Intrabacterial Metabolism Obscures the Successful Prediction of an InhA Inhibitor of Mycobacterium tuberculosis

Article abstract of DOI:10.1021/acsinfecdis.9b00295

Tuberculosis, caused by Mycobacterium tuberculosis (M. tuberculosis), kills 1.6 million people annually. To bridge the gap between structure- A nd cell-based drug discovery strategies, we are pioneering a computer-aided discovery paradigm that merges stru

Full text of DOI:10.1021/acsinfecdis.9b00295

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Article
Intrabacterial Metabolism Obscures the Successful
Prediction of an InhA Inhibitor of Mycobacterium tuberculosis
Xin Wang, Alexander Perryman, Shao-Gang Li, Steve Paget, Thomas Stratton, Alexander
Lemenze, Arthur J. Olson, Sean Ekins, Pradeep Kumar, and Joel S. Freundlich
ACS Infect. Dis., Just Accepted Manuscript • DOI: 10.1021/acsinfecdis.9b00295 • Publication Date (Web): 18 Oct 2019
Downloaded from pubs.acs.org on October 22, 2019
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Intrabacterial Metabolism Obscures the Successful Prediction of an InhA Inhibitor
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of Mycobacterium tuberculosis
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Xin Wang,†1 Alexander L. Perryman,†1 Shao-Gang Li,1 Steve D. Paget,1 Thomas P. Stratton,1 Alex Lemenze,2
Arthur J. Olson,3 Sean Ekins,4,5 Pradeep Kumar,2 and Joel S. Freundlich*1,2
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† Contributed equally.
1 Department of Pharmacology, Physiology, and Neuroscience, Rutgers University–New Jersey Medical School,
Medical Sciences Building, 185 South Orange Avenue, Newark, NJ 07103, USA.
2 Division of Infectious Disease, Department of Medicine, and the Ruy V. Lourenço Center for the Study of
Emerging and Reemerging Pathogens, Rutgers University–New Jersey Medical School, Medical Sciences
Building, 185 South Orange Avenue, Newark, NJ 07103, USA.
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Department of Integrative Structural and Computational Biology, The Scripps Research Institute, Room
MB112/Mail Drop MB5, 10550 North Torrey Pines Road, La Jolla, CA 92037, USA.
4 Collaborations in Chemistry, 5616 Hilltop Needmore Road, Fuquay-Varina, NC 27526, USA.
*Corresponding author : Joel S. Freundlich (freundjs@rutgers.edu)
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Tuberculosis, caused by Mycobacterium tuberculosis, kills 1.6 million people annually. To bridge the gap
between structure- and cell-based drug discovery strategies, we are pioneering a computer-aided discovery
paradigm that merges structure-based virtual screening with ligand-based, machine learning methods trained with
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cell-based
data.
This
approach
successfully
identified
N-(3-methoxyphenyl)-7-
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nitrobenzo[c][1,2,5]oxadiazol-4-amine (JSF-2164) as an inhibitor of purified
InhA with whole-cell efficacy versus in vitro cultured M. tuberculosis.
Leveraging an intrabacterial drug metabolism (IBDM) platform, mechanistic
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studies demonstrated that JSF-2164 underwent
a
rapid F420H2-dependent
biotransformation within M. tuberculosis to afford intrabacterial nitric
oxide and two amines, identified as JSF-3616 and JSF-3617. Thus metabolism
of JSF-2164 obscured the InhA inhibition phenotype within cultured M.
tuberculosis. This study demonstrates a new docking/Bayesian computational strategy to combine cell-
and target-based drug screening and the need to probe intrabacterial metabolism when clarifying antitubercular
mechanism of action.
KEYWORDS: Bayesian,
docking,
InhA,
tuberculosis,
intrabacterial,
metabolism
A significant subset of the chemical biology field interrogates the fundamental biology of pathogenic
bacteria through the discovery and study of small molecule chemical tools. The ramifications from both academic
and humanitarian viewpoints are evident given the global health crisis around drug-resistant bacteria and the need
for innovative therapies and therapeutic strategies.1 We face a common and yet significant challenge in the
identification of small organic molecules with a set of properties consistent with their value as tools2 to probe the
essential biology of the bacteria in the hopes of gaining insights into which proteins and/or metabolic pathways
are requisite for infection in model systems of varying degrees of relevance to pathogenesis in humans.
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We study the causative agent of tuberculosis (TB) – Mycobacterium tuberculosis – and utilize it herein as
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a model system. However, our approach should be applicable to any class of bacteria, or infectious agent. Faced
with the challenge of identifying highly useful chemical tools, TB researchers have relied on a range of techniques
from target-based approaches (both structure-focused and biochemical high-throughput screens) to whole-cell
high-throughput phenotypic screening. Target-based methods, presuming they focus on a gene/protein that is both
essential3 and vulnerable4 under experimental conditions relevant to human infection, have often struggled to
correlate inhibition of protein function and whole-cell activity against in vitro cultured M. tuberculosis.5
Computational methods such as high-throughput docking, despite some success outside of the TB arena, in
general can make educated guesses to increase the probability of finding small molecules that best fit a protein
active site and impair its native function.6 However, the correlation between enzyme functional inhibition and
whole-cell activity is often lacking.7
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To bridge the disconnection between in vitro enzyme inhibition and whole-cell activity, we have sought
to utilize computational studies, in conjunction with chemical and biological techniques, to identify small
molecule tool compounds for M. tuberculosis. We and others have found that high-throughput docking through a
virtual screen (VS) against a single target can identify novel inhibitors of M. tuberculosis enzymes.8 We
hypothesized that high-throughput docking would be complemented by our proven machine learning methods9, 10
to enhance the probability of finding compounds with bacterial growth inhibitory properties and lack of
mammalian cell cytotoxicity.
Herein we describe the design of a high-throughput docking/Bayesian methodology, combining target-
based and whole-cell based screening, and its application to the M. tuberculosis enzyme InhA – an essential11, 12
and vulnerable4 enoyl acyl carrier protein reductase inhibited by the front-line drug isoniazid (INH).13, 14 This
platform identified JSF-2164 as an inhibitor of purified InhA with bacterial growth inhibitory activity. Its
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mechanism within M. tuberculosis was complicated by intrabacterial metabolism and we found our intrabacterial
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drug metabolism (IBDM) platform to be useful in helping detail this process.
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 RESULTS
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A Novel High-Throughput Docking/Bayesian Approach. We examined a previous VS conducted by some of
us that docked a 5.6 x 106 member composite library versus InhA, as part of the GO Fight Against Malaria project
(GO FAM).8 This present work leverages a fraction of this library of commercially available drug-like small
molecules, comprising a 5.07 x 105 collection of Asinex compounds, docked against the Tonge laboratory
structure of InhA complexed with a diaryl ether phenol (PDB ID 2X23).15 This structure was chosen because
InhA is bound to PT70, a very potent inhibitor (Ki = 7.8 nM) with a long residence time (24 min).15 The Asinex
subset was docked into the active site with Auto Dock Vina.16 The average of the estimated free energy of binding
values was -6.93 kcal/mol with a standard deviation of 0.81 kcal/mol. Docking filters narrowed down this subset
to the top 370 compounds, based on the requirement that the candidate’s predicted binding mode formed base-
stacking interactions with the NAD+/NADH cofactor, displayed at least 2 hydrogen bonds with the active site,
and had an estimated free energy of binding ≤ -8.0 kcal/mol. These 370 compounds displayed an average
estimated free energy of binding of -8.66 kcal/mol with a standard deviation of 0.55 kcal/mol in the docking
scores. At this point, current docking strategies typically transition to an experimental stage where the candidate
inhibitors are tested in vitro either with or without prior visual inspection of the compounds. Our strategy diverges
and utilizes our published TB Bayesian models to create a new workflow (Figure 1) that combines ligand-based
machine learning models and structure-based VS data. We proposed that the VS data versus a validated M.
tuberculosis drug target such as InhA could be joined with our Bayesian dual-event models to select for novel
InhA inhibitors with whole-cell activity and lack of mammalian cell cytotoxicity as assessed with Vero cells. Our
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published dual-event models consider both whole-cell efficacy versus in vitro cultured M. tuberculosis (under
actively growing conditions) and Vero cell cytotoxicity, and they have learned via training sets containing on the
order of 103 drug-like small molecules.10 In this case as in our previous work, we define a selectivity index SI =
CC50/MIC, where the MIC is the minimum inhibitory concentration of the molecule that inhibits 90% growth of
the bacteria in culture, and CC50 is the concentration of the compound that inhibits the growth of 50% of the
cultured mammalian cells (Vero cells). Our published models have demonstrated the ability to favorably score
novel whole-cell active compounds with an acceptable selectivity index (SI ≥ 10) from screening collections in
comparison to inactives.
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Figure 1. Innovative workflow to advance the discovery of whole-cell active chemical tools against M.
tuberculosis that inhibit a drug target and lack significant relative toxicity to mammalian cells. (i) GO
Fight Against Malaria experiment 9 docked the Asinex library of 5.07 x 105 models of small molecules against
InhA (PDB ID: 2X23). Docking filters narrowed down the results to the top 370 compounds. (ii) These 370
compounds were filtered with two different dual-event Bayesian models, to identify 131 compounds predicted
to display whole-cell activity against M. tuberculosis and a lack of significant relative toxicity to Vero cells.
(iii) The docked binding modes of the top 131 compounds were visually inspected to identify 19 candidate
compounds, which were then ordered. (iv) The 19 candidate compounds were each tested at 50 M to identify
five hits with at least 50% inhibition of InhA activity. A Principal Component Analysis is shown on the right
that compares the chemical property space of the five hits (four in blue inhibit InhA; one in green is the most
active against M. tuberculosis) to 157 known InhA inhibitors (magenta) in the TB Mobile 2 data set. (v) These
5 novel InhA inhibitors were then tested in M. tuberculosis growth inhibition assays to identify one InhA
inhibitor that significantly inhibited bacterial growth. Shown on the left with light green carbons is the inhibitor
with the best whole-cell activity, an MIC of 8.0 M.
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370 compounds (that passed the initial docking filters) were scored with two of our recently validated
Bayesian dual-event models selecting for bacterial growth inhibition with relatively low cytotoxicity in
mammalian cells (Table S1). We chose to use the dose-response and cytotoxicity (or TAACF-CB2) model, given
its learning based on 1,783 compounds screened versus M. tuberculosis and Vero cells by Reynolds et al. and its
successful identification of four diverse and promising antituberculars from a GlaxoSmithKline antimalarial hit
set.10 The second model utilized was our combined TB dose-response and cytotoxicity (or Combined) model that
draws on a data set of 5,304 small molecules assayed versus M. tuberculosis and Vero cells.9 For the 370
compounds harvested with the initial docking filters: (a) their TAACF-CB2 model scores ranged from -8.61 to
8.38, with 133 compounds predicted as whole-cell active versus M. tuberculosis and relatively non-toxic versus
Vero cells having scores ≥ 0.54; and (b) their Combined model scores ranged from -13.77 to 8.84, with 93
compounds predicted as whole-cell active versus M. tuberculosis and relatively non-toxic versus Vero cells
having scores ≥ 1.17. The top 100 compounds by the TAACF-CB2 model (score ≥ 1.11) along with the highest
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scoring 52 from the Combined model afforded a total of 131 compounds for further analysis (given the overlap
between the two sets equal to 21 molecules).
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Visual inspection of the 131 predicted whole-cell active/non-cytotoxic candidate InhA inhibitors was then
undertaken. As discussed in our previous publication,8 advantageous attributes included (a) almost all of the
heteroatoms were involved in hydrogen bonds, favorable electrostatic interactions,17 or halogen bonds with the
target,18, 19 (b) the hydrophobic groups packed favorably with hydrophobic side chains of InhA,8, 16 and (c)
aromatic rings formed T-stacking, base-stacking20 or π-cation interactions20 with InhA, as measured by the Fox
software (available at http://autodock.scripps.edu/resources/raccoon). Base-stacking and T-stacking interactions
depend on the distance between the centers of the two aromatic rings and the angle between the planes of each
ring, while π-cation interactions can occur when a positively charged group is less than 5 – 6 Å from the center
of an aromatic ring.20 Disadvantageous attributes of a docked pose included displaying (a) one or more large,
solvent-exposed hydrophobic groups (such as i-propyl, t-butyl, or benzyl), (b) polar groups packed in hydrophobic
pockets (unless they had a complementary group from the target in the vicinity,21 or (c) more than one repulsive
electrostatic interaction with charged or polar groups of InhA. This manual inspection afforded 19 candidates for
biological testing, which were available and obtained from the commercial source (Asinex; www.asinex.com).
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Figure 2. The screening hit JSF-2164 (BAS01237231) is a moderate InhA inhibitor in vitro. (A) InhA
inhibition efficacy of the 19 Docking/Bayesian screen hits was assessed by measuring the percent inhibition
of purified InhA at 50 µM compound. The dashed line indicates 50% InhA inhibition efficacy. Triclosan was
used as a positive control shown in red and DMSO was used as a negative control. The five screening hits with
% InhA inhibition > 50% at 50 µM were shown in orange. (B) The InhA enzyme assay was utilized to evaluate
the IC50 for JSF-2164 inhibition of purified InhA. Enzyme activity was determined via the consumption rate
of NADH read at 340 nm. Enzymatic buffer with or without 0.01 CMC of Tween-20 was shown in red or
black, respectively. IC50 values were calculated by non-linear regression via Prism GraphPad (Version 6.0c).
(C), (D) and (E) Docking evidenced JSF-2164 binding to InhA. In (C), the NAD cofactor was displayed in
ball-and-stick with magenta carbons, and the residues Tyr158 and Met199 were shown as thin sticks. In (D)
and (E), the NAD cofactor was displayed as CPK, and the key residues Phe149, Tyr158, Thr196, Ala198,
Met199, and Gln214 as thin sticks with dark purple carbon atoms. The inhibitor that crystallized with InhA in
PDB ID: 2x23, PT70, was shown as thick magenta lines in (E) as a reference.
JSF-2164 (BAS01237091) as an InhA Inhibitor with Whole-Cell Activity. The nineteen candidate molecules
(Table 1) were assessed for whole-cell growth inhibition of in vitro cultured M. tuberculosis wild type H37Rv
strain. Two out of the nineteen compounds, BAS01237091 and BAS01237231, exhibited promising whole-cell
activity (MIC ≤ 12.5 M; i.e., better than our goal of 10 g/mL ~ 33 M for a MW 300 g/mol compound). INH
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and triclosan were used as positive controls in all M. tuberculosis growth inhibition assays.22 Interestingly, both
compounds (BAS01237091 and BAS01237231) are of the benzofurazan class. The most potent whole-cell
inhibitor was BAS01237231, featuring an MIC of 10 M. From the initial set of 370 candidates passing the
docking filters, the worst scoring five molecules (according to their score with the TAACF-CB2 model) were also
assayed, as a supplementary test of the potential for undesirable “false negative” predictions. All demonstrated
MIC values between 125 and 250 M (Table S2).
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We applied an InhA enzyme assay17 to empirically confirm InhA inhibition efficacy of the nineteen
predicted hits. Compound inhibition efficacy was calculated by NADH consumption rate when the purified M.
tuberculosis InhA was co-incubated with compounds and the model substrate 2-dodecenoyl-CoA. Triclosan was
included as a positive control in all InhA inhibition assays.22 17/19 candidates were tested at a compound
concentration of 50 µM, and 2/19 compounds (thiadiazole BAS03794545 and furanone BAS00135708) were
tested at 25 µM due to demonstrated insolubility at 50 M. 5/19 compounds, including the whole-cell active
compound BAS01237231, displayed at least 50% inhibition of InhA at this single concentration (Figure 2A and
Table 1). Their respective IC50 values versus InhA ranged from 4.0 – 51 M (Table 1 and Figure S1). The most
efficacious InhA inhibitor was thiadiazole amide BAS06264740 with an IC50 = 4.0 ± 1.0 M; however, it failed
to inhibit bacteria growth (MIC > 500 µM versus the H37Rv strain). The whole-cell active compound
BAS01237231 was determined as a moderate inhibitor of purified InhA with an IC50 = 51 ± 19 M (Table 1 and
Figure S1).
Table 1. In Vitro Efficacy Profiles for Candidate InhA Inhibitors
MIC (M)
Cmpd Identifiers
Structure
Vero Cell
InhA Inhibition at
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Cytotoxicity
CC50 (M) /
SI a
50 M Cmpd /
IC50 (M) b
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H37Rv mc24914
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BAS06264740
ZINC00378804
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9.2 – 18 /
<0.018
91 ± 2 % c /
4.0 ± 1.0
>500
500
(resynthesized as JSF-
2149)
BAS00131943
ZINC00805451
160 – 230 /
0.35
65 ± 4 % /
40 ± 5
460
250
ASN06745
4.8 – 9.7 /
<0.0096
58 ± 5 % /
48 ± 9
>500
500
ZINC04411524
ASN06744991
ZINC12481353
<2.2 /
55 ± 6 % /
45 ± 5
>500
500
<0.0044
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BAS01237231
ZINC04894756
44 – 87 /
5.5
53 ± 4 % /
51 ± 19
10 d
30
(resynthesized as JSF-
2164)
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BAS00135708
BAS00704577
BAS00396190
ASN03068448
125
>500
>500
250
n.d.
n.d.
n.d.
n.d.
n.d.
n.d.
n.d.
n.d.
36 ± 1 % e
30 ± 5 %
27 ± 3 %
20 ± 2 %
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BAS01237091
BAS24802784
ASN05988636
BAS06979351
BAS01237237
12.5
n.d.
n.d.
n.d.
n.d.
n.d.
n.d.
n.d.
n.d.
n.d.
n.d.
19 ± 1 %
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>500
500
250
500
4.8 ± 5.6 %
1.5 ± 4.3 %
1.2 ± 6.4 %
0.41 ± 11 %
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BAS03794545
ASN03986337
BAS03069066
BAS05024031
BAS05667953
500
n.d.
n.d.
n.d.
n.d.
n.d.
n.d.
n.d.
n.d.
n.d.
n.d.
0 ± 3.6 % e
0 ± 2.4 %
0 ± 1.4 %
0 ± 3.7 %
0 ± 3.1 %
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100 ± 6 % /
0.13 ± 0.02
triclosan
62
120
n.d.
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isoniazid
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n.d.
n.d.
a SI = Vero Cell CC50 / MIC H37Rv using the more conservative estimate of the CC50 in the reported range.
b Values are reported as the mean ± standard error from at least three independent measurements.
c Synthesized material demonstrated an InhA inhibition of 84 ± 2 %.
d While the commercial material demonstrated an MIC = 10 M and an InhA inhibition of 53 ± 4 %, the synthetic material
exhibited an MIC value of 8.0 M versus the H37Rv strain and an InhA inhibition of 44 ± 1%.
e Due to compound insolubility at 50 M, the measurement was made at 25 M.
n.d. = Not Determined
Given their respective InhA inhibition and whole-cell growth inhibition, thiadiazole amide BAS06264740
(renamed JSF-2149) and benzofurazan BAS01237231 (renamed JSF-2164) were selected for further evaluation
through synthesis and confirmation of biological profile. JSF-2149 was prepared (Figure S2) in three steps from
commercially available 2-chlorobenzaldehyde via formation of the thiosemicarbazone 1 that was then cyclized in
the presence of ferric chloride to provide 2-aminothiazole 2.23 Acylation of 2 with 2-norbornane acetyl chloride
provided JSF-2149. JSF-2164 was prepared (Figure S3) via reaction of commercial 4-chloro-7-
nitrobenzo[c][1,2,5]oxadiazole with 3-methoxyaniline, inspired by previously reported SNAr chemistry.24 The
InhA inhibition (84 ± 2 % inhibition at 50 M compound) of synthetic JSF-2149 was similar to that of the
commercial material and the synthetic sample was also inactive versus M. tuberculosis (MIC > 500 M). The
MIC for synthetic JSF-2164 was similar to that of the commercial material (8.0 M vs. 10 M, respectively)
(Table 2) as were their respective percent inhibitions of InhA at 50 M compound (synthetic vs. commercial: 44
± 1 vs. 53 ± 4).
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Table 2. MIC values for JSF-2164 and its two metabolites versus M. tuberculosis strains.
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MIC (µM)
% InhA inhibition at 50 µM
with 1% CMC Tween-20
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6
Compound
Structure
H37Rv mc24914 fgd1::tn ddn::tn
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INH
0.16
8.0
250
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2.5
25
0.078
>200
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N.D.
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JSF-2164
JSF-3617
JSF-3616
44 ± 1.0
77 ± 0.74
46 ± 2.9
125
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250
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IC50 values within the µM range may raise concerns of promiscuous inhibition of purified enzyme InhA
due to aggregates formed by compounds in the enzymatic buffer.25 We, therefore, utilized Tween-20 at 1% of its
critical micelle concentration (CMC) in the enzymatic buffer as a detergent to mitigate aggregate formation
without disrupting enzyme activity (Figure S4). InhA inhibitory potency of the JSF-2149 was evidenced due to
unspecific aggregation, judged by an IC50 shift from 4.0 ± 1.0 µM (without Tween-20) to 70 ± 16 µM (with
Tween-20) (Figure S5). In contrast, the whole-cell active compound JSF-2164 was a specific InhA inhibitor in
vitro given no statistically significant difference between the IC50 with or without Tween-20 (51 ± 19 µM vs. 65
± 16 µM) judged by an unpaired Student’s t-test (Figure 2B). Finally, docking results predicted JSF-2164 to form
the following quaternary interactions with InhA: base-stacking with the NAD cofactor, two hydrogen bonds with
the hydroxyl in the side chain of Tyr158, two hydrogen bonds with the 2'-hydroxyl of the ribose adjacent to the
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nicotinamide ring of NAD, a hydrogen bond with the 3'-hydroxyl of the ribose adjacent to the nicotinamide ring
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2E).
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JSF-2164 Exhibited In Vitro Bacteriostatic Activity Involving More than InhA Inhibition. Over 7 d, JSF-
2164 at 16x MIC demonstrated an ca. 1 log10 reduction in M. tuberculosis colony-forming units/mL (CFU/mL)
(Figure 3A). It indicated that JSF-2164 is not formally bactericidal,26 judged by a failure to determine its minimum
bactericidal concentration (MBC; minimum compound concentration to afford ≥2 log10 reduction in bacterial
CFU/mL) within 4-fold of its MIC range.27 This may be compared to the bactericidal activity of INH as
demonstrated by complete sterilization at 4x MIC with a detection limit of 10 CFU/mL.
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To probe the intrabacterial engagement of InhA by JSF-2164, we assessed the JSF-2164 growth inhibitory
effect against the M. tuberculosis inhA overexpression strain mc24914 (Table 2) which harbors an inhA promoter
c(-15)t mutation.13 INH demonstrated a 16x MIC shift versus the inhA overexpression strain as compared to
H37Rv. JSF-2164, in contrast, exhibited a three-fold shift in MIC versus the inhA overexpression strain as
compared to H37Rv. The effect of JSF-2164 was also assayed on newly synthesized M. tuberculosis short-chain
14
fatty acids and mycolic acids. M. tuberculosis strain H37Rv prelabeled with C-acetate was treated with JSF-
2164 or INH as a positive control.13 After lipid extraction and esterification to afford the corresponding methyl
esters, the samples were analyzed for mycolic acid methyl esters (MAMEs) and their precursors, short-chain fatty
acid methyl esters (FAMEs) via thin-layer chromatography (TLC) (Figures 3B and 3C). INH expectedly13 led to
a depletion of MAMEs and an accumulation of FAMEs as a result of InhA inhibition, whereas JSF-2149 served
as a negative control given its lack of significant whole-cell activity and failure to inhibit purified InhA in the
presence of detergent. JSF-2164 unexpectedly did not exhibit a pattern similar to that for INH. The smaller MIC
shift for JSF-2164, as compared to INH, and its lack of a typical InhA (or more generally FAS-II) inhibition
profile as judged by TLC, coupled with its IC50 versus purified InhA being ca. five-fold larger than its MIC against
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the H37Rv strain, hinted at an intrabacterial process where InhA inhibition is secondary to another mechanism of
action. Consistent with this hypothesis was the observation that JSF-2164 exhibited in vitro efficacy versus the
M. tuberculosis streptomycin-sensitive (ss18b) strain28 that models aspects of non-replicating persistence. This
was differentiated from the negative control INH, which as the prototypical InhA inhibitor selectively inhibits
actively replicating bacteria, and rifampicin as the positive control (Figures 3D, 3E and S6).
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Figure 3. JSF-2164 exhibits an antitubercular profile distinct from INH. (A) 105 CFU/mL of M.
tuberculosis H37Rv were exposed to INH or JSF-2164 at different folds of the MIC for 7 d, followed by
CFU enumeration. The detection limit of 10 CFU/mL is depicted as a dashed line. One-way ANOVA
adjusted with Bonferroni’s multiple comparison test was applied to analyze the CFU statistical difference
between the inoculation and INH or JSF-2164 treated bacteria. * p < 0.05; ** p < 0.01. (B) and (C) Mid-log
phase (OD595 = 0.4) M. tuberculosis H37Rv was pre-treated with INH or JSF-2164 for 2 h followed by 14C-
acetate labeling for 4 h. Lipids were extracted and esterified to afford the corresponding methyl esters. The
mycolic acid methyl esters (MAMEs) and fatty acid methyl esters (FAMEs) were analyzed via thin-layer
chromatography (TLC) and developed via Typhoon phosphorimager. (C) showed quantification of the grey
scale of FAMEs and MAMEs from (B). (D) and (E) M. tuberculosis streptomycin-sensitive strain ss18b was
starved without streptomycin for 2 weeks to maintain OD595 = 0.3, followed by treatment with INH or JSF-
2164 for 7 d. The compound activity against ss18b was determined via the resazurin microtiter assay
(REMA). IC50 and IC90 were quantified via non-linear regression with Prism GraphPad (Version 6.0c). Error
bars indicated standard errors.
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F420H2 Deficiency Linked to JSF-2164 Resistance. To better comprehend the JSF-2164 mechanism of action
within M. tuberculosis, we selected for spontaneous JSF-2164–resistant mutants in the M. tuberculosis strain
H37Rv background. JSF-2164 exhibited a relatively high mutation rate (~10-5), which was similar to the
frequency of resistance of pretomanid (10-5 ~ 10-6),29 a clinical-stage antitubercular requiring activation dependent
by the cofactor F420H2.29 We isolated 26 JSF-2164 resistant colonies selected with a JSF-2164 exposure ranging
from 8 – 32x MIC and confirmed their resistance via a liquid culture MIC test. All tested colonies were resistant
to JSF-2164: one strain (32x4) with a 4-fold MIC shift and the other 23 colonies with ≥16x MIC shift as compared
to the wild type strain H37Rv (with an upper limit of 16-fold established by the solubility of JSF-2164 in the
assay media) (Figure 4A). 5/24 colonies were cross-resistant to INH, consisting of strains 16x6 and 32x4
exhibiting 8x MIC shift and strains 32x3, 32x5 and 32x6 showing 4x MIC shift against INH (Figure 4A).
Four strains (16x2, 16x6, 32x4 and 32x5) were selected for whole-genome sequencing, according to their
resistance to JSF-2164 and varying susceptibility to INH (Table S3). Mutations with high-level JSF-2164
resistance in strains 16x2, 16x6, and 32x5 led to single nucleotide polymorphisms (SNPs) in the gene fbiC (F567S)
or a frameshift in the gene fgd1 (C insertion in between 411 and 412 resulting in a frameshift at Leu138) (Table
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S3). We also noted a promoter mutation in the essential30 thymidylate kinase tmk of the sequenced strain 32x4.
Both enzymes FbiC and Fgd1 comprise the biosynthetic pathway of deazaflavin cofactor F420H2. FbiC is required
for biosynthesis of FO, a precursor of the cofactor F420.31 Fgd1, also known as F420-dependent glucose-6-
phosphate dehydrogenase, recharges F420H2 through the oxidation of glucose-6-phosphate.29 Loss of function
mutations in fgd1 and fbiC led to resistance to pretomanid, a nitro-containing heterocyclic antitubercular requiring
F420H2 dependent activation.29 The cross-resistance of pretomanid was indeed observed in these 3 mutant strains
judged by the >500x MIC shift as compared to wild type H37Rv (Table S3). A strain fgd1::tn, with fgd1
functionally inactivated by transposon insertion,11, 32 led to resistance to both pretomanid (>500x MIC shift) and
JSF-2164 (>20x MIC shift) (Table 2). This further confirmed that F420H2 was essential for JSF-2164 activity.
Strain 16x6, which was resistant to INH, harbored a c(-15)t mutation at the promoter of inhA. This mutation was
identical to the one reported in strain mc24914 and other clinical strains,33 which were resistant to INH due to
statistically significant inhA overexpression as compared to the H37Rv strain (Figure S7).
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Figure 4. Spontaneous mutagenesis and transposon library selection indicate mutations in F420H2
biosynthesis result in JSF-2164 resistance. (A) 26 M. tuberculosis colonies spontaneously resistant to JSF-
2164 were isolated followed by confirmation of JSF-2164 resistance and evaluation of cross-resistance with
INH via liquid culture MIC test. MIC values for JSF-2164 and INH versus strains were normalized by their
corresponding MIC value versus H37Rv, and fold changes were depicted in red (JSF-2164) and in blue (INH).
Resistance was determined as a >4-fold MIC shift as compared to H37Rv with an upper limit of 16-fold
established by the solubility of JSF-2164 in the assay media. (B) A saturated Himar-1 transposon insertion
library with an H37Rv background was selected in the presence of JSF-2164 at 5 – 30x MIC on 7H10 agar
plates supplemented with albumin-dextrose-saline (ADS). 90 viable colonies were isolated and their resistance
to JSF-2164 was verified via liquid culture MIC tests. Transposon insertion sites in the 90 colonies were
determined via SacII digestion, ligation, and sequence alignment of the Sanger sequencing fragment with
primers annealed to the outward-facing T7 promoter.
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The mechanism of action of JSF-2164 was further probed by Himar-1 transposon library screening (Figure
4B).30, 34 A saturated transposon insertion library derived from the H37Rv background was exposed to JSF-2164
at concentrations ranging from 5 – 30x MIC for four weeks. Select single colonies surviving JSF-2164 pressure
were isolated and the transposon location was determined for each strain.34 A total of 90 colonies (54 colonies
from 5x MIC, 8 colonies from 10x MIC, 8 colonies from 20x MIC, and 20 colonies from 30x MIC) exhibited
JSF-2164 resistance on agar plates. JSF-2164 resistance for each of the 90 colonies was confirmed by liquid MIC
test, and was quantified by a ≥16x MIC shift as compared to wild type H37Rv (again, being limited by the
solubility of JSF-2164 in media) for 89/90 strains (Table S4). 89 out of 90 colonies demonstrated significant
resistance to pretomanid, quantified by a ≥500x MIC shift (Table S4). All of the colonies were susceptible to
INH. Genomic DNA was extracted from each of colonies followed by SacII digestion, and the Himar-1 transposon
sequence was ligated into a plasmid with an oriR6K replication initiation site and a kanamycin selection marker.34
Transposon insertion sites were determined via Sanger sequencing of the plasmids containing the Himar-1
sequence followed by mapping results onto the H37Rv genome. 84/90 colonies harbored a transposon insertion
in the open reading frame of fgd1, suggesting the functional inactivation of fgd1 as a leading cause of JSF-2164
resistance. One insertion was observed in the open reading frame of gene pta (rv0408) locating in the same operon
as fgd1. This pta disruption may indicate a polar effect of transposon insertion. The other five insertions were
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harbored in rv3312c (an α/β hydrolase),35 rv1534 (a TetR family transcription regulator),35 and alkB (rv3252c, a
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transmembrane alkane 1-monooxygenase).35
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JSF-2164 released intrabacterial NO• dependent on cofactor F420H2. Nitrobenzofurazan family
compound NBDHEX (6-(7-nitro,2,1,3-benzoxadiazole-4-ylthio)hexanol)36 and NBF-SPh37 (7-nitro-4-
(phenylthio)benzofurazan) are reported to release reactive nitrogen (RNS) and reactive oxygen (ROS) species in
cancer cells,37, 38 and thus we hypothesized that the growth inhibitory activity of JSF-2164 may be due to
production of intrabacterial nitric oxide (NO•) dependent on the F420H2 cofactor. Although of different
chemotypes, the triazine nitrofurans(Wang. X. et al., submitted), pretomanid,39 and delamanid40 have been found
to release NO• within M. tuberculosis through an F420H2-dependent process. We utilized the Griess reagent to
quantify the supernatant concentration of nitrite, arising through the intrabacterial extrusion of NO• followed by
its oxidation and export from the cell.41 Briefly, the M. tuberculosis strains H37Rv and fgd1::tn were treated with
20 or 100 µM pretomanid or JSF-2164 and for consecutive 5 d the supernatant nitrite concentration was assessed
via Griess reagent. Each compound’s NO• production was quantified via a ratio of nitrite concentration to
compound dose. For the H37Rv arm of the experiment, pretomanid treatment led to a burst of NO• production in
the initial 2 d, demonstrated by 49% (9.8 µM) and 17% (17 µM) of NO• release for the 20 µM and 100 µM
pretomanid exposures, respectively (Figure 5A). The lower dose of JSF-2164 treatment (20 µM = 2.5x MIC)
generated a small yet discernible amount of NO• at 2 d post treatment of the H37Rv strain, quantified as 0.84 µM
which is slightly above the 0.78 µM limit of detection (Figure 5A). The higher dose of JSF-2164 (100 µM = 12.5x
MIC) yielded a larger production of NO•, quantified as 6.7 µM, at t = 2 d. F420H2 cofactor deficiency in the
fgd1::tn strain abolished NO• production for both the pretomanid and JSF-2164 treatments, indicating that F420H2
was required to produce NO• from JSF-2164 (Figure 5B). Pretomanid has been shown to produce NO• primarily
via the catalysis of the nitroreductase Ddn which utilizes F420H2 as cofactor.39 We excluded Ddn as being
responsible for the whole-cell activity of JSF-2164 as a comparison of MIC values of JSF-2164 versus H37Rv
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and ddn:tn26, 39 strains showed no difference (Table 2).
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Figure 5. JSF-2164 is metabolized into two amines with NO• release dependent on cofactor F420H2. (A)
and (B) Mid-log phase (OD600 = 0.4) M. tuberculosis H37Rv and fgd1::tn strains were treated with pretomanid
or JSF-2019 at 20 µM or 100 µM. NO• release was measured via Griess reagent in biological triplicates.
Percentage numbers indicated proportion of release NO• from pretomanid or JSF-2019, calculated by a ratio
of nitrite concentration versus compound dose. Error bars showed standard errors. (C) Mid-log phase M.
tuberculosis H37Rv was treated with 20 µM JSF-2164 for 24 h, and metabolites were extracted at several time
points. Two peaks with the same m/z of 257.0 indicated two metabolites JSF-3617 and JSF-3616 with the
same molecular weight. (D) The structure of the two amine metabolites JSF-3617 and JSF-3616 were assigned
via NMR and high-resolution mass spectrometry experiments with the metabolites isolated from the M.
tuberculosis extract. (E) and (F) Mid-log phase (OD600 = 0.4) M. tuberculosis H37Rv and fgd1::tn strains were
treated with 20 µM JSF-2164 in biological triplicates followed by metabolite extraction at various time points
ranging from 0 to 24 h. Metabolites were analyzed and quantified via liquid-chromatography mass
spectrometry (LC-MS), followed by normalization by protein amount. Error bars indicated standard errors.
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JSF-2164 intrabacterial metabolism produces two amines. NO• production suggested an intrabacterial
metabolism of JSF-2164 contributing to its activity. We applied a liquid chromatography-mass spectrometry (LC-
MS) platform for querying intrabacterial drug metabolism (IBDM) (Wang, X. et al., submitted) to probe the fate
of JSF-2164 in M. tuberculosis. Briefly, M. tuberculosis the H37Rv or fgd1::tn strain was treated with 20 µM of
JSF-2164 followed by metabolite extraction at various of time points for identification via liquid chromatography-
mass spectrometry (LC-MS). After 24 h of treatment of H37Rv with JSF-2164, two peaks (one major and one
minor) were observed to gradually accumulate, as detected via LC-MS (250 nm absorption; both peaks were
associated with an m/z = 257.0) (Figure 5C). Suggested by a loss of 30 amu as compared to the parent JSF-2164
(m/z = 287.0), we hypothesized that these peaks corresponded to two amines, deriving from reduction of the nitro
group. We purified the two metabolites from M. tuberculosis treated with JSF-2164, and assigned their chemical
structures (Figure 5D) via NMR and high-resolution mass spectrometry studies. The major metabolite (designated
as M1) was determined to be N5-(3-methoxyphenyl)benzo[c][1,2,5]oxadiazole-4,5-diamine, apparently resulting
from a shift of the 1,2,5-oxadiazole with a reduction of the nitro group to an amine. The minor metabolite
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(designated as M2) was identified as N4-(3-methoxyphenyl)benzo[c][1,2,5]oxadiazole-4,7-diamine – the
apparent reduction product of the nitro moiety of JSF-2164. We synthesized authentic M1 and M2 and renamed
them as JSF-3617 and JSF-3616, respectively (Figure S3). LC-MS data (Figures S8 and S9) demonstrated the
synthetic samples were identical to the metabolites isolated from M. tuberculosis. JSF-3617 was inactive against
M. tuberculosis judged by MIC values of 250 and 125 µM against the H37Rv and fgd1::tn strains, respectively
(Table 2). JSF-3616 demonstrated modest growth inhibitory efficacy with an MIC of 32 µM versus both the M.
tuberculosis and fgd1::tn strains (Table 2). JSF-3616 and JSF-3617 exhibited 46 ± 2.9% and 77 ± 0.73% (Table
2). InhA inhibition at 50 µM in the presence of 1% CMC Tween-20. We were unable to observe the des-nitro
JSF-2164 metabolite via LC-MS comparison to a synthetic standard (JSF-2169; Figure S2).
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JSF-2164 underwent a rapid metabolism in H37Rv, demonstrated by intrabacterial accumulation over 2 h
and followed by a degradation to a negligible level (lower limit of detection = 7.5 pmol) at t ≥ 4 h (Figure 5E).
Intrabacterial JSF-3616 and JSF-3617 were detectable after 30 min of treatment (with lower limits of detection
of 12 and 94 pmol, respectively). Their concentrations increased over the first ca. 2 h and 8 h, respectively, and
then leveled off. JSF-3617 was a major intrabacterial metabolite judged by a higher accumulation amount as
compared to JSF-3616 and the parent JSF-2164. The lack of cofactor F420H2 in the fgd1::tn strain slowed the JSF-
2164 intrabacterial metabolism as compared to the H37Rv strain, judged by an extended intrabacterial half-life
of JSF-2164 in fgd1::tn than in H37Rv. Conversion of JSF-2164 into the metabolites JSF-3616 and JSF-3617 was
also slowed as compared the H37Rv strain. JSF-3616 and JSF-3617 were discernible in fgd1::tn after 4-hour
treatment with intrabacterial concentrations significantly lower than concentrations in H37Rv at each same time
point, judged by the Student’s t-test with p < 0.05. This result indicated an F420-dependent intrabacterial
metabolism consuming JSF-2164 to produce two amine metabolites with NO• generation in M. tuberculosis
(Figure 6).
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Figure 6. Proposed JSF-2164 intrabacterial metabolism in M. tuberculosis. JSF-2164 was a hit of a in
silico Docking/Bayesian screening in search for an InhA inhibitor with antitubercular activity and low
cytotoxicity. JSF-2164 suffered an F420H2-dependent metabolism to afford two amine metabolites JSF-3616
and JSF-3617 with NO• production.
 DISCUSSION
Multi-drug resistant TB is a global epidemic crisis requiring new drugs be added to the current antitubercular
arsenal. We hypothesized that an innovative workflow, combining cell-based data via our dual-event Bayesian
models and target structural data through high-throughput docking, would offer structurally novel small molecules
that inhibit InhA and M. tuberculosis in vitro growth while offering an acceptable lack of Vero cell cytotoxicity.
Our initial foray into large-scale virtual screening of commercial chemical space for InhA inhibitors led to two
novel fragment-sized enzyme inhibitors of InhA that displayed Ki values of 54 and 59 M, respectively.8 That
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previous study used the identical docking program, docking parameters, docking filters, and docking target as the
present study, and the same individual (A.L.P.) visually inspected the docking results for both investigations. The
most significant difference was the previous docking workflow involved algorithms that knew nothing about the
structural and physiochemical properties of small molecules that contribute to antitubercular whole-cell efficacy
and lack of relative Vero cell cytotoxicity. While our workflow depended on a Bayesian model component, other
machine learning strategies could have been used alternatively. However, we have found that Bayesian models
perform at least as well as those derived with Support Vector Machines and Recursive Partitioning.9 Although
several previous studies have investigated different ways of combining or comparing docking calculations with
Bayesian or SVM models, those hybrid workflows focused on increasing the probability of finding inhibitors of
a particular protein target or group of targets (or identifying the target).42-47 They did not involve combining
Bayesian models (trained on cell-based data) with docking studies, to increase the efficiency of finding whole-
cell active, less toxic compounds that could inhibit a particular target within M. tuberculosis. The value of our
workflow has been supported by the virtual processing of 5.07 x 105 commercially available small molecules to
afford 19 candidates, predicted to exhibit acceptable InhA inhibition (≥50 % inhibition at 50 M compound), an
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MIC ≤ 10 g/mL versus M. tuberculosis, and an SI ≥ 10 with respect to Vero cells. One compound, JSF-2164,
met the first two criteria and while its Vero cell-based SI of 5.5 (using the most stringent and conservative metrics)
did not meet the criterion of 10, it was not prohibitively low (SI ≤ 2) in our opinion. Critically, JSF-2164
represented a novel chemotype for an antitubercular agent inhibiting InhA.
The high-throughput docking/Bayesian workflow was, thus, able to find a small molecule (JSF-2164) with
promising InhA inhibition, whole-cell activity, and modest SI versus Vero cells for a virtual screening hit. The
hit rate of 2/19 (10.5%) compounds with acceptable antitubercular efficacy is similar to hit rates we have
witnessed when implementing Bayesian models in previous ligand-based screens,9, 10, 48-50 which are more cost-
and time-efficient than experimental HTS against M. tuberculosis (hit rates < 1%). Additionally, the Bayesian
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approach removed from consideration whole-cell inactive compounds, as the TAACF-CB2 model’s lowest
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The two most potent whole-cell actives (JSF-2164 and BAS01237091) were not found when searching
the CDD database of antitubercular screening data (www.collaborativedrug.com). This analysis supports the
likelihood that optimization efforts with these compounds, or others arising from similar computational screening
of different commercial libraries both within and outside of GOFAM, will lead to novel chemical tools. On the
topic of chemical similarity, it is also noteworthy that the benzofurazans JSF-2164 and BAS01237091 are
dissimilar from the Bayesian training set/s that predicted them to be active as judged by their respective calculated
closest distance to the training set (closest distance of identical molecules = 0; JSF-2164: TAACF-CB2 closest
distance = 0.55, Combined closest distance = 0.54; BAS01237091: Combined closest distance = 0.56). It has
become clear that our Bayesian models thus far are able to predict actives that are structurally dissimilar to the
model’s training set, which supports their general utility when exploring novel areas of chemical space. When
scoring a new set of molecules for prospective testing, a Bayesian model gives favorable scores to molecules that
contain chemical features recognized amongst the actives in its training set. Thus, predicted actives may comprise
a mixture of training set active molecules’ features that have been “mixed and matched," as well as additional
features that were not part of any training set member (as long as those added features do not have properties that
match those of the training set inactives).
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A critical assessment of our workflow was tied to the question as to whether JSF-2164 achieved its whole-
cell growth inhibition primarily through InhA inhibition. Intrabacterial metabolism is clearly a complicating factor
in providing an answer, as this predicted whole-cell active InhA inhibitor has a short lifetime within the bacterium.
In addition to producing NO•, the metabolism of JSF-2164 was shown to afford two derivatives (JSF-3617 and
JSF-3616) which are less active as growth inhibitors and do not exhibit a loss of efficacy upon inhA
overexpression as does JSF-2164, but still maintain modest inhibition of purified InhA. Given the significant
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consumption of JSF-2164 and production of JSF-3617 within the fgd1::tn strain, we cannot discount the
involvement of other genes, despite the results from resistant mutant and transposon insertion experiments, in the
production of JSF-2164 metabolites. The potential also exists for metabolites other than those identified to have
eluded our LC-MS–based methodology. For the parent molecule and its identified metabolites some disconnects
exist between inhibition of purified InhA and the intracellular hallmarks of InhA inhibition that were assayed. At
this level of interrogation, InhA inhibition appears to be a secondary target, but once again we cannot minimize
the role of intrabacterial metabolism in complicating apparent mechanism of action.
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We assert that intrabacterial metabolism must be considered when assessing the connection, or
disconnection, between an antitubercular’s interaction with the purified primary protein target and its effect on
that protein’s function within the bacterium. Thus, our novel computational workflow may be judged successful
in that it identified a compound that inhibited purified InhA and achieved significant whole-cell activity. However,
neither the docking approach nor the Bayesian modeling was capable of understanding intrabacterial metabolism.
It was only comprehended through IBDM studies that were tipped off by the attainment of JSF-2164–resistant
mutants that evidenced 1) engagement of InhA within the cell through the c(-15)t mutation in the inhA promoter
region leading to an overexpression of inhA in JSF-2164 spontaneous mutants and 2) the F420H2-based reduction
of JSF-2164 to amine metabolites JSF-3617 and JSF-3616 accompanied by the production of NO•. We were
unable to observe via LC-MS the presence of the des-nitro JSF-2164 (JSF-2169) that would be formed via loss
of NO• in analogy to pretomanid,39 delamanid,40 and JSF-2019.(Wang, X. et al., submitted)
Finally, it is significant to note that small molecules nitro-containing heterocycles have been reported as
metabolically labile in M. tuberculosis. The Phase III candidate pretomanid39, 51 and EMA-approved delamanid40
are bactericidal in M. tuberculosis due to a release of NO• catalyzed by the nitroreductase Ddn in the presence of
the cofactor F420H2. JSF-2164, however, exhibited bacteriostatic activity even with a demonstrated NO• release.
We have noted a smaller NO• production amount with JSF-2164 treatment as compared to pretomanid treatment
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of H37Rv. This quantitative difference in NO• production may play a significant role in determining bactericidal
versus bacteriostatic activity. Given the demonstrated need to attain follow-up compounds to pretomanid and
delamanid, it is important to better understand nitroheterocycles in terms of their intrabacterial metabolism to
afford NO• and other metabolites that have cidal consequences for M. tuberculosis.
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 CONCLUSIONS
A novel computational workflow combining target-based virtual screening and machine learning from
datasets containing whole-cell efficacy and model mammalian cell cytotoxicity has been designed and assessed
with regard to M. tuberculosis and InhA. This model successfully identified JSF-2164 from a library of ~105
compounds as an in vitro InhA inhibitor with whole-cell activity in M. tuberculosis, supporting our goal of further
developing and applying this novel hybrid workflow. The F420H2-dependent intrabacterial metabolism, revealed
through drug-resistant mutant studies, led to sufficiently rapid biotransformation of JSF-2164 to produce NO• and
two amine metabolites that obscured the mechanism of the parent compound. The IBDM platform was crucial to
probing this phenomenon. We assert the comprehension of molecule intrabacterial metabolism is critical to
investigate the mode of action of antibacterial chemical tools and drug discovery entities.
 METHODS
Synthetic methods:
JSF-2149: (Synthetic route depicted in Figure S2) A mixture of 2-chlorobenzaldehyde (4.5 mL, 40 mmol) and
thiosemicarbazide (3.6 g, 40 mmol) in methanol (40 mL) was heated at 70 °C for 3 h. The reaction mixture was
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