental, Computational and In Vivo Approaches

The main objective of this review is to discuss recent advancements in the overall investigation and in vivo prediction of drug absorption. The intestinal permeability of an orally administered drug (given the value P_eff) has been widely used to determine the rate and extent of the drug’s intestinal absorption (F_abs) in humans. Preclinical gastrointestinal (GI) absorption models are currently in demand for the pharmaceutical development of novel dosage forms and new drug products. However, there is a strong need to improve our understanding of the interplay between pharmaceutical, biopharmaceutical, biochemical, and physiological factors when predicting F_abs and bioavailability. Currently, our knowledge of GI secretion, GI motility, and regional intestinal permeability, in both healthy subjects and patients with GI diseases, is limited by the relative inaccessibility of some intestinal segments of the human GI tract. In particular, our understanding of the complex and highly dynamic physiology of the region from the mid-jejunum to the sigmoid colon could be significantly improved. One approach to the assessment of intestinal permeability is to use animal models that allow these intestinal regions to be investigated in detail and then to compare the results with those from simple human permeability models such as cell cultures. Investigation of intestinal drug permeation processes is a crucial biopharmaceutical step in the development of oral pharmaceutical products. The determination of the intestinal P_eff for a specific drug is dependent on the technique, model, and conditions applied, and is influenced by multiple interactions between the drug molecule and the biological membranes.

Prediction Accuracy of Mechanism-Based Oral Absorption Model for Dogs

The purpose of the present study was to evaluate the prediction accuracy of a mechanism-based oral absorption model for the fraction of a dose absorbed (Fa) in dogs, focusing on poorly soluble drugs. As an open mechanism-based model, the gastrointestinal unified theoretical framework was used in this study. The prediction accuracy of the gastrointestinal unified theoretical framework was evaluated using Fa data in dogs (63 data sets for marketed drugs and proprietary compounds). For neutral compounds, Fa was accurately predicted, suggesting that the physiological parameters of dogs were appropriate except for gastrointestinal pH. An extensive literature survey on the small intestinal pH of dogs was then conducted. The result suggested that the pH value ranged between 6.5 and 7.5, with the midst value of 7.0, but there was a great variation among the literature. To confirm the appropriateness of this pH value, the Fa of free acid compounds was predicted by setting the small intestinal pH to 6.5, 7.0, and 7.5. The proportions of compounds with <2-fold error were 57%, 90%, and 76%, respectively. The results of the present study would enable an appropriate use of a mechanism-based model for drug discovery and development.

Tablet Formulation of a Polymeric Solid Dispersion Containing Amorphous Alkalinized Telmisartan

To overcome the poor dissolution of telmisartan (TMS) at weak acidic pH, amorphous alkalinized TMS (AAT) was prepared by introducing sodium hydroxide as a selective alkalizer. AAT-containing polymeric solid dispersions were prepared by a solvent evaporation method; these solid dispersions were AAT-PEG, AAT-PVP, AAT-POL, and AAT-SOL for the polymers of PEG 6000, PVP K30, Poloxamer 407, and Soluplus, respectively. The characteristics of the different formulations were observed by differential scanning calorimetry, powder X-ray diffraction, Fourier transform infrared spectroscopy, and scanning electron microscopy. To compare the supersaturation behavior, a dissolution test was performed at 37 ± 0.5 °C either in 900 ml (plain condition) or 500 ml (limited condition) of pH 6.8-simulated intestinal fluid used as a medium. AAT-SOL exhibited enhanced dissolution, indicating the probability of extended supersaturation in the limited condition. AAT-SOL was further formulated into a tablet by introducing other excipients, Vivapur 105 and Croscarmellose, as a binder and superdisintegrant, respectively, using a direct compression method. The selected AAT-SOL tablet was superior to Micardis (the reference product) in the aspect of supersaturation maintenance during dissolution in the limited condition, suggesting that it is a promising candidate for practical development that can replace the commercial product in the future.

Population Balance Model for Simulation of the Supersaturation-Precipitation Behavior of Drugs in Supersaturable Solid Forms

We developed a simulation method to describe in vitro drug concentration-time profiles under supersaturated conditions. In a nonsink dissolution test of carbamazepine polymorphic form III (CBZ_III), a model supersaturable solid, the concentration of carbamazepine reached a supersaturated state against its dihydrate form (CBZ_DH). After a certain period, de-supersaturation due to the precipitation of CBZ_DH was observed. In the simulation of this typical dissolution-precipitation profile, the precipitation process of CBZ_DH was simulated by a population balance model in which the rates of primary/secondary nucleation and growth of CBZ_DH were considered. Six rate constants in the precipitation model were determined from de-supersaturation profiles in unseeded isothermal crystallization experiments of CBZ_DH. The dissolution process of CBZ_III was modeled on the basis of its dissolution profile under a sink condition. The simulated concentration versus time curves satisfactorily reproduced the characteristics of dissolution, supersaturation, and precipitation behavior of the model drug. The presented method will enable rational design of formulations and accurate prediction of the oral absorbability of drugs in supersaturable solid forms.

Computational modeling of human oral bioavailability: what will be next?

INTRODUCTION

The oral route is the most convenient way of administrating drugs. Therefore, accurate determination of oral bioavailability is paramount during drug discovery and development. Quantitative structure-property relationship (QSPR), rule-of-thumb (RoT) and physiologically based-pharmacokinetic (PBPK) approaches are promising alternatives to the early oral bioavailability prediction. Areas covered: The authors give insight into the factors affecting bioavailability, the fundamental theoretical framework and the practical aspects of computational methods for predicting this property. They also give their perspectives on future computational models for estimating oral bioavailability. Expert opinion: Oral bioavailability is a multi-factorial pharmacokinetic property with its accurate prediction challenging. For RoT and QSPR modeling, the reliability of datasets, the significance of molecular descriptor families and the diversity of chemometric tools used are important factors that define model predictability and interpretability. Likewise, for PBPK modeling the integrity of the pharmacokinetic data, the number of input parameters, the complexity of statistical analysis and the software packages used are relevant factors in bioavailability prediction. Although these approaches have been utilized independently, the tendency to use hybrid QSPR-PBPK approaches together with the exploration of ensemble and deep-learning systems for QSPR modeling of oral bioavailability has opened new avenues for development promising tools for oral bioavailability prediction.

In Silico Modeling of Gastrointestinal Drug Absorption: Predictive Performance of Three Physiologically Based Absorption Models

Gastrointestinal (GI) drug absorption is a complex process determined by formulation, physicochemical and biopharmaceutical factors, and GI physiology. Physiologically based in silico absorption models have emerged as a widely used and promising supplement to traditional in vitro assays and preclinical in vivo studies. However, there remains a lack of comparative studies between different models. The aim of this study was to explore the strengths and limitations of the in silico absorption models Simcyp 13.1, GastroPlus 8.0, and GI-Sim 4.1, with respect to their performance in predicting human intestinal drug absorption. This was achieved by adopting an a priori modeling approach and using well-defined input data for 12 drugs associated with incomplete GI absorption and related challenges in predicting the extent of absorption. This approach better mimics the real situation during formulation development where predictive in silico models would be beneficial. Plasma concentration-time profiles for 44 oral drug administrations were calculated by convolution of model-predicted absorption-time profiles and reported pharmacokinetic parameters. Model performance was evaluated by comparing the predicted plasma concentration-time profiles, Cmax, tmax, and exposure (AUC) with observations from clinical studies. The overall prediction accuracies for AUC, given as the absolute average fold error (AAFE) values, were 2.2, 1.6, and 1.3 for Simcyp, GastroPlus, and GI-Sim, respectively. The corresponding AAFE values for Cmax were 2.2, 1.6, and 1.3, respectively, and those for tmax were 1.7, 1.5, and 1.4, respectively. Simcyp was associated with underprediction of AUC and Cmax; the accuracy decreased with decreasing predicted fabs. A tendency for underprediction was also observed for GastroPlus, but there was no correlation with predicted fabs. There were no obvious trends for over- or underprediction for GI-Sim. The models performed similarly in capturing dependencies on dose and particle size. In conclusion, it was shown that all three software packages are useful to guide formulation development. However, as a consequence of the high fraction of inaccurate predictions (prediction error >2-fold) and the clear trend toward decreased accuracy with decreased predicted fabs observed with Simcyp, the results indicate that GI-Sim and GastroPlus perform better than Simcyp in predicting the intestinal absorption of the incompletely absorbed drugs when a higher degree of accuracy is needed. In addition, this study suggests that modeling and simulation research groups should perform systematic model evaluations using their own input data to maximize confidence in model performance and output.

Development of a Novel Oral Cavity Compartmental Absorption and Transit Model for Sublingual Administration: Illustration with Zolpidem

Intraoral (IO) delivery is an alternative administration route to deliver a drug substance via the mouth that provides several advantages over conventional oral dosage forms. The purpose of this work was to develop and evaluate a novel, physiologically based oral cavity model for projection and mechanistic analysis of the clinical pharmacokinetics of intraoral formulations. The GastroPlus™ Oral Cavity Compartmental Absorption and Transit (OCCAT™) model was used to simulate the plasma concentration versus time profiles and the fraction and rate of intraoral drug transit/absorption for Intermezzo® sublingual tablets (zolpidem tartrate). The model was evaluated by the goodness-of-fit between simulated and observed concentrations and the deviation of key PK parameters (e.g., C max, T max, and AUC). In addition, a sensitivity analysis was conducted to demonstrate the interplay and impact of key modeling parameters on the fraction absorbed via oral mucosa (F a_IO). The OCCAT™ model captured the observed pharmacokinetics for Intermezzo® sublingual tablets (R (2) > 0.9). The predicted deviations (%) for C max, AUC0-inf, AUC0-20 min, and T max were 5.7, 28.0, 11.8, and 28.6%, respectively, indicating good prediction accuracy. The model also estimated ~18% of total drug was absorbed via the IO route. Furthermore, the sensitivity analysis indicated that the F a_IO was not only associated with drug diffusivity and unbound fraction in epithelium tissue (f ut) but also depended on the physicochemical properties of compounds for IO delivery (e.g., solubility and logD pH = 7.4). The novel physiologically based IO absorption OCCAT™ model showed satisfactory performance and will be helpful to guide development of future intraoral formulations.

Minimum rotation speed to prevent coning phenomena in compendium paddle dissolution apparatus

The purpose of the present study was to investigate the applicability of the Zwietering equation to coning phenomena which often occur during dissolution testing. The minimum rotation speed at which coning phenomena disappeared (no coning rpm, NCrpm) was experimentally determined for various particle and fluid properties in a compendium paddle apparatus with a round-bottom unbaffled vessel. The particle size, relative density and kinematic viscosity exponents in the Zwietering equation were optimized for NCrpm. The particle size and relative density exponents were found to be similar with those for the general tank configurations of cylindrical flat-bottom baffled vessels. However, the kinematic viscosity exponent was significantly different. The equation obtained in this study showed sufficient accuracy (r(2)=0.98, average error=12rpm) to estimate the occurrence of coning. The Zwietering equation was found to be applicable to the coning phenomena in the compendium paddle apparatus.

Lecithin-linker formulations for self-emulsifying delivery of nutraceuticals

Lecithin-linker microemulsions are formulations produced with soybean lecithin in combination with a highly lipophilic (lipophilic linker) and highly hydrophilic (hydrophilic linkers) surfactant-like additives. In this work, lecithin-linker systems were formulated to produce self-emulsifying delivery systems for β-carotene and β-sitosterol. The concentration of the lipophilic linker, sorbitan monooleate, was adjusted to minimize the formation of liquid crystals. The concentration of hydrophilic linkers, decaglyceryl caprylate/caprate and PEG-6-caprylic/capric glycerides, was gradually increased (scanned) until single phase clear microemulsions were obtained. For these scans, the oil (ethyl caprate) to water ratio was set to 1. The single phase, clear microemulsions were diluted with fed-state simulated intestinal fluid (FeSSIF) and produced stable emulsions, with drop sizes close to 200 nm. Using pseudo-ternary phase diagrams to evaluate the process of dilution of microemulsion preconcentrates (mixtures of oil, lecithin and linkers with little or no water) with FeSSIF, it was determined that self-emulsifying systems are obtained when the early stages of the dilution produce single phase microemulsions. If liquid crystals or multiple phase systems are obtained during those early stages, then the emulsification yields unstable emulsions with large drop sizes. An in vitro permeability study conducted using a Flow-Thru Dialyzer revealed that stable emulsions with drop sizes of 150-300 nm produce large and irreversible permeation of β-carotene to sheep intestine. On the other hand, unstable emulsions produced without the linker combination separated in the dialyzer chamber.

Utility of models of the gastrointestinal tract for assessment of the digestion and absorption of engineered nanomaterials released from food matrices

Engineered metal/mineral, lipid and biochemical macromolecule nanomaterials (NMs) have potential applications in food. Methodologies for the assessment of NM digestion and bioavailability in the gastrointestinal tract are nascent and require refinement. A working group was tasked by the International Life Sciences Institute NanoRelease Food Additive project to review existing models of the gastrointestinal tract in health and disease, and the utility of these models for the assessment of the uptake of NMs intended for food. Gastrointestinal digestion and absorption could be addressed in a tiered approach using in silico computational models, in vitro non-cellular fluid systems and in vitro cell culture models, after which the necessity of ex vivo organ culture and in vivo animal studies can be considered. Examples of NM quantification in gastrointestinal tract fluids and tissues are emerging; however, few standardized analytical techniques are available. Coupling of these techniques to gastrointestinal models, along with further standardization, will further strengthen methodologies for risk assessment.

Permeability comparison between hepatocyte and low efflux MDCKII cell monolayer

Determination of passive permeability is not only important for predicting oral absorption and brain penetration, but also for accurately predicting hepatic clearance. High throughput (HT) measurement of passive permeability across hepatocyte cell membrane is technically more challenging than using monolayer cell-based permeability assays. In this study, we evaluated if the HT Madin-Darby canine kidney II-low efflux (MDCKII-LE) cell monolayer permeability assay can be used as a surrogate to predict the passive permeability of hepatocytes. Apparent passive permeability of MDCKII-LE is well correlated to passive diffusion clearance of human and rat hepatocytes, suggesting that the HT MDCKII-LE assay can be used as a surrogate to estimate the passive permeability of hepatocytes. In addition, lipophilicity (Log D determined at pH 7.4) was also found to be well correlated with both MDCKII-LE and hepatocyte permeability for most compounds, hence it may serve as another permeability surrogate.

Quantitative prediction of formulation-specific food effects and their population variability from in vitro data with the physiologically-based ADAM model: a case study using the BCS/BDDCS Class II drug nifedipine

Quantitative prediction of food effects (FE) upon drug pharmacokinetics, including population variability, in advance of human trials may help with trial design by optimising the number of subjects and sampling times when a clinical study is warranted or by negating the need for conduct of clinical studies. Classification and rule-based systems such as the BCS and BDDCS and statistical QSARs are widely used to anticipate the nature of FE in early drug development. However, their qualitative rather than quantitative nature makes them less appropriate for assessing the magnitude of FE. Moreover, these approaches are based upon drug properties alone and are not appropriate for estimating potential formulation-specific FE on modified or controlled release products. In contrast, physiologically-based mechanistic models can consider the scope and interplay of a range of physiological changes after food intake and, in combination with appropriate in vitro drug- and formulation-specific data, can make quantitative predictions of formulation-specific FE including the inter-individual variability of such effects. Herein the Advanced Dissolution, Absorption and Metabolism (ADAM) model is applied to the prediction of formulation-specific FE for BCS/BDDCS Class II drug and CYP3A4 substrate nifedipine using as far as possible only in vitro data. Predicted plasma concentration profiles of all three studied formulations under fasted and fed states are within 2-fold of clinically observed profiles. The % prediction error (%PE) in fed-to-fasted ratio of Cmax and AUC were less than 5% for all formulations except for the Cmax of Nifedicron (%PE=-29.6%). This successful case study should help to improve confidence in the use of mechanistic physiologically-based models coupled with in vitro data for the anticipation of FE in advance of in vivo studies. However, it is acknowledged that further studies with drugs/formulations exhibiting a wide range of properties are required to further validate this methodology.

PBPK models for the prediction of in vivo performance of oral dosage forms

Drug absorption from the gastrointestinal (GI) tract is a highly complex process dependent upon numerous factors including the physicochemical properties of the drug, characteristics of the formulation and interplay with the underlying physiological properties of the GI tract. The ability to accurately predict oral drug absorption during drug product development is becoming more relevant given the current challenges facing the pharmaceutical industry. Physiologically-based pharmacokinetic (PBPK) modeling provides an approach that enables the plasma concentration-time profiles to be predicted from preclinical in vitro and in vivo data and can thus provide a valuable resource to support decisions at various stages of the drug development process. Whilst there have been quite a few successes with PBPK models identifying key issues in the development of new drugs in vivo, there are still many aspects that need to be addressed in order to maximize the utility of the PBPK models to predict drug absorption, including improving our understanding of conditions in the lower small intestine and colon, taking the influence of disease on GI physiology into account and further exploring the reasons behind population variability. Importantly, there is also a need to create more appropriate in vitro models for testing dosage form performance and to streamline data input from these into the PBPK models. As part of the Oral Biopharmaceutical Tools (OrBiTo) project, this review provides a summary of the current status of PBPK models available. The current challenges in PBPK set-ups for oral drug absorption including the composition of GI luminal contents, transit and hydrodynamics, permeability and intestinal wall metabolism are discussed in detail. Further, the challenges regarding the appropriate integration of results from in vitro models, such as consideration of appropriate integration/estimation of solubility and the complexity of the in vitro release and precipitation data, are also highlighted as important steps to advancing the application of PBPK models in drug development. It is expected that the "innovative" integration of in vitro data from more appropriate in vitro models and the enhancement of the GI physiology component of PBPK models, arising from the OrBiTo project, will lead to a significant enhancement in the ability of PBPK models to successfully predict oral drug absorption and advance their role in preclinical and clinical development, as well as for regulatory applications.

Dose selection based on physiologically based pharmacokinetic (PBPK) approaches

Physiologically based pharmacokinetic (PBPK) models are built using differential equations to describe the physiology/anatomy of different biological systems. Readily available in vitro and in vivo preclinical data can be incorporated into these models to not only estimate pharmacokinetic (PK) parameters and plasma concentration-time profiles, but also to gain mechanistic insight into compound properties. They provide a mechanistic framework to understand and extrapolate PK and dose across in vitro and in vivo systems and across different species, populations and disease states. Using small molecule and large molecule examples from the literature and our own company, we have shown how PBPK techniques can be utilised for human PK and dose prediction. Such approaches have the potential to increase efficiency, reduce the need for animal studies, replace clinical trials and increase PK understanding. Given the mechanistic nature of these models, the future use of PBPK modelling in drug discovery and development is promising, however some limitations need to be addressed to realise its application and utility more broadly.