Physiologically Based Absorption Modeling for Amorphous Solid

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Physiologically Based Absorption Modeling for Amorphous Solid Dispersion Formulations Amitava Mitra, Wei Zhu, and Filippos Kesisoglou Mol. Pharmaceutics, Just Accepted Manuscript • DOI: 10.1021/acs.molpharmaceut.6b00424 • Publication Date (Web): 21 Jul 2016 Downloaded from http://pubs.acs.org on July 23, 2016

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Molecular Pharmaceutics

Physiologically Based Absorption Modeling for Amorphous Solid Dispersion Formulations

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Amitava Mitra*#, Wei Zhu#, Filippos Kesisoglou#

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Biopharmaceutics

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Pharmaceutical Sciences and Clinical Supply, Merck & Co. Inc. #

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All authors had equal contribution

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*Corresponding Author

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Amitava Mitra, PhD

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Merck & Co., Inc

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West Point, PA-19486, USA

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Tel.: +1 215 652 8551

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Fax: +1 215 993 1245

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E-mail: [email protected]

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ACS Paragon Plus Environment


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Abstract

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Amorphous solid dispersion (ASD) formulations are routinely used to enable the delivery of

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poorly soluble compounds. This type of formulations can enhance bioavailability due to higher

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kinetic solubility of the drug substance and increased dissolution rate of the formulation, by the

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virtue of the fact that the drug molecule exists in the formulation in a high energy amorphous

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state. In this paper we report the application of physiologically based absorption models to

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mechanistically understand the clinical pharmacokinetics of solid dispersion formulations. Three

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case studies are shown here to cover a wide range of ASD bioperformance in human and

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modeling to retrospectively understand their in-vivo behavior. Case study 1 is an example of

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fairly linear PK observed with dose escalation and the use of amorphous solubility to predict

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bioperformance. Case study 2 demonstrates the development of a model that was able to

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accurately predict the decrease in fraction absorbed (%Fa) with dose escalation thus

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demonstrating that such model can be used to predict the clinical bioperformance in the scenario

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where saturation of absorption is observed. Finally, case study 3 shows the development of an

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absorption model with the intent to describe the observed incomplete and low absorption in

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clinic with dose escalation. These case studies highlight the utility of physiologically based

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absorption modeling in gaining a thorough understanding of ASD performance and the critical

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factors impacting performance to drive design of a robust drug product that would deliver the

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optimal benefit to the patients.

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Keywords - Absorption Modeling, PBPK, Pharmacokinetics, Solid Dispersion, Amorphous, Dissolution

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INTRODUCTION

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The prediction of clinical performance of the formulations, based on preclinical data, is an

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integral component of formulation development and a key deliverable for biopharmaceutics

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scientists. In the last decade, physiologically based absorption modeling has attracted attention as

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a means to achieve this preclinical to clinical translation. (1, 2, 3) Along with the advancement of

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dissolution methods towards biorelevant measurements (4), efforts have been undertaken in the

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field of physiologically based pharmacokinetic (PBPK) modeling to enable incorporation of

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physicochemical and formulation characterization information into the models to simulate

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absorption of drug compounds. (1) The principles of oral absorption PBPK models have been

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reviewed in the literature along with a description of some of the models used in some of the

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most common software such as such as GastroPlus (2), simCYP (3), PK-Sim (5) and

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Intellipharm (6).

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A review of the published literature on PBPK models for characterization or prediction of

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formulation performance shows that the majority of publications have focused on immediate

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release dosage forms or modified release (MR) dosage forms containing crystalline form of the

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drug substance. For the former, a typical strategy is to describe the dissolution data using models

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such as the Noyes-Whitney equation, and subsequent incorporation of the relevant information

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into the absorption models. For example, a good number of publications have demonstrated the

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successful application of these models to study the effect of particle size (7-10) or the impact of

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dissolution rate as measured in-vitro (11-13), on absorption of these “conventional” dosage

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forms. Similarly for MR formulations, given that the release rate from the dosage form is

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typically the rate limiting step to absorption, direct incorporation of the measured in-vitro release

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rates in the absorption model is employed. (10, 14-15) In addition, the development of more



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robust dissolution models that accurately predict dissolution rates of low solubility molecules

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have been recently reported. (16)

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With the increase in the number of insoluble compounds in drug development pipelines (17),

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often new formulation approaches are employed to achieve adequate bioavailability. Such

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approaches may include the utilization of nanosuspensions, lipids or co-solvents, and amorphous

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solid dispersions among others. (18) Among these approaches, amorphous solid dispersions

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(ASD) have recently received increased attention with several new drugs on the market. (19, 20)

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Amorphous solid dispersion (ASD) holds the promise of superior performance because of the

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inherently higher energy state of active pharmaceutical ingredients (API) over the crystalline

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counterpart. (21) The ASDs typically enhance bioavailability due to higher kinetic solubility of

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the drug substance and increased dissolution rate of the formulation, by the virtue of the fact that

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the drug molecule exists in the formulation in a high energy amorphous state. However, a higher

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energy alone does not guarantee higher bioavailability. Amorphous materials have unique

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challenges as compared to other more common approaches for increasing bioavailability due to

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an increased risk of crystallization to a more stable crystalline form. Thus it would appear

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beneficial to expand the application of physiologically based absorption modeling to these

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formulations. However, there are not many published reports available on simulation of

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absorption from these “enabled” dosage forms and from solid dispersions in particular. While the

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principles of absorption from supersaturated solutions have been studied (22), publications on

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applications of such models during product development are very limited. Gao et al. (23)

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described the prediction of performance of a solid dispersion, among other formulations, for rat

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toxicology studies while more recently Zheng et al, (24) discussed the utility of PBPK models in

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the discovery space to drive utilization of a solid dispersion. To the best of our knowledge, there


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have been no detailed reports of clinical data from solid dispersion formulations and application

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of absorption models to describe the clinical data.

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In this manuscript, we report three case studies attempting to highlight successes and challenges

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in utilizing physiologically based absorption models for solid dispersion formulations. Given the

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complexity of the dissolution behavior of solid dispersions (25), we focused on formulations that

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could be categorized as supersaturating systems without evidence of complicated species

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formation such as nanoparticles in in-vitro dissolution studies. The compounds exhibited varied

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absorption and pharmacokinetic behavior ranging from complete absorption with linear

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pharmacokinetics for case study 1 to incomplete and low absorption for case study 3. We discuss

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the development of models either based on the in-vitro solubility and dissolution data or based

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on extrapolation of models developed to first describe formulation performance in a preclinical

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dog model. Extensive physical characterization of these ASDs was carried out to ensure that they

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maintained their amorphous nature prior to in-vitro and in-vivo studies. However, discussion of

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those data is outside the scope of this paper, since the focus here is on development of absorption

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models.

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MATERIALS and METHODS

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Software Used

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GastroPlus v 8.5 (Simulations Plus, Lancaster, CA, USA) was used for absorption modeling in

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all the case studies.

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Case Study 1: Linear pharmacokinetics with high fraction absorbed

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Merck compound A physicochemical properties: Compound A is a BCS class II free base

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molecule. The following key compound properties were used in building the model – molecular

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weight 442, log P 4.21, pKa 3.53, density 1.2 g/mL, calculated human effective permeability

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(based on Caco-2 data) was 3.2 x 10-4 cm/sec and diffusion coefficient was calculated based on

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molecular weight. The amorphous solubility values from dissolution study were input as pH

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solubility profile, as described below. The solubility of the crystalline form in simulated gastric

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fluid (SGF) and fasted state simulated intestinal fluid (FaSSIF) was 0.02 mg/mL and 0.004

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mg/mL, respectively. Diffusion coefficient was adjusted in GastroPlus to account for bile salt

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concentration changes across the gastrointestinal (GI) tract based on diffusion coefficient and in-

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vivo dissolution rate. The precipitation time was fixed at the default value of 900 seconds. The

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blood to plasma ratio was 0.83. Fraction unbound in plasma was 0.9%.

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Dosage form and dissolution data input: The compound was formulated as an amorphous solid

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dispersion using HPMC-AS as the polymer matrix. Immediate release tablet was chosen as the

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formulation option in GastroPlus. The dissolution data for this formulation was generated in 2-

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stage dissolution (SGF followed by FaSSIF). The samples from the dissolution studies were

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ultracentrifuged at 80,000 rpm before analysis to remove any undissolved API. Furthermore the

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dissolution data did not show any indication of crystallization during the time-frame of the study.

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Hence this data represented amorphous solubility of the compound. This dissolution data was

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input in the model as a pH- amorphous solubility profile – 0.43 mg/mL (pH 1.3), 0.2 mg/mL (pH

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6.0), 0.013 mg/mL (pH 6.2), and 0.015 mg/mL (pH 6.5). The solubility profile was input to

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maintain supersaturation in the small in the small intestine, as there were no signs of

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crystallization in the dissolution study. Based on particle size measurement of the dissolving


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solid dispersion in the dissolution study, the mean size of the dissolving particle was set to be 1

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µm (diameter) in the model. Similar particle size was assumed for the crystalline form of the

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API, so that there is direct comparison of the effect of solubility difference of the API forms on

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PK.

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Physiology: The default human fasted physiological model in GastroPlus (Opt logD SA/v6.1)

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was used in these simulations.

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Pharmacokinetic (PK) parameters: Human PK parameters were estimated by fitting the 2 mg oral

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data from first-in-human (FIH) study (Merck data on file) to a two-compartment model in PK-

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plus. The mean PK parameters used in these simulations were CL/F = 0.235 L/hr/kg and V/F =

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0.398 L/kg, k12 = 0.653 hr-1, and k21 = 0.25 hr-1. Based on the low dose and linear

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pharmacokinetics it was assumed that F=1 (i.e. CL/F and V/F were used directly to simulate

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systemic disposition)

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Simulation: Single simulations were conducted to predict the mean PK profiles and parameters

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for several doses from the FIH study using the amorphous solubility data from the dissolution

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study. Additional simulations were conducted using the crystalline solubility of the compound to

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investigate whether these solubility data were able to predict the observed FIH data. Parameter

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sensitivity analysis (PSA) was conducted to assess the impact of precipitation time (90 – 9000

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seconds) on fraction absorbed as function of dose (2 – 400 mg).

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Case Study 2: Saturation of absorption

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Merck compound B physicochemical properties: Compound B is a BCS class II molecule. The

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following key compound properties were used in building the model – molecular weight 624.8,

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log D (pH 7) 2.63, density 1.2 g/mL, calculated human effective permeability (based on Caco-2



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data) was 2.38 x 10-4 cm/sec and diffusion coefficient was calculated based on molecular weight.

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The solubility in buffer was 0.021 mg/mL and there was no pH dependent solubility profile. The

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amorphous solubility in SGF and in FaSSIF was 0.021 mg/mL and 0.035 mg/mL, respectively.

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The amorphous solubility values were obtained from the dissolution data as described in case

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study 1. The solubility and diffusion coefficient was adjusted in GastroPlus to account for

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changes across the gastrointestinal (GI) tract based on bile salt concentration changes, by fitting

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the SGF and FaSSIF solubility data. The size of the dissolving particle was assumed to be 1 µm

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(diameter), based on particle size measurement of the dissolving solid dispersion in the

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dissolution study. The precipitation time was fixed at the default value of 900 seconds because

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there is no pH dependent solubility of this compound and dissolution studies have shown no

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indication of precipitation, therefore precipitation rate is expected to have minimal impact on

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fraction absorbed. The blood to plasma ratio was set as 1. Fraction unbound in plasma was set as

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5%.

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Dosage form input: The compound was formulated as an amorphous solid dispersion using

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HPMC-AS as the polymer matrix. Immediate release tablet was chosen as the formulation option

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in GastroPlus.

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Physiology: The default human fasted physiological model in GastroPlus (Opt logD SA/v6.1)

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was modified by reducing absorption from caecum and ascending colon by manually reducing

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the ASF to 1.0 in both caecum and ascending colon. This was done due to low buffer solubility

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of the compound and no evidence of prolonged absorption in preclinical studies. Based on

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experience with similar compounds, it was assumed that this compound would have minimal

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absorption in the lower GI tract in human.


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Pharmacokinetic (PK) parameters: Human PK parameters were estimated by fitting the 25 mg,

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which was the starting dose, oral data from first-in-human (FIH) study (Merck data on file) to a

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two-compartment model in WinNonin v5.3. The mean PK parameters used in these simulations

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were CL = 0.66 L/hr/kg and V = 1.84 L/kg, k12 = 0.139 hr-1, and k21 = 0.190 hr-1. Based on the

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low starting dose, linear pharmacokinetics up to 500 mg dose and lack of food effect at 125 mg

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dose, it was assumed that fraction absorbed (Fa) = 1 at 25 mg. The first pass extraction (FPE)

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was back-calculated as 51% using hepatic blood flow and blood/plasma ratio.

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Simulation: Single simulations were conducted to predict the mean PK profiles and parameters

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for several doses from the FIH study using the amorphous solubility data from the dissolution

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study.

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Case Study 3: Incomplete and low absorption

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Merck compound C physicochemical properties: Compound C is a BCS IV compound with low

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solubility (~0.1 µg/mL) across the physiological pH range and moderate permeability (LLCPK1

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Papp = 7.9 x 10-6 cm/sec). Given the very low pKa ( 90%) across all the dose levels. Some fluctuations in the PK profile in the

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elimination phase at the higher doses were likely an artifact of the model and have no bearing on

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the overall outcome.

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To investigate whether the use of amorphous solubility was critical to be able to accurately

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predict the observed clinical data at all the doses, additional simulations were carried out using

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the crystalline solubility of compound A. As shown in figure 2A, the low dose (2 mg) was

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simulated accurately. However at 50 mg and above, underprediction of AUC and Cmax was

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observed along with a right shift in Tmax. Figure 2B shows the simulations at 200 mg. These

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simulations clearly demonstrated that solubility of the amorphous form of the API was needed to

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predict the observed PK in the full FIH dose range. It is possible at the intermediate and higher

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doses some mixture of amorphous and crystalline forms co-exists. However the current version

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of the software doesn’t have the ability to handle two solubility values simultaneously hence

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such a scenario cannot be simulated without significant assumptions. Finally, parameter


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sensitivity analysis demonstrated that there was minimal impact on fraction absorbed (90-100%)

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over a wide range of precipitation time in the FIH dose range.

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Case Study 2: Saturation of absorption

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The simulated plasma concentration vs. time profiles and the PK parameters are shown in Figure

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3 and Table 2, respectively, for doses evaluated in FIH study. The observed data are shown for

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comparison. Overall, the model was able to predict observed human PK data with reasonable

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accuracy across the doses, except that delayed Tmax was predicted at 125 mg dose and above,

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which was not consistent with the observed Tmax across doses.

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Figure 4 depicts the observed dose normalized AUC and simulated fraction absorbed (Fa) for

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compound B at individual doses. The trend of saturation of absorption was observed in human

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PK data, as dose normalized AUC0-t decreases from 0.108 to 0.037 µg/mL*hr/mg with increase

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in dose. Similarly, a 3~4 fold decrease in Fa (%) was predicted in the absorption model across

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the doses from 25 mg to 750 mg. This demonstrated that the model was able to capture the

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observed saturation of absorption with reasonable accuracy.

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Case Study 3: Incomplete and low absorption

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The results of the dog pharmacokinetic study at 2 mg/kg dose and the modeling of the data are

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shown in Figure 5. The overall bioavailability was low (approximately 10-15%). As shown in

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Figure 5 an initial simulation based on the amorphous API solubility resulted in significant

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underprediction of the data. An increase in solubility by 4-fold (implemented in the software by

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increasing the bile salt solubilization ratio (SR); very comparable results are obtained by



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adjusting the baseline solubility and maintaining the initial SR value) resulted in a better

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description of the data; the overall extent of absorption was reasonably well captured although

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the absorption phase was not fully captured. Since the intent of this dog simulation was not to

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fully describe the dog data but to get an approximation of in vivo solubility with minimal model

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changes, this solubility estimate was considered acceptable in an early development setting for

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human PK projections. It is likely that the permeability in the dog was somewhat underpredicted

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from the current model. However since there is no direct translation of dog to human

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permeability that aspect of the model was not modified. This 4-fold higher in-vivo solubility

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was subsequently used in the human simulations, with the assumption that the higher in-vivo

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solubility observed in dogs is representative of human in-vivo solubility. It should be noted that

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compound C plasma concentration at 24 hours post dose was unexpectedly high, but such

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observations have been reported previously. (27) It is acknowledged that an increase in solubility

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due to better bile salt solubilization in vivo may not represent the only solution to obtaining an

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improved data fit. Thus additional simulations were conducted exploring alternative permeability

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values or different particle size. An effective permeability of 6.7 × 10-4 cm/sec provided a similar

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fit as that to the increase of in-vivo solubility (data not shown). This is a very high permeability

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value (only a couple of compounds such as ketoprofen and piroxicam have been reported to have

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such high permeability), that would appear unlikely given the cell line permeability and the

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molecular weight (~600 Da) of compound C. Simulations conducted using small in vivo particle

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size could not account for the observed exposures. Even when 0.2 µm (diameter) particle size

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was used for the simulation, plasma concentrations were significantly underpredicted

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(approximately 3-fold), similar to the simulations with the original solubility.

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Figure 6 depicts the simulation of clinical data at doses of 5, 15, 50 and 150 mg. Higher doses

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(400 mg and 800 mg) are not shown as will be explained below. While simulations based on the

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amorphous API solubility accurately predicted the plasma concentration vs time profile for the

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first two doses (5 and 15 mg – Figure 6 panels A and B), they significantly underpredicted the

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pharmacokinetics at the higher doses. Given the significant underprediction at 150 mg (Figure 6

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panel D), simulations of higher doses are not shown. Incorporation of the in-vivo solubility

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estimate from the dog model (i.e. 4 fold higher in-vivo solubility) significantly improved the

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prediction at the 150 mg dose, which was a similar dose to that used in the dog study (2 mg/kg).

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Although similar to the simulations of the dog data some underprediction of the absorption phase

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was observed.

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DISCUSSION

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ASDs enable higher bioavailability as compared to formulation made from crystalline drug

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substance due to higher solubility and dissolution rate of the amorphous product. However, it has

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been observed that rendering a material amorphous does not guarantee success. (28) Hence

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mechanistic understanding of the factors providing enhanced bioperformance (or lack of) is

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imperative. PBPK modeling to prospectively and/or retrospectively understand the absorption

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and bioperformance of ASDs in clinic based on in-vitro dissolution data and/or preclinical PK

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data could be very important to enable successful development of these formulations. The three

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case studies shown here were chosen to cover a wide range of ASD bioperformance in human

337

and modeling to retrospectively understand their in-vivo behavior. Case study 1 is an example of

338

fairly linear PK observed in the FIH study and the use of amorphous solubility to predict



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bioperformance, case study 2 exemplifies saturation of absorption observed in FIH and case

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study 3 demonstrates a formulation showing in-complete and low absorption.

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Compound A (case study 1) was formulated as a spray dried dispersion using HPMC-AS as the

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polymer matrix. In single ascending dose FIH study, reasonable dose proportionality in AUC

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was observed throughout the dose range of 2-400 mg but Cmax showed less than dose

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proportionality at the highest dose (400 mg). Dissolution of this formulation in SGF followed by

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FaSSIF, showed that in FaSSIF the amorphous formulation was able to provide approximately 4-

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fold higher solubility as compared to the crystalline form of the API and also this supersaturation

348

was maintained throughout the duration (120 minutes) of the dissolution experiment. An

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absorption model built using these amorphous solubility data from the dissolution experiment

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was able to predict the PK profile (Figure 1) and parameters (Table 1) with reasonable accuracy.

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Thus demonstrating that in-vivo the formulation was able to achieve similar amorphous

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solubility as shown in the in-vitro dissolution experiment and also maintain supersaturation

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throughout the dose levels. This was further confirmed by the results of simulations conducted

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using solubility of the crystalline API, which showed that while at low dose (2 mg) the model

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predicted the observed data well (Figure 2A), there was significant underprediction at 200 mg

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(Figure 2B). Thus demonstrating that the formulation was able to maintain amorphous solubility

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in-vivo at all doses in-order to achieve the observed dose proportional exposure. Since

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compound A is a weakly basic amorphous compound, precipitation in the GI tract might impact

359

its bioavailability. However, sensitivity analysis (Figure 2C) showed that precipitation time had

360

minimal impact on fraction absorbed. This is likely because the amorphous solubility of the

361

compound is high enough across the GI tract that it maintains supersaturation throughout the


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dose range studied in the clinic. These simulations clearly demonstrate that a thorough

363

understanding of behavior of these amorphous formulations in appropriately designed dissolution

364

experiments and subsequently incorporating that data in the PBPK model is needed to be able to

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predict their bioperformance.

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Compound B (case study 2) was formulated as a spray dried amorphous solid dispersion using

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HPMC-AS as the polymer matrix. In single ascending dose FIH study, both AUC and Cmax

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showed less than dose proportionality PK as the dose increases from 25 mg to 750 mg, indicating

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saturation of absorption at higher doses. Dissolution of this formulation showed that the

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dissolved drug of solid dispersion formulation was able to provide approximately 2-fold higher

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solubility as compared to a formulation with crystalline form of the API, and the supersaturation

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was maintained throughout the duration (90 minutes) of the dissolution experiment in FaSSIF.

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An absorption model was built using the amorphous solubility data from the dissolution

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experiment to predict the clinical PK profiles (Figure 3) and PK parameters (Table 2) with

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reasonable accuracy, although a somewhat delayed Tmax was predicted especially at higher doses.

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The reason for lack of prediction of the absorption phase at higher doses could be due to factors

378

such as inadequate estimation of in-vivo solubility even with use of bile salt based solubilization

379

or inadequate effective permeability estimation or a combination of these factors. To further

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illustrate the saturation of absorption in FIH study, the observed dose normalized AUC and

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simulated %Fa for compound B at individual doses were plotted in Figure 4, which showed that

382

the degree of saturation of absorption in observed human PK data are in agreement with the

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decrease of %Fa predicted by the absorption model across the doses from 25 mg to 750 mg. The

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less than dose proportional PK could be an indication that the amorphous solubility limit has



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been reached at the higher doses resulting in a solubility-limited absorption. In addition, it is

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possible that the supersaturation achieved in-vivo in the GI fluid may not be maintained as well

387

as that suggested by the in-vitro dissolution system, which could also contribute to a less-than-

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dose proportional increase in PK. While it is acknowledged that in this particular case the Tmax

389

was not precisely captured, the model was able to accurately predict the decrease in fraction

390

absorbed with dose escalation. Thus demonstrating that such physiologically based oral

391

absorption model can be used to model the clinical bioperformance in the scenario where

392

saturation of absorption is observed.

393 394

Compound C (case study 3) represents an interesting and challenging case study for oral

395

absorption modeling. Based on our experience, the behavior of compound C would have been

396

difficult to model with the currently available absorption models even if this formulation was a

397

simple crystalline API suspension. Specifically, compound C appears to exhibit relatively low

398

bioavailability (based on preclinical estimates and experience with other compounds in the

399

series) across all doses, however saturation of absorption is not seen until relatively high dose

400

(400 mg and higher). This relatively constant bioavailability across a wide dose range 5-400 mg

401

is very difficult to model based on the known compound and formulation properties. It is likely

402

that the low permeability of the compound is playing a role in the behavior and may be coupled

403

with regiodependent absorption (no data were available during development of Compound C to

404

assess this possibility). It is generally acknowledged that BCS IV compounds are the most

405

difficult to handle from absorption modeling standpoint. (1, 29)

406


Page 19 of 38

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407

The better fit at 150 mg (Figure 6, panel D) based on extrapolated in-vivo solubility from the dog

408

data suggests that this preclinical to clinical translation may be one way to inform simulations for

409

these challenging compounds. Others have also suggested this preclinical to clinical translation

410

step as an initial step in absorption modeling. Recently Zhang et al. used estimates of in-vivo

411

solubilities from rats and dogs to successfully predict FIH pharmacokinetics for eighteen

412

compounds. (30) However one limitation with this approach is that extrapolation to doses outside

413

of those that have been tested in preclinical species may not be straightforward. It is also possible

414

that for solid dispersions (and other solubilizing formulations) the dose to excipient ratio also

415

dictates in-vivo solubilization capacity (or crystallization potential), which can make preclinical

416

to human translation complicated. In the case of compound C, the extrapolation at a comparable

417

mg/kg dose (where dose/excipient/dosing volume ratios were quite similar) appeared to work,

418

although based on the simulations shown in Figure 6 it is evident that the same higher solubility

419

would overpredict exposures at lower doses (data not shown). In a typical drug/formulation

420

development setting, prior to FIH studies would be conducted at a dose representing the

421

projected clinical dose and/or the upper range of the FIH study. This was the case for compound

422

C. While this approach works to project the human PK at the intended dose, it is acknowledged

423

that more studies may be required to project the behavior across the entire FIH dose range (if that

424

is of interest). The final projection at the clinical dose is within 2-fold of the observed data,

425

which would be considered a generally acceptable prediction error for these types of models at

426

early development stage.

427 428 429



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 20 of 38

430

CONCLUSION

431

For amorphous solid dispersion formulations understanding the factors that impact their

432

bioperformance is critical because by design these formulations are thermodynamically unstable

433

with propensity to crystalize and they have complex dissolution characteristics. Physiologically

434

based oral absorption modeling can play an integral part in advancing biopharmaceutics

435

knowledge of these formulations and help in robust design of formulation per the quality by

436

design (QbD) paradigm. The case studies discussed in this manuscript demonstrate the potential

437

application of absorption modeling in such context, with three examples showing increasing

438

levels of biopharmaceutic complexity. Case study 1 is an example of successful application of

439

amorphous solubility obtained from dissolution experiment to predict the linear PK observed

440

with dose escalation in the FIH study. Case study 2 demonstrates that these models can be used

441

to simulate the clinical bioperformance in the scenario where saturation of absorption is observed

442

with dose escalation. Finally, case study 3 exemplifies the application of such model to describe

443

the observed in-complete and low absorption with dose escalation. The biopharmaceutics

444

knowledge gained from these absorption models would maximizes the mechanistic

445

understanding of critical factors affecting the bioperformance of solid dispersion formulations

446

and thereby help in development of a final product that would deliver the optimal benefit to the

447

patients.

448 449

ACKNOWLEDGEMENTS

450

The authors would like to thank the project teams for providing the critical data needed to build

451

the models.

452


Page 21 of 38

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453 454


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Jamei, M.; Lloyd, R.; Pepin, X.; Rostami-Hodjegan, A.; Sjögren, E.; Tannergren, C.;

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Turner, D.B.; Wagner, C.; Weitschies, W.; Dressman, J. PBPK Models for the Prediction

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2. Agoram, B.; Woltosz, W.S.; Bolger, M.B. Predicting the Impact of Physiological and

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3. Jamei, M.; Turner, D.; Yang, J.; Neuhoff, S.; Polak, S.; Rostami-Hodjegan, A.; Tucker,

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G. Population-based Mechanistic Prediction of Oral Drug Absorption. AAPS J. 2009, 11,

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4. Kostewicz, E.S.; Abrahamsson, B.; Brewster, M.; Brouwers, J.; Butler, J.; Carlert, S.;

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Dickinson, P.A.; Dressman, J.; Holm, R.; Klein, S.; Mann, J.; McAllister, M.; Minekus,

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Pharm Sci. 2014, 57, 342-366.

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5. Thelen, K.; Coboeken, K.; Willmann, S.; Dressman, J.B.; Lippert, J. Evolution of a

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6. Johnson, K.C. Dissolution and Absorption Modeling: Model Expansion to Simulate the

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9. Parrott, N.; Hainzl, D.; Scheubel, E.; Krimmer, S.; Boetsch, C.; Guerini, E.; Martin-

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Facklam, M. Physiologically based Absorption Modelling to Predict the Impact of Drug

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Properties on Pharmacokinetics of Bitopertin. AAPS J. 2014, 16, 1077-1084.

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10. Kesisoglou, F.; Chung, J.; Van Asperen,

J.; Heimbach, T. Physiologically Based

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Absorption Modeling to Impact Biopharmaceutics and Formulation Strategies in Drug

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Development—Industry Case Studies. J Pharm Sci. 2016, (article in press)

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11. Mitra, A.; Kesisoglou, F.; Dogterom, P. Application of Absorption Modeling to Predict

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Bioequivalence Outcome of Two Batches of Etoricoxib Tablets. AAPS PharmSciTech.

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2015, 16, 76-84.

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12. Crison, J.R.; Timmins, P.; Keung, A.; Upreti, V.V.; Boulton, D.W.; Scheer, B.J.

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Biowaiver Approach for Biopharmaceutics Classification System Class 3 Compound

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Metformin Hydrochloride Using In Silico Modeling. J Pharm Sci. 2012, 101, 1773-1782.

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13. Sperry, D.C.; Thomas, S.J.; Lobo, E. Dissolution Modeling of Bead Formulations and

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Predictions of Bioequivalence for a Highly Soluble, Highly Permeable Drug. Mol Pharm.

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2010, 7, 1450-1457.

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14. Mathias, N.R.; Crison, J. The Use of Modeling Tools to Drive Efficient Oral Product Design. AAPS J. 2012, 14, 591-600.


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15. Chen, W.; Desai, D.; Good, D.; Crison, J.; Timmins, P.; Paruchuri, S.; Wang, J.; Ha, K.

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Mathematical Model-Based Accelerated Development of Extended-release Metformin

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Hydrochloride Tablet Formulation. AAPS PharmSciTech. 2015, Oct 19. [Epub ahead of

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print]

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16. Wang, Y.; Abrahamsson, B.; Lindfors, L.; Brasseur, J.G. Analysis of Diffusion-

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Controlled Dissolution from Polydisperse Collections of Drug Particles with an Assessed

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Mathematical Model. J Pharm Sci. 2015, 104, 2998-3017.

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17. Venkatesh, S.; Lipper R. A. Role of the Development Scientist in Compound Lead Selection and Optimization. J Pharm Sci. 2000, 89, 145-154.

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18. Williams, H.D.; Trevaskis, N.L.; Charman, S.A.; Shanker, R.M.; Charman, W.N.;

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Pouton, C.W.; Porter, C.J. Strategies to Address Low Drug Solubility in Discovery and

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Development. Pharmacol Rev. 2013, 65, 315-499.

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19. Baghel, S.; Cathcart, H.; O'Reilly, N.J.; Polymeric Amorphous Solid Dispersions: A

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Review of Amorphization, Crystallization, Stabilization, Solid-State Characterization,

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and Aqueous Solubilization of Biopharmaceutical Classification System Class II Drugs. J

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Pharm Sci. 2016 [Epub ahead of print].

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20. Rumondor, A.C..; Dhareshwar, S.S.; Kesisoglou, F. Amorphous Solid Dispersions or

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Prodrugs: Complementary Strategies to Increase Drug Absorption, J Pharm Sci. 2016

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[Epub ahead of print]

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21. Serajuddin, A.T.M. Solid dispersion of Poorly Water-Soluble Drugs: Early Promises,

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Subsequent Problems, and Recent Breakthroughs. J Pharm Sci. 1999, 88, 1058-1066.

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22. Miller, J.M.; Beig, A.; Carr, R.A.; Spence, J.K.; Dahan, A. A Win-Win Solution in Oral

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Delivery of Lipophilic Drugs: Supersaturation via Amorphous Solid Dispersions



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Increases Apparent Solubility without Sacrifice of Intestinal Membrane Permeability.

522

Mol Pharm. 2012, 9, 2009-2016.

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23. Gao, Y.; Carr, R.A.; Spence, J.K.; Wang, W.W.; Turner, T.M.; Lipari, J.M.; Miller, J.M.

524

A pH-dilution Method for Estimation of Biorelevant Drug Solubility along the

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Gastrointestinal Tract: Application to Physiologically based Pharmacokinetic Modeling.

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Mol Pharm. 2010, 7, 1516-1526.

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24. Zheng, W.; Jain, A.; Papoutsakis, D.; Dannenfelser, R.M.; Panicucci, R.; Garad, S.

528

Selection of Oral Bioavailability Enhancing Formulations during Drug Discovery. Drug

529

Dev Ind Pharm. 2012, 38, 235-247.

530

25. Friesen, D.T.; Shanker, R.; Crew, M.; Smithey, D.T.; Curatolo, W.J.; Nightingale, J.A.

531

Hydroxypropyl Methylcellulose Acetate Auccinate-based Spray-Dried Dispersions: An

532

Overview. Mol Pharm. 2008, 5, 1003-1019.

533

26. Kesisoglou, F. Use of Preclinical Dog Studies and Absorption Modeling to Facilitate Late

534

Stage Formulation Bridging for a BCS II Drug Candidate. AAPS PharmSciTech. 2014,

535

15, 20-28.

536

27. Benincosa, L.J.; Audet, P.R.; Lundberg, D.; Zariffa, N.; Jorkasky, D.K. Pharmacokinetics

537

and Absolute Bioavailability of Epristeride in Healthy Male Subjects. Biopharm Drug

538

Dispos. 1996, 17, 249-258.

539 540

28. Newman, A.; Knipp, G.; Zografi, G. Assessing the Performance of Amorphous Solid Dispersions. J Pharm Sci. 2012, 101, 1355-1377.

541

29. Lennernas, H.; Aarons, L.; Augustijns, P.; Beato, S.; Bolger, M.; Box, K.; Brewster, M.;

542

Butler, J.; Dressman, J.; Holm, R.; Julia Frank, K.; Kendall, R.; Langguth, P.; Sydor, J.;

543

Lindahl, A.; McAllister, M.; Muenster, U.; Mullertz, A.; Ojala, K.; Pepin, X.; Reppas, C.;


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544

Rostami-Hodjegan, A.; Verwei, M.; Weitschies, W.; Wilson, C.; Karlsson, C.;

545

Abrahamsson, B. Oral Biopahrmaceuotcs Tools – Time for a New Initiative – An

546

Introduction to the IMI Project OrBito. Eu J Pharm Sci. 2014, 57, 292-299.

547 548

30. Zhang, T.; Heimbach, T.; Lin, W.; Zhang, J.; He, H. Prospective Predictions of Human Pharmacokinetics for Eighteen Compounds. J Pharm Sci. 2015, 104, 2795-2806.

549



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

550

TABLE LEGENDS

551

Table 1: Simulated and observed pharmacokinetic parameters for compound A at dose range of

552

2 - 400 mg.

553

Table 2: Simulated and observed pharmacokinetic parameters for compound B at dose range of

554

25 – 750 mg.

555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572


Page 26 of 38

Page 27 of 38

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


573

FIGURE LEGENDS

574

Figure 1: (A) Simulated and observed plasma concentration vs. time profiles for compound A at

575

2 mg, 4 mg, 8 mg and 15 mg doses. (B) Simulated and observed plasma concentration vs. time

576

profiles for compound A at 25 mg, 50 mg, 100 mg, 200 mg and 400 mg doses.

577 578

Figure 2: Simulated plasma concentration vs. time profiles for compound A at 2 mg dose (A),

579

200 mg dose (B) using solubility of the crystalline form of the compound. The observed plasma

580

concentration vs. time profiles at both doses is shown for comparison. (C) Parameter sensitivity

581

analysis showing the impact of precipitation time and dose on fraction absorbed.

582 583

Figure 3: Simulated and observed plasma concentration vs. time profiles for compound B at 25

584

mg, 50 mg, 125 mg, 250 mg, 500 mg and 750 mg doses.

585 586

Figure 4: Observed dose normalized AUC0-t (µg/mL*hr/mg) and simulated fraction absorbed

587

(%Fa) for compound B at 25 mg, 50 mg, 125 mg, 250 mg, 500 mg and 750 mg doses.

588 589

Figure 5 Simulated (lines) and observed (squares) plasma concentration vs. time profiles for

590

compound C in dogs at a 2 mg/kg dose. Initial simulation based on amorphous API solubility

591

(dashed line) and final model fit to 4-fold higher in-vivo solubility (solid line) are shown.

592 593

Figure 6 Simulated (dashed lines) and observed (squares) plasma concentration vs. time profiles

594

for compound C at 5 mg, 15 mg, 50 mg and 150 mg doses (panels A-D, respectively). For the



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

595

150 mg dose an additional simulation based on the in-vivo solubility obtained from dog data is

596

depicted (solid line).

597


Page 28 of 38

Page 29 of 38

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

598


Table 1 Observed Dose (mg)

Predicted

2

AUC0-24hr (µg/mL*hr) 0.12

Cmax (µg/mL) 0.018

Tmax (hr) 1.0

AUC0-24hr (µg/mL*hr) 0.13

Cmax (µg/mL) 0.022

Tmax (hr) 1.4

4

0.25

0.037

2.0

0.23

0.047

1.4

8

0.49

0.069

2.0

0.46

0.094

1.4

15

1.03

0.163

2.0

0.86

0.177

1.4

25

1.61

0.216

2.0

1.43

0.295

1.7

50

2.89

0.551

2.0

2.86

0.559

1.7

100

6.40

1.106

2.0

5.72

0.978

2.0

200

11.5

1.92

1.0

11.4

1.79

1.0

400

19.1

1.94

2.0

20.3

2.30

1.2

599 600 601 602 603



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

604

Page 30 of 38

Table 2 Observed Dose

Predicted

AUC0-t (µg/mL*hr)

Cmax (µg/mL)

Tmax (hr)

AUC0-t (µg/mL*hr)

Cmax (µg/mL)

Tmax (hr)

Fa (%)

25

0.270

0.043

2.0

0.256

0.051

1.8

100

50

0.398

0.058

1.5

0.528

0.095

2.0

100

125

0.861

0.088

1.5

1.155

0.144

3.5

87

250

1.123

0.113

1.0

1.563

0.161

3.5

59

500

2.131

0.180

2.0

1.908

0.175

4.5

36

750

2.810

0.249

2.0

2.094

0.181

4.8

26

(mg)

605 606 607


Page 31 of 38

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

608


Figure 1 (A)

609

(B)

610



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

611

Figure 2

(A)

612

(B)

613 614


Page 32 of 38

Page 33 of 38

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

615 616


Figure 2

617



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

618

Figure 3

619 620


Page 34 of 38

Page 35 of 38

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

621


Figure 4

622 623



624

Figure 5

625 Observed data

160

Simulation based on amorphous API solubility Simulation with optimized 4-fold higher in vivo solubility

140 Plasma Concentration (ng/mL)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 36 of 38

120 100 80 60 40 20 0 0

4

8

12 Time (hr)

626 627


16

20

24

Page 37 of 38

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

628


Figure 6 (A)

629

(B)

630 631 632



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

633

Figure 6 (C)

634

(D)

635 636 637 638 639


Page 38 of 38

Physiologically Based Absorption Modeling for Amorphous Solid

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