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Monitoring Drug Self-Aggregation and Potential for Promiscuity in Off-Target In Vitro Pharmacology Screens by a Practical NMR Strategy Steven R. LaPlante,* Norman Aubry, Gordon Bolger, Pierre Bonneau, Rebekah Carson, René Coulombe, Claudio Sturino, and Pierre L. Beaulieu Department of Chemistry, Boehringer Ingelheim (Canada) Ltd., 2100 Cunard Street, Laval, Quebec, H7S 2G5, Canada S Supporting Information *

ABSTRACT: A simple NMR assay was applied to monitor the tendency of compounds to self-aggregate in aqueous media. The observation of unusual spectral trends as a function of compound concentration appears to be signatory of the formation of self-assemblies. 1H NMR resonances of aggregating compounds were sensitive to the presence of a range of molecular assemblies in solution including large molecular-size entities, smaller multimers, and mixtures of assembled species. The direct observation of aggregates via unusual NMR spectra also correlated with promiscuous behavior of molecules in off-target in vitro pharmacology assays. This empirical assay can have utility for predicting compound promiscuity and should complement predictive methods that principally rely on the computing of descriptors such as lipophilicity (cLogP) and topological surface area (TPSA). This assay should serve as a practical tool for medicinal chemists to monitor compound attributes in aqueous solution and various pharmacologically relevant media, as demonstrated herein.

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INTRODUCTION One of the most significant challenges in drug discovery is the ability to predict whether or not a medicament will be safe for human consumption.1−9 The pharmaceutical industry faces this challenge because of some simple realities. Although small molecule drugs can be designed and engineered to complement the receptor pocket of the intended target, they can sometimes also interact with other receptors, resulting in adverse and unintended in vivo toxicity outcomes. Drug discovery efforts are further complicated by the fact that the origin of any in vivo toxicological observation is multifactorial and complex in nature, making it difficult to fully understand, predict, and ultimately avoid toxicity in the course of drug design efforts.1−7 As a result, the precise source of encountered toxicity is often not identified nor rationally resolved, leaving large and poorly understood gaps between in vitro studies and in vivo observations. The industry has been implementing many strategies to help prioritize compounds that possess desirable safety profiles and to flag unsafe compounds to avoid the costly advancement of promiscuous and toxic drugs. For example, it has been noted that compounds have a higher likelihood of being toxic if they contain structural subgroups known as toxicophores (e.g., known to generate chemically reactive metabolites).10−18 Other compounds can be toxic due to specific inhibition of key biological mechanisms (referred to as primary or secondary pharmacology).19−24 Also, toxic outcomes can result from unintended interactions with proteins other than the target, © 2013 American Chemical Society

referred to here as off-target promiscuity. Other than assessing in vivo effects on animal models, off-target promiscuity can often be evaluated by testing compounds in panels of unrelated in vitro pharmacology assays.25 Such screens are employed nearly ubiquitously in the industry, and a quantifiable definition of promiscuity has been proposed for compounds that inhibit/ reduce >50% at 10 μM in ≥3 off-target assays, or have a targethit-ratio (THR10) of ≥20% at 10 μM.5,19 These assays are part of more global decision-making processes that are based on evaluating the physical properties of drug candidates that include monitoring the activity toward a target (IC50 and/or EC50), pharmacology attributes (PK, PD, ADME properties), and safety profiles (specificity and toxicity). Interestingly, the behavior of drug candidates in aqueous-based media influence all the above measured properties. The drug discovery community has recognized in recent years that the physicochemical properties25−34 of some compounds, in particular lipophilicity, can somehow predispose them to many properties that include adverse in vivo toxicology outcomes. This correlation has recently been evaluated, and probabilistic guidelines were established based on the calculated partition coefficient (cLogP) and total polar surface area (TPSA) of molecules.1,19 However, how molecular properties can influence and predispose a compound to adverse in vivo outcomes often remains speculative. Some principles are Received: June 12, 2013 Published: August 6, 2013 7073

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Figure 1. Compounds can adopt a three-phase equilibrium when placed in aqueous media. On the bottom are 1H NMR spectra (600 MHz) of compounds A and B shown in Figure 2A,B. (C) Congo red and a copy of an electron micrograph acquired from reference 35b. (D) An insoluble compound in the same series as the compounds shown in Figures 2A and 2B. All NMR spectra were taken of free compound in buffer (50 mM sodium phosphate pH 7.4) at nominal concentrations of 200 μM.

straightforward, such as the view that more lipophilic compounds tend to be more promiscuous by their attraction to hydrophobic off-target receptors.32 Simply stated, “stickier” compounds can bind nonspecifically to “sticky” hydrophobic receptor pockets either in a stoichiometric mode of off-target inhibition (1:1 ligand to target molecule) or nonstoichiometrically if more than one ligand molecule is binding nonselectively to the protein. In both cases, additional and more specific elements of molecular recognition may be involved, which then makes predictions difficult.13 Another potential mode of promiscuity can arise from nonstoichiometric off-target inhibition as a result of compound aggregation (≫1:1 ligand to target molecules). So far, only in vitro studies have been reported on this elusive property,35−40 which led to fundamental questions of how druglike compounds behave in aqueous media. This has implications if one assumes that when druglike compounds are placed in aqueous solution, they must exist in an equilibrium state between a soluble fraction (dissolves in solution) and an insoluble fraction (precipitates as a solid). This long-standing biphasic model has been recently challenged by reports, using dynamic light scattering (DLS), electron microscopy, and recently NMR spectroscopy, that compounds can also exist as soluble, micelle-like colloids (i.e., nanometer scale).35−41 Subsequent reports have argued that these compound aggregates could result in nonstoichiometric inhibition by several potential mechanisms, via binding to multiple sensitive regions of proteins or via more global effects of large aggregates that can result in proteins to experience partial unfolding, restrained dynamics, and physical sequestration.42 The importance of these findings is highlighted by studies that implicate large compound aggregates in alternate and unexpected properties that manifest as, for example, promiscuity in high-throughput inhibitor screens (false positives),35−37 false negatives in cell culture assays due to lack of cell membrane permeability,43 or as having exceptional oral bioavailability.44,45 Despite these reports, the range of properties of these compound aggregates remains poorly characterized, due mainly to the likely existence of a wide range of sizes and physical features that continue to elude detection. Each detection technology applied thus far has its own strengths and weaknesses. For example, DLS and electron microscopy are sensitive to large homogeneous assemblies but are less optimal for small entities and mixtures. More widely accessible NMR spectrometers on the other hand, couple advantages of operational simplicity and an extended dynamic

range for detecting and characterizing the solution state of molecules. Recently, we introduced a straightforward and routinely accessible one-dimensional (1D) 1H NMR assay for monitoring the tendency of compounds to self-aggregate in aqueous media, via serial dilution experiments.41 Here, we use this assay to determine whether or not individual and series of compounds form aggregate assemblies. We also explore whether or not there is a general correlation between the solution-state behavior of compounds, as determined by unusual NMR spectral features and their promiscuous properties in off-target in vitro pharmacology assays.

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RESULTS AND DISCUSSION Unusual NMR Observations Reveal Compound Aggregation. Our interest in exploring NMR as a tool for monitoring compound aggregation arose from observations made over the past decade across several medicinal chemistry programs. We often noted that while many compounds exhibited expected 1H NMR spectra, others exhibited unusual features when placed in aqueous media. Instead of observing the expected sharp 1H NMR resonances of a fast-tumbling small molecule (e.g., see Figure 1A), the NMR spectra of some compounds surprisingly exhibited broad resonances (e.g., see Figure 1B), and others were even broader (e.g., see Figure 1C) although the samples appeared soluble and clear (no precipitate). Those observations suggested that the compound in Figure 1A behaved as a fast-tumbling small molecule, whereas the compound in Figure 1B behaved as a slowtumbling aggregate in solution. Even larger aggregates can exist as revealed in Figure 1C for Congo red, which was corroborated by DLS (∼200 nm diameter particles) and transmission electron microscopy (see picture in Figure 1C).35b Solids or precipitates tumble too slowly in solution, and no resonances are expected (e.g., Figure 1D). The compounds used for Figure 1A,B were then subjected to follow-up NMR NOESY experiments to verify the conclusions made from the 1D NMR data. The NOESY experiment is highly sensitive to the sizes and tumbling rates of assemblies in solution. One expects that a fast-tumbling, small-molecule (∼200−400 Da) would give rise to cross peaks that are of opposite sign (red color in Figure 2A) as the diagonal peaks (black in Figure 2A). A small molecule of ∼800−1200 Da would be expected to give rise to no crosspeaks. However, a slow-tumbling assembly or aggregate would be expected to give rise to cross peaks that have the same sign as the diagonal peaks 7074

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Figure 2. (A) 1H NMR spectrum (600 MHz, 50 mM sodium phosphate pH 7.4) and NOESY spectrum of free compound at 200 μM and 200 ms mixing time. (B) 1H NMR spectrum (600 MHz, 50 mM sodium phosphate pH 7.4) and NOESY spectrum of free compound at 200 μM and 200 ms mixing time.

For compounds that have normal behavior (Figure 4B,D), the resonances are sharp as expected for small, fast-tumbling compounds, and there are no changes in number, shape, and chemical shifts (see straight dotted arrows). The intensities remain the same at the higher concentrations and then decrease, which is consistent with solubility limit or saturation at the higher concentrations. Given the illustration in Figure 1, these compounds have significant soluble and precipitate ranges. They are not considered as compounds that tend to aggregate, which is consistent with the noted absence of promiscuous behavior (vide infra). On the other hand, the compounds in Figure 4A,C show unusual behavior in solution and are considered as compounds that tend to aggregate. Resonances shift left or right upon dilution. At the higher concentrations, there are more resonances than corresponding hydrogens on the compound suggesting that multiple entities exist. Figure 4A has both sharp and broader resonances consistent with molecules that are likely multimeric (dimer, trimer, etc.) rather than the very broad resonances expected from large micelle-like assemblies. This technique allows direct observation of small compound aggregates and leads one to wonder what unusual and unexpected properties these entities can have. Several other points are worth mentioning here. It is interesting that highly related compounds within a series can have such distinct solution behaviors (see Figures 2 and 4). This certainly dismisses the assumption that the phenomenon of aggregation must be a shared property of all members of a chemical series. On the basis of the similar LogD values (bottom of Figure 4), the use of this physicochemical property alone to predict unusual behavior such as aggregation is ineffective in this series. Moreover, it is quite relevant to note that the compounds that exhibited unusual NMR attributes (Figure 4A,C) also displayed promiscuity in a small panel of unrelated off-target in vitro pharmacology assays (four of five

(e.g., both are black in Figure 2B). Thus, it is evident yet surprising that the two highly related compounds in Figure 2 have very distinct behaviors and sizes in solution. This was further corroborated using DOSY NMR experiments which again show that resonance shape can reflect the size of molecular assemblies (see Supporting Information). Interestingly, the NOESY crosspeaks in Figure 2B report interhydrogen proximities that are 50% inhibition was observed at a nominal concentration of 10 μM. Compounds were considered as promiscuous if there were three or more hits in a panel of assays (a lead-optimization panel typically consisted of 30 assays). On the other hand, structurally similar compounds were considered as “clean” as they did not have hits in any of the assays within the same panel (or 50%

HIV1 HIV2 HIV3 HIV4 HIV5 HIV6 HIV7 HIV8 HIV9 HIV10

+ + + + + + − − − −

5 4 4 4 3 3 0 0 0 0

Log D calc pH = 7.4 cLogP 2.4 2.9 2.7 2.3 2.2 3.7 2.8 3.2 2.3 2.8

1.5 2.0 5.7 1.8 0.6 3.1 2.7 4.1 3.0 3.8

calc TPSA

calc predict promisc19

105 98 138 98 114 76 93 73 94 46

N N X N N X N Y N Y

a

The structures for compounds HIV3, HIV10, HIV2, and HIV7 are shown in Figures 4, panels A, B, C, and D, respectively. For the “NMR aggreg” column, + indicates unusual spectral features in the NMR aggregation assay, whereas − indicates normal spectral trends. For the off-target hits column, the value indicates the number of assays where the compound inhibited at >50% at 10 μM out of a panel of five unrelated assays frequently noted to be sensitive to this series of compounds (acetyl cholinesterase, phosphodiesterase III, muscarinicM2, opiate receptors, and norepinephrine transporter). See the Supporting Information for details on LogD and the assays. Calculated physicochemical properties (TPSA and cLogP) were obtained as described in the Supporting Informaton. The letters under the calc predict promisc column are defined as a Y if a compound would have a greater probability of being promiscuous based on the calculated total polar surface area (TPSA) of 3, an N denotes a compound with a greater probability of being nonpromiscuous, based on the calculated TPSA of >75 Å and cLogP < 3. An X indicates that the calculated properties fall outside the applicability domain of this method.19

We also evaluated whether or not the easily calculated physicochemical properties cLogP and TPSA were predictive of promiscuity in this series when evaluated as independent parameters.19 As shown in Table 1, the use of cLogP alone is not predictive of promiscuity in this series either. For example, HIV 3 and HIV 5 have the highest and lowest values (cLogP = 5.7 and 0.6, respectively), but both are promiscuous. On the other hand, the observation and absence of aggregation matches well with the off-target results. Higher values for calculated TPSA agreed somewhat more consistently with compounds that were NMR aggregators and inhibited in offtarget assays, while compounds that had lower TPSA values were nonaggregators and clean in off-target screens (except for HIV 6). Given the above, it appears that lipophilicity assessed by LogD or cLogP alone is not predictive of promiscuity in this series, while some predictivity can be derived from the use of TPSA. The combined use of calculated cLogP and TPSA as a marker of off-target promiscuity as previously described will be discussed later (vida infra).19 A similar study was also undertaken using compounds from a completely unrelated chemical series that targeted HCV NS5B polymerase. NMR dilution spectra of frequent hitters (5−6 of eight assays) clearly exhibited abnormal broadening of resonances as shown in Figure 5A,C and were indicative of the formation of larger aggregates. On the other hand, normal dilution spectra were observed in Figure 5B,D for compounds 7077

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Figure 5. (A−D) Shown on top are four inhibitors of HCV NS5B polymerase. In the middle are a series of 1H NMR spectra (600 MHz) acquired on the respective compounds at various concentrations upon dilution from 200 μM to 6 μM. At the bottom are the number of off-target in vitro pharmacology hits observed where inhibition was greater than 50% (e.g., six hits out of a representative panel of eight unrelated assays were observed for compound A). In this case, the eight assay panel included MaO-A, dopamine-D1, glucocorticoid receptor, COX-2, cathepsin-G, 5-LO, GABAA, adrenergic α2A.

that were clean in the assays (no inhibition in the eight representative assays). This again is consistent with a good correlation between abnormalities in the NMR dilution spectra due to self-aggregation (labeled as +) and a high number of offtarget hits. This study was then extended to additional compounds in this same series, and results are provided in Table 2. Again, there appeared to be a relatively good correlation between abnormalities in the NMR dilution spectra due to selfaggregation and off-target activity with two exceptions (HCV6 and HCV8). In contrast to HIV compounds in Table 1, the HCV inhibitors in Table 2 display a potential correlation between LogD and promiscuity. Most of the compounds with LogD ∼3 or less are clean in the off-target pharmacology assays. In this case again, there appears to be no clear correlations with the calculated physicochemical parameters cLogP and TPSA. Overall, it appears that some physicochemical properties (calculated and experimental) can be used to predict compound promiscuity in some series; however, the general use of these parameters may not always be reliable given that other molecule properties may also influence promiscuity (e.g., overall topology as defined by percent sp3 character, number of aromatic rings, spatial arrangement of polar and charged functionalities, etc.). On the other hand, the generalized use of the 1H NMR assay for predicting promiscuity across multiple chemotypes should serve as a more reliable empirical method since aggregates are directly observed. The assay can also be used to probe whether compound selfaggregation correlates or not with other relevant drug discovery parameters. For example, compounds HCV3 and HCV11 were both determined to have high protein binding tendencies (100% and 99% in a human protein binding assay); however,

Table 2. Data Are Shown for 11 HCV NS5B Polymerase Inhibitors That Belong to the Same Series As the Compounds Shown in Figure 5a

compound

NMRaggreg pH 7.4

off-target hits of eight assays >50%

Log D pH = 7.4

HCV1 HCV2 HCV3 HCV4 HCV5 HCV6 HCV7 HCV8 HCV9 HCV10 HCV11

+ + + + + − − + − − −

7 6 5 5 3 3 2 1 1 0 0

>5.1 >4.7 >4.8 4.3 >5.4 4.1 3.5 3.1 2.1 3.3 3.3

calc calc cLogP TPSA 5.9 5.1 5.7 4.4 5.4 5.7 4.6 4.2 4.9 3.7 5.3

105 138 138 164 121 92 151 151 97 164 118

calc predict promisc X X X X X X X X X X X

a

The structures for compounds HCV2, HCV4, and HCV10 are shown in Figure 5, panels A, C, and D, respectively. Refer to the legend in Table 1 for an explanation of the methods. For the off-target column, compounds were tested in a panel of eight assays that were frequently noted to be sensitive to this series of compounds (MaO-A, dopamine-D1, glucocorticoid receptor, COX-2, cathepsin-G, 5-LO, GABAA, adrenergic α2A).

HCV3 self-aggregates and is promiscuous, whereas HCV11 does not self-aggregate and is off-target clean. Also, HIV9 and HIV7 were found to have different protein binding tendencies (99% and 95% in a rat protein binding assay), but both do not self-aggregate and are off-target clean. Therefore, there does not appear to be a correlation, but a much larger data set would need to be tested to properly establish this. 7078

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Probing Correlations with More Diverse and Larger Sets of Compounds. We then proceeded to further validate the use of the NMR assay on a larger set of compounds from multiple therapeutic areas, and for which off-target pharmacology data had already been acquired, and assess whether the method could serve as an empirical method for flagging promiscuous and potentially problematic compounds. 1 H NMR dilution data were acquired on a total of 124 compounds (including the compounds previously described in Tables 1 and 2), and the dilution data were analyzed for unusual resonances or changes. For compounds that adopted large micelle-like aggregates that tumble very slowly in solution, the NMR spectra appeared similar to those shown in Figure 1C,D (i.e., no resonances or very broad, weak and poorly defined resonances), thus resulting in a so-called “NMR blind spot”. To verify the presence of “NMR-invisible” aggregates, a detergent-based strategy was implemented where addition of detergent resulted in the breaking up of the large aggregates into smaller entities for which narrower resonances were then observed (see Supporting Information). In those cases, an additional 200 μM sample which also included the detergent Triton X-100 was prepared, and 1H spectra acquired to unveil resonances from the NMR “blind spot” (see Experimental Section and the Supporting Information). As a note, we later found that Tween 80 is also capable of breaking up large aggregates and has the advantage that the aromatic window in 1 H NMR spectra are fortuitously unhindered and favorable for quick analyses by medicinal chemists, that was recently reported elsewhere.41 The off-target pharmacological screens performed on the compounds ranged from 5 to 52 assays, where the smaller panels (5−8) included representative assays that were frequent hits for promiscuous compounds within a series as discussed earlier. The definition of compound promiscuity was as previously described19 (vide supra). The conclusions from the NMR aggregation assay (Y for NMR oddity noted, and N for no oddity observed) were then compared to the off-target data (Y for off-target promiscuity, and N for off-target clean), and the results are depicted graphically in Figure 6. A strong correlation between the NMR assay and off-target screening data (i.e., agreement as YY or NN) was observed for 84% of the compounds. The comparison was in disagreement (YN or NY) for the remaining 16% of the compounds. No common structural features were noted for the compounds that belonged to this 16%. The compounds came from various series and were directed at unrelated targets (e.g., kinases, proteases, polymerases, etc.). In conclusion, the above correlation is significant, and the NMR assay was successful in predicting whether or not a compound is off-target promiscuous in a majority of cases, independent of structural class. The off-target promiscuity results from the same set of compounds were then compared to predictions based on calculated physicochemical properties (cLogP and TPSA) as described earlier.19 Unfortunately, of the 124 compounds only 74 fell within the applicability domain of the published model (i.e., LogD < 3 and TPSA > 75 Å2). In this set of 74 compounds, the method was not able to predict the potential for promiscuity (50% agreement and 50% disagreement between the predicted promiscuity and experimentally derived promiscuity). Similar trends can be noted in Tables 1 and 2. In the case of the HCV inhibitors (Table 2), none of the compounds fell within the applicability domain of the

Figure 6. Comparison of observations from the NMR assay versus promiscuity determined from off-target pharmacology assays. For the NMR assay, a Y indicates unusual spectral observations (i.e., aggregation), and an N indicates that normal spectral trends were observed. For the off-target hits, a Y indicates that the compound was found to inhibit at >50% at 10 μM in ≥3 off-target assays and was considered as promiscuous. An N indicates that the compound was found to inhibit at >50% at 10 μM in ≤2 off-target assays and was considered to be clean and not promiscuous. The panel of assays ranged from 5 to 52, where the smaller panels included representative assays that often reported promiscuous compounds (frequent hitters) within a series. The total number of compounds was 124. A statistical analysis using the Fisher Exact Test and randomly chosen data sets gives a P-value of 0.6749 suggesting that the predicted is the same as the observed.

computational method (denoted by an X in the right-most column), and conclusions regarding potential promiscuity could not be derived. In the case of the HIV inhibitors (Table 1), no clear correlation between experimental screening data and predicted promiscuity was evident for those compounds that fell within the criteria (Y for compounds predicted to be promiscuous and N for clean). The specific reasons for why predictions based on the combined physicochemical properties (cLogP and TSA) were ineffective here remains unclear at this point. It may be that specific physical properties are dominant within one series, whereas others predominate in another series. The data in Figures 4 and 5 and Tables 1 and 2 show that sometimes LogD can correlate with experimental promiscuity and other times not, suggesting that cLogP and possibly TPSA alone may not fully capture intrinsic properties of compounds either. The properties of molecules and physical forces implicated in the promotion of aggregation are poorly understood and may vary from case to case (e.g., spatial distribution of polarity, sp3 count, number of rings, solubility, molecular weight, etc.). Until more sophisticated methods become available that can more effectively reflect and predict the behavior of compounds in aqueous solution, the empirical NMR assay described herein offers a simple alternative that allows direct observation of aggregation phenomena in solution and prediction of promiscuous behavior. This method can help flag potentially problematic compounds with a high probability of success since the NMR assay allows direct observation of aggregated states, independently of the properties that triggered the phenomenon. For this reason, the NMR method would be expected to be more successful than predictions based on a limited set of calculated physicochemical properties (e.g., cLogP and TPSA). In summary, there appears to be a good correlation between a compound’s tendency to self-aggregate (as detected by NMR abnormalities in dilutions studies) and its promiscuous 7079

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Figure 7. (A, B) Shown is an overlay of NMR spectra of two compounds in various pharmacology media. Shown are the 1H NMR spectra (aromatic region) of two related HIV reverse transcriptase compounds (all at 200 μM). An “X” indicates NMR resonances that arise from the media and can be disregarded. See the Supporting Information for dilution spectra using the NMR assay.

Extracting this data is possible because the 1H resonances of aromatic groups for compounds have a relatively unhindered detection window, which allows for the NMR assay to report critical physical phenomena. The intensity (area) and sharpness (shape) of the resonances are sensitive to drug concentration and the aggregation state, respectively. It should be kept in mind that the sharpness of drug resonances in human plasma and human serum albumin media are also affected by protein binding and exchange phenomena. For each system, compounds were studied in the usual manner under a range of dilutions (as described in the Supporting Information), but only dilutions where the influence of the aggregates are observable are displayed in Figure 7. The full impact of such data has yet to be explored, but some observations are immediately evident. For instance, the apparent concentrations vary significantly for the compound in Figure 7B. In simulated gastric fluid (SGF), the compound is insoluble (low resonance intensity), which would be expected to affect drug exposure and efficacy. On the other hand, the close analogue in Figure 7A is significantly more soluble and may have favorable pharmacological properties. Also, the influence of formulations may be evaluated. For example, a taurochoric acid formulation was found to be beneficial for in vivo exposure (unreported data), and the NMR resonances shown in Figure 7 suggest that both compounds are available at high concentrations and differ somewhat in aggregation states given the differences in resonance attributes. We believe that this type of evaluation can be useful for the development of more effective formulations for compound delivery. This technique can be used to probe pH effects, such as the ones that would be encountered as a compound travels from the stomach into the gut. These pH studies can also be used in the

tendency in off-target assays. However, one may question whether or not inhibition in the off-target assays is due to aggregates associating to proteins (i.e., nonstoichiometric),41 binding of single molecules to multiple sensitive protein areas, or simply a single, lone compound binding to a receptor (i.e., stoichiometric, 1:1). Although the NMR assay does not distinguish between stoichiometric and nonstoichiometry inhibition modes, it does allow for a direct detection of aggregation which appears to be predictive of compound promiscuity in a majority of cases. Likely, the NMR assay simply detects the tendency of a compound to have “sticky” properties and to self-aggregate, which is also reflective of its potential propensity to bind to “sticky” off-target receptors or surfaces, as long as other molecular recognition elements are also satisfied. Thus, the NMR assay should serve as a facile means to observe microscopic compound properties in solution and to flag potentially promiscuous compounds. Aggregation Occurs in Pharmacologically Relevant Media. During the course of this work, we also questioned whether or not it was possible for compounds to self-aggregate in pharmacologically relevant media.38,43 There was a concern that protein binding may predominate leaving little or low concentrations of free compound to self-aggregate. However, an overlay of NMR spectra of two related compounds in various media shown in Figure 7 and are quite revealing. The simple and clear observation of the aromatic resonances of these compounds demonstrates that they can be studied to address critical questions. There are notable differences in compound resonance shape and intensities, and the dilution data in the Supporting Information confirm that they indeed aggregate in these model media (to different degrees). Note that the data can also be used to determine compound concentrations (solubility) which evidently varies significantly. 7080

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Figure 8. Diagram showing how to prepare the samples for the NMR aggregation assay. Tween 80 can replace Triton X-100 as reported elsewhere to avoid signal overlap between detergent and compound resonances.41 Note that we used a high field NMR spectrometer for the studies described here (due to availability and better sensitivity), which allowed us to monitor the effect of compound aggregation at lower concentrations (i.e., 6 μM). A slightly modified version for the sample preparations was described earlier,41 which employed fewer dilutions to allow for the use of low field NMR spectrometers that are more typically available in medicinal chemistry departments.

logical media. Perhaps the availability of this new tool and others will provide a convenient means to begin addressing the question of mechanism of promiscuity for small molecules. Many factors would need to be monitored, e.g., such as the aggregate sizes and types, effects on protein folding/dynamics, and ligand-protein stoichiometries.

context of different pH buffers used in biological assays (not just pharmacology media).

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CONCLUSIONS A facile NMR assay was applied for monitoring the tendency of compounds to self-aggregate in aqueous solution. The observation of unusual spectral features as a function of compound concentration is signatory of self-assembly/ aggregation and is sensitive to a range of assembly sizes. Also, of value to the medicinal chemist is the strong correlations noted for NMR-misbehaving compounds in solution (buffer or other pharmacologically relevant media) and promiscuous tendencies in off-target in vitro pharmacology assays. This methodology provides a rapid, operationally simple, and inexpensive means to flag compounds with undesirable and potentially problematic behavior during the lead optimization stages of drug discovery. While computational methods may have some value for assigning properties of large data sets, the outcome in predicting compound promiscuity is highly dependent on the ability of specific descriptors to predict the behavior of molecules in solution. This NMR method on the other hand provides direct observation of solution phenomena that can lead to promiscuous behavior (i.e., presence or absence of aggregates) independent of the molecule’s attributes or the physicochemical forces that induce them. Inherently, this empirical method should be more predictive of toxicity due to off-target promiscuity. It also has an attractive potential to address questions related to compound behavior in relevant pharmaco-

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EXPERIMENTAL SECTION

Sample Preparations for NMR Aggregation Assay. Figure 8 provides a descriptive summary of the procedures and materials required for preparing the samples for the NMR aggregation assay and differs slightly from the procedure described earlier.41 Briefly, the required supplies for this aggregation study includes the buffer (50 mM sodium phosphate pH 7.4 in 100% D2O), a Triton X-100 stock (10% vol/vol in above buffer), seven Eppendorf tubes, and two pipettes (2−20 μL and 200−1000 μL). Preparation of the 20 mM compound stock solution is described at the top right of Figure 8. The subsequent procedures are as follows and are depicted in Figure 8. (A) Prepare 20 mM compound stock solution. (B) Portion out aggregation buffer, 1500 μL in tube 6 and 500 μL in each of tubes 1−5. (C) Add/mix 15 μL of above 20 mM compound stock to tube 6. This tube now becomes the 200 μM stock solution. If precipitate forms, take note and proceed. (D) Transfer 500 μL of the 200 μM stock solution to tube 7. (E) Take 500 μL of the 200 μM stock and add to tube 5. Mix with the 500 μL buffer already present. This becomes first 2× dilution. Transfer 500 μL from tube 5 to tube 4. Mix. Repeat dilution and mixing for tubes tubes 3, 2, and 1. (F) Add/mix 8 μL of Triton X-100 stock solution to tube 7. (G) Lightly centrifuge tube 7 (2000 rpm 10 min). Transfer precipitate-free supernatant to NMR tube. (H) Transfer solution from tubes 1−6 to separate NMR tubes. 7081

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Alternate ways of preparing the NMR samples were tested, e.g., keeping the 1% DMSO concentration constant during the dilution step, with no significant differences noted from the method described above. All samples were visually inspected to note whether or not cloudiness or precipitates formed. The buffer employed for these studies (50 mM sodium phosphate pH 7.4 in 100% D2O solvent) was considered to be a representative buffer consistent with those used in the off-target in vitro pharmacology assays. Other buffer conditions were surveyed for a limited set of compounds and similar NMR trends were observed. 100% D2O solvent was employed to facilitate NMR data acquisition (which can be acquired with or without solvent suppression if desired). When preparing the aggregation buffer, note that a pD of 7.8 corresponds to a pH of 7.4. The pulse programs for the NMR aggregation assay are the standard 1D 1H NMR experiments available on all commercial spectrometers. There are several optional parameters that can be modified if desired. Given that the buffer consists of 100% D2O, one can choose to use a standard 1H NMR pulse program or one that includes solvent suppression. The latter may be desirable if large H2O resonance peaks exist due to the hygroscopic property of deuterium oxide. The experiments shown here were mostly run on a 600 MHz NMR equipped with a cryoprobe. The number of scans was typically 1−2k with a relaxation delay plus acquisition time of 2 s, which ensured that the data for a few compounds could be acquired overnight using a sample changer. However, similar NMR data were also obtained using a standard 400 MHz NMR (no cryoprobe) using more scans (2−3k). Data for one compound could be acquired during an evening run but with less signal-to-noise. Data visualization and interpretation are also simple. For the work described here, Bruker’s TOPSPIN software allows for the facile superposition of 1D NMR spectra along with zooming capabilities. The interpretation of the NMR data as unusual was based on an analysis of the superimposed spectra and the observation of major or minor unusual features in resonance shifts, shape, or number. Examples are shown in the main paper and in the Supporting Information. Also, see our recently published paper that describes the NMR assay as a practical tool targeted for medicinal chemists.41

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DOSY, diffusion optimized NMR spectroscopy; DMSO, dimethyl sulphoxide; FASSIF, fasted-state simulated intestinal fluid; FESSIF, fed-state simulated intestinal fluid; HCV, hepatitis C virus; HPLC, high-pressure liquid chromatography; HTS, high-throughput screen; HIV, human immunodeficiency virus; NMR, nuclear magnetic resonance; NOESY, nuclear Overhauser exchange spectroscopy; PK, pharmacokinetic; PD, pharmacodynamics; SAR, structure−activity relationship; SGF, simulated gastric fluid; SPR, surface plasma resonance; T, temperature; THR, target-hit-ratio; TPSA, total polar surface area

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(1) Price, A. P.; Blagg, J.; Jones, L.; Greene, N.; Wager, T. Physicochemical drug properties associated with in vivo toxicological outcomes: a review. Expert Opin. Drug Metab. Toxicol. 2009, 5, 921− 931. (2) Kola, I.; Landis, J. Can the pharmaceutical industry reduce attrition rates? Nat. Rev. Drug Discovery 2004, 3, 711−715. (3) Blagg, J. Structure-activity relationships for in vitro and in vivo toxicity. Annu. Rep. Med. Chem. 2006, 41, 353−368. (4) Kramer, J. A.; Sagartz, J. E.; Morris, D. L. The application of discovery toxicology and pathology towards the design of safer pharmaceutical lead candidates. Nat. Rev. Drug Discovery 2007, 6, 636− 649. (5) Azzaoui, K.; Hamon, J.; Faller, B.; Whitebread, S.; Jacoby, E.; Bender, A.; Jenkins, J. L.; Urban, L. Modeling promiscuity based on in vivo safety pharmacology profiling data. ChemMedChem 2007, 2, 874− 880. (6) Stevens, J. L.; Baker, T. K. The future of drug safety testing: expanding the view and narrowing the focus. Drug Discovery Today 2009, 14, 162−167. (7) Krejsa, C. M.; Horvath, D.; Rogalski, S. L.; Penzotti, J. E.; Mao, B.; Barbosa, F.; Migeon, J. C. Predicting ADME properties and side effects: The BioPrint approach. Curr. Opin. Drug Discovery Dev. 2003, 6, 470−480. (8) Cronin, M. T. D. The role of hydrophobicity in toxicity prediction. Curr .Comput.-Aided Drug Des. 2006, 2, 405−413. (9) Cronin, M. T. D.; Livingstone, D. J. Predicting Chemical Toxicity and Fate; CRC Press: Boca Raton, FL, 2004; pp 391−412. (10) Kalgutkar, A. S.; Fate, G.; Didiuk, M. T.; Bauman, J. Toxicophores, reactive metabolites and drug safety: when is it a cause for concern? Expert Rev. Clin. Pharmacol. 2008, 1, 515−531. (11) Hop, C. E. C.; Kalgutkar, A. S.; Soglia, J. R. Importance of early assessment of bioactivation in drug discovery. Annu. Rep. Med. Chem. 2006, 41, 369−381. (12) Williams, D. P.; Naisbitt, D. J. Toxicophores: groups and metabolic routes associated with increased safety risk. Curr. Opin. Drug Discovery Dev. 2002, 5, 104−115. (13) (a) Bender, A.; Scheiber, J.; Glick, M.; Davies, J.; Azzaoui, K.; Hamon, J.; Urban, L.; Whitebread, S. Analysis of pharmacology data and the prediction of adverse drug reactions and off-target effects from chemical structure. ChemMedChem 2007, 2, 861−873. (b) Dimova, D.; Hu, Y.; Bajorath, J. Matched Molecular Pair Analysis of Small Molecule Microarray Data Identifies Promiscuity Cliffs and Reveals Molecular Origins of Extreme Compound Promiscuity. J. Med. Chem. 2012, 155, 10229−10228. (14) Johnson, D. E.; Smith, D. A.; Park, K. B. Linking toxicity and chemistry: think globally, but act locally? Curr. Opin. Drug Discovery Dev. 2004, 7, 33−35. (15) Alden, C. L. The pathologist and toxicologist in pharmaceutical product discovery. Toxicol. Pathol. 1999, 27, 104−106. (16) Cronin, M. T. D.; Zhao, Y. H.; Yu, R. L. pH-dependence and QSAR analysis of the toxicity of phenols and anilines to Daphnia magna. Environ. Toxicol. 2000, 15, 140−148. (17) Monks, T. J.; Jones, D. C. The metabolism and toxicity of quinones, quinonimines, quinone methides, and quinine-thioethers. Curr. Drug Metab. 2002, 3, 425−438.

ASSOCIATED CONTENT

S Supporting Information *

An extensive Supporting Information section is available that describes materials, methods and other relevant experiments. This material is available free of charge via the Internet at http://pubs.acs.org.

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REFERENCES

AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected] and RESGeneral.LAV@ boehringer-ingelheim.com. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS We are grateful to the following colleagues who provided compounds, data, or helpful discussions during the course of these studies: J. Gillard, S. Bordeleau, C. Brochu, E. Bugan, J. Duan, P. Ettmayer, R. Fryer, M. Hoffmann, O. Hucke, N. Moss, C. Lemke, M. Little, H. Priepke, M. Ribadeneira, S. Srivastava, D. Sun, and E. Young.

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ABBREVIATIONS USED ADME, absorption, delivery, metabolism, excretion; cLogP, calculated partition coefficient; CAC, critical aggregate concentration; CMC, critical micelle concentration; cSLS, confocal static light scattering; DLS, dynamic light scattering; 7082

dx.doi.org/10.1021/jm4008714 | J. Med. Chem. 2013, 56, 7073−7083

Journal of Medicinal Chemistry

Article

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