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Is the full potential of the biopharmaceutics classification system reached?
Accepted Manuscript Is the Full Potential of the Biopharmaceutics Classification System Reached? Christel A. S. Bergström, Sara B. E. Andersson, Jonas H. Fagerberg, Gert Ragnarsson, Anders Lindahl PII: DOI: Reference:
S0928-0987(13)00364-3 http://dx.doi.org/10.1016/j.ejps.2013.09.010 PHASCI 2877
To appear in:
European Journal of Pharmaceutical Sciences
Received Date: Revised Date: Accepted Date:
3 June 2013 5 September 2013 15 September 2013
Please cite this article as: S. Bergström, C.A., E. Andersson, S.B., Fagerberg, J.H., Ragnarsson, G., Lindahl, A., Is the Full Potential of the Biopharmaceutics Classification System Reached?, European Journal of Pharmaceutical Sciences (2013), doi: http://dx.doi.org/10.1016/j.ejps.2013.09.010
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Is the Full Potential of the Biopharmaceutics Classification System Reached? Christel A. S. Bergström,a,* Sara B. E. Andersson,a Jonas H. Fagerberg,a Gert Ragnarsson,b Anders Lindahl b a) Department of Pharmacy, Uppsala University, Biomedical Centre P.O. Box 580, SE-751 23 Uppsala, Sweden b) Medical Products Agency, Scientific Expertise, Box 26,SE- 751 03 Uppsala, Sweden
*Address correspondence to:
Christel A. S. Bergström, PhD Department of Pharmacy Uppsala University Biomedical Centre, P.O. Box 580 SE-751 23 Uppsala, Sweden
Email: [email protected] Phone: +46 – 18 471 4118
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Graphical abstract
Abstract In this paper we analyze how the Biopharmaceutics Classification System (BCS) has been used to date. A survey of the literature resulted in a compilation of 242 compounds for which BCS classes were reported. Of these, 183 compounds had been reported to belong to one specific BCS class whereas 59 compounds had been assigned to multiple BCS classes in different papers. Interestingly, a majority of the BCS class 2 compounds had fraction absorbed (FA) values > 85%, indicating that they were completely absorbed after oral administration. Solubility was computationally predicted at pH 6.8 for BCS class 2 compounds to explore the impact of the pH of the small intestine, where most of the absorption occurs on the solubility. In addition, the solubilization capacity of lipid aggregates naturally present in the intestine was studied computationally and experimentally for a subset of 12 compounds. It was found that all acidic compounds with FA>85% were completely
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dissolved in the pH of the small intestine. Further, lipids at the concentration used in fasted state simulated intestinal fluid (FaSSIF) dissolved the complete dose given of the most lipophilic (logD6.5>3) compounds studied. Overall, biorelevant dissolution media (pure buffer of intestinal pH or FaSSIF) identified that for 18 of the 29 BCS class 2 compounds with FA>85% the complete dose given orally would be dissolved. These results indicate that a more relevant pH restriction for acids and/or dissolution medium with lipids present better forecast solubility-limited absorption in vivo than the presently used BCS solubility criterion. The analysis presented herein further strengthens the discussion on the requirement of more physiologically relevant dissolution media for the in vitro solubility classification performed to reach the full potential of the BCS.
Keywords: Biopharmaceutics classification system, solubility, biorelevant dissolution, dose number, poorly soluble, in silico, in vitro
1. Introduction The Biopharmaceutics Classification System (BCS) categorizes drug molecules on the basis of their permeability and solubility (Amidon et al., 1995), and serves as a tool to identify compounds eligible for a biowaiver of in vivo bioequivalence (FDA, 2000; WHO, 2006; EMA, 2010) The BCS has gained much attention because of its potential to reduce the need for clinical studies during e.g. life cycle management and production of generic compounds. It is also used to identify potential absorption problems after oral administration. The BCS has evolved in many different directions. As an example, it is used to signal need for formulation design (BCS class 2/4) and/or chemical modifications (BCS class3/4) to improve absorption (Pouton, 2006). It has also resulted in the development of the Biopharmaceutics Drug Disposition Classification system (BDDCS) (Wu and Benet, 2005) and the Developability Classification system (DCS) (Butler and Dressman, 2010) . The BCS and BDDCS are based
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on the same solubility definition, but the BDDCS uses metabolism instead of permeability in its classification. It is proposed to be used to identify disposition and drug-drug interaction patterns primarily in the intestine and liver (Wu and Benet, 2005; Benet, 2013). In contrast, the BCS and DCS have the same permeability definition, but the DCS is based on larger volumes for the solubility definition. Butler and Dressman proposed that 500 mL, rather than the 250 mL used in the BCS definition, better reflects volumes in the small intestine (Butler and Dressman, 2010). At the same time, the larger volume (500 mL) is believed to compensate for the disappearance of drug from the intestine (i.e. the permeability) during drug dissolution and solubility assessment. The DCS is particularly useful for indicating whether BCS class 2 compounds are dissolution-rate limited or solubility limited; these are important factors for guiding further formulation design.
From a regulatory perspective the BCS provides a scientific framework to determine whether bioequivalence (BE) studies are needed for new formulations of drugs or if in vitro data suggest a biowaiver to be designated. BE is defined as the compound having an AUC and Cmax in the interval of 80-125% of the reference product. The 90% confidence interval for the test/reference ratio should be contained within this range (EMA, 2010). Attempts have been made to calculate the economic benefits of BCS biowaivers (Cook and Bockbrader, 2002; Cook et al., 2010). It is difficult to perform such economical calculations because of assumptions such as the frequency of compounds in each BCS class and the number of subjects needed to prove BE (which is related to the variability in AUC and Cmax expected for a drug). The economic impact is also higher in Europe as compared to the U.S. due to the EMA extension of biowaivers to BCS class 3 drug products. However, taking into account both BCS class 1 and class 3 biowaivers, it is estimated that 128-147 million dollars could be saved each year in reduced clinical study costs, out of which approximately 66-76 million
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dollars reflect saved costs for BCS class 1 (Cook et al., 2010). Other benefits of increased use of the BCS-based biowaivers are that unnecessary testing in humans is avoided and that the new products reach the market faster. Despite these obvious advantages, pharmaceutical companies appear reluctant to pursue BCS-based biowaivers. In 2010 (i.e. 10 years after the FDA introduced the guidelines for BCS-based biowaivers), Cook et al. reported that the number of applications for biowaivers had started to increase; nevertheless, the FDA had only designated 27 compounds a BCS class 1 biowaiver by this year. In a recent analysis at the Swedish Medical Products Agency, it was revealed that only 10% (21 of 208) and 17% (28 of 167) of the oral solid drug products (immediate release) had applied for a BCS-based biowaiver in 2011 and 2012, respectively. These are low percentages, given that approximately two-thirds of marketed compounds are BCS class 1 or 3, (Takagi et al., 2006; Cook et al., 2010) for which the companies can file for biowaivers in Europe. From the above presented analysis it becomes clear that the pharmaceutical industry currently does not take full advantage of the BCS. One reason may be that not all regulatory authorities approve biowaivers based on the BCS. Although the pharmaceutical industry is global, intending to target all markets so as to fully exploit the investments they have made in their drug products, the regulatory authorities are not equally globalized. For instance, the EMA and the FDA approve biowaivers based on the BCS (although using slightly different criteria), but Japan does not.
The current BCS hence seems not to be fully exploited in that the number of biowaivers designated still is low. However, there may also be room for increased usage of BCS-based biowaivers by changing the in vitro profiles required for active pharmaceutical ingredients (APIs) to be defined as BCS class 1 compounds. The current definition for solubility has, in addition to the amendments made through the DCS system, been subject to debate. The pH-
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interval introduced by the FDA has been discussed, because of the relatively high pH of 7.5 used as the upper limit of the pH interval in which the dose needs to be soluble, and as a response to this discussion the EMA guideline uses a pH interval from 1 to 6.8. The EMA guideline also differs from that of the FDA for the fraction absorbed (FA) level used to signal high permeability. The EMA sets it at >85% whereas FDA uses >90% (FDA, 2000; EMA, 2010). Further it has been argued that acids are unfairly assessed with the pH-interval used in the solubility definition, since the lower limit of pH 1 (reflecting the pH in the stomach) classifies a large number of acids as BCS class 2 although they display high solubility in the small intestine in vivo which is the compartment where most of the absorption takes place (Dressman et al., 2001; Yazdanian et al., 2004). This issue has been addressed in the WHO guidance. Pharmaceutical products containing BCS Class 2 weak acids that are soluble at pH 6.8 are according to WHO eligible for the biowaiver procedure, provided that they dissolve rapidly at pH 6.8 and similarly to the comparator product at pH 1.2 and 4.5 (WHO, 2006). Following the debate on which pH-range to use, more physiologically relevant dissolution media in which the pure buffers are exchanged to buffers spiked with e.g. bile components to also address solubilization likely to occur in vivo, have been suggested (Dressman et al., 2001). It is well-known that lipophilic drug molecules may gain extensively in solubility when dissolved in more physiologically relevant dissolution media such as fasted or fed state simulated intestinal fluids (FaSSIF and FeSSIF, respectively) (Mithani et al., 1996; Fagerberg et al., 2010; Soderlind et al., 2010; Fagerberg et al., 2012), and that the obtained solubility in these simulated fluids better reflect the solubility measured in human intestinal fluids for such compounds (Nicolaides et al., 2001; Söderlind et al., 2010; Clarysse et al., 2011). Hence, the use of simple buffers instead of biorelevant dissolution media may result in a significant underestimation of the concentration reached in the intestinal fluid. The solubility definition of the BCS has been vigorously discussed in particular with regard to pH and lipid content but
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also from the volume perspective (Dressman et al., 2001; Yu et al., 2002; Rinaki et al., 2003; Polli et al., 2004; Rinaki et al., 2004; Yazdanian et al., 2004; Lennernas and Abrahamsson, 2005; Polli et al., 2008; Davit et al., 2012; Varma et al., 2012). In this study we explored the current solubility definition of BCS by studying 242 compounds for which the BCS class could be extracted from the literature. Of these, focus was set on the 58 BCS class 2 compounds for which the impact of pH and lipids present in the fasted state intestinal fluid on the observed solubility was investigated and analysed with an in vivo FA perspective.
2. Materials and methods 2.1 Dataset and Characteristics
A set of compounds (n=242) was extracted from published datasets for which BCS classifications existed (Table 1 and Table 2) (Bergstrom et al., 2003; Lindenberg et al., 2004; Yazdanian et al., 2004; Wu and Benet, 2005; Yang et al., 2007; Butler and Dressman, 2010; Kleberg et al., 2010; Ramirez et al., 2010). Thereafter, we focused on BCS class 2 compounds (n=58). For these substances, molecular descriptors and e.g. pKa, logP and logD6.5 were calculated with the software ADMET Predictor (SimulationsPlus, CA). Fraction absorbed (FA) values were taken from the literature (Table 3). If FA data were not available, oral bioavailability (F) was used as a surrogate for those BCS class 2 compounds with F >85%. Our decision was based on the EMA guideline which uses a FA of 85% as the high permeability cut-off (EMA, 2010). Furthermore, if F is 0.85 or more, then the FA must be 85% or greater.
2.2 Materials
Carbamazepine , dapsone, lansoprazole, nevirapine, ofloxacin, phenazopyridine, praziquantel and rifampicin were purchased from SigmaAldrich (St. Louis, MO), cisapride and rofecoxib were purchased from Toronto Research Chemicals (North York, ON, Canada) and lorazepam
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was purchased from Larodan Fine Chemicals (Malmö, Sweden). For the studies of solubility in FaSSIF, SIF powder original was used which was kindly donated by biorelevant.com.
2.3 Computational Prediction of Biorelevant Solubility: Impact of pH and Lipids
The solubility at pH 1.0 (reflecting stomach) and 6.8 (reflecting small intestine) was predicted with ADMET Predictor (SimulationsPlus, CA). The potential of lipids to further increase the solubility in comparison to a pure buffer was forecasted using the solubilization ratio (SR) and two different computational models in ADMET Predictor. This procedure was used as we did not have large enough datasets at hand to validate the accuracy of the different solubility models in ADMET Predictor. Therefore, the predicted logDpH6.5 values (distribution of a compound between octanol and water at pH 6.5 ) from ADMET Predictor were used to calculate the SR at pH 6.5 (the pH of FaSSIF) using equation 1 logSR=0.39logDpH6.5+3.52
Eq. 1
The obtained SR was then used to calculate the solubilization capacity of the buffer and the FaSSIF using equation 2
SR=SCbs/SCaq
Eq. 2
where SCbs and SCaq are the solubilization capacity of the bile salt and water, respectively. Equation 1 was established by Fagerberg and colleagues in 2010 as a further development of the SR and its relationship to logP presented by Mithani and co-workers in 1996 (Mithani et al., 1996; Fagerberg et al., 2010). In the following, both the FaSSIF solubility and the corresponding solubility in a pure buffer with the same pH as FaSSIF (pH 6.5) were predicted with ADMET Predictor. In step 1, the predicted logDpH6.5 values were used to calculate the logSR (eq. 1) and the resulting SR was used together with predicted FaSSIF solubility (e.g.
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buffer pH 6.5 with 0.75 mM phospholipid and 3.0 mM taurocholate) to calculate the concentration possible to achieve in the buffer (eq. 2). The calculation was repeated in a second round, where instead of predicted FaSSIF, the predicted aqueous solubility at pH 6.5 was used in equation 2 to calculate the solubility in FaSSIF.
The dose number (Do) was calculated from Do=(M0/V0)/Cs
Eq. 3
where M0 is the dose of the compound (here set to the maximum dose strength), V0 is the volume (here set to 250 mL) and Cs is the solubility in the medium used (i.e. FaSSIF or buffer pH 6.5) (Oh et al., 1993). The Do was predicted based on the values obtained from equation 1 and 2.
2.4 Experimental Determinations of Solubility in FaSSIF and Buffer
For the twelve non-acidic BCS 2 compounds with a FA >85% the apparent solubility was measured in FaSSIF and corresponding blank buffer (i.e. FaSSIF without 3 mM taurocholate and 0.75 mM lecithin) to investigate the solubilization capacity of the mixed lipid aggregates present in the FaSSIF. SIF Powder original was used for the production of FaSSIF according to the protocol provided by the manufacturer. For eight of the compounds the apparent solubility was measured using the µDISS Profiler (pION Inc, MA) at 37°C (n=3), according to a previously published protocol (Fagerberg et al., 2010). The pH of the suspensions (buffer and FaSSIF) remained constant during the course of the experiment. For lorazepam, ofloxacin and rifampicin, a small-scale shake flask method was used for the solubility assessment due to low amount of compound available (lorazepam) or high solubility (ofloxacin and rifampicin). The solubility assay used 300 µL of buffer or FaSSIF and was run in duplicate. Excess amount of solid material was weighed into glass vials producing a suspension throughout the
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experiment. Pre-heated solvent was added and the vials were put on a plate-shaker (300 rpm) in an incubator (37°C) and equilibrated for 24 hours. The vials were then centrifuged in a temperature controlled Eppendorf centrifuge Model 5043 at 10,000 g, 37°C and for 10 minutes and the supernatant was sampled, diluted and determined for concentration in a Tecan Sapphire plate reader (Tecan Group, Switzerland).The pH of the suspensions (buffer and FaSSIF) remained constant during the course of the experiment. Solubility data of tamoxifen in buffer and FaSSIF were taken from a previous study performed in-house (Fagerberg et al., 2010).
All solubility data are presented as the mean±standard deviation. Statistical difference between the apparent solubility in buffer and FaSSIF was analysed with Student’s t-test. The obtained solubility data were used to calculate the Do according to equation 3.
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3. Results and Discussion Of the 242 BCS compounds extracted from the literature, 183 compounds had been classified as belonging to one single class (Table 1) and 59 were categorized as belonging to two different BCS classes in different publications (Table 2). The discrepancy between publications may be a result of the different guidelines provided by EMA and FDA or that a compound may be used in different maximum doses when indicated for the treatment of different diseases. As discussed in the introduction, there are different pH-intervals for solubility assessments and FA limits for permeability; however, dose also differs in the two guidelines. The EMA requires the maximum oral dose administered to be soluble whereas the FDA requires the maximum dose strength. For example, if the maximum dose strength of a tablet is 100 mg but it is possible to administer two tablets simultaneously, FDA would base the BCS classification on 100 mg whereas EMA would use 200 mg. Therefore, dependent on which of the two guidelines that is used for the classification, differences are likely to appear. A third reason for the differences found in the literature may be that different methods have been used when investigating solubility and permeability.
The 183 compounds designated to a single BCS class were distributed as follows: 38.2% class 1 compounds, 31.7% class 2 compounds, 26.2% class 3 compounds and 3.8% class 4 compounds (Table 1). For the remainder of this work the analysis focused on the compounds sorted as BCS class 2. These are the compounds for which in vivo conditions (by virtue of the pH gradient and/or the naturally available lipids in the intestine) can improve solubility to the extent that the complete dose becomes soluble. It was possible to extract FA from the literature for 49 of the BCS class 2 compounds (Table 3). The majority (n=29) had a reported FA >85%, 11 compounds had a FA of 50-84% and only 9 compounds had a reported FA <50%.
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The protolytic functions of the 58 class 2 compounds (Table 3) were equally distributed between acids (37.9%) and bases (37.9%), whereas 20.7% were non-ionizable. Only one ampholyte and one zwitterion were included in the dataset. The 29 BCS class 2 compounds with FA >85% were further analysed for their protolytic function. A large majority of these were acids (59%), whereas the remaining were composed of bases (21%), non-ionizable compounds (14%), and the zwitterion (3%) and ampholyte (3%). This shows that acidic compounds in many cases are classified as BCS 2 compounds even though they are absorbed to a high extent (>85%) indicating a considerable risk for incorrect classification of acids as being compounds with solubility-limited absorption.
Predicted buffer solubility clearly identified the impact of pH on solubility (Figure 1). For seven of the acidic BCS class 2 compounds a Do < 1 was predicted at pH 6.8 compared to a Do > 1 at pH 1.0. It should be noted that all BCS class 2 acids had FA > 85% (Table 3); hence, in vivo these acids are not likely to display solubility-limited absorption. As expected the pH dependency for bases resulted in lower Do at low pH. For many of the BCS class 2 bases Do was predicted to be less than 1 at pH 1 but showed Do > 1 at pH 6.8 (Figure 1). This trend may be useful to identify bases that risk precipitating when the gastric content empties into the higher pH found in the duodenum and jejunum. Indeed, several of these (e.g. albendazole, itraconazole and ketoconazole) have been shown to precipitate at least partially in the small intestine (Jung et al., 1998; Mellaerts et al., 2008; Psachoulias et al., 2011). The neutral compounds were, as expected, unaffected by the pH. For these compounds, it may be more relevant to investigate the impact of the mixed micelles secreted by bile on their solubility (Fagerberg et al., 2010; Fagerberg et al., 2012). In particular this is important for highly lipophilic compounds. Lipids have been suggested to significantly impact the solubility
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of drug molecules with a logP > 3 or a logD6.5 > 2 (Dressman and Reppas, 2000; Bergstrom et al., 2007; Fagerberg et al., 2010; Gamsiz et al., 2010; Ottaviani et al., 2010). We therefore analysed whether computational tools predicted lipids to be important for the solubility of the remaining 12 non-acidic BCS class 2 compounds. It was found that these models predicted most of them to have increased solubility in FaSSIF as compared to the corresponding buffer (Figure 2). The Do of tamoxifen (Figure 2a, prediction based on equations 1 and 2 using buffer solubility from ADMET Predictor) and cisapride (Figure 2b, prediction based on equation 1 and 2 using FaSSIF solubility from ADMET Predictor) were predicted to decrease to less than 1 in simulated intestinal fluids. This computational exercise indicated that lipids were likely to improve the solubility of a number of the compounds, maybe even to the extent allowing the oral dose to become completely dissolved. Furthermore, three of the 12 compounds had logP>3 and eight of them displayed logD6.5>2, and hence, also the lipophilicity profile of the compounds pointed at the likelihood of significant solubilization in the lipid aggregates naturally present in the intestinal fluid. We therefore experimentally determined the solubility in buffer and FaSSIF of these compounds and found that all of the compounds obtained equal or higher dissolution rate and apparent solubility in FaSSIF as compared to blank buffer (Figure 3a). As expected, the solubility in FaSSIF was equal to that observed in the buffer for ofloxacin, which was the most hydrophilic compound of the 12 studied with a logDpH6.5 of -0.5. For the most lipophilic compound, tamoxifen, the solubility in FaSSIF was 26-fold higher than in the corresponding buffer. The dose number was then investigated as a mean to link dose to solubility (Figure 3b). Indeed, for the compounds explored here, four of the 12 compounds obtained a Do <1 in FaSSIF. Two of these, lorazepam and ofloxacin, were also completely dissolved in the buffer, and hence, the experimental determinations to a large extent confirmed the computational predictions (Figure 3c and 3d). The other two compounds, rifampicin and tamoxifen, were the most lipophilic
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compounds measured in this study (logD6.5 ≥ 3.0), confirming the importance of having solubilizing aggregates present to better mimic in vivo dissolution of highly lipophilic compounds. All other compounds received a higher solubility in FaSSIF than in buffer, although it was not high enough to produce a Do below 1. To summarize, the computational and experimental efforts employed herein, which used more physiologically relevant conditions to investigate the dose possible to dissolve for BCS class 2 compounds, indicated complete dissolution in intestinal fluid for 18 of the 29 BCS class 2 compounds with FA >85%. When the DCS solubility criterion (i.e. 500 mL instead of 250 mL of solvent) was used additional two compounds (dapsone and phenazopyridine) received Do<1.
4. Conclusion In this study we identified that the BCS is not used to its full potential. The number of biowaivers filed for and designated is still, more than 10 years after the introduction of the regulatory guidelines, relatively low. We speculate that one reason for this is that the regulatory authorities are not globalized to the same extent as the pharmaceutical industry, shown by e.g. that biowaivers are not accepted by all authorities and by the differences in the guidelines by EMA, FDA and WHO. The computational and experimental analyses of BCS class 2 compounds identified that acidic drug molecules are at risk for being deemed having solubility-limited absorption by the current BCS classification although complete absorption is found in vivo. This further supports the WHO guidance in which pharmaceutical products are eligible for biowaivers if the API is a weak acid for which the oral dose is soluble at pH 6.8 and the dissolution profile (pH1.2, 4.5 and 6.8) is similar to the comparator product. In the light of our findings, this combination of a solubility measurement at pH 6.8 for weak acidic BCS class 2 compounds and a formulation dissolution performance in the pH interval of the gastrointestinal tract is an attractive solubility/dissolution profiling approach to eligible
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products for biowaivers. Further, lipids present in the intestine may increase the solubilization of compounds in the intestinal fluid and herein it was found that compounds having logDpH6.5 ≥ 3.0 had apparent solubility in FaSSIF allowing the complete oral dose to become soluble. However, a larger number of non-acidic poorly soluble compounds need to be studied to conclude on possible modifications of the BCS solubility definition for such compounds. In conclusion, a more relevant pH restriction for acids (i.e. assessing solubility solely at the pH of the small intestinal fluid) and/or physiologically relevant medium with lipids present, such as FaSSIF, identified that 18 of the 29 BCS class 2 compounds with FA >85% were expected to be completely dissolved in human intestinal fluid. From the analyses presented herein, globally harmonized guidelines and the implementation of more physiologically relevant media used in the in vitro assessments of solubility and dissolution testing are suggested as two important steps towards an increased number of designated biowaivers.
Acknowledgements Financial support from the Swedish Research Council (Grant 621-2008-3777) and The Swedish Agency for Innovation Systems (Grant 2010-00966) is highly appreciated. We are grateful to biorelevant.com for providing us the SIF original powder used for the solubility experiments, and Simulations Plus (Lancaster, CA) for providing us with a reference site license for the software ADMET Predictor.
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Disclaimer The views and conclusions presented in this paper represent those of the authors and not necessarily those of the Swedish Medical Product Agency where two of the authors are employed.
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Perez, M.A., Sanz, M.B., Torres, L.R., Avalos, R.G., Gonzalez, M.P., Diaz, H.G., 2004. A topological sub-structural approach for predicting human intestinal absorption of drugs. European journal of medicinal chemistry 39, 905-916. Polli, J.E., Abrahamsson, B.S., Yu, L.X., Amidon, G.L., Baldoni, J.M., Cook, J.A., Fackler, P., Hartauer, K., Johnston, G., Krill, S.L., Lipper, R.A., Malick, W.A., Shah, V.P., Sun, D., Winkle, H.N., Wu, Y., Zhang, H., 2008. Summary workshop report: bioequivalence, biopharmaceutics classification system, and beyond. The AAPS journal 10, 373-379. Polli, J.E., Yu, L.X., Cook, J.A., Amidon, G.L., Borchardt, R.T., Burnside, B.A., Burton, P.S., Chen, M.L., Conner, D.P., Faustino, P.J., Hawi, A.A., Hussain, A.S., Joshi, H.N., Kwei, G., Lee, V.H., Lesko, L.J., Lipper, R.A., Loper, A.E., Nerurkar, S.G., Polli, J.W., Sanvordeker, D.R., Taneja, R., Uppoor, R.S., Vattikonda, C.S., Wilding, I., Zhang, G., 2004. Summary workshop report: biopharmaceutics classification system--implementation challenges and extension opportunities. Journal of pharmaceutical sciences 93, 1375-1381. Pouton, C.W., 2006. Formulation of poorly water-soluble drugs for oral administration: physicochemical and physiological issues and the lipid formulation classification system. European journal of pharmaceutical sciences : official journal of the European Federation for Pharmaceutical Sciences 29, 278-287. Psachoulias, D., Vertzoni, M., Goumas, K., Kalioras, V., Beato, S., Butler, J., Reppas, C., 2011. Precipitation in and supersaturation of contents of the upper small intestine after administration of two weak bases to fasted adults. Pharmaceutical research 28, 3145-3158. Ramirez, E., Laosa, O., Guerra, P., Duque, B., Mosquera, B., Borobia, A.M., Lei, S.H., Carcas, A.J., Frias, J., 2010. Acceptability and characteristics of 124 human bioequivalence studies with active substances classified according to the Biopharmaceutic Classification System. British journal of clinical pharmacology 70, 694-702. Rinaki, E., Dokoumetzidis, A., Valsami, G., Macheras, P., 2004. Identification of biowaivers among Class II drugs: theoretical justification and practical examples. Pharmaceutical research 21, 15671572. Rinaki, E., Valsami, G., Macheras, P., 2003. Quantitative biopharmaceutics classification system: the central role of dose/solubility ratio. Pharmaceutical research 20, 1917-1925. Sanghvi, T., Ni, N., Mayersohn, M., Yalkowsky, S.H., 2003. Predicting passive intestinal absorption using a single parameter. Qsar Comb Sci 22, 247-257. Soderlind, E., Karlsson, E., Carlsson, A., Kong, R., Lenz, A., Lindborg, S., Sheng, J.J., 2010. Simulating fasted human intestinal fluids: understanding the roles of lecithin and bile acids. Molecular pharmaceutics 7, 1498-1507. Sugano, K., 2011. Fraction of a dose absorbed estimation for structurally diverse low solubility compounds. International journal of pharmaceutics 405, 79-89. Söderlind, E., Karlsson, E., Carlsson, A., Kong, R., Lenz, A., Lindborg, S., Sheng, J.J., 2010. Simulating fasted human intestinal fluids: Understanding the roles of lecithin and bile acids. Molecular pharmaceutics 7, 1498-1507. Takagi, T., Ramachandran, C., Bermejo, M., Yamashita, S., Yu, L.X., Amidon, G.L., 2006. A provisional biopharmaceutical classification of the top 200 oral drug products in the United States, Great Britain, Spain, and Japan. Molecular pharmaceutics 3, 631-643. Taubert, D., von Beckerath, N., Grimberg, G., Lazar, A., Jung, N., Goeser, T., Kastrati, A., Schomig, A., Schomig, E., 2006. Impact of P-glycoprotein on clopidogrel absorption. Clinical pharmacology and therapeutics 80, 486-501. Varma, M.V., Gardner, I., Steyn, S.J., Nkansah, P., Rotter, C.J., Whitney-Pickett, C., Zhang, H., Di, L., Cram, M., Fenner, K.S., El-Kattan, A.F., 2012. pH-Dependent solubility and permeability criteria for provisional biopharmaceutics classification (BCS and BDDCS) in early drug discovery. Molecular pharmaceutics 9, 1199-1212. WHO, 2006. Proposal to waive in vivo bioequivalence requirements for theWHOModel List of Essential Medicines immediate release, solid oral dosage forms. In: Fortieth report of the WHO Expert Committee on Specifications for Pharmaceutical Preparations. Geneva, World Health Organization. Technical Report Series, no. 937, Annex 8.
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Wu, C.Y., Benet, L.Z., 2005. Predicting drug disposition via application of BCS: transport/absorption/ elimination interplay and development of a biopharmaceutics drug disposition classification system. Pharmaceutical research 22, 11-23. Yang, Y., Faustino, P.J., Volpe, D.A., Ellison, C.D., Lyon, R.C., Yu, L.X., 2007. Biopharmaceutics classification of selected beta-blockers: solubility and permeability class membership. Molecular pharmaceutics 4, 608-614. Yazdanian, M., Briggs, K., Jankovsky, C., Hawi, A., 2004. The "high solubility" definition of the current FDA Guidance on Biopharmaceutical Classification System may be too strict for acidic drugs. Pharmaceutical research 21, 293-299. Yu, L.X., Amidon, G.L., Polli, J.E., Zhao, H., Mehta, M.U., Conner, D.P., Shah, V.P., Lesko, L.J., Chen, M.L., Lee, V.H., Hussain, A.S., 2002. Biopharmaceutics classification system: the scientific basis for biowaiver extensions. Pharmaceutical research 19, 921-925. Zhu, C., Jiang, L., Chen, T.M., Hwang, K.K., 2002. A comparative study of artificial membrane permeability assay for high throughput profiling of drug absorption potential. European journal of medicinal chemistry 37, 399-407.
Figure legends Figure 1. Computationally predicted pH-dependent dose number for BCS class 2 compounds. The graph presents the logarithm of the dose number (logDo), and hence, logDo of 0 corresponds to Do of 1 (here marked with the dashed line). For compounds with a Do less than this value the maximum dose given is completely soluble. The compounds are grouped based on their protolytic function with acids, bases, non-ionizable, ampholyte and zwitterion displayed from left to right. Figure 2. Prediction of solubility in buffer and FaSSIF taking use of the solubilization ratio (SR). a) Solubility in FaSSIF calculated by equation 1 and 2 using predicted buffer solubility from ADMET b) Solubility in buffer calculated by equation 1 and 2 using predicted solubility in FaSSIF from ADMET. Figure 3. Experimentally determined solubility and dose number for non-acidic compounds with FA > 85%.
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a) Solubility data, here presented as the mean±standard deviation. The lipophilicity of the compounds (logD6.5) increases to the right. p<0.05 (*), p<0.01 (**), p<0.001 (***). b) Of the 12 compounds investigated, 4 compounds had a Do<1 in buffer and/or FaSSIF. When the DCS volume criterion was used (i.e. 500 mL instead of the BCS volume of 250 mL) also phenazopyridine and dapsone obtained Do<1 in FaSSIF, but not in the buffer. c) Experimentally determined Do (grey), Do calculated based on ADMET Predictor buffer model (white) and buffer Do calculated based on solubilization ratio (eq. 1 and 2). d) Experimentally determined Do (grey), Do calculated based on ADMET Predictor FaSSIF model (white) and FaSSIF Do calculated based on solubilization ratio (eq. 1 and 2).
Tables Table 1. Compounds reported as belonging to only one BCS class in the survey (n=183). Class 2 (n=58)
Class 1 (n=70) Amlodipinea Benznidazolec Bisoprolola Buspironeb Caffeineb Chloroquinec a Citalopram Cyclophosphamidec Desipraminef Diazepamc c Diethylcarbamazine Diltiazemb Diphenhydramineb Disopyridamideb a Donepezil Doxazosina Doxepinb Doxycyclinec,f Enalaprila Ephedrineb Ergonovinef Ethinyl estradiolf Ethosuximidec Fluoxetineb
Class 3 (n=48)
Glucoseb Imipramineb Ketorolacd Labetalole Levodopac Levonorgestrelc b Lidocaine Lithiumc Lomefloxacinb Loratidinea b Meperidine Metoprololf Metronidazolec Midazolamb b Minocycline Mirtazapinea Mistoprostolb Nicotinamidec Norethiseronec Ondansetrona Phenobarbitalb,c Phenylalanineb Prednisolonec Primaquinec,f
Proguanilc Promazineb Propranololc Pyridoxinec Quinaprila Quinidineb a Ramipril Riboflavinc Rosiglitazoneb Salbutamolc b Salicylic acid Sertralinea Sildenafila Sotalole c Stavudine Terbinafina Theophyllinec,f Timolole Tramadola Venlafaxinea Zidovudinec Zolpidema
Aceclofenaca Albendazolec Amiodaroneb Atorvastatinb Azithromycinb Carbamazepinc a Carvedilol Celecoxibg Cisaprideb Clofaziminec a Clopidogrel Cyclosporineb Danazolg Dapsonec g Diclofenac sodium Diflunisalb Dipyridamoleh Ebastinea Fenoprofend Flurbiprofenb Glipizideb Griseofulvinc
Class 4 (n=7)
Ibuprofenc Indomethacinf Iopanoic acidc Irbesartana Itraconazoleb Ketoconazoleb a Lamotrigine Lansoprazoleb Lorazepama Lovastatina h Mefenamic acid Meloxicamd Montelukastg Mycophenolatea c Nalidixic acid Naproxenf Nevirapinec Nitrofurantoinc Ofloxacinb Oxaprozinb Phenazopyridinef Phenytoinc
Praziquantelc Raloxifeneb Rifampicinc Risperidonea Rofecoxibd Simvastatina b Sirolimus Sulfamethoxazolec Sulindacd Tacrolimusb b Talinolol Tamoxifenf Terfenadinef Tolmetind
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Acebutolole Alendronic acida Allopurinolc Anastrazolea Ascorbic acidc Atenololc,e Bidisomideb Biperidenc Biphosphonatesb a Cefaclor Cefazolinb Chloramphenicolc Cimetidinec b,c Cloxacillin Codeinea,c Colchicinec
Is the full potential of the BCS reached? 21 (27) Dicloxacillinb Didanosinec Ergocalciferolc Ergometrinec Ergotaminec Fexofenadineb Folinic acidf Gabapentina Ganciclovirb c Hydralazine Letrozolea Levetiracetama Levothyroxinec a Lisinopril Losartana Metforminc
Nadolole Neostigminec Nifurtimoxc Penicillaminec Penicillinsb Propylthiouracilc Pyridostigminec Ranitidinea Reserpinec a Risedronic acid Terazosina Tetracyclineb Thiaminec a Topiramate Valsartanb Zalcitabineb
Acetazolamidec Aluminium hydroxidec Azathioprinec Cefiximea
Cefuroxime axetila Oxcarbazepinea Ritonavirc
a (Ramirez et al., 2010) b (Wu and Benet, 2005) c (Lindenberg et al., 2004) d (Yazdanian et al., 2004) e (Yang et al., 2007) f (Bergstrom et al., 2003) g (Kleberg et al., 2010) h (Butler and Dressman, 2010)
Table 2. Compounds sorted into two BCS classes in the present literature (n=59). Class 1/2 (n=9) Class 1/3 (n=20) Class 1/4 (n=3) Amitriptylinea,b Chlorpromazinea,b Digoxinb,c a,c Ketoprofen Nifedipineb,c Piroxicamc,d Valproic acidb,c Warfarina,b Verapamil HClb
Abacavirb,c b,d Acetylsalicylic acid Atropine sulphatea,b Captoprilb,c Cetirizinec,e b Chlorpheniramine Clomipheneb
Class 2/3 (n=2)
Class 2/4 (n=20)
Erythromycina-c Trimethoprimb,c
Ciprofloxacina,b Diloxanideb b Efavirenz Folic acidb Glibenclamideb,g Indinavirb,c Ivermectinb
Dexamethasoneb b,e Fluconazole Isoniazidb,e Lamivudineb,e Levamisoleb c,e Levofloxacin Methyldopaa,b
Metoclopramideb b,f Paracetamol Pravastatinc,e Promethazinea,b Pyrazinamideb,e b Quinine
Amiloridea,b a,b Amoxicillin Acetaminophenc,e
Class 3/4 (n=5) Lopinavirb Mebendazoleb b Mefloquine Nelfinavirb,c Niclosamideb Pyrantelb Pyrimethamineb
Retinolb Spironolactoneb b,c Saquinavir Sulfadiazineb Sulfasalazineb Triclabendazoleb
Acyclovira,b Famotidinec,e b,c Furosemide Hydrochlorothiazidea,b Methotrexatea,b
a (Bergstrom et al., 2003) b (Lindenberg et al., 2004) c (Wu and Benet, 2005) d (Yazdanian et al., 2004) e (Ramirez et al., 2010) f (Butler and Dressman, 2010) g (Kleberg et al., 2010)
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Table 3. Physicochemical properties and FA data for BCS class 2 compounds Compound Albendazole Tacrolimus Cyclosporine Amiodarone Lovastatin Dipyridamole Azithromycin Clofazimine Griseofulvin Clopidogrel Danazol Raloxifene Carvedilol Talinolol Lamotrigine Risperidone Simvastatin Ketoconazole Irbesartan Itraconazole Carbamazepine Fenoprofen Lansoprazole Sulindac Meloxicam Diflunisal Phenazopyridine Tolmetin Nitrofurantoin Phenytoin Flurbiprofen Ibuprofen Rofecoxib Dapsone Lorazepam Nevirapine Rifampicin Mycophenolate Oxaprozin Indomethacin Naproxen Cisapride Atorvastatin
Mw 265.3 804.0 1 202.6 645.3 404.5 504.6 749.0 473.4 352.8 321.8 337.5 473.6 406.5 363.5 256.1 410.5 418.6 531.4 428.5 705.6 236.3 242.3 369.4 356.4 351.4 250.2 213.2 257.3 238.2 252.3 244.3 206.3 314.4 248.3 321.2 266.3 822.9 320.3 293.3 357.8 230.3 466.0 558.6
logP 3.2 3.6 3.0 7.2 4.5 2.2 3.3 6.8 2.5 3.4 3.6 5.5 3.9 3.5 2.0 2.9 4.9 3.9 4.2 5.2 2.6 3.5 1.8 3.2 2.2 4.0 2.8 2.8 0.6 2.2 3.6 3.7 2.7 0.8 2.6 1.7 3.1 3.0 3.5 3.8 3.3 3.5 4.8
logD6.5 3.2 3.6 3.0 5.2 4.5 1.8 0.5 4.3 2.5 3.4 3.6 4.1 2.4 0.8 2.0 1.4 4.9 3.9 4.0 5.2 2.6 1.4 1.8 0.8 1.1 1.0 2.8 0.1 -0.3 2.2 1.5 1.8 2.7 0.8 2.6 1.7 3.0 1.2 1.7 1.5 1.3 2.6 3.0
pKa 4.0
8.5 6.7 8.4 9.0 4.6 8.5 8.1 9.3 3.4 8.0 5.7; 4.1 6.7 3.9 4.4 3.2 4.1 3.9 3.1 5.1 3.8 4.1 8.0 4.3 4.6 3.2 2.3 1.4; 7.4 4.4 4.6 4.1 4.5 7.3 4.7
a/b/n/z/am b n n b n b b b n b n b b b b b n b b b n a b a a a b a a a a a n b n b am a a a a b a
FA (%) 5a 15b 28a 30 (F)c* 31b 36d 37a 45e 45d 50f 58d 60g 65b 65b 70h 70g 73g 75i 78d 80j 85j 85k 85j 88k 89k 90k 90b k 90 90(F)a 90 (F)c 92k 92j k 93 93c 93(F)a 93 (F)c 93 (F)l 94 (F)c 95k k 98 99k 100j 100m
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Compound Mw logP logD6.5 pKa a/b/n/z/am FA (%) Diclofenac sodium 296.2 4.7 2.2 4.0 a 100n b Glipizide 445.5 2.2 1.3 5.5 a 100 Ofloxacin 361.4 -0.4 -0.5 6.1; 7.7 z 100b Praziquantel 312.4 2.2 2.2 n 100j Tamoxifen 371.5 6.7 4.8 8.4 b 100b Sulfamethoxazole 253.3 0.6 -0.2 5.8 a 100 (F)c Aceclofenac 354.2 4.3 1.2 3.2 a N.F. Celecoxib 381.4 3.5 3.5 n N.F. Ebastine 469.7 6.7 4.7 8.5 b N.F. Iopanoic acid 570.9 4.2 2.5 4.8 a N.F. Mefenamic acid 241.3 5.0 2.9 4.4 a N.F. Montelukast 586.2 7.3 5.9 5.1 a N.F. Nalidixic acid 232.2 1.2 0.1 5.5 a N.F. Sirolimus 914.2 4.5 4.5 n N.F. Terfenadine 471.7 5.7 3.5 8.8 b N.F. Abbreviations used: Molecular weight (Mw); partition coefficient between octanol and water (logP); partition coefficient between octanol and water at pH 6.5 (logD6.5); acid (a); base (b); neutral in the pH-range of 2 to 12 (n); zwitterion (z); ampholyte (am); fraction absorbed (FA); not found (N.F.). * F low due to low absorption. References: a (Perez et al., 2004), b (Varma et al., 2012), c (Brunton, 2005), d (Sugano, 2011), e (Mathur et al., 1985), f (Taubert et al., 2006), g (Gan et al., 2009), h (Sanghvi et al., 2003), i (Zhu et al., 2002), j (Chu and Yalkowsky, 2009), k (Yazdanian et al., 2004), l (Loos et al., 1985), m (Lennernas, 2003), n (Chiou and Barve, 1998).
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Figures
Figure 1.
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Lo ra ze O pam flo xa D cin ap R son of ec e C oxi La i s a b ns pr o id Pr pra e az zo i q le ua N n C e ar vir tel b Ph am api en az ne az ep op in e y R ridi ifa n m e Ta pic m in ox ife n
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Lo ra ze O pam flo Ta xac m in o R xif ifa e m n pi Ph c e n Da i n p az s op on e La y n ri C so din ar e p ba ra z m o a z le e N ev pin Pr ira e az pi iq ne ua C nt e is ap l r R of ide ec ox ib
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S0928-0987(13)00364-3 http://dx.doi.org/10.1016/j.ejps.2013.09.010 PHASCI 2877
To appear in:
European Journal of Pharmaceutical Sciences
Received Date: Revised Date: Accepted Date:
3 June 2013 5 September 2013 15 September 2013
Please cite this article as: S. Bergström, C.A., E. Andersson, S.B., Fagerberg, J.H., Ragnarsson, G., Lindahl, A., Is the Full Potential of the Biopharmaceutics Classification System Reached?, European Journal of Pharmaceutical Sciences (2013), doi: http://dx.doi.org/10.1016/j.ejps.2013.09.010
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Is the Full Potential of the Biopharmaceutics Classification System Reached? Christel A. S. Bergström,a,* Sara B. E. Andersson,a Jonas H. Fagerberg,a Gert Ragnarsson,b Anders Lindahl b a) Department of Pharmacy, Uppsala University, Biomedical Centre P.O. Box 580, SE-751 23 Uppsala, Sweden b) Medical Products Agency, Scientific Expertise, Box 26,SE- 751 03 Uppsala, Sweden
*Address correspondence to:
Christel A. S. Bergström, PhD Department of Pharmacy Uppsala University Biomedical Centre, P.O. Box 580 SE-751 23 Uppsala, Sweden
Email: [email protected] Phone: +46 – 18 471 4118
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Graphical abstract
Abstract In this paper we analyze how the Biopharmaceutics Classification System (BCS) has been used to date. A survey of the literature resulted in a compilation of 242 compounds for which BCS classes were reported. Of these, 183 compounds had been reported to belong to one specific BCS class whereas 59 compounds had been assigned to multiple BCS classes in different papers. Interestingly, a majority of the BCS class 2 compounds had fraction absorbed (FA) values > 85%, indicating that they were completely absorbed after oral administration. Solubility was computationally predicted at pH 6.8 for BCS class 2 compounds to explore the impact of the pH of the small intestine, where most of the absorption occurs on the solubility. In addition, the solubilization capacity of lipid aggregates naturally present in the intestine was studied computationally and experimentally for a subset of 12 compounds. It was found that all acidic compounds with FA>85% were completely
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dissolved in the pH of the small intestine. Further, lipids at the concentration used in fasted state simulated intestinal fluid (FaSSIF) dissolved the complete dose given of the most lipophilic (logD6.5>3) compounds studied. Overall, biorelevant dissolution media (pure buffer of intestinal pH or FaSSIF) identified that for 18 of the 29 BCS class 2 compounds with FA>85% the complete dose given orally would be dissolved. These results indicate that a more relevant pH restriction for acids and/or dissolution medium with lipids present better forecast solubility-limited absorption in vivo than the presently used BCS solubility criterion. The analysis presented herein further strengthens the discussion on the requirement of more physiologically relevant dissolution media for the in vitro solubility classification performed to reach the full potential of the BCS.
Keywords: Biopharmaceutics classification system, solubility, biorelevant dissolution, dose number, poorly soluble, in silico, in vitro
1. Introduction The Biopharmaceutics Classification System (BCS) categorizes drug molecules on the basis of their permeability and solubility (Amidon et al., 1995), and serves as a tool to identify compounds eligible for a biowaiver of in vivo bioequivalence (FDA, 2000; WHO, 2006; EMA, 2010) The BCS has gained much attention because of its potential to reduce the need for clinical studies during e.g. life cycle management and production of generic compounds. It is also used to identify potential absorption problems after oral administration. The BCS has evolved in many different directions. As an example, it is used to signal need for formulation design (BCS class 2/4) and/or chemical modifications (BCS class3/4) to improve absorption (Pouton, 2006). It has also resulted in the development of the Biopharmaceutics Drug Disposition Classification system (BDDCS) (Wu and Benet, 2005) and the Developability Classification system (DCS) (Butler and Dressman, 2010) . The BCS and BDDCS are based
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on the same solubility definition, but the BDDCS uses metabolism instead of permeability in its classification. It is proposed to be used to identify disposition and drug-drug interaction patterns primarily in the intestine and liver (Wu and Benet, 2005; Benet, 2013). In contrast, the BCS and DCS have the same permeability definition, but the DCS is based on larger volumes for the solubility definition. Butler and Dressman proposed that 500 mL, rather than the 250 mL used in the BCS definition, better reflects volumes in the small intestine (Butler and Dressman, 2010). At the same time, the larger volume (500 mL) is believed to compensate for the disappearance of drug from the intestine (i.e. the permeability) during drug dissolution and solubility assessment. The DCS is particularly useful for indicating whether BCS class 2 compounds are dissolution-rate limited or solubility limited; these are important factors for guiding further formulation design.
From a regulatory perspective the BCS provides a scientific framework to determine whether bioequivalence (BE) studies are needed for new formulations of drugs or if in vitro data suggest a biowaiver to be designated. BE is defined as the compound having an AUC and Cmax in the interval of 80-125% of the reference product. The 90% confidence interval for the test/reference ratio should be contained within this range (EMA, 2010). Attempts have been made to calculate the economic benefits of BCS biowaivers (Cook and Bockbrader, 2002; Cook et al., 2010). It is difficult to perform such economical calculations because of assumptions such as the frequency of compounds in each BCS class and the number of subjects needed to prove BE (which is related to the variability in AUC and Cmax expected for a drug). The economic impact is also higher in Europe as compared to the U.S. due to the EMA extension of biowaivers to BCS class 3 drug products. However, taking into account both BCS class 1 and class 3 biowaivers, it is estimated that 128-147 million dollars could be saved each year in reduced clinical study costs, out of which approximately 66-76 million
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dollars reflect saved costs for BCS class 1 (Cook et al., 2010). Other benefits of increased use of the BCS-based biowaivers are that unnecessary testing in humans is avoided and that the new products reach the market faster. Despite these obvious advantages, pharmaceutical companies appear reluctant to pursue BCS-based biowaivers. In 2010 (i.e. 10 years after the FDA introduced the guidelines for BCS-based biowaivers), Cook et al. reported that the number of applications for biowaivers had started to increase; nevertheless, the FDA had only designated 27 compounds a BCS class 1 biowaiver by this year. In a recent analysis at the Swedish Medical Products Agency, it was revealed that only 10% (21 of 208) and 17% (28 of 167) of the oral solid drug products (immediate release) had applied for a BCS-based biowaiver in 2011 and 2012, respectively. These are low percentages, given that approximately two-thirds of marketed compounds are BCS class 1 or 3, (Takagi et al., 2006; Cook et al., 2010) for which the companies can file for biowaivers in Europe. From the above presented analysis it becomes clear that the pharmaceutical industry currently does not take full advantage of the BCS. One reason may be that not all regulatory authorities approve biowaivers based on the BCS. Although the pharmaceutical industry is global, intending to target all markets so as to fully exploit the investments they have made in their drug products, the regulatory authorities are not equally globalized. For instance, the EMA and the FDA approve biowaivers based on the BCS (although using slightly different criteria), but Japan does not.
The current BCS hence seems not to be fully exploited in that the number of biowaivers designated still is low. However, there may also be room for increased usage of BCS-based biowaivers by changing the in vitro profiles required for active pharmaceutical ingredients (APIs) to be defined as BCS class 1 compounds. The current definition for solubility has, in addition to the amendments made through the DCS system, been subject to debate. The pH-
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interval introduced by the FDA has been discussed, because of the relatively high pH of 7.5 used as the upper limit of the pH interval in which the dose needs to be soluble, and as a response to this discussion the EMA guideline uses a pH interval from 1 to 6.8. The EMA guideline also differs from that of the FDA for the fraction absorbed (FA) level used to signal high permeability. The EMA sets it at >85% whereas FDA uses >90% (FDA, 2000; EMA, 2010). Further it has been argued that acids are unfairly assessed with the pH-interval used in the solubility definition, since the lower limit of pH 1 (reflecting the pH in the stomach) classifies a large number of acids as BCS class 2 although they display high solubility in the small intestine in vivo which is the compartment where most of the absorption takes place (Dressman et al., 2001; Yazdanian et al., 2004). This issue has been addressed in the WHO guidance. Pharmaceutical products containing BCS Class 2 weak acids that are soluble at pH 6.8 are according to WHO eligible for the biowaiver procedure, provided that they dissolve rapidly at pH 6.8 and similarly to the comparator product at pH 1.2 and 4.5 (WHO, 2006). Following the debate on which pH-range to use, more physiologically relevant dissolution media in which the pure buffers are exchanged to buffers spiked with e.g. bile components to also address solubilization likely to occur in vivo, have been suggested (Dressman et al., 2001). It is well-known that lipophilic drug molecules may gain extensively in solubility when dissolved in more physiologically relevant dissolution media such as fasted or fed state simulated intestinal fluids (FaSSIF and FeSSIF, respectively) (Mithani et al., 1996; Fagerberg et al., 2010; Soderlind et al., 2010; Fagerberg et al., 2012), and that the obtained solubility in these simulated fluids better reflect the solubility measured in human intestinal fluids for such compounds (Nicolaides et al., 2001; Söderlind et al., 2010; Clarysse et al., 2011). Hence, the use of simple buffers instead of biorelevant dissolution media may result in a significant underestimation of the concentration reached in the intestinal fluid. The solubility definition of the BCS has been vigorously discussed in particular with regard to pH and lipid content but
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also from the volume perspective (Dressman et al., 2001; Yu et al., 2002; Rinaki et al., 2003; Polli et al., 2004; Rinaki et al., 2004; Yazdanian et al., 2004; Lennernas and Abrahamsson, 2005; Polli et al., 2008; Davit et al., 2012; Varma et al., 2012). In this study we explored the current solubility definition of BCS by studying 242 compounds for which the BCS class could be extracted from the literature. Of these, focus was set on the 58 BCS class 2 compounds for which the impact of pH and lipids present in the fasted state intestinal fluid on the observed solubility was investigated and analysed with an in vivo FA perspective.
2. Materials and methods 2.1 Dataset and Characteristics
A set of compounds (n=242) was extracted from published datasets for which BCS classifications existed (Table 1 and Table 2) (Bergstrom et al., 2003; Lindenberg et al., 2004; Yazdanian et al., 2004; Wu and Benet, 2005; Yang et al., 2007; Butler and Dressman, 2010; Kleberg et al., 2010; Ramirez et al., 2010). Thereafter, we focused on BCS class 2 compounds (n=58). For these substances, molecular descriptors and e.g. pKa, logP and logD6.5 were calculated with the software ADMET Predictor (SimulationsPlus, CA). Fraction absorbed (FA) values were taken from the literature (Table 3). If FA data were not available, oral bioavailability (F) was used as a surrogate for those BCS class 2 compounds with F >85%. Our decision was based on the EMA guideline which uses a FA of 85% as the high permeability cut-off (EMA, 2010). Furthermore, if F is 0.85 or more, then the FA must be 85% or greater.
2.2 Materials
Carbamazepine , dapsone, lansoprazole, nevirapine, ofloxacin, phenazopyridine, praziquantel and rifampicin were purchased from SigmaAldrich (St. Louis, MO), cisapride and rofecoxib were purchased from Toronto Research Chemicals (North York, ON, Canada) and lorazepam
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was purchased from Larodan Fine Chemicals (Malmö, Sweden). For the studies of solubility in FaSSIF, SIF powder original was used which was kindly donated by biorelevant.com.
2.3 Computational Prediction of Biorelevant Solubility: Impact of pH and Lipids
The solubility at pH 1.0 (reflecting stomach) and 6.8 (reflecting small intestine) was predicted with ADMET Predictor (SimulationsPlus, CA). The potential of lipids to further increase the solubility in comparison to a pure buffer was forecasted using the solubilization ratio (SR) and two different computational models in ADMET Predictor. This procedure was used as we did not have large enough datasets at hand to validate the accuracy of the different solubility models in ADMET Predictor. Therefore, the predicted logDpH6.5 values (distribution of a compound between octanol and water at pH 6.5 ) from ADMET Predictor were used to calculate the SR at pH 6.5 (the pH of FaSSIF) using equation 1 logSR=0.39logDpH6.5+3.52
Eq. 1
The obtained SR was then used to calculate the solubilization capacity of the buffer and the FaSSIF using equation 2
SR=SCbs/SCaq
Eq. 2
where SCbs and SCaq are the solubilization capacity of the bile salt and water, respectively. Equation 1 was established by Fagerberg and colleagues in 2010 as a further development of the SR and its relationship to logP presented by Mithani and co-workers in 1996 (Mithani et al., 1996; Fagerberg et al., 2010). In the following, both the FaSSIF solubility and the corresponding solubility in a pure buffer with the same pH as FaSSIF (pH 6.5) were predicted with ADMET Predictor. In step 1, the predicted logDpH6.5 values were used to calculate the logSR (eq. 1) and the resulting SR was used together with predicted FaSSIF solubility (e.g.
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buffer pH 6.5 with 0.75 mM phospholipid and 3.0 mM taurocholate) to calculate the concentration possible to achieve in the buffer (eq. 2). The calculation was repeated in a second round, where instead of predicted FaSSIF, the predicted aqueous solubility at pH 6.5 was used in equation 2 to calculate the solubility in FaSSIF.
The dose number (Do) was calculated from Do=(M0/V0)/Cs
Eq. 3
where M0 is the dose of the compound (here set to the maximum dose strength), V0 is the volume (here set to 250 mL) and Cs is the solubility in the medium used (i.e. FaSSIF or buffer pH 6.5) (Oh et al., 1993). The Do was predicted based on the values obtained from equation 1 and 2.
2.4 Experimental Determinations of Solubility in FaSSIF and Buffer
For the twelve non-acidic BCS 2 compounds with a FA >85% the apparent solubility was measured in FaSSIF and corresponding blank buffer (i.e. FaSSIF without 3 mM taurocholate and 0.75 mM lecithin) to investigate the solubilization capacity of the mixed lipid aggregates present in the FaSSIF. SIF Powder original was used for the production of FaSSIF according to the protocol provided by the manufacturer. For eight of the compounds the apparent solubility was measured using the µDISS Profiler (pION Inc, MA) at 37°C (n=3), according to a previously published protocol (Fagerberg et al., 2010). The pH of the suspensions (buffer and FaSSIF) remained constant during the course of the experiment. For lorazepam, ofloxacin and rifampicin, a small-scale shake flask method was used for the solubility assessment due to low amount of compound available (lorazepam) or high solubility (ofloxacin and rifampicin). The solubility assay used 300 µL of buffer or FaSSIF and was run in duplicate. Excess amount of solid material was weighed into glass vials producing a suspension throughout the
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experiment. Pre-heated solvent was added and the vials were put on a plate-shaker (300 rpm) in an incubator (37°C) and equilibrated for 24 hours. The vials were then centrifuged in a temperature controlled Eppendorf centrifuge Model 5043 at 10,000 g, 37°C and for 10 minutes and the supernatant was sampled, diluted and determined for concentration in a Tecan Sapphire plate reader (Tecan Group, Switzerland).The pH of the suspensions (buffer and FaSSIF) remained constant during the course of the experiment. Solubility data of tamoxifen in buffer and FaSSIF were taken from a previous study performed in-house (Fagerberg et al., 2010).
All solubility data are presented as the mean±standard deviation. Statistical difference between the apparent solubility in buffer and FaSSIF was analysed with Student’s t-test. The obtained solubility data were used to calculate the Do according to equation 3.
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3. Results and Discussion Of the 242 BCS compounds extracted from the literature, 183 compounds had been classified as belonging to one single class (Table 1) and 59 were categorized as belonging to two different BCS classes in different publications (Table 2). The discrepancy between publications may be a result of the different guidelines provided by EMA and FDA or that a compound may be used in different maximum doses when indicated for the treatment of different diseases. As discussed in the introduction, there are different pH-intervals for solubility assessments and FA limits for permeability; however, dose also differs in the two guidelines. The EMA requires the maximum oral dose administered to be soluble whereas the FDA requires the maximum dose strength. For example, if the maximum dose strength of a tablet is 100 mg but it is possible to administer two tablets simultaneously, FDA would base the BCS classification on 100 mg whereas EMA would use 200 mg. Therefore, dependent on which of the two guidelines that is used for the classification, differences are likely to appear. A third reason for the differences found in the literature may be that different methods have been used when investigating solubility and permeability.
The 183 compounds designated to a single BCS class were distributed as follows: 38.2% class 1 compounds, 31.7% class 2 compounds, 26.2% class 3 compounds and 3.8% class 4 compounds (Table 1). For the remainder of this work the analysis focused on the compounds sorted as BCS class 2. These are the compounds for which in vivo conditions (by virtue of the pH gradient and/or the naturally available lipids in the intestine) can improve solubility to the extent that the complete dose becomes soluble. It was possible to extract FA from the literature for 49 of the BCS class 2 compounds (Table 3). The majority (n=29) had a reported FA >85%, 11 compounds had a FA of 50-84% and only 9 compounds had a reported FA <50%.
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The protolytic functions of the 58 class 2 compounds (Table 3) were equally distributed between acids (37.9%) and bases (37.9%), whereas 20.7% were non-ionizable. Only one ampholyte and one zwitterion were included in the dataset. The 29 BCS class 2 compounds with FA >85% were further analysed for their protolytic function. A large majority of these were acids (59%), whereas the remaining were composed of bases (21%), non-ionizable compounds (14%), and the zwitterion (3%) and ampholyte (3%). This shows that acidic compounds in many cases are classified as BCS 2 compounds even though they are absorbed to a high extent (>85%) indicating a considerable risk for incorrect classification of acids as being compounds with solubility-limited absorption.
Predicted buffer solubility clearly identified the impact of pH on solubility (Figure 1). For seven of the acidic BCS class 2 compounds a Do < 1 was predicted at pH 6.8 compared to a Do > 1 at pH 1.0. It should be noted that all BCS class 2 acids had FA > 85% (Table 3); hence, in vivo these acids are not likely to display solubility-limited absorption. As expected the pH dependency for bases resulted in lower Do at low pH. For many of the BCS class 2 bases Do was predicted to be less than 1 at pH 1 but showed Do > 1 at pH 6.8 (Figure 1). This trend may be useful to identify bases that risk precipitating when the gastric content empties into the higher pH found in the duodenum and jejunum. Indeed, several of these (e.g. albendazole, itraconazole and ketoconazole) have been shown to precipitate at least partially in the small intestine (Jung et al., 1998; Mellaerts et al., 2008; Psachoulias et al., 2011). The neutral compounds were, as expected, unaffected by the pH. For these compounds, it may be more relevant to investigate the impact of the mixed micelles secreted by bile on their solubility (Fagerberg et al., 2010; Fagerberg et al., 2012). In particular this is important for highly lipophilic compounds. Lipids have been suggested to significantly impact the solubility
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of drug molecules with a logP > 3 or a logD6.5 > 2 (Dressman and Reppas, 2000; Bergstrom et al., 2007; Fagerberg et al., 2010; Gamsiz et al., 2010; Ottaviani et al., 2010). We therefore analysed whether computational tools predicted lipids to be important for the solubility of the remaining 12 non-acidic BCS class 2 compounds. It was found that these models predicted most of them to have increased solubility in FaSSIF as compared to the corresponding buffer (Figure 2). The Do of tamoxifen (Figure 2a, prediction based on equations 1 and 2 using buffer solubility from ADMET Predictor) and cisapride (Figure 2b, prediction based on equation 1 and 2 using FaSSIF solubility from ADMET Predictor) were predicted to decrease to less than 1 in simulated intestinal fluids. This computational exercise indicated that lipids were likely to improve the solubility of a number of the compounds, maybe even to the extent allowing the oral dose to become completely dissolved. Furthermore, three of the 12 compounds had logP>3 and eight of them displayed logD6.5>2, and hence, also the lipophilicity profile of the compounds pointed at the likelihood of significant solubilization in the lipid aggregates naturally present in the intestinal fluid. We therefore experimentally determined the solubility in buffer and FaSSIF of these compounds and found that all of the compounds obtained equal or higher dissolution rate and apparent solubility in FaSSIF as compared to blank buffer (Figure 3a). As expected, the solubility in FaSSIF was equal to that observed in the buffer for ofloxacin, which was the most hydrophilic compound of the 12 studied with a logDpH6.5 of -0.5. For the most lipophilic compound, tamoxifen, the solubility in FaSSIF was 26-fold higher than in the corresponding buffer. The dose number was then investigated as a mean to link dose to solubility (Figure 3b). Indeed, for the compounds explored here, four of the 12 compounds obtained a Do <1 in FaSSIF. Two of these, lorazepam and ofloxacin, were also completely dissolved in the buffer, and hence, the experimental determinations to a large extent confirmed the computational predictions (Figure 3c and 3d). The other two compounds, rifampicin and tamoxifen, were the most lipophilic
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compounds measured in this study (logD6.5 ≥ 3.0), confirming the importance of having solubilizing aggregates present to better mimic in vivo dissolution of highly lipophilic compounds. All other compounds received a higher solubility in FaSSIF than in buffer, although it was not high enough to produce a Do below 1. To summarize, the computational and experimental efforts employed herein, which used more physiologically relevant conditions to investigate the dose possible to dissolve for BCS class 2 compounds, indicated complete dissolution in intestinal fluid for 18 of the 29 BCS class 2 compounds with FA >85%. When the DCS solubility criterion (i.e. 500 mL instead of 250 mL of solvent) was used additional two compounds (dapsone and phenazopyridine) received Do<1.
4. Conclusion In this study we identified that the BCS is not used to its full potential. The number of biowaivers filed for and designated is still, more than 10 years after the introduction of the regulatory guidelines, relatively low. We speculate that one reason for this is that the regulatory authorities are not globalized to the same extent as the pharmaceutical industry, shown by e.g. that biowaivers are not accepted by all authorities and by the differences in the guidelines by EMA, FDA and WHO. The computational and experimental analyses of BCS class 2 compounds identified that acidic drug molecules are at risk for being deemed having solubility-limited absorption by the current BCS classification although complete absorption is found in vivo. This further supports the WHO guidance in which pharmaceutical products are eligible for biowaivers if the API is a weak acid for which the oral dose is soluble at pH 6.8 and the dissolution profile (pH1.2, 4.5 and 6.8) is similar to the comparator product. In the light of our findings, this combination of a solubility measurement at pH 6.8 for weak acidic BCS class 2 compounds and a formulation dissolution performance in the pH interval of the gastrointestinal tract is an attractive solubility/dissolution profiling approach to eligible
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products for biowaivers. Further, lipids present in the intestine may increase the solubilization of compounds in the intestinal fluid and herein it was found that compounds having logDpH6.5 ≥ 3.0 had apparent solubility in FaSSIF allowing the complete oral dose to become soluble. However, a larger number of non-acidic poorly soluble compounds need to be studied to conclude on possible modifications of the BCS solubility definition for such compounds. In conclusion, a more relevant pH restriction for acids (i.e. assessing solubility solely at the pH of the small intestinal fluid) and/or physiologically relevant medium with lipids present, such as FaSSIF, identified that 18 of the 29 BCS class 2 compounds with FA >85% were expected to be completely dissolved in human intestinal fluid. From the analyses presented herein, globally harmonized guidelines and the implementation of more physiologically relevant media used in the in vitro assessments of solubility and dissolution testing are suggested as two important steps towards an increased number of designated biowaivers.
Acknowledgements Financial support from the Swedish Research Council (Grant 621-2008-3777) and The Swedish Agency for Innovation Systems (Grant 2010-00966) is highly appreciated. We are grateful to biorelevant.com for providing us the SIF original powder used for the solubility experiments, and Simulations Plus (Lancaster, CA) for providing us with a reference site license for the software ADMET Predictor.
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Disclaimer The views and conclusions presented in this paper represent those of the authors and not necessarily those of the Swedish Medical Product Agency where two of the authors are employed.
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Perez, M.A., Sanz, M.B., Torres, L.R., Avalos, R.G., Gonzalez, M.P., Diaz, H.G., 2004. A topological sub-structural approach for predicting human intestinal absorption of drugs. European journal of medicinal chemistry 39, 905-916. Polli, J.E., Abrahamsson, B.S., Yu, L.X., Amidon, G.L., Baldoni, J.M., Cook, J.A., Fackler, P., Hartauer, K., Johnston, G., Krill, S.L., Lipper, R.A., Malick, W.A., Shah, V.P., Sun, D., Winkle, H.N., Wu, Y., Zhang, H., 2008. Summary workshop report: bioequivalence, biopharmaceutics classification system, and beyond. The AAPS journal 10, 373-379. Polli, J.E., Yu, L.X., Cook, J.A., Amidon, G.L., Borchardt, R.T., Burnside, B.A., Burton, P.S., Chen, M.L., Conner, D.P., Faustino, P.J., Hawi, A.A., Hussain, A.S., Joshi, H.N., Kwei, G., Lee, V.H., Lesko, L.J., Lipper, R.A., Loper, A.E., Nerurkar, S.G., Polli, J.W., Sanvordeker, D.R., Taneja, R., Uppoor, R.S., Vattikonda, C.S., Wilding, I., Zhang, G., 2004. Summary workshop report: biopharmaceutics classification system--implementation challenges and extension opportunities. Journal of pharmaceutical sciences 93, 1375-1381. Pouton, C.W., 2006. Formulation of poorly water-soluble drugs for oral administration: physicochemical and physiological issues and the lipid formulation classification system. European journal of pharmaceutical sciences : official journal of the European Federation for Pharmaceutical Sciences 29, 278-287. Psachoulias, D., Vertzoni, M., Goumas, K., Kalioras, V., Beato, S., Butler, J., Reppas, C., 2011. Precipitation in and supersaturation of contents of the upper small intestine after administration of two weak bases to fasted adults. Pharmaceutical research 28, 3145-3158. Ramirez, E., Laosa, O., Guerra, P., Duque, B., Mosquera, B., Borobia, A.M., Lei, S.H., Carcas, A.J., Frias, J., 2010. Acceptability and characteristics of 124 human bioequivalence studies with active substances classified according to the Biopharmaceutic Classification System. British journal of clinical pharmacology 70, 694-702. Rinaki, E., Dokoumetzidis, A., Valsami, G., Macheras, P., 2004. Identification of biowaivers among Class II drugs: theoretical justification and practical examples. Pharmaceutical research 21, 15671572. Rinaki, E., Valsami, G., Macheras, P., 2003. Quantitative biopharmaceutics classification system: the central role of dose/solubility ratio. Pharmaceutical research 20, 1917-1925. Sanghvi, T., Ni, N., Mayersohn, M., Yalkowsky, S.H., 2003. Predicting passive intestinal absorption using a single parameter. Qsar Comb Sci 22, 247-257. Soderlind, E., Karlsson, E., Carlsson, A., Kong, R., Lenz, A., Lindborg, S., Sheng, J.J., 2010. Simulating fasted human intestinal fluids: understanding the roles of lecithin and bile acids. Molecular pharmaceutics 7, 1498-1507. Sugano, K., 2011. Fraction of a dose absorbed estimation for structurally diverse low solubility compounds. International journal of pharmaceutics 405, 79-89. Söderlind, E., Karlsson, E., Carlsson, A., Kong, R., Lenz, A., Lindborg, S., Sheng, J.J., 2010. Simulating fasted human intestinal fluids: Understanding the roles of lecithin and bile acids. Molecular pharmaceutics 7, 1498-1507. Takagi, T., Ramachandran, C., Bermejo, M., Yamashita, S., Yu, L.X., Amidon, G.L., 2006. A provisional biopharmaceutical classification of the top 200 oral drug products in the United States, Great Britain, Spain, and Japan. Molecular pharmaceutics 3, 631-643. Taubert, D., von Beckerath, N., Grimberg, G., Lazar, A., Jung, N., Goeser, T., Kastrati, A., Schomig, A., Schomig, E., 2006. Impact of P-glycoprotein on clopidogrel absorption. Clinical pharmacology and therapeutics 80, 486-501. Varma, M.V., Gardner, I., Steyn, S.J., Nkansah, P., Rotter, C.J., Whitney-Pickett, C., Zhang, H., Di, L., Cram, M., Fenner, K.S., El-Kattan, A.F., 2012. pH-Dependent solubility and permeability criteria for provisional biopharmaceutics classification (BCS and BDDCS) in early drug discovery. Molecular pharmaceutics 9, 1199-1212. WHO, 2006. Proposal to waive in vivo bioequivalence requirements for theWHOModel List of Essential Medicines immediate release, solid oral dosage forms. In: Fortieth report of the WHO Expert Committee on Specifications for Pharmaceutical Preparations. Geneva, World Health Organization. Technical Report Series, no. 937, Annex 8.
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Wu, C.Y., Benet, L.Z., 2005. Predicting drug disposition via application of BCS: transport/absorption/ elimination interplay and development of a biopharmaceutics drug disposition classification system. Pharmaceutical research 22, 11-23. Yang, Y., Faustino, P.J., Volpe, D.A., Ellison, C.D., Lyon, R.C., Yu, L.X., 2007. Biopharmaceutics classification of selected beta-blockers: solubility and permeability class membership. Molecular pharmaceutics 4, 608-614. Yazdanian, M., Briggs, K., Jankovsky, C., Hawi, A., 2004. The "high solubility" definition of the current FDA Guidance on Biopharmaceutical Classification System may be too strict for acidic drugs. Pharmaceutical research 21, 293-299. Yu, L.X., Amidon, G.L., Polli, J.E., Zhao, H., Mehta, M.U., Conner, D.P., Shah, V.P., Lesko, L.J., Chen, M.L., Lee, V.H., Hussain, A.S., 2002. Biopharmaceutics classification system: the scientific basis for biowaiver extensions. Pharmaceutical research 19, 921-925. Zhu, C., Jiang, L., Chen, T.M., Hwang, K.K., 2002. A comparative study of artificial membrane permeability assay for high throughput profiling of drug absorption potential. European journal of medicinal chemistry 37, 399-407.
Figure legends Figure 1. Computationally predicted pH-dependent dose number for BCS class 2 compounds. The graph presents the logarithm of the dose number (logDo), and hence, logDo of 0 corresponds to Do of 1 (here marked with the dashed line). For compounds with a Do less than this value the maximum dose given is completely soluble. The compounds are grouped based on their protolytic function with acids, bases, non-ionizable, ampholyte and zwitterion displayed from left to right. Figure 2. Prediction of solubility in buffer and FaSSIF taking use of the solubilization ratio (SR). a) Solubility in FaSSIF calculated by equation 1 and 2 using predicted buffer solubility from ADMET b) Solubility in buffer calculated by equation 1 and 2 using predicted solubility in FaSSIF from ADMET. Figure 3. Experimentally determined solubility and dose number for non-acidic compounds with FA > 85%.
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a) Solubility data, here presented as the mean±standard deviation. The lipophilicity of the compounds (logD6.5) increases to the right. p<0.05 (*), p<0.01 (**), p<0.001 (***). b) Of the 12 compounds investigated, 4 compounds had a Do<1 in buffer and/or FaSSIF. When the DCS volume criterion was used (i.e. 500 mL instead of the BCS volume of 250 mL) also phenazopyridine and dapsone obtained Do<1 in FaSSIF, but not in the buffer. c) Experimentally determined Do (grey), Do calculated based on ADMET Predictor buffer model (white) and buffer Do calculated based on solubilization ratio (eq. 1 and 2). d) Experimentally determined Do (grey), Do calculated based on ADMET Predictor FaSSIF model (white) and FaSSIF Do calculated based on solubilization ratio (eq. 1 and 2).
Tables Table 1. Compounds reported as belonging to only one BCS class in the survey (n=183). Class 2 (n=58)
Class 1 (n=70) Amlodipinea Benznidazolec Bisoprolola Buspironeb Caffeineb Chloroquinec a Citalopram Cyclophosphamidec Desipraminef Diazepamc c Diethylcarbamazine Diltiazemb Diphenhydramineb Disopyridamideb a Donepezil Doxazosina Doxepinb Doxycyclinec,f Enalaprila Ephedrineb Ergonovinef Ethinyl estradiolf Ethosuximidec Fluoxetineb
Class 3 (n=48)
Glucoseb Imipramineb Ketorolacd Labetalole Levodopac Levonorgestrelc b Lidocaine Lithiumc Lomefloxacinb Loratidinea b Meperidine Metoprololf Metronidazolec Midazolamb b Minocycline Mirtazapinea Mistoprostolb Nicotinamidec Norethiseronec Ondansetrona Phenobarbitalb,c Phenylalanineb Prednisolonec Primaquinec,f
Proguanilc Promazineb Propranololc Pyridoxinec Quinaprila Quinidineb a Ramipril Riboflavinc Rosiglitazoneb Salbutamolc b Salicylic acid Sertralinea Sildenafila Sotalole c Stavudine Terbinafina Theophyllinec,f Timolole Tramadola Venlafaxinea Zidovudinec Zolpidema
Aceclofenaca Albendazolec Amiodaroneb Atorvastatinb Azithromycinb Carbamazepinc a Carvedilol Celecoxibg Cisaprideb Clofaziminec a Clopidogrel Cyclosporineb Danazolg Dapsonec g Diclofenac sodium Diflunisalb Dipyridamoleh Ebastinea Fenoprofend Flurbiprofenb Glipizideb Griseofulvinc
Class 4 (n=7)
Ibuprofenc Indomethacinf Iopanoic acidc Irbesartana Itraconazoleb Ketoconazoleb a Lamotrigine Lansoprazoleb Lorazepama Lovastatina h Mefenamic acid Meloxicamd Montelukastg Mycophenolatea c Nalidixic acid Naproxenf Nevirapinec Nitrofurantoinc Ofloxacinb Oxaprozinb Phenazopyridinef Phenytoinc
Praziquantelc Raloxifeneb Rifampicinc Risperidonea Rofecoxibd Simvastatina b Sirolimus Sulfamethoxazolec Sulindacd Tacrolimusb b Talinolol Tamoxifenf Terfenadinef Tolmetind
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Acebutolole Alendronic acida Allopurinolc Anastrazolea Ascorbic acidc Atenololc,e Bidisomideb Biperidenc Biphosphonatesb a Cefaclor Cefazolinb Chloramphenicolc Cimetidinec b,c Cloxacillin Codeinea,c Colchicinec
Is the full potential of the BCS reached? 21 (27) Dicloxacillinb Didanosinec Ergocalciferolc Ergometrinec Ergotaminec Fexofenadineb Folinic acidf Gabapentina Ganciclovirb c Hydralazine Letrozolea Levetiracetama Levothyroxinec a Lisinopril Losartana Metforminc
Nadolole Neostigminec Nifurtimoxc Penicillaminec Penicillinsb Propylthiouracilc Pyridostigminec Ranitidinea Reserpinec a Risedronic acid Terazosina Tetracyclineb Thiaminec a Topiramate Valsartanb Zalcitabineb
Acetazolamidec Aluminium hydroxidec Azathioprinec Cefiximea
Cefuroxime axetila Oxcarbazepinea Ritonavirc
a (Ramirez et al., 2010) b (Wu and Benet, 2005) c (Lindenberg et al., 2004) d (Yazdanian et al., 2004) e (Yang et al., 2007) f (Bergstrom et al., 2003) g (Kleberg et al., 2010) h (Butler and Dressman, 2010)
Table 2. Compounds sorted into two BCS classes in the present literature (n=59). Class 1/2 (n=9) Class 1/3 (n=20) Class 1/4 (n=3) Amitriptylinea,b Chlorpromazinea,b Digoxinb,c a,c Ketoprofen Nifedipineb,c Piroxicamc,d Valproic acidb,c Warfarina,b Verapamil HClb
Abacavirb,c b,d Acetylsalicylic acid Atropine sulphatea,b Captoprilb,c Cetirizinec,e b Chlorpheniramine Clomipheneb
Class 2/3 (n=2)
Class 2/4 (n=20)
Erythromycina-c Trimethoprimb,c
Ciprofloxacina,b Diloxanideb b Efavirenz Folic acidb Glibenclamideb,g Indinavirb,c Ivermectinb
Dexamethasoneb b,e Fluconazole Isoniazidb,e Lamivudineb,e Levamisoleb c,e Levofloxacin Methyldopaa,b
Metoclopramideb b,f Paracetamol Pravastatinc,e Promethazinea,b Pyrazinamideb,e b Quinine
Amiloridea,b a,b Amoxicillin Acetaminophenc,e
Class 3/4 (n=5) Lopinavirb Mebendazoleb b Mefloquine Nelfinavirb,c Niclosamideb Pyrantelb Pyrimethamineb
Retinolb Spironolactoneb b,c Saquinavir Sulfadiazineb Sulfasalazineb Triclabendazoleb
Acyclovira,b Famotidinec,e b,c Furosemide Hydrochlorothiazidea,b Methotrexatea,b
a (Bergstrom et al., 2003) b (Lindenberg et al., 2004) c (Wu and Benet, 2005) d (Yazdanian et al., 2004) e (Ramirez et al., 2010) f (Butler and Dressman, 2010) g (Kleberg et al., 2010)
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Table 3. Physicochemical properties and FA data for BCS class 2 compounds Compound Albendazole Tacrolimus Cyclosporine Amiodarone Lovastatin Dipyridamole Azithromycin Clofazimine Griseofulvin Clopidogrel Danazol Raloxifene Carvedilol Talinolol Lamotrigine Risperidone Simvastatin Ketoconazole Irbesartan Itraconazole Carbamazepine Fenoprofen Lansoprazole Sulindac Meloxicam Diflunisal Phenazopyridine Tolmetin Nitrofurantoin Phenytoin Flurbiprofen Ibuprofen Rofecoxib Dapsone Lorazepam Nevirapine Rifampicin Mycophenolate Oxaprozin Indomethacin Naproxen Cisapride Atorvastatin
Mw 265.3 804.0 1 202.6 645.3 404.5 504.6 749.0 473.4 352.8 321.8 337.5 473.6 406.5 363.5 256.1 410.5 418.6 531.4 428.5 705.6 236.3 242.3 369.4 356.4 351.4 250.2 213.2 257.3 238.2 252.3 244.3 206.3 314.4 248.3 321.2 266.3 822.9 320.3 293.3 357.8 230.3 466.0 558.6
logP 3.2 3.6 3.0 7.2 4.5 2.2 3.3 6.8 2.5 3.4 3.6 5.5 3.9 3.5 2.0 2.9 4.9 3.9 4.2 5.2 2.6 3.5 1.8 3.2 2.2 4.0 2.8 2.8 0.6 2.2 3.6 3.7 2.7 0.8 2.6 1.7 3.1 3.0 3.5 3.8 3.3 3.5 4.8
logD6.5 3.2 3.6 3.0 5.2 4.5 1.8 0.5 4.3 2.5 3.4 3.6 4.1 2.4 0.8 2.0 1.4 4.9 3.9 4.0 5.2 2.6 1.4 1.8 0.8 1.1 1.0 2.8 0.1 -0.3 2.2 1.5 1.8 2.7 0.8 2.6 1.7 3.0 1.2 1.7 1.5 1.3 2.6 3.0
pKa 4.0
8.5 6.7 8.4 9.0 4.6 8.5 8.1 9.3 3.4 8.0 5.7; 4.1 6.7 3.9 4.4 3.2 4.1 3.9 3.1 5.1 3.8 4.1 8.0 4.3 4.6 3.2 2.3 1.4; 7.4 4.4 4.6 4.1 4.5 7.3 4.7
a/b/n/z/am b n n b n b b b n b n b b b b b n b b b n a b a a a b a a a a a n b n b am a a a a b a
FA (%) 5a 15b 28a 30 (F)c* 31b 36d 37a 45e 45d 50f 58d 60g 65b 65b 70h 70g 73g 75i 78d 80j 85j 85k 85j 88k 89k 90k 90b k 90 90(F)a 90 (F)c 92k 92j k 93 93c 93(F)a 93 (F)c 93 (F)l 94 (F)c 95k k 98 99k 100j 100m
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Compound Mw logP logD6.5 pKa a/b/n/z/am FA (%) Diclofenac sodium 296.2 4.7 2.2 4.0 a 100n b Glipizide 445.5 2.2 1.3 5.5 a 100 Ofloxacin 361.4 -0.4 -0.5 6.1; 7.7 z 100b Praziquantel 312.4 2.2 2.2 n 100j Tamoxifen 371.5 6.7 4.8 8.4 b 100b Sulfamethoxazole 253.3 0.6 -0.2 5.8 a 100 (F)c Aceclofenac 354.2 4.3 1.2 3.2 a N.F. Celecoxib 381.4 3.5 3.5 n N.F. Ebastine 469.7 6.7 4.7 8.5 b N.F. Iopanoic acid 570.9 4.2 2.5 4.8 a N.F. Mefenamic acid 241.3 5.0 2.9 4.4 a N.F. Montelukast 586.2 7.3 5.9 5.1 a N.F. Nalidixic acid 232.2 1.2 0.1 5.5 a N.F. Sirolimus 914.2 4.5 4.5 n N.F. Terfenadine 471.7 5.7 3.5 8.8 b N.F. Abbreviations used: Molecular weight (Mw); partition coefficient between octanol and water (logP); partition coefficient between octanol and water at pH 6.5 (logD6.5); acid (a); base (b); neutral in the pH-range of 2 to 12 (n); zwitterion (z); ampholyte (am); fraction absorbed (FA); not found (N.F.). * F low due to low absorption. References: a (Perez et al., 2004), b (Varma et al., 2012), c (Brunton, 2005), d (Sugano, 2011), e (Mathur et al., 1985), f (Taubert et al., 2006), g (Gan et al., 2009), h (Sanghvi et al., 2003), i (Zhu et al., 2002), j (Chu and Yalkowsky, 2009), k (Yazdanian et al., 2004), l (Loos et al., 1985), m (Lennernas, 2003), n (Chiou and Barve, 1998).
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Figures
Figure 1.
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Lo ra ze O pam flo xa D cin ap R son of ec e C oxi La i s a b ns pr o id Pr pra e az zo i q le ua N n C e ar vir tel b Ph am api en az ne az ep op in e y R ridi ifa n m e Ta pic m in ox ife n
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Ta m in o R xif ifa en m pi Ph c D en ap in az so o n La pyr e id n C so ine ar pr ba a z o m az le e p N ev in e Pr ira a z pi n iq e ua n C is tel ap r R of ide ec ox ib
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Lo ra ze O pa flo m Ta xac m in o R xif ifa e m n pi Ph en Da cin az ps o o L a py ne n ri C s o din ar e p ba ra m zo az le N ep ev i n Pr ira e az p i iq n e ua C nt is el a R pri o f de ec ox ib
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Figure 3.
Is the full potential of the BCS reached? 27 (27)
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