Systematic Insights from Medicinal Chemistry To Discern the Nature of

Article pubs.acs.org/Macromolecules

Systematic Insights from Medicinal Chemistry To Discern the Nature of Polymer Hydrophobicity Andrew J. D. Magenau,*,† Jeffrey A. Richards,∥ Melissa A. Pasquinelli,‡ Daniel A. Savin,§ and Robert T. Mathers*,∥ †

Materials Science and Engineering, Drexel University, Philadelphia, Pennsylvania 19104, United States Fiber and Polymer Science Program, North Carolina State University, Raleigh, North Carolina 27695, United States § Department of Chemistry, University of Florida, Gainesville, Florida 32611, United States ∥ Department of Chemistry, Pennsylvania State University, New Kensington, Pennsylvania 15068, United States ‡

S Supporting Information *

ABSTRACT: Predicting polymer hydrophobicity based on monomer structure is an illposed problem. Generally, the hydrophobicity of a polymer or a series of polymers has been determined through indirect methods (i.e., contact angle) after polymerization. This sequence presents a problem for the systematic design and rapid evaluation of specialty polymers synthesized via controlled polymerization methods. Here, we propose an approach inspired by medicinal chemistry to predict polymer hydrophobicity based on octanol−water partition coefficients (LogPoct) determined through simple computational approaches. We envisioned that LogPoct, analogous to what is used in drug design, could provide a rational methodology to translate molecular structures of monomers and oligomers into quantifiable hydrophobicity values for polymers. A combination of critical design criteria and the predictive power of LogPoct values, normalized by surface area (LogPoct/SA), accurately assess polymer hydrophobicity. Experimental corroboration with a polarity-sensitive dye (i.e., Nile Red), advancing water contact angles measurements, and swelling ratio experiments verify the method represents a dramatic improvement. A direct and quantitative correlation existed between spectral shifts of Nile Red and calculated LogPoct/SA values, confirming a quantifiable metric for predicting polymer hydrophobicity. Computationally predicted values also resulted in a first approximation of advancing contact angle measurements over a broad spectrum of common polymers providing a basis for estimating contact angles, a screening tool to enhanced monomer design a priori, and a criterion to understand polymer physical properties. Furthermore, swelling ratio measurements elucidated boundary limits for swelling of relatively hydrophobic and hydrophilic polymers in water and hexanes, in addition to alternative alcohol derivative solvents.

■

nanoparticles,18,19 give many options for probing hydrophobic environments. In the area of drug delivery, Lipinski developed a rational approach to predicting oral bioavailability of drug candidates in the human body, termed the Rule of Five,20 in order to reduce the drug discovery timeline and identify pharmaceutical candidates. The success of this method and related strategies that identify potential adverse health effects21 and biocompatibility22 rely on metrics connected to molecular structure, such as molecular weight, number of hydrogen bonding sites, degree of polar surface area (PSA), and a measure of hydrophobicity called the partition coefficient, or LogP.23,24 As defined in eq 1, LogP quantifies how the concentration of molecules partition between water and a nonpolar solvent.

INTRODUCTION The terms hydrophobicity, hydrophilicity, and lipophilicity are ubiquitous throughout many disciplines of science. These concepts provide a framework for understanding physical phenomena including, but not limited to, protein folding,1 surface wetting,2 thermoresponsive solubility,3 self-assembly,4 and drug adsorption,5 and adsorption of proteins.6 Generally, the concept of hydrophobicity in polymeric materials is qualitatively recognized but not readily quantif iable prior to a polymerization. For instance, polymers based on lactide,7 caprolactone,8 adipic acid,9 phellandrene anhydride,10 and trimethylene carbonate11 are all viewed as hydrophobic; however, ranking their relative degrees of hydrophobicity presents a challenging problem that necessitates experimentation. As a result, hydrophobicity has typically been assessed af ter a polymerization by contact angle measurements for nonpermeable surfaces or other informative methods for porous and rough surfaces.12 For example, swelling ratio strategies offer insight into the hydrophobicity of porous hydrogels13 while inverse gas chromatography analysis allows assessment of fibers.14 These approaches and others, such as spectrofluorometric measurements of protein surfaces15 and solvatochromic approaches in micelles,16 microvessels,17 and © XXXX American Chemical Society

⎛ [soluteorganic] ⎞ ⎟⎟ LogP = log⎜⎜ ⎝ [soluteaqueous] ⎠

(1)

Received: August 7, 2015 Revised: September 15, 2015

A

DOI: 10.1021/acs.macromol.5b01758 Macromolecules XXXX, XXX, XXX−XXX

Article

Macromolecules

Chart 1. Theoretical Monomer Library Arranged in Order of Increasing LogPoct Values Based on Comparison of Experimental and Theoretical LogPoct Values for Common Monomers

ease of determining LogPoct values via computational methods and the need for a quantifiable metric to evaluate polymer hydrophobicity, LogPoct was explored as a preferred tool for designing and evaluating macromolecules. The dramatic growth of the field of cheminformatics has led to tools for assessing the connections between the structure of a molecule and its properties, including the prediction of LogPoct.36 Most methods rely on data mining of experimental and/or calculated values for prediction refinement. Substructurebased methods for calculating LogPoct and other properties are broken into four general categories: (1) atom-based methods that split molecules into contributions of individual atoms; (2) reductionist fragment-based approaches that break molecules into fragments and incorporate intramolecular interactions through correction factors that are derived from multiple regression of experimental data; (3) constructionistic fragmentbased approaches, where basic fragment values are derived from measured LogP data of simple molecules and then more complex molecules are constructed from these basic fragments; and (4) mixed approaches that combine both fragment-based approaches and/or atom-based methods. Property-based methods for calculating LogP have also been developed, which take into account descriptions of the entire molecule and are composed of (1) empirical approaches, (2) methods that use the 3-dimensional structure of the molecule, and (3) approaches that are based on topological descriptors. To verify the predictive power of computational LogPoct values, we chose five readily available computational methods: ACD/Laboratories is a mixed atom/fragment constructionist structure approach, EPI Suite is a mixed atom/fragment reductionist structure approach, ChemAxon and ChemBioDraw are both atom structure approaches, and AlogPS is a propery-based approach. For 20 commonly encountered monomers (Table S1), a direct agreement was observed between the experimental and theoretical LogPoct values (Figure S1) for a broad range of monomer classes (Chart 1), e.g., vinyl, diols, lactones, lactams, anhydrides, epoxides, and cycloalkenes. This analysis revealed that the mixed atom/fragment constructionist approach (ACD/ Laboratories) best predicts the LogPoct of the monomers (coefficient of determination, R2 = 0.98), although all computational approaches sufficiently reflect their experimental counterparts with R2 ≥ 0.86. The largest deviations from experimental values were observed for monomers with higher LogPoct values and negative LogPoct values, especially for monomers that have rings in their structure (such as norbornene and caprolactam). These observations indicate that computational LogPoct values afford a systematic method

Two common partition coefficients include octanol−water (LogPoct) and alkane−water (LogPalk). These LogP values, which can be either positive (hydrophobic) or negative (hydrophilic), offer insight into drug adsorption25 and solubility.26 For drugs with significant hydrogen bonding, the difference between LogPoct and LogPalk, which is known as ΔLogP, provides additional insight.27,28 Since drug-like molecules share many of the same functional groups as monomers, LogP values have an underutilized potential as a predictive tool in polymer science. Our motivation is to develop an approach inspired by medicinal chemistry to control the hydrophobicity of polymer materials during the polymer synthesis process. This thought was inspired from efforts to fine-tune the hydrophobicity of polyesters10,29,30 through synthesis strategies,31,32 where a need emerged for a metric that could rank a series of hydrophobic anhydride monomers. Consequently, we hypothesized that certain metrics from medicinal chemistry could provide a general method to translate molecular structures of monomers into a quantifiable degree of hydrophobicity of the corresponding polymers. As a result, we chose to focus on LogPoct due to relevance and utility in streamlining the drug discovery process. As the investigation proceeded, a question arose as to whether LogPoct of oligomers could predict certain physical properties of the corresponding polymers. Since LogPoct can be readily computed using a variety of available software packages, this approach could provide the ability to predict properties of the polymer a priori based on its monomer structure. As such, the following report describes LogPoct values as a metric for predicting polymer hydrophobicity and demonstrating its correlation with water contact angles, swelling ratios, and the solvatochromatic behavior of Nile Red. This general method is envisioned to facilitate design of monomer libraries and diblock copolymers while also promoting a better understanding of how changes at the molecular level, such as functional groups and alkyl substituents, influence the hydrophobicity of monomers and polymeric materials. Thus, this work also has relevance to materials genome efforts for polymer materials.

■

RESULTS AND DISCUSSION At present, a number of experimental methods exist for determining LogPoct values for drugs, monomers, and solvents.33 Advances in computational methods have streamlined the assessment of hydrophobicity of these substances, alleviating the necessity for cumbersome experimental methods, e.g., the shake-flask method or reverse-phase high-pressure liquid chromatography (HPLC).34,35 Therefore, owing to the B


Article

Macromolecules

on LogPoct values and begin to approximate the polymer backbone. In this regard, LogPoct values for gaseous monomers, i.e., ethylene, propylene, 1,3-butadiene, vinyl chloride, and cyclobutene, underestimate the hydrophobicity of their corresponding polymers. Third, models for alternating copolymers should have even numbers of repeat units, such as hexamers (ABABAB) or octamers rather than trimers (ABA and BAB) or pentamers (ABABA and BABAB). Analysis of oligomer size for polyesters (Figure S3) and poly(styrene-alt-maleic anhydride) copolymers (Figure S4) suggest variability of trimers (ABA versus BAB) diminishes as the size of the oligomer increases. Consequently, hexamers (ABABAB) afford a reasonable balance between representing the polymer structure, minimizing the influence of the chain ends, and providing reasonable computation time. Fourth, molecular models for condensation polymerizations or ring-opening polymerization (ROP) that involve chain ends with hydrogen bond donors or acceptors can be modified by replacing the −OH or −COOH group with a methyl ester (−COOCH3) or methyl ether (−OCH3). Although hydrogen bonding lowers the LogPoct/SA value, investigation of poly(ethylene oxide) (Figure S5), polycaprolactone (Figure S6), and poly(trimethylene carbonate) (Figure S7) oligomers indicates the influence of hydrogen bonding begins to diminish as the molecular models approach decamers. As such, Chart 2 shows the methoxy-terminated (−OCH3) end group for polycaprolactone to diminish the chain end influence. Correlation of LogPoct/SA with Spectral Shifts of Solvatochromic Probes. To confirm the prediction that LogPoct/SA values provide an unprecedented metric for quantifying the hydrophobicity of soft materials, a series of cross-linked polymer films were synthesized and evaluated against a polarity-sensitive probe. We speculated that Nile Red, an established solvatochromic,38−41 thermochromic,40,41 and rigidity sensitive dye,42,43 embedded into polyacrylate films of varying polarity would be capable of reflecting the actual polarity and hydrophobicity of their microenvironment. Although Nile Red has been utilized within polymer matrices,42−44 this work provides the first systematic analysis of numerous acrylate derivatives of vast and disparate hydrophobicities correlated against easily computed LogPoct/ SA values. The LogP and SA values (cf. Table S2) of these 13 polyacrylates consist of homopolymers and copolymers calculated from oligomeric trimers (AAA) and hexamers (ABABAB), respectively. Figure 1 gives the absorbance maxima (λmax) of Nile Red embedded into polymer matrices by UV/vis spectroscopy as a function of LogPoct/SA. Visual inspection of the three films (Figure 1 insets) revealed marked color differences between the relatively hydrophilic poly(hydroxyethyl acrylate) (PHEA), moderate hydrophobic poly(ethyl acrylate) (PEA), and hydrophobic poly(ethylhexyl acrylate) (PEHA) films. Overall, these data illustrate that a direct, quantitative correlation exists between spectral shifts in the polarity sensitive Nile Red and computational LogPoct/SA values. This observation confirms the unique potential of LogPoct/SA values as a quantifiable method for predicting polymer hydrophobicity. As films in Figure 1 became progressively more hydrophobic, λmax decreased over a range of ca. 30 nm while LogPoct/SA values were increasingly positive. The majority of samples exhibited a linear correlation between λmax and postitive LogPoct/SA values. For hydrophilic matrices (i.e., negative LogPoct/SA), the change in slope is partially attributed to

to rank a series of monomers (Chart 1) in order of increasing hydrophobicity. Although ranking monomers in Chart 1 illustrates a powerful tool for predicting hydrophobicity on a continuum, monomers inherently transform after polymerization due to changes in hybridization, ring-opening, end groups, etc., which often underestimate hydrophobicity. As a result, a central question arose regarding how to model a polymer in a way that would best capture the physical properties of homopolymers and copolymers in the calculation of LogPoct, while minimizing computation time. Accordingly, oligomers ranging from dimers to dodecamers (Figures S2−S7) for a series of ∼40 homopolymers and copolymers were investigated. After considering issues of hybridization, influence of end groups, molecular weight, and cyclic monomers, oligomers of three to five monomer units (Chart 2) were determined to mimic the Chart 2. Representation of Homopolymers with Molecular Models

molecular structure of the corresponding polymer and provide an accurate method for predicting polymer hydrophobicity. Although oligomers only represent a fraction of the actual polymer, even molecules as small as neopentane elicit a hydrophobic response that represents the hydrophobicity of much larger molecules.37 Four criteria were established to design molecular models: First, normalizing the LogPoct values by the molecular surface area (SA) allows flexibility in choosing the size of the oligomeric molecular model and minimizes molecular weight issues. Without normalization, the LogPoct values increase with increasing size (Figure S8), and choosing a molecular model to represent the hydrophobicity of the polymer becomes challenging. However, it was determined that normalization by the oligomer surface area causes the LogPoct/SA values to plateau after only a few (i.e., 3−5) monomer units, in a computationally convenient region. For example, many hydrogen-terminated oligomers (Figures S2, S5−S7) begin to plateau in the trimer to hexamer range and exhibit similar LogPoct/SA values for higher degrees of oligomerization. Second, molecular models for homopolymers should be oligomers with hybridization that mimics the polymer rather than the monomer. Although vinyl monomers give a general reflection of the hydrophobicity of a polymer, saturated oligomers (Chart 2) eliminate the influence of alkene bond C


Article

Macromolecules

Figure 2 also provides a linear regression of the experimental contact angle as a function of computed LogPoct/SA values (eq 2). Although LogPoct/SA values enable a first approximation of θadv and do not approach the experimental level of accuracy of sessile drop techniques for advancing angles reported by McCarthy,69 several features are worth noting. First, the correlation between LogPoct/SA and θadv exists over a fairly wide range of polymer hydrophobicities and complements the useful application of molecular dynamics (MD) simulations70,71 for predicting surface wetting. For example, based on eq 2, oligomers of polypropylene (Figure S10) have θadv values from 98° to 102° which provide reasonable agreement with MD calculations (106°) 72 as well as experimental values (104° and 95°).46,68 Figure 1. UV/vis absorption maximum of Nile Rile contained within cross-linked polyacrylates films as a function of predictive LogPoct/SA values. The inset images of polyacrylate films illustrate the color change due to polyacrylate hydrophobicity [red: poly(hydroxyethyl acrylate); blue: poly(ethyl acrylate); green: poly(ethylhexyl acrylate)]. All LogPoct/SA values were calculated using Chem3D Pro.

θadv = 1439.3(LogPoct /SA) + 62.47

(2)

Second, calculating LogPoct/SA values gives a first approximation to predicting contact angle of polymers, based solely on monomeric and oligomeric properties. Thus, viewing this quantity as a screening tool enhances monomer design a priori and facilitates a better understanding of physical properties for the resultant polymers. For instance, a comparison of calculated advancing contact angles (Figure S11) for PPO and PEO indicate that the LogPoct/SA values are sensitive to alkyl substituents and end groups. Third, the relation between contact angle and LogPoct/SA defines a critical contact angle (ca. 63°) that corresponds to a polymer that would be equally partitioned between octanol and water (i.e., LogP = 0). For example, PEO, PBS, and poly(vinyl acetate) have LogPoct/SA values nearly equal to zero and contact angles in the range of 61°−67°, consistent with eq 2. Typically, a surface is thought to be hydrophilic when θadv < 90°, but these results suggest that for 63° < θ < 90°, the polymer is more likely to partition into the octanol (hydrophobic) phase than the water phase. Of course, octanol is not a “perfect” hydrophobe, but this approach appears to be a powerful tool to predict polymer surface properties from monomer characteristics prior to polymerization. LogPoct Correlation to Swelling in Hydrogels. To complement insight on bulk material properties (Figure 1) and surface hydrophobicity (Figure 2), further investigation elucidated quantifiable regimes of hydrophilicity and hydrophobicity by swelling (hydro/organo)gels in water and hexanes. In Figure 3, swelling ratios (SR) for a series of polyacrylate films with different substituents were measured in aqueous and organic solvent and plotted against cmputed LogPoct/SA values. For both water and hexane, a clear transition existed, after which point either progressively more hydrophilic or hydrophobic films began to swell to increasing extents. For swelling in water, it was necessary to have negative LogPoct/SA values, whereas in hexane, the onset of swelling occurred as a LogPoct/ SA values became greater than 0.005 Å−2. As shown in Figure 3, swelling in water and hexanes represents the lower limit for hydrophobic and hydrophilic monomers, respectively. In addition to the SR data collected in water (ε = 78.54)73 and hexanes (ε = ∼1.5),73 solvents with intermediate dielectric constants, such as methanol (ε = 32.63),73 ethanol (ε = 24.30),73 butanol, and n-amyl alcohol, suggest LogPoct values approximate a free energy term. As such, Figure 4 provides a comparison between swelling ratio in hexanes and n-amyl alcohol as a function of LogPoct/SA for polyacrylate films. These trends mirror expectations for enthalpic interactions between solvents and polymers as

absorption of ambient moisture during analysis. Water has been shown to have profound influences on the spectral properties of Nile Red containing polymers, and adventitious water absorbing into the matrix complements the spectral shifts of the Nile Red in the more polar polymeric environment.42 Using LogPoct To Predict Contact Angle in Films. Figure 2 provides experimental contact angle (θadv) measure-

Figure 2. Average experimental values for water contact angles (θadv) taken from common polymers (Table S3) versus computational octanol−water partition values (LogPoct) of model molecules normalized with molecular surface area (SA). The linear regression equation (R2 = 0.926) is shown in eq 2.

ments for common polymers as a function of LogPoct/SA values computed from their respective molecular models described above. As detailed in Table S3, these experimental values in Figure 2 represent averages of multiple advancing water contact angles on smooth, nonpermeable surfaces for polystyrene (PS), 4 5 − 4 7 nylon-6, 4 6 , 4 8 poly(methyl methacrylate) (PMMA),49,50 poly(ethylene terephthalate) (PET),48,51−53 poly(butylene terephthalate) (PBT),52,54 poly(vinyl chloride) (PVC),46,55 poly(ethylene oxide) (PEO),53,56 poly(n-butyl methacrylate),47,57,58 1,2-polybutadiene (1,2-PBD),59 polycaprolactone (PCL),45,60,61 poly(butylene succinate) (PBS),62 polyethylene (PE),63,64 poly(vinyl alcohol) (PVA),53,65,66 polyisoprene (PI),67 and polypropylene (PP).46,68 D


Article

Macromolecules

formulated to have an approximate Nile Red concentration of 126 μM, a DMPA mass fraction of 0.25%, a monomer to cross-linker mole ratio of 100:1, and a film volume prior to polymerization of ca. 2.10 mL. All acrylate films utilized poly(ethylene oxide) diacrylate (Mn = 575 g/ mol) as the cross-linker. An example formulation and synthetic procedure for synthesizing a series of three poly(n-butyl acrylate) films containing Nile Red is as follows. To a 20 mL scintillation vial was charged 5.9130 g of n-butyl acrylate, 0.2783 g of Mn = 575 g/mol poly(ethylene oxide) diacrylate, and 0.0151 g of DMPA which was then covered with aluminum foil and vortexed for 6 h. In the meantime, three scintillation vials were prepared each containing 8.4 mg of Nile Red by charging 100 μL of a 0.84 mg/mL Nile Red stock solution, in THF, followed by heating to 60 °C for 2 h to drive off the solvent. Afterward, 2.10 mL of the monomer, cross-linker, and photoinitiator solution was transferred to each of the Nile Red containing vials which was then vortexed overnight to ensure a homogeneous solution. The following day the resulting solutions were spared with nitrogen for 3 min and then transferred by syringe into separate aluminum pans maintained under a nitrogen atmosphere. The solution contained within the aluminum pan was then exposed to UV irradiation at 8.0 mW/cm2 at 365 nm for 3 min and then allowed to cool to room temperature before removal. Each film was allowed to rest for two days in a desiccator prior to analysis with UV−vis. The cross-linked acrylate films used in the swelling studies followed an identical synthesis procedure except with exclusion of Nile Red. Nile Red lambda max (λmax) values were determined from UV−vis spectra using a Shimadzu UV-1650PC spectrophotometer. All reported λmax values were averages of three films from recently synthesized cross-linked polyacrylate films mounted onto freshly cleaned glass slides. Determination of cross-linked acrylate swelling ratio was accomplished by immersing a piece of the polyacrylate film (1.5 mm × 5 mm × 25 mm) in a solution contained in a sealed vial (20 mL). The vial was placed in a temperature-controlled oven for 48 h at 37 °C. Periodically, samples were removed from the liquid, quickly placed on a KimWipe to remove excess solvent, and weighed immediately. This procedure was repeated over the course of 48 h to ensure the sample had equilibrated. Each swelling experiment was done in triplicate, and an average value was reported. Calculation Details. LogPoct values for 20 common monomers were calculated with the following software programs: EPI Suite v. 4.11 using KOWWINTM v. 1.68, ChemBioDraw Ultra 13.0, and Virtual Computational Chemistry Laboratory (accessed on 24 July 2015). In addition, LogP oct were also extracted from the ChemSpider.com database for ChemAxon and ACD/Laboratories (accessed on 21 July 2015). For homopolymers and copolymers, Chem3D Pro version 13.0.2.3021 was used. Each structure was built and minimized with the MM2 force field. Then, the LogPoct values were extracted from the chemical properties module, and the Connolly molecular surface area was calculated using a probe of 1.4 Å.

Figure 3. Correlation between LogPoct/SA and equilibrium swelling ratio for polyacrylate films immersed in water (◆) and hexanes (▲). Films were cross-linked with 1 mol % PEG diacrylate (Mn = 575 g/ mol). LogPoct/SA values were calculated using Chem3D Pro.

Figure 4. Correlation between LogPoct and equilibrium swelling ratio for polyacrylate films immersed in n-amyl alcohol (■) and hexanes (◇). Films were cross-linked with 1 mol % PEG diacrylate (Mn = 575 g/mol). LogPoct/SA values were calculated using Chem3D Pro. The lines represent exponential regression.

described by Hildebrand solubility parameters (δ). Interestingly, although Hildebrand values are published for many solvents and polymers, we expect the convenience and computational availability of software that predicts LogPoct based on the molecular structure will provide new possibilities for assessing and designing new monomers.

■

■

EXPERIMENTAL SECTION

CONCLUSION We have demonstrated that LogPoct/SA values provide several innovative avenues for predicting and assessing hydrophobicity of polymers. For instance, the proposed strategy to assess hydrophobicity has relevance for designing new monomers, diblock copolymers, ranking a series of monomers within a monomer library, and potentially improving the design of polymers with a lower critical solution temperature (LCST). Such a strategy is more insightful than examining the carbon:oxygen ratio of monomers or comparing sizes of substituents along polymer structures. Because of the “qualitative”74 character of hydrophobic and hydrophilic, “the meanings of these terms varies widely from application to application and from scientist to scientist.”75 In this regard, computational predictors of LogPoct values have potential to dramatically improve the description of hydro-

Chemicals and Reagents. All acrylates and diacrylates, such as ethyl acrylate (Sigma-Aldrich, 99%, EA), butyl acrylate (Sigma-Aldrich, >99%, BA), hexyl acrylate (Sigma-Aldrich, 98%, HA), 2-ethylhexyl acrylate (Sigma-Aldrich, 98%), poly(ethylene glycol) methyl ether acrylate, (Sigma-Aldrich, average Mn 480, OEOA), poly(ethylene glycol) methoxy ether acrylate, (Sigma-Aldrich, 98%, EGMEA), tertbutyl acrylate (Sigma-Aldrich, 98%), poly(propylene glycol) acrylate (Sigma-Aldrich, average Mn 475), tetrahydrofurfuryl acrylate (SigmaAldrich), and poly(ethylene glycol) diacrylate (Sigma-Aldrich, average Mn 575), were passed through basic alumina prior to polymerization. 2-Hydroxyethyl acrylate (Sigma-Aldrich, 96%) was passed through basic alumina, dried with magnesium sulfate, and filtered prior to polymerization. 2,2-Dimethoxy-2-phenylacetophenone (Sigma-Aldrich, 99%, DMPA) and Nile Red (Acros Organics, 99%) were used as received. Synthesis and Characterization of Cross-Linked Acrylate Films Containing Nile Red. All cross-linked acrylate films were E


Article

Macromolecules phobicity in polymer science and move away from empirical approaches that are performed post-polymerization. Solvatochromic behavior of Nile Red in a series of polyacrylate films corroborates the hypothesis that normalized LogPoct values of oligomers reflect the chemical environment within a polymer matrix. Based on assessment of model molecules for homopolymers and copolymers, four criteria have been suggested to account for molecular weight issues, the influence of hybridization during polymerization of vinyl monomers, and end groups which may ionize or form hydrogen bonds. As depicted in Chart 2, these criteria offer a framework to predict the hydrophobicity for a variety of monomers. To summarize, appropriate molecular models for homopolymers with nonionizable end groups ranged from trimers or pentamers. In contrast, trimers (i.e., ABA and BAB) for copolymers are too small and necessitate larger oligomers with even numbers of monomer units (i.e., hexamers, octamers, or decamers) to average differences resulting from two different monomers and influence of chain ends. Given the appropriate molecular model, the utility of several computational methods have been demonstrated. Despite the different approaches for predicting LogP oct that were investigated, direct agreement with experimental values was observed for a series of 20 common monomers. In addition, these software programs are either free or easy to use in commonly available software programs such as Chem3D Pro; thus, accessibility is not limited to just medicinal chemists. Based on these software packages and others, a first approximation of certain physical properties, such as water contact angles, presents a method that extends beyond intuition. Several caveats are worth mentioning regarding the strategy to construct oligomers and calculate computational LogPoct values. First, the proposed model has limitations for predicting contact angles and is not expected to account for increases in hydrophobicity due to topographical effects76 or crystallization.77 Likewise, the proposed model has been investigated for polymers without architectural complexity, such as linear homopolymers and alternating or random copolymers. As a result, more than one LogPoct/SA value could distinguish between functional end groups and branch points or to differentiate the hydrophobic environment within diblock copolymers, random copolymers, and tapered block copolymers. Given that monomers share many of the same functional groups as drug-like molecules, LogP values provide a logical predictive tool. In addition, existing LogPoct prediction approaches could be further refined by appending the existing molecular training sets with larger polymeric structures that due to the repeating nature of a polymer, should be more easily to parse into fragments than other large, complex molecules. The training sets could also be further refined by incorporating experimental LogPoct values of oligomers or polymeric units that are not drug-like. Thus, polymer science also provides useful knowledge about hydrophobicity to medicinal chemistry and materials genome efforts.

■

■

Tables S1−S3 and Figures S1−S24 (PDF)

AUTHOR INFORMATION

Corresponding Authors

*E-mail [email protected] (A.J.D.M.). *E-mail [email protected] (R.T.M.). Notes

The authors declare no competing financial interest.

■

ACKNOWLEDGMENTS R.T.M. thanks the National Science Foundation (CHE-MSN and DMR-Polymers) for support under Grant CHE 1308247. Thanks to Professor Tom McCarthy and Dr. James Matthews for helpful disscussions.

■

REFERENCES

(1) Cymer, F.; von Heijne, G.; White, S. H. J. Mol. Biol. 2015, 427, 999. (2) Gao, L.; McCarthy, T. J.; Zhang, X. Langmuir 2009, 25, 14100. (3) Hocine, S.; Li, M.-H. Soft Matter 2013, 9, 5839. (4) Chandler, D. Nature 2005, 437, 640. (5) Gleeson, M. P. J. Med. Chem. 2008, 51, 817. (6) Ostuni, E.; Chapman, R. G.; Holmlin, R. E.; Takayama, S.; Whitesides, G. M. Langmuir 2001, 17, 5605. (7) Bakkour, Y.; Darcos, V.; Coumes, F.; Li, S.; Coudane, J. Polymer 2013, 54, 1746. (8) Du, J.-Z.; Tang, L.-Y.; Song, W.-J.; Shi, Y.; Wang, J. Biomacromolecules 2009, 10, 2169. (9) Dasgupta, Q.; Chatterjee, K.; Madras, G. Biomacromolecules 2014, 15, 4302. (10) Dakshinamoorthy, D.; Weinstock, A. K.; Damodaran, K.; Iwig, D. F.; Mathers, R. T. ChemSusChem 2014, 7, 2923. (11) Nitta, K.; Miyake, J.; Watanabe, J.; Ikeda, Y. Biomacromolecules 2012, 13, 1002. (12) Goddard, J. M.; Hotchkiss, J. H. Prog. Polym. Sci. 2007, 32, 698. (13) Munoz-Pinto, D. J.; Grigoryan, B.; Long, J.; Grunlan, M.; Hahn, M. S. J. Biomed. Mater. Res., Part A 2012, 100A, 2855. (14) Gamelas, J. F. Cellulose 2013, 20, 2675. (15) Cardamone, M.; Puri, N. K. Biochem. J. 1992, 282, 589. (16) Kurniasih, I. N.; Liang, H.; Mohr, P. C.; Khot, G.; Rabe, J. P.; Mohr, A. Langmuir 2015, 31, 2639. (17) Kępińska, D.; Budniak, A.; Kijewska, K.; Blanchard, G. J.; Mazur, M. Polymer 2013, 54, 4538. (18) Klinger, D.; Landfester, K. Macromol. Rapid Commun. 2011, 32, 1979. (19) Fleige, E.; Ziem, B.; Grabolle, M.; Haag, R.; Resch-Genger, U. Macromolecules 2012, 45, 9452. (20) Lipinski, C. A.; Lombardo, F.; Dominy, B. W.; Feeney, P. J. Adv. Drug Delivery Rev. 2001, 46, 3. (21) Muster, W.; Breidenbach, A.; Fischer, H.; Kirchner, S.; Müller, L.; Pähler, A. Drug Discovery Today 2008, 13, 303. (22) Harris, J.; Daugulis, A. J. Biotechnol. Bioeng. 2015, in press. (23) Oprea, T. I. J. Comput.-Aided Mol. Des. 2002, 16, 325. (24) Veber, D. F.; Johnson, S. R.; Cheng, H.-Y.; Smith, B. R.; Ward, K. W.; Kopple, K. D. J. Med. Chem. 2002, 45, 2615. (25) DiCicco, M.; Duong, T.; Chu, A.; Jansen, S. A. J. Biomed. Mater. Res. 2003, 65B, 137. (26) George, M.; Ghosh, I. Eur. J. Pharm. Sci. 2013, 48, 142. (27) Toulmin, A.; Wood, J. M.; Kenny, P. W. J. Med. Chem. 2008, 51, 3720. (28) Kenny, P. W.; Montanari, C. A.; Prokopczyk, I. M. J. Comput.Aided Mol. Des. 2013, 27, 389. (29) Halpern, J.; Weinstock, A. K.; Urbanski, R.; Iwig, D. F.; Mathers, R. T.; Von Recum, H. A. J. Biomed. Mater. Res., Part A 2014, 102, 1467. (30) Dakshinamoorthy, D.; Lewis, S. P.; Cavazza, M. P.; Hoover, A. M.; Iwig, D. F.; Damodaran, K.; Mathers, R. T. Green Chem. 2014, 16, 1774.

ASSOCIATED CONTENT

S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.macromol.5b01758. F


Article

Macromolecules (31) Schroder, K.; Matyjaszewski, K.; Noonan, K. J. T.; Mathers, R. T. Green Chem. 2014, 16, 1673. (32) Mathers, R. T. J. Polym. Sci., Part A: Polym. Chem. 2012, 50, 1. (33) Sangster, J. J. Phys. Chem. Ref. Data 1989, 18, 1111. (34) Buchwald, P. Perspect. Drug Discovery Des. 2000, 19, 19. (35) Murdande, S. B.; Pikal, M. J.; Shanker, R. M.; Bogner, R. H. Pharm. Dev. Technol. 2011, 16, 187. (36) Mannhold, R.; Poda, G. I.; Ostermann, C.; Tetko, I. V. J. Pharm. Sci. 2009, 98, 861. (37) Huang, X.; Margulis, C. J.; Berne, B. J. J. Phys. Chem. B 2003, 107, 11742. (38) Jessop, P. G.; Jessop, D. A.; Fu, D.; Phan, L. Green Chem. 2012, 14, 1245. (39) Deye, J. F.; Berger, T. A.; Anderson, A. G. Anal. Chem. 1990, 62, 615. (40) Golini, C.; Williams, B.; Foresman, J. J. Fluoresc. 1998, 8, 395. (41) Kawski, A.; Kukliński, B.; Bojarski, P. Chem. Phys. 2009, 359, 58. (42) Hou, Y.; Bardo, A. M.; Martinez, C.; Higgins, D. A. J. Phys. Chem. B 2000, 104, 212. (43) Jee, A.-Y.; Park, S.; Kwon, H.; Lee, M. Chem. Phys. Lett. 2009, 477, 112. (44) Dutta, A. K.; Kamada, K.; Ohta, K. J. Photochem. Photobiol., A 1996, 93, 57. (45) Biresaw, G.; Carriere, C. J. J. Polym. Sci., Part B: Polym. Phys. 2001, 39, 920. (46) Extrand, C. W. Langmuir 2003, 19, 646. (47) Kwok, D. Y.; Neumann, A. W. Adv. Colloid Interface Sci. 1999, 81, 167. (48) Yasuda, T.; Okuno, T.; Yoshida, K.; Yasuda, H. J. Polym. Sci., Part B: Polym. Phys. 1988, 26, 1781. (49) Ammonia Plasma-Simulating Treatments and Their Impact on Wettability of PET Fabrics; Johansson, K. S., Ed.; Brill Academic Pub.: Leiden, 2006. (50) Erbil, H. Y.; McHale, G.; Rowan, S. M.; Newton, M. I. Langmuir 1999, 15, 7378. (51) Highly Hydrophobic Textile Surfaces Obtained by Thin-Layer Deposition; Bahners, T., Opwis, K., Textor, T., Schollmeyer, E., Eds.; Brill Academic Pub.: Leiden, 2006. (52) Volpe, C. D.; Migliaresi, C.; Mari, G. In Surface Modification of Polymeric Biomaterials; Ratner, B., Castner, D., Eds.; Springer: New York, 1997; p 107. (53) Kozlov, M.; Quarmyne, M.; Chen, W.; McCarthy, T. J. Macromolecules 2003, 36, 6054. (54) Risbud, M.; Nabi Saheb, D.; Jog, J.; Bhonde, R. Biomaterials 2001, 22, 1591. (55) Erbil, H. Y. Langmuir 1994, 10, 2006. (56) Vasilets, V. N.; Werner, C.; Hermel, G.; Pleul, D.; Nitschke, M.; Menning, A.; Janke, A.; Simon, F. J. Adhes. Sci. Technol. 2002, 16, 1855. (57) Zhang, S.; Liu, Z.; Bucknall, D. G.; He, L.; Hong, K.; Mays, J. W.; Allen, M. G. Appl. Surf. Sci. 2011, 257, 9673. (58) Bruining, M. J.; Blaauwgeers, H. G. T.; Kuijer, R.; Jongsma, F. H. M.; de Brabander, J.; Nuijts, R. M. M. A.; Koole, L. H. Biomacromolecules 2000, 1, 418. (59) Carey, D. H.; Ferguson, G. S. Macromolecules 1994, 27, 7254. (60) Zhu, Y.; Gao, C.; Liu, X.; Shen, J. Biomacromolecules 2002, 3, 1312. (61) Bartolozzi, I.; Solaro, R.; Schacht, E.; Chiellini, E. Eur. Polym. J. 2007, 43, 4628. (62) Huang, C.-L.; Jiao, L.; Zhang, J.-J.; Zeng, J.-B.; Yang, K.-K.; Wang, Y.-Z. Polym. Chem. 2012, 3, 800. (63) Lehocký, M.; Drnovská, H.; Lapčíková, B.; Barros-Timmons, A. M.; Trindade, T.; Zembala, M.; Lapčík, L. R., Jr. Colloids Surf., A 2003, 222, 125. (64) Hoshi, T.; Sawaguchi, T.; Konno, T.; Takai, M.; Ishihara, K. Polymer 2007, 48, 1573. (65) Li, Y.; Neoh, K. G.; Kang, E. T. Polymer 2004, 45, 8779. (66) Tezuka, Y.; Fukushima, A.; Matsui, S.; Imai, K. J. Colloid Interface Sci. 1986, 114, 16.

(67) Quirk, R. P.; Mathers, R. T.; Cregger, T.; Foster, M. D. Macromolecules 2002, 35, 9964. (68) Morra, M.; Occhiello, E.; Garbassi, F. J. Colloid Interface Sci. 1989, 132, 504. (69) Gao, L.; McCarthy, T. J. Langmuir 2007, 23, 3762. (70) De Coninck, J.; Blake, T. D. Annu. Rev. Mater. Res. 2008, 38, 1. (71) Chen, J.; Hanson, B. J.; Pasquinelli, M. A. AIMS Mater. Sci. 2014, 1, 121. (72) Liu, H.; Li, Y.; Krause, W. E.; Rojas, O. J.; Pasquinelli, M. A. J. Phys. Chem. B 2012, 116, 1570. (73) Haynes, W. M. Handbook of Chemistry and Physics, 93rd ed.; CRC Press: Boca Raton, FL, 2012. (74) Gao, L.; McCarthy, T. J. Langmuir 2008, 24, 9183. (75) Vogler, E. A. In Water in Biomaterials Surface Science; Morra, M., Ed.; John Wiley: New York, 2001. (76) Lee, J. A.; McCarthy, T. J. Macromolecules 2007, 40, 3965. (77) Zheng, Y.; Zhang, W.; Gupta, M.; Kankanala, S.; Marks, C.; Carpenter, E.; Carroll, K.; Wynne, K. J. J. Polym. Sci., Part B: Polym. Phys. 2010, 48, 1022.

G


Systematic Insights from Medicinal Chemistry To Discern the Nature of

Recommend Documents