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The mineralocorticoid receptor (MR) is a nuclear receptor whose endogenous ligands are mineralocorticoids, a type of steroid hormone. The activating S810L mutation is known to cause severe early-onset and pregnancy-related hypertension. Progesterone binds to the wild-type (WT) MR as a passive antagonist with fast dissociation; however, it binds to the S810L mutant as a full agonist with slow dissociation. The switch in the biological activity of progesterone is considered to be one of the causes of the disease. First, we used steered molecular dynamics simulations to analyze the dissociation process of progesterone for the WT and the S810L mutant. Progesterone in the WT dissociated from the ligand-binding pocket with a weak force in comparison with progesterone in the S810L mutant due to the large inflow of water molecules into the pocket. Therefore, we used conventional molecular dynamics simulations for the ligand-free structures of the WT and the S810L mutant to investigate the effect of the mutation on the inflow of water. In the WT, water molecules enter the ligand-binding pocket in two ways: in the vicinity of (i) Arg817 and (ii) Ser810. In contrast, few water molecules enter the pocket in the S810L mutant because of the large size and hydrophobic nature of the Leu810 side chain. Fast dissociation is a common feature among passive antagonists of MR; therefore, we inferred that the water inflow could be responsible for the dissociation kinetics of progesterone in the WT and the S810L mutant.

Designing highly selective molecules for a drug target protein is a challenging task in drug discovery. This task can be regarded as a multiobjective problem that simultaneously satisfies a criteria of various objectives, such as selectivity for a target protein, pharmacokinetics endpoints, and drug-like indices. Recent breakthroughs in artificial intelligence have accelerated the development of molecular structure generation methods, and various researchers have applied them to computational drug designs and successfully proposed promising drug candidates. However, designing efficient selective inhibitors with releasing activities against various homologs of a target protein remains a difficult issue. In this study, we developed a de novo structure generator based on reinforcement learning that is capable of simultaneously optimizing multiobjective problems. Our structure generator successfully proposed selective inhibitors for tyrosine-kinases while optimizing 18 objectives consisting of the inhibitory activities against nine tyrosine kinases, three pharmacokinetics endpoints, and six other important properties. These results show that our structure generator and optimization strategy for selective inhibitors will contribute to the further development of practical structure generators for drug designs.

Hydration is a critical factor in the ligand binding process. Herein, to examine the hydration states of ligand binding sites, the three‐dimensional distribution function for the water oxygen site, gO(r), is computed for 3,706 ligand‐free protein structures based on the corresponding small molecule–protein complexes using the 3D‐RISM theory. For crystallographic waters (CWs) close to the ligand, gO(r) reveals that several CWs are stabilized by interaction networks formed between the ligand, CW, and protein. Based on the gO(r) for the crystallographic binding pose of the ligand, hydrogen bond interactions are dominant in the highly hydrated regions while weak interactions such as CH‐O are dominant in the moderately hydrated regions. The polar heteroatoms of the ligand occupy the highly hydrated and moderately hydrated regions in the crystallographic (correct) and wrongly docked (incorrect) poses, respectively. Thus, the gO(r) of polar heteroatoms may be used to distinguish the correct binding poses., The hydration states of ligand binding sites are comprehensively analyzed using a theory of solvation. An analysis of the hydration states at the positions of ligand heavy atoms indicates that the polar heteroatoms of the ligand tend to occupy highly hydrated regions in the correct ligand poses and moderately hydrated regions in the incorrect ligand poses, suggesting a way to distinguish these two poses.

Here, we have constructed neural network-based models that predict atomic partial charges with high accuracy at low computational cost. The models were trained using high-quality data acquired from quantum mechanics calculations using the fragment molecular orbital method. We have succeeded in obtaining highly accurate atomic partial charges for three representative molecular systems of proteins, including one large biomolecule (approx. 2000 atoms). The novelty of our approach is the ability to take into account the electronic polarization in the system, which is a system-dependent phenomenon, being important in the field of drug design. Our high-precision models are useful for the prediction of atomic partial charges and expected to be widely applicable in structure-based drug designs such as structural optimization, high-speed and high-precision docking, and molecular dynamics calculations.

Low-resolution electron density maps can pose a major obstacle in the determination and use of protein structures. Herein, we describe a novel method, quality assessment based on an electron density map (QAEmap), that evaluates local protein structures determined by X-ray crystallography and corrects structural errors using low-resolution maps. QAEmap uses a three-dimensional deep convolutional neural network with electron density maps and their corresponding coordinates as input and predicts the correlation between the local structure and the putative high-resolution experimental electron density map. This estimates how well the structure fits the high-resolution map. Further, we propose that this method may be applied to evaluate ligand binding, which can be difficult to determine at low resolution.

The generation of a wide range of candidate antibodies is important for the successful development of drugs that simultaneously satisfy multiple requirements. To find cooperative mutations and increase the diversity of mutants, an in silico double-point mutation approach, in which 3D models of all possible double-point mutant/antigen complexes are constructed and evaluated using interaction analysis, was developed. Starting from an antibody with very high affinity, four double-point mutants were designed in silico. Two of the double-point mutants exhibited improved affinity or affinity comparable to that of the starting antibody. The successful identification of two active double-point mutants showed that a cooperative mutation could be found by utilizing information regarding the interactions. The individual single-point mutants of the two active double-point mutants showed decreased affinity or no expression. These results suggested that the two active double-point mutants cannot be obtained through the usual approach i.e. a combination of improved single-point mutants. In addition, a triple-point mutant, which combines the distantly located active double-point mutation and an active single-point mutation collaterally obtained in the process of the double-point mutation strategy, was designed. The triple-point mutant showed improved affinity. This finding suggested that the effects of distantly located mutations are independent and additive. The double-point mutation approach using the interaction analysis of 3D structures expands the design repertoire for mutants, and hopefully paves a way for the identification of cooperative multiple-point mutations.

Deep learning is developing as an important technology to perform various tasks in cheminformatics. In particular, graph convolutional neural networks (GCNs) have been reported to perform well in many types of prediction tasks related to molecules. Although GCN exhibits considerable potential in various applications, appropriate utilization of this resource for obtaining reasonable and reliable prediction results requires thorough understanding of GCN and programming. To leverage the power of GCN to benefit various users from chemists to cheminformaticians, an open-source GCN tool, kGCN, is introduced. To support the users with various levels of programming skills, kGCN includes three interfaces: a graphical user interface (GUI) employing KNIME for users with limited programming skills such as chemists, as well as command-line and Python library interfaces for users with advanced programming skills such as cheminformaticians. To support the three steps required for building a prediction model, i.e., pre-processing, model tuning, and interpretation of results, kGCN includes functions of typical pre-processing, Bayesian optimization for automatic model tuning, and visualization of the atomic contribution to prediction for interpretation of results. kGCN supports three types of approaches, single-task, multi-task, and multi-modal predictions. The prediction of compound-protein interaction for four matrixmetalloproteases, MMP-3, -9, -12 and -13, in the inhibition assays is performed as a representative case study using kGCN. Additionally, kGCN provides the visualization of atomic contributions to the prediction. Such visualization is useful for the validation of the prediction models and the design of molecules based on the prediction model, realizing “explainable AI” for understanding the factors affecting AI prediction. kGCN is available at https://github.com/clinfo.

Resistance of prostate cancer to castration is currently an unavoidable problem. The major mechanisms underlying such resistance are androgen receptor (AR) overexpression, androgen-independent activation of AR, and AR mutation. To address this problem, we developed an AR pure antagonist, CH5137291, with AR nuclear translocation-inhibiting activity, and compared its activity and characteristics with that of bicalutamide. Cell lines corresponding to the mechanisms of castration resistance were used: LNCaP-BC2 having AR overexpression and LNCaP-CS10 having androgen-independent AR activation. VCaP and LNCaP were used as hormone-sensitive prostate cancer cells. In vitro functional assay clearly showed that CH5137291 inhibited the nuclear translocation of wild-type ARs as well as W741C- and T877A-mutant ARs. In addition, it acted as a pure antagonist on the transcriptional activity of these types of ARs. In contrast, bicalutamide did not inhibit the nuclear translocation of these ARs, and showed a partial/full agonistic effect on the transcriptional activity. CH5137291 inhibited cell growth more strongly than bicalutamide in VCaP and LNCaP cells as well as in LNCaP-BC2 and LNCaP-CS10 cells in vitro. In xenograft models, CH5137291 strongly inhibited the tumor growth of LNCaP, LNCaP-BC2, and LNCaP-CS10, whereas bicalutamide showed a weaker effect in LNCaP and almost no effect in LNCaP-BC2 and LNCaP-CS10 xenografts. Levels of prostate-specific antigen (PSA) in plasma correlated well with the antitumor effect of both agents. CH5137291 inhibited the growth of LNCaP tumors that had become resistant to bicalutamide treatment. A docking model suggested that CH5137291 intensively collided with the M895 residue of helix 12, and therefore strongly inhibited the folding of helix 12, a cause of AR agonist activity, in wild-type and W741C-mutant ARs. In cynomolgus monkeys, the serum concentration of CH5137291 increased dose-dependently and PSA level decreased 80% at 100 mg/kg. CH5137291 is expected to offer a novel therapeutic approach against major types of castration-resistant prostate cancers.

Purpose: We investigated the antitumor activity of the combination of two different humanized monoclonal human epidermal growth factor receptor (HER) 2 antibodies, pertuzumab and trastuzumab, for gastric cancer. Experimental Design: Tumor mouse xenograft models were used to examine antitumor activity. Cell proliferation was examined using crystal violet staining. HER family proteins' expression was analyzed by ELISA and immunohistochemistry. Phosphorylated proteins and heterodimers were detected by Western blotting and in situ proximity ligation assay (PLA), respectively. Apoptosis activity was examined by caspase 3/7 activity. Antibody-dependent cellular cytotoxicity (ADCC) activity was detected by xCELLigence. Microvessel density was examined by CD31 staining. Results: Pertuzumab in combination with trastuzumab showed significant antitumor activity compared with each monotherapy in NCI-N87, an HER2-positive human gastric cancer xenograft model. The efficacy was stronger than that of the maximum effective dose with each monotherapy. Similar antitumor activity was shown in 4-1ST, another HER2-positive gastric cancer model, but not in MKN-28, an HER2-negative model. Combining pertuzumab with trastuzumab enhanced cell growth inhibition and apoptosis activity by inhibiting EGFR-HER2 heterodimerization and the phosphorylation of these receptors and their downstream factors. This effect was also seen in HER2-HER3 signaling. Furthermore, pertuzumab in combination with trastuzumab potentiated the ADCC activity of those antibodies and reduced tumor microvessel density. Conclusions: We showed the significantly enhanced efficacy of pertuzumab combining with trastuzumab for HER2 overexpressing gastric cancer through the potentiation of cell growth inhibition, apoptosis activity, cell killing activity by ADCC, and antiangiogenic activity. This study suggests the clinical benefit of combination therapy with pertuzumab and trastuzumab for patients with HER2-positive gastric cancers. Clin Cancer Res; 17(15); 5060–70. ©2011 AACR.

Compounds with a medium-sized flexible ring often show atropisomerism that is caused by the high-energy barriers between long-lived conformers that can be isolated and often have different biological properties to each other. In this study, the frequency of the transition between the two stable conformers, aS and aR, of thienotriazolodiazepine compounds with flexible 7-membered rings was estimated computationally by Monte Carlo (MC) simulations and validated experimentally by NMR experiments. To estimate the energy barriers for transitions as precisely as possible, the potential energy (PE) surfaces used in the MC simulations were calculated by molecular orbital (MO) methods. To accomplish the MC simulations with the MO-based PE surfaces in a practical central processing unit (CPU) time, the MO-based PE of each conformer was pre-calculated and stored before the MC simulations, and then only referred to during the MC simulations. The activation energies for transitions calculated by the MC simulations agreed well with the experimental ΔG determined by the NMR experiments. The analysis of the transition trajectories of the MC simulations revealed that the transition occurred not only through the transition states, but also through many different transition paths. Our computational methods gave us quantitative estimates of atropisomerism of the thienotriazolodiazepine compounds in a practical period of time, and the method could be applicable for other slow-dynamics phenomena that cannot be investigated by other atomistic simulations.

Selective factor VIIa-tissue factor complex (FVIIa/TF) inhibition is regarded as a promising target for developing new anticoagulant drugs. In previous reports, we described a S3 subsite found in the X-ray crystal structure of compound 2 that bound to FVIIa/soluble tissue factor (sTF). Based on the X-ray crystal structure information and with the aim of improving the inhibition activity for FVIIa/TF and selectivity against other serine proteases, we synthesized derivatives by introducing substituents at position 5 of the indole ring of compound 2. Among them, compound 16 showed high selectivity against other serine proteases. Contrary to our expectations, compound 16 did not occupy the S3-subsite; X-ray structure analysis revealed that compound 16 improved selectivity by forming hydrogen bonds with Gln217, Thr99 and Asn100.

The 3-(4-cyano-3-trifluoromethylphenyl)-5,5-dimethylthiohydantoin derivatives which have carboxy-terminal side chains were synthesized and their agonistic/antagonistic activities against androgen receptor (AR) measured. Among them, compound 13b showed antagonistic activity (IC50=130 nM) with no agonistic activity even at 10000 nM. This compound exhibited significant metabolic stability and oral antiandrogenic activity (ED50=7 mg/kg).

The crystal structure of human factor VIIa/soluble tissue factor (FVIIa/sTF) in complex with a highly selective peptide-mimetic FVIIa inhibitor which shows 1670-fold selectivity against thrombin inhibition has been solved at 2.6 A resolution. The inhibitor is bound to FVIIa/sTF at the S1, S2 and S3 sites and at the additional S1 subsite. Two charged groups, the amidino group in P2 and the carboxylate group in P4, form ionic interactions with Asp60 and Lys192 of FVIIa, respectively. Structural comparisons between factor VIIa and thrombin show that thrombin has oppositely charged residues, Lys60F and Glu192, in the S2 site and the S1 subsites, respectively. These data suggest that the utilization of the differences of charge distribution in the S2 site and the S1 subsites between FVIIa and thrombin is critical for achieving high selectivity against thrombin inhibition. These results will provide valuable information for the structure-based drug design of specific inhibitors for FVIIa/TF.

Background: Prostate cancer resistant to total androgen depletion therapies is termed castration-resistant prostate cancer and novel therapeutic approaches are in demand. The mechanisms of castration resistance in prostate cancer are reported to be: 1) hypersensitivity to androgen derived from overexpression of androgen receptor (AR), 2) androgen-independent activation of AR, termed “outlaw”, and 3) loss of ligand specificity derived from mutation of AR. To address this demand, we screened and developed an AR antagonist without agonist activity, a so-called AR pure antagonist, CH5137291. Methods: We compared the inhibitory effects of CH5137291 with bicalutamide on the growth of hormone-sensitive and castration-resistant prostate cancer cells. To confirm the in vivo efficacy of CH5137291, we used xenograft models. The antagonist/agonist effect of CH5137291 on AR transcriptional activity was analyzed using reporter gene assays. To elucidate the mechanisms of CH5137291, we examined the effect of CH5137291 on the protein level of nuclear AR in LNCaP-CS10 and the effect of CH5137291 on the subcellular localization of AR in LNCaP cells. Finally, the exposure of CH5137291 and its effect on serum prostate-specific antigen (PSA) level were investigated in cynomolgus monkey. Results: CH5137291 showed in vitro inhibitory effects superior to bicalutamide in all models tested including hormone-sensitive VCaP, LNCaP and castration-resistant, LNCaP-BC2 (overexpression model) and LNCaP-CS10 (outlaw model). CH5137291 treatment (10, 100 mg/kg) inhibited the tumor growth to initial levels and PSA production to lower than initial levels in castration-resistant LuCaP35V, LNCaP-BC2 and LNCaP-CS10 xenograft models. In contrast, bicalutamide (10, 100 mg/kg) did not inhibit tumor growth or PSA production in any of the xenograft models. In addition, CH5137291 exhibited prolongation of the time to progression compared with bicalutamide in the LNCaP xenograft model. In vitro functional assays clarified that CH5137291 showed pure antagonist activity against wild type, bicalutamide-resistant type (W741C), and flutamide-resistant type (T877A) AR in transcription and inhibited the nuclear translocation of all types of AR tested. CH5137291 also exhibited a dose-dependent increase in serum concentration and inhibited PSA production in cynomolgus monkey. Conclusion: Our novel AR pure antagonist CH5137291 inhibited AR nuclear translocation and showed not only superior efficacy in prostate cancers with castration-resistance acquired from AR overexpression, mutation and outlaw mechanisms but also prolonged time to progression in hormone-sensitive prostate cancer compared with bicalutamide. CH5137291 is expected to contribute to novel therapeutic approaches against a broad spectrum of prostate cancers. Citation Information: Mol Cancer Ther 2009;8(12 Suppl):A219.

Background: We hypothesized that the androgen receptor (AR) pure antagonists, which exhibit no agonist activities, would inhibit AR signaling completely and would be efficacious against castration-resistant prostate cancer (CRPC). Based on our understanding of estrogen receptor pure antagonists, we designed dihydrotestosterone (DHT) derivatives and nonsteroidal RU56187 derivatives that inhibit the folding of helix 12 of AR and screened AR pure antagonists. Although a non-steroidal derivative (CH4933468) showed improved metabolic stability compared with DHT derivatives, it metabolized into an extremely small amount of a dealkylated metabolite with strong agonist effect. Therefore, CH4933468 did not show antitumor activity against the CRPC xenograft model. Methods: For metabolic stability, we designed and synthesized sulfonamide-substituted aryl compounds without dealkylated agonist metabolites. The production of agonist metabolites was examined in rat, mouse, monkey and dog. The efficacy of our compounds was measured using an in vitro reporter assay, a cell growth assay and in vivo prostate cancer xenograft models. Results: As a result of our optimization, CH5137291 was discovered and found to be an orally-active androgen receptor pure antagonist without agonistic activities derived from dealkylated agonist metabolites in vivo in rat, mouse, monkey and dog. CH5137291 exhibited AR antagonistic activities of IC50 of 300 nM and no agonist activity even at 30,000 nM in the reporter gene assay. In LNCaP-BC2 cells (AR overexpression CRPC model), CH5137291 was more efficient than bicalutamide. CH5137291 completely inhibited cell growth, in contrast to the partial inhibition of bicalutamide. Furthermore, CH5137291 inhibited the tumor growth and PSA production in the LNCaP-BC2 xenograft model. CH5137291 also inhibited AR nuclear translocation in the presence of R1881. According to the binding models of CH5137291 and bicalutamide to wild type AR, the sulfonamide of CH5137291 directly collided with M895 and thus prevented the folding of helix 12 completely. On the other hand, bicalutamide did not collide with M895 from helix 12 and inhibited helix 12 folding indirectly through W741. Hence, CH5137291 might act as an AR pure antagonist, but bicalutamide apparently does not. Conclusions: CH5137291, a novel AR pure antagonist without agonistic activities derived from dealkylated agonist metabolites, has shown antitumor activities in both in vitro and in vivo CRPC models. Our experimental results for clinical candidate CH5137291 corroborate our hypothesis that pure antagonists inhibit the folding of helix 12 completely, leading to the complete inhibition of the transcription activity of AR. Based on the strong efficacy against CRPC xenograft model and the inhibition of AR nuclear translocation, CH5137291 may offer more benefit than bicalutamide. Citation Information: Mol Cancer Ther 2009;8(12 Suppl):A220.