In Silico Modeling of Gastrointestinal Drug Absorption: Predictive

Article pubs.acs.org/molecularpharmaceutics

In Silico Modeling of Gastrointestinal Drug Absorption: Predictive Performance of Three Physiologically Based Absorption Models Erik Sjögren,† Helena Thörn,‡ and Christer Tannergren*,‡ †

Department of Pharmacy, Uppsala University, Box 580, S-751 23 Uppsala, Sweden Pharmaceutical Technology and Development, AstraZeneca R&D Gothenburg, SE-43183 Mölndal, Sweden

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ABSTRACT: 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 modelpredicted 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 GISim 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. KEYWORDS: drug absorption, in silico model, fraction absorbed, prediction, drug development

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INTRODUCTION The majority of the pharmaceutical products used worldwide are administered in oral drug delivery systems.1 The fraction of the dose absorbed ( fabs) after administration of an oral formulation is hence a critical parameter in the estimation of the in vivo product performance. Research efforts to improve understanding of the gastrointestinal (GI) absorption process and the ability to predict the rate and extent of absorption are therefore currently highly prioritized by the pharmaceutical industry. A general approach to envisaging the potential for absorption involves the interplay between the solubility of the drug, the drug dosage, and the intestinal permeability to the drug, in accordance with the regulatory Biopharmaceutics Classification System (BCS) framework.2 However, GI absorption is actually a more complex process, where the interplay of the physicochemical and biopharmaceutical properties of the active pharmaceutical © 2016 American Chemical Society

ingredient (API), its formulation, and its specific physiological factors determines the rate and extent of absorption.3 One of the main biopharmaceutical and formulation development related challenges during recent years has been the increased number of poorly soluble APIs, which in turn is associated with an increased risk of particle size dependent absorption, significant food effects, and dose-dependent (less than linear) pharmacokinetics. Traditionally, the rate and extent of absorption have been assessed and predicted by in vitro and in vivo models during the drug development process.4 The generated data, such as Caco-2 cell monolayer permeability and the solubility and dissolution Received: Revised: Accepted: Published: 1763

November 13, 2015 January 28, 2016 February 29, 2016 February 29, 2016 DOI: 10.1021/acs.molpharmaceut.5b00861 Mol. Pharmaceutics 2016, 13, 1763−1778

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Molecular Pharmaceutics

Table 1. Physiological Parameters for the Evaluated Intestinal Absorption Models Included in Simcyp (SC), GastroPlus (G+), and GI-Sim (GS)a volume (mL)

transit time (min)

pH

bile

GI compartment

SCb

G+

GS

SC

G+

GS

SC

G+

GS

SCc

G+c

GSd

1 2 3 4 5 6 7 8 9

53 35 24 24 14 14 14 14 13

50 48 175 140 109 79 56 53 57

47 42 150 120 94 71 50

24 9.2 35 35 30 30 30 30 720

6 16 57 46 35 26 19 270 810

15 16 56 44 35 25 17

1.5 6.4 6.5 6.6 6.8 7.0 7.1 7.3 6.5

1.3 6.0 6.2 6.4 6.6 6.9 7.4 6.4 6.8

1.3 6.0 6.2 6.4 6.6 6.9 7.4

0.29 3.3 2.3 3.6 1.3 1.3 1.3 1.3 0.6

0 2.80 2.33 2.03 1.41 1.16 0.14 0 0

0 2 × 10−4 2 × 10−4 2 × 10−4 2 × 10−4 2 × 10−4 2 × 10−4

a

The GI compartments are numbered from the proximal to the distal end of the GI tract. GI compartment 1 represents the stomach in all models. The large intestine is represented by GI compartment 9 in SC and 8 and 9 in G+ (Opt logD Model SA/V 6.1). No evaluation of colonic absorption was performed for GS. bBaseline values in the dynamic volume model used in the ADAM model. cLuminal bile concentration (mM). dMicellar volume fraction.

explore the strengths and limitations of the in silico absorption models Simcyp, GastroPlus, and GI-Sim, with respect to their prediction of human intestinal absorption, using an a priori modeling approach and well-defined input data, for a set of drugs with incomplete absorption because of known solubility/ dissolution or permeation limitations.

rate of the drug in biorelevant dissolution media, are routinely used in compound risk assessment and to guide formulation development strategies. This enables assessment of the absorption potential of a specific API, but has limitations in terms of the simultaneous integration of several sources of in vitro data for a holistic assessment of the implications for in vivo absorption. Understandably, the application of mechanistic, physiologically based, in silico absorption models as prediction tools for GI absorption has gained considerable interest.5 These mechanistic models take the relationships between the drug characteristics, the formulation factors, and human physiology into account in the simulation of the GI absorption of an orally administered drug.6 Provided that well-defined input data are used, mechanistic and sophisticated physiologically based absorption models have the potential to improve understanding of the factors that limit intestinal absorption and to enable accurate predictions of the rate and extent of absorption in humans. Such models have previously been shown to be useful in the predictions of fabs and plasma exposure to drugs.7,8 To date, a number of mechanistic models for the prediction of intestinal absorption have been published and several commercial software packages are currently available. The two most commonly used commercial software packages are GastroPlus, which is based on the advanced compartmental absorption and transit (ACAT) model, and Simcyp, which is based on the advanced dissolution absorption and metabolism (ADAM) model.9,10 However, although widely used by the industry, regulatory authorities, and academic institutions, there are few reports of systematic investigation of the in vivo predictive performance of the models, especially for incompletely absorbed drugs for which prediction of the extent of absorption is more challenging.11 It would be of interest to the scientific community to understand the comparative performance of these models because this would help modeling and simulation scientists in the pharmaceutical industry, regulatory authorities, and academic research groups select the appropriate model/software for their specific purposes. To date, no study has input the same data into each of these two absorption models to enable a direct performance comparison, which for instance previously has been reported for the models GastroPlus 3.1.0 and IDEA 2.0.12 In a previous report, we investigated how well the in silico absorption model GI-Sim, which has been internally developed by AstraZeneca, predicted the in vivo absorption of a number of incompletely absorbed drugs.13 The aim of this study was to

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MATERIALS AND METHODS General Study Strategy. General software settings were adopted, recommendations in user manuals were followed when available, colonic absorption was allowed for when possible, and a selection of APIs expected to be incompletely absorbed but with well-defined and well-characterized physicochemical and biopharmaceutical properties was used. The modeling strategy was performed strictly bottom-up with no fitting to observations or remodeling with adjustments after preruns. Case-specific selection of software settings was not allowed, so as to simulate a standardized operational procedure and to enable direct comparison of the results. Care was taken to ensure that the input data used in the predictions were of sufficient quality to enable interpretation of the results with high degree of confidence. To address the lack of in vitro formulation input data, such as particle size and in vitro dissolution data, corresponding to the in vivo data of model drugs selected from literature, a particle size based dissolution approach was selected in this study. A strategy to address any lack of data on particle size was defined before any predictions were performed.13 Investigated Absorption Models. Three absorption models were used in this study: the commercially available software packages Simcyp and GastroPlus, and the AstraZeneca in silico model GI-Sim. All three models employ a similar model structure in which the GI physiology was represented by compartments coupled in series. In the models, unreleased material, undissolved particles, dissolved molecules, and degradation products flow through the GI tract from the stomach, via the small intestinal (SI) compartments (duodenum, jejunum, and ileum), and into the colon compartment(s).14−16 Each GI compartment is defined by parameters of relevance to the drug absorption processes, such as luminal volume, pH, area available for drug absorption, etc., to mimic the physiology and physiological environment in the intestinal lumen (Table 1). Further descriptions of the models are provided in the following sections. Information about software settings, choice of internal model when multiple options were available, and management of 1764

DOI: 10.1021/acs.molpharmaceut.5b00861 Mol. Pharmaceutics 2016, 13, 1763−1778

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Table 2. APIs Used in Evaluating the Simcyp, GastroPlus, and GI-Sim Models with Values for the Fundamental Input Parameters Including Solubility, Permeability, and Other Physicochemical Propertiesa Mw (g/mol) aprepitant

534

carbamazepine

236

danazol digoxin felodipine fenofibrate fexofenadine

337 781 384 361 502

griseofulvin irbesartan ketoconazole

353 429 531

AZ1 AZ2

475 ± 5 545 ± 5

pKa 2.4 b 9.15 a 12 a 0.26 b neutral neutral neutral neutral 4.2 a 7.84 b neutral 4.9 a 2.9 b 6.5 b neutral 0.75 b 2.74 b

logD7.4

ρ (g/mL)

D (10−9·m2/s) Peff (10−4·cm/s)

SpH6.5 (μg/mL)

SFaSSIF (μg/mL)

Sintrinsic (μg/mL)

logKm:wd

BCS

c

0.37

23

0.37

6.1

II

6.9

1.51

0.63

7.1

1.6

1.27

0.78

4.3b

127

236

127

4.2

II

3.7 1.3 4.3 6.9 0.23

1.21 1.36 1.28 1.18 1.17

0.68 0.54 0.67 0.66 0.59

7.5c 0.41c 7.8c 7.7c 0.07b

0.5 11.4 1.0 0.25 530f

8.7 14.3e 53 13.7 530

0.5 11.4 1 0.2 520

5.5 3.7 6.0 6.1 NA

II II II II III

2.9 1.5 4.1

1.38 1.31 1.38

0.70 0.65 0.61

7.3c 4.6c 3.3c

15 102 6.5

20 112 26

15 2.5 3.2

3.9 4.7 5.0

II I II

2.4 1.5

1.34 1.43

0.63 0.62

6.6c 0.46c

80 3.7

110 8.7

80 3.7

3.9 4.4

II IV

a

For pKa values, the notations a and b indicate acid and base, respectively. Parameter values (except log Km:w) were previously reported in Sjögren et al. 2013.13 bPeff measured in vivo. cPeff predicted from Caco-2 Papp measurements. dlog Km:w applied for iteration of biorelevant solubility in Simcyp. e Measurement performed in human intestinal fluid. fSame value as SFaSSIF because of lack of buffer solubility data. Mw, molecular weight; ρ, density; D, diffusion coefficient; Peff, effective permeability; SpH6.5, solubility at pH 6.5; SFaSSIF; solubility in fasted simulated small intestinal fluid; Sintrinsic, intrinsic solubility; BCS, Biopharmaceutical Classification System; log Km:w, micelle:buffer partition coefficient.

GastroPlus. GastroPlus (Simulations Plus, Inc., Lancaster, CA, USA) is a whole-body physiologically based pharmacokinetic (PBPK) model, which uses the Advanced Compartmental Absorption Transit (ACAT) mechanistic absorption model to mimic the human intestinal absorption of oral formulations from the GI tract.9 Simulations were conducted with version 8.0.0002 GastroPlus. In this work, either the “immediate release solution” or “immediate release suspension” (for tablets, capsules and suspensions) was used as the formulation type. The default value for gastric transit time in GastroPlus is 6 min for solutions, suspensions, and capsules, while it is 15 min for tablets. To align settings for GastroPlus across different formulations and considering that the majority (66%) of formulations simulated were nontablets, the gastric transit time was set to 6 min. The effect of bile salts on the dissolution/solubility of the drug is modeled by taking into account the solubilization of the drug by bile and the relevant concentrations of bile salts in each region of the GI tract. In practice, this effect was estimated by the in vitro solubility, as measured in biorelevant media. Precipitation was modeled with the default value for mean precipitation time (900 s). The Nernst−Brunner modification of the Noyes−Whitney equation was used to describe the dissolution kinetics.21 The diffusion layer thickness was adjustable up to a maximum of 30 μm. The initial particle size was described by a normal distribution on entering the mean particle radius (± the standard deviation), and 10 particle-size bins were used. The absorption rate in the ACAT model is estimated by an absorption scale factor logD model (the Opt logD Model SA/V 6.1 was used in this work) which scales regional permeability according to changes in trans- and paracellular transport, surface area, abundance of the villi and microvilli, and regional pHs. Only transcellular transport was considered for this work as the majority of the compounds were highly permeable/lipophilic compounds and no specific information on paracellular transport was available. A plasma protein binding (fraction of unbound drug in plasma; f up) value of 1 was adopted as the default

input data is also provided in these sections. Detailed descriptions of the models are available elsewhere.9,10,13 Simcyp. The ADAM model is a multicompartmental GI transit model integrated into the Simcyp human populationbased Simulator (Certara, CA, USA).10,17 Version 13.1 of the software was used for this work. The ADAM model treats the GI tract as one stomach, seven SI compartments, and one colon compartment. A time-dependent fluid volume dynamics model handles basal luminal fluid, additional fluid taken with the dose, the biological fluid secretion rates, and the fluid absorption rate. The formulations used in this work included the “solution with precipitation” and the “solid formulation immediate release (IR)” (for tablets, capsules, and suspensions). The initial particle size was described as a polydispersed log-normal distribution on entering the mean particle radius (± the coefficient of variation), and 10 particle-size bins were used. The ADAM model includes physiologically relevant regional luminal pH; the bile salt concentration and the effects of these factors on the solubility and dissolution rate of the drug are accounted for via a bile micelle solubilization model and the generalized diffusion-layer model.10,18,19 The diffusion layer thickness was adjustable up to a maximum of 30 μm. In the ADAM model, the solubility of ionizable drugs is described by the Henderson−Hasselbalch equation.20 The aqueous solubility in the simulations was entered as “intrinsic solubility”, as calculated from the solubility measurement reported at pH 6.5 (Table 2). The total solubility was set to match the reported solubility in the biorelevant medium by manual iteration of the micelle:buffer partition coefficient (log Km:w). Under supersaturated conditions, precipitation will take place according to a first-order process. Default values for the maximum supersaturation ratio (10) and precipitation rate constant (4 h−1) were used. For this particular work, the Sim-Healthy Volunteers population, with a population representative, was used in all simulations. The reference person in this model is a 25-year-old, 70 kg male in fasted state. 1765


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and data on first-pass extraction (Table 3). This procedure was used to exclude the influence of differences in PK algorithms between the models and thus facilitate direct comparison of the capacity for prediction of GI absorption. Convolution was carried out using WinNonlin 6.3 software (Certara, CA, USA). The evaluation comprised three parts. First, we evaluated how well the models could predict the AUC, the peak plasma concentration (Cmax), and the time to Cmax (tmax). The accuracy of these predictions was categorized according to three levels, depending on the extent of deviation (observation−prediction): highly accurate (≤25% deviation), acceptably accurate (25%−2fold deviation) and inaccurate (>2-fold deviation). The overall predictive performance was assessed in terms of the geometric mean of the ratio of absolute predicted and actual values (eq 1).

throughout this study. However, GastroPlus always performs an automatic prediction of f up, and it is recommended that the predicted f up (f up.pred) is used if it is five times lower than the original input value ( 1). A value of AFE = 1 indicates that no systematic trends exist. The second part of the evaluation assessed the ability of the models to capture any dependency of fabs on dose and particle size, which are important aspects of clinical formulation development. The rationale for this was that, even if the total exposure is inaccurately predicted, it would be very helpful to be able to capture trends in dose or particle size dependency when making decisions on new formulation strategies and assessing absorption risks. The results of this part of the investigation were assessed by comparing the observed and predicted plasma exposure, normalized to the dose (AUC/dose). The relative changes in AUC/dose were also assessed by normalizing the results to the lowest dose or smallest particle size, respectively. It was important that these first two parts of the evaluation were carried out in as similar a manner as possible for the three software packages, so as to facilitate a straightforward comparison. For some input parameters, e.g., particle size distribution and solubility in biorelevant media, this was not possible because of model differences. Instead, these input parameters were handled according to the software manuals when instructions were available, or otherwise in a manner that would preserve as much similarity between the models as possible. Details for data input management are specified in the description of each software package. Since it is recommended in GI-Sim that colonic drug absorption is excluded by default, as this feature has not yet been fully evaluated, only SI drug absorption was permitted for this model, while the simulations in 1766


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Table 3. Pharmacokinetic (PK) Parameters and Fraction Escaping First Pass Extraction Used for the Convolution of Plasma Concentration−Time Profilesa A/dose (ng/mL/mg) aprepitant carbamazepine danazol digoxin felodipine fenofibrate fexofenadine griseofulvin irbesartan ketoconazole

b

31 38c 2.4 10 29 64 50 62 5.3 140 75d 190e 440f

B/dose (ng/mL/mg)

C/dose (ng/mL/mg)

b

9.8 12c 14 3.2 1.9 9.0 69 13 6.0 26 0.14d 0.43e 1.3f

α (1/h) b

0.093 1.6 4.1 29 1.8 3.0

1.5 2.5c 0.28 6.0 2.0 32 2.1 2.8 0.39 4.4 0.34d 0.30e 0.27f

β (1/h)

γ (1/h)

b

0.044 0.047c 0.016 0.40 0.35 2.3 0.22 0.38 0.049 0.87 0.031d 0.031e 0.031f

first pass (%) b

0.015 0.018 0.27 0.029 0.078 0.065

7.4 7.4c 1.3 56 10 87 0 14 8 39 0 0 0

f up.predg 0.000021 0.000021 0.81 0.032 0.89 0.0082 0.000021 0.99 0.17 0.84 0.013 0.013 0.013

a

Values for AZ1 and AZ2 are not displayed for reasons of business confidentiality. bAprepitant dose 80 mg. cAprepitant dose 125 mg. dKetoconazole dose 200 mg. eKetoconazole dose 400 mg. fKetoconazole dose 800 mg. gPredicted fraction of unbound drug in plasma used in GastroPlus evaluation.

The predicted fabs (fabs.pred) and AUC values for all simulations of the model APIs are presented in Table 4. The corresponding observed and predicted plasma profiles for Simcyp, GastroPlus, and GI-Sim are shown in Figure 1 and Figure 2. The distribution of the predicted SI fabs values for the respective models is shown in Table 5. The predicted SI fabs values covered a broad range from the lowest fabs.pred values of 4, 5, and 8% for Simcyp, GastroPlus, and GI-Sim, respectively, to the highest level of 100% for all models. For all models, 25−30% of the simulations were in the high fabs range (fabs.pred = 80−100%). However, significant differences were observed between the models for the mid and low fabs ranges. A large proportion of the Simcyp and GastroPlus simulations (48% and 32%) were in the low range ( fabs.pred ≤ 20%) while only 13% of the GI-Sim simulations were within this range. Instead, the majority of the GI-Sim simulations (57%) were in the midrange (fabs.pred = 20−80%), while 27% and 39% of the Simcyp and GastroPlus simulations were within this range. This analysis of fabs illustrates the general sensitivity of the models. For poorly soluble APIs with Peff > ∼1, accurate prediction of absorption in this range is very sensitive to correct modeling of solubility/dissolution.28 The predicted fabs will be very low if the contribution of either one of these parameters to the driving force behind absorption is underestimated. Further, the interactive balance of solubility and Peff increases with decreased solubility and increased Peff. The results indicate that GI-Sim has the greatest capacity to predict the absorption of drugs with fabs in the midrange, followed by GastroPlus and then Simcyp. The overall accuracy of the predictions for the PK variables AUC, Cmax, and tmax is summarized in Table 6 and shown in Figure 3. The accuracy of the same PK variables for each respective API is shown in Figure 4. Ketoconazole solutions and carbamazepine solutions at a dose of 200 mg were excluded from this evaluation because the disposition PK parameters were based on the observed plasma concentration−time profiles from these administrations because of the lack of intravenous data. Many of the Simcyp predictions for AUC (42.5%) and Cmax (47.5%) were inaccurate. High accuracy was seen for 32.5% and 17.5% of the Simcyp AUC and Cmax predictions, respectively. 40% and 32.5% of GastroPlus AUC and Cmax values were predicted with high accuracy, while 30% and 25%, respectively, were inaccurate. This level of accuracy was in agreement with a

Simcyp and GastroPlus were performed both with and without the colon. In Simcyp, the simulations without colon absorption included a Peff,man value of 0.00001 in the colon segment. In GastroPlus, absorption without the colon was simulated by setting the absorption scale factors in the cecum and ascending colon compartments to 0. The intention of the third part of the evaluation was to evaluate whether allowing for colonic drug absorption in the Simcyp and GastroPlus models would improve the overall result. The precision of the predicted AUC when allowing for colonic drug absorption was evaluated and compared with the general performance without colonic absorption. The simulated plasma concentration−time profiles were also visually inspected in this assessment. In addition, the effect of the f up.pred functionality on the predictive outcome was evaluated for GastroPlus. Input Data. The simulations were based on previously reported biopharmaceutical and physicochemical properties (Table 2), PK parameters (Table 3), and formulation/dose information (Table 4).13 In vitro measurements of the solubility of the APIs in phosphate buffer at pH = 6.5 and in fasted simulated small intestinal fluid (FaSSIF) at 37 °C were used for the in vivo predictions. The solubilizing effect of colloidal structures was accounted for according to the instructions for the respective software packages. Permeability was represented by human effective permeability (Peff) values for the APIs, using previously reported in vivo Peff data when available. If no in vivo values were available, the Peff was predicted via previously published Caco-2-derived apparent permeability (Papp)−Peff correlations (the Papp−Peff correlations included in the respective software were not used).13 To account for differences in ABL thickness due to inadequate stirring in the in vitro experiments, the correlations were based on membrane permeability rather than total permeability. A f up value of 1 was adopted as the default throughout this study. Details of how input data were used are specified in each software section.

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RESULTS AND DISCUSSION Overall Predictive Performance of Simcyp, GastroPlus, and GI-Sim. The objective of this part of the study was to evaluate how well the Simcyp, GastroPlus, and GI-Sim absorption models predicted the oral absorption of drugs with identified limitations in GI absorption. 1767


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Table 4. Predictions of fabs and Accuracy of Plasma Exposure Predictions for the Investigated Drugs Using Simcyp (SC), GastroPlus (G+), and GI-Sim (GS)a fabs.pred (%)

AUCpred/AUCobs

fabs.pred (LI/SI)

drug

dose (mg)

form.

r (μm)

SC

G+

GS

SC

G+

GS

SC

G+

abs lim

aprepitant aprepitant ketoconazole ketoconazole ketoconazole ketoconazole ketoconazole ketoconazole AZ2 AZ2 carbamazepine carbamazepine danazol danazol griseofulvin griseofulvin griseofulvin griseofulvin felodipine felodipine felodipine AZ1 AZ1 AZ1 AZ1 AZ1 AZ1 AZ1 digoxin digoxin digoxin fexofenadine fexofenadine fexofenadine fexofenadine fexofenadine fexofenadine irbesartan irbesartan irbesartan irbesartan irbesartan fenofibrate fenofibrate

80 125 200 200 400 400 800 800 20 60 200 400 100 400 250 472 1000 1000 10 10 100 2 6 18 30 50 100 180 0.25 0.25 0.25 0.1 20 60 120 240 120 150 150 300 600 900 145 160

nano nano solu tabl solu tabl solu tabl susp susp susp tabl caps caps tabl susp tabl tabl solu tabl caps susp susp susp susp susp susp susp solu tabl tabl solu solu solu solu solu tabl solu tabl tabl tabl tabl nano susp

0.06 0.06

0.52 0.37 0.64 0.70 0.58 0.95 0.59 0.62 0.37 0.41 0.81 0.56 0.49 0.16 0.35 0.17 0.10 0.14 0.96 1.17 1.95 0.98 1.25 1.18 0.95 0.94 1.35 0.78 1.19 1.15 0.92 0.29 0.28 0.24 0.17 0.09 0.24 0.92 0.87 0.74 0.62 0.53 0.24 0.29

1.08 1.09 1.53 0.76 1.46 0.95 1.38 0.45 1.46 1.84 0.87 0.59 1.20 0.47 1.14 0.63 0.39 0.49 0.96 1.08 2.65 0.96 1.22 1.16 0.93 0.93 1.38 1.04 1.63 1.35 0.35 0.49 0.45 0.43 0.43 0.32 0.60 1.06 0.61 0.57 0.59 0.57 0.45 0.49

1.00 0.89 1.30 1.16 0.90 1.42 0.89 0.86 1.42 1.21 1.00 1.05 0.73 0.31 1.77 1.19 0.70 0.88 0.95 0.98 1.68 0.98 1.25 1.18 0.95 0.95 1.40 1.07 1.88 1.69 1.10 0.73 0.68 0.64 0.66 0.52 0.92 1.08 0.84 0.86 1.00 1.02 0.62 0.36

38 27 22 22 13 13 7.6 7.6 7.5 3.6 81 42 13 4.0 16 9.6 4.9 4.9 100 100 59 100 100 100 100 100 96 73 44 43 13 9.7 9.7 9.0 6.2 3.9 6.2 85 85 67 44 33 15 14

80 62 53 24 34 13 18 5.6 29 16 87 44 33 12 54 35 19 17 100 93 80 98 98 98 98 98 98 98 60 50 5.1 16 16 16 16 15 16 98 59 52 42 36 29 24

74 65 45 37 21 20 11 11 28 11 100 79 20 8 83 67 34 30 99 83 51 100 100 100 100 100 100 100 69 62 16 24 24 24 24 24 24 100 82 78 71 64 40 17

1.04 1.04 1.07 1.07 1.05 1.05 1.05 1.04 1.16 1.11 1.14 1.31 1.02 1.02 1.37 1.34 1.31 1.31 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.04 1.20 1.65 1.69 2.99 2.28 1.99 1.44 1.33 1.26 1.33 1.12 1.12 1.15 1.12 1.10 1.02 1.02

1.09 1.10 1.61 2.69 1.61 2.84 1.46 2.77 1.26 1.19 1.14 1.96 1.36 1.27 1.76 2.12 2.43 2.60 1.00 1.06 1.08 1.02 1.02 1.02 1.02 1.02 1.02 1.02 1.45 1.66 5.88 1.38 1.38 1.38 1.28 1.16 1.28 1.02 1.59 1.71 1.76 1.67 0.95 0.97

sol sol precipitation sol precipitation sol precipitation sol perm/sol perm/sol

10 10 10 2.35 2.35 5 75 2.23 2.23 2 0.6 3 8 2.5 2.5 2.18 2.18 2.18 2.18 2.18 2.18 2.18 6.5 51

10 50b 50b 50b 50b 0.2 1.1

dis sol sol sol sol sol sol dis sol dis dis dis dis dis dis dis perm perm perm/dis perm perm perm perm perm perm dis dis sol sol sol sol

a

Doses, formulations (form.), particle radius (r), accuracy of plasma exposure prediction (AUCpred/AUCobs), predicted fabs, and relative increase in predicted fabs when including absorption from the colon ( fabs.pred (LI/SI)) are shown for the reference APIs included in the study. Suggested key processes limiting absorption (abs lim) are denoted as follows: permeability (perm), solubility (sol) and dissolution (dis). bNo particle size information available. caps = capsule, nano = nanosuspension, solu = solution, susp = suspension, tabl = tablet.

previous evaluation by Jones et al.11 Predictions of AUC and Cmax by GI-Sim were inaccurate in 5% and 10% of the cases, respectively, while 57.5% and 55% were predicted with high accuracy. The overall predictions of accuracy for the AUC, given as AAFE, were 2.2, 1.6, and 1.3 for Simcyp, GastroPlus, and GISim, respectively. The corresponding AAFE values were 2.2, 1.6, and 1.3, respectively, for Cmax predictions and 1.7, 1.5, and 1.4, respectively, for tmax predictions.

Simcyp generally underpredicted AUC and Cmax (AFEAUC = 0.51, AFECmax = 0.49), as also seen in the reduced accuracy with decreasing predicted fabs (Figure 5). This demonstrates the value of reporting the predicted fabs in any absorption simulation, even when the corresponding observed fabs is not available. Underpredictions were observed regardless of formulation (solutions or solids), which indicates that the general capacity for absorption is generally underpredicted in Simcyp for these challenging type of APIs. A trend for general underprediction was 1768


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Figure 1. Observed and predicted plasma concentration−time profiles. Observed (symbols) and predicted (dotted lines) plasma concentration−time profiles for aprepitant, ketoconazole, AZ2, carbamazepine, danazol, and griseofulvin. Simcyp, GastroPlus, and GI-Sim simulations are displayed in the left, middle, and right columns, respectively. Values for AZ2 are in arbitrary units for reasons of business confidentiality. Errors in the observations are indicated as standard deviations. A linear or log scale for the y-axis was chosen based on the best vizualization of the plasma profiles.

All three models showed a general overprediction of tmax; the accuracy decreased with decreasing observed tmax (Figure 3E). Several processes may have been involved in this result. For example, gastric emptying is modeled as a first-order process with a half-life of approximately 5−10 min. However, gastric emptying

also observed for GastroPlus predictions (AFEAUC = 0.77, AFECmax = 0.80), but this was not correlated with the predicted fabs. There was no tendency for general over- or underprediction, using the AFE analysis, for GI-Sim (AFEAUC = 0.95, AFECmax = 1.08). 1769


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Figure 2. Observed and predicted plasma concentration−time profiles. Observed (symbols) and predicted (dotted lines) plasma concentration−time profiles for felodipine, AZ1, digoxin, fexofenadine, irbesartan, and fenofibrate. Simcyp, GastroPlus, and GI-Sim simulations are displayed in the left, middle, and right columns, respectively. Values for AZ1 are in arbitrary units for reasons of business confidentiality. An asterisk (*) is used to show when the drug was administered as a solution. Errors in the observations are indicated as standard deviations. A linear or log scale for the y-axis was chosen based on best vizualization of the plasma profiles.

in vivo in the fasted state is an intricate process controlled by the phases of the interdigestive migrating myoelectric complex and also by the volume and gastric content.29,30 Furthermore, particles are emptied differently from the stomach compared to

solutions, and the size and density of the particles will have implications for the residence time in the stomach.31 A more physiologically relevant model for gastric emptying with possibilities for replicating formulation dependencies could 1770


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in biorelevant media is inserted directly into the model. However, in Simcyp, this parameter is implemented manually by the operator, by iteratively changing the bile micelle partition coefficient until the FaSSIF solubility is acquired. A part of the modeling strategy was to select model drugs with well-defined PK parameters with incomplete absorption reported in the literature. The potential drawback with such an approach is the difficulty in finding the true formulation properties in the reported in vivo study, such as particle size distribution and in vitro dissolution. To address the lack of in vitro formulation input data, a particle size based dissolution approach was selected in this study along with a predefined strategy to handle lack of particle size data.13 The assumptions made are not expected to affect the overall results or the conclusions of this work. It could be argued that the Papp−Peff correlation used in this study may have biased the Simcyp and GastroPlus outcomes and that better results would have been acquired for these models if their modelspecific Papp−Peff correlation had been used. However, a direct model comparison would not have been possible if this approach had been used. In addition, the general trend of differences between the models in the accuracy of their predictions for APIs with direct in vivo measurements of Peff (carbamazepine and fexofenadine) was similar to the overall results for APIs with estimated Peff values. This strongly indicates that the differences in predictive performance between the models were not a consequence of the approach used to estimate Peff. The main reasons for the overall success rate and model performance were in the differences between models in the numerous underlying equations describing the mechanisms and the physiological system parameters. However, it was outside the scope of this study to identify and investigate these crucial equations and parameters. As a consequence of the high fraction of inaccurate predictions and the clear trend toward decreased accuracy with decreased predicted fabs observed with Simcyp, the results of this study indicate that the evaluated version of the ADAM model in Simcyp 13.1 should be used with caution for drugs with an observed or estimated risk of incomplete absorption. This could be of specific relevance when a higher degree of accuracy is needed in the prediction, for example in predictions of bioequivalence between two formulations. As the underpredictions obtained with Simcyp were independent of the type of formulation, exemplified by the underprediction of the permeability limited compounds AZ2, digoxin, and fexofenadine, it can be speculated that the result is related to the model for permeability used in Simcyp. Indeed, a new mechanistic model for prediction of passive permeation is reported to be under development.33 With regard to the fluid volumes there are differences between Simcyp and the other tools (Table 1). The intestinal fluid volume is generally discussed in terms of its impact on the dissolution and fraction dissolved of a drug. However, the luminal volume will also impact the absorption rate of the drug if it is related to the available area for absorption. Theoretically, for a compound at saturation, a reduction in available area for absorption caused by a decrease in luminal volume will result in a corresponding reduction in absorption rate. The differences in luminal volume between the models could therefore also be relevant for the absorption in a permeability perspective. Prediction of Dose-Dependent and Particle Size Dependent Absorption of APIs from Different Formulations. A priori based absorption predictions relying on wellevaluated models are particularly useful in candidate drug risk assessment and the early development of clinical formulations as

Table 5. Distribution of the Predicted Fraction of the Dose Absorbed ( fabs.pred) Results in the Small Intestinea fabs.pred (%)

Simcyp

GastroPlus

GI-Sim

80−100 50−80 20−50 10−20 0−10

25 (11) 6.8 (3) 20 (9) 16 (7) 32 (14)

30 (13) 16 (7) 23 (10) 27 (12) 4.6 (2)

30 (13) 23 (10) 34 (15) 11 (5) 2.3 (1)

a

The results are shown as the percentage of the simulations falling into each range, with the number of simulations within brackets.

Table 6. Summary of the Overall Accuracy of Predictions of the Pharmacokinetic Parameters AUC, Cmax, and tmax by the Simcyp, GastroPlus, and GI-Sim Modelsa

AUC

Cmax

tmax

Simcyp GastroPlus GI-Sim Simcyp GastroPlus GI-Sim Simcyp GastroPlus GI-Sim

high

acceptable

inaccurate

0−25%

25%−2-fold

>2-fold

AAFE

AFE

32.5 40 57.5 17.5 32.5 55 20 32.5 35

25 30 37.5 35 42.5 35 60 52.5 47.5

42.5 30 5 47.5 25 10 20 15 17.5

2.16 1.59 1.32 2.18 1.58 1.34 1.71 1.45 1.44

0.51 0.77 0.95 0.49 0.80 1.08 1.53 1.34 1.15

a

The simulations allowed for small-intestinal absorption only. The results are shown as the percentage of the simulations in each specific accuracy level, and as the absolute average fold error (AAFE) and average fold error (AFE).

potentially increase the accuracy of tmax predictions. Furthermore, for poorly soluble APIs, dissolution is traditionally regarded as a major determinant of tmax.2 However, to accurately capture tmax, it is equally important to correctly describe the permeability. This is especially crucial for freely permeating but poorly soluble APIs, as described by the solubility/permeability interplay. An absorption model predicting poorer permeation than that occurring in vivo will consequently overpredict tmax for these compounds. This may be the reason why the shorter gastric transit time (6 min) in GastroPlus simulations did not result in better tmax predictions (AFE = 1.3) than those from Simcyp (gastric transit time = 25 min, AFE = 1.5) and GI-Sim (gastric transit time = 15 min, AFE = 1.2) simulations. In summary, the results indicated that the ranked order of accuracy in models predicting the GI absorption of drugs with absorption problems was GI-Sim, followed by GastroPlus, and then Simcyp. In the development of GI-Sim, great care was taken to include algorithms that were aligned with established physical theory, as well as a mechanistic description of the additive effects of biorelevant media and nanoparticles on drug absorption.13,22,23,26,27,32 In addition, the superior outcome for GISim may be related to the input data as it has been recognized that input data with a higher degree of quality than early screen data are required to maximize the prediction performance of GISim for low solubility drugs. Therefore, crystalline solubility in buffer and biorelevant media is always generated as is Caco-2 permeability data in the presence of bovine serum albumin on the receiver compartment under maximized stirring conditions. This to improve recovery and minimize the in vitro ABL, respectively, for low solubility drugs resulting in an increased in vitro permeability. Moreover, in GI-Sim and GastroPlus, the solubility 1771


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Figure 3. Prediction of AUC, Cmax, and tmax: overall results. Overview of the results from the investigated API evaluation by Simcyp (green squares), GastroPlus (blue triangles), and GI-Sim (red dots). The graphs depict the accuracy of the predicted AUC (A), Cmax (C), and tmax (E) values and give the relative accuracy as AUC.pred/AUC.obs (B), Cmax.pred/Cmax.obs (D), and tmax.pred/tmax.obs (F). The solid and dotted lines represent the line of unity and that indicating a 2-fold difference, respectively.

The models had a similar capacity to capture the relative changes in dose-normalized AUC as a consequence of a decrease in fabs that were due to dose and particle size (Figures 6 and 7). The AAFE values for the observations (given as relative change in AUC) were 1.44, 1.40, and 1.29 for Simcyp, GastroPlus, and GISim, respectively. This indicates that all three models are suitable for early API absorption risk assessment and predictions of in vivo performance of different formulations, which in turn will guide the selected formulation strategy, decision for particle size reduction, and need for enhanced solubility formulations. However, the results for aprepitant and fenofibrate suggest that

no human PK data are available at that point and parameter optimization is difficult to justify. The purpose of the second part of the study was therefore to evaluate how well the three absorption models predicted dose and particle size dependency, including prediction of the in vivo performance of nanoparticles, and oral absorption of poorly soluble drugs. Reliable predictions provide confidence in the use of the absorption models in risk assessment and in the guidance of formulation development even in situations when the plasma profile has not been fully captured because of unknown PK parameters and/or the presence of extrahepatic first-pass metabolism or efflux. 1772


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Figure 5. Accuracy of AUC versus predicted fraction absorbed. Overview of the relative accuracy of the predicted AUC values (AUCpred/AUCobs) as a function of the predicted fraction of the dose absorbed (fabs.pred) for the investigated APIs as predicted by the Simcyp (green squares), GastroPlus (blue triangles), and GI-Sim (red dots) models. The solid and dotted lines represent the line of unity and that indicating a 2-fold difference, respectively.

in AUC when increasing the dose from 100 mg to 400 mg. The opposite result was obtained when outcomes were predicted after increasing the dose of felodipine from 10 mg to 100 mg: the relative reduction of AUC was underpredicted. Both danazol and felodipine are neutral molecules which permeate the GI wall freely but have low solubility; the formulations investigated were micronized suspensions. Hypothetical explanations for the discrepancies include saturation or autoinhibition of the firstpass extraction of danazol and a potential miss-specification of the particle size for the 100 mg felodipine formulation.34 The prediction results of the tablets of irbesartan, the only weak acid in the data set, deviate somewhat from the general trend as Simcyp show higher predicted fabs than GastroPlus in the 150− 600 mg dose range. This may be attributed to the somewhat higher overall pH in the ADAM model which increases the luminal concentration of solubilized irbesartan and consequently also the absorption rate. One can further speculate that difference in luminal volume between the ADAM and ACAT model will affect the trend in dose dependency differently, resulting in a relatively higher fabs for GastroPlus compared to Simcyp when the dose increases (Figure 6). The fexofenadine simulations serve as an example for the direct relationship between dissimilarities in physiological parameters between the models and differences in the simulation outputs. There was no dose dependency in the exposure results for GastroPlus and GI-Sim, which is in agreement with observations, while subproportional dose exposure was predicted by Simcyp (Figure 6).35,36 This was related to the low volume of intestinal fluids specified in the ADAM model, which resulted in luminal saturation at higher doses of fexofenadine. However, in most cases it was not possible to draw such straightforward conclusions between model features and the results. In all, no general conclusion regarding the ranking of the model performances could be made as a consequence of the variable outcome. Evaluation of the Colonic Drug Absorption Models in Simcyp and GastroPlus. Sufficient colonic absorption is often important for the in vivo performance of extended release formulations, and compounds with incomplete SI absorption may be absorbed in the colon to some extent. However, the factors determining the rate and extent of colonic absorption are currently not fully understood, and there is therefore a risk that the colon compartments in the available absorption models do not fully reflect the in vivo situation. The accuracy of the prediction of the contribution of the colon to the overall

Figure 4. Prediction of AUC, Cmax, and tmax: results per API. Overview of the results of pharmacokinetic predictions for the investigated APIs by the Simcyp (green squares), GastroPlus (blue triangles), and GI-Sim (red dots) models, sorted by API. The graphs depict the accuracy of the predicted AUC (A), Cmax (B), and tmax (C) values. The solid and dotted lines represent the line of unity and that indicating a 2-fold difference, respectively.

GI-Sim is superior in predicting the in vivo performance of nanoformulations, followed by GastroPlus, while Simcyp should not be used for these investigations. Although the overall capacity for capturing exposure dose dependency was acceptable (AAFE < 2) for all three software systems, there were APIs for which the results were less accurate. For example, all three models underpredicted plasma exposure to the micronized danazol formulation at 400 mg and overpredicted the relative reduction 1773


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Figure 6. Prediction of dose-dependent drug absorption. Graphs show dose-normalized plasma exposure (ng h/mL/mg; solid lines) and changes in AUC/dose relative to the lowest dose investigated (dotted lines). Observations are shown in black, and predictions by the Simcyp, GastroPlus, and GISim models are shown in green, blue, and red, respectively. Observed dose-normalized AUC values are displayed as means with standard deviations. Information on variability was unavailable for felodipine. Values for AZ1 and AZ2 are in arbitrary units because of business confidentiality.

by allowing for absorption in the colon compartment(s). There was a modest improvement in the overall accuracy of predictions of AUC for both Simcyp (AAFE from 2.16 to 1.99) and GastroPlus (AAFE from 1.59 to 1.48) when allowing absorption from the colon compartment(s) (Table 7). The level of colon absorption was variable; the total predicted fraction absorbed

absorption is currently under debate, and no systematic evaluation of the different colon models has been reported to date.5,37,38 As a consequence, absorption from the colon compartments is not allowed in the current version of GI-Sim. The purpose of this part of the work was to evaluate how the predictive performance of Simcyp and GastroPlus was affected 1774


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digoxin and griseofulvin in Figure 8). This indicates that using the AUC may not be the best way to evaluate the capacity of the models to allow for colonic absorption. Overall, the level of colonic absorption was lower in the ADAM model than in the ACAT model. The average increase in fabs.pred when allowing for absorption in the colon for APIs with an SI fabs.pred < 90% was 29% for Simcyp and 73% for GastroPlus. The variable and ambiguous results acquired in this study confirm that colonic absorption models need to be improved. This supports the continued efforts that are made to increase knowledge of factors affecting this process (permeability, transit, luminal conditions, etc.) for the development of a more reliable colon absorption model.39−41 Implications of f up for the Prediction of fabs in GastroPlus. The implications of f up for the prediction of fabs were briefly evaluated for GastroPlus since the general software settings automatically provide an f up prediction and a subsequent recommendation on whether or not to choose this input. Overall, there were only minor effects on the SI absorption when f up.pred values were used instead of the default value ( f up = 1). However, when allowing for colonic absorption, the use of f up.pred values significantly increased fabs.pred. As a consequence, the accuracy was improved for some APIs and reduced for others, as reflected by the changes in AFE (0.77 to 1.20) and AAFE (1.59 to 1.47). It is possible that this model functionality is included to guarantee that basolateral sink conditions are maintained throughout the absorption and to avoid artifacts like colonic excretion (as exemplified by the results for fenofibrate, Figure 8). The conclusion is that f up should be considered for GastroPlus predictions but that the f up.pred functionality should be used with caution as the implications of this parameter for the prediction of fabs are not completely apparent. Drug Development and Regulatory Perspectives. Positive experiences from using absorption models during the process of drug development have increased over recent decades as the reliability of the models has been continuously improved. However, the inevitable increases in model complexity, as a result of the fundamental nature of intestinal absorption, make the absorption models more and more difficult to scrutinize. The increased complexity also increases the number of possible approaches to include and describe the different mechanisms involved. It was therefore not surprising that differences in the prediction results were obtained between the models investigated in this study. Different absorption or PBPK software may be used in parallel for different purposes by different functions within a pharmaceutical company throughout the drug discovery and development process. The consequence of occasional contradictory results between different models increases the risk of a reduction in confidence when applying in silico absorption models in general. Thus, there is a risk that the use of several in silico models in conjunction will result in counterproductive uncertainties. The findings of this study emphasize how important it is that each modeling and simulation group knows how to use and implement their in vitro data to validate and standardize the data implementation in the models used throughout the company. Also, homogeneous procedures will not only increase the confidence of the operators in how to perform simulations but will also reduce interoperator variability and hence increase the possibility of cross-comparing simulations between projects within a company. An ongoing model comparison similar to this is underway as part of the IMI project OrBiTo (Innovative Tools for Oral Biopharmaceutics).42,43 We believe that the results of the two evaluations will complement each other; this work focuses on

Figure 7. Prediction of particle size-dependent drug absorption. Graphs show dose-normalized plasma exposure (ng h/mL/mg; solid lines) and changes in AUC/dose relative to the smallest particle size or the solution investigated (dotted lines). Observations are shown in black, and predictions by the Simcyp, GastroPlus, and GI-Sim models are shown in green, blue, and red, respectively. The observed dose-normalized AUC values are displayed as means with standard deviations.

Table 7. Summary of the Overall Accuracy of the Predictions for AUC, Using Simcyp and GastroPlus, When Excluding or Including Colonic Drug Absorption and When Using the Predicted Fraction of Unbound Drug in Plasma (f up.pred)a 0−25% Simcyp GastroPlus -f up.pred

32.5 40 40

Simcyp GastroPlus -f up.pred

30 42.5 45

25% (2-fold) Excluding Colon 25 30 37.5 Including Colon 30 30 30

>2-fold

AAFE

AFE

42.5 30 22.5

2.16 1.59 1.53

0.51 0.77 0.81

40 27.5 25

1.99 1.48 1.47

0.61 1.11 1.20

a

The results are shown as the percentage of the simulations in each specific accuracy level, and as the absolute average fold error (AAFE) and average fold error (AFE).

(fabs.pred) was significantly increased for some APIs, e.g., 1.5- to 5.9-fold for digoxin, but only to a minor degree for others, e.g., 1.1- to 1.3-fold for AZ2. One should also consider that increasing the accuracy of exposure predictions occasionally coincided with a reduction in plasma profile resemblance (exemplified by 1775


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Figure 8. Contribution of colonic absorption. Graphs show the observed (symbols) and predicted Simcyp (green) and GastroPlus (blue) plasma concentration−time profiles for digoxin, griseofulvin, and fenofibrate. The dashed lines indicate simulations allowing for small-intestinal drug absorption only. The solid lines depict predictions that also allowed for absorption from the colon. The dotted lines represent GastroPlus simulations allowing for colonic absorption and using a predicted value for the fraction of unbound drug in plasma ( f up.pred). Variability in the observations is indicated as standard deviations.

absorption predictions for biopharmaceutically challenging compounds using well-defined data input and known PK parameters, while the OrBiTo evaluation will include a significantly more extensive dataset with unknown PK parameters and varying quality of input data. The OrBiTo evaluation will also investigate potential operator effects as the simulations will be performed with the same dataset and models by many different modeling and simulation scientists with different backgrounds and experience. The results of this study also help to address the question of how absorption predictions are communicated to regulatory agencies. Simulations have been increasingly added as supporting information to regulatory files over the past decade.44,45 Although regulatory agencies have gradually embraced the potential value of PBPK and biopharmaceutical modeling, they still recommend that further evaluations of the models are performed and that frameworks for the evaluation of such data are constructed.45−47 To increase confidence in the process, our suggestion is that each company perform an evaluation of the in silico model(s) they use, in order to optimize the in vitro and formulation data, and to acquire confirmation of the general in vivo accuracy of the in vitro/in silico predictions. We suggest that this is a pragmatic approach to countering intercompany variability while attaining increased confidence in in silico simulation-based evaluations for regulatory purposes. Submis-

sion of such individual software evaluations as supporting information to the regulatory agency will allow the assessment of the overall model performance and consequently the potential value of the supporting in silico simulations. Such evaluations should preferably be done with a heterogeneous data set, such as the one used in this study, that includes challenging APIs and formulations. A similar approach has been included in a recent suggestion for a regulatory guideline on how to report the results and performance of a PBPK analysis.47 We want to conclude this section by recognizing that simulations such as those performed in this study, conducted with a strict a priori and bottom-up approach, are rarely sent to regulatory agencies. Those submitted to regulatory agencies have often been made in a late development stage. As a consequence these simulations are regularly optimized and evaluated through top-bottom simulations, based on additional experimental and preclinical data, to validate the adopted model approach and its predictive performance.48−51 However, an evaluation of the potential capacity of the investigated models for such an approach was not within the scope of this study.

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CONCLUSIONS There are numerous potential benefits to the application of physiologically based biopharmaceutical models in the process of drug discovery and development and in supporting proof-of1776


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(5) Kostewicz, E. S.; Aarons, L.; Bergstrand, M.; Bolger, M. B.; Galetin, A.; Hatley, O.; Jamei, M.; Lloyd, R.; Pepin, X.; Rostami-Hodjegan, A.; Sjögren, E.; Tannergren, C.; Turner, D. B.; Wagner, C.; Weitschies, W.; Dressman, J. PBPK models for the prediction of in vivo performance of oral dosage forms. Eur. J. Pharm. Sci. 2014, 57, 300−21. (6) Sugano, K. Introduction to computational oral absorption simulation. Expert Opin. Drug Metab. Toxicol. 2009, 5 (3), 259−93. (7) Parrott, N.; Lave, T. Applications of physiologically based absorption models in drug discovery and development. Mol. Pharmaceutics 2008, 5 (5), 760−75. (8) Sugano, K. Fraction of a dose absorbed estimation for structurally diverse low solubility compounds. Int. J. Pharm. 2011, 405 (1−2), 79− 89. (9) Agoram, B.; Woltosz, W. S.; Bolger, M. B. Predicting the impact of physiological and biochemical processes on oral drug bioavailability. Adv. Drug Delivery Rev. 2001, 50 (Suppl. 1), S41−67. (10) Jamei, M.; Turner, D.; Yang, J.; Neuhoff, S.; Polak, S.; RostamiHodjegan, A.; Tucker, G. Population-based mechanistic prediction of oral drug absorption. AAPS J. 2009, 11 (2), 225−37. (11) Jones, H. M.; Gardner, I. B.; Collard, W. T.; Stanley, P. J.; Oxley, P.; Hosea, N. A.; Plowchalk, D.; Gernhardt, S.; Lin, J.; Dickins, M.; Rahavendran, S. R.; Jones, B. C.; Watson, K. J.; Pertinez, H.; Kumar, V.; Cole, S. Simulation of human intravenous and oral pharmacokinetics of 21 diverse compounds using physiologically based pharmacokinetic modelling. Clin. Pharmacokinet. 2011, 50 (5), 331−47. (12) Parrott, N.; Lave, T. Prediction of intestinal absorption: comparative assessment of GASTROPLUS and IDEA. Eur. J. Pharm. Sci. 2002, 17 (1−2), 51−61. (13) Sjögren, E.; Westergren, J.; Grant, I.; Hanisch, G.; Lindfors, L.; Lennernäs, H.; Abrahamsson, B.; Tannergren, C. In silico predictions of gastrointestinal drug absorption in pharmaceutical product development: application of the mechanistic absorption model GI-Sim. Eur. J. Pharm. Sci. 2013, 49 (4), 679−98. (14) Yu, L. X.; Amidon, G. L. Saturable small intestinal drug absorption in humans: modeling and interpretation of cefatrizine data. Eur. J. Pharm. Biopharm. 1998, 45 (2), 199−203. (15) Yu, L. X.; Amidon, G. L. A compartmental absorption and transit model for estimating oral drug absorption. Int. J. Pharm. 1999, 186 (2), 119−25. (16) Yu, L. X.; Crison, J. R.; Amidon, G. L. Compartmental transit and dispersion model analysis of small intestinal transit flow in humans. Int. J. Pharm. 1996, 140 (1), 111−118. (17) Jamei, M.; Dickinson, G. L.; Rostami-Hodjegan, A. A framework for assessing inter-individual variability in pharmacokinetics using virtual human populations and integrating general knowledge of physical chemistry, biology, anatomy, physiology and genetics: A tale of ’bottomup’ vs ’top-down’ recognition of covariates. Drug Metab. Pharmacokinet. 2009, 24 (1), 53−75. (18) Glomme, A.; März, J.; Dressman, J. B. Predicting the Intestinal Solubility of Poorly Soluble Drugs. In Pharmacokinetic Profiling in Drug Research; Wiley-VCH Verlag GmbH & Co. KGaA: 2007; pp 259−280. (19) Wang, J.; Flanagan, D. R. General solution for diffusion-controlled dissolution of spherical particles. 1. Theory. J. Pharm. Sci. 1999, 88 (7), 731−8. (20) Henderson, L. J. Concerning the relationship between the strength of acids and their capacity to preserve neutrality. Am. J. Physiol. 1908, 21 (2), 173−9. (21) Lu, A. T.; Frisella, M. E.; Johnson, K. C. Dissolution modeling: factors affecting the dissolution rates of polydisperse powders. Pharm. Res. 1993, 10 (9), 1308−14. (22) Persson, E. M.; Gustafsson, A. S.; Carlsson, A. S.; Nilsson, R. G.; Knutson, L.; Forsell, P.; Hanisch, G.; Lennernäs, H.; Abrahamsson, B. The effects of food on the dissolution of poorly soluble drugs in human and in model small intestinal fluids. Pharm. Res. 2005, 22 (12), 2141−51. (23) Mazer, N. A.; Benedek, G. B.; Carey, M. C. Quasielastic lightscattering studies of aqueous biliary lipid systems. Mixed micelle formation in bile salt-lecithin solutions. Biochemistry 1980, 19 (4), 601− 15.

concept investigations. However, this study shows that differences in outcome between different models exist; it was shown that GI-Sim and GastroPlus performed better than Simcyp in predicting the intestinal absorption of the incompletely absorbed drugs included in this data set. Moreover, it was also concluded that all three absorption models can be used to guide early clinical formulation development, as they had equal predictive performances in capturing particle size and dose dependencies. Further development of colonic absorption models is warranted as only minor and ambiguous improvements in prediction accuracy were observed when the colon compartment(s) in Simcyp and GastroPlus were included. Finally, this study suggests that each modeling and simulation group should perform systematic model evaluations using well-defined input data to maximize model performance and output confidence.

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AUTHOR INFORMATION

Corresponding Author

*Pharmaceutical Technology and Development, AstraZeneca R&D Gothenburg, Pepparedsleden 1, SE-43183 Mölndal, Sweden. Tel: +46 31 7761976. Fax: +46 31 7763700. E-mail: [email protected]. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS The authors wish to thank Anna Lundahl for excellent support and assistance. GI-Sim has been developed by AstraZeneca for internal use. AstraZeneca has ongoing license agreements for both Simcyp and GastroPlus.

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ABBREVIATIONS USED AAFE, absolute average fold error; ABL, aqueous boundary layer; ACAT, advanced compartmental absorption and transit; ADAM, advanced dissolution absorption and metabolism; AFE, average fold error; API, active pharmaceutical ingredient; AUC, area under the plasma concentration−time curve; BCS, Biopharmaceutics Classification System; Cmax, peak plasma concentration; fabs, fraction of the dose absorbed; fabs.pred, predicted fabs; FaSSIF, fasted simulated small intestinal fluid; GI, gastrointestinal; IR, immediate release; log Km:w, micelle:buffer partition coefficient; Papp, apparent permeability; PBPK, physiologically based pharmacokinetic; Peff, human effective permeability; PK, pharmacokinetic; tmax, time to Cmax

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REFERENCES

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