Effective Virtual Screening Strategy toward heme-containing proteins: Identification of novel IDO1 inhibitors

European Journal of Medicinal Chemistry 184 (2019) 111750

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European Journal of Medicinal Chemistry journal homepage: http://www.elsevier.com/locate/ejmech

Research paper

Effective Virtual Screening Strategy toward heme-containing proteins: Identification of novel IDO1 inhibitors Yi Zou a, d, 1, Yue Hu b, 1, Shushan Ge a, 1, Yingbo Zheng a, Yuezhen Li c, Wen Liu b, Wenjie Guo b, ***, Yihua Zhang a, Qiang Xu b, **, Yisheng Lai a, * a

State Key Laboratory of Natural Medicines, Jiangsu Key Laboratory of Drug Discovery for Metabolic Diseases, Center of Drug Discovery, China Pharmaceutical University, Nanjing, 210009, PR China State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, Nanjing, 210093, PR China c Department of Organic Chemistry, School of Science, China Pharmaceutical University, Nanjing, 211198, PR China d CAS Key Laboratory of Receptor Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences (CAS), Shanghai, 201203, PR China b

a r t i c l e i n f o

a b s t r a c t

Article history: Received 12 June 2019 Received in revised form 22 September 2019 Accepted 28 September 2019 Available online 3 October 2019

Developing small molecules occupying the heme-binding site using computational approaches remains a challenging task because it is difficult to characterize heme-ligand interaction in heme-containing protein. Indoleamine 2,3-dioxygenase 1 (IDO1) is an intracellular heme-containing dioxygenase which is associated with the immunosuppressive effects in cancer. With IDO1 as an example, herein we report a combined virtual screening (VS) strategy including high-specificity heme-binding group (HmBG)-based pharmacophore screening and cascade molecular docking to identify novel IDO1 inhibitors. A total of four hit compounds were obtained and showed proper binding with the heme iron coordinating site. Further structural optimization led to a promising compound S18e3, which exerted potent anti-tumor efficacy in BALB/c mice bearing established CT26 tumors by activating the host's immune system. These results suggest that S18e3 merits further study to assess its potential for the intervention of cancer. Furthermore, our study also unveils a novel in silico-based strategy for identifying potential regulators for hemeproteins within short timeframe. © 2019 Elsevier Masson SAS. All rights reserved.

Keywords: IDO1 Heme T cell Cancer immunotherapy Virtual screening Drug design Molecular dynamics simulation

1. Introduction Heme-containing protein, or hemeprotein, is a large class of metalloproteins that contain a heme prosthetic group inside the protein. The heme group, consisting of iron cation bound at the center of the conjugate base of the porphyrin, is an indispensable participant in oxygen carrying, enzyme catalysis, active membrane transport, electron transfer, sensory functions, and other processes [1,2]. The development of small molecules occupying the hemebinding site is an effective strategy to regulate these functions, which could be developed into drugs once some of these hemecontaining proteins become abnormal in pathologic conditions

* Corresponding author. ** Corresponding author. *** Corresponding author. E-mail addresses: [email protected] (W. Guo), [email protected] (Q. Xu), [email protected] (Y. Lai). 1 Y.Z., Y$H., and S.G. contributed equally to this work. https://doi.org/10.1016/j.ejmech.2019.111750 0223-5234/© 2019 Elsevier Masson SAS. All rights reserved.

[3e5]. Indoleamine 2,3-dioxygenase 1 (IDO1), for instance, is an intracellular heme-containing dioxygenase that catalyzes the first and rate-limiting step of the kynurenine (Kyn) pathway of tryptophan (Trp) metabolism, which is initiated by triplet O2 binding to the ferrous high-spin state of heme iron through charge transfer. The activation of the dioxygen leads to the radical addition reaction of O2 to Trp [6], which subsequently converts to N-formyl-L-Kyn, this intermediate is then converted into a series of biologically active Trp catabolites such as Kyn [7]. This process results in a highly tolerogenic microenvironment characterized by reduced effector T (Teff) lymphocytes and natural killer (NK) cells, and promotes the induction and activation of immunosuppressive regulatory T (Treg) cells and the activation, recruitment and expansion of dendritic cells (DCs) and myeloid-derived suppressor cells (MDSCs) [8]. These alterations influence the pathology of autoimmune diseases and, in particular, cancer immune tolerance [9e11]. Accordingly, pharmacological inhibition of the enzymatic activity of IDO1 is currently being pursued as a potential

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therapeutic tool to leverage cancer therapy. Several IDO1 inhibitors are currently under preclinical or clinical investigation [12], and some of them are designed to coordinate with the heme iron [13,14], such as NLG919, epacadostat and Amg-1 (Fig. 1). Despite of the recent negative result from a phase III clinical trial (ECHO-301) combining epacadostat with pembrolizumab, which may be attributed to several issues such as the possible structure-related pharmacokinetics limitations, inapplicable pharmacodynamics indicators, unreasonable combination therapy strategy or clinical trial design [15], many IDO1-related clinical trials are still in active state for the treatment of patients with many cancer types [16]. Furthermore, several studies showed that high expression of IDO1 was significantly associated with higher tumor grade, vascular invasion, PD-L1 expression, and adverse clinical outcomes [17e20]. Additionality, in a preclinical anti-PD1-resistant lung adenocarcinoma model, IDO1 inhibition can reactivate antitumor immune responses by blocking IDO1-expressing MDSCs [21]. All of these studies indicate that the treatment targeting IDO1 remains a highprofile field [22]. Thus, it is still a need for novel IDO1 inhibitors with appropriate pharmaceutical properties that may help accelerate the understanding of the basic biological properties of IDO1 in cancer immunotherapy. Virtual screening (VS) has emerged as a popular alternative approach to combinatory chemistry and high-throughput screening (HTS) in identifying drug candidates with new skeletons because it is less time-consuming and allows researchers to narrow down the research scope to a small pool of promising compounds [23]. In fact, many VS studies aiming at seeking novel IDO1 inhibitors have been hitherto reported [24]. However, most of the resulting compounds showed weak biological activity in vitro, and no evidence of in vivo efficacy in activating the immune system was reported. This could be partially attributed to the incapability of the current VS methods in addressing the heme-coordination interaction, which may be essential for hit identification. Furthermore, it is difficult to reliably describe the iron-ligand interaction in the active site of IDO1 using classical molecular force-field parameters [25]. Although the hybrid quantum mechanical/molecular mechanical (QM/MM) method has been proven to be a powerful computational technique in heme-containing systems [26,27], this strategy is computationally inefficient for large-scale VS. Therefore, developing effective VS strategies for the identification of novel and potent compounds targeting heme-containing protein, such as IDO1, is a challenging task.

Recently, we reported the identification of a series of benzoxazolinone derivatives as novel IDO1 inhibitors using a combination of the scaffold-hopping strategy and molecular electrostatic potentials (ESP) calculation. These compounds, derived from IDO1 substrate Trp, could coordinate with heme iron [28]. In our continuous efforts to discover potent and novel immunotherapy agents with potential IDO1 inhibitory activities, herein, the customized HmBG (heme-binding group)-based pharmacophore screening and cascade molecular docking as well as molecular ESP calculation were combined to accomplish a VS protocol where both the protein-ligand nonbonding and heme-ligand interactions were taken into account (Fig. 2). Four hits with different scaffolds were found exhibiting potent activity against IDO1 (3.2e20.6 mM). Encouraged by the initial results, S18e3 (IC50: 59.8 nM) was identified through similarity search and hit optimization. Subsequent in vitro and in vivo experiments demonstrated that S18e3 could effectively suppress colon cancer growth in mice by increasing CD8þ T cell infiltration and reducing Foxp3þ Treg cell fraction, which led to the relief of immune suppression in the tumor microenvironment. 2. Results and discussion 2.1. Dynamic structure analysis of binding mode Like other hemeproteins, IDO1 has its heme prosthetic group at the catalytic site, which contributes to the interaction energy between the ligand and protein in the distal heme pocket. Therefore, this research started with the investigation of the detailed interactions between IDO1 and its inhibitor, epacadostat (PDB ID: 5WN8), at the atomic level using molecular dynamics (MD) simulation, considering its relatively high inhibitory activity and participation in heme iron coordination. Three independent MD simulations were performed for epacadostat-binding, holo- and apo-IDO1 in explicit aqueous solution. Generally, more fluctuations were observed in holo- and apo-IDO1 systems than that in epacadostat-binding complex system (Fig. S1). The root-mean square fluctuation (RMSF) values, which reflected the flexibility of each residue during the MD process, demonstrated a small fluctuation in all three systems (epacadostat-IDO1, holo-IDO1 and apo-IDO1), whereas the RMSF values of the residues Phe163 (0.6458, 0.7138 and 0.9772, respectively), Phe226 (0.6534, 0.776 and 1.0692, respectively), Phe227 (0.6896,

Fig. 1. (A) Crystal structure of human indoleamine 2,3-dioxygenase 1 (PDB ID: 5WN8, epacadostat was removed). The yellow region represents the small domain, the gray region represents the large domain, and the red part represents the DE-loop connecting the two domains. (B) Representative compounds that binds to heme iron are shown in 2D and 3D models. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

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Fig. 2. An integrated VS procedure combining pharmacophore- and docking-based screening.

0.9733 and 0.9961, respectively) and Leu234 (0.798, 0.9978 and 1.0821, respectively) varied significantly (Fig. S2). These results indicated that the residues within the active site were much more rigid once apo-IDO1 bound to the cofactor heme and the inhibitor, successively. As to the residues in DE-loop, the binding of these two molecules significantly immobilized its conformation, and little changes were found on the inhibitor itself during the MD simulation. The difference of RMSF value was mainly derived from the conformational change of the (sulfamoylamino)alkylamino group (EPA2, colored by purple) toward the solvent accessible region, while the moiety (EPA1, colored by green) occupying both pockets A and B kept in a stable conformation. As seen in Fig. 3B, the conformational changes of the two heme propionate groups were quite different, where 7-propionate was more flexible than 6propionate in epacadostat- and holo-IDO1 systems. This may be ascribed to the electrostatic interaction between Arg343 and 6propionate that stabilizes the binding conformation. In contrast, 7-propionate is near pocket B and the solvent accessible region, where the motion patterns are mainly depended on the conformation of the inhibitor and DE-loop. In addition, the distance between the centroids of 7-propionate and EPA2 also reflected their relative location changes in simulation (Fig. 3C). The representative conformations of complex, extracted from the equilibrium trajectories via clustering analysis based on the distance between the centroids of 7-propionate and EPA2, showed us the various possibilities of the conformations (Ca to Cd-2) between them (Figs. S3 and S4). This information will provide clues to the development of the following pharmacophore model based on this co-crystal structure. 2.2. Generation of the customized HmBG-based pharmacophore model Structure-based pharmacophore (SBP) modeling generated from the features of receptor-ligand interactions (usually from one or more co-crystal structures) has been employed in the successful identification of novel structures with significant activity toward various biological targets [29,30]. In our study, the cocrystallization structure of epacadostat in complex with hIDO1 (PDB ID: 5WN8) was employed as the template to auto-generate the original pharmacophore model using the Receptor-Ligand Pharmacophore Generation protocol in Discovery Studio (DS) 3.0

[31], and the pharmacophore features generated represented the non-bonded interactions between hIDO1 and epacadostat. However, modifications of the hydroxyamidine moiety in epacadostat analog significantly affected its biological activity, indicating the importance of choosing an HmBG in IDO1 inhibitor design [32]. Therefore, to properly represent the HmBG in IDO1 inhibitors, we created a customized pharmacophore feature, named HmBG, which was defined mainly based on the QM calculations (MMBPcorrected classical ligand-heme interaction energies (EMM) and €hrig (Fig. S5) distances (rMM)) from the literature reported by Ro [33]. Ultimately, the customized HmBG-based pharmacophore model (HypoS1) consisted of one HmBG feature, two hydrogen bond donor (HBD) features, two hydrogen bond acceptor (HBA) features, two hydrophobic (HYD) features, one ring aromatic (RA) feature as well as several excluded volumes (Fig. 4A). In addition, HypoS2 was also established as a control pharmacophore, whose HmBG was replaced by the default HBD feature (Fig. S6). To elaborate the crucial residues participating in the proteinligand interaction and determine the pharmacophore mapping prioritization, the molecular mechanics PoissoneBoltzmann surface area (MM/PBSA) free energy calculation and decomposition analysis were performed based on the aforementioned MD simulation trajectories at the equilibration stage, which was considered to indirectly reflect the relative importance of 8 pharmacophore features. Similar to our previous study [34], the major contributor to ligand binding was the intermolecular van der Waals term (DGVdw), which can be associated with the hydrophobic nature of the IDO1 binding pocket (Fig. S7). The calculated binding free energy was then decomposed into the contribution of a single residue with the default parameters embedded in Amber 14. Fig. 4B displayed the most important residues contributing to the binding free energy. Clearly, Phe163, Arg231, Leu234, Ser263 and heme were the predominant contributors to the binding energy. In pocket A (Fig. S8), the favorable contribution from hydrophobic interaction of Phe163 might be derived from the p-p interaction between Phe163 and the aniline moiety of epacadostat, which validated the necessity of the RA feature. Two hydrogen bond features (HBA1 and HBA2) between Arg231 and the sulfamine group of epacadostat, reflecting the electrostatic interaction (DGele) in essence, provided the driving force for ligand binding. The HBD1 feature was assigned to the additional hydrogen-bond interaction between epacadostat and 7-propionate of the heme group which is the main contributor

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Fig. 3. Dynamic structure analysis of epacadostat-heme system during MD simulation. (A) The epacadostat-heme system extracted from the complex. (B) Comparison of the RMSF value of each fragment in the system. (C) Monitoring the distance between the centroid of the (sulfamoylamino) alkylamino (EPA2, colored by purple) and 7-propionate group (colored by yellow). Ca to Cd-2 are the representative conformations of the epacadostat-heme moiety, extracted from the equilibrium trajectories through clustering analysis. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

Fig. 4. (A) 3D arrangements of the pharmacophore model, HypoS1. The pharmacophore features are colored with magenta (HBD), green (HBA), orange (RA), cyan (HY) and purple blue (HmBG). Excluded volumes were hidden. (B) Per-residue contribution to the binding effective energy of epacadostat-IDO1 complex. Heme contribution item refers to the porphyrin ring without iron. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

to the ligand-heme interaction. Phe164 and Leu234 constituted the hydrophobic feature HYD1 at the top of pocket A, meanwhile another hydrophobic feature HYD2 represented the hydrophobic interaction between the ligand and the residue Phe226 in pocket B. HBD2 demonstrated the hydrogen bonding interaction between epacadostat and the hydroxyl group of the residue Ser263. Nonetheless, the distance of this H-bond was relatively long and the decomposition binding energy of Ser263 was significantly lower than those of other primary residues that participated in the protein-ligand interaction. In summary, the first pharmacophore mapping criterion (PMC1) was set as follows: the HmBG, RA and HBD1 features were required to be present in the library screen; the screening will include 0e2 features from the group of HBA1 and HBA2, while 0e3 features

from the group of HYD1, HYD2 and HBD2. In order to determine the effectiveness of PMC1, the second pharmacophore mapping criterion (PMC2: the total minimum features were set to 3 and the maximum features were set to 8 without any additional required features) was set for comparison. 2.3. VS using the customized HmBG-based pharmacophore model Before the pharmacophore screening, the discriminatory capability of the two pharmacophore models with two PMCs was evaluated using two external validation databases, which were constructed using a combination of several reported IDO1 inhibitors and the customized DUD database (31 potent IDO1 inhibitors and 2000 drug-like non-inhibitors with similar physical

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properties) or the NCI diverse database (the identical 31 IDO1 inhibitors and 1945 compounds with diverse chemotypes) (Fig. S9). The area under the curve (AUC) value of a receiver operating characteristics (ROC) curve as well as the enrichment factor (EF) and goodness of hit (GH) score were calculated for each pharmacophore model based on their performance in the library screening. In our study, HypoS1(PMC1) demonstrated high AUC values in the screening of the two databases (0.902 for DUD database, 0.912 for NCI database), whereas HypoS2(PMC1) and HypoS1(PMC2) failed to identify several active ligands (0.803 for DUD database, 0.732 for NCI database and 0.728 for DUD database, 0.783 for NCI database, respectively) (Fig. 5). The overall EF values of HypoS1(PMC1) were 12.90 in 1% of the both databases screened, while HypoS2(PMC1) and HypoS1(PMC2) showed lower EF values. The final GH test scores of the pharmacophore model were 0.81 and 0.54 in 1% of two databases, respectively, which were better than 0.5, a threshold value for a good pharmacophore model (Tables S1 and S2) [35]. Apparently, HypoS1(PMC1) exhibited significantly better performance in discriminating active compounds from inactives ones. Moreover, it was found that the HmBG feature, as well as PMC1, were significantly vital for hit identification, thus implying that HypoS1(PMC1) was suitable for screening purpose. The databases Specs and ChemBridge were pre-filtered by Lipinski's rule of five, Veber rule and pan-assay interference compounds (PAINS) filter sequentially to obtain a smaller database, which was then subjected to VS using the validated pharmacophore model HypoS1(PMC1) as the queries. After the pharmacophore screening, a total of 12,500 compounds (1% of the pre-filtered database) were selected to enter the next step.

2.4. VS using cascade molecular docking Given that GOLD has been proven to be effective for docking studies of hemeproteins [36], the compounds obtained from the pharmacophore-based VS were first docked into the binding site of IDO1 by this algorithm. The binding poses obtained were submitted to MOE2009 for rescoring analysis using the Affinity dG scoring and London dG scoring functions which contain particular metal coordination item. These results, together with GoldScore and Chemscore, were further analyzed using the consensus score (Cscore) calculation, and the chemscore-metal value which reflects the formation of an interaction between the ligand and metal was also analyzed. According to the Cscore and chemscore-metal values, the

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top-ranked compounds were clustered using FCFP_6 fingerprints and 127 compounds with multifarious coordinating groups of the heme iron were retained for further analysis. Considering that the flexibility of IDO1 protein upon ligand binding is crucial to enhance the docking performance, we performed the flexible docking €dinger and the obtained experiment using the IFD protocol in Schro IFD docking score was used for ranking the binding poses [37]. As one of the major contributors to the binding affinity between the inhibitor and IDO1 protein, the coordinate bond between an atom with an unshared pair of electrons and the heme iron of IDO1 had been taken into consideration in our research. It is well-known that the molecular electrostatic potential (ESP), the potential energy of a proton at a particular location near a molecule, had been particularly useful as an indicator of the sites or regions of a molecule to which an approaching electrophile is initially attracted [38]. Although no evidence has shown that the value of ESP was associated with coordination bond energies, the maximum and minimum ESP value on the molecular surface are strongly correlated with the intermolecular interaction energy [39,40]. In metalligand complex (refer in particular to central metal atom is regarded as a positive ion and is surrounded by negative or neutral ligands which have a lone pair of electrons), monomers always contact with each other in a maximally ESP complementary manner when the electrostatic interaction mainly exists. Therefore, the ESP value can be regarded as a criterion for the tendency of interaction between the coordinating atom and Fe in the process of selecting compounds in VS. Through careful visual inspection of the IFD results, all the predicted coordinating atoms interacted with the heme iron in the expected ways, and their coordination ability with the heme iron was further assessed by the molecular ESP calculation using the density functional theory (DFT) method [41]. Eventually, 33 representative compounds with diverse scaffolds were selected based on the IFD scores (<4.5 kcal/mol) and the rank of the ESP values (Table S4). All hits were then subjected to ADME filters to eliminate molecules with poor PK profiles. Fortunately, most identified hits have favorable predicted PK properties and high human oral absorption (Table S6).

2.5. IDO1 inhibitory activities of the hits The resulting 33 compounds obtained from the aforementioned in silico screening were purchased from Specs and ChemBridge for

Fig. 5. Validation of different pharmacophore models with two PMC using DUD database (A) and NCI diverse database (B).

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subsequent bioassay. Due to the close biological relevance between IDO1 enzymatic and cellular assays, the HeLa cell line expressing native human IDO1 induced by IFN-g was used to investigate the potency of these compounds [42]. Cell viability was evaluated at the end of the assay to exclude the possibility that their IDO1 inhibitory activity was due to cytotoxicity. Epacadostat and NLG919 analog were used as positive controls. All hit compounds were first tested at a concentration of 30 mM for their inhibitory activity against IDO1 in a HeLa cell-based enzymatic assay. Among them, 12 compounds displayed an inhibitory potency >30% (Table S4) and were further evaluated at various concentrations in order to calculate their IC50 values. Compounds V2, V8, V19 and V27 were proved to be potent IDO1 inhibitors with IC50 values of 3.2, 20.6, 6.0 and 18.6 mM, respectively, and demonstrated no significant cytotoxicity under the experimental conditions (Fig. S15).

2.6. Structural analysis and predicted binding modes It was worth noting that the four hits which had diverse structural skeletons were significantly different from the reported IDO1 inhibitors. The 2D chemical structures and pharmacophore mapping results of the hits were shown in Fig. 6. All four hits were successfully mapped to our customized pharmacophore model HypoS1, and the HmBG feature was matched by the 3-N of thienopyrimidinone ring in V2, 7- or 8-N of triazolothiadiazin ring in V8, the carbonyl oxygen atom in V19, and 3- or 4-N (potential tautomerized) of pyrazole ring in V27, respectively. In order to gain a detailed insight into the interaction mechanism between the four hit compounds and IDO1, their binding modes obtained by IFD were analyzed. The thienopyrimidinone moiety, the main scaffold in V2, directly bound to the heme iron via the 3-N atom, and showed a pp interaction with Phe163. The methyl group of the tolyl fragment was located at the top of the hydrophobic pocket A, while the p-methoxyphenyl moiety extended to the pocket B and formed hydrophobic interactions with Phe226, Ile354 and Leu384. Moreover, the methoxy oxygen atom of V2 formed a hydrogen bond (H-bond) with Arg231, while the amide proton of its linker formed another H-bond with 7-

propionate of the heme group, which could contribute to the major affinity for protein-ligand interaction (Fig. 7A). In the case of compound V8, the 7- or 8-N atom of triazolothiadiazin ring bound to the heme iron, and the p-bromophenyl moiety filled the top space of pocket A, where the Br atom formed a halogen bond with the carbonyl oxygen atom of Leu124. The m-methoxyaniline moiety bent sharply by the flexible methylene amine linker and stretched into the pocket B to interact with Phe226 though a pp stacking interaction (Fig. 7B). For compound V19, the chlorobenzene scaffold was situated in the pocket A, and Cl atom engaged in a halogen bonding interaction with the carbonyl oxygen atom of Leu124. The naphthol group extended to pocket B, and showed a pp stacking interaction with Phe226. Additionally, the acetohydrazide, connecting the hydroxynaphthalene and p-chlorophenyl moieties, interacted with the heme iron via its carbonyl oxygen atom (Fig. 7C). In V27, the phenylpyrazole ring was situated in pocket A with its pyrazole group interacting with the heme iron, while ohydroxy benzyl group was involved in a pp interaction with Phe226 in pocket B. The protonated piperidine ring acted as a linker connecting the phenylpyrazole moiety and o-hydroxy benzyl group, and interacted with 7-propionate of the heme group through a hydrogen bonding interaction (Fig. 7D).

2.7. The hits interact with heme group within the active site of IDO1 The ESP calculation of the extracted ligand from the IFD results showed that the ESP value of the coordinating atom of V2, V8, V19 and V27 was 34.87, 57.63, 68.55 and 37.73 kcal/mol, respectively, which corresponded to the minimum ESP value (blue region) on the vdW surface around the probable coordinate atoms (Fig. 8A). The blue region was inclined to interact with the positive ESP value (þ31.49 kcal/mol) surrounding the heme iron (shown in red above the heme iron in Fig. 8B). In consideration of the improper comparison between negatively charged epacadostat and the neutral species, we computed the molecular ESP of 4PI, a known IDO1 inhibitor that predominantly interacts with ferric IDO1 for reference [44], and the minima around the imidazole N atom was 31.02 kcal/mol (Fig. S11). Furthermore, to investigate

Fig. 6. (A) The 2D structure of VS hits. The blue part represents the scaffold of each hit, and the atom in red represents the probable coordinating atom ligating with the heme cofactor. (B) Hypo1 mapped with the hits. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

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Fig. 7. The proposed binding modes of V2 (A), V8 (B), V19 (C) and V27 (D). Polar contacts were shown as black dot lines. Docking studies were performed using IFD method, and the figures were drawn using Pymol [43].

the molecular ESP in the complex state, we constructed the regularized porphine model including the interceptive inhibitor, heme and histidine groups for ESP calculation (Fig. S10). As shown in Fig. 8C, the complex was formed in an ESP positive-negative complementary way according to the inter-penetration between the vdW surfaces of the two fragments, revealing the electrostatic nature of the interaction between the ligand and the heme. The use of the spectral changes in the Soret peak of the heme is an effective and rapid means of assessing substrate binding, because the absorbance spectra of the heme group is highly sensitive to the local surroundings upon ligand binding, which changes the corresponding spectroscopic properties of the heme [45]. Therefore, it has been widely used to investigate the direct interaction between the ligand and heme group [46a,b,c,d,e]. In order to verify if compounds V2, V8, V19 and V27 could bind to the heme iron within the IDO1 active site, their ultraviolet (UV) absorption spectra were obtained. Epacadostat, which is known to interact with the heme iron, was used as a control. As shown in Fig. 9A, in the absence of inhibitors, the deoxy-ferrous IDO1 enzyme exhibited the Soret peak at 427 nm. In the case of epacadostat addition, a blue-shift of the Soret maximum to 419 nm is observed. In the presence of V2, V8 and V27, the Soret peak obviously shifted to 421 nm together with a hypochromic effect, while it shifted to 424 nm in the presence of V19. The UV spectra of compounds alone did not show any peak in this region (410e430 nm), which implied that this spectral shift was not caused by the spectral characteristics of these compounds themselves (Fig. 9B). These UV spectral data

demonstrated that V2, V8 and V27 might be bound to the heme iron. In the case of V19, however, the shift in the Soret peak of the absorption spectra is relatively small. Thus, much more validation would be required to verify the direct binding of V19 to the active site of IDO1. Additionally, DFT calculations were performed to determine the bond characteristics of the geometry-optimized hemeesubstrate bound complexes for the cases of the fragments F1, F2, F3, F4 and F5 (Fig. S10). The topology analysis technique proposed by Bader was firstly used for analyzing electron density in “the quantum theory of atoms in molecules” (QTAIM) [47], and all critical points (CPs), including the bond critical point (BCP) between the coordinating atom and heme iron, have been found (Figs. 10A and S11) [48]. Then the dg value was obtained at BCP, which was significantly smaller when compared to the chemical bond regions from the color-filled plane map (the area labeled by red box) (Figs. 10B and S12). According to the heme-ligand interaction energy obtained from DFT calculation (Table S3), it was found that F1 (32.16 kcal/ mol) possessed a higher interaction energy with the heme iron than F4 (15.32 kcal/mol), while F3 and F5 (36.86 and 38.61 kcal/mol) clearly exhibited more favorable interaction energy compared with F2 (25.72 kcal/mol). Furthermore, we decomposed electron density at BCP into the contributions from a range of localized molecular orbitals (LMO) [49]. As shown in Fig. 10C, the orbital, namely LMO113, had remarkable contribution to the BCP for F1 binding and the isosurface graph showed that LMO113 was located between Fe and O atoms. The same binding

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Fig. 8. (A) ESP colored vdW surface of the hits. The heme (colored by green) was placed under the hit molecule as a reference for easily observation. (B) ESP colored vdW surface of the 5-fold coordinated system. (C) ESP colored penetration map of vdW surface of the 6-fold coordinated system. The default lower and upper limits of color scale is 70 and 60 kcal/mol, respectively, and the default color transition is BWR (Blue-White-Red). (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

Fig. 9. (A) Absorption spectra of deoxy-ferrous IDO1 enzyme in the absence and presence of the compounds (200e500 mM) in phosphate buffer (pH: 6.5). [rhIDO1] ¼ 3 mM. (B) Absorption spectra of the selected compounds.

pattern also existed in the case of F2, F3 and F5 binding models, which confirmed the existence of chemical bond between the coordinating atom and heme iron (Fig. S13). We also noticed that the results were different for F4-iron-porphin-imidazole system, in which. The most contributing LMO45 was almost around the hydrazide oxygen atom. In order to further analyze the characteristics of the Fe-X bond (X means the coordinate atom O or N) at BCP, Wiberg bond order was calculated for the atom pair that constituted the coordinate bond in each model under atomic natural

orbital (ANO) basis [50], and subsequently decomposed as atomic shell pair contributions (Figs. 10D and S14). It was found that the bonds between the coordinating atom and iron gave a similar atomic shell pair contributions distribution, and had both coordinate and classical covalent characters. The interaction between 3d orbital of iron and 2p orbital of the coordinating atom had the greatest contribution to total bond order in each model compared to other atomic shell pair contributions, indicating the nature of the coordinate bond formation. In addition, 2s orbital of oxygen and 2s

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Fig. 10. (A). QTAIM analysis of F1-iron-porphin-imidazole system. Orange sphere correspond to BCP. (B) The distribution character of dg function was plotted as color-filled plane map. (C) Orbital contributions of the FeeO bond at BCP and the LMO with the greatest contribution to BCP. (D) Decomposition analysis of Wiberg bond order in NAO basis for FeeO bond. The contributions from various atomic shell pairs were plotted using histogram. The proportion was marked on the edge of the histogram. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

orbital of nitrogen also took part in the formation of the corresponding coordinate bond. It was noticeable that the interaction between 3d orbital of iron and 2p orbital of oxygen for F4 binding had relatively weaker contribution to the FeeO bond (<0.3) than that for F1 binding, as the negatively charged oxygen of F1 provided more electrons occupying the empty 3d orbit of the iron atom, leading to better orbital overlapping. This might be the reason for the low interaction energies between F4 and heme. 2.8. Similarity-based analog searching To gain an initial understanding of the SAR of the obtained 4 hits, a similarity-based analog search was conducted using the Specs

database. Unfortunately, no analog of compound V27 was available in this database. Thus, SAR studies mainly focused on compounds V2, V8 and V19. After cluster analysis, seven analogs of V2, seven analogs of V8, and sixteen analogs of V19 were selected for biological evaluation. In addition, these analogs also possessed favorable PK properties according to the ADME prediction (Table S6). A total of 13 compounds displayed more than 50% inhibition of the IDO1 activity at 30 mM, and their IC50 values against IDO1 were measured (Table S5). At the end of the assay, cell viability was also evaluated, and these compounds had no obvious cytotoxicity except S5, S16, S20 and S21. Among the analogs of V2, only S3 (IC50: 5.6 mM) containing 3,4dimethyoxy showed similar inhibitory activity to V2. S4 (IC50:

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12.4 mM) exhibited slightly decreased inhibition against IDO1 in comparison with V2, which might be due to the small size of the fragment (furan ring) in pocket B. Significant reduction in inhibitory potency was observed for S1 (IC50: 20.1 mM), where a relatively larger naphthalene-substituted group existed in pocket B compared with V2. This result indicated that the suitable steric effect of the fragment in pocket B had great impact on inhibitory activity. Compounds S2, S6 and S7 encompassing a relatively long and rigid linker and S5 which contains a furan ring instead of the benzene significantly lost its potency in this cellular assay. This suggested that different para-substitutions on the phenyl group and linker variations had great impacts on the activity, which provides clues for further optimization in future. For V8 analogs, no compound displayed more potent IDO1 inhibitory effect than V8 itself (IC50: 20.6 mM). For V19 and its analogs, N'-((2hydroxynaphthalene-1-yl)methylene)acetohydrazide was identified as the basic common scaffold (S15eS26). S18 (IC50: 2.1 mM) was observed to be the most potent compound in this series, followed by S24 (IC50: 4.8 mM). The linker between the hydrazide and substituted phenyl group had a substantial influence on the inhibitory activity, where the bulky group resulted in a considerable loss in potency (S15 and S20), and smaller group such as ethyl group (S18) led to increased inhibitory activities compared to S23 (IC50: 8.6 mM). Moreover, switching the linker from hydrazide to hydrazine (S29 and S30) or 2-oxopropanehydrazide (S21 and S28) resulted in a loss in potency, which can be explained by the unfavorable steric interactions with the protein. 2.9. Chemical modification of S18 by simplification of the structure In consideration of the promising inhibitory activity and synthetic feasibility, S18 was chosen as the lead and subsequent structural optimization was carried out in order to further improve the potency. To gain a better understanding of the molecular basis of the inhibitory activity, we analyzed the putative binding mode of S18. As depicted in Fig. 11, the 4-chloro-2-methylaniline moiety in pocket A formed two p-p interactions with the residues Tyr126 and Phe164, simultaneously, which is the main contributor of its binding affinity. The naphthol fragment was located in pocket B with a rigid acethydrazide linker connecting 4-chloro-2methylaniline group, similar to that of V19. Nonetheless, some bad contacts and unfavorable interactions appeared in this area which required further modification of the naphthol moiety to develop a compound with higher inhibitory activity. Subsequently, our emphasis switched to optimizing alternative

regions of the molecule by the replacement of the naphthol unit with a smaller group as a synthetic handle, with the aim of avoiding the bad contacts between the inhibitor and the residues at the active site (Fig. 12). Although the compound S18e1 (removal of the hydroxyl group in S18) did not have any substantial influence on the inhibitory effect, the replacement of the naphthol with phenol group (S18e2) caused a modest increase in activity (2-fold). The putative binding mode suggested that the naphthalene moiety around the pocket B displayed several unfavorable steric interactions between atom pairs in close contacts, and the rigid linker could not allow proper conformational adjustment, thus hindering the molecules from fitting into the pocket B. Removal of a benzene ring of S18, therefore, led to an enhanced activity by relieving these bad contacts (S18e2, IC50: 954 nM). Accordingly, we synthesized compound S18e3 without any substitutions on the phenyl group. As expected, it did exhibit a significant improvement (35-fold) in activity when compared to S18. This could be attributed to the removal of the hydroxyl group which led to an enhanced hydrophobic interaction with the surrounding residues. Taken together, this modification of S18 led to the identification of the most potent compound S18e3 (IC50: 59.8 nM), demonstrating that the naphthol unit was not essential for the inhibitory activity of IDO1 in this series. This modification laid a strong foundation for further lead optimization. Finally, in order to investigate if S18e3 could bind to apo-IDO1 instead of holo-IDO1, we performed the enzymatic assay according to the method of GSK's article [89]. As expected (Fig. S16), S18e3 was not an apo-IDO1 inhibitor, albeit some resemblance to the 2D structure of the known apo-IDO1 inhibitor BMS986205. 2.10. Effect on IDO1 protein expression in the cell-based assay To examine whether these compounds could influence IDO1 protein expression, Western blot analysis was carried out for V2, S18, S18e2, S18e3 and S24 in HeLa cells. As shown in Fig. 13A and B, all the tested compounds turned out to be incapable of suppressing IDO1 expression in the cell-based assay. Taken together, our data indicated that these compounds inhibited the Kyn production by affecting IDO1 enzymatic activity rather than its expression and HeLa cell viability. 2.11. Selectivity toward TDO2 TDO2, another heme-containing enzyme that catalyzes the first step of the Kyn pathway, is constitutively expressed in the liver,

Fig. 11. Putative binding mode of S18 within the active site of IDO1. In pocket A, cyan dotted lines represent the pp interaction. In pocket B, red and orange dotted line represent the bad and unfavorable contacts, respectively. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

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Fig. 12. Chemical modification of S18.

Fig. 13. (A) IDO1 inhibitors did not alter expression of IDO1. HeLa cells were treated with IFN-g with or without the inhibitors at their 3-fold HeLa IC50 concentrations for 24 h, and analyzed by Western blot using an anti-IDO antibody. (B) Quantification of IDO protein expression. (C) TDO2 inhibitory rate of the compounds. HeLa cells were transfected with TDO2 plasmid for 24 h, then the cells were collected and seeded them into 96 plates. After 6 h, the transfected HeLa cells were treated with IFN-g and the compounds for 24 h. The test method for TDO2 enzyme activity was the same as the HeLa cell-based enzyme assays. Data were averages from three independent experiments with standard deviations.

where it is believed to be responsible for maintaining systemic Trp levels [51]. Although the Trp metabolism via TDO2 represents an alternative route to IDO1 in certain types of tumors, it remains to be confirmed whether dual IDO1/TDO2 inhibitors could achieve therapeutic effects in human cancers [52]. Therefore, in order to test the selectivity of the compounds in inhibiting the IDO1 enzyme, their inhibitory activities against TDO2 were measured in a cell-expressing human TDO2 (hTDO2) assay. As shown in Fig. 13C, like the IDO1-selective inhibitor epacadostat, the inhibitory rates of all tested compounds were less than 20% at the concentration of 10fold of the IC50 values obtained from the previous HeLa-based inhibition assay, while a dual IDO1/TDO2 inhibitor NLG919 analog showed moderate TDO2 inhibitory activity [53]. These results suggested that these tested compounds were IDO1-selective inhibitors. 2.12. S18e3 could reverse IDO1-mediated immunosuppression in vitro Teffs such as CD8þ T cells play a dominant role in negatively controlling cancer development, whereas Treg cells suppress immunogenic response and lead to immune escape of cancer cells [54]. Previous studies suggested that IDO1 suppressed T cell proliferation by degrading Trp and increasing the level of Trp degradation products, and IDO1 inhibition could reverse this effect [55].

Here, to determine whether our compounds could improve T cell proliferation in the presence of tumor cells, we performed T cell proliferation assay, where CT26 murine colon carcinoma cells, which have been shown to express IDO1 at a high level [56], were used to co-culture with naïve T cells. As shown in Fig. 14A, compared with the control, compounds V2, S18 and S18e3 displayed a significant augmentation on the rate of T cell proliferation, particularly in the case of V2 and S18e3. It is known that IFN-g is a major immune-modulating molecule produced mainly by T cells and NK cells activated by antigens, mitogens or alloantigens [57]. In this co-culture system, IFN-g was produced predominantly by Teffs, which were reactivated once the immunosuppressive environment was lifted through IDO1 inhibition. As shown in Fig. 14B, S18e3 displayed the strongest production of IFN-g, and an approximately 2-fold increase in IFN-g levels was seen in the S18e3 treatment group compared to DMSO control. These results demonstrated that treatment of IDO1-expressing CT26 cells with V2 and S18e3 could increase cytokine production as well as the growth of the neighboring T cells. In addition, high IDO1 expression in the tumor microenvironment can also promote the differentiation of naïve T cells into highly immunosuppressive Treg cells, inducing immunoescape and the subsequent tumor cell growth [58]. Therefore, we also tested if our compounds could affect Treg cell conversion. As shown in Fig. 14C, when naïve T cells were co-cultured with CT26 cells under

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Fig. 14. (A) T cell proliferation assays in the CT26-T cells co-culture system. Data represent mean ± SEM, *p < 0.05, **p < 0.01 versus control. (B). IFN-g levels in the CT26-T cells coculture system. Data represent mean ± SEM, *p < 0.05, **p < 0.01 versus control. (C). Decreased differentiation of Treg cells in the CT26-naïve T cells co-culture system. Data represent mean ± SEM, *p < 0.05, **p < 0.01 versus T þ CT26 group. A representative plot of FACS analysis is presented. CD4þCD25þFOXP3þ positive cells were defined as Treg cells. Compounds were added to the system at the concentration of 0.3 mM in all three assays. In these experiments, V2, S18 and S18e3 at concentrations of 0.3 mM had yielded no appreciable inhibitory activity against T cells or CT26 cells alone (data not shown). Each bar of the graph indicates the mean of three replicate wells with standard error of the mean.

Con A stimulation, treatment of testing compounds gave rise to an approximately 4-fold increase in the number of Treg cells compared with naïve T cells cultured alone. To our surprise, the addition of S18 and S18e3, but not V2, to the system resulted in significant reduction of Treg cell conversion. Taken together, S18e3, the most potent compound in this research, could reverse IDO1mediated immunosuppression in vitro through the promotion of T cell proliferation, the increase of IFN-g production and the blockage of Treg cell conversion, which is critical for T cells-based immunotherapies.

2.13. S18e3 suppressed colon cancer growth in immunocompetent but not in immunodeficient mice Considering the promising biological profile in vitro as well as their structural novelty, compounds V2 and S18e3 was further evaluated in a mouse xenograft model of colon cancer in order to determine whether IDO1 inhibition and reversing immune tolerance could curtail tumor growth in vivo. Xenograft tumors were generated by subcutaneous implantation of CT26 cells into BALB/c mice. Compounds V2 and S18e3 were administered by oral gavage at 50 mg/kg daily for 17 days. Tumor volumes were measured every 3 days for 17 days. 5-Fluorouracil (5-Fu) (25 mg/kg) was used as a reference compound. After 17 days of treatment, compound S18e3 significantly impeded the growth of tumor and reduced average tumor weight (36%) in mice compared to the vehicle group without significant body weight loss (Fig. 15). Whereas, compound V2 showed less effect on tumor growth in mice, which could be partly attributed to its relatively weaker inhibitory activity against IDO1

and lower ability to block Treg cell conversion in vitro. Furthermore, to investigate the in vivo anti-tumor mechanism of the testing compounds, immunodeficient BALB/c nude mice bearing established CT26 tumors were treated by oral gavage with S18e3 for 10 days. As shown in Fig. 16, administration of S18e3 did not affect tumor growth in BALB/c nude mice, while traditional cytotoxic drug 5-Fu dramatically suppressed tumor growth. These data implied that S18e3 inhibited tumor growth in mice by activating the functional immune system rather than cytotoxicity.

2.14. S18e3 inhibited tumor cell growth and induced apoptosis in mice To observe the changes of the structure and appearance of cancerous cellular structures after administration, the suspected tumor tissue samples were initially stained using hematoxylin and eosin (H&E) to highlight different tissue structures such as cell nuclei and cytoplasm. As shown in Fig. 17A, administration of V2 and S18e3 decreased the amount of tumor cells, leading to the change of morphological features including cell shrinkage and condensation and margination of nuclear chromatin. In order to investigate the mechanism of tumor cell death, the dissected tumor tissues were subjected to immunohistochemistry (IHC) experiment. Since proliferating cell nuclear antigen (PCNA) is known as an indicator for evaluating the state of cell proliferation, we subsequently analyzed the immunostaining of PCNA in tumor tissue. The profiling data showed that the expression of PCNA were significantly decreased in the treated groups, especially in S18e3 treated tumors, suggesting that the proliferation of tumor cells was

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Fig. 15. IDO1 inhibitors suppressed colon cancer growth in mice. 1  106 CT26 cells were transplanted subcutaneously into the armpit of the BALB/c mice. Three days after transplantation, mice were randomly allocated to vehicle control or treatment groups (n ¼ 6). Drugs were administered on days 1e17. (A) Tumor volume and mouse body weight (B) were recorded. (C) After sacrifice, solid tumors were separated and weighed (D). Data represent mean ± SEM, n ¼ 6, **p < 0.01 versus vehicle.

arrested after the treatment with IDO1 inhibitor (Fig. 17B). In another experiment, terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL) assay was used to identify and quantify apoptotic cells, which exhibited a strong nuclear green fluorescence. The pathological pictures of tumor sections showed that there were more cell apoptosis occurring in the S18e3 treatment group than those in the vehicle-treated group (Fig. 17C). Based on these data, S18e3 was proven to be effective in suppressing the proliferation of tumor cells, and inducing apoptosis in mice. 2.15. S18e3 increased CD8þ T cell infiltration while reduced the number of Foxp3þ treg cells in murine tumor tissue Tumor-infiltrating T cells are a hallmark of continuous immune surveillance in cancer and have prognostic and therapeutic correlations in mice and human. Increasing T cell infiltration into tumor tissues has proven to be an effective strategy to improve the efficacy of tumor immunotherapy [59]. Therefore, we conducted an IHC analysis to further investigate the exact state of T lymphocytes in tumor specimens (Fig. 18). Administration of S18e3 resulted in an increase of infiltrating CD8þ T cells as well as IFN-g expression. Meanwhile, less Foxp3þ T cells (Treg cells) were detected in IDO1 inhibitor treated tumors, especially in the S18e3 treated group. It is well-known that Granzyme B is a serine protease most commonly found in the granules of CTLs, which can mediate cells apoptosis [60]. IHC analysis demonstrated that the expression of Granzyme B was dramatically increased in the tumor specimens treated with S18e3, suggesting the elevated function of CTLs. Taken together, our results revealed that S18e3 could induce CD8þ T cell recruitment to the tumor microenvironment in mice, decrease Foxp3þ

Treg cell population as well as elevate the function of CTLs.

3. Conclusion In this paper, we described the process of our medicinal chemistry program for the discovery of novel IDO1 inhibitors from hit identification to structural optimization via using several technological means including VS, chemical synthesis and bioactivity testing both in vitro and in vivo. In the process of VS, the customized HmBG-based pharmacophore, cascade molecular docking using metal-containing scoring functions, and molecular ESP calculation were successively utilized to obtain novel IDO1 inhibitors with four different scaffolds. All the hits could interact with heme iron, which was confirmed by UV absorption spectra experiment and QM calculations. Subsequently, a similarity-based analog search combined with subsequent structural optimization led to the discovery of S18e3, which showed an IC50 value of 59.8 nM in the HeLa cellbased kyn assay. In addition, in vitro T cell proliferation experiments revealed that S18e3 could take effect in the immune system and play a role of immunomodulation. Further results from indepth biological evaluations have shown an intriguing efficacy in suppressing the growth of CT26 tumors in BALB/c xenograft mice via promoting CD8þ T cell infiltration, increasing IFN-g production, and reducing the Foxp3þ Treg cell fraction. Its antitumor efficacy response only seen in immunocompetent mice. This work provides an excellent starting point for further optimization of the potency and pharmaceutical properties of this series to obtain IDO1 inhibitors that are suitable for preclinical development. Meanwhile, we anticipate that our VS strategy can provide a broadly applicable method for the rapid identification of potential regulators for other

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Fig. 16. S18e3 did not suppress colon cancer growth in immunodeficiency mice. 1  106 CT26 cells were transplanted subcutaneously into the armpit of the BALB/c nude mice. Three days after transplantation, mice were randomly allocated to vehicle control or treatment groups (n ¼ 6). Drugs were administered on days 1e10. (A) Tumor volume and mouse bodyweight (B) were recorded. (C) After sacrifice, solid tumors were separated and weighed (D). Data represent mean ± SEM, n ¼ 6, *p < 0.05, **p < 0.01 versus vehicle.

hemeproteins. 4. Experimental 4.1. Computational methods 4.1.1. Protein structure preparation X-ray crystal structure of hIDO1 in complex with epacadostat was downloaded from Protein Data Bank (PDB) (PDB ID: 5WN8) [61], which was prepared by Protein Preparation Wizard module in Maestro [62] with all hydrogen added and water deleted. Other parameters were set as default. 4.1.2. Database preparation The Specs and ChemBridge databases, which contain more than 1,250,000 commercially-available compounds in total, were filtered with Lipinski's rule of five (molecular weight  500, SlogP  5, number of hydrogen bond donors  5, number of hydrogen bond acceptors  10, number of rotatable bonds  10) [63], Veber rule (polar surface area 140 Å2, total number of hydrogen bond donors and acceptors  12) [64] and PAINS [65] to construct a theoretical drug-like database. The customized DUD database was constructed with 31 annotated IDO1 inhibitors and the identified 2000 decoy molecules. Namely, the decoys should have similar physical properties with seeded known IDO1 inhibitors and can also be distinguished topologically. With this concept in mind, three steps were employed to choose the proper decoy molecules. First, compounds' similarity with 31 annotated IDO1 inhibitors was scaled by the Tanimoto coefficient using the extended-connectivity fingerprints (FCFP_6).

Compounds with the Tanimoto coefficient less than 0.6 were selected. Second, five physical properties including molecular weight, number of HBAs and HBDs, number of rotatable bonds, and AlogP were calculated for both known IDO1 inhibitors and molecules obtained from last steps. The choices were made only when the calculated physical properties of certain molecules were close to those of any annotated IDO1 inhibitors. Besides, in order to mimic the unbalanced nature of inactives and actives in the real database screening, a high ratio of decoy molecules versus true actives (200:3) was adopted in this research, which led to a total of 2000 decoys from ZINC. Finally, the identified 2000 decoy molecules were combined with 31 annotated IDO1 inhibitors to form the DUD database. All the molecules were converted into 3D structures and energy minimization process was performed with CHARMM general force field [87] in DS3.0 and the ionization states of the molecules were generated at pH 7.4. Other parameters were set to the default values. During ligand preparation, only one 3D structure was constructed per achiral compound. For chiral molecule, we have specified to enumerate isomers of each structure.

4.1.3. MD simulations The understanding of proteineligand binding mode is of critical importance for biomedical research, yet the process itself has been very difficult to study because of its intrinsically dynamic character. MD simulation, first developed in the late 70s [66], has become a highly important technique to consider the physical movements of atoms and molecules, which is allowed to interact for a fixed period of time, giving a view of the dynamic evolution of the system. Chain A of the IDO1-epacadostat co-crystal structure (PDB ID:

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Fig. 17. S18e3 inhibited growth and induced apoptosis of tumor in mice. Tumor sections were infused in formaldehyde solution for immunohistochemistry. (A) H&E staining of tumor tissue. (B) Expression of PCNA. (C) TUNEL staining of tumor sections. The areas of apoptotic cells can be detected by fluorescence microscopyeequipped with fluorescein isothiocyanate (FITC) filter where they appear green (TUNEL positive). Scale bar, 50 mm. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

5WN8), holo-IDO1 and apo-IDO1 were used as the initial structures for MD simulations. The general AMBER force field (gaff) [67] was used for the inhibitor and the AMBER14SB force field [68] was used for the protein. After all the ionizable residues of IDO1 were set to the standard protonated or deprotonated states, the hydrogen atoms were added by using the tleap module of Amber14 program. In IDO1-epacadostat system, epacadostat was treated as a deprotonated state. The parameters of the heme site were constructed with MCPB. py software [73], which facilitates metal site modeling with classical force field parameters derived from quantum mechanics calculations with B3LYP functional and Grimme's D3 dispersion correction with Becke-Johnson (BJ)-damping [69,70]

using Gaussian 09 software package [71]. The atomic partial charges for the heme site (His346-heme-epacadostat model of epacadostat-IDO1 and His346-heme model of holo-IDO1) were the restrained electrostatic potential (RESP) charges determined by fitting with the RESP procedure using ChgModB method implemented in MCPB. py [74]. Then a hybrid bonded/restrained nonbonded model was built, in which the harmonic restraint was applied for FeeO bond, and other atoms connected to Fe were treated in the bonded pattern. The harmonic restraint was defined by the bond force constant and the equilibrium bond distance between Fe atom and its ligating oxygen atom. The detailed force field parameters and RESP charges of the heme active center were given

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Fig. 18. IDO1 inhibitors increased CD8þ T cell infiltration while reduced Foxp3þ T cell number in tumor tissue. Tumor sections were infused in formaldehyde solution for immune stain. (A) CD8þ T cells in tumor tissue. (B) Expression of IFN-g. (C) Foxp3þ T cells in tumor tissue. (D). Expression of Granzyme B. Scale bar, 50 mm.

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in the Supplementary Information (Document S2, S3, S4 and S5). MD simulations of all complexes were performed using pmemd or sander module of AMBER 14 [72]. Each system was immersed in a periodic truncated octahedron box of TIP3P [75] water molecules with a margin distance of 10.0 Å. The counter ions were added to the solvent to keep the system neutral. The geometry of the system was minimized in two steps before MD simulation. First, the water molecules were refined through 2500 steps of steepest descent minimization followed by 2500 steps of conjugate gradient minimization, while the protein was kept fixed with a constraint of 10.0 kcal mol1 Å2. Second, the complexes were relaxed by 5000 cycles of minimization procedure (2500 cycles of steepest descent and 2500 cycles of conjugate gradient minimization). During the simulation, the Particle Mesh Ewald method [76] was employed to calculate the long-range electrostatic interactions, while the SHAKE method [77] was applied to constrain all covalent bonds involving hydrogen atoms to allow the time step of 2 fs. A 10 Å cutoff value was used for the non-bonded interactions. After system optimization, running of MD simulations was started on the systems by gradually heating each system in the NVT ensemble from 0 to 300 K for 50 ps using a Langevin thermostat with a coupling coefficient of 1.0/ps and with a force constant of 2.0 kcal (mol Å2)1 on the complex. And then 500 ps of density equilibration with a force constant of 2.0 kcal (mol$ Å2)1 on the complex was performed. Subsequently, the systems were again equilibrated for 500 ps by releasing all the restraints, in which Berendsen barostat was used to control the pressure. Finally, production runs for 20 ns MD simulations were performed under a constant temperature of 300 K in the NPT ensemble with periodic boundary conditions for each system. The snapshots saved at 1 ps intervals were used for further analysis. 4.1.4. MD trajectories analysis Trajectories generated from MD simulations were analyzed via cpptraj module of AmberTools 14. RMSD value is a useful estimation for quantifying conformational changes of the same protein. The RMSDs for backbone atoms of proteins (complex) and the heavy atoms of the inhibitor and the residues in the active site (pocket) relative to their starting structures were analyzed for three systems, as shown in Fig. S1. The RMSF plots were used to determine the flexibility of residues in the binding pocket during the simulation. Radius of gyration (Rg), reflecting the compactness of a structure, was then calculated. Cluster analysis is the way of determining structure populations from simulations. Clustering is a means of partitioning data so that data points inside a cluster are more similar to each other than they are to points outside a cluster. The cluster analysis of protein conformations was carried out with average linkage as the clustering algorithm, and backbone atom RMSD as the distance metric. This average linkage algorithm is recommended in all cluster algorithms. VMD [78] and PyMOL [79] softwares were employed to visualize the trajectories and to depict structural representations. 4.1.5. MM/PBSA binding free energy calculation and binding energy decomposition analysis The obtained stable MD trajectory was used to estimate the binding free energy (DGbind) using the MM/PBSA technique implemented in AMBER14 [80]. This is a post-processing method in which representative snapshots from an ensemble of conformations are used to calculate the free energy change between two states (typically a bound and free state of a receptor and ligand). The MM/PBSA approach combines molecular mechanics and continuum solvent models to predict the protein-ligand binding free energy. Free energy differences are calculated by combining the gas phase energy contributions that are independent of the chosen

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solvent model as well as solvation free energy components (both polar and non-polar) calculated from an implicit solvent model for each species. The binding free energy, D Gbind, can be calculated as:

DGbind ¼ Gcomplex - (Gprotein þ Gligand) Each term can be estimated as follows:

DG ¼ DGMM þ DGsol - TDS Where DGMM is the molecular mechanics free energy, DGsol is the solvation free energy, and TDS represents the entropy term. The molecular mechanics energy is calculated by the electrostatic and van der Waals interactions, while the solvation free energy is composed of the polar and the nonpolar contributions:

DGMM ¼ DGele þ DGvdW DGsol ¼ DGpolar þ DGnpolar The contribution of polar solvation energy, DGpolar, is calculated with the Poisson-Boltzmann (PB) implicit solvent model, whereas the nonpolar part of the solvation energy, DGnpolar, is dependent on the solvent accessible surface area (SASA) [81]. -TDS is the contributions arising from changes in the degrees of freedom of the solute molecules, which was often neglected in practical applications. Before the formal MMPBSA calculation, we analyzed the stability of binding free energy during the 200 ns simulation. A total of 2000 snapshots were extracted from the entire trajectories every 100 frames. For each snapshot, MM/PBSA binding free energy was calculated as the difference between the free energy of the complex and the total of the free energies of the receptor and the ligand. The binding free energies were decomposed into contributions from protein-ligand interaction pairs, which consists of four terms: DGele, DGVdw, DGPB, and DGNpolar. As can be seen in the plots (Fig. S7), more fluctuations were displayed during the first 75 ns simulation, especially in DGele and DGNpolar terms. After 75 ns of simulation, only small fluctuations were detected. Besides, the standard deviations for all values during last 100 ns simulation were smaller than that during the first 75 ns simulation. Thus, these results demonstrated that the MMPBSA binding free energy reached convergence after the first 100 ns simulation. Ultimately, 100 snapshots extracted from the last 50 ns stable MD trajectories were submitted to the ultimate MM/PBSA binding free energy calculation. The interactions between single residue in IDO1 and epacadostat were analyzed using the MM/PBSA free energy decomposition analysis. 4.1.6. Structure-based pharmacophore modeling and screening The Receptor-Ligand Pharmacophore Generation protocol implemented in DS3.0 generates a default pharmacophore model from IDO1-epacadostat co-crystal complex (PDB ID: 5WN8) with default parameters. The protocol is capable of identifying various types of ligand-receptor interactions in a predefined binding site, such as hydrogen bond interactions, hydrophobic areas and charge transfer interactions. Because the HmBG feature was not provided in the feature dictionary of Discovery Studio 3.0, we generated a customized HmBG feature by replacing an HBA feature representing the ligating atom using the Customize Pharmacophore Features. The customized HmBG feature was defined by specifying hydroxyl oxygen atom of epacadostat as a query atom. The specification of €hrig, in which this query atom was based on a paper published by Ro MMBP-corrected classical ligand-heme interaction energies (EMM) and distances (rMM) are calculated by DFT method [33]. In this study, we chose the ligating group that showed EMM < 10 kcal/

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mol, and defined the HmBG feature in the parameter settings panel. Excluded volumes were placed around receptor atoms within 4 Å of the above feature's centers and a default 1.2 Å tolerance radius was applied. In terms of hydrophobic features, both aromatic and aliphatic interactions were regarded as hydrophobic interactions in the analysis. Excluded volumes around the 7-propionate group of heme were removed to simulate the flexibility of this group. Additional and unnecessary excluded volumes were manually removed so as to still form a representation of the pocket's surface, whilst reduced the time cost for the database searching. The constraint tolerance of spheres and excluded volumes were further refined to default values of DS3.0 except HmBG feature (1.0 Å, according to the average value of rMM). The reliability of pharmacophore models was measured by two different matrices: enrichment factor (EF) and goodness of hit (GH). The EF value was calculated as fraction of retrieved actives in the hit lists divided by the fraction of total actives in the database, and the GH value was computed through the following equation, where Ha and Ht represent the number of positives in the hits and the total number of hits, respectively, and A and D refer to the number of actives and the total number of molecules in the database correspondingly. EF ¼ (HaⅹD) / (HtⅹA) GH ¼ {[Haⅹ(3A þ Ht)]/(4HtⅹA)}ⅹ[1 - (Ht - Ha)/(D - A)] Conformations of all 3D structures in the Specs and ChemBridge databases were generated using the Generate Conformations protocol with the Best/Caesar option and a maximum of 255 conformations were generated within the energy threshold of 20.0 kcal/ mol. The pharmacophore model was employed to screen the multiconformational database using the Screen Library protocol with flexible and no features omitting method. After removal of the duplicated molecules, hit compounds from pharmacophore-based screening were selected for further molecular docking study. 4.1.7. Cascade docking screening All the molecules passing the pharmacophore screening were subjected to semi-flexible docking to further narrow down the hits list. The IDO1 co-crystal structure with epacadostat was utilized as the docking receptor. Gold 5.3 with metal parameters were applied in this process, as well as GoldScore and ChemScore with heme scoring function derived from statistics of the PDB database were adopted to rank the docking results. The region within a radius of 10 Å centered on the iron atom of heme was defined as the active site for docking study. Ten genetic algorithm (GA) runs were performed for docking. The standard default parameters were adopted for the docking experiment except the early termination option which was set to off. The annealing parameters of van der Waals and H-bond interactions were applied within 4.0 and 2.5 Å, respectively, to allow poor geometry at the beginning of a GA run. In addition to the scoring functions mentioned above, the Affinity dG Scoring and London dG Scoring in MOE2009 were used for rescoring the binding pose obtained from Gold. Affinity dG Scoring which estimates the enthalpy contribution to the free energy of binding and considers the interactions between nitrogen/sulfur atom and transition metals; London dG Scoring which estimates the free energy of binding of the ligand from a given pose by measuring geometric imperfections of metal ligations and the energy of an ideal metal ligation. These results were further refined using the Cscore calculations, which could improve the predictive power of in-silico screening for drug design [82]. Cscore is a combination of four different scoring functions, namely GoldScore, Chemscore, Affinity dG Scoring and London dG Scoring. The Cscore

value was set to 1 if a molecular was ranked at the top 60% by a single scoring function. Therefore, the Cscore value is an integer ranging from 4 (when all of the four scoring functions ranked the ligand at the top 60%) to 0 (none of the four scoring functions ranked the ligand at the top 60%) and helps to distinguish the most promising compounds from the massive plausible hits. In addition to Cscore, the chemscore-metal value which reflects the formation of an interaction between the ligand and metal was also analyzed. Thus, compounds were kept if characterized with a Cscore value  3 and a chemscore-metal value  0.3, respectively. Then, the top ranking compounds were clustered based on the FCFP_6 fingerprints calculation and then selected manually. These compounds were subsequently submitted to IFD calculations, which considered the flexibility of both ligand and protein. During the calculation, ligands were first docked into the rigid receptor using softened energy function in Glide. By default, a maximum 20 poses per ligand were retained. Then, the protein degrees of freedom for each complex were sampled and the protein-ligand complexes were minimized. The protein structure in each pose now reflected an induced fit to the ligand structure and conformation. The best protein-ligand complex was then identified based on the predicted binding affinities of the docked ligand. Here, the residues within 5 Å of each of the 20 ligand poses were subjected to a conformational search and energy minimizations, and the residues outside this range were fixed. Finally, the minimized ligand was rigorously redocked into the induced-fit protein structure using Glide XP scoring mode, and metal constraints was applied to both Glide docking stages in induced fit docking protocol. The choice of the best-docked structure for each ligand was made using an IFD docking score that combines the energy grid score, the binding affinity predicted by GlideScore, and the internal strain energy for the model potential used to direct the conformationalsearch algorithm. 4.1.8. QM calculations Small molecular and heme-HIS346 complex were subtracted from the complex in the IFD calculation results, and then the regularized porphin models (fragment-ironporphinimidazole), in which lateral substituents of the protoporphyrin and the main chain atom of histidine were neglected for the sake of clarity, were employed to keep both the coordination environment on the iron and the p electron delocalization of the macrocycle. In F1porphinimidazole model, fragment F1 was treated as a deprotonated state [83]. All of the DFT ground electronic state calculations were conducted in an implicit solvent by employing the SMD solvent model using Gaussian 09 software package [71]. In the case of molecular ESP calculation, the optimization of hydrogen atom of small molecular was conducted using B3LYPD3(BJ)/6-311G* level of theory. The regularized porphin models were fully optimized with the B3LYP-D3(BJ) method and 6-311G* base set. Frequency calculations have been performed at the same level and confirmed the minimum nature of the complexes. Moreover, single-point energy calculations for QTAIM and ANO analysis were carried out at the B3LYP-D3(BJ)/def2-TZVP level, while the binding energy calculations were performed at the B3LYP-D3(BJ)/ma-TZVP level. Binding energies were calculated by subtracting the energy of the 5-fold coordinated system and the energy of the isolated ligand from the energy of the 6-fold coordinated system. For the 5-fold coordinated system (ferrous ironporphinimidazole) (total charge ¼ 0), the ground state with the hybrid DFT functional is a quintuplet. For the 6-fold coordinated systems (total charge ¼ 1 for F1 binding model, and 0 for other 6fold coordinated systems), a low-spin complex was assumed [83]. Wave function stability check was performed for all calculations. Multiwfn [84], an extremely powerful wavefunction analysis

Y. Zou et al. / European Journal of Medicinal Chemistry 184 (2019) 111750

program, was utilized in this study to perform wavefunction analyses based on outputted file of Gaussian program, which contained molecular ESP calculation, QTAIM, LMO and ANO analysis, etc. VMD program is needed in the ESP, QTAIM and LMO plotting. Unless otherwise specified, all units are in a. u. 4.1.9. ADME properties prediction The identified hits were studied for their ADME properties by using the QikProp [85]. The program was executed by employing a default mode for the principal descriptors and physicochemical properties of all hit compounds, with detailed analysis of the QPpolrz (predicted polarizability), QPlogPo/w (predicted octanol/ water partition coefficient), QPlogHERG (blockage of HERG Kþ channels), QPPCaco (Caco-2 cell permeability), QPlogBB (the predicted partition coefficient of the brain/blood barrier), and percentage of human oral absorption. Significant ADME parameters with their recommended values were polarizability (QPpolrz: 13.0e70.0), octanol/water partition coefficient (QPlogPo/w: 2.0e6.5), the blockage of HERG Kþ channels (QPlogHERG: concern below 5), cell permeability (QPPCaco: value < 25 is poor, value > 500 is great), brain/blood partition coefficient (QPlogBB: 3.0e1.2), and percentage human oral absorption (value > 80% is high, value < 25% is poor) [86]. 4.2. Biology 4.2.1. Determination of inhibitory activity in HeLa cell-based IDO1/ Kyn assay The compounds obtained from VS were purchased form Specs and ChemBridge Corp. and all compounds’ purities were equal or greater than 95%. HeLa cells were seeded in 96-well culture plates at a density of 5  103 per well. On the next day, human IFN-g (100 ng/mL) and compounds in a total volume of 200 mL culture medium containing 15 mg/mL of L-Trp were added to the cells. After incubation for 24 h, 140 mL of the supernatant was mixed with 10 mL of 6.1 N trichloroacetic acid and the mixture was incubated for 30 min at 50  C. The reaction mixture was then centrifuged for 10 min at 4000 rpm to remove sediments. 100 mL of the supernatant was mixed with 100 mL of 2% (w/v) p-dimethylaminobenzaldehyde in acetic acid and measured at 480 nm. The initial wells containing the cells in the remaining volume of 50 mL were used to estimate cell viability in a classical MTT assay. To that end, 50 mL of culture medium (Iscove medium with 10% FCS and amino acids) were added to the wells together with 20 mL 4 mg/mL of MTT. After 4 h of incubation at 37  C, 200 mL of DMSO were added to dissolve the crystals of formazan blue and the absorbance at 570 nm was measured after overnight incubation at 37  C. Graphs of inhibition curves with IC50 values were generated using Prism v.7.0. 4.2.2. Western blot analysis HeLa cells were seeded in 6-well culture plates at a density of 2  105 per well. On the next day, human IFN-g (100 ng/mL) and compounds in a total volume of 2 mL culture medium were added to the cells. After incubation for 24 h, the cells were collected and washed with PBS twice. Proteins were extracted from cells by lysis buffer consisting of 50 mM TriseHCl, pH 8.0, 50 mM KCl, 5 mM DTT, 1 mM EDTA, 0.1% SDS, 0.5% Triton X-100 (Sunshine Biotechnology Co. Ltd.,) and protease inhibitor cocktail tablets (Roche, Indianapolis, IN). The protein lysates were separated by 10% SDS-PAGE and subsequently electrotransferred onto a polyvinylidene difluoride membrane (Millipore, Bedford, MA). The membrane was blocked with 5% nonfat milk for 1 h at room temperature. The blocked membrane was incubated with the indicated primary Abs, and then with a horseradish peroxidase-conjugated secondary Ab. Protein bands were visualized using Western blotting detection

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system according to the manufacturer's instructions (Cell Signaling Technology, MA). 4.2.3. Measurement of IDO1 absorbance spectra The coding region for human IDO1 were subcoloned into a modified pET28 vector, thereby introducing a thrombin-cleavable His tag (6  ) at the N terminus of the resultant protein. The recombinant plasmid was transformed into E. coli BL21 (DE3). For expression, cells were grown at 37  C until the OD at 600 nm reached 0.5. To boost heme biosynthesis, 5-aminolevulinic acid hydrochloride was added from a 250 mM stock to each culture to a final concentration of 1.0 mM, and the cultures were incubated at 37  C until the OD at 600 nm reached 0.7. The flasks were removed from the incubator, placed in an ice bath, and chilled for 5 min. Isopropyl b-D-1-thiogalactopyranoside (0.5 mM) was added to each culture, and the flasks were returned to the shaker and incubated at 30  C for 6 h. The cells were harvested and washed by buffer A (50 mM potassium phosphate, 0.3 M potassium chloride, 25 mM imidazole, 5% glycerol, 0.1 mM Tris (2-carboxyethyl)phosphine, pH 7.1) and stored at 80  C. The purification of the full-length His-TbIDO1 protein was done as previously reported [88]. Absorbance spectra (370e600 nm) were measured immediately after addition of compounds (500 mM) to rhIDO1 (3 mM) in phosphate buffer (pH: 6.5) using Safire multifunctional microplate reader. Ferrous deoxy reaction environment was generated by adding Na2S2O4 to the solution under N2 atmosphere. Changes in the 428 nm maxima indicated binding to the ferrous iron of the heme. 4.2.4. T cell proliferation and cytokine assay CT26 cells were seeded in 6-well culture plates at a density of 1  105 per well. On the next day, compounds containing 15 mg/mL of L-tryptophan in a total volume of 2 mL culture medium were added to the cells. 48 h later, culture supernatant was collected for T cell proliferation assay performed as follows. T lymphocytes prepared from splenocytes of BALB/c mice were resuspended with 100 mL supernatant and 100 mL RPMI 1640 containing 10% FBS and 5 mg/mL ConA. 48 h later, supernatant was collected for ELISA assay for IFN-g while T cell proliferation was examined by MTT assay. To that end, an amount of 100 mL of MTT (1 mg/mL) added to the wells. After 4 h of incubation at 37  C, an amount of 100 mL of DMSO was added to dissolve the crystals of formazan blue and the absorbance was measured at 570 nm. Supernatants collected from the coculture system were subjected to ELISA analysis for IFN-g by using kits from Dakawe (Beijing, China). 4.2.5. Treg cell experiments Treg cells were analyzed by using an eBioscience intracellular staining kit according to the manufacturer's instructions. After cocultured with CT26 cells, T cells were collected. Surface staining was performed with a CD4-FITC and CD25-PE for 15 min at 4  C. After this, the cells were fixed and permeabilized with fixation buffer and permeabilization wash buffer. The intracellular staining was performed with FOXP3-APC for 20 min. The cells were then analyzed by flow cytometry analysis. 4.2.6. TDO2 inhibition assay HeLa cells were transfected with a plasmid construct encoding Human TDO2 for the cellular assay. The assay was performed in 96well flat bottom plates seeded with 5  103 cells in a final volume of 200 mL. On the next day, compounds containing 15 mg/mL of L-Trp in a total volume of 200 mL culture medium were added to the cells. After incubation for 24 h, 140 mL of the supernatant was mixed with 10 mL of 6.1 N trichloroacetic acid and the mixture was incubated for 30 min at 50  C. The reaction mixture was then centrifuged for

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10 min at 4000 rpm to remove sediments. 100 mL of the supernatant was mixed with 100 mL of 2% (w/v) p-dimethylaminobenzaldehyde in acetic acid and measured at 480 nm. 4.2.7. Animal model and treatments BALB/c mice (6e8 weeks old) were purchased from Model Animal Research Center of Nanjing University (Nanjing, China). Briefly, mice were fed with free access to pellet food and water in plastic cages at 21 ± 2  C and kept on a 12-h light-dark cycle. Animal welfare and experimental procedures were carried out strictly in accordance with the Guide for the Care and Use of Laboratory Animals (The Ministry of Science and Technology of China, 2006) and the related ethical regulations of our university. All efforts were

made to minimize animals’ suffering and to reduce the number of animals used. Mouse colon cancer cells (CT26) were cultured and collected by centrifugation (1000 rpm, 5 min) and washed twice with ice-cold PBS. Then cells were diluted to 1  107/ml and 1  106 CT26 cells (in 0.1 ml PBS) were injected subcutaneously into the right flanks of mice. All mice formed tumors three days after injection. Then mice were randomly distributed into four groups (n ¼ 6) according to tumor volumes. V2 and S18e3 (50 mg/kg) were administered (i.g) to each group respectively every day since day 0.5-Fu (25 mg/kg) were administered (i.p) every two days. Tumor length and tumor width were measured with a Vernier caliper every three days. Tumor volumes were measured and calculated using the equation volume ¼ a  b2/2, where “a” is the maximal width and “b” is maximal orthogonal width. On the 17th day, mice were weighed, euthanized, and tumors were removed and the weight were taken. Drugs were administered on days 1e17. 4.2.8. Immunohistochemistry analysis For immunohistochemistry staining, the sections were deparaffinized, rehydrated, and washed in 1% PBS-Tween 20, and then treated with 2% hydrogen peroxide, blocked with 3% goat serum (Life Technology, 16210e064) and incubated for 2 h at room temperature with specific primary antibodies. Then the slides were incubated with streptavidin-HRP (Shanghai Gene Company, GK500705) for 40 min, then stained with DAB (Shanghai Gene Company, GK500705) substrate and counter-stained with hematoxylin. Images were acquired by microscopy (Olympus IX51).

determined by HPLC chromatograms acquired on a shimadzu DGU20A3 HPLC instrument. Analyses were conducted by an Agilent TCC18, 250 mm  4.6 mm column, using a watereMeOH gradient with MeOH from 50% to 85% in 10 min. Detection was at 254 nm, and the average peak area was used to determine purity. The purity of the compounds from commercial databases and organic synthesis is more than 95%. All the reagents and solvents were reagent grade and were used without further purification unless otherwise specified. All products were obtained in racemic form. 4.3.1. Preparation of 2-((4-chloro-2-methylphenyl)amino) butanehydrazide

To a solution of 4-chloro-2-methylaniline (1 g, 7.06 mmol) in DMF (15 mL) was added DL-Ethyl 2-bromobutyrate (4.11 g, 21.19 mmol), potassium carbonate (4.39 g, 31.78 mmol). The reaction mixture was stirred at 90  C for 48 h. After the reaction was completed, the reaction mixture was cooled to room temperature, poured into water and extracted with EA. The organic layer was washed with brine, dried over anhydrous Na2SO4, filtered, and concentrated under reduced pressure. The solid was pure enough without any further purification. A mixture of ethyl 2-((4-chloro-2-methylphenyl)amino)butanoate (1.33 g, 5.2 mmol) and hydrazine hydrate (3.67 g, 52 mmol) in ethanol (45 mL) was stirred at 90  C for 10 h. The reaction mixture was concentrated under reduced pressure. The residue was partitioned between DCM and water. The organic layer was washed with brine, dried over anhydrous Na2SO4, filtered, and concentrated under reduced pressure. The solid residue was washed with diethyl ether and hexane to afford the intermediate 2-((4-chloro-2methylphenyl)amino)butanehydrazide without any further purification. 4.3.2. General procedure for preparation of N0 -benzylidene-2-((4chloro-2-methylphenyl)amino)-butanehydrazide derivatives (S18e1, S18e2 and S18e3)

4.3. Chemistry Melting points were determined on a RDCSY-I capillary apparatus and were uncorrected. All materials used were commercially available and used as supplied. HG/T2354-92 silica gel 60 F254 sheets were used for analytical thin-layer chromatography (TLC). Column chromatography was performed on silica gel (200e300 mesh). 1H NMR spectra were recorded on a Bruker AV-300 spectrometer. 13C NMR spectra were recorded on a Bruker AC500 NMR spectrometer using tetramethylsilane as an internal reference. Chemical shifts (d) were given in parts per million (ppm) relative to the solvent peak. Mass spectra (MS) were measured using a Thermo Fisher FINNIGAN LTQ spectrometer. Purity of compounds was

To a solution of 2-((4-chloro-2-methylphenyl)amino)butanehydrazide (0.44 g, 1.99 mmol) in ethanol (10 mL) was added dropwise the solution that 2-hydroxy-1-naphthaldehyde (0.313 g, 1.99 mmol) in ethanol (4 mL). Then, the reaction mixture was stirred at 80  C for 6 h. After the completion of the reaction, the mixture was cooled to room temperature and filtered off. The remained solid was washed with ethanol and water for 3 times. The target compound was pure enough without any further purification.

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4.3.2.1. 2-((4-chloro-2-methylphenyl)amino)-N'-(naphthalen-1ylmethylene)butane-hydrazide (S18e1). This compound was prepared from the corresponding 1-naphthaldehyde. White solid. Yield 85%; HPLC purity: 98.4%; mp 153e155  C; 1H NMR (300 MHz, DMSO‑d6) d 11.59 (s, 1H), 8.83 (t, J ¼ 4.2 Hz, 1H), 8.69 (d, J ¼ 7.3 Hz, 1H), 8.00 (t, J ¼ 8.3 Hz, 2H), 7.88 (dd, J ¼ 15.8, 7.2 Hz, 1H), 7.66e7.54 (m, 2H), 7.10e6.92 (m, 2H), 6.48 (m, 1H), 4.89 (t, J ¼ 12.3 Hz, 1H), 3.89 (t, J ¼ 7.4 Hz, 1H), 2.17 (d, J ¼ 9.0 Hz, 3H), 1.89 (q, J ¼ 8.3, 7.7 Hz, 2H), 1.02 (t, J ¼ 7.2 Hz, 3H). 13C NMR (126 MHz, DMSO‑d6) d 170.23, 147.69, 145.28, 144.92, 144.33, 134.04, 131.09, 130.96, 130.55, 129.89, 129.86, 129.80, 129.42, 129.24, 128.60, 128.14, 127.81, 127.78, 126.80, 126.75, 126.72, 126.69, 126.14, 125.98, 125.40, 124.85, 124.18, 120.61, 120.40, 111.86, 111.71, 58.69, 55.16, 26.30, 25.41, 17.80, 17.77, 11.28, 11.19. MS (ESI): m/z 402.3 [MþNa]þ. 4.3.2.2. 2-((4-chloro-2-methylphenyl)amino)-N'-(2hydroxybenzylidene)butane-hydrazide (S18e2). This compound was prepared from the corresponding salicylaldehyde. White solid. Yield 88%; HPLC purity: 96.8%; mp 164e166  C; 1H NMR (300 MHz, DMSO‑d6) d 11.74 (s, 1H), 11.03 (s, 1H), 8.45 (s, 1H), 7.51 (d, J ¼ 7.7 Hz, 1H), 7.26 (q, J ¼ 7.1, 6.4 Hz, 1H), 7.04 (d, J ¼ 10.8 Hz, 2H), 6.89 (t, J ¼ 8.3 Hz, 2H), 6.46 (t, J ¼ 7.5 Hz, 1H), 5.10e4.66 (m, 1H), 3.85 (q, J ¼ 7.2 Hz, 1H), 2.16 (d, J ¼ 9.1 Hz, 3H), 1.86 (p, J ¼ 7.4, 6.8 Hz, 2H), 0.99 (t, J ¼ 7.3 Hz, 3H). 13C NMR (126 MHz, DMSO‑d6) d 170.02, 157.73, 147.95, 144.82, 141.74, 131.89, 131.68, 129.88, 129.83, 129.62, 126.67, 125.39, 120.62, 120.03, 119.82, 119.06, 116.80, 116.65, 111.75, 58.47, 54.96, 26.21, 25.28, 17.78, 17.70, 11.14, 10.99. MS (ESI): m/z 368.3 [MþNa]þ. 4.3.2.3. N0 -benzylidene-2-((4-chloro-2-methylphenyl)amino)butanehydrazide (S18e3). This compound was prepared from the corresponding benzaldehyde. White solid. Yield 90%; HPLC purity: 98.8%; mp 160e162  C; 1H NMR (300 MHz, DMSO‑d6) d 11.44 (s, 1H), 8.11 (s, 1H), 7.56 (ddd, J ¼ 14.5, 6.7, 2.3 Hz, 2H), 7.31 (td, J ¼ 8.2, 7.3, 3.5 Hz, 3H), 7.01e6.78 (m, 2H), 6.34 (dd, J ¼ 8.6, 2.7 Hz, 1H), 4.81e4.66 (m, 1H), 3.70 (q, J ¼ 7.4 Hz, 1H), 2.03 (d, J ¼ 7.5 Hz, 3H), 1.70 (q, J ¼ 7.3 Hz, 2H), 0.85 (t, J ¼ 7.4 Hz, 3H). 13C NMR (126 MHz, DMSO‑d6) d 174.78, 170.25, 147.68, 144.87, 144.25, 134.57, 130.55, 130.44, 129.85, 129.83, 129.39, 129.26, 127.52, 127.30, 126.69, 126.64, 125.39, 125.14, 120.57, 111.81, 111.79, 58.61, 54.87, 26.28, 25.37, 17.78, 17.71, 11.15, 10.97. MS (ESI): m/z 352.3 [MþNa]þ. Acknowledgements This work was supported by grants from the National Natural Science Foundation of China (No. 21772233, 81730100, 21807104, 81922067), the Postgraduate Research & Practice Innovation Program of Jiangsu Province (No. KYCX18_0790) and the Fundamental Research Funds for the Central Universities (No. YK2017121107, 6204070087 and 020814380114). Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi.org/10.1016/j.ejmech.2019.111750. References [1] M. Sono, M.P. Roach, E.D. Coulter, J.H. Dawson, Heme-containing oxygenases, Chem. Rev. 96 (1996) 2841e2887. [2] Heme protein database. http://hemeprotein.info/heme.php. [3] I. Sevrioukova, Interaction of human drug-metabolizing CYP3A4 with small inhibitory molecules, Biochemistry 58 (2019) 930e939. [4] K.F. Greish, L. Salerno, R. Al Zahrani, E. Amata, M.N. Modica, G. Romeo, A. Marrazzo, O. Prezzavento, V. Sorrenti, A. Rescifina, G. Floresta, S. Intagliata, , Novel structural insight into inhibitors of heme oxygenase-1 (HO-1) V. Pittala by new imidazole-based compounds: biochemical and in vitro anticancer

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activity evaluation, Molecules 23 (2018) E1209. [5] L. Salerno, E. Amata, G. Romeo, A. Marrazzo, O. Prezzavento, G. Floresta, , Potholing of the hydrophobic V. Sorrenti, I. Barbagallo, A. Rescifina, V. Pittala heme oxygenase-1 western region for the search of potent and selective imidazole-based inhibitors, Eur. J. Med. Chem. 148 (2018) 54e62. [6] L. Capece, A. Lewis-Ballester, S.R. Yeh, D.A. Estrin, M.A. Marti, Complete reaction mechanism of indoleamine 2,3-dioxygenase as revealed by QM/MM simulations, J. Phys. Chem. B 116 (2012) 1401e1413. [7] J.C. Mbongue, D.A. Nicholas, T.W. Torrez, N.S. Kim, A.F. Firek, W.H. Langridge, The role of indoleamine 2, 3-dioxygenase in immune suppression and autoimmunity, Vaccines 3 (2015) 703e729. ndi, L.J. Ve csei, The kynurenine system and immunoregulation, J. Neural [8] Y. Ma Transm. 119 (2012) 197e209. [9] D.H. Munn, A.L. Mellor, Indoleamine 2,3 dioxygenase and metabolic control of immune responses, Trends Immunol. 34 (2013) 137e143. [10] J. Godin-Ethier, L.A. Hanafi, C.A. Piccirillo, R. Lapointe, Indoleamine 2,3dioxygenase expression in human cancers: clinical and immunologic perspectives, Clin. Cancer Res. 17 (2011) 6985e6991. , [11] I. Theate, N. Van Baren, L. Pilotte, P. Moulin, P. Larrieu, J.C. Renauld, C. Herve I. Gutierrez-Roelens, E. Marbaix, C. Sempoux, B.J. Van den Eynde, Extensive profiling of the expression of the indoleamine 2,3-dioxygenase 1 protein in normal and tumoral human tissues, Canc. Immunol. Res. 3 (2015) 161e172. k, N. Dobos, G. Koncz, Z. Kar , G. Halmos, [12] L. Hornya anyi, D. P all, Z. Szabo kvo €lgyi, The role of indoleamine-2,3-dioxygenase in cancer developL. Sze ment, diagnostics, and therapy, Front. Immunol. 9 (2018) 151. [13] S. Kumar, D. Jaller, B. Patel, J.M. LaLonde, J.B. DuHadaway, W.P. Malachowski, G.C. Prendergast, A.J. Muller, Structure based development of phenylimidazole-derived inhibitors of indoleamine 2,3-dioxygenase, J. Med. Chem. 51 (2008) 4968e4977. €hrig, L. Awad, A. Grosdidier, P. Larrieu, V. Stroobant, D. Colau, [14] U.F. Ro V. Cerundolo, A.J. Simpson, P. Vogel, B.J. Van den Eynde, V. Zoete, O. Michielin, Rational design of indoleamine 2,3-dioxygenase inhibitors, J. Med. Chem. 53 (2010) 1172e1189. [15] A.J. Muller, M.G. Manfredi, Y. Zakharia, G.C. Prendergast, Inhibiting Ido pathways to treat cancer: lessons from the ECHO-301 trial and beyond, Semin. Immunopathol. 41 (2019) 41e48. [16] https://www.clinicaltrials.gov/ct2/results? term¼IDO&Search¼Apply&recrs¼b&recrs¼a&recrs¼d&age_ v¼&gndr¼&type¼&rslt¼. [17] Y. Kozuma, K. Takada, G. Toyokawa, K. Kohashi, M. Shimokawa, F. Hirai, T. Tagawa, T. Okamoto, Y. Oda, Y. Maehara, Indoleamine 2,3-dioxygenase 1 and programmed cell death-ligand 1 co-expression correlates with aggressive features in lung adenocarcinoma, Eur. J. Cancer 101 (2018) 20e29. [18] E.A. Dill, P.M. Dillon, T.N. Bullock, A.M. Mills, Ido expression in breast cancer: an assessment of 281 primary and metastatic cases with comparison to PD-L1, Mod. Pathol. 31 (2018) 1513e1522. [19] C.P. Yu, S.F. Fu, X. Chen, J. Ye, Y. Ye, L.D. Kong, Z. Zhu, The clinicopathological and prognostic significance of Ido1 expression in human solid tumors: evidence from a systematic review and meta-analysis, Cell. Physiol. Biochem. 49 (2018) 134e143. [20] W. Ma, H. Duan, R. Zhang, X. Wang, H. Xu, Q. Zhou, L. Zhang, High expression of indoleamine 2, 3-dioxygenase in adenosquamous lung carcinoma correlates with favorable patient outcome, J. Cancer 10 (2019) 267e276. [21] A. Li, H.B. Barsoumian, J.E. Schoenhals, T.R. Cushman, M.S. Caetano, X. Wang, D.R. Valdecanas, S. Niknam, A.I. Younes, G. Li, W.A. Woodward, M.A. Cortez, J.W. Welsh, Indoleamine 2,3-dioxygenase 1 inhibition targets anti-PD1resistant lung tumors by blocking myeloid-derived suppressor cells, Cancer Lett. 431 (2018) 54e63. [22] M.M.T. Zhu, A.R. Dancsok, T.O. Nielsen, Indoleamine dioxygenase inhibitors: clinical rationale and current development, Curr. Oncol. Rep. 21 (2019) 2. [23] E. Lionta, G. Spyrou, D.K. Vassilatis, Z. Cournia, Structure-based virtual screening for drug discovery: principles, applications and recent advances, Curr. Top. Med. Chem. 14 (2014) 1923e1938. [24] (a) E. Dolusi c, P. Larrieu, S. Blanc, F. Sapunaric, J. Pouyez, L. Moineaux, D. Colette, V. Stroobant, L. Pilotte, D. Colau, T. Ferain, G. Fraser, M. Galleni, re, B. Masereel, B.J. Van den Eynde, J. Wouters, R. Fre de rick, Discovery J.M. Fre and preliminary SARs of keto-indoles as novel indoleamine 2,3-dioxygenase (Ido) inhibitors, Eur. J. Med. Chem. 46 (2011) 3058e3065; (b) J.R. Smith, K.J. Evans, A. Wright, R.D. Willows, J.F. Jamie, R. Griffith, Novel indoleamine 2,3-dioxygenase-1 inhibitors from a multistep in silico screen, Bioorg. Med. Chem. 20 (2012) 1354e1363; (c) A. Coluccia, S. Passacantilli, V. Famiglini, M. Sabatino, A. Patsilinakos, R. Ragno, C. Mazzoccoli, L. Sisinni, A. Okuno, O. Takikawa, R. Silvestri, G. La Regina, New inhibitors of indoleamine 2,3-dioxygenase 1: molecular modelling studies, synthesis and biological evaluation, J. Med. Chem. 59 (2016) 9760e9773; (d) D. Gao, Y. Li, Identification and preliminary structure-activity relationships of 1-Indanone derivatives as novel indoleamine-2,3-dioxygenase 1 (Ido1) inhibitors, Bioorg, Med. Chem. 25 (2017) 3780e3791; (e) H. Xu, Y. Song, Q.A. Yang, Novel high throughput virtual screening protocol to discover new indoleamine 2,3-dioxygenase 1 (Ido1) inhibitors, Chem. Pharm. Bull. 65 (2017) 714e717; (f) H. Zhang, W. Liu, Z. Liu, Y. Ju, M. Xu, Y. Zhang, X. Wu, Q. Gu, Z. Wang, J. Xu, Discovery of indoleamine 2,3-dioxygenase inhibitors using machine learning based virtual screening, MedChemComm 9 (2018) 937e945;

22

[25]

[26]

[27] [28]

[29]

[30]

[31] [32]

[33] [34]

[35]

[36]

[37] [38]

[39]

[40]

[41]

[42]

[43] [44]

[45]

[46]

Y. Zou et al. / European Journal of Medicinal Chemistry 184 (2019) 111750 (g) X. Xu, J. Ren, Y. Ma, H. Liu, Q. Rong, Y. Feng, Y. Wang, Y. Cheng, R. Ge, Z. Li, J. Bian, Discovery of cyanopyridine scaffold as novel indoleamine-2,3dioxygenase 1 (Ido1) inhibitors through virtual screening and preliminary hit optimization, J. Enzym. Inhib. Med. Chem. 34 (2019) 250e263; (h) Y. Zhou, J. Peng, P. Li, H. Du, Y. Li, Y. Li, L. Zhang, W. Sun, X. Liu, Z. Zuo, Discovery of novel indoleamine 2,3-dioxygenase 1 (Ido1) inhibitors by virtual screening, Comput, Biol. Chem. 78 (2019) 306e316.  F. Sebesta, V. Sl ama, J. Melcr, Z. Futera, J.V. Burda, Estimation of transitionmetal empirical parameters for molecular mechanical force fields, J. Chem. Theory Comput. 12 (2016) 3681e3688. H. Hirao, N. Thellamurege, X. Zhang, Applications of density functional theory to iron-containing molecules of bioinorganic interest, Front. Chem. 2 (2014) 14. V. Guallar, F.H. Wallrapp, QM/MM methods: looking inside heme proteins biochemistry, Biophys. Chem. 149 (2010) 1e11. Y. Zou, Y. Wang, F. Wang, M. Luo, Y. Li, W. Liu, Z. Huang, Y. Zhang, W. Guo, Q. Xu, Y. Lai, Discovery of potent Ido1 inhibitors derived from tryptophan using scaffold-hopping and structure-based design approaches, Eur. J. Med. Chem. 138 (2017) 199e211. J. Deng, K.W. Lee, T. Sanchez, M. Cui, N. Neamati, J.M. Briggs, Dynamic receptor-based pharmacophore model development and its application in designing novel HIV-1 integrase inhibitors, J. Med. Chem. 48 (2005) 1496e1505. R. Kurczab, A.J. Bojarski, New strategy for receptor-based pharmacophore query construction: a case study for 5-HT₇ receptor ligands, J. Chem. Inf. Model. 53 (2013) 3233e3243. Accelrys Discovery Studio 3.0, Accelrys, San Diego, CA, 2010. E.W. Yue, B. Douty, B. Wayland, M. Bower, X. Liu, L. Leffet, Q. Wang, K.J. Bowman, M.J. Hansbury, C. Liu, M. Wei, Y. Li, R. Wynn, T.C. Burn, H.K. Koblish, J.S. Fridman, B. Metcalf, P.A. Scherle, A.P. Combs, Discovery of potent competitive inhibitors of indoleamine 2,3-dioxygenase with in vivo pharmacodynamic activity and efficacy in a mouse melanoma model, J. Med. Chem. 52 (2009) 7364e7367. € hrig, A. Grosdidier, V. Zoete, O. Michielin, Docking to heme proteins, U.F. Ro J. Comput. Chem. 30 (2009) 2305e2315. Y. Zou, F. Wang, Y. Wang, W. Guo, Y. Zhang, Q. Xu, Y. Lai, Systematic study of imidazoles inhibiting Ido1 via the integration of molecular mechanics and quantum mechanics calculations, Eur. J. Med. Chem. 131 (2017) 152e170. K. Boppana, P.K. Dubey, S.A. Jagarlapudi, S. Vadivelan, G. Rambabu, Knowledge based identification of MAO-B selective inhibitors using pharmacophore and structure based virtual screening models, Eur. J. Med. Chem. 44 (2009) 3584e3590. S.B. Kirton, C.W. Murray, M.L. Verdonk, R.D. Taylor, Prediction of binding modes for ligands in the cytochromes P450 and other heme-containing proteins, Proteins 58 (2005) 836e844. Induced Fit Docking Protocol, Glide Version 5.5, Prime Version 2.1, € dinger, LLC, New York, NY, 2009. Schro P. Politzer, P.R. Laurence, K. Jayasuriya, Molecular electrostatic potentials: an effective tool for the elucidation of biochemical phenomena, Environ. Health Perspect. 61 (1985) 191e202. T. Brinck, J.S. Murray, P. Politzer, Surface electrostatic potentials of halogenated methanes as indicators of directional intermolecular interactions, Int. J. Quantum Chem. 14 (1992) 57e64. J.S. Murray, P. Lane, P. Politzer, Simultaneous s-hole and hydrogen bonding by sulfur- and selenium-containing heterocycles, Int. J. Quantum Chem. 108 (2008) 2770e2781. M.N. Glukhovtsev, R.D. Bach, C.J. Nagel, Performance of the B3LYP/ECP DFT calculations of iron-containing compounds, J. Phys. Chem. A 101 (1997) 316e323. J.A. Markwalder, S.P. Seitz, Y. Blat, L. Elkin, J.T. Hunt, J.G. Pabalan, M.N. JureKunkel, G.D. Vite, K. Covello, Identification and optimization of a novel series of indoleamine 2,3-dioxygenase inhibitors, Bioorg, Med. Chem. Lett. 27 (2017) 582e585. €dinger, LLC. The PyMOL molecular graphics system, Version 2.3 Schro H. Sugimoto, S. Oda, T. Otsuki, T. Hino, T. Yoshida, Y. Shiro, Crystal structure of human indoleamine 2,3-dioxygenase: catalytic mechanism of O2 incorporation by a heme-containing dioxygenase, Proc. Natl. Acad. Sci. 103 (2006) 2611e2616. M. Sono, T. Taniguchi, Y. Watanabe, O. Hayaishi, Indoleamine 2,3-dioxygenase. Equilibrium studies of the tryptophan binding to the ferric, ferrous, and CObound enzymes, J. Biol. Chem. 255 (1980) 1339e1345. € hrig, S.R. Majjigapu, A. Grosdidier, S. Bron, V. Stroobant, L. Pilotte, (a) U.F. Ro D. Colau, P. Vogel, B.J. Van den Eynde, V. Zoete, O. Michielin, Rational design of 4-aryl-1,2,3-triazoles for indoleamine 2,3-dioxygenase 1 inhibition, J. Med. Chem. 55 (2012) 5270e5290; (b) S.P. Fung, H. Wang, P. Tomek, C.J. Squire, J.U. Flanagan, B.D. Palmer, D.J. Bridewell, S.M. Tijono, J.F. Jamie, L.M. Ching, Discovery and characterisation of hydrazines as inhibitors of the immune suppressive enzyme, indoleamine 2,3-dioxygenase 1 (Ido1), Bioorg. Med. Chem. 21 (2013) 7595e7603; (c) Y.H. Peng, S.H. Ueng, C.T. Tseng, M.S. Hung, J.S. Song, J.S. Wu, F.Y. Liao, Y.S. Fan, M.H. Wu, W.C. Hsiao, C.C. Hsueh, S.Y. Lin, C.Y. Cheng, C.H. Tu, L.C. Lee, M.F. Cheng, K.S. Shia, C. Shih, S.Y. Wu, Important hydrogen bond networks in indoleamine 2,3-dioxygenase 1 (Ido1) inhibitor design revealed by crystal structures of imidazoleisoindole derivatives with Ido1, J. Med. Chem. 59 (2015) 282e293;

[47] [48] [49] [50]

[51] [52]

[53]

[54]

[55] [56]

[57] [58]

[59] [60] [61]

[62] [63]

[64]

[65] [66] [67] [68]

[69]

[70] [71]

[72]

(d) W.P. Malachowski, M. Winters, J.B. DuHadaway, A. Lewis-Ballester, S. Badir, J. Wai, M. Rahman, E. Sheikh, J.M. LaLonde, S.R. Yeh, G.C. Prendergast, A.J. Muller, O-alkylhydroxylamines as rationally-designed mechanism-based inhibitors of indoleamine 2,3-dioxygenase-1, Eur. J. Med. Chem. 108 (2016) 564e576; (e) S. Panda, A. Roy, S.J. Deka, V. Trivedi, D. Manna, Fused heterocyclic compounds as potent indoleamine-2,3-dioxygenase 1 inhibitors, ACS Med. Chem. Lett. 7 (2016) 1167e1172. B. Silvi, A. Savin, Classification of chemical bonds based on topological analysis of electron localization functions, Nature 371 (1994) 683e686. S. Dinda, A.G. Samuelson, The nature of bond critical points in dinuclear copper(I) complexes, Chem. Eur J. 18 (2012) 3032e3042. C. Edmiston, K. Ruedenberg, Localized atomic and molecular orbitals, Rev. Mod. Phys. 35 (1963) 457e465. K.B. Wiberg, Application of the pople-santry-segal CNDO method to the cyclopropylcarbinyl and cyclobutyl cation and to bicyclobutane, Tetrahedron 24 (1968) 1083e1096. S.J. Thackray, C.G. Mowat, S.K. Chapman, Exploring the mechanism of tryptophan 2,3-dioxygenase, Biochem. Soc. Trans. 36 (2008) 1120e1123. M. Kanai, H. Funakoshi, H. Takahashi, T. Hayakawa, S. Mizuno, K. Matsumoto, T. Nakamura, Tryptophan 2,3-dioxygenase is a key modulator of physiological neurogenesis and anxiety-related behavior in mice, Mol. Brain 2 (2009) 8. M.R. Mautino, F.A. Jaipuri, J. Waldo, S. Kumar, J. Adams, C. Van Allen, A. Marcinowicz-Flick, N. Vahanian, C.J. Link, novel specific- and dual- tryptophan-2,3-dioxygenase (TDO) and indoleamine-2,3-dioxygenase (Ido) inhibitors for tumor immunotherapy, AACR (2014) abstract 1633.  , G. Halmos, L. Horny ak, N. Dobos, G. Koncz, Z. Kar anyi, D. P all, Z. Szabo kvo €lgyi, The role of indoleamine-2,3-dioxygenase in cancer developL. Sze ment, diagnostics, and therapy, Front. Immunol. 9 (2018) 151. G.C. Prendergast, W.P. Malachowski, J.B. DuHadaway, A.J. Muller, Discovery of Ido1 inhibitors: from bench to bedside, Cancer Res. 77 (2017) 6795e6811. C. Uyttenhove, L. Pilotte, I. Theate, V. Stroobant, D. Colau, N. Parmentier, T. Boon, B. Van den Eynde, Evidence for a tumoral immune resistance mechanism based on tryptophan degradation by indoleamine 2,3dioxygenase, J. Nat. Med. 9 (2003) 1269e1274. J.R. Schoenborn, C.B. Wilson, Regulation of interferon-gamma during innate and adaptive immune responses, Adv. Immunol. 96 (2007) 41e101. F. Fallarino, U. Grohmann, Using an ancient tool for igniting and propagating immune tolerance: Ido as an inducer and amplifier of regulatory T cell functions, Curr. Med. Chem. 18 (2011) 2215e2221. J. Zhang, S. Endres, S. Kobold, Enhancing tumor T cell infiltration to enable cancer immunotherapy, Immunotherapy 11 (2019) 201e213. I.S. Afonina, S.P. Cullen, S.J. Martin, Cytotoxic and non-cytotoxic roles of the CTL/NK protease granzyme B, Immunol. Rev. 235 (2010) 105e116. H.M. Berman, J. Westbrook, Z. Feng, G. Gilliland, T.N. Bhat, H. Weissig, I.N. Shindyalov, P.E. Bourne, The protein data bank, Nucleic Acids Res. 28 (2000) 235e242. €dinger, LLC, New York, NY, 2009. Maestro, Version 9.0, Schro C.A. Lipinski, F. Lombardo, B.W. Dominy, P.J. Feeney, Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings, Adv. Drug Deliv. Rev. 23 (1997) 3e25. D.F. Veber, S.R. Johnson, H. Cheng, B.R. Smith, K.W. Ward, K.D. Kopple, Molecular properties that influence the oral bioavailability of drug candidates, J. Med. Chem. 45 (2002) 2615e2623. A. Whitty, Growing PAINS in academic drug discovery, Future Med. Chem. 3 (2011) 797e801. J.A. McCammon, B.R. Gelin, M. Karplus, Dynamics of folded proteins, Nature 267 (1977) 585e590. J. Wang, R.M. Wolf, J.W. Caldwell, P.A. Kollman, D.A. Case, Development and testing of a general amber force field, J. Comput. Chem. 25 (2004) 1157e1174. J.A. Maier, C. Martinez, K. Kasavajhala, L. Wickstrom, K.E. Hauser, C. Simmerling, ff14SB: improving the accuracy of protein side chain and backbone parameters from ff99SB, J. Chem. Theory Comput. 11 (2015) 3696e3713. T. Lu, F. Chen, Revealing the nature of intermolecular interaction and configurational preference of the nonpolar molecular dimers (H2)2, (N2)2, and (H2)(N2), J. Mol. Model. 19 (2013) 5387e5395. S. Grimme, S. Ehrlich, L. Goerigk, Effect of the damping function in dispersion corrected density functional theory, J. Comput. Chem. 32 (2011) 1456e1465. Gaussian 09, revision D.01 M.J. Frisch, G.W. Trucks, H.B. Schlegel, G.E. Scuseria, M.A. Robb, J.R. Cheeseman, G. Scalmani, V. Barone, B. Mennucci, G.A. Petersson, H. Nakatsuji, M. Caricato, X. Li, H.P. Hratchian, A.F. Izmaylov, J. Bloino, G. Zheng, J.L. Sonnenberg, M. Hada, M. Ehara, K. Toyota, R. Fukuda, J. Hasegawa, M. Ishida, T. Nakajima, Y. Honda, O. Kitao, H. Nakai, T. Vreven, J.A. Montgomery Jr., J.E. Peralta, F. Ogliaro, M. Bearpark, J.J. Heyd, E. Brothers, K.N. Kudin, V.N. Staroverov, T. Keith, R. Kobayashi, J. Normand, K. Raghavachari, A. Rendell, J.C. Burant, S.S. Iyengar, J. Tomasi, M. Cossi, N. Rega, J.M. Millam, M. Klene, J.E. Knox, J.B. Cross, V. Bakken, C. Adamo, J. Jaramillo, R. Gomperts, R.E. Stratmann, O. Yazyev, A.J. Austin, R. Cammi, C. Pomelli, J.W. Ochterski, R.L. Martin, K. Morokuma, V.G. Zakrzewski, G.A. Voth, P. Salvador, J.J. Dannenberg, S. Dapprich, A.D. Daniels, O. Farkas, J.B. Foresman, J.V. Ortiz, J. Cioslowski, D.J. Fox, Gaussian, Inc., Wallingford CT, 2013. D.A. Case, R.M. Betz, W. Botello-Smith, D.S. Cerutti, T.E. Cheatham III, T.A. Darden, R.E. Duke, T.J. Giese, H. Gohlke, A.W. Goetz, N. Homeyer, S. Izadi,

Y. Zou et al. / European Journal of Medicinal Chemistry 184 (2019) 111750

[73] [74] [75]

[76] [77]

[78] [79] [80]

[81]

P. Janowski, J. Kaus, A. Kovalenko, T.S. Lee, S. LeGrand, P. Li, C. Lin, T. Luchko, R. Luo, B. Madej, D. Mermelstein, K.M. Merz, G. Monard, H. Nguyen, H.T. Nguyen, I. Omelyan, A. Onufriev, D.R. Roe, A. Roitberg, C. Sagui, C.L. Simmerling, J. Swails, R.C. Walker, J. Wang, R.M. Wolf, X. Wu, L. Xiao, D.M. York, P.A. Kollman, AMBER 14, University of California, San Francisco, 2014. P. Li, K.M. Merz, Metal ion modeling using classical mechanics, Chem. Rev. 117 (2017) 1564e1686. P. Li, K.M. Merz, MCPB.py: a python based metal center parameter builder, J. Chem. Inf. Model. 56 (2016) 599e604. W.L. Jorgensen, J. Chandrasekhar, J.D. Madura, R.W. Impey, M.L. Klein, Comparison of simple potential functions for simulating liquid water, J. Chem. Phys. 79 (1983) 926e935. T. Darden, D. York, L. Pedersen, Particle mesh Ewald: an N log(N) method for Ewald sums in large systems, J. Chem. Phys. 98 (1993) 10089e10092. J.P. Ryckaert, G. Ciccotti, H.J.C. Berendsen, Numerical integration of the cartesian equations of motion of a system with constraints: molecular dynamics of n-alkanes, J. Comput. Phys. 23 (1977) 327e341. W. Humphrey, A. Dalke, K. Schulten, VMD: visual molecular dynamics, J. Mol. Graph. 14 (1996) 33e38. € dinger, Portland, OR, 2016. www. PyMOL Molecular Graphics System, Schro pymol.org. B.R. Miller, T.D. McGee Jr., J.M. Swails, N. Homeyer, H. Gohlke, A.E. Roitberg, MMPBSA.py: an efficient program for end-state free energy calculations, J. Chem. Theory Comput. 8 (2012) 3314e3321. T. Hou, W. Zhang, D.A. Case, W. Wang, Characterization of domain-peptide interaction interface: a case study on the amphiphysin-1 SH3 domain, J. Mol. Biol. 376 (2008) 1201e1214.

23

[82] R.X. Wang, S.M. Wang, How does consensus scoring work for virtual library screening? An idealized computer experiment, J. Chem. Inf. Comput. Sci. 41 (2001) 1422e1426. € hrig, V. Zoete, O. Michielin, The binding mode of N-hydroxyamidines to [83] U.F. Ro indoleamine 2,3-dioxygenase 1 (Ido1), Biochemistry 56 (2017) 4323e4325. [84] T. Lu, F. Chen, Multiwfn: a multifunctional wavefunction analyzer, J. Comput. Chem. 33 (2012) 580e592. [85] L. Ioakimidis, L. Thoukydidis, A. Mirza, S. Naeem, J. Reynisson, Benchmarking the reliability of QikProp. Correlation between experimental and predicted values, QSAR Comb. Sci. 27 (2008) 445e456. [86] Recommended values are given in Schrodinger's maestro software suite manual. [87] K. Vanommeslaeghe, E. Hatcher, C. Acharya, S. Kundu, S. Zhong, J. Shim, E. Darian, O. Guvench, P. Lopes, I. Vorobyov, A.D. Mackerell Jr., CHARMM general force field: a force field for drug-like molecules compatible with the CHARMM all-atom additive biological force fields, J. Comput. Chem. 31 (2010) 671e690. [88] M.T. Nelp, P.A. Kates, J.T. Hunt, J.A. Newitt, A. Balog, D. Maley, X. Zhu, L. Abell, A. Allentoff, R. Borzilleri, H.A. Lewis, Z. Lin, S.P. Seitz, C.H. Yan, J.T. Groves, Immune-modulating enzyme indoleamine 2,3-dioxygenase is effectively inhibited by targeting its apo-form, Proc. Natl. Acad. Sci. U. S. A. 115 (2018) 3249e3254. [89] R. Ortiz-Meoz, L. Wang, R. Matico, A. Rutkowska, M. Rosa, S. Bedard, R. Midgett, K. Strohmer, D. Thomson, C. Zhang, J. Guss, R. Totoritis, T. Consler, N. Campobasso, D. Taylor, T. Lewis, K. Weaver, M. Mülbaier, J. Seal, R. Dunham, W. Kazmierski, D. Favre, G. Bergamini, L. Shewchuk, A. Rendina, G. Zhang, Characterization of Novel Inhibition of Indoleamine 2,3-dioxygenase by Targeting its Apo Form, bioRxiv, https://doi.org/10.1101/324947.