Please cite this article in press as: Savardi et al., Discovery of a Small Molecule Drug Candidate for Selective NKCC1 Inhibition in Brain Disorders,
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Figure 1. In Vitro Selection of the Selective NKCC1 Inhibitor ARN23746 as a Lead Compound
(A) Schematic representation of the intervention point in bumetanide’s structure for synthesizing novel bumetanide analogs.
(B) Quantification of the inhibitory activity of bumetanide (Bume) and bumetanide analogs (10, 100 mM) in NKCC1-(left) or NKCC2-(right) transfected
HEK293 cells in the ClÀ influx assay. Data are presented as a percentage of the respective control DMSO. Data represent mean G standard error of the
mean (SEM) from 3–4 independent experiments (Kruskal-Wallis one way ANOVA on Ranks, NKCC1 10 mM: H = 84.898, DF = 6, p < 0.001; NKCC1 100 mM:
H = 86.799, DF=6, p < 0.001; NKCC2 10 mM: H = 40.700, DF = 6, p < 0.001; NKCC2 100 mM: H = 70.569, DF = 6, p < 0.001, Dunn’s post hoc test, *p < 0.05,
**p < 0.01, ***p < 0.001).
(C) Representation of the ligand-based computational strategy to discover novel molecular scaffolds that inhibit NKCC1. The obtained bumetanide
pharmacophore (1) consists of three H-bond acceptor (HBA) features (red spheres), three H-bond donor (HBD) interactions (blue spheres), one
lipophilic feature (green sphere), and one stacking feature (brown sphere) anchored around the central aromatic core. Ligand disposition was then
implemented by superimposing other known unspecific NKCC1 inhibitors (2), revealing shared features and different dihedral dispositions of
substituent around the central aromatic core. This model was used as a search filter for the virtual screening (3) of our internal chemical collection
(ꢀ15,000 compounds). Results generated from in vitro testing of the 165 initial hits (4) were then used to retrain the model (5) and perform a second more
specific screening of our chemical library and commercial chemical libraries (ꢀ135,000 compounds). This iterative computational cycle led to hit
compounds (6) ARN22393 and ARN22394. (D) Quantification of the inhibitory activity of the indicated compounds (10, 100 mM) in NKCC1-transfected
HEK293 cells (ClÀ influx assay). Data are presented as a percentage of the respective control DMSO. Data represent mean G SEM from 3–4 independent
experiments (Kruskal-Wallis one way ANOVA on Ranks, 10 mM: H = 37.119, DF = 3, p < 0.001; 100 mM: H = 33.724, DF = 3, p < 0.001, Dunn’s post hoc test,
*p < 0.05, **p < 0.01, ***p < 0.001).
(E) Chemical structures of NKCC1 inhibitors with novel scaffold. (F) Left, example traces obtained in the ClÀ influx assay on NKCC1-transfected HEK293
cells for each compound (100 mM). The arrow indicates the addition of NaCl (74 mM) to initiate the NKCC1-mediated ClÀ influx. Right, quantification of
the NKCC1 inhibitory activity of the indicated compounds (10, 100 mM) in experiments such as those on the right. Data are presented as a percentage of
the respective control DMSO. Data represent mean G SEM from 3–4 independent experiments (10 mM: one way ANOVA, F(4,84) = 33.048, p < 0.001,
Dunnett’s post hoc test, *p < 0.05, ***p < 0.001; 100 mM: Kruskal-Wallis one way ANOVA on Ranks, H = 50.796, DF = 4, p < 0.001, Dunn’s post hoc test,
***p < 0.001.
(G) Left, example traces obtained in the Ca2+ influx assay on 3DIV hippocampal mouse neurons for each tested compound (100 mM). The arrows
indicates the addition of GABA (100 mM) and KCl (90 mM). Right, quantification of the effect of the indicated compounds (10, 100 mM) in the Ca2+ influx
assay on 3DIV neurons. Data are presented as a percentage of the control DMSO. Data represent mean G SEM from three independent experiments.
10 mM: one-way ANOVA, F(5,161)= 77.184, p < 0.001, Dunnett’s post hoc test **p < 0.01, ***p < 0.001; 100 mM: Kruskal-Wallis One ANOVA on Ranks, H =
134.681, DF = 5, p < 0.001, Dunn’s post hoc test, ***p < 0.001).
(H) Amplitude change average and single cell data points of GABA-evoked currents obtained by voltage-clamp recordings of 12–20 DIV hippocampal
mouse neurons before (gray example recordings in the inset above) and after (black example traces) bath application of the indicated drugs (10 mM).
Data are presented as mean G SEM (Paired t test, *p < 0.05, **p < 0.01). Scale bars: 250 pA, 250 ms. See also Figures S1A–S1D; Tables S1 and S2.
modifications that reduced NKCC2 inhibition also led to a significant loss of potency
for NKCC1 inhibition (Figure 1B). Altogether, these data indicated a major difficulty
to develop new and potent bumetanide derivatives with significant selectivity for
NKCC1 over NKCC2. This is in line with the literature on other existing bumetanide
analogs25 and prodrugs.26 We therefore sought for new molecular entities, structur-
ally unrelated to bumetanide, as selective NKCC1 inhibitors.
Because of the importance of the carboxylic group and the contribution to the selec-
tivity of a linear alkyl chain attached to an aromatic core, we applied a ligand-based
computational strategy to build a pharmacophore model based on bumetanide and
other known NKCC1 inhibitors (Figure 1C). We first performed a force field-based
conformational search on bumetanide’s structure. This approach returned the
preferred spatial arrangement of the pharmacophoric features of bumetanide,
such as H-bond donor and acceptor, lipophilic, and aromatic groups (Figure 1C).
We then implemented ligand disposition by superimposing this template pharma-
cophore with other structures of unselective NKCC1 inhibitors (e.g., diuretics furose-
mide, asozemide, piretanide, and chlorothiazide) to identify shared chemicophysical
properties and three-dimensional (3D) localization (Figure 1C). We used this phar-
macophore model as a filter for the virtual screening of our institution’s diverse
and non-redundant internal library of ꢀ20,000 molecules (Figure 1C). In a first round
of experiments, 165 new compounds from the library emerged as initial hits and
were individually tested for their ability to inhibit NKCC1 in the ClÀ influx assay
(not shown). Of these compounds, 20% showed NKCC1 inhibition between
5%–10% at 10 mM. The other 80% showed no activity (not shown). Using the struc-
tural data acquired in the first round, we refined the pharmacophore model
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Chem 6, 1–24, August 6, 2020