Computationally guided discovery of novel non-steroidal AR-GR dual antagonists demonstrating potency against antiandrogen resistance.

As a major class of medicine for treating the lethal type of castration-resistant prostate cancer (PCa), long-term use of androgen receptor (AR) antagonists commonly leads to antiandrogen resistance. When AR signaling pathway is blocked by AR-targeted therapy, glucocorticoid receptor (GR) could compensate for AR function especially at the late stage of PCa. AR-GR dual antagonist is expected to be a good solution for this situation. Nevertheless, no effective non-steroidal AR-GR dual antagonist has been reported so far. In this study, an AR-GR dual binder H18 was first discovered by combining structure-based virtual screening and biological evaluation. Then with the aid of computationally guided design, the AR-GR dual antagonist HD57 was finally identified with antagonistic activity towards both AR (IC50 = 0.394 µM) and GR (IC50 = 17.81 µM). Moreover, HD57 could effectively antagonize various clinically relevant AR mutants. Further molecular dynamics simulation provided more atomic insights into the mode of action of HD57. Our research presents an efficient and rational strategy for discovering novel AR-GR dual antagonists, and the new scaffold provides important clues for the development of novel therapeutics for castration-resistant PCa.

Discovery and Optimization of N-Acyl-6-sulfonamide-tetrahydroquinoline Derivatives as Novel Non-Steroidal Selective Glucocorticoid Receptor Modulators.

Selective glucocorticoid receptor modulators (SGRMs), which can dissociate the transactivation from the transrepression of the glucocorticoid receptor (GR), are regarded as very promising therapeutics for inflammatory and autoimmune diseases. We previously discovered a SGRM HP-19 based on the passive antagonistic conformation of GR and bioassays. In this study, we further analyzed the dynamic changes of the passive antagonistic state upon the binding of HP-19 and designed and synthesized 62 N-acyl-6-sulfonamide-tetrahydroquinoline derivatives by structural optimization of HP-19. Therein, compound B53 exhibits the best transrepression activity (IC50 NF-κB = 0.009 ± 0.001 µM) comparable with dexamethasone (IC50 NF-κB = 0.005 ± 0.001 µM) and no transactivation activity. B53 can efficiently reduce the expression of inflammatory factors IL-6, IL-1ß, TNF-α, and so on and makes a milder adverse effect and is highly specific to GR. Furthermore, B53 is able to significantly relieve dermatitis on a mouse model via oral drug intervention.

Discovery of novel non-nucleoside inhibitors with high potency and selectivity for DNA methyltransferase 3A.

DNA methyltransferases (DNMTs) are important epigenetic regulatory enzymes involved in gene expression corresponding to many diseases including cancer. As one of the major enzymatically active mammalian DNMTs, DNMT3A has been regarded as an attractive target for the treatment of cancer particularly in hematological malignancy. Discovery of promising inhibitors toward this target with low toxicity, adequate activity and target selectivity is therefore pivotal in the development of novel cancer therapy and the inhibitory mechanism investigation. In this study, a multistep structure-based virtual screening and in vitro bioassays were conducted to search for potent novel DNMT3A inhibitors. Compound DY-46 was then identified as a promising new scaffold candidate (IC50 = 1.3 ± 0.22 µM) that can occupy both the SAM-cofactor pocket and the cytosine pocket of DNMT3A. Further similarity searching led to the discovery of compound DY-46-2 with IC50 of 0.39 ± 0.23 µM, which showed excellent selectivity against DNMT1 (33.3-fold), DNMT3B (269-fold) and G9a (over 1000-fold). These potent compounds significantly inhibited cancer cell proliferation and showed low cytotoxicity in peripheral blood mononuclear cells. This study provides a promising scaffold for the further development of DNMT3A inhibitors, and the possibility to design proper analogs with broad or specific selectivity.

Discovery of novel non-steroidal selective glucocorticoid receptor modulators by structure- and IGN-based virtual screening, structural optimization, and biological evaluation.

Glucocorticoids (GCs) are the most commonly used anti-inflammatory drugs. However, their excellent therapeutic effects are often accompanied by undesirable side effects. To discover selective glucocorticoid receptor modulators (SGRMs) that preferentially induce transrepression with little or no transactivation activity, a structure-based virtual screening by combining molecular docking and InteractionGraphNet (IGN) rescoring was performed, and compound HP210 was identified. HP210 did not induce the transactivation functions of GR while still acted on the NF-κB mediated tethered transrepression function (IC50 = 2.32 µM), and suppressed the secretion of pro-inflammation cytokines IL-1ß and IL-6. Compared with dexamethasone, HP210 showed no cross activities with phylogenetically related mineralcorticoid receptor and progesterone receptor and no significant effect on osteoprotegerin, exhibiting a reduced side-effect profile. Then, guided by the molecular dynamics simulations and binding free energy calculations, compound HP210_b4 with over two-fold higher transrepression activity (IC50 = 0.99 µM) was discovered. This study reported a group of non-steroidal new-scaffold SGRMs, providing valuable clues for the development of novel anti-inflammatory drugs.

Discovery of a novel nonsteroidal selective glucocorticoid receptor modulator by virtual screening and bioassays.

Synthetic glucocorticoids (GCs) have been widely used in the treatment of a broad range of inflammatory diseases, but their clinic use is limited by undesired side effects such as metabolic disorders, osteoporosis, skin and muscle atrophies, mood disorders and hypothalamic-pituitary-adrenal (HPA) axis suppression. Selective glucocorticoid receptor modulators (SGRMs) are expected to have promising anti-inflammatory efficacy but with fewer side effects caused by GCs. Here, we reported HT-15, a prospective SGRM discovered by structure-based virtual screening (VS) and bioassays. HT-15 can selectively act on the NF-κB/AP1-mediated transrepression function of glucocorticoid receptor (GR) and repress the expression of pro-inflammation cytokines (i.e., IL-1ß, IL-6, COX-2, and CCL-2) as effectively as dexamethasone (Dex). Compared with Dex, HT-15 shows less transactivation potency that is associated with the main adverse effects of synthetic GCs, and no cross activities with other nuclear receptors. Furthermore, HT-15 exhibits very weak inhibition on the ratio of OPG/RANKL. Therefore, it may reduce the side effects induced by normal GCs. The bioactive compound HT-15 can serve as a starting point for the development of novel therapeutics for high dose or long-term anti-inflammatory treatment.

Discovery of N-(4-(Benzyloxy)-phenyl)-sulfonamide Derivatives as Novel Antagonists of the Human Androgen Receptor Targeting the Activation Function 2.

Androgen receptor (AR) antagonists have been widely used for the treatment of prostate cancer (PCa). As a link between the AR and its transcriptional function, the activation function 2 (AF2) region has recently been revealed as a novel targeting site for developing AR antagonists. Here, we reported a series of N-(4-(benzyloxy)-phenyl)-sulfonamide derivatives as new-scaffold AR antagonists targeting the AR AF2. Therein, compound T1-12 showed excellent AR antagonistic activity (IC50 = 0.47 µM) and peptide displacement activity (IC50 = 18.05 µM). Furthermore, the in vivo LNCaP xenograft study confirmed that T1-12 offered effective inhibition on tumor growth when administered intratumorally. The study represents the first successful attempt to identify a small molecule targeting the AR AF2 with submicromolar AR antagonistic activity by structure-based virtual screening and provides important clues for the development of novel therapeutics for PCa treatment.

Molecular View on the Dissociation Pathways and Transactivation Regulation Mechanism of Nonsteroidal GR Ligands.

As a major drug target for anti-inflammatory therapy, the glucocorticoid receptor (GR) regulates a wide range of physiological processes through transactivation (TA) or transrepression. GR TA is involved in many adverse effects of GR-targeting drugs, and therefore, the discovery of novel GR ligands with lower TA activity and longer residence time is quite urgent. Undoubtedly, understanding the ligand dissociation mechanisms and the structural basis of the TA regulation is crucial for the development of novel GR-targeting drugs. Here, we used random accelerated molecular dynamics (RAMD) and funnel metadynamics (FM) simulations to explore the dissociation mechanisms of 5 classic glucocorticoids and 6 nonsteroidal GR ligands. Multiple ligand dissociation pathways were discovered. The classic glucocorticoids exhibit a strong preference for Path I, and most nonsteroidal ligands tend to dissociate along mixed pathways. We also find that the distinct unbinding preferences for AZD2906 and AZD9567, two representative nonsteroidal ligands with similar scaffolds but different TA activities, are primarily determined by their different polar interactions with the surrounding residues. Notably, the binding of AZD9567 poses a substantial impact on the conformation of the GR homodimer interface, which provides a valuable clue to understand the mechanisms of the TA-related side effects induced by the adjustments of the homodimerization process. These findings are critical for the structure-based rational design of novel GR ligands with more potent anti-inflammatory potency and reduced side effects.

Opportunities for overcoming tuberculosis: Emerging targets and their inhibitors.

Tuberculosis (TB), an airborne infectious disease mainly caused by Mycobacterium tuberculosis (Mtb), remains a leading cause of human morbidity and mortality worldwide. Given the alarming rise of resistance to anti-TB drugs and latent TB infection (LTBI), new targets and novel bioactive compounds are urgently needed for the treatment of this disease. We provide an overview of the recent advances in anti-TB drug discovery, emphasizing several newly validated targets for which an inhibitor has been reported in the past five years. Our review presents several attractive directions that have potential for the development of next-generation therapies.

Discovery of novel antagonists targeting the DNA binding domain of androgen receptor by integrated docking-based virtual screening and bioassays.

Androgen receptor (AR), a ligand-activated transcription factor, is a master regulator in the development and progress of prostate cancer (PCa). A major challenge for the clinically used AR antagonists is the rapid emergence of resistance induced by the mutations at AR ligand binding domain (LBD), and therefore the discovery of novel anti-AR therapeutics that can combat mutation-induced resistance is quite demanding. Therein, blocking the interaction between AR and DNA represents an innovative strategy. However, the hits confirmed targeting on it so far are all structurally based on a sole chemical scaffold. In this study, an integrated docking-based virtual screening (VS) strategy based on the crystal structure of the DNA binding domain (DBD) of AR was conducted to search for novel AR antagonists with new scaffolds and 2-(2-butyl-1,3-dioxoisoindoline-5-carboxamido)-4,5-dimethoxybenzoicacid (Cpd39) was identified as a potential hit, which was competent to block the binding of AR DBD to DNA and showed decent potency against AR transcriptional activity. Furthermore, Cpd39 was safe and capable of effectively inhibiting the proliferation of PCa cell lines (i.e., LNCaP, PC3, DU145, and 22RV1) and reducing the expression of the genes regulated by not only the full-length AR but also the splice variant AR-V7. The novel AR DBD-ARE blocker Cpd39 could serve as a starting point for the development of new therapeutics for castration-resistant PCa.

Comprehensive assessment of deep generative architectures for de novo drug design.

Recently, deep learning (DL)-based de novo drug design represents a new trend in pharmaceutical research, and numerous DL-based methods have been developed for the generation of novel compounds with desired properties. However, a comprehensive understanding of the advantages and disadvantages of these methods is still lacking. In this study, the performances of different generative models were evaluated by analyzing the properties of the generated molecules in different scenarios, such as goal-directed (rediscovery, optimization and scaffold hopping of active compounds) and target-specific (generation of novel compounds for a given target) tasks. In overall, the DL-based models have significant advantages over the baseline models built by the traditional methods in learning the physicochemical property distributions of the training sets and may be more suitable for target-specific tasks. However, both the baselines and DL-based generative models cannot fully exploit the scaffolds of the training sets, and the molecules generated by the DL-based methods even have lower scaffold diversity than those generated by the traditional models. Moreover, our assessment illustrates that the DL-based methods do not exhibit obvious advantages over the genetic algorithm-based baselines in goal-directed tasks. We believe that our study provides valuable guidance for the effective use of generative models in de novo drug design.

Discovery of Novel GR Ligands toward Druggable GR Antagonist Conformations Identified by MD Simulations and Markov State Model Analysis.

Binding of different ligands to glucocorticoid receptor (GR) may induce different conformational changes and even trigger completely opposite biological functions. To understand the allosteric communication within the GR ligand binding domain, the folding pathway of helix 12 (H12) induced by the binding of the agonist dexamethasone (DEX), antagonist RU486, and modulator AZD9567 are explored by molecular dynamics simulations and Markov state model analysis. The ligands can regulate the volume of the activation function-2 through the residues Phe737 and Gln738. Without ligand or with agonist binding, H12 swings from inward to outward to visit different folding positions. However, the binding of RU486 or AZD9567 perturbs the structural state, and the passive antagonist state appears more stable. Structure-based virtual screening and in vitro bioassays are used to discover novel GR ligands that bias the conformation equilibria toward the passive antagonist state. HP-19 exhibits the best anti-inflammatory activity (IC50 = 0.041 ± 0.011 µm) in nuclear factor-kappa B signaling pathway, which is comparable to that of DEX. HP-19 also does not induce adverse effect-related transactivation functions of GR. The novel ligands discovered here may serve as promising starting points for the development of GR modulators.

Discovery of a Novel Androgen Receptor Antagonist Manifesting Evidence to Disrupt the Dimerization of the Ligand-Binding Domain via Attenuating the Hydrogen-Bonding Network Between the Two Monomers.

Androgen receptor (AR) has proved to be a vital drug target for treating prostate cancer. Here, we reported the discovery of a novel AR antagonist 92 targeting the AR ligand-binding pocket, but distinct from the marketed drug enzalutamide (Enz), 92 demonstrated inhibition on the AR ligand-binding domain (LBD) dimerization, which is a novel mechanism reported for the first time. First, a novel hit (26, IC50 = 5.57 µM) was identified through virtual screening based on a theoretical AR LBD dimer bound with the Enz model. Then, guided by molecular modeling, 92 was discovered with 32.7-fold improved AR antagonistic activity (IC50 = 0.17 µM). Besides showing high bioactivity and safety, 92 can inhibit AR nuclear translocation. Furthermore, 92 inhibited the formation of the AR LBD dimer, possibly through attenuating the hydrogen-bonding network between the two monomers. This interesting finding would pave the way for the discovery of a new class of AR antagonists.

Accuracy or novelty: what can we gain from target-specific machine-learning-based scoring functions in virtual screening?

Machine-learning (ML)-based scoring functions (MLSFs) have gradually emerged as a promising alternative for protein-ligand binding affinity prediction and structure-based virtual screening. However, clouds of doubts have still been raised against the benefits of this novel type of scoring functions (SFs). In this study, to benchmark the performance of target-specific MLSFs on a relatively unbiased dataset, the MLSFs trained from three representative protein-ligand interaction representations were assessed on the LIT-PCBA dataset, and the classical Glide SP SF and three types of ligand-based quantitative structure-activity relationship (QSAR) models were also utilized for comparison. Two major aspects in virtual screening campaigns, including prediction accuracy and hit novelty, were systematically explored. The calculation results illustrate that the tested target-specific MLSFs yielded generally superior performance over the classical Glide SP SF, but they could hardly outperform the 2D fingerprint-based QSAR models. Although substantial improvements could be achieved by integrating multiple types of protein-ligand interaction features, the MLSFs were still not sufficient to exceed MACCS-based QSAR models. In terms of the correlations between the hit ranks or the structures of the top-ranked hits, the MLSFs developed by different featurization strategies would have the ability to identify quite different hits. Nevertheless, it seems that target-specific MLSFs do not have the intrinsic attributes of a traditional SF and may not be a substitute for classical SFs. In contrast, MLSFs can be regarded as a new derivative of ligand-based QSAR models. It is expected that our study may provide valuable guidance for the assessment and further development of target-specific MLSFs.

CovalentInDB: a comprehensive database facilitating the discovery of covalent inhibitors.

Inhibitors that form covalent bonds with their targets have traditionally been considered highly adventurous due to their potential off-target effects and toxicity concerns. However, with the clinical validation and approval of many covalent inhibitors during the past decade, design and discovery of novel covalent inhibitors have attracted increasing attention. A large amount of scattered experimental data for covalent inhibitors have been reported, but a resource by integrating the experimental information for covalent inhibitor discovery is still lacking. In this study, we presented Covalent Inhibitor Database (CovalentInDB), the largest online database that provides the structural information and experimental data for covalent inhibitors. CovalentInDB contains 4511 covalent inhibitors (including 68 approved drugs) with 57 different reactive warheads for 280 protein targets. The crystal structures of some of the proteins bound with a covalent inhibitor are provided to visualize the protein-ligand interactions around the binding site. Each covalent inhibitor is annotated with the structure, warhead, experimental bioactivity, physicochemical properties, etc. Moreover, CovalentInDB provides the covalent reaction mechanism and the corresponding experimental verification methods for each inhibitor towards its target. High-quality datasets are downloadable for users to evaluate and develop computational methods for covalent drug design. CovalentInDB is freely accessible at http://cadd.zju.edu.cn/cidb/.

Beware of the generic machine learning-based scoring functions in structure-based virtual screening.

Machine learning-based scoring functions (MLSFs) have attracted extensive attention recently and are expected to be potential rescoring tools for structure-based virtual screening (SBVS). However, a major concern nowadays is whether MLSFs trained for generic uses rather than a given target can consistently be applicable for VS. In this study, a systematic assessment was carried out to re-evaluate the effectiveness of 14 reported MLSFs in VS. Overall, most of these MLSFs could hardly achieve satisfactory results for any dataset, and they could even not outperform the baseline of classical SFs such as Glide SP. An exception was observed for RFscore-VS trained on the Directory of Useful Decoys-Enhanced dataset, which showed its superiority for most targets. However, in most cases, it clearly illustrated rather limited performance on the targets that were dissimilar to the proteins in the corresponding training sets. We also used the top three docking poses rather than the top one for rescoring and retrained the models with the updated versions of the training set, but only minor improvements were observed. Taken together, generic MLSFs may have poor generalization capabilities to be applicable for the real VS campaigns. Therefore, it should be quite cautious to use this type of methods for VS.

Advances in the computational development of androgen receptor antagonists.

The androgen receptor is a ligand-dependent transcriptional factor and an essential therapeutic target for prostate cancer. Competitive binding of antagonists to the androgen receptor can alleviate aberrant activation of the androgen receptor in prostate cancer. In recent years, computer-aided drug design has played an essential part in the discovery of novel androgen receptor antagonists. This review summarizes the recent advances in the discovery of novel androgen receptor antagonists through computer-aided drug design approaches; and discusses the applications of molecular modeling techniques to understand the resistance mechanisms of androgen receptor antagonists at the molecular level.

Novel androgen receptor antagonist identified by structure-based virtual screening, structural optimization, and biological evaluation.

Androgen receptor (AR) plays important roles in the development of prostate cancer (PCa), and therefore it has been regarded as the most important therapeutic target for both hormone-sensitive prostate cancer (HSPC) and advanced PCa. In this study, a novel hit (C18) with IC50 of 2.4 µM against AR transcriptional activity in LNCaP cell was identified through structure-based virtual screening based on molecular docking and free energy calculations. The structure-activity relationship analysis and structural optimization of C18 resulted in the discovery of a structural analogue (AT2), a more potent AR antagonist with 16-fold improved anti-AR potency. Further assays indicated that AT2 was capable of effectively inhibiting the transcriptional function of AR and blocking the nuclear translocation of AR like the second-generation AR antagonists. The antagonists discovered in this study may be served as the promising lead compounds for the development of AR-driven PCa therapeutics.

A magic drug target: Androgen receptor.

Androgen receptor (AR) is closely associated with a group of hormone-related diseases including the cancers of prostate, breast, ovary, pancreas, etc and anabolic deficiencies such as muscle atrophy and osteoporosis. Depending on the specific type and stage of the diseases, AR ligands including not only antagonists but also agonists and modulators are considered as potential therapeutics, which makes AR an extremely interesting drug target. Here, we at first review the current understandings on the structural characteristics of AR, and then address why and how AR is investigated as a drug target for the relevant diseases and summarize the representative antagonists and agonists targeting five prospective small molecule binding sites at AR, including ligand-binding pocket, activation function-2 site, binding function-3 site, DNA-binding domain, and N-terminal domain, providing recent insights from a target and drug development view. Further comprehensive studies on AR and AR ligands would bring fruitful information and push the therapy of AR relevant diseases forward.

Discovery of Novel Androgen Receptor Ligands by Structure-based Virtual Screening and Bioassays.

Androgen receptor (AR) is a ligand-activated transcription factor that plays a pivotal role in the development and progression of many severe diseases such as prostate cancer, muscle atrophy, and osteoporosis. Binding of ligands to AR triggers the conformational changes in AR that may affect the recruitment of coactivators and downstream response of AR signaling pathway. Therefore, AR ligands have great potential to treat these diseases. In this study, we searched for novel AR ligands by performing a docking-based virtual screening (VS) on the basis of the crystal structure of the AR ligand binding domain (LBD) in complex with its agonist. A total of 58 structurally diverse compounds were selected and subjected to LBD affinity assay, with five of them (HBP1-3, HBP1-17, HBP1-38, HBP1-51, and HBP1-58) exhibiting strong binding to AR-LBD. The IC50 values of HBP1-51 and HBP1-58 are 3.96 µM and 4.92 µM, respectively, which are even lower than that of enzalutamide (Enz, IC50 = 13.87 µM), a marketed second-generation AR antagonist. Further bioactivity assays suggest that HBP1-51 is an AR agonist, whereas HBP1-58 is an AR antagonist. In addition, molecular dynamics (MD) simulations and principal components analysis (PCA) were carried out to reveal the binding principle of the newly-identified AR ligands toward AR. Our modeling results indicate that the conformational changes of helix 12 induced by the bindings of antagonist and agonist are visibly different. In summary, the current study provides a highly efficient way to discover novel AR ligands, which could serve as the starting point for development of new therapeutics for AR-related diseases.