Abstract
Despite the STING-type-I interferon pathway playing a key role in
effective anti-tumor immunity, the therapeutic benefit of direct STING
agonists appears limited. In this study, we use several artificial
intelligence techniques and patient-based multi-omics data to show that
Ectonucleotide Pyrophosphatase/Phosphodiesterase 1 (ENPP1), which
hydrolyzes STING-activating cyclic GMP-AMP (cGAMP), is a safer and more
effective STING-modulating target than direct STING agonism in multiple
solid tumors. We then leverage our generative chemistry artificial
intelligence-based drug design platform to facilitate the design of
ISM5939, an orally bioavailable ENPP1-selective inhibitor capable of
stabilizing extracellular cGAMP and activating bystander
antigen-presenting cells without inducing either toxic inflammatory
cytokine release or tumor-infiltrating T-cell death. In murine
syngeneic models across cancer types, ISM5939 synergizes with targeting
the PD-1/PD-L1 axis and chemotherapy in suppressing tumor growth with
good tolerance. Our findings provide evidence supporting ENPP1 as an
innate immune checkpoint across solid tumors and reports an AI
design-aided ENPP1 inhibitor, ISM5939, as a cutting-edge STING
modulator for cancer therapy, paving a path for immunotherapy
advancements.
Subject terms: Drug discovery, Cancer therapy, Machine learning
__________________________________________________________________
While the STING-type-I interferon pathway plays a key role in
anti-tumour immunity, current direct STING agonists have limited
therapeutic benefit. Here, the authors identify ENPP1 as a safer and
more effective STING-modulating target than direct STING agonism, and
use an AI-based drug design platform to design the ENPP1-selective
inhibitor ISM5939.
Introduction
Activation of the STING pathway promotes the production of type I
interferons (IFNs), thereby enhancing antitumor immunity via
cell-autonomous and non-autonomous mechanisms^[56]1. STING activation
in antigen-presenting cells (APCs), such as dendritic cells (DCs) and
macrophages, licenses the cells for enhanced antigen presentation and
upregulates co-stimulatory molecules, thereby enhancing the ability to
prime and activate cytotoxic T cells^[57]2–[58]4. Additionally, STING
signaling in the tumor microenvironment (TME) has been demonstrated to
dampen tumorigenic and pro-metastatic processes, such as
myeloid-derived suppressor cell activation, T-cell suppression, and the
tumor-intrinsic epithelial-mesenchymal transition^[59]5. The pathway’s
central role in regulating immune responses makes it a promising target
for cancer immunotherapy.
Despite its potential as an activator of antitumor immunity, the
development of direct STING agonists has so far encountered substantial
difficulty. Cyclic dinucleotide (CDN) STING agonists, such as ADU-S100
and MK-1454, suffer from poor bioavailability and can only be
administrated intratumorally, with a marginal therapeutic
benefit^[60]6–[61]8. Non-CDN STING agonists, such as MSA-2 and SR-717,
designed with improved bioavailability, lead to systemic inflammatory
responses and toxic cytokine release when administered systemically in
preclinical models^[62]9,[63]10, thus presenting substantial safety
concerns. Given the significant challenges associated with direct STING
agonists, there is a pressing need for developing alternative
strategies to safely and effectively modulate the STING pathway.
Detection of cytoplasmic DNA by cyclic GMP–AMP synthase (cGAS)
catalyzes formation of second-messenger cyclic GMP-AMP (cGAMP), which
then activates STING^[64]11. The cellular export and import of cGAMP
among the various cell types within the TME facilitates endogenous
STING agonism and effective antitumor immunity^[65]5,[66]12–[67]16.
However, the intracellular accumulation of imported cGAMP is
significantly impeded by cell surface-bound ectonucleotide
pyrophosphatase/phosphodiesterase 1 (ENPP1), which is the main degrader
of cGAMP^[68]12,[69]17–[70]19. High expression of ENPP1 within the
tumor bed in various solid tumors suggests a link between ENPP1’s role
in antagonizing cGAMP-STING-mediated antitumoral immunity and cancer
onset, progression, and metastasis^[71]13,[72]15,[73]20,[74]21
Inhibition of ENPP1 results in increased extracellular cGAMP, allowing
for sustained, controlled activation of the STING pathway by promoting
consistent and localized STING activation without the systemic toxicity
associated with direct STING agonists. Accordingly, ENPP1 inhibitors
show promise as next-generation STING modulators effective in
overcoming resistance to immunotherapy characteristic of “immune-cold”
cancers^[75]22.
However, the development of ENPP1 inhibitors has so far proved
challenging, with only a few candidates such as [76]TXN10128, RBS2418,
and SR-8541A entering phase 1 studies. Common shortcomings include low
potency and specificity at physiological pH, as many inhibitors lose
efficacy outside optimal assay conditions (e.g., pH 9)^[77]18, as well
as unfavorable pharmacokinetic properties^[78]23. Moreover, any such
inhibitor must also sufficiently avoid the challenges common among
small-molecule inhibitors, including poor oral absorption, low overall
efficacy in physiological settings, and non-specific interactions, such
as blocking cGAMP export. Accordingly, there is an urgent need to
develop a highly potent and selective ENPP1 inhibitor that can be
administered orally to enhance cancer immunotherapy.
Artificial intelligence (AI)-driven molecular design has recently
emerged as an efficient strategy to discover hit
compounds^[79]24–[80]26 by facilitating and integrating the discovery
of disease-associated molecular targets^[81]27–[82]29, designing
compounds specific to those targets^[83]30–[84]36, and identifying
disease indications and patient populations most likely to respond to
targeted therapy^[85]37–[86]44. Several generative chemistry approaches
have designed experimentally validated molecules^[87]30, with
Chemistry42^[88]45 emerging as one of the most frequently validated
multi-model platforms^[89]30.
In this work, our generative AI-integrated workflow extended ENPP1 as
an immune checkpoint among multiple solid tumors and assisted in
developing a highly specific oral ENPP1 inhibitor, ISM5939.
Mechanistically, ISM5939 specifically suppresses cGAMP degradation and
augments non-cell autonomous STING signaling in the tumor
microenvironment, which synergizes with current standard-of-care
treatments, including anti-PD(L)1 therapy and DNA-destabilizing agents.
The identification and development of ISM5939 as a specific oral ENPP1
inhibitor marks a significant advance in cancer immunotherapy, offering
a promising strategy to harness the cGAS-STING pathway for enhanced
antitumor immunity with minimized systemic toxicity.
Results
Integrative analysis validates ENPP1 as a potential innate immune checkpoint
in multiple solid tumor types
ENPP1 supports immune evasion in triple-negative breast cancer
(TNBC)^[90]46, but its role as an innate immune checkpoint across a
broad range of solid tumor types remains to be fully elucidated. To
address this, we used PandaOmics, a commercially available
target-discovery platform which integrates large-scale patient omics
data sets with multiple omics- and text-based algorithms to explore
additional oncology indications^[91]34. Briefly, we utilized the
indication prioritization tools embedded in PandaOmics to rank
indications by composite score for potential response to ENPP1
inhibition. This score incorporates biological relationships (e.g.,
gene networks, expression, pathways, causal directionality) and
text-based information (e.g., publication text-mining, research trends,
strength of evidence) to nominate diseases in which ENPP1 may play a
central, pathogenic role. Several cancer types, such as TNBC,
hepatocellular carcinoma, acute myeloid leukemia, ovarian carcinoma,
colorectal adenocarcinoma, colorectal cancer, breast cancer, head and
neck cancer and ER-negative breast cancer, were identified as top
candidates for ENPP1 inhibitor treatment (Fig. [92]1A). Complementing
the PandaOmics prediction, we also analyzed the expression of ENPP1 and
its correlation with prognostically beneficial STING-type I interferon
signaling across all solid tumor types in the TCGA database. We
predicted that cancer indications consistently fulfilling the following
criteria would emerge as prime candidates for ENPP1-targeted therapies:
1) high expression of cGAS, which catalyzes the production of cGAMP; 2)
presence of ENPP1 in tumor tissue (either on malignant cells or
mesenchymal/immune cells within tumors); 3) a negative correlation
between ENPP1 expression and STING-type I IFN signaling; and 4)
association of STING-type I IFN pathway expression with favorable
prognosis. Based on our analysis, eight solid tumor types stand out as
priority indications: breast carcinoma, gastric carcinoma, colorectal
carcinoma, head and neck carcinoma, esophageal carcinoma, ovarian
carcinoma, lung squamous carcinoma, and cervical squamous carcinoma
(Supp. Figs. [93]S1A, [94]S1B). Together, the robust insights provided
by PandaOmics coupled with the TCGA analysis underscore the potential
of ENPP1 as a therapeutic target across a diverse array of solid
tumors.
Fig. 1. Validation of ENPP1 as an innate immune checkpoint in solid tumors
and indication prioritization for ENPP1 targeting strategies.
[95]Fig. 1
[96]Open in a new tab
A Indication prioritization tools embedded in PandaOmics to rank
indications for ENPP1. B The correlation analysis of type-I IFN related
immune parameters across cancer types: Correlation between ENPP1
expression in tumor cells and ISG score in conventional dendritic cells
(cDC) in gastric cancer (OMIX001073, n = 10) and colorectal cancer
(CRC, [97]GSE132465, n = 23), as well as correlation between ENPP1
expression in tumor cells and CXCL10 expression in dendritic cells (DC)
and cDC percentage in triple-negative breast cancer (TNBC,
[98]GSE176078, n = 10). Each dot represents a single patient sample.
The x-axis indicates the ENPP1 expression in tumor cells and the y-axis
displays the corresponding immune parameters: ISG score, CXCL10
expression or cDC percentage. The P value and correlation were
performed using Pearson’s correlation coefficient, with the resulting
trend line depicted in blue. The gray shaded area surrounding the trend
line represents the 95% confidence intervals. Source data are provided
as a Source Data file. C Spatial distribution of ENPP1, ISG score and
predicted proportion of CD8 T and pro-inflammatory macrophage cells per
spot in histology slide SN84_A120838_Rep2 from CRC patient (Zenodo
record: 7760264). The color represents the value of ENPP1 expression,
ISG score and predicted proportion of DC and NK cells per spot. Source
data are provided as a Source Data file. D Spatial distribution of
ENPP1, ISG score and predicted proportion of CD4 T and DC cells per
spots in slide GSM6433591_094A from a TNBC patient (GEO:
[99]GSE210616). The color represents the value of ENPP1 expression, ISG
score and predicted proportion of DC and NK cells per spot. Source data
are provided as a Source Data file. E Spatial distribution of ENPP1,
ISG score and predicted proportion of DC and NK cells per spot in slide
21_00734_LI_SING from a GC patient (GEO: [100]GSE251950). The color
represents the value of ENPP1 expression, ISG score and predicted
proportion of DC and NK cells per spot. Source data are provided as a
Source Data file.
A study recently reported the role of ENPP3 in cGAMP degradation and
immune modulation^[101]47, raising the questions of relative
contribution and sufficiency of targeting ENPP1. We found that ENPP1 is
likely the predominant enzyme responsible for cGAMP degradation in
various solid tumors, as evidenced by its higher and more widespread
expression across cancer cell lines (Supp. Fig. [102]S2A) and various
tumor tissues (Supp. Fig. [103]S2B). Single-cell gene expression data
showed ENPP1 was widely expressed across diverse cell types within the
tumor microenvironment, whereas ENPP3 was mainly restricted to
fibroblasts and mast cells (Supp. Fig. [104]S2C). We also found that
across various cancer subtypes and treatment regimens, ENPP1 exhibited
a significant association with resistance to standard of care (SOC)
treatments, but ENPP3 expression was not significantly correlated with
the response to either chemotherapy or immune checkpoint therapy (Supp.
Fig. [105]S2D). Given its broad expression and clinical relevance,
targeting ENPP1 may be more effective in overcoming treatment
resistance and improving therapeutic outcomes.
Given the critical role of type-I IFN signaling in tumor antigen
presentation mediated by conventional dendritic cells (cDCs), we
utilized single-cell sequencing data from tumor tissues of patients
with TNBC, gastric cancer (GC), and colorectal cancer (CRC) to
investigate the correlation between the tumor cell-intrinsic expression
of ENPP1 and cDC-intrinsic STING-type I IFN signaling. A significant
negative correlation was found between ENPP1 expression in tumor cells
and the expression of CXCL10, a typical chemokine downstream of type I
IFN^[106]48, or the interferon-stimulated gene (ISG)
score^[107]49–[108]51 in DCs in all three cancer types (Fig. [109]1B),
consistent with the immunosuppressive role of tumor-derived ENPP1 and
decreased cGAMP in the TME.
Further, due to intratumoral heterogeneity common across solid
tumors^[110]52–[111]54, we posited that within a given tumor tissue,
regions with high ENPP1 expression would exhibit more “immune
desert”-like characteristics, such as low immune cell infiltration and
activation. To test this hypothesis, we integrated single-cell RNA
sequencing (scRNA-seq) data and spatial transcriptome data from tumor
samples^[112]55. In CRC, TNBC, and GC samples, regions abundant in
ENPP1 were found to be mutually exclusive with those with high ISG
scores and those enriched with CD8^+ T or pro-inflammatory macrophage
cells (Fig. [113]1C–E)^[114]56–[115]58, further supporting the role of
ENPP1 in modulating the immune composition within the TME.
Collectively, our integrative analysis reveals ENPP1 as a negative
regulator of the STING-type-I IFN pathway and as a therapeutic target
in multiple solid tumors.
Discovery of potent, selective, and orally bioavailable ENPP1 inhibitor
ISM5939
We implemented structure-based drug design of ENPP1 inhibitors via the
multi-model, commercially available Chemistry42 generative AI
platform^[116]45, which has so far yielded among the most
experimentally validated molecules across generative chemistry
approaches^[117]30. We started with the known binding poses of
previously reported ENPP1 inhibitors QS1 and QPS2^[118]59–[119]61 and
designated key ligand-interacting amino acids of ENPP1 as mandatory
binding points. Diverse candidate structures were selected for
validation based on medicinal chemistry filters, self-organizing maps
(SOM) rewards, pharmacophore hypothesis rewards, synthetic
accessibility, and novelty, leading to the discovery of initial hit
compound ISM7516 (Fig. [120]2A). ISM7516 is a sulfonamide with a
7-phenyl-1H-imidazo[4,5-c]pyridine core scaffold, which inhibited the
hydrolysis of cGAMP with an IC[50] of 2.55 nM, whereas QS1 in the same
assay exhibited an IC[50] of 142 nM. Furthermore, ISM7516 stabilized
extracellular cGAMP secreted by MDA-MB-231 breast cancer cells
transfected with double-stranded DNA (dsDNA), with an EC[50] of 0.50 µM
(Fig. [121]2D). To optimize this promising hit compound, we conducted
molecular docking of ISM7516 with ENPP1 (Fig. [122]2B), aiming to
explore critical binding interactions and elucidate the
structure-activity relationship (SAR). The docking results suggested
that the sulfonamide and 7-phenyl-1H-imidazo[4,5-c]pyridine core of
ISM7516 form major contacts with ENPP1, while the benzyl group provides
additional hydrophobic interactions (Fig. [123]2B, [124]C), which were
iteratively modified to obtain potentially improved structures.
Alchemistry^[125]62, an efficient physics-based module within
Chemstry42 to estimate binding free energy, was employed to prioritize
the compounds with lower calculated binding energy (△G[cal]) than
ISM7516 (△G[cal] = −13.29 kcal/mol) (Supp. Fig. [126]S3), ultimately
leading to the discovery of ISM3576 (△G[cal] = −14.59 kcal/mol)
(Fig. [127]2D).
Fig. 2. Generative AI-aided discovery of an ENPP1 inhibitor.
[128]Fig. 2
[129]Open in a new tab
A Flow scheme for AI-facilitated ENPP1 inhibitor discovery. ReRSA,
retrosynthesis related synthetic accessibility; SA, synthetic
accessibility; PXR, pregnane xenobiotic receptor. Novelty calculated
based on the dataset compiled from ChEMBL. Alchemistry, accurately
estimates the relative binding free energy to prioritize molecules with
efficient physics-based methods. ADMET prediction, predict
physicochemical and ADMET molecular properties. B Docking pose of
ISM7516 with human ENPP1. The source data are provided as a Source Data
file. ENPP1 (white) is shown in ribbon and ISM7516 (cyan) is drawn in
sphere, while key residues (white) and ISM7516(cyan) are shown in
stick. C Docking pose of ISM5939 with human ENPP1. The source data are
provided as a Source Data file. ISM5939 (yellow) was superimposed with
AMP (purple, PDB ID: 6wfj) and QPS2 (green, PDB ID: 6wev). Zn atoms are
shown as gray sphere and dashed lines indicate hydrogen bonds or
coordination bonds. D Biochemical/cellular activity, △G[cal], CYP3A4
induction, hERG inhibition and predicted hERG inhibition value of
candidate inhibitors. The data are colored by profiling results. Green
represents ideal profiling, orange indicates potential issues, black
represents acceptable results, and red represents unacceptable
outcomes. CYP3A4 induction calculated as ratio of CYP3A4 mRNA following
compound treatment at 10 µM compared to rifampicin. △G[cal] was
calculated by Alchemistry tool. hERG, human ether-a-go-go-related gene.
Predicted hERG inhibition values were achieved by ADMET prediction
module.
ISM3576 showed in vitro biochemical and cellular potency superior to
ISM7516, likely attributable to the substitution of the methoxyl group
with a methyl group and the addition of two fluorine atoms at the
3,5-positions of the benzyl group in ISM7516. However, ISM3576 was
suboptimal in its strong CYP3A4 enzyme induction in human hepatocytes,
a phenomenon that can indicate severe drug-drug interactions or
self-induction in clinical use predominately mediated through the
activation of pregnane xenobiotic receptor (PXR)^[130]63. Our strategy
to reduce PXR binding involved fine-tuning the geometry of the
difluorophenyl group to disrupt hydrogen bonds, as well as modifying
the 7-phenyl-1H-imidazo[4,5-c]pyridine core to decrease the hydrophobic
interactions engaged in PXR binding, while maintaining affinity for
ENPP1, as these components also contribute to ENPP1 binding (Supp
Figs. [131]S4 and[132]S5). ISM3576 also demonstrated increased
potential for cardiotoxic inhibition of hERG channels (hERG
IC[50] = 11.68 µM) compared to ISM7516 (hERG IC[50] > 30 µM)
(Fig. [133]2D), so the newly designed compounds were further screened
for predicted hERG inhibition in the Chemistry42 ADMET prediction
module along with △G[cal]. This filtering aimed to balance CYP3A4
induction, hERG liability, and ENPP1 inhibition (Fig. [134]2A). Through
systematic SAR exploration and AI-enabled filtering (Fig. [135]2A,
Supplemental Figs. [136]S4 and [137]S5), we eventually identified
ISM5939 (△G[cal] = −14.06 kcal/mol, predicted hERG inhibition
value = 23.44 µM), which exhibited promising drug-like features, such
as low CYP3A4 induction, high ENPP1 inhibitory potency, high metabolic
stability, minimal hERG liability, and favorable animal pharmacokinetic
(PK) profiles (Fig. [138]2D, Table [139]1). ISM5939 was thus nominated
for further development, highlighting the ability for integrated
AI-driven platforms like Chemistry42 to accelerate the entire drug
discovery hit-to-lead process.
Table 1.
ADMET and Pharmacokinetic Properties of ISM5939
KS pH 7.4 (µM) 66
Caco-2 Papp A to B (10^-6 cm/s), efflux ratio 1.8, 9.4
human, mouse, rat, dog LM CLint (mL/min/kg) <8.6, <38, <17, <14
human, mouse, rat, dog hepatocyte CLint (mL/min/kg) <18, 177, <30, <44
human, mouse, rat, dog plasma protein binding (fu%) 15, 39, 45, 35
CYP 1A2, 2D6, 2C19, 3A4, 2C9 inhibition., IC[50] (µM) all >40
mouse F (%), CL (iv, mL/min/kg), V[ss] (iv, L/kg), 32, 14, 0.53
rat F (%), CL (iv, mL/min/kg), V[ss] (iv, L/kg) 49, 14, 1.1
dog F (%), CL (iv, mL/min/kg), V[ss] (iv, L/kg) 45, 2.0, 0.63
[140]Open in a new tab
According to the docking results, ISM5939 has a modified
imidazole[4,5-c]pyridine core pack between Phe257 and Tyr340 with a
hydrogen bond forming with Lys295 in the adenine pocket (Fig. [141]2C).
The 7-phenyl substitution on the core of ISM5939 projects deep into the
pocket surrounded by multiple aromatic amino acids. The additional π-π
interactions with Phe257, Phe321 and Tyr371 may contribute to the
increased affinity of ISM5939, considering that a methoxy group in QPS2
was extended into this pocket instead (Fig. [142]2C). We superimposed
ISM5939 with AMP (Kd = 103 nM), a tightly bound hydrolysis nucleotide
product of ATP and cGAMP that acts as a natural ENPP1 ligand. The fused
ring of ISM5939 occupies the same region as the adenine of AMP but
extends further into the pocket. Additionally, the 5-fluoropyridine
extends out of the adenine pocket and occupies a similar space as the
ribose ring of AMP, but the sulfonamide group does not project to the
zinc site like the phosphate of AMP but instead forms hydrogen bonds
with Glu373 and Asp376. Despite the different interactions of ISM5939
and AMP with the zinc site, the virtual analysis suggests that ISM5939
binds to the ATP/cGAMP active site and competes with natural substrates
for ENPP1 binding.
ISM5939 is a potent and ENPP1-selective Inhibitor
To evaluate the inhibitory effect of ISM5939, we used recombinant ENPP1
protein and both 2,3-cGAMP and ATP as substrates. Under physiological
pH conditions (pH 7.4), ISM5939 demonstrated a remarkable inhibition of
ENPP1 (Fig. [143]3A), with IC50 values of 0.63 nM against the 2,3-cGAMP
degradation and 9.28 nM against ATP hydrolysis (Fig. [144]3B). This
potency far surpasses that of ENPP-IN-1^[145]64, the positive control
in the assay, which exhibited significantly higher IC50 values of
259.0 nM for 2,3-cGAMP and 1328.0 nM for ATP, highlighting the superior
inhibitory efficacy of ISM5939. Under tumor microenvironment-mimicking
pH conditions (pH 6.5), ISM5939 retained significant inhibitory
activity against ENPP1, with IC50 values of 11.41 nM for 2,3-cGAMP
degradation and 170.30 nM for ATP degradation (Fig. [146]3B). In
alignment with the docking analysis (Supplemental Fig. [147]S4), the
ENPP1 inhibition assay revealed that increasing concentrations of
either ATP or cGAMP led to a decrease in the inhibitory potency of
ISM5939 (Fig. [148]3C). This suggests that the inhibitory action of
ISM5939 is competitive with the natural substrates of ENPP1.
Additionally, enzymatic assays were performed using recombinant ENPP1
proteins from cynomolgus monkeys, dogs, and mice, which yielded
comparable inhibition effects of ISM5939 on ENPP1 (Supp.
Figs. [149]S6A–C). Furthermore, to assess the selectivity of ISM5939
for ENPP1, we simultaneously evaluated its inhibitory effects on ENPP2
and ENPP3, the most closely related members within the ecto-nucleotide
pyrophosphatase/phosphodiesterase (ENPP) family, themselves often
inhibited by ENPP1-targeting inhibitors^[150]65–[151]67. For the ENPP2
assay (Fig. [152]3D), we included the reported ENPP2 inhibitor
HA130^[153]68 as a positive control. To verify the specificity of the
ENPP3 assay (Fig. [154]3E), we used STF-1084^[155]17, a recognized
potent ENPP1 inhibitor, as the positive control, and also included a
range of unrelated compounds such as HA130 (an ENPP2
inhibitor)^[156]68, SN-38 (a topoisomerase I inhibitor)^[157]69, and
paclitaxel (a microtubule stabilizer)^[158]70 as negative controls. The
IC[50] value for ENPP1 was at the sub-nanomolar level, while the IC[50]
values for ENPP2 and ENPP3 were over 15,000 and 3400 times greater,
respectively (Fig. [159]3F), suggesting that ISM5939 exhibits high
selectivity for ENPP1. In addition, we also evaluated the effect of
ISM5939 on the ICESTP™ SafetyOne44 panel, which encompasses a
comprehensive set of 44 clinically relevant off-targets, including
GPCRs, ion channels, enzymes, nuclear receptors, and key transporters
(Supp. Fig. [160]S6C). The results demonstrated that ISM5939 does not
exhibit any significant agonistic or antagonistic effects on any of
these targets, with the maximum observed induction or inhibition being
less than 20% (Fig. [161]3G, Supplementary Data. [162]1). This
comprehensive evaluation underscores the specificity and safety profile
of ISM5939 as a potential therapeutic agent targeting ENPP1.
Fig. 3. ISM5939 inhibits ENPP1-mediated cGAMP and ATP degradation with high
potency and selectivity.
[163]Fig. 3
[164]Open in a new tab
A Concentration response curves of ISM5939 or positive control
(ENPP-1-IN-1) in ENPP1 enzymatic assay (pH 7.4, n = 2 biological
replicates, 2 independent experiments). Left, 2,3- cGAMP as substrate;
right, ATP as substrate. B IC[50] values for ISM5939 against human
ENPP1 catalytic activity using 2’,3’ cGAMP or ATP as substrates at pH
7.4 and pH 6.5. C Concentration response curves of ISM5939 in the ENPP1
enzymatic assay at different concentrations of ATP and cGAMP (n = 2
biological replicates). D–F The inhibitory activity of ISM5939 against
the catalytic activity of (D) human ENPP2 (E) human ENPP3 and (F) table
summary for IC[50] values (n = 2 biological replicates). G The Rador
map illustrating the effect of ISM5939 (10 μM) on SafetyOne44 Panel
targets (n = 2 biological replicates). H Inhibitory effects of ISM5939
on cGAMP degradation mediated by soluble ENPP1 in human plasma detected
by ELISA. I Stabilization of cGAMP in vivo in murine plasma by ISM5939
(n = 3 biological replicates). Data were analyzed by two‐tailed,
unpaired Student’s t‐test. P-value < 0.05 were shown. J Potency of
ISM5939 in maintaining extracellular cGAMP levels when applied to
cancer cells in vitro (n = 2 biological replicates). Data were
represented as mean ± SEM. Source data are provided as a Source Data
file.
Subsequently, we evaluated the inhibitory effects of ISM5939 on cGAMP
degradation mediated by both soluble ENPP1 in plasma and membrane-bound
ENPP1 on tumor cells. We found that cGAMP was degraded in plasma
samples within 3 h (Fig. [165]3H) or when intravenously injected in
vivo into mice within 1 h (Fig. [166]3I); however, degradation was
inhibited with the addition of ISM5939 to the assay system
(Fig. [167]3H, [168]I). We then assessed the potency of ISM5939 in
maintaining extracellular cGAMP levels in the presence of
ENPP1-expressing cancer cells, including MDA-MB-231 breast cancer cells
and H1792 lung adenocarcinoma cells, finding an IC[50] of approximately
40 nM (Fig. [169]3J). Notably, this potency was absent in MV4-11 cells,
which naturally lack expression of ENPP1 (Fig. [170]3H, Supp.
Fig. [171]S6E).
ISM5939 stabilizes tumor-secreted cGAMP and orchestrates non-cell autonomous
STING activation in antigen-presenting cells
In the tumor microenvironment, tumor cells serve as the predominant
source of active cGAMP secretion^[172]17, which can be taken up by
neighboring APCs to activate the STING pathway and enhance antigen
presentation^[173]71,[174]72. To determine whether ISM5939 could
enhance the accumulation of tumor cell-secreted cGAMP, a panel of human
(MDA-MB-231, ZR-75-30, 786-O, H1792) and murine (4T1, MC38, EMT6,
B16F10) cancer cell lines that naturally express high levels of ENPP1
were transfected with dsDNA to induce cGAS activation. The cells were
treated with a range of concentrations of ISM5939 followed by
extracellular cGAMP measurement. Our results confirmed that ISM5939
potently promotes the accumulation of extracellular cGAMP, in a
dose-dependent manner (Fig. [175]4A, Supp. Fig. [176]S7A). In contrast,
this effect is much weaker in ENPP1-deficient CT26 tumor cells (Supp.
Fig. [177]S7B, C). Consistent with cGAMP accumulation, co-culturing
dsDNA-transfected cancer cells with THP1-Dual cells, a monocytic line
expressing an IFN reporter, revealed that ISM5939 significantly
enhanced dsDNA-induced IFN expression (Fig. [178]4B, C, Supp.
Fig. [179]S7D, E). However, the reporter signal response to ISM5939 was
completely inhibited by the addition of the STING inhibitor H-151
(Fig. [180]4B, C, Supp. Fig. [181]S7D, E). Ablation of IRF3, a key
downstream transcriptional factor of STING signaling, reversed the IFN
reporter signal gain in response to ISM5939 (Fig. [182]4D, E, Supp.
Fig. [183]S7E). These results together demonstrate that ISM5939
effectively activates non-cell autonomous STING signaling in APCs via
stabilizing cancer cell-secreted cGAMP.
Fig. 4. ISM5939 protects tumor-secreted cGAMP and orchestrates non-cell
autonomous STING activation in APCs.
[184]Fig. 4
[185]Open in a new tab
A Dose-dependent accumulation of extracellular cGAMP by ISM5939. Cancer
cell lines as indicated were treated with a gradient of ISM5939
concentrations followed by detection of supernatant cGAMP levels via
ELISA (n = 2 biological replicates, two independent experiments). The
color represents cGAMP concentrations. B, C Type I interferon (IFN)
pathway activation in THP1-Dual cells co-cultured with (B) MDA-MB-231
(B) or (C) ZR-75-30 tumor cells transfected with dsDNA (n = 2
biological replicates, two independent experiments). D, E Type I IFN
pathway activation in IRF3-deficient THP1-dual cells co-cultured with
(D) MDA-MB-231 or (E) ZR-75-30 tumor cells transfected with dsDNA
(n = 2 biological replicates, two independent experiments). F, G 4T1
syngeneic tumor-bearing mice treated with a single dose of ISM5939
(10 mg/kg, p.o.), and plasma and tumor samples assayed for (F) drug
concentrations of ISM5939 and (G) cGAMP (orange) and IFNβ (green) in
tumor tissues (n = 3 biological replicates). H Quantification of tumor
infiltrating immune cells by flow cytometry in 4T1 syngeneic tumor
bearing mice treated with ISM5939 (n = 6 biological replicates). Data
were analyzed by two‐tailed Student’s t‐test. P-value < 0.05 were
shown. All data were represented as mean ± SEM. Source data are
provided as a Source Data file.
To investigate whether this effect indeed occurs in vivo, 4T1 syngeneic
tumor-bearing mice were orally administered a single dose of ISM5939 at
10 mg/kg followed by monitoring both the drug exposure and STING
signaling in tumor tissue. Along with the rapid absorption of ISM5939
into plasma and distribution within tumor tissues (Fig. [186]4F), both
cGAMP and IFN-β levels increased in tumors and were retained for up to
48 h (Fig. [187]4G), indicating sustained activation of STING
signaling. Additionally, long-term treatment with ISM5939 remodeled the
tumor immune microenvironment into a more immunologically active state,
characterized by enhanced polarization of macrophages into M1 status
and augmented production of granzyme B and IFN-γ from CD8^+ T cells
(Fig. [188]4H). These results show that ISM5939 inhibits ENPP1 in vivo,
thereby suppressing cGAMP degradation and subsequently augmenting STING
signaling and antitumor immune programs in TME-resident immune cells.
To directly compare ISM5939 with other ENPP1 inhibitors that have
progressed to phase 1 clinical testing, we synthesized analogs based on
available patents^[189]73–[190]75 and conducted a series of comparative
analyses (Supp. Fig. [191]S8A). ISM5939 demonstrated superior potency
in enzymatic inhibition (Supplementary Fig. [192]8B, [193]C), in
preventing cancer cell-mediated cGAMP degradation (Supplementary
Fig. [194]8D), and in binding affinity for ENPP1 compared to the
synthesized analogs (Supplementary Fig. [195]8E). ISM5939 also
demonstrated potential PK advantage over the other inhibitors with
higher systemic exposure and significantly lower clearance in mice
compared to analogs of RBS2418 and SR-8541A (Supp. Fig. [196]S8F).
Using in vitro-in vivo extrapolation (IVIVE) and allometric scaling
analyses (Supplementary Data. [197]9), we predicted a projected human
dose range of 70 to 358 mg/day, which is significantly lower than the
RBS2418 dosage of 100 to 800 mg twice daily (ClinicalTrials.gov ID:
[198]NCT05270213). Although STF-1084^[199]17, a potential analog of the
ENPP1 inhibitor ANG-3132 (Angarus Therpeutics), which is nearing phase
1 clinical trials, exhibits similar potency to ISM5939, its
administration is restricted to intravenous or subcutaneous routes.
This limitation may impact patient convenience and quality of
life^[200]76,[201]77.
ISM5939 exhibits a wider therapeutic index compared with direct systemic
STING agonists
The development of direct STING agonists has been hampered
substantially by the risk of eliciting systemic cytokine release and
low antitumor efficacy in clinical trials^[202]78. This has led to a
series of studies focused on STING-modulation strategies that aim to
retain efficacy while curbing potential adverse effects, such as the
encapsulation of direct STING agonists in nanoparticles^[203]79. Given
ENPP1’s selective modulation of the STING pathway in the tumor
microenvironment, we anticipated that ISM5939 exhibits a much broader
therapeutic index than traditional STING agonists– that is, exhibiting
less toxicity at similar, effective dosages. To this end, we compared
the impact of ISM5939 and ADU-S100, a CDN STING agonist, on the
function of human peripheral blood mononuclear cells (PBMCs). In
contrast to ADU-S100, which induced a significant release of
inflammatory cytokines such as IFN-α, IL-6, and TNF-α, no such effect
was observed with ISM5939 treatment, even at high concentrations
(Fig. [204]5A). In vivo studies yielded similar results, with systemic
administration of the STING agonists diABZI and MSA-2 at dosages known
to induce modest to optimal antitumor effects^[205]80–[206]83 leading
to a marked increase in inflammatory cytokine levels in mouse plasma.
In contrast, no significant cytokine induction was observed in the
ISM5939-treated group (Fig. [207]5B). To further establish the safety
profile of our compound, we conducted in vitro evaluations of cytokine
induction and 28-day Good Laboratory Practice (GLP) toxicity studies in
Sprague Dawley (SD) rats and Beagle dogs. Consistent with the absence
of cytokine induction in rat and dog PBMCs (Supp. Fig. [208]S9A),
long-term treatment with ISM5939 at varying doses was well-tolerated,
with no obvious alterations in the ratios of T cell subtypes (Supp.
Fig. [209]S9B, C).
Fig. 5. ISM5939 has a wider therapeutic index than direct STING agonists.
[210]Fig. 5
[211]Open in a new tab
A Drug-induced cytokine release from human PBMCs treated with ISM5939
or ADU-S100 for 24 h, followed by cytokine detection in the supernatant
using ELISA. B Cytokine concentrations in plasma obtained from mice
administered with STING agonists (diABZI, MSA-2) or ISM5939 for 7 days
via ELISA (n = 5 biological replicates). C Viability of T cells
isolated from cultured human PBMCs treated with ISM5939 or STING
agonists by flow cytometry (n = 2 biological replicates). The color
represents cell numbers. D–F Proportions and immunophenotypes of immune
cells collected from plasma and tumor samples of MC38 tumor-bearing
mice treated with STING agonists (orange) or ISM5939 (green) by flow
cytometry 24 h after treatment (n = 6 biological replicates). D Live
CD8 + T cells and CD4 + T cells within all live immune cells from
blood. E Live total immune cells (left), CD8^+ (middle) and CD4^+ T
cells (right) within all live immune cells in the tumor. F
IFN-γ + (left) and TNF-α + (right) CD8 + T cells within all
tumor-infiltrating CD8 + T cells. For B and D–F, data were analyzed by
two‐tailed Student’s t‐test. P-value < 0.05 were shown. Data were
represented as mean ± SEM. Source data are provided as a Source Data
file.
In addition, oral ISM5939 did not cause any severe adverse change in
clinical signs, body weight, clinical pathology, or microscopic
examination in 28-day repeat dose toxicity studies in rats
(Supplementary Data [212]2 and [213]5) and dogs (Supplementary
Data [214]6 and [215]8) at doses up to 150 mg/kg/day and 30 mg/kg/day,
respectively. The main adverse findings were limited to clinical
pathology and histopathological findings of kidneys in rats, which had
completely or partially recovered by the conclusion of the recovery
period (Supplementary Data [216]2 and [217]5). No adverse findings were
noted in dogs (Supplementary Data 6 and [218]8). These data support a
strong safety profile of ISM5939, further emphasizing its distinct
advantage over STING agonists in mitigating systemic inflammation and
enhancing patient safety.
Another challenge for direct STING agonists is that STING activation
can induce apoptosis within various cell subtypes, such as lymphocytes,
thus diminishing therapeutic efficacy^[219]84. Considering that
extracellular cGAMP cannot freely diffuse into cells and relies on the
limited expression of cGAMP importers^[220]85,[221]86, we predicted
that modulating cGAMP levels through ISM5939 would be less likely to
cause massive lymphocyte cell death than direct STING agonists. To test
this hypothesis, we compared the impact of ISM5939 and three STING
agonists on T-cell viability. In stark contrast to the STING agonists,
which demonstrated a dose-dependent reduction in CD4^+ and CD8^+ T cell
viability, both ISM5939 and cGAMP itself did not exhibit such an
effect, even at very high concentrations (Fig. [222]5C). To further
validate these findings in vivo, we administered ISM5939 or a single
dose of a STING agonist (MSA-2 or diABZI) at therapeutic levels to mice
bearing MC38 tumors. While treatment with MSA-2 and diABZI resulted in
decreased T cell proportions in circulation (Fig. [223]5D) and in
harvested tumor tissues (Fig. [224]5E), no such reduction was observed
in ISM5939-treated mice (Fig. [225]5D, [226]E). In contrast, both the
IFN-γ and TNF-α producing T cells significantly increased in the tumor
bed upon ISM5939 treatment (Fig. [227]5F, Supp. Fig. [228]S9D),
indicating enhanced antitumor immunity. These data confirmed that
ISM5939 exhibits a wider therapeutic index than conventional STING
agonists, supporting its development as a safer and potentially more
effective option for antitumor therapy.
ISM5939 synergizes with anti-PD(L)1 therapy
We next asked whether ISM5939 could improve the efficacy of
mechanistically orthogonal immune-stimulatory therapy, such as
anti-PD(L)1 checkpoint inhibition. To this end, we assessed the
expression of ENPP1 in tumor tissues and its correlation to the
efficacy of anti-PD-1 therapy in breast cancer patients^[229]87.
Interestingly, we found that non-responders had significantly higher
baseline tumoral expression of ENPP1 compared to responders
(Fig. [230]6A). Additionally, in a proportion of esophageal
adenocarcinoma patients with high level of LRRC8A^[231]88 —a
transporter necessary for both the efflux and influx of
cGAMP^[232]89—non-responders to atezolizumab (anti-PD-L1) had higher
expression of ENPP1 (Fig. [233]6B). These findings suggest ENPP1 may
act as an intrinsic driver or biomarker of immune checkpoint blockade
resistance, particularly in tumor microenvironments with the potential
to utilize cGAMP.
Fig. 6. ISM5939 augments antitumor immunity and synergizes with anti-PD(L)1
immunotherapy.
[234]Fig. 6
[235]Open in a new tab
A Baseline ENPP1 expression levels in Paclitaxel+anti-PD1 responsive or
non-responsive breast cancer patients (GEO: [236]GSE194040). n = 38 for
responders and n = 31 for non-responders. B Baseline ENPP1 expression
levels in anti-PD-L1 responsive or non-responsive esophageal
adenocarcinoma patients with high LRRC8A expression. Data were
extracted from [237]GSE165252. n = 6 for responders and n = 3 for
non-responders. For boxplots, boxes indicate 25th and 75th percentiles,
the lines within boxes mark medians, whiskers extend over 1.5 times the
interquartile range (IQR, the distance from 25th to 75th percentile);
dots represent patient samples. C Combinational effect of ISM5939 and
anti-PD-L1 therapy. MC38 tumor‐bearing C57BL/6 J mice were treated with
anti‐PD‐L1 antibody (3 mg kg^−1, twice per week) alone or in
combination with ISM5939 (30 mg/kg, twice a day) for indicated days
(n = 6 biological replicates per group). D Combinational effect of
ISM5939 and anti-PD1 therapy. MC38 tumor‐bearing C57BL/6 J mice were
treated with anti‐PD1 antibody (5 mg kg^−1, twice per week) alone or in
combination with ISM5939 at the indicated doses (n = 8 biological
replicates per group). E–H Tumor infiltrating immune cells analyzed by
flow cytometry. MC38 tumor‐bearing C57BL/6 J mice were treated with
anti‐PD1 antibody (5 mg kg^−1, twice per week) alone or in combination
with ISM5939 (20 mg kg^−1, twice a day) for 7 consecutive days (n = 6
biological replicates). E, F The proportion of the indicated immune
cells in tumor infiltrating CD45^+ cells; G The ratio of CD8^+ T cells
(left) and CD4^+ T cells (right) to Tregs. H CD69 positive ratios among
the indicated immune subsets. I Tumor growth of CT26 syngeneic tumors.
Tumor bearing mice (n = 6 biological replicates per group) were treated
with anti-PD1 antibody alone or in combination with ISM5939
(20 mg kg^−1, twice a day). Data are represented as mean ± SEM. For (A,
B) and (E–G), data were analyzed by two‐tailed Student’s t‐test. P
value < 0.05 were shown. Data were represented as mean ± SEM. Source
data are provided as a Source Data file.
To translate these clinical findings into therapeutic potential, the
combination of ISM5939 with a suboptimal dose of either anti-PD-1
(5 mg kg^-1) or anti-PD-L1 (3 mg kg^-1) was evaluated in the MC38 tumor
model, which has been shown to recapitulate the microsatellite instable
(MSI) tumor subset, characterized by spontaneous cGAS
activation^[238]90,[239]91. Compared to each monotherapy group, ISM5939
combined with either anti-PD-L1 (Fig. [240]6C) or anti-PD-1 antibodies
(Fig. [241]6D) significantly suppressed tumor growth, with negligible
body weight change (Supp. Figs. [242]S10A-B). The coefficient drug
interaction (CDI) confirmed synergy between ISM5939 with either
anti-PD-L1 (CDI[day20] = 0.40) or anti-PD-1(For ISM5939 at 5 mg kg^-1,
CDI[day16] = 0.80; For ISM5939 at 20 mg kg^−1, CDI[day16] = 0.34).
Tumor-infiltrating lymphocytes were then quantified to determine how
the tumor immune microenvironment was remodeled with treatment. ISM5939
alone and in combination with anti-PD-1 significantly increased the
ratio of M1 to M2 macrophages (Fig. [243]6E). Additionally, the ratios
of CD8^+ T cells to regulatory CD4^+ T cells (Tregs) and effector CD4^+
T cells to Tregs were also enhanced upon ISM5939 treatment
(Fig. [244]6F). Of note, both the infiltration of T cells, especially
of CD8^+ T cells (Fig. [245]6G, Supp. Fig. [246]S10C), and the
expression level of CD69, an activation marker of lymphocytes
(Fig. [247]6H), were highest in mice receiving the combination regimen,
consistent with a reduction in immunosuppressive cell types and an
increase in cytotoxic cell types in the TME. We also conducted efficacy
study in LLC1, a murine lung cancer model that is highly resistant to
anti-PD1 therapy^[248]42. Despite a relatively lower ranking in the
indication prioritization list for lung cancer (Supp. Fig. [249]1B),
this model also demonstrated a significant antitumor effect for the
combination of ISM5939 with an anti-PD1 antibody (Supp.
Fig. [250]S10D).
To identify mechanisms driving response and patient populations
potentially most likely to benefit from ENPP1 inhibition, we studied
the efficacy of ISM5939 against CT26 tumors, a validated microsatellite
stable (MSS) CRC model with low ENPP1 and cGAS expression (Supp.
Fig. [251]S10E). We speculated that low basal cGAS activation or the
lack of ENPP1 in the tumor would make this model relatively insensitive
to further cGAMP-mediated immune activation and therefore ISM5939
combinational synergism. As expected, while anti-PD-1 alone achieved
some tumor inhibition, the addition of ISM5939 failed to show a
significant therapeutic advantage (Fig. [252]6I, Supp. Fig [253]S10F).
Altogether, these results demonstrated ISM5939 as a potentially
effective and safe combinatorial strategy to sensitize tumors to
anti-PD(L)1 therapy, especially in tumors with high expression of the
ENPP1-cGAS axis.
ISM5939 synergizes with genome-destabilizing chemotherapeutic drugs
Chemotherapy remains the mainstay of frontline cancer therapy, whose
induction of DNA damage and chromosome instability not only kills tumor
cells but also triggers immune-stimulating signal cascades, including
activation of cGAS due to increased cytosolic dsDNA^[254]92. We
hypothesized that ISM5939 has the potential to synergize with
chemotherapeutics by boosting activation of the cGAS-STING pathway in
the TME mediated by tumor-secreted cGAMP. To explore the relationship
between ENPP1 levels and chemotherapy responses, we analyzed a diverse
collection of clinical cohorts covering multiple solid tumor
types^[255]87,[256]88,[257]93–[258]96. These cohorts included patient
responses to chemotherapy along with gene expression profiles in tumor
tissues (Fig. [259]7A). In breast cancer cohorts, ENPP1 expression was
consistently higher in chemotherapy non-responsive patients, regardless
of the presence of estrogen receptor (ER) and human epidermal growth
factor receptor 2 (HER2). Similarly, chemotherapy non-responsive CRC
patients exhibited significantly higher ENPP1 level compared to
responders. Notably, ovarian cancer patients showed significantly
increased levels of ENPP1 following chemotherapy compared to their
levels prior to treatment (Supp. Fig. [260]S11A), reinforcing the role
of ENPP1 as an underlying driver of resistance to chemotherapy. Given
these results, we next evaluated the effect of ISM5939 in combination
with multiple chemotherapeutic agents on cGAMP accumulation. As
expected, in two human (MDA-MB-231 and HCC1395) and one murine (EMT6)
breast cancer cell lines, the concurrent use of ISM5939 with either
cisplatin or paclitaxel resulted in significant cGAMP accumulation in
culture medium (Fig. [261]7B, Supp. Fig. [262]S11B). Similar results
were obtained for the combination of ISM5939 with either oxaliplatin or
SN-38, the active metabolite of irinotecan, in murine colorectal cancer
cells (Supp. Fig. [263]S11C).
Fig. 7. The combination of ISM5939 with chemotherapy maximizes cGAMP release
and synergistically inhibits tumor growth.
[264]Fig. 7
[265]Open in a new tab
A Baseline ENPP1 expression levels in chemotherapy responsive or
non-responsive TNBC, HER2 positive breast cancer, ER positive breast
cancer and colorectal cancer patients. Data were extracted from
[266]GSE22513, [267]GSE50948, [268]GSE22093 and [269]GSE28702. For
TNBC, n = 8 for responders and n = 20 for non-responders; for HER2
positive breast cancer, n = 22 for responders and n = 71 for
non-responders; for ER positive breast cancer, n = 10 for responders
and n = 32 for non-responders; for CRC, n = 42 for responders and
n = 41 for non-responders. For boxplots, boxes indicate 25th and 75th
percentiles, the lines within boxes mark medians, whiskers extend over
1.5 times the interquartile range (IQR, the distance from 25th to 75th
percentile); dots represent patient samples. B Maximal cGAMP release
induced by combined use of cisplatin or paclitaxel with ISM5939 (n = 2
biological replicates, two independent experiments). C–E EMT6 syngeneic
tumor bearing mice treated with cisplatin and ISM5939 alone or in
combination for 2 weeks to profile (C) differential tumor growth and
(D, E) endpoint tumor sample bulk RNA sequencing showing (D) pathway
enrichment (The color represents adjust P-value) and (E) deconvoluted
immune cell populations in the tumor microenvironment post-treatment
(n = 6 biological replicates for tumor growth detection, n = 3
biological replicates for RNA sequencing). Color represents z-scores of
cell population, values range from red (low population) to green (high
population). F Tumor growth of 4T1 orthotopic tumor-bearing mice
treated with docetaxel and ISM5939 alone or in combination. G In vitro
release of cGAMP by BRCA-proficient (MDA-MB-231, ID8) or BRCA-deficient
(MDA-MB-436, UWB1.289) cells treated with Olaparib or ISM5939 alone or
in combination via ELISA (n = 2 biological replicates). H Tumor growth
of 4T1 orthotopic tumor-bearing mice treated with Olaparib and ISM5939
alone or in combination (n = 7 biological replicates). Data were
represented as mean ± SEM. For (A), (C), (F) and (H), data were
analyzed by two‐tailed Student’s t‐test. P value < 0.05 were shown.
Source data are provided as a Source Data file.
Building on these promising in vitro results, we then explored the
therapeutic benefits of ISM5939 with chemotherapeutic agents in vivo,
using the EMT6 TNBC model, which naturally bears high ENPP1 levels
(Supp. Fig. [270]S7C). While either ISM5939 or cisplatin alone achieved
modest tumor inhibition, their combination significantly synergized
(CDI = 0.70), rendering a superior tumor inhibition (Fig. [271]7C) with
good tolerability (Supp. Fig. [272]S11D). Tumors harvested from mice
treated with the combination regimen showed the highest levels of cGAMP
compared to those from untreated or monotherapy-only groups, as
measured by LC-MS (Supp. Fig. [273]S11E). To further investigate how
the tumor immune microenvironment was remodeled, tumor tissues from all
groups were subjected to bulk RNA sequencing followed by pathway
enrichment analysis. The combination of ISM5939 and cisplatin
demonstrated a pronounced enhancement of both innate and adaptive
immune pathways, consistent with the increased tumoral cGAMP
accumulation. Key features included the interferon gamma response,
inflammation, and interferon-alpha response (Fig. [274]7D). Cell type
deconvolution pointed to a concomitant increase of CD8^+ T cells, M1
macrophages, and dendritic cells, along with a reduction in M2
macrophages, effects uniquely observed in the combination group
(Fig. [275]7E). Extending these findings, we observed similar results
with the combined use of ISM5939 and docetaxel, which showed a superior
synergistic effect in retarding tumor growth in a 4T1 orthotopic breast
cancer model (CDI = 0.86, Fig. [276]7F). Interestingly, despite the
lack of ENPP1 expression in CT26 tumor cells, the combination of
ISM5939 with oxaliplatin, the standard-of-care treatment for colorectal
cancers, resulted in a moderate reduction in tumor burden (Supp.
Fig. [277]S11F), suggesting that inhibition of non-tumor–derived ENPP1
contributes to the efficacy of chemotherapeutics. Taken together, these
data suggest that administration of ISM5939 is a promising strategy to
enhance the efficacy of chemotherapeutic agents.
Finally, considering the capability of Poly (ADP-ribose) polymerase
(PARP) inhibitors to induce cGAS activation^[278]97, we explored the
potential of combining ISM5939 with olaparib. Using both
BRCA1/2-proficient and -deficient breast and ovarian cancer cell lines,
we found that the concurrent use of ISM5939 with olaparib resulted in
maximal cGAMP accumulation in the extracellular space, regardless of
the mutation status of BRCA1/2 (Fig. [279]7G). In accordance with these
findings, ISM5939 and olaparib exhibited striking synergy in a 4T1
orthotopic tumor model in vivo (CDI = 0.93, Fig. [280]7H).
Discussion
Modulating the STING pathway effectively and safely to enhance
antitumor immunity remains a challenge for targeted drug development.
In this study, we employed PandaOmics, the commercially available
target-discovery platform, and integrated patient-based multi-omic data
to highlight ENPP1 as a STING-modulating target among broad
hard-to-treat solid tumors. Leveraging the Chemistry42 drug design
platform, we also created ISM5939, an AI-aided oral ENPP1 inhibitor,
which demonstrates favorable bioavailability and high potency in
enhancing non-cell autonomous STING signaling in the TME (Fig. [281]8).
In multiple murine tumor models, ISM5939 synergized well with
anti-PD(L)1 antibodies and genome-destabilizing chemotherapy regimens
in inhibiting tumor growth with good tolerance. Our findings thus
provide ISM5939 as a cutting-edge STING modulator for cancer therapy
with the potential of combining with standard-of-care treatments,
paving a path for immunotherapy advancements.
Fig. 8. Diagram illustrating the mechanism through which ISM5939 potentiates
antitumor immune responses.
[282]Fig. 8
[283]Open in a new tab
ISM5939 facilitates the accumulation of extracellular cGAMP by blocking
ENPP1-dependent degradation, thereby triggering the cGAS-STING
signaling pathway in antigen-presenting cells (APCs). This activation
effectively enhances the activity of CD8 + T cells. Additionally,
ISM5939 diminishes adenosine production by inhibiting ENPP1-mediated
ATP hydrolysis, potentially mitigating the suppression of
tumor-infiltrating T cells.
Despite the growing interest in ENPP1 as a tumor target, it remains
unclear which patients, beyond those with breast cancer, would benefit
from ENPP1 inhibition. Leveraging the PandaOmics platform, we have
expanded the potential application of ENPP1 targeting strategies to a
wide range of hard-to-treat solid tumors, including gastric,
colorectal, esophageal, ovarian, and lung carcinomas. While the
combination of anti-PD(L)1 therapy or chemotherapy with ISM5939 has
demonstrated significant potency in syngeneic tumor models, the optimal
combination for a given patient can vary based on cancer type and
certain key features. Because neither ISM5939 nor anti-PD(L)1
stimulates the cGAS pathway on their own, we speculate their
combination is more suitable for cancers with microsatellite
instability-high (MSI-H), chromosomal instability, or ATM
mutations^[284]98,[285]99. These cancers exhibit cytosolic dsDNA
accumulation and endogenous cGAS activation, facilitating abundant
cGAMP release. Additionally, our analysis of esophageal adenocarcinoma
patients revealed that higher ENPP1 levels are enriched in anti-PD-1
non-responders, but only in those who highly express the cGAMP
transporter, LRRC8A. This suggests that the expression level of LRRC8A
can serve as a predictive biomarker for selecting patients for this
treatment, potentially paving the way for the development of a
companion diagnostic tool in this therapeutic context. Chemotherapy, on
the other hand, elicits broad cytotoxic effects and offers the
advantage of fewer biomarker requirements and less stringent patient
selection criteria compared to immunotherapy. Complementing
chemotherapy’s mechanism of action, ISM5939 enhances overall efficacy
regardless of specific genetic or molecular markers. This flexibility
makes the ISM5939-chemotherapy combination a potentially more
universally applicable treatment option, increasing its relevance for a
wider patient population with diverse tumor profiles.
Our ENPP1 inhibitor offers a wider safety margin compared to direct
STING agonists. Systemic administration of non-CDN STING agonists often
leads to systemic inflammatory responses, such as cytokine storms,
which restrict their clinical application. However, in the context of
cancer patients, malignant cells serve as the predominant source of
extracellular cGAMP due to dsDNA accumulation in the cytosol caused by
endogenous DNA damage and genome instability^[286]17,[287]100. The
effects of tumor-derived cGAMP have been shown to be spatially
restricted to the TME, which presents an opportunity for exploiting
this mechanism therapeutically. It has been shown that depletion of
CD8^+ T cells in tumor-bearing mice ablates the abscopal antitumor
activity of intratumoral cGAMP injection but, importantly, not its
proximal activity^[288]101, suggesting that non-CD8^+ T cell-mediated
antitumor effects of cGAMP are restricted to the local cGAMP-high
microenvironment. Other studies have found intercellular transfer of
cGAMP mediated by direct cell-cell connexin channels dependent on cell
density^[289]102. Together with our safety profiling experiments
finding no systemic cytokine induction or changes in circulating T cell
phenotypes, this supports our hypothesis that ENPP1 inhibition allows
for the localized activation of STING in tumors, thereby avoiding the
risk of systemic inflammation.
Moreover, STING agonists often face limited clinical efficacy, partly
due to the pleiotropic functions of STING across various immune cell
types. While activation of STING in APCs is beneficial for licensing
antitumor T cells, intrinsic STING activation in T cells can induce
their apoptosis, hindering effective antitumor responses^[290]84. In
contrast to most synthesized STING agonists, the diffusion of
endogenous cGAMP into immune cells of the TME is gatekept by limited
expression of cGAMP transporters, thus avoiding unrestricted STING
activation within the TME. Particularly, our in vitro head-to-head
assessment demonstrated that cGAMP had a negligible impact on the
viability of CD8^+ and CD4^+ T cells, even at high concentrations, in
stark contrast to other synthesized STING agonists that adversely
affect T cell viability. In vivo treatment in diverse animal models
also supports the safety of ISM5939 for endogenous levels of T cells
and circulating cytokines. These findings confirm that enhancing
extracellular cGAMP levels via ENPP1 inhibition is a feasible strategy
to induce non-cell autonomous STING activation in APCs while preserving
the viability of antitumor T cells.
Although our work describes an ENPP1 inhibitor that acts broadly and
safely across solid tumors, challenges remain to fully characterize its
application for use in the clinic. While the number of indications
supported by our PandaOmics analysis and experimental testing is
encouraging, understanding why some cancer types express ENPP1 highly
but have no association between ENPP1 expression and STING-type I IFN
response, and even have negative prognostic scoring for STING-IFN
pathway expression, such as sarcoma and uterine carcinosarcoma, is
important to identify determinants of response to STING agonism and
immunotherapy more broadly. Because we identified the cGAMP transporter
LRRC8A as a potential biomarker for ISM5939 use in patients
non-responsive to anti-PD-L1 monotherapy, it can be reasoned that
related mechanisms can render patients resistant to ISM5939. Mutations
in LRRC8A have been associated with tumorigenesis and resistance to
chemotherapeutics in various cancers, including cervical^[291]103,
ovarian, alveolar^[292]104, mammary^[293]105, and gastric
cancers^[294]106, so elucidating how mutations in or expression of the
various components of the cGAS-STING pathway, including LRRC8A and
ENPP1 itself, affect response to ENPP1 inhibition is crucial for
predicting response to treatment and identifying patients who are most
likely to benefit. Our work began to investigate potential drivers of
resistance by showing lower efficacy against ENPP1-low CT26 cells in
combination with anti-PD-L1 in vivo, but future work will need to
carefully profile potential mechanisms of resistance and incorporate
the significant heterogeneity of TME cell types to pinpoint the most
informative cell types and biomarkers for response.
So far, no ENPP1 inhibitors have been approved for clinical use, but
three have progressed to clinical trials, including RBS2418
(Riboscience), [295]TXN10128 (Txinno Bioscience), and SR-8541A
(Stingray Therapeutics). We therefore conducted head-to-head comparison
with best-guess molecules based on patent literature, as their
structures have not been disclosed yet. ISM5939 demonstrates
significant advantage over other current ENPP1 inhibitors in binding
affinity, potency, and PK profile, making ISM5939 a compelling
candidate for further clinical development likely to outperform
existing ENPP1 inhibitors. As such, the FDA has approved our dose
design (IND: 172399) for a Phase 1 clinical trial of ISM5939.
In conclusion, the development and application of ENPP1 inhibitors,
particularly ISM5939, offers a promising direction in cancer
immunotherapy. By expanding therapeutic indications, ensuring localized
immune activation with minimal systemic toxicity, and providing a
robust, orally bioavailable therapeutic option, our ENPP1 inhibitor
ISM5939 has the potential to revolutionize cancer treatment,
particularly when combined with existing therapies such as anti-PD(L)1
and chemotherapy.
Methods
Ethical statement
All animal experiments followed the National Institutes of Health Guide
for the Care and Use of Laboratory Animals. For in vivo efficacy
studies in mice, the protocols and procedures were approved by the
Institutional Animal Care and Use Committee (IACUC) of Wuxi AppTec
(Code: AUF# 105-7), Pharmaron Beijing Co., Ltd. (Code:
ON-SYN-06012023), Biometas (Code: BMM-23001061) and HD Biosciences
(Code: AUF# 105-7). For all tumor-bearing mice studied in this article,
the maximal allowable tumor size/burden was defined as an individual
tumor volume of ≤2000 mm³. Throughout all in vivo studies, these limits
were strictly adhered to.
For in vivo PK studies, the protocols and procedures were approved by
the IACUC of Shanghai Medicilon Inc. (For Mice PK, Code:
09013-21275/21277/21299/22372/22446/22451/ 22457/22504/24792; For Rat
PK, Code: 09013-22545; For Dog PK, Code: 09013-22638).
For 28-day GLP, an animal care and use application for studies was
reviewed and approved by Pharmaron TSP IACUC before the initiation of
study (For dog GLP, Code: No.: 23-333; For Rat GLP. Code: No.: 23-332).
PandaOmics and TCGA indication expansion analysis
Indication prioritization tools embedded in PandaOmics (Insilico
Medicine) was applied to rank indications for ENPP1 inhibition within
the realm of oncology therapy. The target, ENPP1, was used as the input
gene in the Indication Prioritization tool. The composite score
encompassed models for network neighbors, causal inference, pathways,
relevance, expression and text-based information (evidence, attention
score, and trend), resulting in the top 10 ranking indications regarded
as the priority indications.
The expression of ENPP1, the STING-type I IFN score, and patient
clinical information were downloaded from TCGA Pan-Cancer (PANCAN) in
UCSC Xena. The correlation between the ENPP1 and STING-type I IFN score
was calculated using Pearson correlation for each cancer type.
Prognostic analyses were conducted using the Cox proportional hazards
regression model. The analysis was performed using the ‘survival’
package in R software version 4.1.2. The model was fitted to assess the
association between STING-type I IFN score and overall survival
(OS)/progression-free survival (PFS). Hazard ratios (HR) with 95%
confidence intervals (CI) were calculated to estimate the risk of
events. Statistical significance was considered at a p-value < 0.05.
The proportional hazards assumption was tested using the Schoenfeld
residuals method.
Correlation analysis between ENPP1 expression and inflammatory signatures
Previously published single-cell RNA-seq data from patients with TNBC
(GEO: [296]GSE176078)^[297]107, GC (Omix: OMIX001073)^[298]108, and CRC
(GEO: [299]GSE132465)^[300]109 were downloaded from their respective
repositories. Samples from each dataset were sequenced using the 10x
Genomics Chromium platform. Downstream analysis was performed using the
Seurat package (version 4.3.0) in R. The data underwent quality
control, scaling, and normalization according to protocols described by
the original authors. Cell annotations were determined by the original
authors.
TNBC: ENPP1 expression in tumor cells was calculated as the mean
expression of ENPP1 in the ‘CAFs’, ‘Cancer Epithelial’, ‘PVL’, and
‘Normal Epithelial’ clusters within each sample. CXCL10 expression in
DCs was calculated as the mean expression of CXCL10 in the
‘Myeloid_c0_DC_LAMP3’ cluster within each sample. The cell percentage
of cDC2 was calculated as the percentage of ‘Myeloid_c11_cDC2_CD1C’
cells among all quality-controlled cells within each sample.
GC: ENPP1 expression in tumor cells was calculated as the mean
expression of ENPP1 in the ‘Endocrine cells’, ‘Epithelial cells’, and
‘Fibroblasts’ clusters within each sample. The ISG signature in cDC2
cells was calculated as the mean expression of ISG signature
genes^[301]50,[302]51 in the ‘cDC2_CD1C’ cluster within each sample.
CRC: ENPP1 expression in tumor cells was calculated as the mean
expression of ENPP1 in the ‘Epithelial cells’ and ‘Stromal cells’
clusters within each sample. The ISG signature in cDC cells was
calculated as the mean expression of ISG signature genes in the ‘cDC’
cluster within each sample.
Pearson correlation analysis was performed using the ‘cor.test’
function in R.
Spatial Transcriptomics Analysis
Spatial transcriptomics data for colorectal cancer (CRC,
[303]https://zenodo.org/records/7760264, slide
SN84_A120838_Rep2;)^[304]56, gastric cancer (GC,
[305]GSE251950)^[306]58, and triple-negative breast cancer (TNBC,
[307]GSE210616)^[308]57 were obtained from publicly available
repositories, and single-cell RNA-seq data was obtained for TNBC
([309]GSE176078)^[310]107 and GC ([311]GSE183904)^[312]110.
The scRNA-seq data sets were processed using Seurat. Quality control
steps included filtering out cells with less than 500 nFeature_RNA and
more than 3000 nFeature_RNA. Mitochondrial gene expression was used to
identify and remove low-quality or dying cells, with a cutoff set at
15% mitochondrial gene content. Gene expression was normalized using a
global-scaling normalization method, where the counts were
log-transformed and scaled to 10,000 transcripts per cell. To address
batch effects arising from different samples, we employed
Harmony^[313]111 for batch correction. The integrated analysis was
performed utilizing the Harmony function in Seurat. Briefly, the
principal component analysis (PCA) space was computed using the 3,000
most variable genes, and Harmony was applied to the PCA embeddings to
align datasets and reduce batch-associated variances. Following batch
correction, the Harmony-corrected embeddings were used for clustering
analysis. The Uniform Manifold Approximation and Projection (UMAP)
method was utilized for dimensionality reduction and visualization of
the single-cell data. Clustering was performed using the Louvain
algorithm, implemented in the FindNeighbors and FindClusters functions
in Seurat, with a significance resolution parameter tuned to ensure
optimal clustering granularity.
For spatial transcriptomics, data preprocessing was conducted using the
scanpy package. For each individual sample, we filtered out spots for
which the number of UMI counts detected were below 1000 or above 40000.
In addition, spots containing a fraction of more than 0.2 mitochondrial
genes were not considered in the analysis. We normalized the UMI counts
from the remaining spots using global-scaling normalization. Spatial
transcriptomics deconvolution was performed using the Cell2Location
method or RCTD^[314]112. Spatial distribution of Interferon-Stimulated
Genes (ISGs), ENPP1, and immune cellular populations were visualized
using scanpy.
In vitro ENPP1 inhibition assay
The inhibitory effect of ISM5939 on ENPP1 enzyme activity at two
different pH conditions (6.5 and 7.4) was evaluated using the AMP-Glo
luminescence-based assay (Promega). Recombinant human ENPP1 protein
(R&D) or purified mouse/dog/monkey ENPP1 protein (Biometas, see below)
was incubated with a final concentration of 2.5 µM of either ATP
(Promega) or 2’3’-cGAMP (InvivoGen) in assay buffer adjusted to the
respective pH. ISM5939 was added at varying concentrations diluted in
DMSO, and the reaction was initiated by the addition of AMP-Glo Reagent
(Promega) followed by Kinase Detection Reagent (Promega), according to
the kit instructions. ENPP-1-IN-1 (WO2019046778A1), the reported ENPP1
inhibitor was used as the positive control. Luminescence signal was
measured using a microplate reader (BMG LABTECH), and percentage of
inhibition was calculated relative to a control group without the test
compound. IC50 values were determined using nonlinear regression
analysis in GraphPad Prism 8 software.
Expression and purification of mouse, dog or monkey ENPP1 protein
For protein expression, Expi293F cells (Invitrogen, A14257) were
cultured at 1.5 × 10^6 cells/ml and passaged 24 h before transfection
in a shaking incubator set to 120 rpm, 37 °C, and 8% CO2. On the day of
transfection, the cell density was increased to 3×10^6 cells/ml with
fresh OPM-293 CD05 medium (OPM, 81075-001). The respective DNA plasmids
for mouse (pcDNA3.4-kappa-mENPP1(80-906)-His), dog
(pcDNA3.4-kappa-8His-dog ENPP2(144-239)-dog
ENPP1(182-582)-ENPP2(627-958)), and monkey (pcDNA3.4-kappa-8His-cyno
ENPP2(149-237)-cyno ENPP1(189-589)-ENPP2(678-1034)) were diluted in
Opti-MEM medium (Invitrogen, 31985088), filtered through a 0.22μm
filter, and used at a final concentration of 1ug/ml. PEIMAX
(Polyscience, 24765-1) was diluted in Opti-MEM and mixed with the DNA
at a 1:4 DNA/PEI (w:w) ratio, incubated for 20 minutes at room
temperature before addition to the cell culture.
Twenty-four hours post-transfection, 1/20th of the culture volume of
10% peptone (Sigma, P0521-1KG) was added to the flask, and glucose
(Sigma, G7528-5KG) was supplemented at 4 g/L when the supernatant
glucose concentration fell below 2 g/L. Cell growth and viability were
monitored with Countstar six days after transfection. The cells were
then harvested by centrifugation at 4000 rpm for 40 minutes in shaking
flasks.
The purification process included affinity chromatography, size
exclusion chromatography, and concentration steps. In affinity
chromatography, a pre-packed 5 mL Ni-Excel column (Cytiva, 17371203)
was pre-equilibrated with 4 column volumes of PBS at pH 7.4, loaded
with the culture supernatant, and washed with PBS at pH 7.4 containing
20 mM imidazole. The protein was eluted with the same buffer containing
500 mM imidazole, and purity was assessed by SDS-PAGE. For size
exclusion chromatography, a 120 ml Superdex 200 column (Cytiva,
17-1043-01) was pre-equilibrated with two column volumes of PBS at pH
7.4. The elution fractions from the Ni column were concentrated using a
30 kDa cut-off concentrator at 4000RPM, and 2 mL of the sample was
injected into the column. Purity was again evaluated by SDS-PAGE.
Finally, selected fractions were combined and concentrated to the
desired concentration using centrifugation at 4000 rpm with Amicon
Ultra centrifugal filters (Millipore, MWCO 30 kDa, UFC9030).
Novelty calculation
The compound ISM7516 was represented as a Morgan fingerprint and
compared against the fingerprints of molecules in ChEMBL dataset. For
each molecule in ChEMBL, the Tanimoto Coefficient was calculated to
determine the similarity between the fingerprint of ISM7516 and the
molecule from the dataset. The Tanimoto Coefficient is defined as:
[MATH: TanimotoA,B=
∣A∩B∣A+B−∣A∩B∣ :MATH]
1
[MATH: A∩B :MATH]
is the number of bits that are shared between the two sets being
compared; A is the number of bits in fingerprint of ISM7516. B is the
number of bits in fingerprint of the molecule from ChEMBL. The novelty
score is determined as:
[MATH: Novelty=1−TanimotoA,Bmax :MATH]
2
[MATH: Tanimoto(A,
B)max
:MATH]
represents the highest level of similarity found between ISM7516 and
compound in ChEMBL.
Molecular docking
Molecular docking studies were carried out using the Molecular
Operating Environment (MOE) (2022.02; Chemical Computing Group
ULC,1010Sherbrooke St. West, Suite #910, Montreal, QC, Canada, H3A2R7,
2022). The active site of the protein was defined as a sphere with a
5 Å radius around the co-crystalized inhibitor. The template-based
docking was employed to position the compounds within the protein’s
active site. The optimal pose was selected manually considering both
the similarity to the reference ligand binding mode and the scoring
provided by the software’s default scoring function.
Medicinal chemistry analysis of ISM5939
CYP3A4 induction
55,000 hepatocytes were plated per well in a collagen I-coated 96-well
plate for 24 h. Fresh dosing medium with the test compounds at 10 µM or
the appropriate control was added to the cells. The positive control
inducer for CYP3A4 was rifampicin (10 µM), flumazenil (30 µM) was used
as the negative control compound, and DMSO (0.1%) was used as vehicle
control. The dosing medium was renewed every 24 h, and all incubations
were performed in triplicate. After 72-h of treatment, cell viability
was determined using a Cell Titer-Fluor™ cell viability assay kit.
Quantitative PCR was performed with the Cells-to-Ct kit (Thermo Fisher)
to detect mRNA expression of CYP3A4. Gene expression was calculated as
2^Ct(ACTB)-Ct(induced).
The percentage of positive control was calculated as following:
[MATH: Percentageofpositivecontrol(%)=Fold
−change<
mstyle>induced
Fold−chan
gepositive
control×100<
/mn> :MATH]
hERG channel inhibition
CHO-hERG cells were cultured in a humidified incubator at 37 °C and 5%
CO[2]. 24 to 48 h prior to electrophysiological recordings, the cells
were plated on glass cover slips placed in culture dishes and
maintained under the same incubation and media conditions. The HEKA EPC
10 USB patch clamp amplifier (HEKA Elektronik, Germany) was used in the
whole-cell recording. A cover slip with single CHO hERG cells on the
surface was removed and placed into a continuously perfused
(approximately 1 ml/minute) recording chamber mounted on an inverted
microscope. hERG channel currents were recorded from single cells using
standard whole-cell recording techniques. The cells were voltage
clamped at a holding potential of −80 mV. The hERG current was
activated by depolarizing at +20 mV for 5 sec, after which the current
was taken back to −50 mV for 5 sec to remove the inactivation and
observe the deactivating tail current. The K+ tail current through hERG
channels observed during this step was allowed to stabilize under
continuous bath perfusion. Cells were then superfused with drug until
steady state block was achieved. Steady state was considered reached
when three consecutive super-imposable current records were collected.
At this point, cells were once again superfused with extracellular
solution until the current amplitude returned to values close to those
measured before application of drug. One or more compounds or
concentrations of drugs were tested on each cell with washout in
between each drug application. Cisapride was used in the experiments to
ensure the normal response and good quality of the hERG cells. Data
were analyzed using Assay Software provided by Patchmaster and Graphpad
Prism.
Kinetic solubility
10 μL of 10 mM solution of test and control compounds in DMSO was added
into lower chambers of Whatman mini-uniprep vials. 490 μL of 50 mM PB
(pH 7.4) was added into lower chambers of the Whatman mini-uniprep
vials. Solubility samples were vortexed for at least 2 minutes.
Mini-uniprep vials were placed on a shaker for 24 h at room temperature
at the speed of 800 rpm. Vials were then centrifuged for 20 minutes at
4000 rpm. Mini-unipreps were compressed to prepare the filtrates for
injection into HPLC-UV & UPLC-UV systems to calculate the
concentrations.
Caco-2 permeability assay
Caco-2 cells (ATCC) were seeded onto polyethylene membranes (PET) in
96-well Corning Insert plates at 1 ×105 cells/ cm^2. Media was
refreshed every 4-5 days until a confluent cell monolayer was reached
at 21 to 28 days. Compounds were tested at 2.00 μM bi-directionally in
duplicate. Digoxin was tested at 10.0 μM bi-directionally in duplicate,
and nadolol and metoprolol were tested at 2.00 μM in A to B direction
in duplicate. The transport buffer was HBSS with 10.0 mM HEPES at pH
7.40 ± 0.05, and final DMSO concentration was adjusted to less than 1%.
Concentrations of test and control compounds in starting, donor, and
receiver solutions were quantified by LC-MS/MS using peak area ratio of
analyte/internal standard. Following the transport assay, lucifer
yellow rejection assay was applied to determine the Caco-2 cell
monolayer integrity.
Liver microsome metabolic stability assay
Microsomes (final concentration 0.5 mg/mL), 100 mM phosphate buffer pH
7.4, and compound (final concentration 1 μM) were added to the assay
plate and allowed to preincubate for 10 min at 37 °C. The reaction was
initiated by the addition of NADPH (final concentration 1 mM), and the
plate was constantly shaken at 37 °C. After 0, 5, 15, 30, 45, and
60 minutes, aliquots were taken, and the reaction was quenched using
cold acetonitrile. The samples were shaken for 10 min, then centrifuged
at 4000 rpm for 20 min at 4 °C and analyzed by LC-MS/MS. The in vitro
intrinsic clearance was calculated from the rate of compound
disappearance.
Hepatocyte metabolic stability assay
All incubations were carried out in duplicate in a 95% humidified, 5%
CO2 incubator at 37 °C, and the final volume of the mixture was 200 μL
containing test compound (1 μM) and human/dog/rat/mouse hepatocytes
(0.5 × 10^6 cells/mL) in Williams’ Medium E. After incubations, 25 μL
of each sample at each time point (15, 30, 60 and 90 min) were
transferred to wells containing 125 μL of ice-cold stop solution (ACN
containing 200 ng/mL tolbutamide and 200 ng/mL labetalol as internal
standards). For T0, 125 μL/well of ACN stop solution were added prior
to the addition of cell suspensions. Medium Control (MC) sample plates
(labeled as T0-MC and T90-MC) are prepared at T0 and T90 by adding the
same components to each well except cell suspensions. The plates were
then sealed and shaken for 10 minutes prior to centrifugation at
4000 rpm and 4 °C for 20 minutes. The resulting supernatant was diluted
1:3 with pure water and sealed and shaken for 10 minutes prior to
LC-MS/MS analysis.
Plasma protein binding assay
The extent of protein binding was determined by equilibrium dialysis
with a 96-well device. The test compound was spiked into CD-1 mouse, SD
rat, Beagle dog and human plasma and the final concentration was 2 μM.
100 μL of plasma samples were dialyzed against an equal volume of
dialysis buffer (Phosphate Buffered Saline, 100 mM, pH 7.4 ± 0.1) for
4 h with 5% CO[2] at 37 °C. Triplicate incubations were performed. An
aliquot of plasma sample was harvested before the incubation and used
as T0 samples for recovery calculation. After incubation, 50 μL of
samples were transferred from the plasma side as well as the buffer
side into new 96-well plates. Each sample was added with equal volume
of opposite blank matrix (buffer or plasma) to reach a final volume of
100 μL with 1:1 ratio of plasma: Dialysis Buffer volume in each well.
300 μL stop solution (Acetonitrile/Methanol (50:50, v:v)) containing
internal standards (tolbutamide at 200 ng/mL) was then added. The
resulting supernatant was diluted 1:1 with pure water and sealed and
shaken for 10 minutes prior to LC-MS/MS (API 4000, AB Sciex, MA, USA)
analysis.
[MATH: %Unbound=100×
[F]/
[T], :MATH]
[MATH: %Bound=100−%Unbound, :MATH]
[MATH: %Recovery=100×([F]+
[T])/[T0] :MATH]
[MATH: [F]=Concentrationofcompoundinfreefraction(fromreceiverchamber) :MATH]
[MATH: [T]=Concentrationofcompoundintotalplasmafraction(fromdonorchamber) :MATH]
[MATH: [T0]=
ConcentrationofcompoundinT0samples(beforedialysis) :MATH]
CYP Inhibition
Pooled human liver microsomes supplied by Corning (Cat No. 452117) were
used. CYP isoform and substrate-specific probe reactions were employed
as previously reported: Phenacetin O-deethylation for CYP1A2,
diclofenac 4-hydroxylation for CYP2C9, S-mephenytoin 4-hydroxylation
for CYP2C19, dextromethorphan O-demethylation for CYP2D6, midazolam
1′-hydroxylation for CYP3A4. The incubation systems consisted of 100 mM
potassium phosphate buffer (pH 7.4), 33 mM MgCl[2], 10 mM NADPH and HLM
(0.253 mg/mL). Substrates, compounds/positive controls, HLM, and NADPH
solutions were added to corresponding wells. The plate was mixed and
incubated for 10 minutes for all CYPs at 37 °C. The reaction was
terminated by adding 400 uL of cold stop solution (200 ng/ml
Tolbutamide and Labetalol in CAN). Samples were centrifuged at 4000 rpm
for 20 minutes to precipitate protein. 200 uL supernatant was
transferred to 100 uL of HPLC water and shaken for 10 minutes. Samples
were analyzed by LC/MS/MS. XL fit was used to plot the percent of
vehicle control versus the test compound concentrations, and for
non-linear regression analysis of the data.
In vivo mouse and rat PK studies
Male CD-1 mice (at the age of 6 to 8 weeks, Shanghai Medicilon Inc.,
IACUC No.: 09013-21275/21277/21299/22372/22446/22451/22457/22504/24792)
(n = 3) and male SD rats (at the age of 6 to 8 weeks, Shanghai
Medicilon Inc., IACUC No.: 09013-22545) (n = 3) received either a
single intravenous (bolus) injection or single oral administration (by
gavage) of the compound in a cocktail preparation. Doses of 1 mg/kg
(per compound, intravenous administration) and 5 mg/kg (per compound,
perorally) were given as solutions in different formulations as needed,
e.g. 5% DMSO + 95% (10% HP-β-CD in saline). Consecutive blood samples
were taken via submandibular vein or other suitable vein from n = 3
animals per route of administration after 0.083 (iv only), 0.25, 0.5,
1, 2, 4, 6 (po only), 8 and 24 h and were further processed to obtain
plasma.
In vivo dog PK studies
Male Beagle dogs (at the age of 1 to 3 years, Shanghai Medicilon Inc.,
IACUC No.: 09013-22638) received either a single intravenous (bolus)
injection or single oral administration (by gavage) of the compound in
a cocktail preparation. Doses of 0.5 mg/kg (intravenous administration)
and 1 mg/kg (perorally) were given as solutions in different
formulations as needed, e.g. 5% DMSO + 95% (10% HP-β-CD in saline).
Consecutive blood samples were taken via jugular vein or other suitable
vein from n = 3 animals per route of administration after 0.083 (iv
only), 0.25, 0.5, 1, 2, 4, 6 (po only), 8, 24 and 48 h and were further
processed to obtain plasma.
In vitro ENPP2 and ENPP3 inhibition assay
The Amplex® Red-based fluorescence assay was used to evaluate the
inhibitory effects of test compounds on human ENPP2 (or Autotaxin, ATX)
and ENPP3. Compounds were dissolved in DMSO and prepared into 10 mM
stock solutions. Working solutions with 10 concentration gradients were
prepared by diluting the stock solutions with DMSO. The assay buffer
for ATX contained choline oxidase, horseradish peroxidase (HRP), and
Amplex® Red reagent (Invitrogen), while the buffer for ENPP3 contained
Tris-HCl, MgCl2, and ZnCl2. For the ENPP2 assay, compounds were
transferred to a 384-well plate using an Echo liquid handler, followed
by the addition of ENPP2 protein (Echelon Biosciences, Cat No. E-4000),
HRP, choline oxidase, LPC, and Amplex® Red reagent, according to the
manufacturer’s instructions. HA130, the reported ENPP2
inhibitor^[315]68 was used as the positive control. The plate was
incubated at room temperature for 30 minutes and fluorescence was
measured at 530 nm excitation and 590 nm emission using an Envision
plate reader (PerkinElmer). For the ENPP3 assay, compounds were
transferred to a 384-well plate using an Echo liquid handler, followed
by the addition of recombinant human ENPP3 protein (ACRO, Cat No.
EN3-H52H4), TMP-pNP, and the assay buffer, according to the
manufacturer’s instructions. The reported ENPP3 inhibitor was used as a
positive control, while the ENPP2 inhibitor HA130, the topoisomerase I
inhibitor SN-38, and the microtubule stabilizer paclitaxel were
selected as the negative control. The plate was incubated at room
temperature and fluorescence was measured at 405 nm excitation and
492 nm emission using an Envision plate reader (PerkinElmer). Data
analysis was performed using GraphPad Prism software to determine IC50
values and inhibition percentages.
In vitro safety pharmacology profiling
The SafetyOne44 Panel
([316]https://en.ice-biosci.com/index/show.html?catname=safety4490&id=1
73) which assessed the in vitro safety pharmacology of ISM5939 was
performed by ICE Bioscience. For ion channel modulation, both manual
and automated patch clamp techniques were utilized. The manual patch
clamp method involved the formation of whole-cell configurations,
followed by the application of defined voltage protocols to measure the
resulting ionic currents for Cav1.2, IKs, and NR1/NR2B channels. The
automated patch clamp system, QPatch 48 X, was employed for the
assessment of Nav1.5 and hERG channels, providing high-throughput data
with consistent quality control. In the realm of G-protein coupled
receptors (GPCRs), the FLIPR assay was used to investigate the effects
of test compounds on GABAA (α1β2γ2), 5-HT3A, and nAChRα4β2 receptors.
This assay utilized fluorescent dyes to detect changes in membrane
potential and calcium flux, offering a rapid and sensitive method for
screening potential modulators. For enzymatic targets, a series of
assays were conducted to measure the inhibitory effects of test
compounds on COX1, COX2, PDE3A, PDE4D2, LCK, AchE, and MAO A. These
assays utilized a combination of fluorescence and luminescence-based
detection methods, providing quantitative data on the potency of the
test compounds against these enzymes. For the study of transporter
targets, a reuptake assay for DAT, NET, and 5-HTT was performed. This
assay measured the ability of test compounds to inhibit the reuptake of
neurotransmitters into cells, using a fluorescent detection method that
provided a quantitative assessment of the compounds’ potency.
Surface plasmon resonance (SPR) for ENPP1
SPR assay was performed by Pharmaron Inc. The BIAcoreTM S200 instrument
was used for data collection in surface plasmon resonance experiments,
and the data collection temperature was set to 25°. In this study, the
ENPP1 (MCE) protein was immobilized on the surface of a CM5 sensor chip
(Cytiva) via amine-coupling method, and the operation was performed in
strict accordance with the standard S-series CM5 sensor chip
instructions. The ENPP1 protein was diluted to a concentration of 20
ug/mL using running buffer HBS-P (Cytiva), and the injection time was
set to 1200 seconds, and the final chip surface density reached ~15000
response units. A certain number of diluted compounds were set to the
surface of the CM5 sensor chip at a flow rate of 30 uL/min, the
association time was 120 s, and the dissociation time was 3000 s in a
single-cycle mode. The running buffer is HBS-P (Cytiva), 2% DMSO. The
data were solvent corrected for different proportions of diluted DMSO
concentrations. All data are double subtracted to exclude the effects
of buffers and reference channels. The data were processed and analyzed
using BIAcoreTM S200 evaluation software version 1.1. The binding
constant Ka, the dissociation constant Kd and the affinity constant KD
were confirmed by 1:1 binding model fitting. The protein immobilization
amounts on the surface of the CM5 sensor chip is calculated according
to the following formula:
[MATH: (MWofanalyte/MWofligand)*RU(Ligand)*stoichiometry=Rmax :MATH]
Affinity constant KD was confirmed according to the following formula:
[MATH: KD=Kd/Ka
:MATH]
Cell lines
Cancer cell lines, including MDA-MB-231 (ATCC Cat# HTB-26), MDA-MB-436
(ATCC Cat# HTB-130), ZR-75-30 (ATCC Cat# CRL-1504), MV411 (ATCC Cat#
CRL-9591), NCI-H1792 (ATCC Cat# CRL-5895), 786-O (ATCC Cat# CRL-1932),
4T1 (ATCC Cat# CRL-2539), EMT6 (ATCC Cat# CRL-2755), B16F10 (ATCC Cat#
CRL-6475), CT26 (ATCC Cat# CRL-2638), HCC1395 (ATCC Cat# CRL-5868), and
UWB1.289 (ATCC Cat# CRL-2945) were purchased from ATCC. Additionally,
the MC38 cell line (Cobioer Cat# CBP60825) was acquired from Cobioer.
the ID8 cell line (SUNNCELL Cat# SNL-498) was obtained from SUNNCELL.
the THP1-DUAL cell line (InvivoGen Cat# thpd-nfis) was obtained from
InvivoGen.
Cell culture
The THP1-DUAL, ZR-75-30, NCI-H1792, 786-O, 4T1, EMT6, CT26, HCC1395,
and UWB1.289 cell lines were cultured in RPMI1640 medium (Invitrogen),
supplemented with 10% FBS (Gibco) and 1% P/S, except UWB1.289, which
also included 50% MEGM and 3% FBS. The MDA-MB-231 and MDA-MB-436 cell
lines were cultured in L-15 medium (Invitrogen), supplemented with 10%
FBS, 1% P/S, and additional factors for MDA-MB-436: 10ug/ml insulin and
16ug/ml glutathione. The MC38, MV411, and B16F10 cell lines were
cultured in DMEM (Invitrogen), supplemented with 10% FBS and 1% P/S.
The ID8 cell line was also cultured in DMEM with the same supplements.
All cell lines were maintained in a humidified incubator at 37 °C with
5% CO[2], except MDA-MB-231 and MDA-MB-436, which were cultured without
CO[2]. All cell lines were authenticated by STR DNA profiling and
tested for Mycoplasma contamination. All cell-based experiments were
conducted within 15 passages.
Coculture assay
THP1-Dual™ cell is a human monocyte cell line engineered to express the
Lucia luciferase reporter gene under the control of an ISG54 promoter
and IFN-stimulated response elements. Lucia luciferase activity serves
as a measure of IRF pathway activation. MDA-MB-231, ZR-75-30, or MC38
cells were pretreated with ISM5939, H-151, or their combination at the
indicated concentrations for 24 h, followed by transfection with dsDNA
using Lipofectamine 3000 (Thermo Fisher). Briefly, for dsDNA
transfection, dsDNA at the final concentration of 800 or 2000 ng/ml was
incubated with P3000 reagent for 5 min at 37 °C, then Lipofectamine
3000 reagent was added, incubated at 37 °C for 5 min, and then 10 µL of
transfection mix was added to each 96-well plate well containing cells
and incubated for 24 h, according to the manufacturer’s protocol.
ISM5939 was dissolved in DMSO and diluted to 10,000 nM, 1,000 nM, and
100 nM in cell culture medium, while H-151 STING inhibitor (InvivoGen)
was diluted to 10 μM. After transfection, dsDNA-containing medium was
removed, and THP1-Dual™ cells were seeded on top of tumor cells at 1:1
(ZR-75-31, MC38 and 4T1) or 3:1 (MDA-MD-231) tumor:immune cells ratios.
Co-cultures were incubated with indicated compounds for an additional
24 or 48 h. Supernatants were collected at 24 and 48 h post-tumor cell
seeding, and Lucia luciferase activity was measured using the
QUANTI-Luc™ Lucia kit and a luminometer.
Detection of cancer cell secreted cGAMP
For detection of dsDNA-induced cGAMP release, the compounds were
diluted as indicated and incubated with MV4-11, MDA-MB-231, H1792,
ZR-75-30, 786-O, 4T1, MC38, EMT6, or B16F10 cells, followed by
transfection with dsDNA, as described above. At time points indicated,
the supernatant was analyzed for cGAMP levels using an ELISA kit
(Cayman Chemical), while cell viability was assessed using the
CellTiter-Glo® 2.0 assay (Promega). The data were analyzed using
standard curve fitting to determine cGAMP concentrations (XLFIT
software) and GraphPad Prism software to generate column charts
representing cell viability.
For detection of compound-induced cGAMP release, MDA-MB-231,
MDA-MB-436, ID8, UWB1.289, HCC1395, or EMT6 cancer cell lines were
seeded at a confluence of 30% and treated with ISM5939 (1 µM) alone or
in combination with either Cisplatin (5 µM, 1 µM, or 0 µM;
MedChemExpress), Paclitaxel (10 nM, 5 nM, 1 nM, or 0 nM;
MedChemExpress), Olaparib (5 µM, 2 µM, 0.5 µM, 0.2 µM, or 0 µM;
MedChemExpress), or SN-38 (0.1 µM; MedChemExpress, HY-13704) for
48-72 h. Cell confluence was monitored using an Incucyte live-cell
imaging system. At the end of the treatment period, cell supernatants
were collected and analyzed for cGAMP levels using a 2’3’-cGAMP ELISA
kit (Cayman). Data analysis involved calculating cGAMP concentrations
from a standard curve and generating bar charts to visualize the impact
of treatments on cGAMP secretion and cell confluence.
Plasma mediated cGAMP degradation
To assess the effect of test compounds on cGAMP degradation in human
and mouse plasma, plasma was isolated from 8-10 week-old C57BL/6 J
female mice (Shainghai Lingchang, Ltd) and human heparinized whole
blood (Shanghai Liquan hospital, approved by hospital ethics committee,
Code: [2023]−0) by centrifugation at 4 °C at 2000 × g for 15 minutes.
Aliquots of 50 μL of plasma were mixed with 0.5 μM or 3 μM cGAMP
(MedChemExpress) and 10 μM or 1 μM ISM033-154 or vehicle control in
1.5 mL EP tubes and incubated for 0, 3, or 6 h. The samples were then
stored at −80 °C until 2’3’-cGAMP levels were quantified using the
Cayman 2’3’-cGAMP ELISA Kit (Cat.No. 501700). The absorbance was read
at the specified wavelength to determine the remaining cGAMP
concentrations.
RT-qPCR
Total RNA was extracted from cell lysates using the MagMAX™ mir Vana™
Total RNA Isolation Kit (Applied Biosystems). Reverse transcription was
conducted with the High-Capacity RNA-to-cDNA Kit (Invitrogen), followed
by real-time PCR with the PowerUp™ SYBR™ Green Master Mix (Invitrogen),
according to manufacturers’ protocols. Relative gene expression was
calculated using the 2-ΔΔCt method, with ACTB as the internal reference
gene. The primers used for amplification were as follows: human ENPP1
forward: CAAAGGTCGCTGTTTCGAGAG,
human ENPP1 reverse: TGCACGTCTCCTGGTAATCTAAA;
human cGAS forward: ACATGGCGGCTATCCTTCTCT,
human cGAS reverse: GGGTTCTGGGTACATACGTGAAA;
human ACTB forward: CATGTACGTTGCTATCCAGGC,
human ACTB reverse: CTCCTTAATGTCACGCACGAT;
mouse ENPP1 forward: CTGGTTTTGTCAGTATGTGTGCT,
mouse ENPP1 reverse: CTCACCGCACCTGAATTTGTT;
mouse cGAS forward: GAGGCGCGGAAAGTCGTAA,
mouse cGAS reverse: TTGTCCGGTTCCTTCCTGGA;
mouse ACTB forward: GGCTGTATTCCCCTCCATCG,
mouse ACTB reverse: CCAGTTGGTAACAATGCCATGT;
In vivo pharmacokinetics and efficacy
Female Balb/cAnN mice at the age of 6 to 8 weeks (n = 3 per timepoint)
were implanted with 5 × 10^5 4T1 cells into the mammary fat pad and
allowed to grow until tumor volume reached 60-80 mm^3 before single
oral dosing with ISM5939 at 10 mg/kg or vehicle control. Blood and
tumors were collected from mice at 0 hr, 0.25 hr, 0.5 hr, 1 hr, 2 hr,
4 hr, 8 hr, 24 hr, 48 hr post-dosing, and ISM5939 was detected with
liquid chromatography with tandem mass spectrometry (LC-MS/MS), cGAMP
was detected with LS-MS/MS, and IFN-β was detected by ELISA (PBL Assay
Science, Cat No. 41410-1) according to manufacture instructions.
HD Biosciences Co. (Shanghai, China) analyzed anti-tumor efficacy of
ISM5939 as a monotherapy or in combination against MC38 tumors.
Briefly, C57BL/6 J mice at the age of 6 to 8 weeks (n = 6 per
condition; Beijing Vital River Laboratory Animal Technology Co.) were
inoculated subcutaneously with 2×10^5 MC38 cells in the right flank and
treated with orally administered ISM5939 (30 mg/kg, twice a day) and/or
anti-PD-L1 antibody (5 mg kg^−1, twice a week]] (Bio X Cell, Cat No.
BE0101) injected intravenously when tumors reached 51 mm^3. Data
analysis included reporting mean and SEM, with statistical differences
calculated with one-way ANOVA or Student’s t-test. Synergy was
evaluated with the coefficient of drug interaction (CDI), calculated by
the formula CDI = AB/(A×B), AB is the ratio of the combination group to
the control group in mean TV; A or B is the ratio of the corresponding
single drug group to the control group in mean TV. CDI < 1 indicates
synergism, CDI < 0.7 indicates a significant synergistic effect,
CDI = 1 indicates additive and CDI > 1 indicates antagonism.
The efficacy of ISM5939 as a single or combination therapy against 4T1
orthotopic tumors was carried out in 8-10 week-old female Balb/cAnN
mice (n = 6 per condition) implanted with 5 × 10^5 4T1 cells into the
mammary fat pad and allowed to grow until tumor volume reached 60-80
mm^3 before single oral dosing with ISM5939 (10 mg/kg, BID), docetaxel
(10 mg/kg, i.v., QW), olarparib (50 mg/kg, oral, QD), and/or cisplatin
(5 mg/kg, i.p., QW), or control non-treatment.
The efficacy study in the CT26 syngeneic model was conducted by
Pharmaron (Beijing). Briefly, 6 to 8-week female BALB/cAnN mice (Vital
River Laboratory Animal Technology Co., Ltd.) were subcutaneously
inoculated with 3 × 10^5 CT26 cells and treated with orally
administered ISM5939 and/or anti-PD1 (Bio X Cell, BE0146) when tumors
reached 70 mm^3 (n = 8 per condition). The measurement of tumor size
was conducted three times per week with calipers and the tumor volume
(mm^3) was estimated using the formula: TV = a × b 2/2 throughout the
study, where a and b are the long and short diameters of a tumor,
respectively.
Detection of immune cells and cytokines in vitro and in vivo
2.5 × 10^5 Human PBMCs (Hemarea, CNHBC100C) were incubated in 96-well
plates and treated with ISM5939 or STING agonist ADU-S100 (InvivoGen)
at the indicated concentrations for 6 h. Supernatants were collected
for analysis with Cytometric Bead Array (CBA; BD Biosciences) for TNF-α
(Cat No. 558273), IFN-α (Cat No. 560379), and IL-6 (Cat No. 558276).
For rat or dog PBMCs, 0.1 × 10^5 PBMCs ([SOURCE]) were treated with
ISM5939, ADU-S100 or diABZI at the indicated concentrations for 6 h.
Rat TNF-α was detected by CBA (BioLegend, Cat No.741391), while dog
TNF-α was detected by ELISA (AssayGenie, CNFI00020).
The proportions of circulating TAM and T cell subtypes were quantified
in 4T1 orthotopic Balb/cAnN mice receiving ISM5939 or vehicle control
(see above), C57BL/6 J mice bearing MC38 subcutaneous tumors receiving
oral ISM5939 (10 mg/kg or 30 mg/kg BID) or STING agonists diABZI
(1.5 mg/kg, i.v., single dose) and MSA-2 (60 mg/kg, oral, single dose),
and in rat and dog models without tumors. For cell typing and
quantification, tumors and blood were collected and single-cell
suspensions were analyzed by flow cytometry for immune subtypes using
markers for live/dead (Zombie NIR dye), CD45, CD3, CD4, CD8, CD25,
Foxp3, CD44, CD62L, CD69, NK1.1, CD19, IFN-γ, TNF-α. For cytokine
detection, plasma from the MC38 tumor-bearing C57BL/6 J mice treated
with ISM5939, diABZI, or MSA-2 at the indicated concentrations was
collected and analyzed for IFN-β by ELISA (R&D, VAL612), or TNF-α and
IL-6 using CBA (BD Biosciences, Cat No. 558301 and 558299) according to
manufacturer instructions.
28-day repeat dose GLP-compliant toxicity studies with 28-day recovery
period were conducted in Sprague–Dawley (SD) rats (both the male and
female; 7 weeks; n = 10 per condiion) and Beagle dogs (both the male
and female, 7.2-8 months, n = 5 per condition) by Pharmaron (Beijing)
TSP Services Limited. The SD rats and Beagle dogs were obtained from
Vital River Laboratory Animal Technology, Co. Ltd and Beijing Marshall
Biotechnology, Co. Ltd., respectively. The animals were dosed by oral
gavage once daily for 28 consecutive days. The SD rats were
administrated at 15, 50 or 150 mg/kg/day in a 20 ml/kg dose volume and
the beagle dogs were at 3, 10 or 30 mg/kg/day in a 7.5 ml/kg dose
volume. Approximately 1.0 mL of the whole blood was collected via
puncture of peripheral vessels from dogs and via puncture of abdominal
aorta from rats at the end of dosing period and recovery period.
Blood samples from SD rats were used to assess the absolute numbers and
percentages of Total T cells (CD45 + CD3 + ), Helper T cells
(CD45 + CD3 + CD4 + CD8a-) and Cytotoxic T cells
(CD45 + CD3 + CD8a + CD4-) using flow cytometry (FCM).
The percentages and absolute numbers of lymphocyte subpopulations of
Total T cells (CD3e + ), Helper T cells (CD3e + CD4 + CD8a-) and
Cytotoxic T cells (CD3e + CD4-CD8a + ) in the peripheral blood from
Beagle dogs were assessed by flow cytometry (FCM).
RNA-seq for post-treatment TME analysis
Tumors from subcutaneous syngeneic EMT6 tumor-bearing mice (n = 6 per
condition) were treated with ISM5939 and/or cisplatin, as described
above. Tumors were collected when tumors reached 36-79 mm^3 and
processed for bulk RNA-seq. Total RNA was isolated from cell pellets
using the Magnetic Tissue/Cell/Blood Total RNA Kit (TIANGEN, DP761) on
the KingFisher Flex. RNA samples were quantitated with the Quant-IT RNA
kit (Invitrogen, [317]R11490) and evaluated for integrity by using
Agilent 5400 Bioanalyzer. RNA-seq libraries were constructed using KAPA
mRNA HyperPrep Kit (Roche, KR1352) in the Insilico Robotics Lab
(Suzhou, China) on the Biomek i7 workstation with custom scripts. The
libraries were sequenced on the Illumina NovaSeq6000 platform with 150
nt paired-end reads. Read quality control was conducted with FastQC,
and raw reads were filtered and mapped to the mouse mm10 reference
genome using the DRAGEN RNA Pipeline (Illumina). The quantitation of
gene expression was based on raw read counts using transcripts per
million normalization.
Differential expression analysis was performed using DESeq2. A ranked
gene list was generated based on log2-fold change. Pathway enrichment
was done with R package “clusterProfiler” according to the pre-ranked
gene lists. The enrichment results were evaluated based on Normalized
Enrichment Score (NES) and False Discovery Rate (FDR). The FDR < 0.05
is regarded as significant. Immune cell proportions were estimated by
[318]CIBERSORT^[319]113 according to the gene TPM matrix of the bulk
RNA-seq samples. All analyses were performed using R (v. 4.1.2).
Analysis of tumor ENPP1 and ENPP3 gene expression for association with
response to treatments
Previously published pre-treatment gene expression data from breast
cancer tissue from patients treated with combination
paclitaxel+anti-PD1 therapies were accessed from GEO:
[320]GSE194040^[321]87; from esophageal adenocarcinoma patients treated
with anti-PDL1 therapy, subset to patients with high LRRC8A expression
(top 25%), was accessed from GEO: [322]GSE165252^[323]88; from TNBC
patients treated with paclitaxel + radiation were accessed from GEO:
[324]GSE22513^[325]93; from HER2 + BC patients treated with
doxorubicin/paclitaxel followed by
cyclophosphamide/methotrexate/fluorouracil plus trastuzumab were
accessed from GEO: [326]GSE50948^[327]94; from ER + BC patients treated
with various chemotherapy regimens were accessed from GEO:
[328]GSE22093^[329]95; and from CRC patients treated with FOLFOX
therapy were accessed from GEO: [330]GSE28702^[331]96 and were
stratified by response. Statistical significance was calculated by
Student’s t-test.
Reporting summary
Further information on research design is available in the [332]Nature
Portfolio Reporting Summary linked to this article.
Supplementary information
[333]Supplementary Information^ (2.2MB, pdf)
[334]Peer Review File^ (2.2MB, pdf)
[335]41467_2025_59874_MOESM3_ESM.pdf^ (112.6KB, pdf)
Description of Additional Supplementary Files
[336]Supplementary Data 1-9^ (154.3KB, xlsx)
[337]Reporting Summary^ (6.4MB, pdf)
Source data
[338]Source Data^ (2.1MB, xlsx)
Acknowledgements