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General Platform for Systematic Quantitative Evaluation of Small-Molecule Permeability in Bacteria

The chemical features that impact small-molecule permeability across bacterial membranes are poorly understood, and the resulting lack of tools to predict permeability presents a major obstacle to the discovery and development of novel antibiotics. Antibacterials are known to have vastly different structural and physicochemical properties compared to nonantiinfective drugs, as illustrated herein by principal component analysis (PCA). To understand how these properties influence bacterial permeability, we have developed a systematic approach to evaluate the penetration of diverse compounds into bacteria with distinct cellular envelopes. Intracellular compound accumulation is quantitated using LC-MS/MS, then PCA and Pearson pairwise correlations are used to identify structural and physicochemical parameters that correlate with accumulation. An initial study using 10 sulfonyladenosines in Escherichia coli, Bacillus subtilis, and Mycobacterium smegmatis has identified nonobvious correlations between chemical structure and permeability that differ among the various bacteria. Effects of cotreatment with efflux pump inhibitors were also investigated. This sets the stage for use of this platform in larger prospective analyses of diverse chemotypes to identify global relationships between chemical structure and bacterial permeability that would enable the development of predictive tools to accelerate antibiotic drug discovery.


Supporting Information
A. Supporting Information Figures S1-S15 S2 B. Supporting Information Tables S1-S4 S17 C. Materials and methods S20 D. Synthesis of acyl-AMS analogues S22 E. LC-MS/MS analysis S31 F. Compound accumulation studies S32 G. Principal component analysis S34 H. 1

H-NMR and 13 C-NMR spectra S43
A. SUPPORTING INFORMATION FIGURES S1-S14       Figure 4 from the manuscript. Data are reported as mean ± SD for 4 experiments. Statistical significance was assessed using two-way ANOVA and Tukey's multiple comparison test and 95% confidence intervals. *p < 0.05, **p < 0.01, ***p < 0.001.      Data are reported as mean ± SD for 4 experiments. Statistical significance was assessed relative to cells treated with salicyl-AMS using one-way ANOVA and Tukey's multiple comparison test and 95% confidence intervals: * p < 0.05, ** p < 0.01.    Table S1. Non-antiinfective drugs and antibacterials used in PCA Table S2. Structural and physicochemical properties used in PCA Table S3. Average structural and physicochemical properties by compound class.

Table S4. Structural and physicochemical properties of sulfonyladenosines evaluated in bacterial compound accumulation assays
Physicochemical properties shown are for the non-ionized forms of sulfonyladenosines (as shown in Figure 3 of the manuscript), because the algorithm used to compute ALogPs and ALogpS (vcclab.org) of the ionized compounds as they would exist at pH 7.4 neutralizes the negatively charged sulfamate nitrogen by addition of an ammonium counterion: for OSB-AMS, the algorithm adds two ammonium counterions to neutralize both the sulfamate and the carboxylate; for L-alanyl-AMS, the algorithm adds ammonium and chloride to neutralize the sulfamate and positively charge amine, respectively. Computation of the physicochemical properties of compounds in their ionized state at pH 7.4 did not substantially change the values. The largest observed differences between the non-ionized and pH 7.4 ionized compounds were the ALogPs values for L-alanyl-AMS (ALogPs -1.03 for ionized form vs. -1.36 for non-ionized form) and OSB-AMS (ALogPs 0.19 for ionized form vs. -0.49 for non-ionized form).

Reagents
Reagents were obtained from Aldrich Chemical (www.sigma-aldrich.com) or Acros Organics (www.fishersci.com) and used without further purification. Phenylalanine-arginine-betanaphthylamide (PAβN) was purchased from MP Biomedicals. Optima or HPLC grade solvents were obtained from Fisher Scientific (www.fishersci.com), degassed with Ar, and purified on a solvent drying system as described 1 unless otherwise indicated.

Reactions
All reactions were performed in flame-dried glassware under positive Ar pressure with magnetic stirring unless otherwise noted. Liquid reagents and solutions were transferred through rubber septa via syringes flushed with Ar prior to use. Cold baths were generated as follows: 0 °C, wet ice/water; -10 °C, wet ice/brine; -20 °C, dry ice/isopropanol monitored with a thermometer; -44 °C, dry ice/CH 3 CN; -63 °C, dry ice/chloroform; -78 °C, dry ice/acetone; -100 °C, dry ice/Et 2 O.

Chromatography
TLC was performed on 0.25 mm E. Merck silica gel 60 F254 plates and visualized under UV light (254 nm) or by staining with potassium permanganate (KMnO 4 ), cerium ammonium molybdenate (CAM), phosphomolybdic acid (PMA), iodine (I 2 ), or p-anisaldehyde. Silica flash chromatography was performed on E. Merck 230-400 mesh silica gel 60. Lyophilization of small aqueous samples was performed using a GeneVac HT-4X centrifugal evaporator. Lyophilization of larger aqueous samples was performed using a Labconco Freezone 2.5 instrument.

Analytical Instrumentation
IR spectra were recorded on a Bruker Optics Tensor 27 FTIR spectrometer with peaks reported in cm -1 . NMR spectra were recorded on a Bruker UltraShield Plus 500 MHz Avance III NMR or UltraShield Plus 600 MHz Avance III NMR with DCH CryoProbe at 24 °C in CDCl 3 unless otherwise indicated. Chemical shifts are expressed in ppm relative to TMS ( 1 H, 0 ppm) or solvent signals: CDCl 3 ( 13 C, 77.0 ppm), C 6 D 6 ( 1 H, 7.16 ppm; 13 C, 128.0 ppm) or acetone-d 6 ( 13 C, 206.2 ppm); coupling constants are expressed in Hz. NMR spectra were processed using Bruker TopSpin or nucleomatica iNMR (www.inmr.net) software. Mass spectra were obtained at the MSKCC Analytical Core Facility on a Waters Acuity SQD LC-MS or PE SCIEX API 100 by electrospray (ESI) ionization or atmospheric pressure chemical ionization (AP-CI). High resolution mass spectra were obtained on a Waters Acuity Premiere XE TOF LC-MS by electrospray ionization.
Liquid chromatography tandem mass spectrometry (LC-MS/MS) was carried out on an Agilent Technologies 6410 triple quad LC-MS/MS system with autosampler in electrospray ionization (ESI) mode, with an Agilent Zorbax Eclipse XDB-C18 reverse phase column (50 × 4.6 mm, 5 μm) using a flow rate of 0.4 mL/min and an isocratic mobile phase of CH 3 CN in 0.1% aq formic acid (mixture optimized for each analyte, see Table S5) over 5 min.

Nomenclature
Atom numbers shown in chemical structures herein correspond to the standard nucleoside numbering system used in the text of the article and Supporting Information and not to IUPAC nomenclature, which was used solely to name each compound. Compounds not cited in the paper are numbered herein from S1.

General procedure for deprotection of S3a-f
In a 20 mL vial, N-acyl sulfamoyl adenosine S3a-f (70 µmol), 344 µL H 2 O was added and cooled to 0 °C. TFA (1.7 mL) was added and the reaction was warmed to rt and stirred for 2-24 h. The solution was cooled to 0 °C, then MeOH was added (5 mL) and the mixture was concentrated by rotary evaporation. The mixture was azeotroped from MeOH (2 x 5 mL) and cyclohexane (3 x 5 mL). Purification by silica flash chromatography (5:1 → 3:1 EtOAc/MeOH) yielded the acyl sulfamoyl adenosines.

E. LIQUID CHROMATOGRAPHY-TANDEM MASS SPECTROMETRY (LC-MS/MS)
To quantify the amount of the sulfonyladenosine of interest in each sample, a standard curve (0.0025-100 µM) was constructed by mixing 200 µL analyte (in PBS) with 200 µL internal standard (2 µM, acetonitrile). Approximately 4 µL of sample were injected into the LC-MS/MS. For the purposes of platform methodology validation herein, we selected close structural analogues of each analyte for the internal standards to maximize sensitivity and linear range of detection. For example, we obtained a wider linear range of detection for salicyl-AMS when we used benzoyl-AMS, a close structural analogue as an internal standard, compared to when we used alanyl-AMS, a more structurally distinct compound (Figure S3). Full details regarding internal standards for each analyte and the multiple reaction monitoring (MRM) transitions are provided below in Table S5.

Calculation of cellular concentration
The total number of cells was determined via viable cell counts and plating of colony forming units (CFUs). After incubation with analyte, serial dilutions in fresh media were plated on agar. Colonies were grown for 16-24 h and plates containing 25-250 colonies were used to calculate the total number of cells. Individual cell volumes of B. subtilis, 8 E. coli, 9 and M. smegmatis 10 were taken from the literature and total cell volume and cellular analyte concentration was calculated using this information.

Optimization of Wash Protocol
To determine whether four washes were adequate to remove extracellular salicyl-AMS in the compound accumulation studies, we incubated B. subtilis with 1 mM salicyl-AMS for 1 h and varied the number of wash steps. We reasoned that cell-associated salicyl-AMS would reach an asymptote as the number of wash steps was increased. Indeed, the amount of cell associated salicyl-AMS decreased between no washes and three washes and reached an asymptote after three washes.

G. PRINCIPAL COMPONENT ANALYSIS
To generate the plots shown in Figure 1 of the manuscript, a total of 141 compounds (Table S1) were compared by principal component analysis (PCA). 11 The compounds analyzed by PCA included the following: • 50 top selling brand-name, non-antiinfective small molecule drugs by revenue in 2012 ( Figure S20) 12 • 91 antibacterials (Figure S21-S29 A set of 20 physicochemical descriptors (Table S2) were calculated using cheminformatics tools (Instant JChem, VCCLab 14 ). The descriptors were selected based on several criteria. First, Lipinski's parameters 15 (MW ≤ 500, HBD ≤ 10, HBA ≤ 5, LogP ≤ 5) and Veber's parameters 12 (RotB ≤ 10, tPSA ≤ 140 Å 2 ) have been correlated with oral bioavailability. 16 While oral bioavailability is not an immediate goal of most academic screening campaigns, some attention to these parameters is useful to the extent that they partially correlate to cell permeability. 6 Second, Tetko's calculated logS aqueous solubility (ALogpS) and log P hydrophobicity (ALogPs) were included since a balance between compound solubility and hydrophobicity are critical for oral bioavailability. 6 The distribution coefficient (logD 7.4 ) was included to approximate aqueous solubility of ionizable molecules at pH = 7.4. Third, several stereochemical parameters (nStereo, nStMW, Fsp 3 ) were included to approximate three-dimensional complexity. 17 Fourth, most antibacterials are natural products or derived from natural products, 15 and additional parameters previously found to differentiate synthetic drugs and natural products were included. 18 Synthetic drugs tend to have higher nitrogen content, while natural products tend to have higher oxygen content (N, O). Natural products tend to have fewer aromatic rings and more complex, fused ring sytems (Rings, RngAr, RngSys, RngLg, RRSys). Lastly, relative polar surface area (relPSA) was included because it has been shown to be a distinguishing factor between Gram-positive and Gram-negative antibacterials. 19 These data were assembled in a Microsoft Excel spreadsheet and average values for each parameter were calculated within each compound series. The hypothetical average values for each antibacterial class, non-antiinfectives, and the sulfonyladenosine datasets were included in the PCA analysis (Table S3).

Detailed PCA Protocol (Windows)
To provide a visual representation of the position of each component in chemical space, we conducted PCA with the "R" open source statistical computing package 20 to rotate the 20dimensional vector corresponding to each compound to a 2-dimensional vector, with minimal loss of information. The detailed protocol is as follows: 23. To generate biplots, overlay the PCA plots with loading plots using the Layout feature in GraphPad Prism.
Following PCA, all 141 compounds were plotted on newly generated, unitless, orthogonal axes (principal components) that are based on linear combinations of the original 20 parameters (Figure 1a, Figures S1). Summary information from R is shown below in Table S6. These data indicate that 90% of the variation in the complete 20-dimensional dataset is accounted for by the five six principal components (PC1-PC5), due to correlations between some of the original 20 parameters. For visualization purposes, the first two principal components (PC1, PC2) were used to generate the plots shown in Figure 1a of the manuscript. Together, these principal components account for 66% of the variation in the complete dataset, with individual contributions of 44% and 21%, respectively ( Table S6).
The component loadings generated from the PCA were used to construct loading plots using GraphPad Prism ( Figure S2). These data indicate that MW, O, HBD, HBA, and tPSA have the largest loadings on PC1 and shift molecules to the right along PC1 in the PC1 vs. PC2 ( Figures  S2a, d) and PC1 vs. PC3 (Figure S2b, d) plots. The descriptors with the largest loadings on PC2 are ALogPs and RngAr, which shifts molecules to the top of the PC1 vs. PC2 (Figure S2a, d) and PC3 vs. PC2 (Figures S2c, d) plots, and ALogpS and relPSA, which shifts molecules to the bottom of these plots. The descriptors with the largest loadings on PC3 are nStMW and Fsp 3 , which shifts molecules to the left along PC3, and N and RngAr, which shifts molecules to the right along PC3.
To evaluate the robustness of PCA on a smaller dataset, we conducted separate PCAs on the 10 sulfonyladenosines used in the compound accumulation assays ( Figure S7) and the 9 sulfonyladenosines that accumulated to detectable levels in the compound accumulation assays (data not shown). The relative positions of each compound were not substantially affected when each plot was compared to the other and the PCA with non-antiinfectives and antibacterials.
For PCA analyses that included sulfonyladenosines, we used the physicochemical properties of sulfonyladenosines in their non-ionized forms. Using the physicochemical properties of the pH 7.4 ionized forms did not substantially alter the PCA results for correlations between properties and E. coli accumulation shown in Figure 5a and Figure S8.