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Evaluation of the Structural Determinants of Polymeric Precipitation Inhibitors Using Solvent Shift Methods and Principle Component Analysis Dallas B. Warren,† Christel A. S. Bergström,‡,§ Hassan Benameur,∥ Christopher J. H. Porter,*,§ and Colin W. Pouton*,† †

Drug Discovery Biology, Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, Victoria, Australia Uppsala University Drug Optimization and Pharmaceutical Profiling Platform, Department of Pharmacy, Uppsala University, Uppsala, Sweden § Drug Delivery, Disposition and Dynamics, Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, Victoria, Australia ∥ Capsugel R&D, Strasburg, France

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‡

S Supporting Information *

ABSTRACT: The presence of polymers within solid dose forms, such as solid dispersions, or liquid or semisolid formulations, such as lipid-based formulations, can promote the maintenance of drug supersaturation after dissolution or dispersion/digestion of the vehicle in the gastrointestinal tract. Transiently stable supersaturation delays precipitation, increases thermodynamic activity, and may enhance bioavailability and reduce variability in exposure. In the current study a diverse range of 42 different classes of polymers, with a total of 78 polymers across all classes, grades, and molecular weights were examined, to varying degrees, as potential polymeric precipitation inhibitors (PPIs) using a solvent shift method to initiate supersaturation. To provide a deeper understanding of the molecular determinants of polymer utility the data were also analyzed, along with a range of physicochemical descriptors of the polymers employed, using principle component analysis (PCA). Polymers were selectively tested for their ability to stabilize supersaturation for nine poorly water-soluble model drugs, representing a range of nonelectrolytes, weak acids, and weak bases. In general, the cellulose-based polymers (and in particular hydroxypropylmethyl cellulose, HPMC, and its derivatives) provided robust precipitation inhibition across most of the drugs tested. Subsequent PCA indicate that there is consistent PPI behavior of a given polymer for a given drug type, with clear clustering of the performance of polymers with each of the nonelectrolytes, weak bases, and weak acids. However, there are some exceptions to this, with some specific drug type−polymer interactions also occurring. Polymers containing primary amine functional groups should be avoided as they are prone to enhancing precipitation rates. An inverse relationship was also documented for the number of amide, carboxylic acid, and hydroxyl functional groups; therefore for general good PPI performance the number of these contained within the polymer should be minimized. Molecular weight is a poor predictor of performance, having only a minor influence, and in some cases a higher molecular weight enhances the precipitation process. The importance of ionic interactions to the ability of a PPI to stabilize the supersaturated state was demonstrated by the advantage of choosing a polymer with an opposite charge with respect to the drug. Additionally, when the polymer charge is the same as the supersaturated drug, precipitation is likely to be enhanced. A PCA model based on polymer molecular properties is presented, which has a central oval region where the polymer will general perform well across all three drug types. If the polymer is located outside of this region, then they either show compound-specific inhibition or enhance precipitation. Incomplete separation of the PPI performance based on the molecular properties on the polymers indicates that there are some further molecular properties that might improve the correlation. KEYWORDS: supersaturation, lipid formulation, crystallization, polymer, inhibition, metastable

1. INTRODUCTION

formulations such as solid dispersions or lipid based formulations.1 Each of these approaches aims to increase the apparent solubility of a poorly water-soluble drug within the

The successful delivery and subsequent absorption of poorly water-soluble drugs after oral administration is challenging, and the bioavailability of many Biopharmaceutical Classification System (BCS) Class II drugs is limited by slow or incomplete dissolution. Strategies to address the issue of low water solubility include techniques such as modulation of crystal form, particle size reduction, salt formation, and the development of enabling © 2013 American Chemical Society

Received: Revised: Accepted: Published: 2823

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and methyl cellulose), polymethacrylates, poloxamers, poly(vinyl alcohol), poly(styrene sulfonate), and polyvinylpyrrolidone. A total of 78 polymers across all classes, grades, and molecular weights were tested to varying degrees. The molecular structures of the well-defined polymers are shown for the cellulose derivatives in Table 2 and all other polymers in Figure 2. A number of the polymers tested have poorly characterized molecular structures and are omitted from the table and figure. Principle component analysis (PCA) was used to identify specific and generalized PPIs and drug molecules that cluster together with similar variable profiles, to determine the link between PPI performance and polymer molecular properties, to determine whether PPI performance is a function of differences in polymer and drug charge, and to validate this model. The data have allowed the identification of the key properties of PPIs that dictate optimal utility when coadministered with poorly watersoluble drugs.

gastrointestinal tract or to increase the rate of dissolution. Recently, however, it has become apparent that the requirement for effective solubilization within the gastrointestinal tract need not require a significant change to equilibrium solubility.2−4 Instead, transient periods of supersaturation may be sufficient to maintain solubilization and to provide the driving force for absorption. Indeed supersaturation may provide advantages for absorption over and above that of subsaturated solutions by virtue of an increase in thermodynamic activity. The generation of a supersatured state and subsequent inhibition of precipitation has been described, by analogy, as a “spring and parachute”.5 The “spring” to create supersaturation may occur, for example, via dissolution of solid dose forms containing amorphous drug, by dissolution of pharmaceutical salts or by the dispersion or digestion of lipid-based delivery systems.4 Approaches to reduce the rate of precipitation from supersaturated solutions, that is, potential “parachutes”, include the addition of polymers, surfactants, and cyclodextrins.6 Polymeric precipitation inhibitors (PPIs) have been shown to stabilize supersaturation and therefore enhance oral absorption in both solid dose forms such as solid dispersions7 and liquid and semisolid formulations such as lipid-based formulations.3,4,8−11 For example, Gao et al.3 found that the addition of 5% w/w hydroxypropylmethyl cellulose (HPMC) to a self-emulsifying drug delivery system (SEDDS) of paclitaxel resulted in oral bioavailability of 9.5% in male Sprague−Dawley rats compared to 0.9% in the absence of the polymer. PPIs aim to maintain drug in a kinetically stabilized (but thermodynamically unstable) supersaturated state for sufficient time to allow absorption. In doing so, PPIs slow down drug precipitation/crystallization via inhibition of nucleation, crystal growth, or both. In contrast, PPIs typically have little impact on drug solubility directly, i.e. via cosolvency, when incorporated into formulations at relatively low proportions.12 The ability of PPIs to kinetically stabilize the supersaturated state is thought to result from intermolecular interactions between the drug and polymer in solution (through hydrogen bonding, hydrophobic, and ionic interactions), the ability of the polymer to sterically hinder the crystallization process, a decrease in the drug selfdiffusion coefficient, or a combination thereof.13−15 The current study presents the results of an extensive screen of the potential utility of a range of PPIs with a series of model poorly water-soluble drugs and represents a significant expansion of previously published studies.13 Data from the literature suggests that common attributes of a “good” PPI exists. However, the current scarcity of data and complexity of the gastrointestinal lumen are hindering their identification beyond specific examples. To obtain the fundamental information to address this gap in current understanding, this study of PPI using a wide variety of polymers classes and model drugs was initiated. A simplified approach was adopted to remove other phenomenological effects. This method does not model the processes that may occur within a physiological system; however it does allow isolation of the influence of the PPI on supersaturated drug precipitation and the relationship between the drugs and the polymers inhibiting precipitation. The drugs employed include nonelectrolytescarbamazepine, danazol, and ethinylestradiol, weak basesamiodarone, halofantrine, and itraconazole, and weak acidsmeclofenamic acid, mefenamic acid, and tolfenamic acid. The properties and structures of these drugs are shown in Table 1. The PPIs cover a diverse range of 42 different classes of polymers, including cellulose derivatives (such as hydroxypropylmethyl cellulose, hydroxypropylmethyl cellulose acetate succinate,

2. MATERIALS AND METHODS 2.1. Materials. The following polymers were obtained from Sigma-Aldrich Pty Ltd., Australia: alginic acid (G Alginic), gum arabic (G Arabic), locust bean gum (G Locust), xanthan gum (G Xanthan), hydroxylethyl cellulose ethoxylate quaternized (HECEQ), hydroxypropylmethyl cellulose (HPMC), methyl 2-hydroxyethyl cellulose (MHEC), poly(acrylic acid) (PAA 1.8, PAA 1250, and PAA 3000), polyallylaimine hydrogen chloride (PAAH), poly(2-acrylamido-2-methyl-1-propanesulfonic acid) (PAAMPS), polyacrylamide (PAC), poly(acrylamide-co-acrylic acid) (PACAA 200 and PACAA 5000), poly(acrylamide-codiallyldimethylammonium chloride) (PACDDA), polyanetholesulfonic acid (PAESA), poly(bis(2-chloroethyl) ether-alt-1, 3-bis(3-(dimethylamino)propyl)urea) (PCEDPU), polydiallyldimethylammonium chloride (PDDA), poly(dimethylamino-coepichloorohydrin-co-ethylenediamine) (PDEE), polyethyleneimine (PEI), poly(ethylene glycol)-block-poly(propylene glycol)-blockpoly(ethylene glycol) (PEPE 1.1 PEPE 14.6), poly(2-ethyl 2oxazoline) (PEOX 50, PEOX 200 and PEOX 500), polyepoxysuccinic acid (PESA), poly(glycidyl methacrylate) (PGMA), poly(methylvinyl ether-alt-maleic anhydride) (PMVEM), poly(propylene glycol) bis(2-aminopropyl ether) (PPGAE), poly(sodium 4-styrene sulfonate) (PSSS 70 and PSSS 1000), poly(4styrenesulfonic acid-co-maleic acid) (PSAMA), poly(vinyl alcohol) (PVA 50 and PVA 94), polyvinylpyrrolidone (PVP 10, PVP 40, and PVP 360) and cross-linked PVP (PPVP), and poly(1vinylpyrrolidone-co-2-dimethylaminoethyl methacrylate) (PVPDAM). HPMC E5, HPMC E4M(1), HPMC E4M(2), HPMC E10 M, and HPMC F4M and methylcellulose (MC A15 and MC A4M) were supplied by The Dow Chemical Company, c/o Colorcon Asia Pacific Pty Ltd., Australia. Hercules Chemical Company Inc., c/o APS Healthcare, Nuplex Industries Pty Ltd. Australia, supplied the following polymers: ethyl cellulose N100 (EC), hydroxyethyl cellulose (HEC 250GF), hydroxypropyl cellulose HXF (HPC), HPMC K200M and K4M, MC A4C, and sodium carboxylmethyl cellulose (SCMC 7H, SCMC 9M, and SCMC 12M). HEC 30000 was supplied by Shandong Head Co Ltd. and carbomers (C 340 and C 934) by Suichang Tinci Materials Technology Co. Ltd., all c/o Ceechem Pty Ltd., Australia. The Eudragit polymers (E E100, E L100, E L10055, E RL100, and E S100) were supplied by Evonik Degussa Australia Pty Ltd. Pluronic polymers (P F68, P L62, P L64, P L92, P P103, and P P105) were obtained from BASF Australia Ltd. HPMC 606 and HPMC 904, hydroxypropylmethyl cellulose acetate succinate 2824

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Table 1. Structure and Physical Properties of the Nine Model Poorly Water-Soluble Drugs Studied; Non-Electrolyte Carbamazepine, Danazol, and Ethinylestradiol, Weak BaseAmiodarone, Halofantrine, and Itraconazole, and Weak Acid Meclofenamic Acid, Mefenamic Acid, and Tolfenamic Acid

a

Measured in this study (halofantrine-free base).

(HPMCAS LF, HPMCAS MF, and HPMCAS HF), hydroxypropylmethyl cellulose phthalate 55S (HPMCP), and MC SM4

samples were obtained from ShinEtsu Chemical Co. Ltd., c/o ANZChem Pty Ltd., Australia. 2825

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Table 2. Structure of the Cellulose Based Polymers Studieda

The cellulose repeating unit has three substitution positions, and a given cellulose polymer may have up to five different substituents R groups in any one of those positions. The possible R groups are tabulated as R1 to R5. The relative frequency of each substituent may vary, as can the molecular weight of the repeating units in the R groups. It is apparent that each “polymer” represents a wide range of different materials.

a

10.0 carbamazepine, 1.0 danazol, 1.0 ethinylestradiol, 1.0 halofantrine, 1.0 itraconazole, 8.0 meclofenamic acid, 2.0 mefenamic acid, and 3.0 tolfenamic acid. For a particular drug, this maximized the precipitation “challenge” and therefore provided conditions under which the relative performance of the polymers could be most readily probed. This approach led to variability in the ultimate extent of supersaturation across the different drugs. However, in the current studies, the ability of the polymers to inhibit precipitation was the focus of the work conducted, rather than an exploration of intrinsic precipitation patterns, and this was not highly dependent on the supersaturation ratio generated (see Supporting Information, influence of supersaturation ratio on precipitation inhibition performance). Propylene glycol was selected as the cosolvent due to more similarities in its physical properties to lipid formulation components than other cosolvents previously used to generate a supersaturated solution, that is, dimethylformamide,16,17 dimethylacetamide,18,19 and 1,3-dioxolane.20 Propylene glycol is also an acceptable component for oral administration. The cosolvent

A complete alphabetical listing of the abbreviations used to identify the polymers can also be found in the Supporting Information. Danazol was supplied by Sterling Pharmaceuticals, Australia, halofantrine HCl by GlaxoSmithKline Pharmaceuticals, India, itraconazole by Lee Pharma Ltd., India, amiodarone HCl, carbamazepine, ethinylestradiol, meclofenamic acid sodium salt, mefenamic acid, and tolfenamic acid by Sigma-Aldrich Pty Ltd., Australia. Analytical grade sodium dihydrogen phosphate, disodium hydrogen phosphate, and sodium chloride were used for the aqueous phase buffer. Propylene glycol was obtained from Merck Pty Ltd., Australia. 2.2. Supersaturation Challenge Using Solvent Shift. The solvent shift method was used to generate a supersaturated solution of the studied poorly water-soluble drugs in solution containing different polymers at varying concentrations. Drugs were initially dissolved in the cosolvent (propylene glycol) at close to maximum concentration (90% of saturated solubility), with the following concentrations (mg cm−3); 1.0 amiodarone, 2826

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phase of propylene glycol (300 μL) was then added into 3000 μL of buffered aqueous phase (simulated endogenous intestinal fluid, pH 6.5, 18 mM NaH2PO4, 12 mM Na2HPO4, 98 mM NaCl, ionic strength 0.152 M21) within a 20 mL glass vial (Econo Glass Vial, PerkinElmer) containing dissolved polymer as required, thereby generating the supersaturated solution. These final dispersed solution conditions were maintained for all experiments. The phosphate buffer system was utilized due to its simplicity and common use. Nonetheless, aqueous phase conditions can have an effect on the degree of supersaturation,22 and future studies might usefully explore the impact of differing, for example, biorelevant, media on PPI performance. The resulting aqueous dispersed system (90.6% w/w water and 9.4% w/w propylene glycol) generated the following approximate drug supersaturation ratios (based on aqueous solubilities from Table 1); 1 amiodarone, 3 carbamazepine, 145 danazol, 11 ethinylestradiol, 545 halofantrine, 1090 itraconazole, 13 meclofenamic acid, 8 mefenamic acid, and 628 tolfenamic acid. The principal polymer aqueous phase concentrations tested were 0.001 and 0.1% w/v. This range was selected based on the realization that an approximate concentration of 0.02% w/v would be obtained when a 1 g capsule containing 5% w/w polymer is dispersed within stomach contents of 250 cm3. After cosolvent addition, four 300 μL samples of the dispersion were taken and immediately placed into wells of a 96-well microplate and the plate introduced into the nephelometer. Blanks containing propylene glycol (without dissolved drug) dispersed into the appropriate aqueous phase were run in parallel. The delay, 30−60 s, between mixing of solutions and the first nephelometer turbidity reading was recorded. All measurements were performed at room temperature, 20 °C. Solution turbidity was monitored for a period of 1.5 h (5400 s) using a NEPHELOstar Galaxy (BMG Labtechnologies) microplate nephelometer (λ = 635 nm laser), which records the turbidity as a function of back scattered light, not light adsorption. The following program settings were used; gain = 70, cycle time = 30 s, measurement time per well = 0.30 s, position delay 0.5 s, orbital shaking with 2 mm width for 5 s at end of each cycle. 96-well microplates, comprising flat bottomed wells made from polystyrene (NUNC) were used. 2.3. Data Classification and Analysis. The signal versus time data provided by the nephelometer provides a measure of the amount of precipitate present within the sample well. Details of how this assertion was validated are given below. In general, precipitation leads to the generation of particulates that in turn result in scatter, and ultimately, this manifests as an increase in turbidity. Due to the complex nature of the signal versus time curves observed, a classification system of three characteristic precipitation profiles has been developed;13 see Figure 1. Precipitation curves of Type C, see Figure 1c, have a high initial rate of turbidity increase (i.e., precipitation) that slows with time, reaching an asymptotic maximum value when complete. This is further divided into two subtypes; C1 (typical of profiles obtained in the absence of a PPI) and C2, which exhibits a second phase in which the signal subsequently fluctuates and then decreases due to precipitate flocculation. Type B (of which there are three subtypes, see Figure 1b) is the more complex of the three types, with 2 or 3 distinct precipitation rates that all reach an asymptotic maximum value by the end of the experiment. B1 has a higher initial rate which slows dramatically and after a further delay accelerates again, B2 has a moderate initial rate which also accelerates after a delay, and B3 has a slow initial rate that also accelerates after a delay time. The final type is Type A,

see Figure 1a, where precipitation is inhibited for the entire period of the experiment; A1 has precipitation almost completely inhibited within detection limits. A2 shows precipitation occurring at a very slow and decreasing rate. PPIs that show Type A1 behavior are the most desirable, Types A2, B1 and B2 may be suitable depending on the period required to support absorption. Systems showing C1/C2 profiles are the least effective at precipitation inhibition, with some even increasing the precipitation rate. For each combination of drug and polymer, the precipitation curve was classified and precipitation rate(s) determined using linear regression of the appropriate sections of the signal versus time curve, see Figure 1d. The lower limit of detection for the precipitation rate using this technique was 0.0010 units s−1 or 5.4 units over the time period of the precipitation experiments. (Note that in an attempt to simplify classification, labeling of the regions has changed relative to that originally proposed by Warren et al. (2010);13 with Region III and Region I being interchanged). The performance of the PPIs were quantified by calculating the relative rate of precipitation (RR), defined as the logarithm of the ratio of the precipitation rate (R) in the presence of polymer to the rate with no polymer present (Rno polymer), see eq 1. A logarithmic scale was required since the precipitation rates observed ranged from 0.001 to 100 units s−1, a variation of over 5 orders of magnitude. For example, a RR of −2 indicates that the precipitation rate was decreased by a factor of 100. RI, RII and RIII denote the relative rate of precipitation in Region I, II and III (see Figure 1d), respectively. The RR was used to classify the performance of a given PPI using the following performance ranking system; superior (RR < 2, colored green), good (−2 < RR < 1, colored blue), minor (−1 < R < 0, colored purple), no effect (R ∼ 0, colored yellow) and precipitation (ppt) enhancer (R > 0, colored red). The term “precipitation enhancer” has replaced “enhanced”, as used previously,13 to denoted PPIs that increase the rate of precipitation. ⎛ ⎞ R ⎟⎟ relative rate = log⎜⎜ ⎝ R no polymer ⎠

(1)

2.4. Principal Component Analysis. Principle component analysis (PCA) provides a means of identifying the most important determinants (i.e., molecular properties or experimental measures) of a particular behavior (in this case stabilization of supersaturation, or conversely, precipitation). PCA is well suited to large data sets and is used to extract the most important information from the data set, to compress the size of the data set (and therefore to simply the description of the data set) by keeping only this important information, and then to utilize the simplified framework for description to analyze the structure of the observations and to identify trends in performance as a function of these descriptors.23 PCA uses a mathematical procedure that attempts to explain as much of the total variation in the data set using as few factors (principle components, or weighted linear combinations of the variables) as possible.24 In doing so the components that describe most of the variation are identified as those that have the largest impact on behavior. The first principle component (PC1) accounts for the largest amount of total variation in the data. The second principle component (PC2), which is orthogonal to the first, accounts for the largest amount of the remaining total variation not already accounted for by PC1. It is possible to determine as many PCs as there are original variables, but typically only a few are needed to cover the 2827

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Figure 1. Classification of precipitation profiles and determination of precipitation rates and delay times using nephelometric experimental data of drug precipitation. Panels a−c show precipitation profiles of Type A to C, and panel d illustrates the means of precipitation rate measurement. Exemplar profiles display the precipitation of danazol in the presence of (panel a) A1 () 0.1% w/v PSSS 1000 and A2 (···) 0.1% w/v HPMC 606, (panel b) B1 () 0.001% w/v HEC 250, B2 (···) 0.1% w/v PVA 50 and B3 (---) 0.001% w/v E L100, (panel c) C1 () no polymer and C2 (···) 0.001% w/v PACAA 200, and (panel d) () experimental data with 0.001% w/v HPMC K4M and (···) fitted curve slopes. Panel d reproduced with permission and modifications from Warren et al.13 Note that the signal scale is different on each of the four graphs.

The scoring plot shows how the observations are clustered together and how strongly a variable influences those observations. Thus observations with similar variable profiles are clustered together and sit diagonally opposite those in the scoring plot that have the inverse profile. Subsequently, by using the scoring plot together with the loading plot, information on how the observations are related to the variables may be obtained. For example, in a hypothetical analysis where lipophilicity is an important determinant of the experimental end point (as might be the case, for example, in an examination of trends in drug adsorption behavior), the logP parameter would be located on the loading plot at a significant distance from the origin.

information given by the variables to allow sufficient statistical accuracy. PCA is suitable for large data sets and facilitates the identification of trends, groupings, and outliers. The results of a PCA are interpreted by examination of a “scoring plot” and a “loading plot”, both of which are critical to data evaluation. The loading plot shows the influence that each variable has on the model, with variables that contribute similarly to the total variation located within the same region of the plot and those that are inversely correlated to the variation found diagonally opposite. The stronger the influence that a variable has on the model, the further that variable point is located/ plotted from the loading plot origin. 2828

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clarify the desired activity, and also a small number of the most significant polymer molecular properties. The latter were included to allow separation of precipitation inhibition behavior not only based on measured performance (which is self-evident), but also to probe for similarities in trends based on molecular properties. PCA2. This second analysis focused on the identification of more generalized PPI performance trends that were not drugspecific and that were retained across different drug types. This was achieved via identification of the polymer clusters and trends using the performance data of the three model drugs (danazol, halofantrine, and meclofenamic acid) in the same data set along with a small number of the most significant polymer molecular properties (as per PCA1). PCA3. The aim of this analysis was to further investigate the clustering and trends observed in PCA1 and PCA2 and to determine whether the clustering behavior was affected by differences in molecular properties or whether it was driven entirely by the experimental performance variables. Only the molecular properties of the polymers were included in this data set. PCA4. The fourth analysis undertaken was to determine whether there was a link between drug class (non-electrolyte, weak base, and weak acid) and the precipitation inhibition of the polymers. In this case, the experimental performance variables for eleven polymers (E E100, HPC, HPMC E4M2, HPMCAS LF, PDDA, PEOX 500, PMVEM, PPGAE, PVA 50, PVP 360, and SCMC 7H) with all of the nine model drugs (non-electrolyte carbamazepine, danazol, and ethinylestradiol, weak base amiodarone, halofantrine, and itraconazole, and weak acid meclofenamic acid, mefenamic acid, and tolfenamic acid) were included in the data set. PCA5. The focus of PCA5 was to determine if the polymer clusters and trends of precipitation inhibition observed in PCA3 were general for all drugs and classes (non-electrolyte, weak base, and weak acid). This was determined by repeating PCA3 using a the separate data set of eleven polymers (E E100, HPC, HPMC E4M2, HPMCAS LF, PDDA, PEOX 500, PMVEM, PPGAE, PVA 50, PVP 360, and SCMC 7H) with nine model drugs (nonelectrolytecarbamazepine, danazol, and ethinylestradiol, weak baseamiodarone, halofantrine, and itraconazole, and weak acidmeclofenamic acid, mefenamic acid, and tolfenamic acid). The data set included only the molecular properties of the polymer (as per PCA3). PCA6. To validate the observations from PCA3 (i.e., that polymer precipitation inhibition performance was related to the molecular properties of the polymers, and not just the performance measures) the scoring plot from PCA3 was reproduced with only the eleven polymers (E E100, HPC, HPMC E4M2, HPMCAS LF, PDDA, PEOX 500, PMVEM, PPGAE, PVA 50, PVP 360, and SCMC 7H) tested with the nine model drugs (non-electrolytecarbamazepine, danazol, and ethinylestradiol, weak baseamiodarone, halofantrine, and itraconazole, and weak acidmeclofenamic acid, mefenamic acid, and tolfenamic acid).

Under these circumstances, in the scoring plot, highly lipophilic compounds would be clustered together in the same region as logP on the loading plot, whereas the hydrophilic substances would be located diagonally opposite to this cluster. The compounds which have a higher logP will be located within the cluster at a larger distance from the origin. For a more detailed discussion on the use of principle component analysis see Wold et al.25 and Jackson.26 PCA of the data sets was performed using Simca-P v11 (Umetrics, Sweden), with each variable mean centered and scaled to unit variance before the analysis. The purpose of each analysis determined which properties were included within the PCA. When the effect of the polymer on precipitation was the focus, only the molecular properties of the polymers that had a nonskewed distribution (some after being transformed with the cubic root) were added to the experimental results, in order to avoid giving the molecular properties too heavy weighting in the analysis. In the PCAs of polymer properties, all calculated properties were used in their original form in order to identify trends, clustering, and the role of the molecular properties on precipitation inhibition. The polymer molecular properties available included the molecular weight, number of functional groups (hydroxyl, ester, ether, carboxylic acid, primary to tertiary amines and amide), number of hydrogen bond acceptors, number of hydrogen bond donors, charge at pH 5.5, and charge at pH 6.5. Each of these properties, except molecular weight, were normalized with respect to both the polymer repeating unit (i.e., number per polymer repeating unit) or 100 MW (i.e., number per 100 MW) for analysis. The experimental results included the precipitation curve type (Type), the relative rate of precipitation in Region I (RRI), the slowest RR at 0.001 and 0.1% w/v, the concentration dependence of precipitation inhibition (CPI, calculated as the absolute change in RR between 0.1 and 0.001% w/v polymer), the gradient of slowest RR from 0.001 to 0.1% w/v polymer (logSlowest), and the gradient of RRI from 0.001 to 0.1% w/v polymer (Slope RRI). A number of the PCA plots involving multiple drugs have been colored using a performance score for the polymers that captures the overall PPI performance and complements the performance ranking system for single polymers. The performance ranking for each polymer with each drug was given the score; superior = 1, good = 2, minor = 4, none = 5, and precipitation enhancer = 7. These scores for the polymer with danazol, halofantrine, and meclofenamic acid were summed, and the overall polymer performance was determined based on the following scale: 3 (minimum score obtained from 3× scores of 1) to 5 = superior, 6 = good, 7 = good to minor, 8 to 13 = variable, and 14 to 21 (maximum score obtained by 3× scores of 7) = precipitation enhancer. In the case of the eleven polymers that were tested against nine different drugs (rather than just danazol, halofantrine, and meclofenamic acid), the sum value was divided by 3 (to provide the same comparative scale). The technique of PCA was utilized in this study to perform a number of different analyses of the data sets. The aims and details of each PCA are summarized below. PCA1. The focus of this first analysis was on the precipitation inhibition behavior of the different polymers for a particular drug and was used to cluster and separate polymers based on their ability to inhibit precipitation for a single drug. The analysis was performed for three drugs; danazol (as an example of a nonelectrolyte), halofantrine (as a weak base), and meclofenamic acid (as a weak acid). The variables examined included both experimental performance indicators (i.e., precipitation rates), to

3. RESULTS Nine model poorly water-soluble drugs were tested, to varying degrees, against 42 polymer classes, with a total of 78 polymers included across all grades and molecular weights. The structure and properties of the model drugs are shown in Table 1. The drugs examined include non-electrolytecarbamazepine, danazol, and ethinylestradiol, weak basesamiodarone, halofantrine, 2829

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is dependent on the drug species; meclofenamic acid (4.6 × 103 units mg−1), danazol (2.2 × 104 units mg−1), and halofantrine (4.9 × 104 units mg−1). This difference is likely to be due to the difference in size and shape of the precipitated particles formed by each drug; therefore, it is not possible to simply state that a given signal equates to a given mass for any precipitation. The signal per mass of precipitate was found to be consistent for a given drug; however, this may no longer hold if that in the presence of different polymers the drug forms a different type of crystal habit or polymorph. 3.2. Polymer Screening, Three Drugs. Of the polymers tested, a preliminary data set comprising data obtained for danazol alone and for a subset of 53 polymers has been published previously.13 In the current analysis this data set for danazol has been extended to include a total of 78 polymers. Also reported here are data for 63 polymers that have been tested against halofantrine and meclofenamic acid. Fifteen polymers (E L10055, E RL100, E S100, HEC 30000S, PAA 1.8, PAA 1250, PEPE 1.1, P F68, P L62, P L64, P L92, P P103, P P105, and PPVP) that were initially evaluated with danazol were excluded from further testing due to poor performance or because the same polymer (but a different molecular weight) was among the included 63. Of the 63 polymers included in the broader testing protocol, 21 were negatively charged, 12 positively charged, and the remaining 30 were neutral under the conditions of the precipitation challenge. To illustrate the data obtained, the danazol precipitation curves obtained for a selection of the polymers are shown in Figure 1, with examples shown for each of the seven different precipitation profiles. To exemplify the range of data generated across the different drugs and polymers, a cross section of the entire data set is presented in Table 3 and is discussed in detail below. The selected data include the precipitation data for danazol, halofantrine, and meclofenamic acid in the presence of five different polymers (E E100, HPMCAS HF, PCEDPU, PDDA, and PSSS 1000). This cross-section was chosen to illustrate PPI utility ranging from superior precipitation inhibition (e.g., E E100 with danazol and halofantrine, with RRs decreased to −3.74 and −2.27, respectively) to minor inhibition (e.g., PCEDPU with danazol, RR = −0.84) or precipitation enhancement (e.g., PSSS 1000 with meclofenamic acid, with RR increased to approximately 0.6) and the variation that is found with different drug types (e.g., PDDA is a precipitation enhancer with danazol, no effect with halofantrine and superior precipitation inhibitor with meclofenamic acid). The complete data set for all drugs and all polymers tested can be found in the Supporting Information, Table S2. Data for the performance of 53 of the polymers with danazol are from Warren et al.,13 and these reproduced data are highlighted in both tables. The drug dependency of the impact of the differing PPIs can be illustrated by examination of the data obtained for E E100. Absolute precipitation rates for danazol, halofantrine, and meclofenamic acid in the absence of polymer are 5.5, 51, and 69 units s−1, respectively. As stated previously, the turbidity signal is variable for different drugs due to differences in the size and shape of the precipitated particles. Therefore, it cannot be concluded from these values that danazol precipitates 10 times slower than the other two drugs. What is of interest, and of primary importance here, however, is how these absolute precipitation rates are altered in the presence of a PPI. For example, when 0.1% w/v of E E100 was added to the aqueous phase, the absolute precipitation rates were changed to 0.0010,

and itraconazole, and weak acidsmeclofenamic acid, mefenamic acid, and tolfenamic acid. The range of poorly water-soluble drugs was selected to provide variation in structure and properties. Selection of the model drugs was limited by the experimental requirements, that is, low aqueous solubility and sufficient solubility in propylene glycol, to attain a supersaturated state on aqueous dispersion. The molecular structures of the polymers tested are shown in Table 2 (cellulose derived) and Figure 2 (noncellulose). The molecular properties of the polymers (molecular weight, number of functional groups, pKa, and charge) are listed in the Supporting Information, Table S1. For some polymers (mostly the gums) the structure is ill-defined, and therefore data for G-Arabic, G-Locust, G-Xanthan, G-Xylan, C-340, and C-934 are not provided. A complete alphabetical listing of the abbreviations used to identify the polymers can be found in the Supporting Information. Some of the polymers are classified by the manufacturer as “insoluble in water” at pH 6.5 (for example E E100 is said to be insoluble above pH 5.0). Typically, however, the limit for designation as “insoluble” is in the order of 1% w/w, and in contrast, the concentrations tested in this study were significantly below this limit, that is, 0.001 and 0.1% w/v. 3.1. Nephelometry Validation. The amount of drug precipitated within a dispersed sample was determined as a function of time using a nephelometer. Nephelometry measures the amount of back-scattered light from a sample and provides the degree of obscuration from the scattering signal; it is not a measure of light adsorption. The degree of obscuration of the sample reflects the number of particles within the sample; therefore nephelometry provides an indication of precipitated material. The attraction of nephelometry is that it allows the simultaneous and automated analysis of a large number of samples using a plate reader and provides significantly improved time resolution over other current techniques.13,58 The assumption that turbidity provides a measure of the amount of precipitated material was checked using polystyrene spheres of known dimension. The precipitation of halofantrine was also monitored using both the nephelometer and dynamic light scattering to confirm that precipitation led to particles of a size that could be detected by nephelometry (results not shown). The data suggest that the turbidity produced by precipitation is a function of both particle concentration and particle size and that it can be accurately detected by nephelometry. The technique was further validated by monitoring the precipitation of three drugs at different concentrations, into aqueous buffered solution (no polymer present). The concentration of the drugs within the cosolvent was varied, thereby changing the mass available for precipitation. Figure 3 shows the precipitation profiles obtained for (a) danazol, (b) halofantrine, and (c) meclofenamic acid. These profiles reveal that, as the mass available for precipitation decreases (i.e., as the drug concentration within the cosolvent phase decreases), the signal describing precipitation also decreases. As noted previously, danazol shows a more complex precipitation profile than halofantrine or meclofenamic acid.13 The exact nature of this complexity has not been explored here; however the current working hypothesis is that it reflects discrete stages of nucleation and crystal growth in the precipitation process. Linear regression of the signal asymptote for each concentration versus the mass of precipitate produced (difference between the aqueous phase mass and equilibrium mass) fits the data well (r2 = 0.9954, 0.9683, and 0.9944 for danazol, halofantrine, and meclofenamic acid, respectively); see Figure 3d. The signal per mass of precipitate within the well (line gradient) 2830

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Figure 2. Structure of the noncellulose based polymers. Abbreviation “P ?##” denotes the six Pluronic surfactants, where ? represents the letter and ## the number, i.e. Pluronic F68 is denoted as P F68.

0.27, and 220 units s−1, for danazol, halofantrine, and meclofenamic acid, respectively. These equate to a decrease in the absolute rates of 5500 and 188 fold for danazol and halofantrine and an increase of 3.2 fold for meclofenamic acid. Alternatively, when expressed as the RR (as per eq 1) with values of −3.74, −2.27, and +0.50,

respectively. E E100 is therefore ranked as a superior PPI for danazol and halofantrine and a precipitation enhancer for meclofenamic acid. A summary of the ranking of the PPI performance at 0.1% w/v polymer for these three drugs is shown in Table 4. 2831

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Figure 3. Influence of cosolvent drug concentration on nephelometer signal from buffer only precipitation challenge; precipitation profiles of (a) danazol, (b) halofantrine, and (c) meclofenamic acid at different cosolvent drug concentrations (mg cm−3) and (d) signal versus mass precipitated for (●) danazol, (▼) halofantrine, and (■) meclofenamic acid with linear regression lines.

For danazol, at the lower polymer concentration of 0.001% w/v, eleven polymers (HPMC E4M2, MC A4M, MHEC, PAC, PAAMPS, PAESA, PGMA, PMVEM, PSAMA, PSSS 70, and PSSS 1000) were superior inhibitors of precipitation, indicating a strong interaction between the polymers and the drug molecules or crystal nuclei. All of these, with the exception of PSSS 1000, maintain superior performance with increase in the polymer concentration to 0.1% w/v. Halofantrine has four polymers that show superior inhibition at 0.001% w/v and that is maintained at 0.1% w/v; E L100, PAESA, PSAMA, and PSSS 70. The behavior of all of the polymers was markedly different with meclofenamic acid at 0.001% w/v, with only a single polymer (HPMC 606) having any inhibitory influence, where the effect was classified as “minor inhibition”. All other polymers either had no effect on meclofenamic acid precipitation or enhanced precipitation rates. The representative data in Table 3 illustrate some typical behavior observed and the complexity of the interactions that are occurring. HPMCAS HF is a good example of a polymer that performs as a general good to superior precipitation inhibitor for all three of the drugs, with the other four polymers within this table showing drug specific performance. The quaternary ammonium polymer PDDA (charge of +0.79 per 100 MW) enhances the precipitation of danazol, has little influence on

halofantrine, and is a superior inhibitor for meclofenamic acid. The meclofenamic acid inhibition is only seen at the higher polymer concentration, and the interaction of the opposing charges between the polymer and drug is likely to be the reason for this stabilization. PCEDPU (+0.67 per 100 MW), another positively charged polymer, is also a superior PPI for meclofenamic acid. Interestingly, PCEDPU is also a minor PPI for danazol and a good PPI for halofantrine, indicating that for halofantrine it is not simply the charge that is important, and that other interactions are driving the supersaturation stabilization. The importance of difference in charge between the drug and the polymer is illustrated by the negatively charged polymer, PSSS 1000 (−0.55. per 100 MW); for danazol and halofantrine it acts as a superior precipitation inhibitor but enhances the precipitation rate for meclofenamic acid. Changing the concentration of the polymer also displays the three possible types of behavior. For example, an increase in polymer concentration resulted in little change in the degree of precipitation inhibition for danazol with PCEDPU, decreased the precipitation rate of halofantrine with HPMCAS HF, and increased the precipitation rate of meclofenamic acid with E E100. The influence of polymer concentration on their PPI performance is covered in further detail below, section 3.3. 2832

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Table 3. Polymeric Precipitation Inhibitor Precipitation Challenge Results for a Select Group of Polymers When Tested against Danazol, Halofantrine, and Meclofenamic Acida 0.001% w/v polymer

type

RRI

E E100 HPMCAS HF PCEDPU PDDA PSSS 1000

B1 C1 C2 C2 A2

−0.17 0.04 −0.80 0.77 −1.66

E E100 HPMCAS HF PCEDPU PDDA PSSS 1000

C1 C1 C1 C1 C1

−0.73 −1.62 −1.33 0.28 0.19

E E100 HPMCAS HF PCEDPU PDDA PSSS 1000

C1 C1 C1 C1 C1

0.08 0.13 0.75 0.51 0.78

RRII −0.52

−3.74

0.1% w/v RRIII

type

Danazol 0.01 A1 A1 C2 C2 A1 Halofantrine C1 C1 C1 C1 A1 Meclofenamic Acid C1 B3 B2 B2 C1

RRI

RRII

RRIII

PPI ranking

−3.74 −3.74 −0.84 0.58 −1.10

superior superior minor PPT enhancer superior

−2.27 −2.08 −1.56 0.22 −4.71

superior good good none superior

0.50 −2.01 −2.49 −3.60 0.56

−1.25 −1.36 −2.33

PPT enhancer good good superior PPT enhancer

PPI ranking system; superior: RR < −2, good: −2 < RR < −1, minor: −1 < RR < 0, no effect: R ∼ 0 and precipitation enhancer: RR > 0. For complete results for all the polymers tested, see the Supporting Information, Table S2. a

included as parameters along with a range of polymer molecular properties (Supporting Information, Table S1; molecular weight, number of functional groups (hydroxyl, ester, and carboxylic acid), number of hydrogen bond acceptors, and number of hydrogen bond donors per polymer repeating unit or 100 MW). The intent of PCA1 was to identify clusters of polymers with similar abilities to inhibit precipitation and to provide some indication of common properties of the polymers that cluster in areas indicative of “good” precipitation inhibition potential. To allow insight into the latter, that is, the influence of general molecular properties on the precipitation kinetics (and not to simply separate polymers based on the experimental data), a small number of the most significant nonskewed molecular properties of the polymers (i.e., properties that were found to have a Gaussian distribution across the polymers) were also included. The scoring and loading plots of the resulting PCAs for danazol, halofantrine, and meclofenamic acid are shown in Figure 4. All of the scoring plots presented have a large circle indicating the location of the 95% confidence interval. These PCAs are not comparative; therefore it is not possible to make a direct comparison between the scoring plots (i.e., Figure 4a, c, and e) since the variables are distributed differently for each drug, as shown by the loading plots (Figure 4b, d, and f). In this analysis the slowest relative precipitation rate obtained at a 0.1% w/v polymer concentration was chosen as the primary indicator of “utility”. The color scheme in the plots therefore reflect this parameter; with polymers with superior precipitation inhibition at 0.1% w/v polymer (RR < −2) colored green, those with good to no effect (RR from −2 to 0) are colored blue, and those which enhanced precipitation (RR > 0) are colored red. From the loading plots for danazol, halofantrine, and meclofenamic acid (Figure 4b, d, and f, respectively) it is clear that RRSlowest 0.1% (circled) is diagonally opposed to the polymers that are colored green in the scoring plots (Figure 4a, c, and e, respectively), and therefore have superior precipitation inhibition. This position may appear counterintuitive; however it should be noted that a lower value of RR indicates better

In an attempt to provide a more concise indicator of polymer utility across the polymer concentration range tested for each of the three drug molecules, the complete precipitation experimental data (Supporting Information, Table S2) were condensed and summarized in Table 4. Within this table the polymers are classified based on the PPI ranking system for the relatively precipitation rates and colored appropriately; green superior (RR < −2), bluegood (−2 < RR < −1), purple minor (−1 < RR < 0), yellowno effect (R ∼ 0), and red precipitation enhancer (RR > 0). The general trends revealed by this table are: cellulose derivatives (HPC, HPMC, HPMCAS, HPMCP, MC, and MHEC) are good precipitation inhibitors for all three drugs, with the exception of SCMC which only performs well with halofantrine; PVPs are generally good precipitation inhibitors; PAC and its associated copolymers (PACAA and PACDDA) have no effect or enhance precipitation rates; and the polymers containing primary amine functional groups, that is, PAAH, PAC, PDEE, PEI, and PPGAE, should be avoided as they are prone to enhancing precipitation rates. A number of novel polymers (in terms of application to the field of precipitation inhibition of poorly water-soluble drugs) standing out as potential candidate PPIs and warrant further exploration, for example, PAESA, PEOX, PSSS, and the PVP copolymer PVPDAM. In order to better understand the trends in behavior and to better understand the reasons that underlie PPI performance, a series of principle components analyses were undertaken and are described below. 3.2.1. Drug and PPI Performance. PCA was initially performed separately for each of the three model drugs with the tested polymers data set (PCA1). The focus of PCA1 was on the precipitation inhibition behavior of the different polymers for a particular drug. The most significant performance parameters obtained from the precipitation experiments [Supporting Information, Table S2; relative rates in Region I, slowest RR at 0.001 and 0.1% w/v, concentration dependence of precipitation inhibition (CPI), the gradient of slowest RR from 0.001 to 0.1% w/v polymer (logSlowest), the gradient of RRI from 0.001 to 0.1% w/v polymer (Slope RRI) and precipitation type] were 2833

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Table 4. Polymeric Precipitation Inhibitor Ranking for Polymers Tested against Danazol, Halofantrine, and Meclofenamic Acida

Coloring is based on the PPI ranking system; greensuperior (RR < −2), bluegood (−2 < RR < −1), purpleminor (−1 < RR < 0), yellow no effect (R ∼ 0), and redprecipitation enhancer (RR > 0). bResults published previously.13

a

performance as a PPI. Therefore, the polymers with the higher RRSlowest 0.1% (colored red) are located in the same quadrant of the scoring plot as RRSlowest 0.1% within the loading plot, and conversely those with the lower RRSlowest 0.1% (colored green) are found in the diagonally opposed quadrant. 3.2.1.1. Danazol. The scoring plot of PPI performance in inhibiting the precipitation of danazol (Figure 4a) shows a clear

distribution of polymers from the worst (colored red) to the best (green) precipitation inhibitors along a diagonal from the upper right to lower left. The first principle component (PC1) explains 34% of the total variation in the data, with the second principle component (PC2) explaining a further 18%. Similar polymers and those of the same type are clustered together, with all of the cellulose derived polymers located in the center left region of the 2834

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Figure 4. Scoring (a, c, and e) and loading (b, d, and f) plots for the principle component analysis (PCA1) of PPI performance with danazol (a and b, PC1 = 34%, PC2 = 18%), halofantrine (c and d, PC1 = 34%, PC2 = 23%), and meclofenamic acid (e and f, PC1 = 30% and PC2 = 23%). Coloring based on slowest RR at 0.1% w/v polymer (RRSlowest 0.1% and circled in loading plots); green < −2, blue −2 to 0 and red > 0. Note that these PCAs are not comparative.

plot (not labeled on the diagram for clarity). The danazol loading plot (Figure 4b) indicates the importance of each of the variables to the PCA; the further that a variable is located from the origin, the stronger its influence on the data. Molecular weight (labeled as MW in Figure 4) is found in the lower right-hand quadrant of the loading plot, relatively close to the origin. The proximity to the origin indicates that molecular weight has only a minor influence on either of the principle components (PC1 and PC2) and being equidistant from each axis that it has a similar influence on both PC1 and PC2. Of the

functional groups, the number of hydroxyl, ether, and carboxylic acid groups was found to have a significant effect on PCA1 for danazol. The number of hydroxyl (−OH/Unit and −OH/100MW) and ether groups (−O−/Unit and −O−/100MW) are both in the upper left quadrant of the loading plot, and all four have almost twice as much influence on the value of PC2 versus PC1, that is, −OH/Unit is at 0.41 for PC2 versus −0.24 for PC1. However, while molecular weight, hydroxyl groups, and ether groups were found to have an influence on PC1 and PC2, their location orthogonal to the diagonal indicating the worst to best 2835

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with danazol. The number of hydroxyl groups is located in the upper right-hand quadrant and carboxylic acids in the upper central region, exhibiting the same placement relative to the worst to best diagonal as seen with danazol. This therefore suggests again that hydroxyl groups had little impact on the PCA and that the number of carboxylic acid groups had an inverse relationship between the number of functional groups and polymer precipitation performance. In contrast, however, the raw precipitation data (from Table 4) show that a number of the negatively charged polymers containing carboxylic acid groups are good PPIs. It appears, therefore, that in some cases some other property of the polymer can override this supersaturation stabilization. In the case of the number of ether functional groups, this is found close to the PC1 axis on the right-hand side of the loading plot. This is the most significant difference between the PCA for halofantrine and that of danazol and suggests that the number of ethers has a slight positive correlation with the polymer precipitation performance for halofantrine. The polymer hydrogen bonding indicators, HAcc and HDon, are located in the upper half of the loading plot, with hydrogen bond accepts to the right (alongside the number of hydroxyl groups) and hydrogen bond donors just to the left of the PC2 axis. This is the same relatively location as that observed with danazol, signifying that the number of hydrogen bond acceptors does not influence the precipitation inhibition performance of the polymers and that there is an inverse relationship with hydrogen bond donors. The polymer inhibition performance variables for halofantrine (RRSlowest etc.) are all placed in the same relative locations to the worst to best diagonal as seen for danazol, with the exception of CPI, which in the case of halofantrine appears to be an indicator of precipitation inhibition performance. 3.2.1.3. Meclofenamic Acid. The scoring plot for meclofenamic acid PPI performance (Figure 4e, PC1 explains 30% of the data, while PC2 explains a further 23%) clearly demonstrates the difficulty that the tested polymers have at slowing the precipitation rate of this weak acid; it is dominated by red, and only has four polymers colored green. The same distribution of polymer inhibition performance as found with halofantrine is visible, with a distribution of the worst to best along a diagonal from the upper left to the lower right and the same clustering of similar polymers. An outlier is also evident at the bottom right of the scoring plot. This is the positively charged polymer PDDA. Molecular weight has the same location within the loading plot (Figure 4f) as with halofantrine; consequently there is a minor inverse correlation between the molecular weight and polymer precipitation inhibition with meclofenamic acid. Similar locations of the number of hydroxyl, ether, and carboxylic acid functional groups are exhibited as for halofantrine with no significant influence, slight positive correlation, and strong negative correlation, respectively. The stronger negative correlation between the number of carboxylic acid groups and polymer precipitation inhibition indicates that charge repulsion is an important factor in the precipitation enhancement that was demonstrated with a significant number of the polymers with meclofenamic acid (see Table 4). Both of the hydrogen bonding variables are similarly located in the upper half of the loading plot, both are relatively close to the PC2 axis. The number of hydrogen bond donors, again, has an inverse correlation with precipitation inhibition. However, the hydrogen bond acceptors is a small distance away from being orthogonal to the worst to best diagonal, indicating that there is a slight negative correlation to the precipitation inhibition.

axis of polymer precipitation inhibition performance in the scoring plot suggests that these functional groups do not change the performance of polymers significantly. In contrast, the number of carboxylic acid groups (−COOH/100MW) is found in the upper right quadrant of the loading plot relatively close to the origin, indicating that carboxylic acid groups only has a minor influence on either principle component,and is diagonally opposed to the best performers and in the same quadrant as the worst. Therefore, there is an inverse relationship between the number of carboxylic acid groups a polymer contains and its precipitation inhibition performance with danazol. For hydrogen bond acceptors (HAcc/100MW) and hydrogen bond donors (HDon/100MW), the number of acceptors can be found in the upper left quadrant of the loading plot, while the number of donors is in the upper right quadrant. The number of hydrogen bond acceptors is located in the same area as the number of hydroxyl and ether groups and therefore also does not influence the precipitation inhibition performance of the polymers. However, the number of hydrogen bond donors is diagonally opposed to the best performers; therefore a smaller number of hydrogen bond donors within the polymer is advantageous to good PPI performance. The variables that are a direct measure of the polymer inhibition performance (such as RRSlowest) are found in the upper right quadrant of the PCA1 for danazol. That these performance variables are located along the worst to best precipitation inhibiting polymer diagonal is self-evident, since these variables were used to determine the precipitation inhibition performance. Data points depicting RRI, RRSlowest, and Type at polylmer concentrations of 0.001 and 0.1% w/v are found in the same area of Figure 1, since broadly similar precipitation inhibition behavior was evident at both concentrations. The three 0.001% w/v variables are located further from the origin than 0.1% w/v, indicating that these variables have a larger influence on both PC1 and PC2. This likely reflects that fact that polymers that have a detectable influence at this relatively low concentration, will have more pronounced effects at the higher concentration. The variables Slope RRI (decrease in the relative rate from 0.001 to 0.1% w/v polymer) and logSlowest (gradient of slowest RR from 0.001 to 0.1% w/v polymer) are located opposite to the other main performance indicators, since a larger value of both variables are indicative of better performance. The final performance indicator, the concentration dependence of precipitation inhibition (CPI), is located in the lower right quadrant orthogonal to the diagonal indicating best to worst precipitation inhibition performance and is therefore a poor indictor of the polymer performance. 3.2.1.2. Halofantrine. The scoring plot for the PCA1 for halofantrine (Figure 4c) shows a clear distribution from the worst (red) to the best (green) along a diagonal from the upper left to the lower right. PC1 explains 34% of the data, while PC2 explains a further 23%. Similar clustering of the polymer types was seen with halofantrine as was evident with danazol previously. A smaller number of input variables were significant contributors to the PCA as compared with danazol, with Type at both 0.001 and 0.1% w/v and Slope RRI omitted. The loading plot for halofantrine is shown in Figure 4d. Molecular weight is found in proximity to the origin of the halofantrine loading plot, in the upper left quadrant, indicating that there is a small inverse correlation between the molecular weight and the polymer performance. The same three polymer functional groups (hydroxyl, ether, and carboxylic acid) were found to be significant contributors to the PCA as was the case 2836

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Figure 5. Comparative scoring plots for principle component analysis (PCA2) of PPI performance (PC1 = 25%, PC2 = 15%) for (a) danazol, (b) halofantrine, (c) meclofenamic acid, and (d) polymer identification. Coloring is based on the slowest RR at 0.1% w/v polymer for the given drug, green < −2, blue −2 to 0, and red > 0.

The polymer inhibition performance variables are similarly placed as with danazol and halofantrine, with the exception of the two 0.001% w/v polymer concentration variables, RRI and Slowest. In the case of meclofenamic acid, these are both poor predictor of precipitation inhibition performance; due to the requirement a higher polymer concentration to have any significant impact on the precipitation rate of meclofenamic acid as compared to both danazol and halofantrine. 3.2.2. General PPI Performance. The experimental precipitation data for the three model drugs (danazol, halofantrine, and meclofenamic acid) and the polymer molecular properties were then combined into a single data set and a PCA performed. This second PCA (PCA2) focused on the identification of more generalized PPI performance trends that are not drug-specific and are retained across the three different drug types (nonelectrolyte, weak base, and weak acid). The resulting scoring plot for PCA2 is shown in Figure 5, with the locations of each of the polymers presented in Figure 5d. This scoring plot was then colored separately for danazol, halofantrine, and meclofenamic acid based on the slowest RR at 0.1% w/v polymer (as per PCA1, Figure 4) for each drug, Figure 5a, b, and c, respectively. Some polymers are absent from the plots for halofantrine and meclofenamic acid. These are the 15 polymers that were excluded from further testing beyond danazol. The loading plot for PCA2 is unimportant for this analysis (and has been omitted), since the focus is on the clustering and relative location of the

polymers within the scoring plot itself, where clusters of polymers in similar locations were expect to have similar properties. A clustering of related polymers, similar to that observed in the PCAs relating to individual drugs, was observed for the overall PCA (Figure 5d). The majority of the cellulose derived polymers are located in the lower left quadrant of the scoring plot, with the exception of SCMC 7H, SCMC 9 M, and SCMC 12M, which are located slightly above the origin along with HPMCP. These four cellulose-derived polymers are also located within a horizontal band across the plot that contains the other polymers containing carboxylic acid functional groups (E L100, EL10055, PAA, PACAA, PESA, and PSAMA). The primary amine- and amidecontaining polymers (PACAA, PDEE, PEI, and PPGAE) are found to the right-hand side, with the exception of PAC, which is located toward the center of the upper half of the scoring plot. The distribution of the precipitation inhibition performance of the polymers with danazol, Figure 5a, has the best PPIs located on the left and the worst toward the lower right. For halofantrine, Figure 5b, a very similar overall distribution is observed, although the best PPIs are concentrated to the top left of the scoring plot. This is the region where the polymers containing the sulfonate functional groups are located, that is, PAESA, PAAMPS, PSAMA, and PSSS. Conversely, meclofenamic acid, Figure 5c, shows a different distribution with the worst now to the upper right of the origin and the best in the lower half. The colored scoring plots for all three drugs have a region of overlap of 2837

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Figure 6. Scoring plot of PPI performance principle component analysis (PCA2, PC1 = 25%, and PC2 = 15%) including danazol, halofantrine, and meclofenamic acid. Coloring is based on the net PPI performance score for all three drugs; green = superior, blue = good, cyan = good to minor, yellow = variable, and red = precipitation enhancer. Data shown are the same as Figure 5, with some data points removed. Figure 5d contains the complete polymer labeling. Polymers represented by an asterisk (*) and labeled are those used for subsequent testing against an additional six model drugs.

performance across all three drugs as shown in Figure 6 (green = superior, blue = good, cyan = good to minor, yellow = variable, and red = precipitation enhancer). The complete PCAs of these two scoring plots, with the scoring plot polymer identification key and loading plots can be found in the Supporting Information, Figures S6, S7, S8, and S9. While the clustering of the PPIs is not as clear-cut as that observed when considering both experimental performance and polymer properties (Figure 6), there are regions within the polymer molecular property space where a given polymer either performs well or poorly as a general precipitation inhibitor. Both of the scoring plots show a central oval region (marked by the dashed ovals) where the polymers generally perform well across all three types of drugs. Where the polymers are located outside this region, they show either compound specific inhibition or enhance precipitation. The first scoring plot (Figure 7a) has six data points that deviate from this generalization of a central oval region with generally good PPI performance. The three red data points within the oval region correspond to PGMA, EC, and PVA, while the three good polymers that sit outside the central oval region in the bottom left quadrant correspond to PVPDAM (green), PVP (blue), and PEOX (cyan). The first scoring plot also has two regions where the properties of the polymer result in poor precipitation inhibition, that is, the upper right and upper left quadrants. When considered alongside the loading plot (Figure S7), it is apparent that these regions correspond to polymers with larger numbers of amide, carboxylic acid, and hydroxyl functional groups and higher numbers of hydrogen bond acceptors. The second scoring plot (Figure 7b) has the same six polymers present outside the central oval shaped region of good PPI performance as the first scoring plot; PGMA (red), EC (red), PVA (red), PVPDAM (green), PVP (blue), and PEOX (cyan). In this case, there is one quadrant where the polymer properties produce a poor precipitation inhibitor, the lower left. From the loading plot (Figure S9) this region corresponds to polymers containing a larger number of primary amine and carboxylic acid functional groups. 3.3. Polymer Concentration and PPI Performance. One requirement for successful PPI implementation in an oral dosage form is that the inhibition is not highly sensitive to the concentration

superior/good PPIs. This region contains the majority of the cellulose-derived polymers. To facilitate clearer identification of the polymers that are the better generic PPIs for all three drugs, the PCA2 data previously presented (Figure 5) were recolored based on the polymers’ precipitation inhibition behavior across all three drugs; see Figure 6. Those PPIs that exhibited superior to good performance for all three drugs are shown in green, blue denotes good performance for all three drugs, good to minor performance for all three drugs is shown in cyan, yellow are the PPIs which are drug-specific and show different performance for each drug, and those in red denote polymers that have no effect or enhance precipitation. A clear trend is apparent, with the best general PPIs found in the lower left quadrant of the scoring plot, moving toward the central PC2 axis of the plot are the PPIs with compound-specific responses, and the PPIs to avoid (which provide no protection or even enhance the precipitation) are located to the right-hand side. The best general PPIs include HPMC E4M(2), HPMC E10M, HPMCAS HF, MC A4M, MC SM4, and PVPDAM, while the worst are EC, PPGAE, PEI, PDEE, PACAA 500, PACDDA, and PAAH. 3.2.3. Polymer Molecular Properties and PPI Performance. The previous PCAs (PCA1 and PCA2, Figures 4, 5, and 6, respectively) included not just the molecular properties of the polymers, but also the experimental precipitation inhibition performance. The weakness of this type of analysis is that the groupings of the polymers observed within the scoring plots are potentially dominated by their PPI performance. Thus, the position in the scoring plot is dictated only by the measured PPI performance and influenced little by molecular properties. Under those circumstances the colocalization of a PPI in a given region of the scoring plot with another PPI may reflect similar PPI performance, rather than similarities in molecular properties. To probe further the reason for the clustering, another PCA (PCA3) was performed using only the molecular properties of the polymers, and the obtained scoring plot was subsequently colored based on the PPI performance. The scoring plot for the first (20%) and second (17%) PCs is shown in Figure 7a and for the third (15%) and fourth (9%) PCs presented in Figure 7b. The coloring is based on the polymers’ precipitation inhibition 2838

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Figure 7. Scoring plots for the principle component analysis (PCA3) of polymer properties (a) PC1 (20%) versus PC2 (17%) and (b) PC3 (15%) versus PC4 (9%). Coloring is based on the net PPI performance score for danazol, halofantrine, and meclofenamic acid; green = superior, blue = good, cyan = good to minor, yellow = variable, and red = precipitation enhancer. Dashed ovals indicate regions containing mostly the good to superior ranked polymers. A polymer identification key can be found in the Supporting Information, Figures S6 and S8. See Supporting Information, Figures S7 and S9 for the corresponding loading plots.

of the polymer since this may change in vivo. As noted previously, the effects of increasing polymer concentration on the precipitation rate of the drugs are divided into three observed types of behavior; little to no effect of increasing polymer concentration, decreasing rates of precipitation with increasing polymer concentration, and increasing drug precipitation rate with increasing polymer. The best PPIs are those with good precipitation inhibition at low concentrations and where increasing concentration has little effect. In the case of danazol, at the lower concentration of 0.001% w/v polymer eleven polymers (HPMC E4M(2), MC A4M, MHEC, PAC, PAAMPS, PAESA, PGMA, PMVEM, PSAMA, PSSS 70, and PSSS 1000) were superior inhibitors of precipitation, indicating a strong interaction between the polymers and the drug molecules or crystal nuclei. With the exception of PSSS 1000, all of these maintain superior performance with an increase in polymer concentration to 0.1% w/v. Halofantrine has four polymers that show superior inhibition at 0.001% w/v and this

behavior was also maintained at 0.1% w/v; E L100, PAESA, PSAMA, and PSSS 70. In contrast, the behavior of all of the polymers with meclofenamic acid at 0.001% w/v was different to the other two drugs, with only a single polymer (HPMC 606) having a minor inhibitory influence at 0.001% w/v. All other polymers either had no effect or enhanced precipitation. Six of the “good” performing PPIs, identified during testing with danazol, halofantrine, and meclofenamic acid were selected (E E100, HPMC E4M(2), HPMCAS HF, HPMCP, PEOX 500, and PVP 360) and further evaluated to determine a more detailed view of the effect of an increase in the polymer concentration from 0.0001 to 0.3% w/v. This concentration range encompasses the approximate physiological concentrations of 0.02% w/v and 0.3% w/v, obtained from dispersion of a 1 g capsule containing 5% w/w polymer and a 1 g tablet containing 75% w/w polymer, in 250 cm3 of stomach contents. Precipitation curves for each of the drugs against the polymers E E100 and HPMACAS HF are shown in the Supporting Information, Figure S5. All six polymers 2839

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3.4.1. Drug Type and PPI Performance. To establish whether a relationship exists between PPI performance and drug class (i.e., for non-electrolytes, weak bases, and weak acids), a PCA (PCA4) was carried out on the experimental precipitation inhibition data obtained for all of the nine drugs (non-electrolytes carbamazepine, danazol, and ethinylestradiol, weak bases amiodarone, halofantrine, and itraconazole, and weak acids meclofenamic acid, mefenamic acid, and tolfenamic acid) with eleven polymers (E E100, HPC, HPMC E4M(2), HPMCAS LF, PDDA, PEOX 500, PMVEM, PPGAE, PVA 50, PVP 360, and SCMC 7H). The scoring plot for PCA4 is presented in Figure 9. Clustering of the drugs based on their drug class is evident, with the non-electrolytes to the left, weak bases in the bottom right quadrant, and weak acids in the top right quadrant. Based on the range, albeit limited, of drug molecules tested here, PPIs are expected to behave in a similar manner with drugs with similar ionization behavior. An attempt was made to find trends in the distribution of the drugs within this scoring plot based on fundamental molecular properties (i.e., molecular weight, melting point, logP and hydrogen bonding). However, due to the limited number of drugs involved and the range of the molecular properties within the data set, no trends were identifiable. 3.4.2. Drug Type and General PPI Performance. A PCA (PCA5) was then performed on this data set (nine drugs and eleven polymers) using the polymer molecular properties (analogous to PCA3, Figure 7) to ascertain whether the polymer clusters and trends in precipitation inhibition observed in PCA3 were common for a wider range of drug molecules. The resulting scoring plot is shown in Figure 10, and the corresponding loading plot is available in the Supporting Information, Figure S10. The scoring plot has been colored separately for precipitation inhibition performance (green = superior, blue = good, cyan = good to minor, yellow = variable, and red = precipitation enhancer) for the non-electrolytes, weak bases, weak acids, and all drugs, as presented in Figure 10a, b, c and d, respectively. A direct comparison between this scoring plot and that presented for PCA3 in Figure 7 cannot be made, since they are both constructed from a separate data set. For the non-electrolytes (Figure 10a) six of the eleven polymers were classified as superior PPIs, and these are found clustered around the origin. In the case of the weak bases (Figure 10b) only three polymers are superior PPIs, and the clustering is similar to that of the nonelectrolytes. The precipitation inhibition properties of the polymers for the weak acids (Figure 10c) are consistent with that observed previously and that weak acids were difficult to stabilize and only one polymer, HPMC E4M(2), was classified as a superior PPI. The distributions of the PPI performance between the classes of drugs is consistent with that found in PCA2 (Figure 5), in which the non-electrolyte and weak base displayed a similar distribution on the scoring plot and a larger number of polymers were able to stabilize the supersaturated non-electrolyte. When combining the PPI performance across all drug types (Figure 10d), the best general polymer precipitation inhibitors are found in an oval region close to the origin: HPMCAS LF, HPMC E4M(2), E E100, and PVP 360. 3.4.3. Polymer Molecular Properties and PPI Performance. The precipitation inhibition performance of the polymers was found by PCA3 to be related to the molecular properties of the polymers. To validate this observation and determine whether this was specific to the three initial model drugs (danazol, halofantrine, and meclofenamic acid), the scoring plot for PCA3 (Figure 7) was reproduced using the eleven polymers that were

showed the same family of precipitation curves for a given drug with increasing polymer concentration. The shapes of these precipitation curves suggest that the effect observed is predominately kinetic in nature and does not affect equilibrium solubility. If an affect on equilibrium solubility was evident, then the precipitation curves would be expected to behave in a similar manner to those observed when the amount of drug available for precipitation was changed (i.e., an asymptotic signal decrease with increasing polymer concentration); see Figure 3. When the precipitation rates observed are plotted against the polymer concentration, they display a sigmoidal type relationship; see Figure 8. The shaded area of the figure illustrates the likely attainable polymer concentration in vivo (from 0.02 to 0.3% w/v). At low polymer concentration there is little change in precipitation behavior with increasing concentration; however, once a critical concentration is reached (and realizing that the critical concentration is dependent on both the drug and the polymer), then the precipitation rate drops rapidly. In general, a minimum rate is reached after a 10-fold increase in polymer concentration from the critical concentration, and any increase beyond this concentration has a minimal effect. A promising PPI should be on the minimum, flat region of the sigmoidal curve at all concentrations expected to be encountered within the gastrointestinal tract. All six of the polymers tested here show this favorable behavior. 3.4. Focused Polymer Screening, Larger Drug Set. An important remaining question is whether the PPI performance observed thus far is general and valid across other similar types of drugs, or whether it is specific to particular drug molecules. To address this question, a selection of eleven polymers (E E100, HPC, HPMC E4M(2), HPMCAS LF, PDDA, PEOX 500, PMVEM, PPGAE, PVA 50, PVP 360, and SCMC 7H) covering a widespread of PPI performance and polymer properties (these eleven polymers are labeled and marked with an asterisks in Figure 6) were then tested against an additional six different drugs (two non-electrolytescarbamazepine and ethinylestradiol, two weak basesamiodarone and itraconazole, and two weak acidsmefenamic acid and tolfenamic acid). See Table 1 for the structure and properties of these drugs. The results for the precipitation challenge tests are summarized in Table 5, and the complete results can be found in the Supporting Information, Table S4. The data suggest that there is consistent PPI behavior for a given polymer and drug type. For example, E E100 is a superior PPI for all neutral and weakly basic molecules, PDDA has no effect for any of the weak bases, and SCMC 7H acts as a precipitation enhancer for all the weak acids. However, some specific drug−polymer interactions do occur; for example PDDA with the non-electrolytes and weak acids, E E100 with the weak acids, PPGAE with the non-electrolytes and weak acids, and SCMC 7H with the weak bases. From this data set it can be concluded that best general PPIs (regardless of the electrolyte properties of the drug) are HPMC E4M(2) and HPMCAS HF, and the worst are SCMC 7H and PPGAE. Where the drug is a non-electrolyte, then E E100, HPMC E4M(2), and HPMCAS HF are the best performers, followed by PVA 50, PVP 360, PEOX 500, and HPC, with PPGAE and PDDA performing poorly. For weak bases, E E100, HPMC E4M(2), HPMCAS LF, and PMVEM perform best, followed by PVA 50 and PPGAE, with PDDA and HPC performing poorly. If the drug is a weak acid, then PDDA and PVP 360 are the best performing PPIs, followed by HPC and HPMCAS LF, with PPGAE, SCMC 7H, PMVEM, and E E100 performing poorly. 2840

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Figure 8. Effect of polymer concentration on the relative precipitation rate (RRI ●, RRII ▼, RRIII ■) of danazol (black), halofantrine (red), and meclofenamic acid (green) in the presence of (a) E E100, (b) HPMC E4M(2), (c) HPMCAS HF, (d) HPMCP, (e) PEOX 500, and (f) PVP 360. The shaded area is in the concentration range 0.02 to 0.3% w/v, representative of 5% w/w polymer in a 1 g capsule, and 75% w/w polymer in a 1 g tablet dispersed into 250 cm3, respectively.

maintained for this wider set of model drugs. If the molecular properties of the polymer fall within this region, then it will generally perform as a good to superior precipitation inhibitor.

tested against all nine model drugs (PCA6) and colored based on the PPI performance for all nine drug molecules; see Figure 11. The same oval regions (marked by the dashed ovals) were 2841

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Table 5. Polymeric Precipitation Inhibitor Ranking for a Selection of Eleven Polymers against Nine Drug Moleculesa

Coloring is based on the PPI ranking system; greensuperior (RR < −2), bluegood (−2 < RR < −1), purpleminor (−1 < RR < 0), yellow no effect (R ∼ 0), and redenhancer (RR > 0). bResults published previously.13

a

These results are consistent with five previous publications screening potential PPIs; however care must be taken with direct comparisons since four of these (Ilevbare et al.,12 Vandecruys et al.,17 Curatolo et al.,18 and Megrab et al.59) were performed at significantly higher polymer concentrations (20−25 times higher than the highest concentration used in this study, 2.0−2.5% w/v versus 0.1% w/v). As has been demonstrated previously (and within this study), a change in polymer concentration can have a dramatic influence on the degree of precipitation inhibition. Bevernage et al.60 detail the precipitation behavior of three neutral drugs (danazol, fenofibrate, and loviride) and two weak bases (etravirine and ritonavir) in the presence of 6 polymers (HPMCAS LF, HPMC E5, HPMC E50, HPMC E4M, HPMCP, and PVP) at a more realistic physiological concentration of 0.05% w/v polymer. The authors report that HPMC E4M, HPMC E5, and HPMCAS LF worked well as precipitation inhibitors for danazol and loviride and that HPMC and PVP (K25 grade, MW = 24 000) also had some effect. However, fenofibrate was difficult to stabilize in the supersatured state, and the weak base etravirine was similarly difficult to inhibit its precipitation. Ilevbare et al. screened 34 polymers, including a series of novel cellulose derivatives, for the effect on the solution crystal growth of ritonavir.12 In these studies, PAA was an ineffective crystallization inhibitor for the nonelectrolyte, ritonavir, consistent with the observation for danazol in this study. Vandercruys et al.17 screened 25 unidentified drugs with five different polymers (HPC, HPMC E5, PVP K30, PVP-co-VA, and PEG) at 2.5% w/v and failed to find one that would perform as a PPI for all drugs. It is not possible to separate the drugs used in the latter study into weak acids or weak bases as they are not identified within the article. Megrab et al.59 studied the inhibitory effects of eight polymers (HPMC, HPC, PVA, PEG × 2 and PVP × 3) at a concentration of 2% w/v on the precipitation of oestradiol. These authors ranked the PPIs examined in the order PVP > PVA > HPC > HPMC with PEG being recorded as ineffective. Finally, Curatolo et al.18 preformed a systematic screening of the precipitation behavior of a single drug (2-(4-ethoxybenzyl)-1,2dihydroimidazo[1,5-a] quinoxalin-3(5H)-one) (weak base, pKa 9.2) in the presence of 34 polymers at a concentration of 2% w/v. The data obtained were in general consistent with the current study, and they found that, of all the polymers tested,

Figure 9. Scoring plot for principle component analysis (PCA4, PC1 = 41%, and PC2 = 22%) based on experimental data only for all eleven polymers and nine drugs; non-electrolyte (△), weak base (□), and weak acid (◇).

Those polymers falling out side this area performed poorly and were not able to maintain the drugs in a supersaturated state.

4. DISCUSSION The initial wide ranging screen of polymer precipitation activity for danazol, halofantrine, and meclofenamic acid (section 3.2) reveal the following phenomena. As observed previously, the cellulose-derived polymers are generally good PPIs,13 with the majority ranking as at least good precipitation inhibitors. The best general PPIs that are either good or superior precipitation inhibitors for all three drugs include HPMC (6 out of 10 grades), HPMCAS HF, MC (3 out of 4 grades), PVP 360, and PVPDAM. For most poorly water-soluble drug molecules, attention can be more directly focused on the data obtained for the nonelectrolytes and weak bases, since most weak acids tend to have higher solubility under physiologically relevant conditions. Under these circumstances, a broader range of potential PPIs are apparent; E E100, HPC, HPMC (all grades), HPMCAS (all grades), HPMCP, MC (all grades), PAAMPS, PAESA, PEOX (all MWs), PMVEM, PSSS (all MWs), PSAMA, PVP 360, and PVPDAM. 2842

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Figure 10. Comparative scoring plot for principle component analysis (PCA5) of eleven polymer’s properties with nine drugs (PC1 46%, PC2 23%). Coloring based on the net PPI performance score for (a) neutrals, (b) weak bases, (c) weak acids, and (d) all drug types; green = superior, blue = good, cyan = good to minor, yellow = variable, and red = precipitation enhancer. A corresponding loading plot can be found in Supporting Information, Figure S10.

HPMCAS-MF was the most effective at maintaining drug in a supersaturated state and that the best performing polymers were HPMCP-50, HPMC K100, PVP, and PVA, followed by the poorly performing HEC, HPC, and PEI. No inhibition of precipitation was observed when using PAA, CMC, Pluronics (P-EPE), or sodium alginate (sodium salt of alginic acid). The only inconsistency of these results with the current study was the finding that sodium alginate/alginic acid had no inhibitor effects on precipitation, whereas here this polymer had a minor influence on danazol precipitation. In the current studies, the data obtained with PAC were highly variable, performing as superior, no effect and precipitation enhancer with danazol, halofantrine, and meclofenamic acid, respectively. The enhanced precipitation observed with the weakly acidic drug may be due to a direct induction of precipitation, as PAC has been demonstrated to aggregate in the presence of sodium cholate.61 For this reason, even though PAC may stabilize neutral compounds, once in small intestine aggregation is possible and may preclude ongoing utility. The weak acid meclofenamic acid was the more difficult drug of the three to sustain in the supersaturated state, with only one polymer (PDDA) able to provide “superior” precipitation inhibition. We hypothesize that the reason for this difficulty in maintaining super saturation may be the charge density of the ionised carboxylic acid group and the smaller molecular weight of the weak acid, resulting in stronger electrostatic interactions

favoring crystal formation. The importance of ionic interactions in the PPI performance is shown by the advantage of choosing a polymer with oppositely charge to the drug candidate. For example, the negatively charged polymers E L100, HPMCP, PAESA, PSAMA, and PSSS are all superior PPIs for halofantrine, and in the case of meclofenamic acid the positively charged polymer PDDA was the only PPI that was ranked as superior, with two further positively charged polymers (PCEDPU, PVPDAM) classified as good. The importance of ionic interaction is further illustrated by the fact that polymers with the same charge sign as the supersaturated drug typically enhanced precipitation. Thus, for halofantrine, the positively charged polymers PACDDA, PAAH, PDDA, PDEE, PEI, and PPGAE all enhanced precipitation (although HECEQ, PEOX (all molecular weights), and PVPDAM were exceptions to this rule). For meclofenamic acid, the negatively charged polymers E E100, EL100, G Alginic, G Xylan, PACAA 5000, PAAMPS, PAESA, PESA, PSSS (both molecular weights), PSAMA, and SCMC (all grades) also enhanced precipitation, although exceptions were also observed and HPMCAS (all grades), HPMCP and PAA 3000 inhibited precipitation. These observations are consistent with those of Van Eerdenbrugh and Taylor,62 who reported the crystallization inhibition potential of drug films formed by rapid solvent evaporation in the presence of polymers. The acidic polymers PAA and PSSS were good stabilizers for drugs with basic and amide functional groups but extremely poor 2843

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Figure 11. Scoring plots for the principle component analysis (PCA3) of polymer properties showing only the eleven polymers tested against all nine drugs, (a) PC1 (20%) versus PC2 (17%) and (b) PC3 (15%) versus PC4 (9%). Coloring is based on the net PPI performance score for each polymer across all nine drugs tested; green = superior, blue = good, cyan = good to minor, yellow = variable, and red = precipitation enhancer. See Supporting Information, Figures S7 and S9 for the corresponding loading plots.

first analysis, molecular weight had a marginal effect on precipitation inhibition, with lower molecular weight polymers tending toward improved performance, in particular with halofantrine and meclofenamic acid. Interestingly, this marginal inverse effect of polymer molecular weight on precipitation inhibition contradicts the previous observations that higher molecular weight PVP are more efficient complexing agents for salicylic acid and its derivatives63 and that higher molecular weight PVP and HPMC more effectively maintain the supersaturation of itraconazole.64 The data are, however, consistent with previous findings of limited correlation between polymer molecular weight and the ability to inhibit ritonavir crystallization for a wide variety of polymers.12 The improved precipitation inhibition with increasing molecular weight is typically attributed to an increase in viscosity, thereby decreasing the rate at which drug molecules can diffuse to the growing crystal surface.13,64 However, the molecular weight effect is clearly drug-specific, since increasing molecular weight for PVP had no influence on PPI performance for danazol or meclofenamic acid, and precipitation inhibition was improved by increasing molecular

stabilizers for acidic drugs. However, the choice of a polymer with opposite charge to the drug does not guarantee good or superior performance as a PPI. For example; the negatively charged G Xylan enhances the precipitation of halofantrine; the positively charged polymers PACDDA, PAAH, PDEE, PEI, PEOX, and PPGAE all perform as precipitation enhancers for meclofenamic acid, and the quaternary ammonium polymer PDDA has little influence on tolfenamic acid. In an attempt to better understand the patterns of precipitation behavior for the different polymers studied here, a series of principle component analyses were performed. The first principle component analysis (PCA1, section 3.2.1, Figure 4) focused on the ability of a wide range of polymers to inhibit the precipitation of a single drug and developed a cluster model based on this PPI performance. The key results for the polymer molecular properties and their relationship to the PPI performance are summarized in Table 6. It should be noted, however, that this summary table is only applicable for the specific drugs listed, and when the study is extended to a broader range of drugs, the relationships are more complex. Nonetheless, in this 2844

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Table 6. Apparent Correlations between Precipitation Inhibition of the Given Drugs for the Tested Polymers and the Polymer Molecular Properties Obtained from Two Separate Principle Component Analyses (PCA1 and PCA3) correlation drug danazol, nonelectrolyte (PCA1)

halofantrine, weak base (PCA1) meclofenamic acid, weak acid (PCA1)

none

positive

negative

molecular weight hydroxyls ethers hydrogen bond acceptors hydroxyls hydrogen bond acceptors

none

carboxylic acids hydrogen bond donors

ethers

hydroxyls

ethers

molecular weight carboxylic acids hydrogen bond donors molecular weight carboxylic acids hydrogen bond donors hydrogen bond acceptors amides 1° amines carboxylic acids hydroxyls hydrogen bond acceptors

danazol, halofantrine, and meclofenamic acid (PCA3)

The incomplete separation of PPI performance by PCA3 and PCA6 indicates that there are further molecular properties of the polymers that might improve the correlation, but which at this stage are missing from the data set. These missing descriptors might include hydrophilic or hydrophobic solvent accessible surface area and dipole moment. These parameters are not easily accessible for polymer species, although a theoretical calculation may be possible and may provide insight into the importance of these polymer properties in the precipitation inhibition process. To determine the likely performance of a polymer, input of as many molecular properties as can be identified is desirable. The solubility parameter (δ) would also be a desirable inclusion, and might be calculated using a group contribution from the summation of atomic and group contribution to the energy of vaporization and molar volume of the polymer.65 The data on the effect of polymer concentration (section 3.3, Figure 8) on precipitation inhibition show that a minimum critical concentration is required to have an influence on the precipitation rate of a supersaturated system. The precipitation rate decreases to a minimum over an order of magnitude increase in polymer concentration. The shapes of these precipitation curves suggest that the effect observed is predominately kinetic in nature, rather than representing a change to equilibrium solubility of the drug within the dispersed phase. Clustering of the performance of the PPIs with drug type (PCA4, section 3.4.1, Figure 9) was found, with non-electrolytes, weak bases, and weak acids each clustering together on the scoring plot. The performance of a given PPI is therefore seemingly consistent for similar types of drugs. In agreement with this suggestion, Bevernage et al. found that the performance of a series of HPMC grades, HPMCP and PVP, was similar for two non-electrolytes (danazol and loviride) but was different for a third non-electrolyte (fenofibrate) and different again for two weak bases (etravirine and ritonavir).60 Van Eerdenbrugh and Taylor also reported consistent behavior of polymers in inhibiting drug crystallization of drugs. In the latter cases, the polymers tested included HPMC, HPMCAS, E E100, PAA, PVP, PSSS, and poly(vinylpyrrolidone-vinyl acetate). For each of these polymers the authors reported consistent behavior for two nonelectrolytes (benzamide and phenacetin), different behavior for the two weak bases (bifonazole and lidocaine), and consistent behavior with four weak acids (chlorpropamide, chlorzoxazone, flufenomic acid, and flurbiprofen).62

weight for halofantrine (Supporting Information, Table S1). The reason for this variability is that the polymer−drug interactions are the key phenomena, rather than simply bulk diffusional properties. A negative correlation of PPI performance and the number of carboxylic acid groups was found for many drugs; however there were some exceptions with HPMCAS, HPMCP, and PSAMA performing well for danazol and halofantrine and PESA for danazol. A relationship between increasing carboxylic acid groups and decreasing precipitation inhibition is also evident in the negative correlation with the number of hydrogen bond donors. This inverse correlation with carboxylic acid groups is in contrast to that found by Ilevbare et al., who concluded that the cellulose polymers that contained more carboxylic acid groups were better inhibitors of ritonavir crystallization than those neutral or less ionized cellulose polymers.12 This is likely to be a drug specific effect for ritonavir. Our observation that there was no correlation between precipitation inhibition and hydrogen bond acceptors is also consistent with previous published results.12 The precipitation data were subsequently combined into a single data set (PCA2, section 3.2.2, Figures 5 and 6) to enable the identification of more generalized PPI performance trends that are not drug-specific and are retained across all three drug types. From this it was concluded that for danazol and halofantrine (non-electrolyte and weak base, respectively) the patterns of precipitation inhibition for the different polymers are similar but for meclofenamic acid (weak acid) the patterns are significantly different. However, an overlapping region of polymers with good precipitation inhibition behavior for all three drugs is evident, and this is where the majority of the cellulose-derived polymers were found. When the PPI performance metrics were removed from the analysis (PCA3, section 3.2.3, Figure 7) and only the molecular properties of the polymers considered, more subtle relationships were apparent between the molecular properties and precipitation performance. A central oval-shaped region of best performance was evident containing the polymers that perform well across all three of the drugs. Those polymers that are located outside the oval regions show either compound-specific inhibition or are poor inhibitors or precipitation enhancers. This observation was further validated with a larger data set of model drugs (PCA6, section 3.4.3, Figure 11), and similar performance regions were observed on the scoring plot. 2845

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indicate utility when coadministered with poorly water-soluble drugs. These properties include a negative correlation between the precipitation inhibition performance and the number of amide, primary amine, carboxylic acid, and hydroxyl functional groups. Consistent PPI behavior for a given polymer for a given drug type was also shown, suggesting that a PPI will behave in a similar manner for drugs with similar charge characteristics. The principle component analysis models present regions where polymers perform well for non-electrolytes, weak bases, and weak acids. In contrast, where the polymer is located outside of this region, compound specific inhibition or enhanced precipitation is common. The PCA models can be utilized for future development and for broader PPI evaluation; however consideration of additional molecular properties of the polymers is likely to reveal more robust relationships between the polymer structure and PPI performance. The following applications of the data generated here are envisaged. (1) For oral formulation development (non-electrolytes, weak bases, and weak acids): (i) When selecting from existing polymers, identify polymers based in Table 4 and Figure 6. Polymers with consistently high PPI performance include HPMC (6 out of 10 grades), HPMCAS HF, MC (all grades), PVP 360, and PVPDAM. (ii) When designing a new polymer, identify the position of novel polymer on scoring plots from Figure 7 using the polymer molecular properties and determine if it is within the region of good general PPI performance. (2) When working with a specific compound or class of compounds: from existing polymers, a more informed decision can be made based on Table 4 and Figure 5. For example, when limiting to non-electrolytes or weak bases, better performing polymers include E100, HPC, HPMC (all grades), HPMCAS (all grades), HPMCP, MC (all grades), PAAMPS, PAESA, PEOX (all grades), PMVEM, PSSS (all grades), PSAMA, PVP 360, and PVPDAM. (3) Screening for a suitable PPI with a given drug can be performed at 0.001% w/v polymer. If there is no significant precipitation inhibition at this concentration, then the polymer is unlikely to be a usable PPI.

To validate the observations from PCA3 (where polymer precipitation inhibition performance was related to the molecular properties of the polymers and not performance measures) the scoring plot was reproduced using the performance data for a subset of eleven polymers tested against nine model drugs (PCA5, section 3.4.2, Figure 10). The similarity in the distribution of PPI performance with the nonelectrolytes and weak bases and the significant difference in the behavior of the weak acids is consistent with that found in PCA2 (Figure 5), with a limited number of PPIs able to inhibit the precipitation process. When the PPI performance across all drug types is combined, the polymers with the best general precipitation inhibition properties are found in an oval region close to the origin and comprise HPMCAS LF, HPMC E4M(2), E E100, and PVP 360 (Figure 10d), consistent with the observations made in PCA3 (Figure 7). The working hypothesis for the mechanism by which the PPIs inhibit precipitation is that drug supersaturation is kinetically stabilized (via hydrogen bonding, hydrophobic and ionic interactions, and/or decrease in self-diffusion rates) by the presence of polymers. The importance of hydrogen bonding and ionic interactions is confirmed by this study. However, some polymers were found to act as precipitation enhancers, indicating that the reduction in the rate of precipitation for good PPIs cannot be solely due to a decrease in the effective drug diffusion coefficient. Rather, as stated previously, there must be direct interaction between the drug molecules and the polymer and not simply the bulk diffusion rate being decreased. This observation of direct interactions between the polymer and the growing precipitate crystal is supported by the results published by Yin et al.66 for the precipitation of calcium carbonate in the presence of polyacrylates. Yin et al. observed that even in the presence of the poly acrylate monomer units (acrylic acid, 2-acrylamindo-2-methypropanesulfonic acid, and 2-hydropropyl acrylate), the induction period for the crystallization period was increased with increasing monomer unit concentration. Across the range of PPIs tested, there are differences in relative solubility, and this can vary with conditions within the aqueous phase. Application of the PPI inhibition performance data therefore requires consideration of the location within the gastrointestinal tract at which precipitation inhibition is required. An exemplar pair of polymers that illustrate the importance of this issue are HPMC and HPMCAS, both having different solubilities under the various conditions found within the gastrointestinal tract. HPMC’s solubility is not influenced to a significant degree by solution pH and will dissolve within the low pH conditions of the stomach. However, HPMCAS is insoluble at stomach pH, requiring a higher pH before it dissolving. HPMCAS LF is soluble at pH > 5.5, MF > 6.0, and HF > 6.8.69 Therefore, even though HPMCAS may be a superior PPI, if the inhibition is required when the formulation disperses within the stomach, then the low solubility of HPMCAS at low pH may limit utility. However, if HPMCAS is incorporated into a delayed release formulation, whereupon it is dispersed upon exiting the stomach, then it will be present in the solubilized state at the correct time to inhibit precipitation from the supersaturated state.

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ASSOCIATED CONTENT

S Supporting Information *

Tabulated precipitation challenge data, selected profiles demonstrating the effect of polymer concentration on the precipitation challenge data, additional loading plots and detailed scoring plots for some of the principle component analyses, and a complete alphabetical listing of the polymer abbreviations. This material is available free of charge via the Internet at http://pubs. acs.org.

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AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. Tel.: +61 3 9903 9649 (C.J.H.P.); E-mail: [email protected]. Tel.: +61 3 9903 9562 (C.W.P.). Notes

The authors declare no competing financial interest.

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5. CONCLUSIONS The link between precipitation inhibition performance and the molecular properties of a range of potential PPIs has been explore using principle component analysis. The data has allowed identification of polymer properties that dictate optimal utility with respect to inhibition of drug precipitation and that might

ACKNOWLEDGMENTS The authors wish to acknowledge the financial support of D. B. Warren by ARC Linkage Project Grant (LP0884059), the financial support given by VINNOVA (Grant 2010-00966) for C.A.S. Bergström’s Marie Curie Fellowship at Monash University, and 2846

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the support of Capsugel R&D (formerly Research and Development, a division of Pfizer).

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