Distinguishing of cocrystals from simple eutectic mixtures: phenolic

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Distinguishing of cocrystals from simple eutectic mixtures: phenolic acids as potential pharmaceutical coformers Maciej Przyby#ek, and Piotr Cysewski Cryst. Growth Des., Just Accepted Manuscript • DOI: 10.1021/acs.cgd.8b00335 • Publication Date (Web): 23 Apr 2018 Downloaded from http://pubs.acs.org on April 24, 2018

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Crystal Growth & Design

Distinguishing of cocrystals from simple eutectic mixtures: phenolic acids as potential pharmaceutical coformers Maciej Przybyłek* and Piotr Cysewski

Chair and Department of Physical Chemistry, Faculty of Pharmacy, Collegium Medicum of Bydgoszcz, Nicolaus Copernicus University in Toruń, Kurpińskiego 5, 85-950 Bydgoszcz, Poland

ABSTRACT The multiparameter model comprising 1D and 2D QSPR/QSAR descriptors was proposed and validated for phenolic acids binary systems. This approach is based on the optimization of regression coefficients for maximization of true positives percentage in the pool of systems comprising either simple binary eutectics or cocrystals. The training set consisted of 58 eutectics and 168 cocrystals. The solid dispersions collection used for models generation comprised literature data enriched with our new experimental results. From all 1445 descriptors computable in PaDEL only 13 orthogonal descriptors with the highest predicting power were taken into account. The analysis revealed importance of the parameters characterizing atom types (naaN, SHsOH, SsssN, nHeteroRing, maxHBint6, C1SP2), autocorrelation functions (ATSC1i, AATSC1v, MATS8m, GATS1i) and also other molecule structure measures (WTPT-5, MLFER_A, MDEN-22). The proposed approach is very simple and requires only information about the structure encoded in canonical SMILES string. The inverting of the problem of cocrystals screening and focusing on the homogeneous group of 1 ACS Paragon Plus Environment

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coformers for cocrystallization with variety of drugs rather than seeking coformers for particular active pharmaceutical ingredient proved to be very efficient. This led to very valuable clues for selection of pairs for cocrystallization with probability about 80%.

Keywords: Pharmaceutical cocrystals, Phenolic acids, Cocrystal screening, Molecular descriptors, Nutraceutical cocrystals

Introduction It is well known that the pharmacokinetic properties of drugs are very closely related to the crystal form. Recently, extensive studies have been conducted to improve active pharmaceutical ingredients’ (APIs) relevant properties via crystal engineering including polymorphism and crystal morphology control1,2 and cocrystallization.3–6 Cocrystals deserve particular attention, due to the wide range of potential coformers, which can be utilized for tuning the physicochemical properties of APIs. Apart from bioavailability improvement and related aspects, there are several other features important from the pharmaceutical viewpoint which can be modified through cocrystallization, such as mechanical properties and stability.7,8 The latter feature seems to be interesting in the context of preserving properties of certain phytochemicals such as phenolic acids. These compounds are typical strong antioxidants, since they exhibit high radical scavenging activity,9–11 lipid peroxidation diminishing properties5,9,12 and endogenous defense systems activation ability.9,13 Phenolic acids can be divided into two major subclasses, namely hydroxybenzoic acids and hydroxycinnamic acids. Compounds belonging to the former subclass can be found in many fruits, teas, and vegetables.12,13 The latter subclass, cinnamic acid derivatives, are stronger antioxidants than hydroxylated benzoic acid analogues.14 Similarly to other phenolic acids,

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they are also effective anti-inflammatory agents.15 Due to their properties, these nutraceuticals exhibit promising therapeutic effects for the treatment of diabetes and obesity.15 Phenolic acids cocrystals are interesting from both structural and practical aspects. Due to the hydroxyl group attached to the benzene ring hydroxybenzoic acids can form various intermolecular interactions. This diversity is manifested by the variety of supramolecular synthons or even synthon polymorphism.16–18 From the pharmaceutical viewpoint APIs cocrystallization with phenolic acids seems to be advantageous for several reasons. First, it has been demonstrated that these coformers are solubility and dissolution rate enhancers.19–23 Second, recent studies7 showed that phenolic acids cocrystals exhibit antimicrobial and anticancer activity improvement, which is probably caused by their properties (high antioxidant potential, antifungal and antibacterial activity). This shows that phenolic acids can not only play the role of excipient, but can also act as active ingredients in drug-drug cocrystals. Third, it has been demonstrated that phenolic acids cocrystals are more stable than pure API. This is understandable, since these compounds are commonly used as preservatives in food industry, pharmacy and cosmetics.24 Some interesting examples of stability improvement via cocrystallization are molecular complexes of popular antituberculosis agent, isoniazid with ferulic and caffeic acids which exhibited significant stability under accelerated ICH conditions (relative humidity of 75% and 40°C).18 Cocrystals screening is one of many important strategies for seeking new forms of drugs.25 Since numerous documented examples of cocrystals and quite a lot of simple eutectics can be found in the literature, it is possible to create theoretical models that allow to reduce experimental effort by narrowing down the list of potential coformers. Based on different methodologies, several approaches enabling new multicomponent crystals designing have been raised including hypothetical cocrystal lattice energy calculation,26 Hansen solubility parameters analysis,27,28 Conductor like Screening Model for Real Solvents



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(COSMO-RS) based enthalpy of mixing calculation29–34 and various theoretical models utilizing structural parameters for thermodynamic characteristics of solids including melting point prediction.35–39 The last mentioned approaches deserves special attention due to the application of molecular descriptors, which nowadays can be easily calculated using many available on the market or freeware software. The main goal of this study is to develop effective and accurate phenolic acids cocrystals screening model based on the common 1D and 2D QSPR/QSAR descriptors. Although these parameters are very fast to compute, they give simplified characteristics of molecules’ structural features compared to the quantumchemical indices. Nevertheless, constitutional and topological descriptors were utilized in many QSPR/QSAR models and their significant contribution in predicting physicochemical properties and biological activity was confirmed.40 Our recent studies showed that hydrophilicity indices can be successfully used for classification of dissolution rate enhancing/reducing cocrystal formers.31 Therefore, theoretical predictive models based on the 1D and 2D molecular descriptors are promising tools for designing new drug formulations with improved properties. In this paper a novel approach of linear combination of descriptors screening model will be applied.

Materials and Methods Chemicals All chemicals used in this study were purchased at the highest available purity from commercial suppliers and used as received. Amides, subjected to the cocrystallization screening tests, namely urea (CAS: 57-13-6), benzamide (CAS: 55-21-0), salicylamide (CAS: 65-45-2) and ethenzamide (CAS: 938-73-8) and the following phenolic acids cocrystal formers:

2,6-dihydroxybenzoic

acid

(γ-resorcylic

acid,

CAS:

303-07-1),

3,4-

dihydroxybenzoic acid (protocatechuic acid, CAS: 99-50-3), 3,5-dihydroxybenzoic acid (α-


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resorcylic acid, CAS: 99-10-5), ferulic acid (CAS: 1135-24-6) were obtained from SigmaAldrich. Methanol used as solvent was purchased from Avantor (Gliwice, Poland).

Experimental cocrystal screening procedure During the experimental stage of this study, solid dispersions of different compounds were prepared and analyzed in terms of new cocrystal phase formation. All measurements were performed using deposited on glass slides crystallite layers obtained via droplet evaporative crystallization (DEC) technique.32,33,41–43 As it was reported in the previous works32,33,43 this method of sample preparation can be successfully used for fast and efficient cocrystal screening. The procedure involved mixing of pure components methanolic solutions (0.3 M) in 1:1 proportion and evaporation of 20 µL droplets of these mixtures on glass microscope slides at 43°C. Crystallite deposits prepared in such way were measured using powder X-ray diffractometry (PXRD) and attenuated total reflection-Fourier-transform infrared spectroscopy (ATR-FTIR). Powder patterns were recorded by applying PW3050/60 goniometer and Empyrean XRD tube Cu LFF DK303072 (2θ angle range: 5° to 40°, width step: 0.02°). Then, the obtained raw diffractograms were cleaned from Kα2 contribution, background subtracted, smoothed and normalized. ATR-FTIR spectra were recorded using Alpha-PFT-IR instrument (Bruker Optik, Ettlingen).

Computational protocol The datasets of cocrystals and eutectics were collected based on the extensive literature studies and Cambridge Structural Database (CSD)44 survey. This collection includes solid dispersions formed by phenolic acids and their precursors (benzoic and cinnamic acid). For each compound canonical SMILES codes were retrieved from the PubChem website45 and were directly used for 1D and 2D molecular descriptors calculation using PaDEL 2.21 software.46 The values of all 1444 computed descriptors were collected and used for 5 ACS Paragon Plus Environment


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distinguishing of binary cocrystals from simple eutectics by analyzing the absolute differences between descriptors corresponding to each pair. For this purpose 2×2 contingency statisitcs47 was applied, which relies on classification of true positives (TPs=properly classified experimentally observed pairs forming cocrystals) and true negatives (TNs=properly classified cases of simple binary eutectics or non-successful cocrystallizations). Before computing the absolute differences characterizing each pair the incomplete distributions of molecular descriptors (non-computable for all cases) as well as those comprising more than 80% zeroth values were rejected from further analysis. The remaining data were normalized and ranked in terms of potential of predictive power defined by the highest percentage of true positives TP% with the assumption of zeroth error (FP%=0). Linearly correlated sets (Pearsons’ correlation coefficient R2>=0.6) were rejected, which narrowed further analysis to only 13 orthogonal descriptors. Based on these variables the linear combinations were defined and their coefficients were optimized during regression analysis. Hence, the computational problem was reduced to weighting of descriptors contributions for maximization of TP%. Hence, for each size of the problem the regression function (RF) was defined as follows

∑ = ∑ |∆ |

(1)

where |∆ | = | ℎ − | and where DkI denotes the value of k-th descriptor characterizing i-th compound being either phenolic acid (PhA) or other excipients. Among the latter there are many active pharmaceutical ingredients (API). Next, threshold value defining false positives percentage equal zero (FP%=0) was estimated within 58 eutectics (EU) subset and TP% was computed for remaining 168 cocrystals (CC). The values of regression parameters were optimized in each model for maximizing the value of TP%. Hence, the applied procedure leads to identification of the threshold level guaranteeing the highest TP%


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values with as low as possible false positives in the eutectics subset. In the case of models with lower dimensions than 13 the values of corresponding regression coefficients were set to zero. This led to a total of 8178 models resulting from all combinations of 13 parameters examined in this study. The best models were analyzed in details using internal validation procedure. This step, aiming for verification sensitivity of each model on accidental data collection, relied on random exclusion of 10%, 20% and 30% of cases and re-optimization of suitable regression parameters included in the particular model. This procedure was repeated 100 times for each validation step and for every model. The error of each coefficient was calculated based on Student’s t-test approach at 99.9% confidence level.48 As a result, the set of 300 solutions of regression function was used for formulation of parameters and their uncertainties for every model. Finally, the model of the most predictive power of distinguishing cocrystals from simple binary eutectics was identified based on maximum value of TP%-FP% provided 95% confidence of prediction. Hence, models with FP% higher than 5% were excluded as too noisy. This means that in each proposed model the probability of inclusion of false positive cases is less than 5%. Details characterizing the top 20 solutions are provided in Supporting Information in Table S1.

Results and discussion Both experimental and theoretical screening of cocrystallization propensities of phenolic acids with variety of compounds was performed. In the first step the models were generated and validated according to the three-step procedure described in the methodology part. Final part presents the potential range of applications for practical purposes of selecting the most promising candidates for cocrystallization. It is worth mentioning that typical screening procedures are usually performed for seeking suitable coformers for given API. Here the reverse strategy is utilized in which for range of coformers the potential drug-like



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candidates are searched. This approach seems to be more promising since for a homogeneous set of molecules common molecular descriptors are expected to be found. Of course this narrows the range of potential applications but still seems to be very effective for practical screening of new solid forms of drugs.

Dataset characteristics Theoretical model of cocrystal screening was developed utilizing dataset comprising binary solid dispersions formed by phenolic acids with different compounds. We were able to collect 216 systems found in the literature augmented with ten new mixtures prepared and characterized in this study. The detailed characteristic of the whole training set is provided in supplementary as Table S1. It is worth emphasizing that the phenolic acids are quite often used excipients for various classes of APIs. For examples they were used in formulations with non-steroidal anti-inflammatory drugs (e.g. meloxicam49), iron chelators (e.g. deferiprone50), cardiovascular agents (e.g. caffeine51), muscle relaxants (e.g. chlorzoxazone52), anti-microbial agents (e.g. sulfamethazine53), anti-asthmatic agents (e.g. theophylline34) and anti-depressants (e.g. agomelatine54). According to our literature survey, among all mixtures, 58 systems are immiscible in the solid state. This subclass comprises simple eutectics of all considered types of phenolic acids including their precursors, namely benzoic acid26,55–67 and cinnamic acid57,68,69 and their hydroxylated analogues.26,62–66,70–77 Although in simple eutectics counterpart the non-substituted benzoic acids are dominant there are, however, some interesting exclusions. Indeed examples of solid state associates formed by these coformers include many pharmaceutically relevant complexes, such as carbamazepine-cinnamic acid molecular complex78 and benzoic acid cocrystals with many various APIs and drug-like compounds

such

as

aromatic

amides

(Isonicotinamide,79

4-(1H-pyrazol-1-

ylmethyl)benzamide80), amines (1,2-diaminobenzene,81 1,2-diaminobenzene81) diazepams


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(nevirapine82),

methylxanthines

(caffeine,83

etofylline84)

and

other

heterocycles

(trimethroprim,84 flucytosine85). The abundant hydroxybenzoic acids subclass contains cocrystals

of

4-hydroxybenzoic

acid,17,86–90

2-hydroxybenzoic

acid,4,27,90–104

3-

hydroxybenzoic acid,105–108 3,5-dihydroxybenzoic acid,107–109 2,4-dihydroxybenzoic acid,51–53 2,5-dihydroxybenzoic acid,110–112 2,6-dihydroxybenzoic acid,113–115 3,4-dihydroxybenzoic acid,33,63,88 4-hydroxy-3-methoxybenzoic acid116–118 and 3,4,5-trihydroxybenzoic acid.119–122 Hydroxycinnamic acids subclass contains binary mixtures formed by 4-hydroxycinnamic acid, ferulic, caffeic and sinapic acids.3,123–125 The dataset used for the model formulation contains also three hydroxynaphthoic acids (1-hydroxy-2-naphthoic acid, 2-hydroxy-1naphthoic acid and 6-hydroxy-2-naphthoic acid) solid dispersions.76,126–130 Although these compounds are not naturally occurring nutraceuticals, they can be assigned to phenolic acids because of their structure. Due to the presence of two condensed benzene rings, these compounds seem to be less attractive as pharmaceutical cocrystal formers than the analogous hydroxybenzoic acids. Nevertheless, hydroxynaphthoic acids were cocrystalized with various APIs, namely sulfamethazine,53 caffeine,127,129 theophylline,34,131 gabapentin,21 meloxicam,49 nicotinamide (vitamin B3),126 pyrazinamide118 and stanozolol.128

Theoretical crystals screening The binomial classification has been already successfully applied30 for crystals screening using one parameter model based on the values of mixing enthalpy. The main difference of the approach documented in this paper is related to the alternate formulation of the target function utilizing the multivariable equation. The maximization of TP% with varying regression parameters allows, in principle, for much more precise tuning of the target function and more accurate separation of the cases belonging to two considered subsets. The whole procedure requires relative values of some descriptors, which are suitable for adequate



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classification of pairs. Hence, the main paradigm standing behind the procedure discussed here is the assumption that there are molecular descriptors being able to capture structural, energetic or electronic diversities of components in formulation of either physical mixtures or intermolecular complex in the solid state. It is worth emphasizing that this intuitively appealing assumption may however, not be so exact as it seems from the first glance. For example, lidocaine is able to form 1:1 molecular complex with L-menthol resulting in stable cocrystals in the molten mixture.132,133 However, liquids of the components in unimolar proportions do not systematically recrystallize even by the thaw-melt method.134 This is an important note since DSC experiments provide different thermograms depending on whether equimolar stoichiometric compound of L-menthol and lidocaine is present in solution or not. Hence, in this case cocrystallization requires doping with cocrystals, or else a simple binary eutectic mixture is obtained instead. Fortunately such strange cases are quite rare although the interplay between kinetic and thermodynamic factors must be always considered, if a given pair of compounds is to be classified into cocrystals or eutectics subsets. Keeping this in mind, the training set was carefully inspected and authors believe that each of 216 considered pairs is properly assigned to the either subset. As mentioned in the methodology part, the molecular descriptor selected for the purpose of classification into cocrystallizing and non-crystallizing pairs comes from computable PaDEL descriptors set. It happened that in the cases of considered populations of binary eutectics mixtures and cocrystals the following types of molecular information were found to be important, namely: •

WTPT-5 weighted path, which is the summed path lengths starting from nitrogen atoms135

•

naaN is one of eccentric connectivity indices,136–138 counting E-State of :NH atom-type


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•

SHsOH, SsssN characterize atom type electrotopological state index values for hydrogen atoms of OH groups and >N- atoms (sp3 nitrogen atoms) respectively136–138

•

nHeteroRing belongs to ring count subset characterizing the number of hererocyclic rings

•

ATSC1i, AATSC1v descriptors defined using autocorrelation functions40 using averaged centered Broto-Moreau autocorrelation weighted by first ionization potential or van der Waals volumes, respectively

•

MATS8m is Moran autocorrelation with lag 8 weighted by mass40

•

GATS1i is Geary autocorrelation - lag 1 / weighted by first ionization potential40

•

MLFER_A associated with molecular linear free energy relations139 summarizing solute hydrogen bond acidity

•

maxHBint6 is atom type electrotopological state Maximum E-State descriptors of strength for potential Hydrogen Bonds of path length 6136–138

•

MDEN-22 parameters quantifying the molecular distance edges140

•

C1SP2 counting number of sp2 carbon atom types

It is worth emphasizing that distributions of these values are orthogonal and using all of them in the regression analysis justified. All possible combinations of descriptors, starting from selection of two and ending on inclusion of all 13, resulted in as high as 8178 regression equations. For each mode of size from 2 up to 13 the values of coefficients were optimized. The results of this first step of model seeking are documented in Fig. 1. It represents the distribution of obtained TP% values plotted against dimension of the regression function for all computed models.



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Fig. 1. The distribution of predictive powers of all possible 8178 models for distinguishing cocrystals (TP) from simple binary eutectics (TN) formulated at the first stage of the procedure (threshold for FP%=0).

From results presented in Fig. 1 it can be inferred that there are several models worth considering, which include only few parameters. Indeed, the most surprising is that the simplest two-parameter model has quite efficient power of distinguishing CC from EU. It comprises atom counts descriptors interrelated via the following formula: = 42.021.44 ∙ |∆naaN| # 9.680.33 ∙ |∆SHsOH|

(2)

The predicative power of the simplest model is provided in Fig. 2. Even such a crude representation of the components’ structure is quite efficient in assessments of crystallization probability. About 69.7% of TP are already embraced by the model with only 1.7% of the false positive cases. Furthermore, such simplified two-parameter quantification has quite predictive power as it was documented in left panel of Fig. 2. The classification of EU and CC according to distribution of regression function values is sufficient for practical purposes. It is


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worth mentioning that one of our cases of unsuccessful crystallization is properly classified and among nine newly synthesized cocrystals, 6 are properly assigned. Another surprising factor comes from comparison of this simple model with approach using Hmix as criterions of crystallization. In such approach29–34 the affinity of potential cocrystals formers is quantified in terms of enthalpy of mixing of hypothetical super-cooled liquids under ambient conditions. Although this model was applied with some success29,30,32,33 it seems to be less adequate for phenolic acids characteristics. Indeed, as it is documented in Fig. 2 much steeper plot of FP% cases is observed for -Hmix distribution compared to the one resulting from two-parameter regression. The same behavior can be attributed to corresponding TP% plots.

Fig. 2. Predicative power of the simples two-parameters regression model. Right panel documents the distribution of true positive cases (cocrystals) and false negative cases (simple binary eutectic mixtures) included in the regressions solution of the highest predictive power. Additionally the plot of Matthews correlation coefficient (MCC) was provided. Left panel shows results of screening for new cocrystals based on regression A. Open circles denote data from training set, while red circles stand for results of our experiments.



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The above presented two-parameter model does not belong to the top ten solutions obtained from the calculations. They are characterized in Supporting Information in Table S2. It has been found that the best model (denoted as C) which outperforms most other both in terms of maximizing TP% and minimizing FP% happens to be rich in eleven descriptors. The detailed regression formula has the following form:

= −0.700.02 ∙ |∆WTPT- 5| # 43.750.05 ∙ |∆naaN| # 8.200.01 ∙ |∆SHsOH| # 13.070.01 ∙ |∆nHeteroRing| # 1.260.01 ∙ |∆ATSC1i| # 1.660.01 ∙ |∆MLFER_A| # 10.410.01 ∙ |∆GATS1i| # 3.730.00 ∙ |∆maxHBint6| − 16.380.01 ∙ |∆SsssN| # 31.470.03 ∙ |∆MDEN- 22| − 15.730.02 ∙ |∆C1SP2| (3)

This model has much higher precision and can properly account for about 80% positive cases (%TP=79.9%, Table S2). Interestingly, model denoted as H (Table S2) consists of only six parameters and it is slightly less accurate than the best model (%TP=79.4). Therefore this classification system, represented by the following equation also deserves attention due to its simplicity.

= 43.112.43 ∙ |∆WTPT- 5| # 72.672.63 ∙ |∆naaN| # 20.411.70 ∙ |∆SHsOH| − 31.481.68 ∙ |∆MLFER_A| # 21.221.07 ∙ |∆MATS8m| # 42.993.93 ∙ |∆MDEN- 22|

(4)

Since relative values computed for each pair were normalized for given molecular descriptor, the values of regression coefficients directly inform about the significance of each parameter. In the case of all models with the highest predicting power, the most significant positive contribution to cocrystallization comes from the number of nitrogen atoms included in the


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aromatic ring, naaN. This is not surprising since carboxylic and phenolic groups can be actively involved in intermolecular hydrogen bonding with such proton acceptor centers. This conclusion is further strengthened by contribution coming from nHeteroRing descriptor, which essentially brings the same kind of information. Another quite intuitive factor positively affecting ability of cocrystallization in the case of the best model represented by eq. 3 is included in the SHsOH descriptor. However, in this case the high contributions can be attributed rather to phenolic acids than to other coformers. Since SHsOH descriptor characterizing electrotopological state of hydrogen atoms from OH groups is frequently used in phenolic compounds QSAR studies its significance is probably related to the hydrogen bonding ability of ligands to the receptor.141–143 Similar contribution and also positive comes from Geary autocorrelation descriptor weighted by first ionization potential, GATS1i. The other positive influence comes from maxHBint6. There were negatives contributions to the cocrystallization propensities of the phenolic acids, the strongest of which was the number of sp2 carbons atoms quantized by the C1sp2 descriptor. This suggests that the presence of apolar regions is not beneficial for cocrystallization of PhA. Also closed structures of nitrogen atoms in sp3 configuration make it difficult for PhA to form intermolecular complexes. Other negative contributions of minor importance can be attributed to distributions of Broto-Moreau autocorrelation function weighted by van der Waals volumes, ATSC1v. Among ten systems tested experimentally for the purpose of this work this model properly assigns to EU subset the only case of unsuccessful cocrystallization. Two of nine newly synthesized cocrystals are misclassified, but this is within accuracy of the model. It predicts exclusion of about 30% of positive cases what is in full agreement with performed experiments. In summarizing it is worth emphasizing that the best regression model selected among many possible ones leads not only to efficient classification of pairs into cocrystals and simple binary eutectics (Fig. 3) but also provides very clear structural rationale of this effectiveness.



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This shows that parameters of the proposed models are closely related to the intermolecular interactions formation potential, which is a similar concept to Hansen solubility parameters analysis.27,28 Hence restricting the interest to homogeneous classes of cocrystals formers seems to be beneficial despite the fact that the obtained model is probably not suited in general for screening purposes. However, it is directly applicable to finding such active pharmaceutical ingredient, for which synthesis of new cocrystals is expected to be very probable.

Fig. 3. Predictive power of the best model found among all 2036 studied. Notation is the same as in Fig. 2. Detailed values are provided in Supporting Information.

Application of the regression model The real strength of any QSPR model comes from its predictive potential and practical applications. Thus, full screening was performed for selected APIs using eq. 1. In the list of considered systems enumerated in Table S1 one can find several approved drugs. After selection of 48 of them, all possible combinations of pairs with 21 phenolic acids were generated and values of regression function were computed using the best model provided by 16 ACS Paragon Plus Environment

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eq. 1. The resulting distributions were graphically collected in Fig. 4. It is evident that phenolic acids are supposed to be very good cocrystals formers of many drugs. Indeed, according to our prediction each of 21 phenolic acids can be used for cocrystallization with the following drugs: Voriconazole, Fluconazole, Adenine, Sulfamethazine, Epoxiconazole, Trimethroprim, Didanosine, Phenazine, Pyrazinamide, Theophylline, Caffeine, Nevirapine, Paliperidone, Meloxicam, Danazol, Etofylline, Stanozolol, Pentoxifylline, Piroxicam, Lornoxicam, Metronidazole, Iloperidone, Etodolac, Tadalafil, Imazamox, Imazethapyr, Isoniazid or Pyraclostrobin. On the contrary, none of PhA is suitable for cocrystallziation with curcumin and only few can be chosen for synthesis of molecular complexes in the solid state with Carbamazepine, Indomethacin, Progesterone or Pregnenolone. The full list of predictions made using eg.1 is provided in Supporting Information in Table S3 and S4.

Fig. 4. Distribution of the regression function values computed using eg. 1 for the set of potential cocrystals of 21 phenolic acids with 46 API. Red line represents threshold above which the propensity of cocrystallization is claimed to by high.

Experimental cocrystal screening In our previous works,32,33,43 cocrystallization abilities of phenolic acids with several popular drugs and drug-like compounds belonging to the class of amides were examined using fast 17 ACS Paragon Plus Environment


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and efficient DEC method and theoretical COSMO-RS based calculations. These studies and many other reports22,62,92,110 showed that amides exhibit significant affinity to the phenolic acids. However, there are some exceptions. For instance benzamide cocrystalizes with salicylic acid, but on the other hand cannot form multicomponent crystals with structurally similar benzoic acid and 4-hydroxybenzoic acid.26 At this stage of the study new mixtures that have not been reported in the literature were examined. As a result of this, 9 new cocrystals were found. Noteworthy, only in the case of the benzamide-ferulic acid system was the cocrystal phase not detected. The results of experimental cocrystal screening were summarized in the Table 1. In Fig. 5 exemplary PXRD patterns and FTIR spectra of ethenzamide-ferulic acid were presented. The data for the other compounds in this study are summarized in the Supporting Information (Fig. S1-S9). The new cocrystal phase can be detected by the appearance of new diffraction peaks on the PXRD pattern, which cannot be assigned to the pure coformers and by the absorption bands shifts corresponding to the new hydrogen bonds formation on FTIR-ATR spectra. In the case of the ethenzamide-ferulic acid system, the cocrystal can be identified by the appearance of intense diffraction peak at 2θ=27.1° and two less abundant peaks at 2θ=11.3° and 13.6° (Fig. 5). Cocrystal formation can be also easily confirmed by the characteristic absorption bands shifts. As one can see from Fig. 5, new intermolecular interactions between ethenzamide and ferulic acid are manifested by the blue shifts of N-H absorption stretching modes, ν(NH) at 3169 and 3368 cm-1 on pure ethenzamide FTIR-ATR spectra, leading to the appearance of three peaks at 3186, 3289 and 3427 cm-1. Similarly a characteristic ν(NH) band shifts can be observed for benzamide-3,4dihydroxybenzoic acid (Fig. S3), nicotinamide-2,6-dihydroxybenzoic (Fig. S6), nicotinamide3,4-dihydroxybenzoic acid (Fig. S7) and nicotinamide-3,5-dihydroxybenzoic acid (Fig S8) solid dispersions. On the other hand, in the case of several other positive examples, due to the overlapping of absorption bands coming from the pure components, it is difficult to assign


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


unambiguously absorption bands to the amide ν(NH) stretching modes or to ν(OH) of phenolic acids. Nevertheless, in all these cases new absorption bands appeared on the spectra of the mixture which confirms new molecular complex formation. As one can see from Table. 1, new crystal phase can be observed in almost all cases except the benzamide-ferulic acid solid dispersion. In the case of this system all peaks corresponding to the mixture are overlapping with pure components diffraction signals. Another confirmation of immiscibility in the solid state comes from FTIR-ATR spectra, since no absorption bands shifts can be observed (Fig. S4).

Table 1. The results of experimental cocrystal screening, “+” denotes positive cases (cocrystal formation), while “-“ denotes negative cases. amide

phenolic acid

screening characteristic score

urea

benzamide

new crystal phase 2θ signals [°]

3,4-dihydroxybenzoic acid +

10.8, 17.2, 27.1

ferulic acid

8.8, 19.6, 24.4, 28.0

+


23.2, 27.9

ferulic acid

-

-

salicylamide

ferulic acid

+

16.1, 18.0, 19.6, 23.1, 25.9, 27.2

ethenzamide

ferulic acid

+

11.3, 13.6, 27.1

nicotinamide 2,6-dihydroxybenzoic acid +

26.0, 27.8


8.2, 16.5


9.6, 14.0, 14.6, 16.8, 17.3

ferulic acid

19.2, 25.3, 26.1, 27.0

+



mixture ferulic acid

Intensity

ethenzamide

5.0

10.0

15.0

20.0

25.0

30.0

35.0

40.0

2θ [° ] 1.0

normalized transmitance

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

Page 20 of 39

0.8

0.6

0.4

mixture

0.2

ferulic acid ethenzamide 0.0 2400

2600

2800

3000

3200

3400

3600

wave number [cm-1]

Fig. 5. PXRD pattern and FTIR-ATR spectra of ethenzamide-ferulic acid system.

Conclusions In this paper novel screening approach involving simple molecular indices for assessment of phenolic acids cocrystal potential was developed. The selection of these particular molecular compounds was guided by their practical applicability since phenolic acids containing solid dispersions have been extensively studied. Proper selection of pairs of


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chemical compounds for successful cocrystallization is not a trivial task. Hence, many theoretical models of cocrystals thermodynamic properties prediction involving combination of structural features and descriptors have been proposed.35–39 This paper presents new strategy for theoretical cocrystals screening which utilizes 1D and 2D molecular descriptors. This method offers several unique and advantageous features. First of all, this approach enables for finding adequate molecular descriptors properly assigning both similarities and diversions of structural, electronic and energetic contributions for homogenous set of cocrystals. The crucial step is addressed to proper definition of the target function enabling for maximization of involved true positive cases and at the same time minimization of number of false positives included in the prediction. Here it is done by optimization of weighting coefficients of relative values of selected molecular descriptors. As a result, a series of equations were generated with very promising predictive power. The real strength of the proposed approach is clearly documented by finding very simple two-parameter regression formula, which is only slightly less precise compared to the best model defined using eleven molecular descriptors. The proposed and validated procedure is very simple and does not require complex computations since all necessary information about the structure is decoded from canonical SMILES string. Since the focus was restricted exclusively to phenolic acids as cocrystals formers proposed model is probably not adequate for other sets of compounds. However, it is general in the sense that broad range of active pharmaceutical ingredients can be tested for cocrystallization with considered here group of coformers. The extension to other classes of compounds seems to be straightforward and worth further exploration.

SUPPORTING INFORMATION



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Supporting information contains phenolic acids cocrystals and eutectics database, classification models details, theoretical cocrystal screening results and collection of PXRD patterns and FTIR-ATR spectra recorded for newly synthesized dispersions.

ASSOCIATED CONTENT AUTHOR INFORMATION Corresponding Author *

E-mail: [email protected]

ACKNOWLEDGMENT This study was supported by the Nicolaus Copernicus University in Toruń (grant MN2/WF/2017). REFERENCES (1)

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For Table of Contents Use Only Distinguishing of cocrystals from simple eutectic mixtures: phenolic acids as potential pharmaceutical coformers Chair and Department of Physical Chemistry, Faculty of Pharmacy, Collegium Medicum of Bydgoszcz, Nicolaus Copernicus University in Toruń, Kurpińskiego 5, 85-950 Bydgoszcz, Poland

Synopsis: The model comprising 1D and 2D molecular descriptors was proposed and validated for phenolic acids cocrystal screening. This approach based on the methodology involving optimization of regression coefficients for maximization of true positives percentage leads to very valuable clues for predicting cocrystallization with probability about 80%.


Distinguishing of cocrystals from simple eutectic mixtures: phenolic

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