Int J Pharm Pharm Sci, Vol 14, Issue 4, 20-27Reviewl Article

BIPHASIC DISSOLUTION MODEL: NOVEL STRATEGY FOR DEVELOPING DISCRIMINATORY IN VIVO PREDICTIVE DISSOLUTION MODEL FOR BCS CLASS II DRUGS

PRASENJIT SARKAR* , SAUMYAJYOTI DAS, SUTAPA BISWAS MAJEE

Division of Pharmaceutics, Department of Pharmaceutical Technology, NSHM Knowledge Campus, Kolkata Group of Institutions, 124 BL Saha Road, Kolkata 700053, West Bengal
*Email: sarkar.prasenjit5113@gmail.com

Received: 03 Jan 2021, Revised and Accepted: 04 Feb 2021

ABSTRACT

In vitro dissolution study should ideally be designed to predict in vivo performance precisely, providing key information on the bioavailability and establishing IVIVC. Development of discriminatory in vivo predictive dissolution model and the establishment of IVIVC is difficult to achieve with BCS Class 2 drugs as they exhibit variable absorption along the GI tract owing to pH-dependent solubility, especially for Classes IIa and IIb. In this context, the biphasic dissolution model is a powerful technique for investigating the interplay between dissolution, precipitation and partitioning of various poorly soluble molecules. The dissolution test medium comprising of immiscible aqueous and organic phases enables maintenance of sink conditions and easy quantification of poorly soluble drug partitioning into the organic phase. In the review, novel efforts have been taken to provide comprehensive information on challenges associated with the establishment of IVIVC for BCS Class II drugs, various approaches being adopted for developing discriminatory in vivo predictive dissolution model, significant outcomes of studies on biphasic dissolution model to predict the in vivo dissolution behaviour of BCS Class II drugs and the problems with the use of biphasic dissolution model including the status of FDA on the same.

Keywords: BCS, Biorelevant dissolution medium, Biphasic dissolution model, IVIVC, In vivo predictive dissolution

© 2022 The Authors. Published by Innovare Academic Sciences Pvt Ltd. This is an open-access article under the CC BY license (https://creativecommons.org/licenses/by/4.0/)
DOI: https://dx.doi.org/10.22159/ijpps.2022v14i4.44042. Journal homepage: https://innovareacademics.in/journals/index.php/ijpps.

INTRODUCTION

Pharmaceutical industry, in recent years, has attempted and adopted several integrated and multidisciplinary approaches to achieve a more logical and assertive development flow with a new drug molecule [1-3]. For many decades, poor aqueous solubility of therapeutically active molecules is one of the most critical issues in the pharmaceutical industry [4-6].

For biopharmaceutics classification system (BCS) Class II drugs, adequate dissolution in an aqueous environment is difficult to achieve at physiological pH [7-9]. BCS Class IIa drugs which are usually weak acids, behave like BCS Class I molecules and dissolve rapidly in the small intestine [10]. On the other hand, BCS class IIb drugs exhibit high solubility and dissolution rates in the acidic environment of the stomach but may precipitate in the small intestine [11]. Solubility of BCS Class IIc drugs has been observed to be pH-independent [10]. However, surfactants and lipids present in the biological milieu play a significant role in the dissolution of BCS Class IIc drugs [12].

Dissolution testing is being employed routinely as the primary tool for evaluating the potential ability of a dosage form to deliver a drug and make it bioavailable [13]. It is an important tool for guiding formulation development, monitoring stability, studying drug-release mechanisms, ensuring batch-to-batch consistency and demonstrating bioequivalence [7, 14, 15]. For compounds with poor aqueous solubility, maintaining the sink condition during the dissolution study, an essential criterion to obtain reproducible, meaningful data can be challenging complete rendering dissolution characterization difficult [16]. In vivo predictive dissolution (IPD) methods will be considerably different from and more complex than USP quality control (QC) methodologies [17]. IPD is considered as a surrogate to forecast the in vivo drug release and potentially reduces the number of bioavailability/bioequivalence studies required.

In vitro in vivo correlations (IVIVC) play an important role in the drug development process and optimization of the formulation [7, 18, 19]. The main objective of an IVIVC is to serve as a surrogate for in vivo bioavailability studies [20, 21]. Establishment of an IVIVC is highly dependent on the ability of in vitro tests to predict in vivo absorption, wherein IPD methodology can be beneficial. In the last two decades, great efforts have been employed to develop dissolution tests with biorelevant media (e. g. FaSSIF, FeSSIF, SGF, etc.) to characterize immediate release dosage forms of poorly water-soluble drugs in order to simulate the dissolution and absorption processes in the GI tract [22]. However, establishing IVIVC of BCS Class II drugs from immediate-release (IR) dosage forms is a difficult task to achieve [23-25]. Table 1 has been framed to provide an overview on the status of in vivo studies and establishment of IVIVC with immediate-release (IR)/sustained-release (SR) formulations of BCS Class II drugs in last 5 y. For the purpose and also for writing the review, a systematic search of PubMed and Web of Science databases was conducted in the period October 2021 to January 2022 to retrieve all articles reporting studies on BCS Class II drugs since 1990 till date. Search terms included ‘in vivo predictive dissolution model’, ‘BCS Class II’, ‘in vitro drug release study’, ‘in vivo study’, ‘pharmacokinetic parameters’, ‘IVIVC’, ‘biphasic dissolution model’ combined with the Boolean operator “AND”, were applied for all database fields. Restrictions were applied to the article language (only in English).

Since conventional dissolution tests fail to fulfil the objectives of IPD due to the lack of biorelevance, more physiologically relevant dissolution methods have been developed to predict in vivo performance [19, 38]. Examples are artificial stomach-duodenum models, physical stress models, dissolution-permeation models, digestion model, biphasic dissolution model etc [39-43]. A biphasic dissolution system is one attractive technique, expected to be particularly applicable in studying the in vitro dissolution and predicting in vivo behaviour of both IR and modified-release formulations of BCS Class II compounds [4, 13, 22, 44].

The present review focuses on challenges in establishing IVIVC of BCS Class II drugs approaches for developing discriminatory in vivo predictive dissolution model for BCS Class II drugs with special emphasis on the biphasic dissolution model. No previous attempt has been made till date to highlight the significance of the biphasic dissolution model in predicting in vivo dissolution behaviour of BCS Class II drugs.

Table 1: Update on studies to establish IVIVC with IR/SR formulations of BCS Class II drugs in 2016-2021

Drug Formulation In vitro drug release study In vivo studies IVIVC Remarks Reference
Etoricoxib Tablets

Computer-controlled multicompartmental device (GIS) equipped with 3 dissolution chambers representing stomach, duodenum, and jejunum.

Dissolution media: SGF (pH 2.0), phosphate buffer (5 mmol, pH6.5)

Data obtained from BE cross-over studies in healthy subjects and in silico simulation to predict pharmacokinetic

profile

 Not done

GIS able to detect dissolution differences between formulations in SGF

-Impact of excipients on in vivo duodenal and jejunal behaviour observed

 [26]
Fenofibrate Microemulsion

USP Type II;

Dissolution media: Buffers of pH 1.2, 4.5, and 6.8

Not done.

Permeability studies done using biomimetic artificial membrane and everted gut sac technique

 - Permeability determined by everted gut sac technique was of higher magnitude than that determined using artificial membrane [27]
Oxcarbazepine Microfibres pressed into tablets

USP Type II;

Dissolution medium: Phosphate buffer (pH 6.8)

 In healthy human volunteers Not done In vitro dissolution was>90% within 2 min which is 5 times faster than that of pure drug, improvement in Cmax, Tmax, AUC [28]
Atorvastatin calcium Gelatin nanoparticles Dialysis bag diffusion technique In Sprague-dawley rats Not done Cmax and AUC 0-24 of nanoparticles 4-fold and 11-fold higher than that of pure drug suspension [29]
Sorafenib tosylate Self-nano emulsifying drug delivery system (SNEDDS) Done. Details could not be obtained Done. Details could not be obtained Not done Cmax of optimized SNEDDS higher than pure suspension and the AUC 0-∞ of optimized SNEDDS increased by 5 times than pure drug [30]
Telmisartan Nanosuspension

USP Type II;

0.1N HCl

 Not done NA - [31]
Ezetimibe Liquisolid compacts

USP Type II;

Dissolution media: 0.5% SLS, 0.05 M acetate buffer (pH4.5)

 Not done - Better drug release compared to marketed tablet [32]
Sildenafil IR and SR tablets

USP Type II;

Dissolution media: 0.1N HCl (pH 1.2), acetate buffer (pH 4.5), phosphate buffer (pH 6.8)

 In beagle dogs. Non-compartmental pharmacokinetic parameters determined such as t1/2, AUC, Vd, CL, Cmax, tmax

Population pharmacokinetics for establishing IVIVC.

Level A IVIVC

 Unusual patterns in complex, pH/site-dependent solubility and dissolution explained by population pharmacokinetics [33]
Gliclazide Tablets with cyclodextrin complex

USP Type I;

Dissolution medium: phosphate buffer (pH 6.8)

 Not done - Dissolution rate enhanced by complexation [34]
Carvedilol Marketed tablets

USP Type II;

Dissolution media: 0.7% HCl (USP 38, pH 1.45), SGF sans pepsin (pH1.2), FaSSGF (pH 1.6), FeSSGF (pH 5.0), SIF sans pancreatin (pH 6.8), 0.05M phosphate buffer (pH 6.8), acetate buffer (pH 4.5), FaSSIF (pH 6.5), FeSSIF (pH 5.0), phosphate buffer (pH 6.8, 7.2, 7.8, 6.25, 12.5, 25, 50, 100 mmol)

 Not done NA Dissolution of carvedilol completed within 60 min in SGF (pH 1.2-5.0) relatively low within 240 min in SIF (pH 6.5-7.8) [35]
Efavirenz IR suspension, modified-release cubosomes

USP Type II (Minipaddle) for IR suspension;

Dialysis method using Minipaddle for cubosomes;

Dissolution media:

FaSSIF, FeSSIF, phosphate buffer (pH 6.8, 7.4), water with 0.5, 1.0, or 2.0% SLS, SGF, SGF sans pepsin, lipolytic medium, water, 0.1N HCl

 Done in rats. Cubosome in vivo data fit two-compartment model and IR suspension followed one-compartment model after extravascular administration. PK parameters evaluated. Cmax, AUC, Ke, tmax, t1/2, the fraction of dose absorbed at each time point evaluated Levy plots done and r2 values for all media were calculated. Level A IVIVC obtained with 0.5% SLS in water for cubosomes and FaSSIF for IR suspension Rate of absorption not dramatically different for cubosomes and suspension but difference in the extent of absorption [36]
Nifedipine IR capsules

USP Type II;

Dissolution medium: FaSSGF

 In healthy human volunteers Not done Effect of administration of large volumes of water with capsules on pharmacokinetic parameters studied [37]

FaSSIF: Fasted-state simulated intestinal fluid; FeSSIF: Fed State Simulated Intestinal Fluid; FaSSGF: Fasted State Simulated Gastric Fluid; FeSSGF: Fed State Simulated Gastric Fluid; SLS: Sodium lauryl sulphate; SGF: simulated gastric fluid; GIS: Gastrointestinal Simulation

Challenges in establishing IVIVC of BCS class II drugs

BCS Class II drugs are characterized by aberrant biopharmaceutical properties. Dissolution of BCS Class IIa and Class IIb drugs is highly dependent on drug pKa, its solubility, formulation variables, in addition to the in vivo luminal environment [10, 45]. Gastric content with food and orally administered drug gradually enters the small intestine, where bile and the pancreatic juice are secreted to neutralize the gastric content, and the drug ultimately gets absorbed into the systemic circulation across the intestinal membrane [46, 47]. Some BCS Class IIa weak acids, particularly the small molecule nonsteroidal anti-inflammatory drugs (NSAIDs), are reported to dissolve quickly and behave like BCS Class I drugs in the small intestine, even though they exhibit low solubility at acidic gastric pH. Weakly basic drugs with poor intrinsic solubility (BCS Class IIb) quickly dissolve and are minimally absorbed in gastric pH owing to ionization but may precipitate upon entry in the upper small intestine due to the pH shift to the higher side [26, 48]. Therefore, dissolved BCS Class IIb drugs may be present in a supersaturated state [46]. While BCS Class II weak acids and bases demonstrate pH-dependent solubility in environments of varying pH in the human GI tract, the solubility of BCS Class IIc drugs would not be affected by this in vivo pH change. However, for BCS Class IIc drug products, surfactants and lipids present in the biological environment play a significant but difficult to predict role in drug dissolution.

IVIVC development becomes thus challenging owing to complexities in the phenomena of drug dissolution and absorption from the dosage forms of BCS Class IIa and IIb drugs, especially [12]. Various factors that usually determine the feasibility and success of IVIVC include physicochemical, biological, and pharmacokinetic properties of a drug substance, formulation design, in vitro and in vivo study designs for characterizing dosage form performance, modeling methodology, and level of understanding of the inter-relationship among the variables involved in the in vivo processes of release and absorption [12, 49].

Approaches for developing discriminatory in vivo predictive dissolution model

For developing a discriminatory in vivo predictive dissolution model, empirical and first-principles-based approaches have been documented for a range of intended purposes. Combinations of these two, such as the use of empirical model-derived parameters as inputs for the first principles and vice versa, are also common [50]. Based on the type of model used for decision-making, modeling approaches have been categorized, regardless of the input source.

First-principles approach

Early in drug product development, with a lack of data, first-principles-based models are created to aid in formulation development and process screening. With increased data and knowledge, these models mature into data-driven predictive models to enable real-time release testing (RTRt) and QC testing [51]. The first-principles study of dissolution profiles of drugs has been initiated by Arthur Noyes and Willis Whitney in 1897 [52]. The first-of-its-kind study characterized dissolution as a first-order rate process, dependent only on a rate parameter k and the material solubility. At sink conditions, this model further reduces to a zero-order one. This 0th- and 1st-order kinetics are the most basic mechanistic descriptions of dissolution [50].

Empirical approach

An empirically predictive in vitro dissolution model is dependent upon a traditional dissolution method due to the nature of the empirical approach that is used to forecast the dissolution profiles either directly (i.e., release level at specific time points or time to reach a specified release) or through predicting values of coefficients for fit to functional forms of dissolution profiles [50, 53, 54]. Exploring the formulation and process knowledge space provides an opportunity to understand the impact of various formulation and process variables on dissolution, which also establishes a foundation for the subsequent model-building exercise [53, 54].

To predict in vivo performance, there are more physiologically relevant dissolution methods other than the above-mentioned approaches. Examples are artificial stomach-duodenum models, physical stress models, dissolution-permeation models, digestion models, etc [19].

Artificial stomach-duodenum (ASD) model

This in vitro model mimics the functions of parts of the human digestive system (particularly stomach and duodenum), thus enabling scientists to understand how food and drug formulations are dissolved and transported within the GI tract [55, 56]. Also, it allows studying the influence of physical properties of drugs such as solubility and wettability on drug absorption, assuming that duodenal drug concentration is proportionally related to absorption [56, 57]. ASD modeling provides an important dynamic tool for the investigation of transient effects. This type of model has been successfully used to improve the dynamic in vitro assessment of antacid-induced resistance to stomach acidification and measure their activity in the duodenal milieu as well as in evaluating the effect of proteins and magnesium oxide in neutralizing stomach pH [57, 58].

Physical stress model

The novel dissolution stress test device, developed by Garbacz and Weitschies, exposes the dosage forms to physiological mechanical stress as an arbitrary sequence of movements, pressure waves, and phases of rest in the manner they may occur under in vivo conditions during passage of the dosage form through GI tract in a fasted state [41, 59]. Moreover, it enables the simulation of an intermittent contact of the dosage form with the dissolution medium [60].

Dynamic gastric model (DGM)

To address the need for a model, DGM was developed at the Institute of Food Research (Norwich, UK) that could simulate both the biochemical and mechanical processes occurring during human gastric digestion in a physiologically relevant manner [61]. It mimics the mechanical stress of mixing and the gastric fluid secretion depends on the volume of gastric content. Its close simulation of physiological conditions has enabled the application of DGM in the pharmaceutical industry as an in vitro tool to study the effect of food matrices on the disintegration and dissolution of dosage forms and the drug delivery profile to the duodenum [61, 62].

GastroPlus simulation

GastroPlus is a mechanistically based simulation software package that simulates absorption, biopharmaceutics, pharmacokinetics, and pharmacodynamics in humans and animals, following several routes of administration [63, 64]. The PBPK Plus™ Module in GastroPlus is the top-ranked physiologically based pharmacokinetic (PBPK) software for IVIVEs, pre-first-in-human (FIH) predictions, pediatric dose selection, and formulation optimization. In the convolution technique, a set of in vitro data from different dissolution scenarios are used as input functions to estimate the expected drug plasma concentration-time profile. In deconvolution, in vivo dissolution profiles created by GastroPlus are plotted against in vitro obtained dissolution profiles so that "bio-performance" dissolution conditions can be identified [64]. GastroPlus offers several advantages in predicting in vivo dissolution and absorption behavior in both fasted and fed conditions. Based on known solubility at a particular pH and drug pKa, GastroPlus enables the calculation of regional solubility on the basis of the fraction of drug ionized at each compartmental pH from the Henderson-Hasselbach equation. It can predict the effect of bile salt on in vivo drug solubility and dissolution. GastroPlus is capable of modeling the possible precipitation of poorly soluble weak bases when moving from stomach to small intestine [40].

pH-stat lipolysis

One way to overcome the solubility limitations of emerging drug candidates is through the use of lipid-based formulations (LBFs) [65-69]. The current in vitro setups to assess the performance of LBFs have limitations and often produce results that are quite different from the in vivo performance [70-72]. This lack of predictive and high throughput in vitro tests hampers the efficient development of LBFs. Commonly, LBFs are tested for their dispersibility, micellar size, and behavior upon digestion, in line with the lipidic formulation classification system [73-77]. The pH-stat lipolysis method is the most widespread standard method for in vitro assessment of LBFs [78-80]. It typically involves the dispersion of LBF in a medium representing the fasted intestinal environment with the addition of digestive lipases while controlling pH throughout the experiment by the addition of NaOH solution [5, 81, 82].

Biphasic dissolution model

A biphasic dissolution study is expected to be particularly suitable to study the in vitro dissolution and predict in vivo behavior of formulations of BCS Class ΙΙ compounds. It involves the use of two immiscible liquid layers, an organic phase and an aqueous one [83, 84].

Biphasic dissolution model

Biphasic dissolution test medium consists of immiscible aqueous and organic phases, where maintenance of sink condition is possible due to continuous partitioning of drug into the organic phase, depending on its distribution coefficient [13, 15, 85]. The basis for the biphasic dissolution model was first reported by Levy, who proposed that the presence of an upper organic phase within the dissolution medium could act as a reservoir or sink for the dissolved drug. Sink conditions are conventionally maintained in dissolution tests by means of surfactants, a large volume of dissolution medium, or cosolvents, but they have no physiological relevance [4, 86]. Combined dissolution and partition kinetics in biphasic dissolution models provide a discriminative power to dissolution tests for formulations of BCS Class II drugs [15, 44, 85]. In biphasic dissolution, 1-octanol is used for generating distribution or absorption-sink conditions due to its physicochemical properties and its application in logP determination [14, 16, 44].

The biphasic dissolution system is simple, easy to implement, and uses commonly available equipment [44, 87]. The advantage of this system lies in its single-step likeness to in vivo release, dissolution, and absorption of therapeutically active molecules in the gastrointestinal tract. Moreover, in the biphasic dissolution test, quantification of the drug concentration in the organic phase avoids the analytical challenges of measuring the free drug concentration in the aqueous phase [44]. Reliable and accurate determination of the free drug concentration belonging to BCS Class II becomes difficult due to the presence of precipitated drug particles in the aqueous phase under a non-sink condition. In contrast, the drug concentration in the organic phase can be reliably and accurately determined. Particle movement into the organic phase by the process of partition effectively acts as an analytical “filter” [88, 89].

Biphasic dissolution models: Past studies and significant outcomes

Anishetty et. al performed an experiment to assess the dissolution behavior of tablets with meloxicam nanoparticles in the biphasic medium of octanol-buffer of varying pH and it was concluded that excipients play a major role in the release behavior of drug with lactose as diluent promoting rapid wetting of drug particles by dissolution medium than dicalcium phosphate [90].

In an experiment to develop a biphasic dissolution test for deferasirox dispersible tablets and its application in establishing an in vitro-in vivo correlation, flow-through apparatus and the USP paddle apparatus were employed as dissolution apparatuses and octanol-phosphate buffer (pH 6.8) was used as biphasic dissolution medium and level-A IVIVC correlation was successfully established. Level A IVIVC was obtained with both dissolution apparatuses and the system was demonstrated to be superior to the single-phase system with respect to its ability to discriminate between different formulations [13].

In another study, an absorptive compartment (biphasic system) was introduced to a gastrointestinal simulator (GIS) and USP dissolution apparatus II for the assessment of in vivo prediction from immediate-release dosage forms for BCS Class IIc drugs, donepezil and danazol. Donepezil exhibited complete dissolution with the presence of an absorptive phase at 180 min (10 % in simulated intestinal fluid+90% in 1-octanol). Danazol also exhibited a 2-fold improvement in its dissolution with a distinct absorptive phase at 180 min [83].

It was difficult to establish IVIVC for different polymorphic forms of carbamazepine using single-phase dissolution. The difference between the anhydrous and dihydrate forms could be noted under non-sink conditions but it failed to establish meaningful IVIVC. However, when a biphasic in vitro dissolution test was employed with USP Type II apparatus as dissolution apparatus and phosphate buffer pH 6.8 containing octanol as dissolution medium, satisfactory discriminative power could be observed for the different polymorphic forms (form III>form I>dihydrate form) [21].

Gastrointestinal simulator (GIS) with biphasic platform proved beneficial in predicting in vivo dissolution profiles for BCS Class IIb drugs, ketoconazole, and raloxifene, including supersaturation and precipitation as well as mass transport analysis. In the study, sodium phosphate buffer (pH 6.5)-octanol was employed as the dissolution medium [11].

In a study to ascertain the application of a biphasic dissolution model as a discriminating tool for HPMC matrices containing BCS Class II drug, nifedipine, several combinations of aqueous and organic phases were investigated, which included SIF, HCl, sodium dodecyl sulfate (SDS), and sodium phosphate-buffer as aqueous phase whereas chloroform, ethyl acetate, cyclohexene/octanol, and nonanol/octanol as the organic phase. From the study, it was concluded that a biphasic dissolution medium consisting of an optimized deionized water/octanol system exhibited discriminatory power in revealing differences among nifedipine formulations with different HPMC percentages [7].

Biphasic dissolution model in establishing IVIVC

IVIVC has been defined by the FDA as “a predictive mathematical model describing the relationship between an in vitro property of a dosage form and an in vivo response”. Generally; the in vitro property is the rate or extent of drug dissolution or release, while the in vivo response is the plasma drug concentration or amount of drug absorbed. The United States Pharmacopoeia (USP) also defines IVIVC as “the establishment of a relationship between a biological property, and a parameter derived from a biological property produced from a dosage form, and a physicochemical property of the same dosage form” [18, 20, 91].

The basic requirements for establishing IVIVC are: Data obtained from human studies are essential for regulatory consideration of the correlation, two or more drug product formulations with different release rates are compared by employing an appropriate in vitro dissolution method, the development of IVIVC enables the standardization of the same dissolution method for all the formulations, and plasma concentration data from a bioavailability study for each of the formulations [22, 92].

In order to establish quantitative IVIVC with the biphasic test, an optimal hydrodynamic condition coupled with the use of biorelevant media is required. In vivo absorption profile is analogous to the drug concentration-time profile in the organic phase due to the presence of an “absorptive” phase in the biphasic dissolution model [22, 49].

Challenges with biphasic dissolution model

The biphasic dissolution model cannot be used as a regulatory test without the approval of the FDA, although it can act as a valuable bio relevant dissolution strategy enabling prediction of the outcome of bioequivalence studies and assisting in formulation selection [16]. The sampling of biphasic systems is associated with several analytical problems. The use of an automated sampling unit in standard USP I/II dissolution vessels with a medium such as octanol may block the sampling lines. In the biphasic dissolution model, the switch from gastric to intestinal conditions occurs very rapidly, whereas the process of gastric emptying is usually more gradual in vivo. Rapid pH transition may lead to an overestimation of the precipitation rate [5].

CONCLUSION

Dissolution testing is employed for in vitro assessment and prediction of the in vivo behavior of a pharmaceutical dosage form. Various approaches are being adopted in order to develop the discriminatory in vivo predictive dissolution model, which includes the First-Principles approach and the Empirical approach. There are several other models such as artificial stomach-duodenum models, physical stress models, dissolution-permeation models, digestion models, etc. In order to overcome the challenges associated with the development of a discriminatory in vivo predictive dissolution model and the establishment of IVIVC for BCS Class II drugs, the biphasic dissolution model has been reported to be very effective as discussed in the review with case studies on BCS Class IIa and IIb drugs. Although the model shows promise, limitations do exist regarding quantification and rapid pH transition, and also FDA approval with respect to its use for regulatory purposes is still awaited. Further studies in this direction will establish the biphasic dissolution model as an extremely beneficial tool in IVIVC studies.

FUNDING

No funding was received from any source.

AUTHORS CONTRIBUTIONS

The authors have contributed equally to content designing, writing and editing of the manuscript.

CONFLICT OF INTERESTS

No conflict of interest by authors.

REFERENCES

  1. Davanço MG, Campos DR, Carvalho PO. In vitro-in vivo correlation in the development of oral drug formulation: A screenshot of the last two decades. Int J Pharm. 2020;580(2):119210. doi: 10.1016/j.ijpharm.2020.119210, PMID 32173499.

  2. Pramod K, Tahir MA, Charoo NA, Ansari SH, Ali J. Pharmaceutical product development: quality by design approach. Int J Pharm Investig. 2016;6(3):129-38. doi: 10.4103/2230-973X.187350, PMID 27606256.

  3. Yu LX, Amidon G, Khan MA, Hoag SW, Polli J, Raju GK, Woodcock J. Understanding pharmaceutical quality by design. AAPS J. 2014;16(4):771-83. doi: 10.1208/s12248-014-9598-3, PMID 24854893.

  4. Pestieau A, Evrard B. In vitro biphasic dissolution tests and their suitability for establishing in vitro-in vivo correlations: A historical review. Eur J Pharm Sci. 2017;102:203-19. doi: 10.1016/j.ejps.2017.03.019, PMID 28315463.

  5. O’Dwyer PJ, Box KJ, Koehl NJ, Bennett Lenane H, Reppas C, Holm R, Kuentz M, Griffin BT. Novel biphasic lipolysis method to predict in vivo performance of lipid-based formulations. Mol Pharm. 2020;17(9):3342-52. doi: 10.1021/acs.molpharmaceut.0c00427, PMID 32787274.

  6. Kaur L, Kaur T, Singh AP, Singh AP. “Formulation development and solubility enhancement of rosuvastatin calcium by using hydrophilic polymers and solid dispersion method”. Int J Curr Pharm Sci 2021;13(6):50-5. doi: 10.22159/ijcpr.2021v13i6.1910.

  7. Phillips DJ, Pygall SR, Cooper VB, Mann JC. Toward biorelevant dissolution: application of a biphasic dissolution model as a discriminating tool for HPMC matrices containing a model BCS class II drug. Diss Technol. 2012;19(1):25-34. doi: 10.14227/DT190112P25.

  8. Wagh MP, Patel JS. Biopharmaceutical classification system: scientific basis for biowaiver extensions. Int J Pharm Pharm Sci. 2010;2(1):12-9.

  9. Sawant G, Bhagwat G. Niosomes as an approach to improve the solubility and bioavailability of BCS class II drugs. Int J Appl Pharm. 2021;13(2):94-101. doi: 10.22159/ijap.2021v13i2.40423.

  10. Tsume Y, Mudie DM, Langguth P, Amidon GE, Amidon GL. The biopharmaceutics classification system: subclasses for in vivo predictive dissolution (IPD) methodology and IVIVC. Eur J Pharm Sci. 2014;57:152-63. doi: 10.1016/j.ejps.2014.01.009, PMID 24486482.

  11. Tsume Y, Igawa N, Drelich AJ, Amidon GE, Amidon GL. The combination of GIS and biphasic to better predict in vivo dissolution of BCS Class IIb drugs, ketoconazole and raloxifene. J Pharm Sci. 2018;107(1):307-16. doi: 10.1016/ j.xphs.2017.09.002, PMID 28919384.

  12. Zhou D, Qiu Y. Understanding biopharmaceutics properties for pharmaceutical product development and manufacturing II: Dissolution and in vitro-in vivo correlation. J Validation Technol. 2010:57-70.

  13. Al Durdunji AA, Alkhatib HS, Al-Ghazawi M. Development of a biphasic dissolution test for deferasirox dispersible tablets and its application in establishing an in vitro-in vivo correlation. Eur J Pharm Biopharm. 2016;102:9-18. doi: 10.1016/ j.ejpb.2016.02.006, PMID 26898545.

  14. Denninger A, Westedt U, Rosenberg J, Wagner KG. A rational design of a biphasic dissolution setup-modeling of biorelevant kinetics for a ritonavir hot-melt extruded amorphous solid dispersion. Pharmaceutics. 2020;12(3):1-23. doi: 10.3390/pharmaceutics12030237, PMID 32155962.

  15. Silva DA, Al-Gousous J, Davies NM, Chacra NB, Webster GK, Lipka E, Amidon GL, Lobenberg R. Biphasic dissolution as an exploratory method during early drug product development. Pharmaceutics. 2020;12(5):1-17. doi: 10.3390/ pharmaceutics12050420, PMID 32370237.

  16. Phillips DJ, Pygall SR, Cooper VB, Mann JC. Overcoming sink limitations in dissolution testing: a review of traditional methods and the potential utility of biphasic systems. J Pharm Pharmacol. 2012;64(11):1549-59. doi: 10.1111/j.2042-7158.2012.01523.x, PMID 23058042.

  17. Krieg BJ, Taghavi SM, Amidon GL, Amidon GE. In vivo predictive dissolution: comparing the effect of bicarbonate and phosphate buffer on the dissolution of weak acids and weak bases. J Pharm Sci. 2015;104(9):2894-904. doi: 10.1002/jps.24460, PMID 25980464.

  18. Sakore S, Chakraborty B. In vitro-in vivo correlation (IVIVC): A strategic tool in drug development. J Bioequiv Availab. 2011;8(4):1-12.

  19. Deng J, Staufenbiel S, Hao S, Wang B, Dashevskiy A, Bodmeier R. Development of a discriminative biphasic in vitro dissolution test and correlation with in vivo pharmacokinetic studies for differently formulated racecadotril granules. J Control Release. 2017;255:202-9. doi: 10.1016/j.jconrel.2017.04.034, PMID 28450206.

  20. Nainar S, Rajiah K, Angamuthu S, Prabakaran D, Kasibhatta R. Biopharmaceutical classification system in in vitro in vivo correlation: concept and development strategies in drug delivery. Trop J Pharm Res. 2012;11(2):319-29.

  21. Deng J, Staufenbiel S, Bodmeier R. Evaluation of a biphasic in vitro dissolution test for estimating the bioavailability of carbamazepine polymorphic forms. Eur J Pharm Sci. 2017;105:64-70. doi: 10.1016/j.ejps.2017.05.013, PMID 28487146.

  22. Xu H, Shi Y, Vela S, Marroum P, Gao P. Developing quantitative in vitro-in vivo correlation for fenofibrate immediate-release formulations with the biphasic dissolution-partition test method. J Pharm Sci. 2018;107(1):476-87. doi: 10.1016/j.xphs.2017.06.018, PMID 28666964.

  23. Uppoor VRS. Regulatory perspectives on in vitro (dissolution)/in vivo (bioavailability) correlations. J Control Release. 2001;72(1-3):127-32. doi: 10.1016/s0168-3659(01)00268-1, PMID 11389991.

  24. Qureshi SA. In vitro-in vivo correlation (IVIVC) and determining drug concentrations in blood from dissolution testing: A simple and practical approach. Open Drug Deliv J. 2010;4(2):38-47.

  25. Cardot JM, Beyssac E, Alric M. In vitro-in vivo correlation: importance of dissolution in IVIVC. Diss Technol. 2007;14(1):15-9. doi: 10.14227/DT140107P15.

  26. Gonzalez Alvarez I, Bermejo M, Tsume Y, Ruiz Picazo A, Gonzalez Alvarez M, Hens B, Garcia Arieta A, Amidon GE, Amidon GL. An in vivo predictive dissolution methodology (iPD methodology) with a BCS Class IIb drug can predict the in vivo bioequivalence results: Etoricoxib products. Pharmaceutics. 2021;13(4):1-12. doi: 10.3390/pharmaceutics13040507.

  27. Tlijani M, Lassoued MA, Bahloul B, Sfar S. Development of a BCS Class II drug microemulsion for oral delivery: design, optimization, and evaluation. J Nanomater. 2021;2021:1-9. doi: 10.1155/2021/5538940.

  28. Nasir S, Hussain A, Abbas N, Bukhari NI, Hussain F, Arshad MS. Improved bioavailability of oxcarbazepine, a BCS class II drug by centrifugal melt spinning: In-vitro and in-vivo implications. Int J Pharm. 2021;604(3):120775. doi: 10.1016/j.ijpharm.2021.120775, PMID 34098052.

  29. Kasekar NM, Singh S, Jadhav KR, Kadam VJ. BCS class II drug-loaded protein nanoparticles with enhanced oral bioavailability: in vitro evaluation and in vivo pharmacokinetic study in rats. Drug Dev Ind Pharm. 2020;46(6):955-62. doi: 10.1080/03639045.2020.1764021, PMID 32362144.

  30. Sandhya P, Poornima P, Bhikshapathi DVRN. Self nano emulsifying drug delivery system of sorafenib tosylate: development and in vivo studies. Pharm Nanotechnol. 2020;8(6):471-84. doi: 10.2174/2211738508666201016151406, PMID 33069205.

  31. Bhargav E, Barghav GC, Reddy YP, kumar CP, Ramalingam P, Haranath C. A design of experiment (DoE) based approach for development and optimization of nanosuspensions of telmisartan, a BCS class II antihypertensive drug. Future J Pharm Sci. 2020;6(1):1-13.

  32. Marapur SC, Jat RK, Patil JS. Formulation and development of some BCS class II drugs. J Drug Deliv Ther. 2019;9(2):321-9. doi: 10.22270/jddt.v9i2.2424.

  33. Kim TH, Shin S, Jeong SW, Lee JB, Shin BS. Physiologically relevant in vitro-in vivo correlation (IVIVC) approach for sildenafil with site-dependent dissolution. Pharmaceutics. 2019;11(6):1-9. doi: 10.3390/pharmaceutics11060251, PMID 31159390.

  34. Sunitha R, Venugopal K, Satyanarayana SV. Formulation and development studies on enhancement of dissolution rate of BCS class II antidiabetic drug. Int J Drug Dev Res. 2018;10(3):1-6.

  35. Hamed R, Awadallah A, Sunoqrot S, Tarawneh O, Nazzal S, AlBaraghthi T, Al Sayyad J, Abbas A. PH-dependent solubility and dissolution behavior of carvedilol-case example of a weakly basic BCS Class II drug. AAPS PharmSciTech. 2016;17(2):418-26. doi: 10.1208/s12249-015-0365-2, PMID 26202065.

  36. Deshmukh S, Avachat A, Garka A, Khurana N, Cardot JM. Optimization of a dissolution method in early development based on IVIVC using small animals: application to a BCS class II drug. Diss Technol. 2016;23(4):34-41. doi: 10.14227/DT230416P34.

  37. Nader AM, Quinney SK, Fadda HM, Foster DR. Effect of gastric fluid volume on the in vitro dissolution and in vivo absorption of BCS class II drugs: A case study with nifedipine. AAPS J. 2016;18(4):981-8. doi: 10.1208/s12248-016-9918-x, PMID 27106837.

  38. Jambhekar SS, Breen PJ. Drug dissolution: significance of physicochemical properties and physiological conditions. Drug Discov Today. 2013;18(23-24):1173-84. doi: 10.1016/j.drudis.2013.08.013, PMID 24042023.

  39. Blanquet S, Zeijdner E, Beyssac E, Meunier JP, Denis S, Havenaar R, Alric M. A dynamic artificial gastrointestinal system for studying the behavior of orally administered drug dosage forms under various physiological conditions. Pharm Res. 2004;21(4):585-91. doi: 10.1023/ b:pham.0000022404.70478.4b, PMID 15139514.

  40. Tsume Y, Takeuchi S, Matsui K, Amidon GE, Amidon GL. In vitro dissolution methodology, mini-gastrointestinal simulator (mGIS), predicts better in vivo dissolution of a weak base drug, dasatinib. Eur J Pharm Sci. 2015;76:203-12. doi: 10.1016/j.ejps.2015.05.013, PMID 25978875.

  41. Garbacz G, Wedemeyer RS, Nagel S, Giessmann T, Monnikes H, Wilson CG, Siegmund W, Weitschies W. Irregular absorption profiles observed from diclofenac extended-release tablets can be predicted using a dissolution test apparatus that mimics in vivo physical stresses. Eur J Pharm Biopharm. 2008;70(2):421-8. doi: 10.1016/j.ejpb.2008.05.029, PMID 18582568.

  42. Koziolek M, Gorke K, Neumann M, Garbacz G, Weitschies W. Development of a bio-relevant dissolution test device simulating mechanical aspects present in the fed stomach. Eur J Pharm Sci. 2014;57(57):250-6. doi: 10.1016/j.ejps.2013.09.004, PMID 24051217.

  43. Li ZQ, He X, Gao XM, Xu YY, Wang YF, Gu H, Ji RF, Sun SJ. Study on dissolution and absorption of four dosage forms of isosorbide mononitrate: level a in vitro-in vivo correlation. Eur J Pharm Biopharm. 2011;79(2):364-71. doi: 10.1016/j.ejpb.2011.04.015, PMID 21569844.

  44. Shi Y, Gao P, Gong Y, Ping H. Application of a biphasic test for characterization of in vitro drug release of immediate-release formulations of celecoxib and its relevance to in vivo absorption. Mol Pharm. 2010;7(5):1458-65. doi: 10.1021/mp100114a, PMID 20704265.

  45. Vertzoni M, Diakidou A, Chatzilias M, Soderlind E, Abrahamsson B, Dressman JB, Reppas C. Biorelevant media to simulate fluids in the ascending colon of humans and their usefulness in predicting intracolonic drug solubility. Pharm Res. 2010;27(10):2187-96. doi: 10.1007/s11095-010-0223-6, PMID 20676736.

  46. Kou D, Zhang C, Yiu H, Ng T, Lubach JW, Janson M, Mao C, Durk M, Chinn L, Winter H, Wigman L, Yehl P. In vitro, in silico, and in vivo assessments of intestinal precipitation and its impact on the bioavailability of a BCS class 2 basic compound. Mol Pharm. 2018;15(4):1607-17. doi: 10.1021/acs.molpharmaceut.7b01143, PMID 29522347.

  47. Buchwald P. Direct, differential-equation-based in vitro-in vivo correlation (IVIVC) method. J Pharm Pharmacol. 2003;55(4):495-504. doi: 10.1211/002235702847, PMID 12803771.

  48. Tsume Y. Dissolution effect of gastric and intestinal pH fora BCS class II drug, pioglitazone: new in vitro dissolution system to predict in vivo dissolution. J Bioequiv Availab 2013;5(6):224-7. doi: 10.4172/jbb.1000162.

  49. Sirisuth N, Eddington ND. In vitro in vivo correlations, systemic methods for the development and validation of an IVIVC metoprolol and naproxen drug examples. Int J Generic Drugs. 2002;3:250-8.

  50. Zaborenko N, Shi Z, Corredor CC, Smith Goettler BM, Zhang L, Hermans A, Neu CM, Alam MA, Cohen MJ, Lu X, Xiong L, Zacour BM. First-principles and empirical approaches to predicting in vitro dissolution for pharmaceutical formulation and process development and for product release testing. AAPS J. 2019;21(3):32. doi: 10.1208/s12248-019-0297-y, PMID 30790200.

  51. Mitra A, Kesisoglou F, Dogterom P. Application of absorption modeling to predict bioequivalence outcome of two batches of etoricoxib tablets. AAPS PharmSciTech. 2015;16(1):76-84. doi: 10.1208/s12249-014-0194-8, PMID 25182387.

  52. Dokoumetzidis A, Macheras P. A century of dissolution research: from noyes and whitney to the biopharmaceutics classification system. Int J Pharm. 2006;321(1-2):1-11. doi: 10.1016/j.ijpharm.2006.07.011, PMID 16920290.

  53. Chen Y, Jiao T, McCall TW, Baichwal AR, Meyer MC. Comparison of four artificial neural network software programs used to predict the in vitro dissolution of controlled-release tablets. Pharm Dev Technol. 2002;7(3):373-9. doi: 10.1081/pdt-120005733, PMID 12229268.

  54. Lennernas H, Lindahl A, Van Peer AV, Ollier C, Flanagan T, Lionberger R, Nordmark A, Yamashita S, Yu L, Amidon GL, Fischer V, Sjogren E, Zane P, McAllister M, Abrahamsson B. In vivo predictive dissolution (IPD) and biopharmaceutical modeling and simulation: future use of modern approaches and methodologies in a regulatory context. Mol Pharm. 2017;14(4):1307-14. doi: 10.1021/acs.molpharmaceut. 6b00824, PMID 28195732.

  55. Carino SR, Sperry DC, Hawley M. Relative bioavailability estimation of carbamazepine crystal forms using an artificial stomach-duodenum model. J Pharm Sci. 2006;95(1):116-25. doi: 10.1002/jps.20495. PMID 16315223.

  56. Vatier J, Celice Pingaud C, Farinotti R. Interests of the ‘artificial stomach’ techniques to study antacid formulations: comparison with in vivo evaluation. Fundam Clin Pharmacol. 1998;12(6):573-83. doi: 10.1111/j.1472-8206.1998.tb00989.x, PMID 9818289.

  57. Vatier J, Malikova Sekera E, Vitre MT, Mignon M. An artificial stomach-duodenum model for the in vitro evaluation of antacids. Aliment Pharmacol Ther. 1992;6(4):447-58. doi: 10.1111/j.1365-2036.1992.tb00558.x, PMID 1420737.

  58. Polster CS, Atassi F, Wu SJ, Sperry DC. Use of artificial stomach-duodenum model for investigation of dosing fluid effect on clinical trial variability. Mol Pharm. 2010;7(5):1533-8. doi: 10.1021/mp100116g, PMID 20669969.

  59. Garbacz G, Kandzi A, Koziolek M, Mazgalski J, Weitschies W. Release characteristics of quetiapine fumarate extended-release tablets under bio relevant stress test conditions. AAPS PharmSciTech. 2014;15(1):230-6. doi: 10.1208/s12249-013-0050-2, PMID 24297600.

  60. Garbacz G, Klein S, Weitschies W. A biorelevant dissolution stress test device - background and experiences. Expert Opin Drug Deliv. 2010;7(11):1251-61. doi: 10.1517/ 17425247.2010.527943, PMID 20977290.

  61. Alegria A, Garcia Llatas G, Cilla A. Static digestion models: general introduction. In: Verhoeckx K, Cotter P, Lopez Exposito I, Kleiveland C, Lea T, Mackie A, Requena T, Swiatecka D, Wichers H, editors. The impact of food bio-actives on health: in vitro and ex vivo models. The Netherlands: TNO-Zeist; 2015. p. 3-15.

  62. Dupont D, Alric M, Blanquet Diot S, Bornhorst G, Cueva C, Deglaire A, Denis S, Ferrua M, Havenaar R, Lelieveld J, Mackie AR, Marzorati M, Menard O, Minekus M, Miralles B, Recio I, Van den Abbeele PVD. Can dynamic in vitro digestion systems mimic the physiological reality? Crit Rev Food Sci Nutr. 2019;59(10):1546-62. doi: 10.1080/10408398.2017.1421900, PMID 29359955.

  63. Barker R, Abrahamsson B, Kruusmägi M. Application and validation of an advanced gastrointestinal in vitro model for the evaluation of drug product performance in pharmaceutical development. J Pharm Sci. 2014;103(11):3704-12. doi: 10.1002/jps.24177, PMID 25223814.

  64. Naylor TA, Connolly PC, Martini LG, Elder D, Minekus M, Havenaar R, Zeijdner E. Use of a gastro-intestinal model and GastroPLUS for the prediction of in vivo performance. J Appl Ther Res. 2006;6(1):15-9.

  65. O’Driscoll CMO, Griffin BT. Biopharmaceutical challenges associated with drugs with low aqueous solubility-the potential impact of lipid-based formulations. Adv Drug Deliv Rev. 2008;60(6):617-24. doi: 10.1016/j.addr.2007.10.012, PMID 18155800.

  66. Kuentz M. Lipid-based formulations for oral delivery of lipophilic drugs. Drug Discov Today Technol. 2012;9(2):e97-e104. doi: 10.1016/j.ddtec.2012.03.002.

  67. Müllertz A, Ogbonna A, Ren S, Rades T. New perspectives on lipid and surfactant-based drug delivery systems for oral delivery of poorly soluble drugs. J Pharm Pharmacol. 2010;62(11):1622-36. doi: 10.1111/j.2042-7158.2010.01107.x, PMID 21039546.

  68. Mu H, Holm R, Mullertz A. Lipid-based formulations for oral administration of poorly water-soluble drugs. Int J Pharm. 2013;453(1):215-24. doi: 10.1016/j.ijpharm.2013.03.054, PMID 23578826.

  69. Hauss DJ. Oral lipid-based formulations. Adv Drug Deliv Rev. 2007;59(7):667-76. doi: 10.1016/j.addr.2007.05.006, PMID 17618704.

  70. Feeney OM, Crum MF, McEvoy CL, Trevaskis NL, Williams HD, Pouton CW, Charman WN, Bergstrom CAS, Porter CJH. 50 y of oral lipid-based formulations: Provenance, progress and future perspectives. Adv Drug Deliv Rev. 2016;101:167-94. doi: 10.1016/j.addr.2016.04.007.

  71. Griffin BT, Kuentz M, Vertzoni M, Kostewicz ES, Fei Y, Faisal W, Stillhart C, O’Driscoll CM, Reppas C, Dressman JB. Comparison of in vitro tests at various levels of complexity for the prediction of in vivo performance of lipid-based formulations: case studies with fenofibrate. Eur J Pharm Biopharm. 2014;86(3):427-37. doi: 10.1016/j.ejpb.2013.10.016, PMID 24184675.

  72. Stillhart C, Kuentz M. Trends in the assessment of drug supersaturation and precipitation in vitro using lipid-based delivery systems. J Pharm Sci. 2016;105(9):2468-76. doi: 10.1016/j.xphs.2016.01.010, PMID 26935881.

  73. Pouton CW, Porter CJH. Formulation of lipid-based delivery systems for oral administration: materials, methods and strategies. Adv Drug Deliv Rev. 2008;60(6):625-37. doi: 10.1016/j.addr.2007.10.010, PMID 18068260.

  74. Strickley RG. Solubilizing excipients in oral and injectable formulations. Pharm Res. 2004;21(2):201-30. doi: 10.1023/b:pham.0000016235.32639.23, PMID 15032302.

  75. Pouton CW. Formulation of poorly water-soluble drugs for oral administration: physicochemical and physiological issues and the lipid formulation classification system. Eur J Pharm Sci. 2006;29(3-4):278-87. doi: 10.1016/j.ejps.2006.04.016, PMID 16815001.

  76. Williams HD, Sassene P, Kleberg K, Calderone M, Igonin A, Jule E, Vertommen J, Blundell R, Benameur H, Mullertz A, Porter CJH, Pouton CW, Communicated on Behalf of the LFCS Consortium. Toward the establishment of standardized in vitro tests for lipid-based formulations, part 4: Proposing a new lipid formulation performance classification system. J Pharm Sci. 2014;103(8):2441-55. doi: 10.1002/jps.24067, PMID 24985238.

  77. Pouton CW. Lipid formulations for oral administration of drugs: non-emulsifying, self-emulsifying and ‘self-micro emulsifying drug delivery systems. Eur J Pharm Sci. 2000;11(2) Suppl 2:S93-8. doi: 10.1016/s0928-0987(00)00167-6, PMID 11033431.

  78. Williams HD, Sassene P, Kleberg K, Bakala-N’Goma JC, Calderone M, Jannin V, Igonin A, Partheil A, Marchaud D, Jule E, Vertommen J, Maio M, Blundell R, Benameur H, Carriere F, Mullertz A, Porter CJH, Pouton CW. Toward the establishment of standardized in vitro tests for lipid-based formulations, part 1: Method parameterization and comparison of in vitro digestion profiles across a range of representative formulations. J Pharm Sci. 2012;101(9):3360-80. doi: 10.1002/jps.23205, PMID 22644939.

  79. Stillhart C, Imanidis G, Griffin BT, Kuentz M. Biopharmaceutical modeling of drug supersaturation during lipid-based formulation digestion considering an absorption sink. Pharm Res. 2014;31(12):3426-44. doi: 10.1007/s11095-014-1432-1, PMID 24962509.

  80. Thomas N, Richter K, Pedersen TB, Holm R, Müllertz A, Rades T. In vitro lipolysis data does not adequately predict the in vivo performance of lipid-based drug delivery systems containing fenofibrate. AAPS J. 2014;16(3):539-49. doi: 10.1208/s12248-014-9589-4, PMID 24687210.

  81. Sassene PJ, Michaelsen MH, Mosgaard MD, Jensen MK, Van Den Broek EVD, Wasan KM, Mu H, Rades T, Müllertz A. In vivo precipitation of poorly soluble drugs from lipid-based drug delivery systems. Mol Pharm. 2016;13(10):3417-26. doi: 10.1021/acs.molpharmaceut.6b00413, PMID 27533712.

  82. Kilic M, Dressman J. A simplified method to screen for in-vivo performance of oral lipid formulations. J Pharm Pharmacol. 2014;66(5):615-23. doi: 10.1111/jphp.12182, PMID 24313318.

  83. Tsume Y, Igawa N, Drelich AJ, Ruan H, Amidon GE, Amidon GL. The in vivo predictive dissolution for immediate release dosage of donepezil and danazol, BCS class IIc drugs, with the GIS and the USP II with biphasic dissolution apparatus. J Drug Deliv Sci Technol. 2020;56(Part B):3-36. doi: 10.1016/j.jddst.2019.01.035.

  84. Jantratid E, Dressman JB. Biorelevant dissolution media simulating the proximal human gastrointestinal tract: A update. Diss Technol. 2009;16(3):21-5. doi: 10.14227/DT160309P21.

  85. Fan X, Shi S, He J, Deng J, Ji J. Development of in vivo predictive pH-gradient biphasic dissolution test for weakly basic drugs: optimization by orthogonal design. Diss Technol. 2021;28(3):24-9. doi: 10.14227/DT280321P24.

  86. Klein S. The use of biorelevant dissolution media to forecast the in vivo performance of a drug. AAPS J. 2010;12(3):397-406. doi: 10.1208/s12248-010-9203-3, PMID 20458565.

  87. Franc A, Museliłk J, Gonec R, Vetchy D. Biphasic dissolution method for quality control and assurance of drugs containing active substances in the form of weak acid salts. Acta Pharm. 2016;66(1):139-45. doi: 10.1515/acph-2016-0010, PMID 26959550.

  88. Carlert S, Paalsson A, Hanisch G, von Corswant CV, Nilsson C, Lindfors L, Lennernas H, Abrahamsson B. Predicting intestinal precipitation--a case example for a basic BCS class II drug. Pharm Res. 2010;27(10):2119-30. doi: 10.1007/s11095-010-0213-8, PMID 20717839.

  89. Amidon KS, Langguth P, Lennernäs H, Yu L, Amidon GL. Bioequivalence of oral products and the biopharmaceutics classification system: science, regulation, and public policy. Clin Pharmacol Ther. 2011;90(3):467-70. doi: 10.1038/clpt.2011.109, PMID 21775984.

  90. Anishetty R, Singh SK, Garg V, Singare DS, Yadav A, Gulati M, Kumar B, Pandey NK, Narang R, Mittal A. Discriminatory potential of biphasic medium over compendial and biorelevant medium for assessment of dissolution behavior of tablets containing meloxicam nanoparticles. Asian J Pharm Clin Res. 2016;9(4):253-64.

  91. Manikandan M, Kannan K. Study on in vivo release and in vivo absorption of camptothecin-loaded polymeric nanoparticles: level a in vitro-in vivo correlation. Asian J Pharm Clin Res. 2016;9(3):71-4.

  92. Jaber E, Emami J. In vitro-in vivo correlation: from theory to applications. J Pharm Pharm Sci. 2006;9(2):169-89. PMID 16959187.