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Evaluating LC–MS/MS to measure accumulation of compounds within bacteria Ramkumar Iyer, Zhengqi Ye, Annette Ferrari, Leonard Duncan, M Angela Tanudra, Hong Tsao, Tiansheng Wang, Hong Gao, Christopher L Brummel, and Alice L. Erwin ACS Infect. Dis., Just Accepted Manuscript • DOI: 10.1021/acsinfecdis.8b00083 • Publication Date (Web): 30 Jun 2018 Downloaded from http://pubs.acs.org on July 1, 2018

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ACS Infectious Diseases

1 Evaluating LC–MS/MS to measure accumulation of compounds within bacteria

Ramkumar Iyer, Zhengqi Ye, Annette Ferrari, Leonard Duncan, M Angela Tanudra, Hong Tsao, Tiansheng Wang, Hong Gao, Christopher L Brummel and Alice L Erwin* Vertex Pharmaceuticals Incorporated, 50 Northern Avenue, Boston, Massachusetts 02210

*Corresponding author: [email protected]

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2 A general method for determining bacterial uptake of compounds independent of antibacterial activity would be a valuable tool in antibacterial drug discovery. LC-MS/MS assays have been described, but it has not been shown whether the data can be used directly to inform medicinal chemistry. We describe the evaluation of an LC-MS/MS assay measuring association of compounds with bacteria, using a set of over a hundred compounds (inhibitors of NAD-dependent DNA ligase, LigA) for which in-vitro potency and antibacterial activity had been determined. All compounds were active against an effluxdeficient strain of Escherichia coli with reduced LigA activity (E. coli ligA251 ∆tolC). Testing a single compound concentration and incubation time, we found that for equipotent compounds, LC-MS/MS values were not predictive of antibacterial activity. This indicates that measured bacteria-associated compound was not necessarily exposed to the target enzyme. Our data suggest that while exclusion from bacteria is a major reason for poor antibacterial activity of potent compounds, the distribution of compound within the bacterial cell may also be a problem. The relative importance of these factors is likely to vary from one chemical series to another. Our observations provide directions for further study of this difficult issue.

Keywords: compound accumulation, Gram-negative, antibacterial, mass spectrometry, ligase Antibacterial activity is the net result of the ability of a compound to inhibit its molecular target (biochemical target engagement, or potency) and the extent to which it accumulates at the target within the cell (target exposure).

Medicinal chemistry

programs routinely use in vitro biochemical (cell-free) assays, protein structural ACS Paragon Plus Environment

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3 information and molecular modeling to generate testable hypotheses aimed at rapidly improving the biochemical potency of compounds. However, a major problem faced by early discovery programs is our inability to design compounds with good accumulation within bacteria.1-3 Consequently, a direct assay of intracellular compound concentration that is independent of antibacterial activity would be extremely useful in a medicinal chemistry program. We describe the development of an LC-MS/MS assay to measure bacterial cellassociated compound, and the evaluation of the assay in the context of a concurrent antibacterial drug discovery project at Vertex Pharmaceuticals. At the time we began this work, no assays of this type had been reported. Several have been reported since then, and the topic has recently been reviewed.4-8 We emphasize the difference between this study and others that have recently used LC-MS/MS to study the association of experimental compounds with bacteria. Davis et al. used principal component analysis to identify physical properties associated with high accumulation of sulfonyl adenosines in Escherichia coli, Bacillus subtilis, and Mycobacterium smegmatis.5

More recently, Richter et al. studied over 180 diverse

compounds and described chemical and physical characteristics associated with high accumulation in E. coli. Their analysis allowed them to predict that addition of a primary amine would confer Gram-negative activity on compounds with a certain profile (low globularity, high rigidity) that were already active against Gram-positive bacteria.6

For

each of these studies, the broad goal was to understand the structure-activity relationship (SAR) of accumulation within bacteria. The researchers analyzed their LC-MS/MS data together with chemical features and predicted physical properties. Biologic activity of the compounds was not part of either analysis. In contrast, we compared bioanalytic data to ACS Paragon Plus Environment


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4 biologic data, asking whether the two technologies give the same information about exposure of study compounds to their target. In validating assays of bacteria-associated compound, most researchers have used mutants with altered susceptibility to antibiotics. For example, Cai et al reported that accumulation of ciprofloxacin in the Pseudomonas aeruginosa pump deletion mutants PAO200 and PAO314 was greater than in the parent strain, PAO1, consistent with the lower MIC of the two mutants.7 Zhou et al reported that the effect of efflux pump deletion on the MICs of linezolid and meropenem for P. aeruginosa and E. coli was consistent with the ability of the mutant and parent isolates to deplete these antibiotics from culture medium.8 Strains with regulated over- or under expression of porin genes9,10 could be used in the same way. Other researchers have used chemical treatment, such as addition of colistin, uncoupling agents or efflux pump inhibitors, to demonstrate that these additives affect measured compound uptake by E. coli in the same way that they affect antibiotic susceptibility.5,6 These validation studies provide confidence that a substantial portion of measured compound is internalized, rather than simply stuck to the bacterial surface. But none of the reported methods for sample preparation provide information on compound distribution among the different subcellular locations.

Thus they do not

necessarily quantify the intracytoplasmic concentration of compounds, which is the information that would be most useful in a medicinal chemistry program. In this work, we tested over a hundred inhibitors of E. coli NAD-dependent DNA ligase (LigA) that had antibacterial activity for at least one of the mutant strains we tested.11,12 We were then able to ask whether the LC-MS/MS data were consistent with the biological data obtained during routine evaluation of compounds as they were synthesized. One limitation of this approach is that what is measured is the total amount ACS Paragon Plus Environment

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5 of compound associated with bacteria in a sample (i.e., the net effect of entry and efflux), including any compound adherent to the cell surface. The value of this measurement in a medicinal chemistry program depends on how closely it reflects the amount of compound that is actually exposed to the target in the cytoplasm or periplasmic space. If analogs within the chemical series vary widely in distribution within the bacterial cell, then measurements of total compound may be of little value. For LC-MS/MS measurements, we chose a simple experimental design, with a single time point and one concentration. Because there is some uncertainty about where the compound we measure is located, we use the term bacteria-associated compound (BAC) for our LC-MS/MS data, rather than the terms uptake, permeation, or accumulation. We used the ratio of in-vitro LigA IC50 to antibacterial activity as a way to compare the exposure of compounds to the target, as our data set included compounds with a range of biochemical potencies. We anticipated that compounds with poor antibacterial activity despite good in-vitro potency would be found to be excluded from bacterial cells. Surprisingly, this was not the case. When we controlled for potency, either by studying a subset of compounds with similar LigA IC50 values and a wide range of whole cell antibacterial activities or by calculating the IC50/MIC ratio (TExp), we found that BAC values failed to predict whole cell antibacterial activity. To the best of our knowledge this is the first study to evaluate the correlation between (i) exposure of experimental antibacterial compounds to their intracellular target and (ii) association of the same compounds with bacteria, determined by LC–MS/MS.



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6 RESULTS AND DISCUSSION Assay development. Preliminary development of the LC-MS/MS assay is shown in the Supporting Information. Briefly, bacteria were incubated with the test compound (10 µM), sampled at various times up to 50 min, and removed from unbound drug by centrifugation through silicone oil. Bacteria-associated compound was extracted from bacterial lysates using acetonitrile and analyzed by LC-MS/MS. We found, as others have, that the association of antibiotics with E. coli cells was readily detectable within 2 minutes, increased over the next several minutes, and in most cases reached a plateau by about ten minutes. For each antibiotic studied, the amount of compound detected was substantially greater for an efflux-deficient strain (∆tolC), consistent with the effect of tolC deletion on MIC of these drugs (Figure S1). Subsequent studies used a single incubation time, 15 min, similar to previous studies.5,6 This assay had good day-to-day reproducibility and low background values (Figure S2). Effect of efflux on both BAC and antibacterial activity - quinolones. As a first step in assessing whether LC–MS/MS data are consistent with biological data, we tested several quinolone antibiotics in isogenic efflux-competent and efflux-deficient strains of E. coli. These antibiotics differ widely in the extent to which ∆tolC affects susceptibility. The MIC of moxifloxacin for E. coli is 0.125 µM; deletion of tolC reduces the MIC to 0.008 µM, a 16-fold shift. At the other extreme, the MIC of cinoxacin is shifted only two-fold by tolC deletion, from 12.5 µM to 6.25 µM. We noted a similarly wide range in the effect of ∆tolC on BAC values determined by LC–MS/MS for the same quinolones in these two strains (Figure 1A).

Cinoxacin BAC was very similar for both strains, while much larger

differences between the strains were seen for moxifloxacin and enrofloxacin. Plotting the MIC ratio(tolC+/∆tolC) vs BAC ratio (∆tolC/tolC+), we noted a general trend for the two ACS Paragon Plus Environment

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7 assays to give similar assessments of the effect of tolC deletion (Figure 1B). A larger set of compounds would be required to adequately power this study to be able to produce a rigorous statistical measure of the correlation. Characterization of study set: LigA inhibitors. We have previously described two related classes of compounds that inhibit bacterial LigA (Figure 2A; tables S1, S2).11,12 We had available 132 compounds with in-vitro ligase activity (IC50 for recombinant E. coli LigA) from 0.01 to 10 µM. Twenty-five of these were from the aminoalkoxypyrimidine carboxamide (AAPC) scaffold,11 and 107 from the pyridopyrimidinone (PP) scaffold.12 None of these compounds was active against wt E. coli (MIC > 100 µM; data not shown). This inactivity was due in part to the effect of multidrug efflux pumps on intracellular accumulation. Inactivation of TolC-dependent pumps increases the susceptibility of E. coli to many antibacterial compounds.13 We found that nearly half the LigA inhibitors had modest activity (MIC 12.5 to 100 µM) in an efflux-deficient (∆tolC) mutant (Figure 2B). A second reason for limited antibacterial activity of the AAPC and PP compounds was that bacteria can tolerate a substantial loss of ligase activity.14,15 Lavesa-Curto et al. described a temperature-sensitive mutant of E. coli in which ligA contained a point mutation (ligA251, resulting in L15F) that reduced ligase activity by 20- to 60-fold.16 The ligA251 mutation sensitized E. coli to the antibacterial activity of ligase inhibitors. Although none of our inhibitors were active against an efflux-competent (tolC+) strain that carried the wild-type ligA allele (ligA+), 27 compounds had detectable activity (MIC 12.5 to 100 μM) for the temperature-sensitive mutant (ligA251 tolC+) (Figure 2C). A strain with both mutations (ligA251 ΔtolC) was even more sensitive, with all except eight compounds having MIC from 0.2 to 100 μM (Figure 2D).



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8 If all compounds accumulated to a similar degree (i.e., had similar rates of entry & efflux susceptibilities), and achieved similar exposure to the ligase enzyme in the cytoplasm, then IC50 values would be a strong predictor of MIC. In practice, although there was a general tendency for the more potent compounds to have better antibacterial activity in the ligA251 ∆tolC strain, we also noted that compounds with comparable IC50 values often differed in MIC. For example, the boxed region of Figure 2D shows 61 compounds with a narrow range of ligase inhibition (IC50 from 0.05 µM to 0.2 µM) and a wide range of MIC (0.4 µM to >100 µM). This compound set thus offered the possibility of evaluating the extent to which LC–MS/MS data reflect the effective intracellular concentration of compounds and test our hypothesis.

Importantly, the differential

susceptibility of E. coli ligA251 ∆tolC and E. coli ligA+ ∆tolC gave us confidence that the antibacterial activity we detected was on target. In addition, for the small number of compounds active against ligA251 tolC+, we were able to assess whether BAC and MIC were affected similarly by TolC-dependent efflux. Effect of efflux on both BAC and antibacterial activity - ligase inhibitors. Of the full set of ligase inhibitors, twenty-seven compounds had measurable antibacterial activity against the ligA251 tolC+ strain (Figure 2C). Deleting tolC improved the antibacterial activity of most compounds in the set. Using this subset of 27 compounds that spanned both AAPC and PP chemotypes, we evaluated whether BAC ratios determined for the ligA251 tolC+ and its ∆tolC derivative would follow the corresponding MIC ratios. The results differed by scaffold, with AAPC affected much less by TolC-dependent efflux than PP, consistent with previous observations of the effect of ∆tolC on AAPC compounds.11 Overall, the effect of tolC deletion on antibacterial activity of these compounds was indeed ACS Paragon Plus Environment

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9 roughly consistent with the effect of the tolC deletion on BAC (Figure 3), as we had seen for eight quinolone antibiotics (Figure 1) and for a smaller set of ligase inhibitors in a pilot study (Figure S2). Most compounds could be classified as high-efflux or low-efflux, while a few compounds fell between the two groups. At this point, these early data supported the conclusion that a single data point (15 minutes, 10 µM), similar to other recent studies,5,6 might be sufficient for rank ordering of these compounds by efflux. The association of compounds with wild-type and ∆tolC bacteria over time was not evaluated in this study, as our primary goal was to assess a simple experimental design. BAC measurements do not predict exposure to ligase enzyme. Of the 132 compounds (25 AAPC, 107 PP) shown in Figure 2, we were able to obtain BAC values using the E. coli ∆tolC strain for all except two AAPC and four PP. In most cases, these values were substantially higher than for the isogenic tolC+ strain, consistent with antibacterial data (Table S1). As in the pilot study, the background (no-cell) values were negligible for most compounds. For one AAPC and 19 PP, background values exceeded 20% of the ∆tolC values. These compounds were eliminated from further analysis, as were those for which MIC > 100 µM. We thus had 100 compounds (20 AAPC and 80 PP) with which to evaluate the hypothesis that BAC data reflect intracellular exposure of compounds to the ligase enzyme. We tested this hypothesis in two ways. First, as noted above, 61 of the ligase inhibitors were within a narrow range of invitro potency (ligase IC50 from 0.5 µM to 2 µM) and displayed a wide range of antibacterial activity (MIC from 0.4 µM to >100 µM) for the ligA251 ∆tolC strain. We hypothesized that BAC measurements for these compounds would correlate with MICs, with greater ACS Paragon Plus Environment


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10 accumulation for compounds with better antibacterial activity. This was not the case (Figure 4A). Two compounds with the lowest BAC values (15 nM) lacked antibacterial activity. Apart from these two points, there was no tendency for compounds with low BAC to have poor antibacterial activity. Compounds with the lowest MICs (0.4 µM to 1.6 µM) had BAC values ranging from 85 nM to >500 nM. The compounds with the highest BAC (>500 nM; 6 compounds) had MICs ranging from 0.4 µM to >100 µM. Additionally, for a given MIC (e.g., 3.1 µM) there was a large spread of BAC values ranging from 40 nM to >500 nM. There were no obvious features of compounds that had particularly high or low BAC relative to their antibacterial activity. Six compounds in the upper right quadrant of the plot (i.e., BAC of 200 nM or higher and MIC of 12.5 µM or higher) were noted to be diverse in scaffold and in predicted charge. The second analysis used all 100 compounds for which we had ∆tolC BAC data and on-scale antibacterial activity for ligA251 ∆tolC. Compounds with high MIC relative to their in-vitro potency are considered to have poor exposure to the ligase enzyme. There was no correlation between BAC and TExp (Figure 4B). For example, the boxed region of Figure 4B indicates a set of compounds with very similar BAC values and a wide range of TExp. If one were working with a series of potent inhibitors that did not have antibacterial activity, it is doubtful whether BAC data of this type would provide information that would allow chemists to improve intracellular concentration and thereby achieve antibacterial activity. Considering the poor antibacterial activity of our ligase inhibitors, the extent to which they were taken up by ∆tolC bacteria seemed surprisingly high. Most compounds had BAC values of 50 to 500 nM, determined in lysates of ∆tolC bacteria. Using an ACS Paragon Plus Environment

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11 assumed bacterial cell volume of 1 µm3 (1 femtoliter),17 these lysate concentrations corresponded to apparent intracellular concentrations of 25 to 250 µM if compounds were evenly distributed throughout the bacterial cell. These concentrations are up to 25 fold above the initial external concentration in the medium and far above those required for half-maximal inhibition of ligase activity, as determined in an in-vitro enzyme assay. In principle, one might consider that exceeding the intracellular concentration of an inhibitor beyond a certain value would saturate the enzyme.

Consequently, increasing the

intracellular concentration would not be expected to reduce the MIC further. Most of our compounds had poor antibacterial activity even for the ligA251 mutant, in which ligase is defective, and were inactive against ligA+ E. coli. In this context, we propose that that of the compound we detect in steady-state BAC determinations, only some of it is actually available to bind the target in the cytoplasm. The remainder may be bound to non-target components of the periplasm, cytoplasm, or either leaflet of either membrane. Attempts to localize compound (e.g., by fractionation) are outside the scope of this study. It is unlikely that measured compound is all simply associated with the bacterial cell surface. For the 92 compounds for which we determined BAC in both tolC+ and ∆tolC cells, ∆tolC BAC was much higher than tolC+ BAC (Table S1). This suggests that some proportion of compound penetrated into cells at least far enough to be subject to efflux – i.e., reached the periplasmic space or the cytoplasmic membrane. It is possible, however, that the surface properties of the two strains differ in such a way that compounds adhere to ∆tolC cells to a greater extent than to tolC+ cells. Loss of tolC affects production of the porin OmpF and has a variety of other effects on bacterial function beyond susceptibility to antibiotics.18,19



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12 Physical properties affecting accumulation.

While research over the past

several decades has told us a great deal about the bacterial processes that affect antibiotic entry and intracellular accumulation,13 it has provided little guidance for designing compounds that have high exposure to periplasmic or cytoplasmic targets. It has been observed that antibiotics active against Gram-negative bacteria tend to be lower in molecular weight and more hydrophilic (low clogD) than Gram-positive antibiotics or than drugs in general.2,20 This is consistent with the general understanding that permeation across the Gram-negative outer membrane via porins is most efficient for small, hydrophilic compounds21 and that the best substrates for multidrug efflux pumps are often hydrophobic or amphiphilic.22 One might then predict that reducing the size or logD of compounds within a chemical series would increase their intracellular concentration and thereby improve TExp. In general, we saw no effect of molecular weight on either BAC or exposure of ligase inhibitors (Table S1). Of our ligase inhibitors, the AAPC compounds were generally smaller (median MW 236) and more hydrophilic (median clogD 0.8) than the PP compounds (median MW 359, median clogD 2.7). These properties might account for the observation that both BAC and MIC data showed that the AAPC are little affected by efflux. However, these favorable properties did not lead to better intracellular accumulation of this scaffold, whether assessed by TExp or by BAC. Although the small size and relative hydrophilicity of the AAPC compounds might enable porin-permeation and periplasmic accumulation, these same properties may hinder permeation across the hydrophobic cytoplasmic membrane.

The divergent sieving properties of the outer and inner

membranes might account for the poor antibacterial activity of the AAPCs relative to their target potency. ACS Paragon Plus Environment

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13 The effect of clogD on BAC was the opposite of that predicted: in the PP scaffold, the more hydrophobic compounds generally had higher BAC values, which is suggestive of compounds sticking to the bacterial cell or more readily partitioning into hydrophobic cellular compartments. Predicted charge also had an effect: a group of twelve positivelycharged compounds had some of the lowest clogD values and were taken up similarly to compounds with much higher clogD (Figure 5A). Consistent with the lack of correlation between BAC and TExp, we saw no effect of clogD on TExp (Figure 5B). The route by which the ligase inhibitors cross the Gram-negative outer membrane is not known. The route of permeation across the outer membrane has been studied primarily for β-lactams, which enter via porins,13 and aminoglycosides, which cross the lipopolysaccharide-phospholipid bilayer by a process that has been termed self-promoted uptake.23 Less is known about how other classes of antibiotics enter the periplasm. Despite the general observation that Gram-negative antibiotics have low molecular weight and clogD, it cannot necessarily be concluded that reduction of molecular weight or logD within a specific chemical series will increase intracellular accumulation and improve antibacterial activity relative to potency. For example, recent electrophysiology studies using porins embedded in planar lipid bilayers and controlled porin overexpression studies have shown that translocation of small, hydrophilic antibacterial compounds depends not simply on size but also on specific interactions between the antibiotic and the porin channel.9,24,25 Our data can be compared to those in two recent studies that evaluated the association of experimental compounds with bacteria to identify physical properties associated with high accumulation. Davis et al. reported for ten related compounds that hydrophobicity was the property most strongly correlated with accumulation in E. coli,5 as ACS Paragon Plus Environment


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14 we saw for most of our compounds. Similarly to our finding that a set of positivelycharged PP compounds were among those with the highest BAC values, Richter et al. reported for a set of 100 small molecules that the parameter most strongly associated with accumulation in E. coli was positive charge. They also found that of 68 compounds containing primary amines charged at pH 7.4, those with the highest uptake had low flexibility and low globularity. In that study, neither molecular weight nor clogD was a major contributor to accumulation in bacteria.6 Comparison with earlier studies of compound accumulation. Studying the kinetics of association of radioactive or fluorescent antibiotics with susceptible bacteria led

to

the

recognition

that

for

aminoglycosides,

tetracyclines,

and

perhaps

fluoroquinolones, entry is in part energy-dependent, although none of these has an apparent specific receptor.26-28

Measuring the differences between isogenic strains

differing in susceptibility to one or more antibiotics allowed identification of genetic loci (porins, LPS synthesis) that affect permeation across the outer membrane or that encode or regulate efflux pumps.29-37 More recently, LC/MS-MS was used to study the role of efflux in intrinsic resistance of gram-negative bacteria to linezolid,38 and to evaluate the hypothesis that two compounds potentiating tetracycline in Acinetobacter baumannii are efflux pump inhibitors.39 These experiments examined one antibacterial compound at a time, so there was no need to control for potency towards the molecular target. In contrast to these reports on bacterial processes affecting uptake of individual antibiotics, a few studies compared two or more compounds within a chemical class (fluoroquinolones or tetracyclines), allowing assessment of the effect of structure or physical properties on accumulation in bacteria. Several studies noted differences among quinolones,40-42 and in one case it was shown that the maximum velocity of transport ACS Paragon Plus Environment

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15 (Vmax) for accumulation of quinolones in P. aeruginosa was roughly predictive of antibacterial activity.43

Most studies that have compared multiple compounds have

chosen to incubate bacteria with a single concentration of each compound and to measure steady-state concentrations, as we did.5,6,44 Calculating the ratio of IC50 to MIC allowed us to rank-order compounds by relative exposure even if they differ in both potency and antibacterial activity, and to ask how well our BAC measurements reflect exposure, a question that has rarely been addressed. Twenty-five years ago, Bazile et al. measured the association of 11 quinolones with bacteria, using their intrinsic fluorescence as a readout. Using a gel-based supercoiling assay and E. coli gyrase enzyme to determine the minimum effective dose (MED) of each compound, they found that MED was only moderately predictive of MIC for Staphylococcus aureus, E. coli, or P. aeruginosa. Adjusting MED by accumulation improved the correlation with MIC substantially,44 suggesting that for these quinolones, the measurements of accumulation reflected exposure to the target. In contrast, as described above, we found for our ligase inhibitors that BAC did not predict exposure. This suggests that simply determining steady-state concentrations may not be appropriate for all chemical scaffolds. Samra et al. studied 12 tetracyclines, monitoring the increase in fluorescence (reflecting association with membrane) when tetracyclines are added to a suspension of S. aureus or E. coli. Noting that fluorescence was not correlated with growth inhibition, they speculated that these compounds differ in distribution within the cell and perhaps also in affinity for ribosomes.45

For the most hydrophobic tetracyclines, both bacterial

fluorescence measurements and antibacterial activity were greater in deep-rough LPS mutants (in which patches of exposed phospholipid in the outer membrane are thought to allow permeation of hydrophobic compounds) than in strains with longer LPS.33 McMurry ACS Paragon Plus Environment


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16 et al. used radioactivity to monitor uptake of tetracyclines by E. coli and reported that the hydrophobic analog minocycline accumulated much faster than tetracycline itself and reached higher levels.46 As the two are similar in both MIC and ribosome inhibition,47 this suggests that not all intracellular minocycline is exposed to its target. In contrast, Chopra and Hacker found uptake of five tetracyclines to be inversely correlated with hydrophobicity. They assessed uptake by induction of a tetA-lacZ reporter,48 which may be a more accurate representation of cytoplasmic accumulation than is fluorescence. Much more recently, a study used molecular dynamic simulation and X-ray diffraction to compare several carbapenems in their interaction with a phospholipid bilayer. The carbapenems were found to differ in membrane partitioning, and it was suggested that these differences in membrane partitioning might affect the exposure of these compounds to their transpeptidase targets in the periplasm.49 These reports on tetracyclines and carbapenems provide precedent for the idea that not all compound that enters the cell is available to bind a cytoplasmic target. For our compounds, we saw that either hydrophobicity or positive charge may increase the amount of total cell-associated compound, but those high measurements do not necessarily translate to better antibacterial activity for analogs of equal potency. Limitations of this study. The idea that IC50 and MIC would be correlated if exposure to the target were similar for all compounds is an oversimplification. These assays are very different, particularly in regard to time frame. Standard protocols for MIC determination require overnight incubation to allow bacterial growth to be scored by eye. The transcriptional changes that occur during antibiotic exposure50,51 may affect compound accumulation, so that susceptibility to an antibacterial compound may change during the assay. In contrast, enzyme assays are typically run under conditions ACS Paragon Plus Environment

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17 deliberately optimized to get maximal signal in the assay in a short period, usually well under an hour. With the use of luminescent bacteria or fluorescent probes for monitoring bacteria, it is possible to detect growth inhibition within a few hours.52,53 A bacterial cellbased assay that reflects early engagement of the compound with the target, rather than growth inhibition, might also be useful. For example, the effect of antibiotics on the incorporation of radiolabeled precursors into macromolecules is typically detected in less than one bacterial generation time.54 A bacterial adaption of the cellular thermal shift assay55 might also be feasible. Two recent reports describe methods for assaying intracellular compound that combine image analysis with spectrophotometric measurements.56,57 It is possible that these will make it possible to measure cytoplasmic concentrations rather than total bacteria-associated compound. IC50 is the enzymatic parameter most often obtained in medicinal chemistry programs, but it may not be the most appropriate. A cell-free enzyme assay is very different from the intracellular conditions in substrate concentration and in the presence of other cellular components. It is possible that conducting the enzyme assay in a bacterial lysate rather than in a defined system would yield results that were more predictive of antibacterial activity. In our case, a further issue is that the enzyme assays used wild-type recombinant LigA, while the antibacterial activity was in cells with mutated ligA. Enzyme assays using the LigA251 protein might have produced a different rank-ordering of IC50/MIC ratios. For this study, we were limited to the biochemical and microbiologic data (IC50 and MIC) that were already being obtained for the LigA hit-to-lead chemistry project. With regard to the BAC assay, it is possible that a different method of separating bacteria from unbound compound might have been more predictive of biological data. ACS Paragon Plus Environment


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18 Multiple means of cell washing and separation have been described, as summarized in a recent review.4 Most early studies used vacuum filtration onto membranes, a technique that requires only a few seconds to recover bacteria from a suspension and wash the membrane with buffer. An alternative technique was centrifugation through silicone oil.30,37,44,58 Reasons for adapting this latter method included observing nonspecific binding of a drug to filter membrane59 or concern about drug leaking out of bacteria during washing of the filter.34,35 Hooper et al. reported that the two methods gave similar results for evaluating the uptake of norfloxacin by E. coli, but that after initial uptake of norfloxacin by E. coli, nearly all compound was lost if the bacteria were diluted into drug-free medium.60 Consistent with this, Bedard et al. reported that ciprofloxacin could readily be washed out of E. coli cells, though P. aeruginosa cells retained a higher proportion of compound.61 We chose centrifugation through silicone oil after extensive preliminary experimentation and consideration of methods previously described for radioactive antibiotics. This method is rapid, allowing sampling at early time points without the need for washing steps that might allow internalized compound to elute into the wash buffer. In preliminary studies (not shown), we found that 96-well filter plates worked well for determining association of tetracycline, ciprofloxacin and other antibiotics with E. coli and P. aeruginosa in a method similar to that described by Cai et al.7 However, when we began to test experimental ligase inhibitors, we noted very high background levels in bacteriafree ("no-cell") control samples. We ascribed this to compound binding to either the filters or the plastic plates. We tested many different filter plates and washing methods and were unable to eliminate nonspecific binding of these rather hydrophobic ACS Paragon Plus Environment

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19 experimental compounds, although each wash reduced the amount of compound associated with bacterial cells. Centrifugation through oil gave much cleaner results with very little no-cell background and allowed the use of higher cell densities than was possible in the filter method. The Piddock laboratory compared several methods and recommended that the silicone oil method include determination of surface binding in a parallel set of samples incubated at 0˚ C.59,62 This may not be an ideal control, as cold temperatures are likely to alter membrane properties. Indeed, it has been reported that the outer membrane of P. aeruginosa is leaky after even brief exposure to cold.34,43,63 Our hope was to develop a method that we could use for both E. coli and P. aeruginosa, so we did not include controls incubated in an ice bath. We conducted preliminary studies with both S. aureus and P. aeruginosa, but had no suitable compound sets with which to validate the data.

Specifically, our ligase

inhibitors were only weakly active versus S. aureus and even less so versus P. aeruginosa. The hope is that this assay can provide guidance to medicinal chemistry for designing compounds with high cellular accumulation. It was important to determine whether the SAR of BAC values were consistent with biological data. Testing this hypothesis required a set of related compounds that had (i) measurable antibacterial activity and (ii) a known molecular target for which potency could be determined in a cell-free assay. Few such compound collections are available outside of academic or industrial antibiotic discovery programs.

The susceptibility of E. coli ∆tolC ligA251 to our LigA inhibitors offered the

possibility of validating the BAC assay using such a controlled set of more than a hundred inhibitors. No other antibacterial scaffold with known potency against a single target was available to us at the time of this study. ACS Paragon Plus Environment


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20 Concluding remarks. In pharmaceutical antibiotic discovery programs, it is not uncommon for compounds with high in-vitro potency against the proposed target to have poor cellular activity, especially versus Gram-negative bacteria. This observation is often interpreted as the failure of compounds to "get in" to bacterial cells. Our observations suggest that although the exclusion of compounds by bacteria is a major problem, exposure of intracellular compound to the target is another variable that influences antibacterial activity. Most of the compounds we studied had high BAC values, yet this did not necessarily translate into better antibacterial activity, offering the possibility that antibacterial activity can be improved not only by increasing the total amount of compounds in cells but also by improving intracellular distribution of the compounds. Our observations indicate that the association of compounds with bacterial cells can be measured accurately using LC-MS/MS, just as in earlier studies using radioactive compounds.

When the goal is to study the contribution of the outer membrane

permeability barrier or efflux, LC-MS/MS can be used appropriately to evaluate the effect of a mutation or of altered conditions on accumulation of a single compound in bacteria. However, for comparing compounds to each other, single-point measurements of total accumulation are not necessarily predictive of the amount of compound that will be available to an intracellular target. Bacteria-associated compound is not necessarily cytoplasmic. Other experimental designs may be necessary to make the approach more predictive. To our knowledge, the study by Bazile et al. is the only one that reported biochemical potency and MIC determinations as well as uptake measurements for a group of related compounds.44 It may be that determining the kinetics of drug accumulation into Gram-negative bacteria64 may be more useful than the steady-state measurements we and others have made. ACS Paragon Plus Environment

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21 This is a very active area of research, with recent reports describing localizing intracellular azide-containing compounds to the periplasm or cytoplasm65 and studying the interaction of compounds with the inner and outer membranes in a model of the Gram-negative cell envelope.66

For researchers seeking to understand the SAR of

accumulation in Gram-negative bacteria, further development of these technologies will be valuable.

METHODS Bacterial strains. E. coli MC4100 (referred to as E. coli wt) and a mutant in which tolC was inactivated by insertion of Tn10 were used for BAC determinations. Bacteria were cultivated in cation-adjusted Mueller Hinton broth (MH2B) (Becton, Dickinson and Company, Franklin Lakes, New Jersey). Susceptibility of bacteria to antibiotics and other compounds was determined in microplate assays using CLSI protocols and reported as MIC in µM for the experimental ligase inhibitors and µg/ml for antibiotics. Chemical compounds. Antibiotics were purchased from Sigma-Aldrich. LigA inhibitors were prepared and in-vitro potency determined as previously described.11,12 A list of compounds is provided in Table S1, and structures are shown in Table S2. Determination of bacteria-associated compound (BAC). Detailed methods are provided in Supporting Information. In brief, bacterial cells at a fixed cell density were incubated with compound at room temperature. At the end of the incubation (15 min, unless otherwise indicated), the cell suspension was transferred onto a silicone oil layer. Centrifugation of the cells through the oil layer quickly separated them from unbound compound in the aqueous layer. The tubes with the cell pellet at the bottom were then ACS Paragon Plus Environment


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22 flash-frozen in a dry-ice bath to reduce diffusion of the compound back out of the cell. The tips with the tiny, compact cell pellet were sliced off. The cell pellet was then lysed in a larger volume of buffer, and the concentration of compound in the lysate was estimated using LC-MS/MS.

ASSOCIATED CONTENT Supporting Information Detailed methods for BAC assay. Development of the BAC assay. Figure S1, Uptake of antibiotics by wild-type and efflux-deficient E. coli - time course. Figure S2, Pilot study with ligase inhibitors: BAC is reproducible and consistent with observed efflux (PDF). Table S1: 132 ligase inhibitors, with chemical structure (SMILES), biological data (ligase IC50; MIC for three E. coli strains, ligA+ ∆tolC, ligA251 tolC+, and ligA251

∆tolC; BAC for E. coli tolC+ and ∆tolC, and no-cell controls) and calculated properties (molecular weight, charge, and clogD7.4) (XLS). Table S2: 132 LigA inhibitors, with chemical structure (drawing and SMILES), biological data (ligase IC50; MIC for E. coli ligA251 ∆tolC); BAC for E. coli tolC+ and ∆tolC, and no-cell controls) and calculated properties (molecular weight, charge, and clogD7.4) (XLS)

AUTHOR INFORMATION Corresponding Author *[email protected]


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23 Present affiliations: Ramkumar Iyer, Entasis Therapeutics, Waltham, Massachusetts; Annette Ferrari, Seqiris, Cambridge, Massachusetts; Leonard Duncan, JMI Laboratories, North Liberty, Iowa; M Angela Tanudra, Entasis Therapeutics, Waltham, Massachusetts; Christopher Brummel, Concert Pharmaceuticals, Inc., Lexington, Massachusetts; Alice Erwin, Erwin Consulting LLC, Seattle, Washington. ORCID Alice L Erwin

0000-0002-1416-7149

Ramkumar Iyer

0000-0002-3638-2421

Tiansheng Wang

0000-0001-5073-2670

Author Contributions R.I and A.L.E. conceived the study, analyzed data and wrote the manuscript with input from all authors. A.L.E., R.I. and C.L.B. designed experiments with input from H.G., H.T., Z.Y., A.F. and M.A.T. R.I., A.F. and M.A.T. developed the BAC methods and performed the bacterial assays. Z.Y. and H.T. conducted bioanalytical assays. T.W. designed ligase inhibitors, and L.D. analyzed and evaluated their biological activity. Conflict of Interest Statement The authors declare the following competing financial interest(s): All authors were working at Vertex Pharmaceuticals Incorporated at the time this work was conducted. Z.Y., H.T. T.W., and H.G are current Vertex employees. Z.Y., H.T. T.W., H.G and L.D. own Vertex stock and/or stock options.



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24 ACKNOWLEDGEMENTS This work was supported by Vertex Pharmaceuticals Incorporated. We thank Paul Charifson and Adam Shapiro for critical reading of the manuscript. REFERENCES (1) Silver, L. L. (2016) A Gestalt approach to Gram-negative entry, Bioorg. Med. Chem. 24, 6379-6389. 10.1016/j.bmc.2016.06.044. (2) Brown, D. G., May-Dracka, T. L., Gagnon, M. M., and Tommasi, R. (2014) Trends and exceptions of physical properties on antibacterial activity for Gram-positive and Gramnegative pathogens, J. Med. Chem. 57, 10144-10161. 10.1021/jm501552x. (3) Tommasi, R., Iyer, R., and Miller, A. A. (2018) Antibacterial drug discovery: Some assembly required, ACS Infect. Dis. 4, 686-695. 10.1021/acsinfecdis.8b00027. (4) Six, D. A., Krucker, T., and Leeds, J. A. (2018) Advances and challenges in bacterial compound accumulation assays for drug discovery, Curr. Opin. Chem. Biol. 44, 9-15. 10.1016/j.cbpa.2018.05.005. (5) Davis, T. D., Gerry, C. J., and Tan, D. S. (2014) General platform for systematic quantitative evaluation of small-molecule permeability in bacteria, ACS Chem. Biol. 9, 25352544. 10.1021/cb5003015. (6) Richter, M. F., Drown, B. S., Riley, A. P., Garcia, A., Shirai, T., Svec, R. L., and Hergenrother, P. J. (2017) Predictive compound accumulation rules yield a broad-spectrum antibiotic, Nature 545, 299-304. 10.1038/nature22308. (7) Cai, H., Rose, K., Liang, L.-H., Dunham, S., and Stover, C. (2009) Development of a liquid chromatography/mass spectrometry-based drug accumulation assay in Pseudomonas aeruginosa, Anal. Biochem. 385, 321-325. 10.1016/j.ab.2008.10.041. ACS Paragon Plus Environment

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33

Figure 1. Characterization of BAC for quinolone antibiotics. (A) For eight quinolone antibiotics, association with ∆tolC E. coli was greater than that with tolC+ bacteria. In the absence of bacterial cells, compound concentrations were close to the limit of detection (1 nM). For levofloxacin, enrofloxacin, and moxifloxacin, no compound was detected in the no-cell controls. (B) Deletion of tolC increased susceptibility to quinolones as well as their association with bacterial cells. The magnitude of these effects differed from one quinolone to another and were roughly correlated.



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34

Figure 2. (A) Two DNA ligase inhibitor scaffolds described previously. AAPC, aminoalkoxypyrimidine carboxamide;11 PP, pyridopyrimidinone.12 (B-D) Ligase inhibitors display heterogeneity in apparent exposure, with antibacterial activity increased by ligA251 and ∆tolC mutations. Inhibition of DNA ligase was assayed in vitro and plotted on the X axis as IC50 (µM). Antibacterial activity is plotted on the Y axis as MIC (µM) for E. coli strains ∆tolC (panel B), ligA251 (panel C) and ligA251 ∆tolC (panel D). The boxed region of panel D indicates 61 compounds with similar potency (IC50 0.05 - 0.2 µM) but widely varying antibacterial activity (MIC 0.2 - >100 µM).


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35

Figure 3. The effect of ∆tolC is similar for both BAC and antibacterial activity. The X axis shows the ratio of MIC for E. coli ligA251 tolC+ to E. coli ligA251 ∆tolC.



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Figure 4. BAC does not directly reflect exposure to DNA ligase within bacteria. (A) Of 61 ligase inhibitors with very similar in-vitro potency (boxed region of Figure 2D), we obtained BAC data for 46. Measured association of these compounds with ∆tolC bacteria was not predictive of MIC for an efflux-deficient derivative of ligA251. (B) Using the ratio of IC50 to MIC (TExp) as an indication of exposure to DNA ligase, we evaluated a larger set of compounds and again failed to see a correlation between accumulation and exposure. The box in 4B indicates a set of compounds with very similar BAC values and a wide range of TExp.


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Figure 5. Physical properties: Association of compounds with bacteria is affected by both charge and hydrophobicity. (A) For both AAPC and PP predicted to be uncharged at pH 7.4, BAC was generally increased by calculated logD7.4. BAC was also high for PP with predicted +1 charge and low clogD. (B) The high uptake of compounds with either high clogD or +1 charge was not associated with high exposure to DNA ligase.



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MS to measure accumulation of compounds within

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