Development of Small-Molecules Targeting Receptor Activator of Nuclear Factor-κB Ligand (RANKL)—Receptor Activator of Nuclear Factor-κB (RANK) Protein–Protein Interaction by Structure-Based Virtual Screening and Hit Optimization

Antiresorptive Medications Prior to Stereotactic Body Radiotherapy for Spinal Metastasis are Associated with Reduced Incidence of Vertebral Body Compression Fracture

STUDY DESIGN

Retrospective Cohort.

OBJECTIVE

Antiresorptive drugs are often given to minimize fracture risk for bone metastases, but data regarding optimal time or ability to reduce stereotactic body radiotherapy (SBRT)-induced fracture risk is limited. This study examines the association between antiresorptive use surrounding spinal SBRT and vertebral compression fracture (VCF) incidence to provide information regarding effectiveness and optimal timing of use.

METHODS

Patients treated with SBRT for spinal metastases at a single institution between 2009-2020 were included. Kaplan-Meier analysis was used to compare cumulative incidence of VCF for those taking antiresorptive drugs pre-SBRT, post-SBRT only, and none at all. Cox proportional hazards and Fine-Gray competing risk models were used to identify additional factors associated with VCF.

RESULTS

Of the 234 patients (410 vertebrae) analyzed, 49 (20.9%) were taking bisphosphonates alone, 42 (17.9%) were taking denosumab alone, and 25 (10.7%) were taking both. Kaplan-Meier analysis revealed a statistically significant lower VCF incidence for patients initiating antiresorptive drugs before SBRT compared to those taking none at all (4% vs 12% at 1 year post-SBRT, P = .045; and 4% vs 23% at 2 years, P = .008). On multivariate analysis, denosumab duration (HR: .87, P = .378) or dose (HR: 1.00, P = .644) as well as bisphosphonate duration (HR: .98, P= .739) or dose (HR: .99, P= .741) did not have statistical significance on VCF incidence.

CONCLUSION

Initiating antiresorptive agents before SBRT may reduce the risk of treatment-induced VCF. Antiresorptive drugs are underutilized in patients with spine metastases and may represent a useful intervention to minimize toxicity and improve long-term outcomes.

Pan-histone deacetylase inhibitor vorinostat suppresses osteoclastic bone resorption through modulation of RANKL-evoked signaling and ameliorates ovariectomy-induced bone loss

BACKGROUND

Estrogen deficiency-mediated hyperactive osteoclast represents the leading role during the onset of postmenopausal osteoporosis. The activation of a series of signaling cascades triggered by RANKL-RANK interaction is crucial mechanism underlying osteoclastogenesis. Vorinostat (SAHA) is a broad-spectrum pan-histone deacetylase inhibitor (HDACi) and its effect on osteoporosis remains elusive.

METHODS

The effects of SAHA on osteoclast maturation and bone resorptive activity were evaluated using in vitro osteoclastogenesis assay. To investigate the effect of SAHA on the osteoclast gene networks during osteoclast differentiation, we performed high-throughput transcriptome sequencing. Molecular docking and the assessment of RANKL-induced signaling cascades were conducted to confirm the underlying regulatory mechanism of SAHA on the action of RANKL-activated osteoclasts. Finally, we took advantage of a mouse model of estrogen-deficient osteoporosis to explore the clinical potential of SAHA.

RESULTS

We showed here that SAHA suppressed RANKL-induced osteoclast differentiation concentration-dependently and disrupted osteoclastic bone resorption in vitro. Mechanistically, SAHA specifically bound to the predicted binding site of RANKL and blunt the interaction between RANKL and RANK. Then, by interfering with downstream NF-κB and MAPK signaling pathway activation, SAHA negatively regulated the activity of NFATc1, thus resulting in a significant reduction of osteoclast-specific gene transcripts and functional osteoclast-related protein expression. Moreover, we found a significant anti-osteoporotic role of SAHA in ovariectomized mice, which was probably realized through the inhibition of osteoclast formation and hyperactivation.

CONCLUSION

These data reveal a high affinity between SAHA and RANKL, which results in blockade of RANKL-RANK interaction and thereby interferes with RANKL-induced signaling cascades and osteoclastic bone resorption, supporting a novel strategy for SAHA application as a promising therapeutic agent for osteoporosis.

Predicting the Efficacy of Novel Synthetic Compounds in the Treatment of Osteosarcoma via Anti-Receptor Activator of Nuclear Factor-κB Ligand (RANKL)/Receptor Activator of Nuclear Factor-κB (RANK) Targets

BACKGROUND

Osteosarcoma (OS) currently demonstrates a rising incidence, ranking as the predominant primary malignant tumor in the adolescent demographic. Notwithstanding this trend, the pharmaceutical landscape lacks therapeutic agents that deliver satisfactory efficacy against OS.

OBJECTIVE

This study aimed to authenticate the outcomes of prior research employing the HM and GEP algorithms, endeavoring to expedite the formulation of efficacious therapeutics for osteosarcoma.

METHODS

A robust quantitative constitutive relationship model was engineered to prognosticate the IC50 values of innovative synthetic compounds, harnessing the power of gene expression programming. A total of 39 natural products underwent optimization via heuristic methodologies within the CODESSA software, resulting in the establishment of a linear model. Subsequent to this phase, a mere quintet of descriptors was curated for the generation of non-linear models through gene expression programming.

RESULTS

The squared correlation coefficients and s2 values derived from the heuristics stood at 0.5516 and 0.0195, respectively. Gene expression programming yielded squared correlation coefficients and mean square errors for the training set at 0.78 and 0.0085, respectively. For the test set, these values were determined to be 0.71 and 0.0121, respectively. The s2 of the heuristics for the training set was discerned to be 0.0085.

CONCLUSION

The analytic scrutiny of both algorithms underscores their commendable reliability in forecasting the efficacy of nascent compounds. A juxtaposition based on correlation coefficients elucidates that the GEP algorithm exhibits superior predictive prowess relative to the HM algorithm for novel synthetic compounds.

Development of 3-acetylindole derivatives that selectively target BRPF1 as new inhibitors of receptor activator of NF-κB ligand (RANKL)-Induced osteoclastogenesis

Bromodomain and PHD finger-containing (BRPF) proteins function as epigenetic readers that specifically recognize acetylated lysine residues on histone tails. The acetyl-lysine binding pocket of BRPF has emerged as an attractive target for the development of protein interaction inhibitors owing to its potential druggability. In this study, we identified 3-acetylindoles as bone antiresorptive agents with a novel scaffold by performing structure-based virtual screening and hit optimization. Among those derivatives, compound 18 exhibited potent and selective inhibitory activities against BRPF1B (IC50 = 102 nM) as well as outstanding inhibitory activity against osteoclastogenesis (73.8% @ 1 μM) and differentiation (IC50 = 0.19 μM) without cytotoxicity. Besides, cellular mechanism assays demonstrated that compound 18 exhibited a strong bone antiresorptive effect by modulating the RANKL/RANK/NFATc1 pathway. Structural and functional studies on BRPF1 inhibitors aid in making advances to understand the epigenetic mechanisms of bone cell development and create innovative therapeutics for treating bone metastases from solid tumors and other bone erosive diseases.

Structure-Based Discovery of Receptor Activator of Nuclear Factor-κB Ligand (RANKL)-Induced Osteoclastogenesis Inhibitors

Receptor activator of nuclear factor-κB ligand (RANKL) has been actively pursued as a therapeutic target for osteoporosis, given that RANKL is the master mediator of bone resorption as it promotes osteoclast differentiation, activity and survival. We employed a structure-based virtual screening approach comprising two stages of experimental evaluation and identified 11 commercially available compounds that displayed dose-dependent inhibition of osteoclastogenesis. Their inhibitory effects were quantified through TRAP activity at the low micromolar range (IC₅₀ < 5 μΜ), but more importantly, 3 compounds displayed very low toxicity (LC₅₀ > 100 μΜ). We also assessed the potential of an N-(1-aryl-1H-indol-5-yl)aryl-sulfonamide scaffold that was based on the structure of a hit compound, through synthesis of 30 derivatives. Their evaluation revealed 4 additional hits that inhibited osteoclastogenesis at low micromolar concentrations; however, cellular toxicity concerns preclude their further development. Taken together with the structure-activity relationships provided by the hit compounds, our study revealed potent inhibitors of RANKL-induced osteoclastogenesis of high therapeutic index, which bear diverse scaffolds that can be employed in hit-to-lead optimization for the development of therapeutics against osteolytic diseases.

Mechanistic advances in osteoporosis and anti-osteoporosis therapies

Osteoporosis is a type of bone loss disease characterized by a reduction in bone mass and microarchitectural deterioration of bone tissue. With the intensification of global aging, this disease is now regarded as one of the major public health problems that often leads to unbearable pain, risk of bone fractures, and even death, causing an enormous burden at both the human and socioeconomic layers. Classic anti-osteoporosis pharmacological options include anti-resorptive and anabolic agents, whose ability to improve bone mineral density and resist bone fracture is being gradually confirmed. However, long-term or high-frequency use of these drugs may bring some side effects and adverse reactions. Therefore, an increasing number of studies are devoted to finding new pathogenesis or potential therapeutic targets of osteoporosis, and it is of great importance to comprehensively recognize osteoporosis and develop viable and efficient therapeutic approaches. In this study, we systematically reviewed literatures and clinical evidences to both mechanistically and clinically demonstrate the state-of-art advances in osteoporosis. This work will endow readers with the mechanistical advances and clinical knowledge of osteoporosis and furthermore present the most updated anti-osteoporosis therapies.

Harnessing the cyclization strategy for new drug discovery

The design of new ligands with high affinity and specificity against the targets of interest has been a central focus in drug discovery. As one of the most commonly used methods in drug discovery, the cyclization represents a feasible strategy to identify new lead compounds by increasing structural novelty, scaffold diversity and complexity. Such strategy could also be potentially used for the follow-on drug discovery without patent infringement. In recent years, the cyclization strategy has witnessed great success in the discovery of new lead compounds against different targets for treating various diseases. Herein, we first briefly summarize the use of the cyclization strategy in the discovery of new small-molecule lead compounds, including the proteolysis targeting chimeras (PROTAC) molecules. Particularly, we focus on four main strategies including fused ring cyclization, chain cyclization, spirocyclization and macrocyclization and highlight the use of the cyclization strategy in lead generation. Finally, the challenges including the synthetic intractability, relatively poor pharmacokinetics (PK) profiles and the absence of the structural information for rational structure-based cyclization are also briefly discussed. We hope this review, not exhaustive, could provide a timely overview on the cyclization strategy for the discovery of new lead compounds.

G Protein-Coupled Receptor Interaction Prediction Based on Deep Transfer Learning

G protein-coupled receptors (GPCRs) account for about 40% to 50% of drug targets. Many human diseases are related to G protein coupled receptors. Accurate prediction of GPCR interaction is not only essential to understand its structural role, but also helps design more effective drugs. At present, the prediction of GPCR interaction mainly uses machine learning methods. Machine learning methods generally require a large number of independent and identically distributed samples to achieve good results. However, the number of available GPCR samples that have been marked is scarce. Transfer learning has a strong advantage in dealing with such small sample problems. Therefore, this paper proposes a transfer learning method based on sample similarity, using XGBoost as a weak classifier and using the TrAdaBoost algorithm based on JS divergence for data weight initialization to transfer samples to construct a data set. After that, the deep neural network based on the attention mechanism is used for model training. The existing GPCR is used for prediction. In short-distance contact prediction, the accuracy of our method is 0.26 higher than similar methods.

The NF-κB Pharmacopeia: Novel Strategies to Subdue an Intractable Target

NF-κB transcription factors are major drivers of tumor initiation and progression. NF-κB signaling is constitutively activated by genetic alterations or environmental signals in many human cancers, where it contributes to almost all hallmarks of malignancy, including sustained proliferation, cell death resistance, tumor-promoting inflammation, metabolic reprogramming, tissue invasion, angiogenesis, and metastasis. As such, the NF-κB pathway is an attractive therapeutic target in a broad range of human cancers, as well as in numerous non-malignant diseases. Currently, however, there is no clinically useful NF-κB inhibitor to treat oncological patients, owing to the preclusive, on-target toxicities of systemic NF-κB blockade. In this review, we discuss the principal and most promising strategies being developed to circumvent the inherent limitations of conventional IκB kinase (IKK)/NF-κB-targeting drugs, focusing on new molecules that target upstream regulators or downstream effectors of oncogenic NF-κB signaling, as well as agents targeting individual NF-κB subunits.

Development of an Orally Active Small-Molecule Inhibitor of Receptor Activator of Nuclear Factor-κB Ligand

Receptor activator of nuclear factor-κB (RANK) and its ligand, RANKL, play pivotal roles in bone remodeling. The monoclonal antibody denosumab successfully inhibited the maturation of osteoclasts (OCs) by binding to RANKL in the clinic. We continued our efforts to develop small-molecule inhibitors of RANKL. In this work, 41 β-carboline derivatives were synthesized based on previously synthesized compound Y1599 to improve its drug-like properties. Compound Y1693 was identified as a potent RANKL inhibitor that improved absorption-distribution-metabolism-excretion properties and effectively prevented RANKL-induced osteoclastogenesis and bone resorption. Furthermore, Y1693 also suppressed the expression of OC marker genes. Moreover, Y1693 demonstrated good tolerability and efficacy in an orally administered mouse model of osteoporosis as well as the ability to rescue alveolar bone loss in vivo caused by periodontal disease. Collectively, the above findings may provide a valuable direction for the development of novel antiresorptive therapies that target RANKL.

Decision Making in Structure-Based Drug Discovery: Visual Inspection of Docking Results

Molecular docking is a computational method widely used in drug discovery. Due to the inherent inaccuracies of molecular docking, visual inspection of binding modes is a crucial routine in the decision making process of computational medicinal chemists. Despite its apparent importance for medicinal chemistry projects, guidelines for the visual docking pose assessment have been hardly discussed in the literature. Here, we review the medicinal chemistry literature with the aim of identifying consistent principles for visual inspection, highlighting cases of its successful application, and discussing its limitations. In this context, we conducted a survey reaching experts in both academia and the pharmaceutical industry, which also included a challenge to distinguish native from incorrect poses. We were able to collect 93 expert opinions that offer valuable insights into visually supported decision-making processes. This perspective shall motivate discussions among experienced computational medicinal chemists and guide young scientists new to the field to stratify their compounds.

Osteoporosis: Mechanism, Molecular Target and Current Status on Drug Development

CDATA[Osteoporosis is a pathological loss of bone mass due to an imbalance in bone remodeling where osteoclast-mediated bone resorption exceeds osteoblast-mediated bone formation resulting in skeletal fragility and fractures. Anti-resorptive agents, such as bisphosphonates and SERMs, and anabolic drugs that stimulate bone formation, including PTH analogues and sclerostin inhibitors, are current treatments for osteoporosis. Despite their efficacy, severe side effects and loss of potency may limit the long term usage of a single drug. Sequential and combinational use of current drugs, such as switching from an anabolic to an anti-resorptive agent, may provide an alternative approach. Moreover, there are novel drugs being developed against emerging new targets such as Cathepsin K and 17β-HSD2 that may have less side effects. This review will summarize the molecular mechanisms of osteoporosis, current drugs for osteoporosis treatment, and new drug development strategies.

Discovery of Small-Molecule Inhibitors of Receptor Activator of Nuclear Factor-κB Ligand with a Superior Therapeutic Index

Receptor activator of nuclear factor-κB ligand (RANKL) constitutes the master mediator of osteoclastogenesis, while its pharmaceutical inhibition by a monoclonal antibody has been approved for the treatment of postmenopausal osteoporosis. To date, the pursuit of pharmacologically more favorable approaches using low-molecular-weight inhibitors has been hampered by low specificity and high toxicity issues. This study aimed to discover small-molecule inhibitors targeting RANKL trimer formation. Through a systematic screening of 39 analogues of SPD-304, a dual inhibitor of tumor necrosis factor (TNF) and RANKL trimerization, we identified four compounds (1b, 3b, 4a, and 4c) that selectively inhibited RANKL-induced osteoclastogenesis in a dose-dependent manner, without affecting TNF activity or osteoblast differentiation. Based on structure-activity observations extracted from the most potent and less toxic inhibitors of RANKL-induced osteoclastogenesis, we synthesized a focused set of compounds that revealed three potent inhibitors (19a, 19b, and 20a) with remarkably low cell-toxicity and improved therapeutic indexes as shown by the LC₅₀ to IC₅₀ ratio. These RANKL-selective inhibitors are an excellent starting point for the development of small-molecule therapeutics against osteolytic diseases.

Small Molecule NF-κB Pathway Inhibitors in Clinic

Nuclear factor kappa B (NF-κB) signaling is implicated in all major human chronic diseases, with its role in transcription of hundreds of gene well established in the literature. This has propelled research into targeting the NF-κB pathways for modulating expression of those genes and the diseases mediated by them. In-spite of the critical, but often promiscuous role played by this pathway and the inhibition causing adverse drug reaction, currently many biologics, macromolecules, and small molecules that modulate this pathway are in the market or in clinical trials. Furthermore, many marketed drugs that were later found to also have NF-κB targeting activity were repurposed for new therapeutic interventions. Despite the rising importance of biologics in drug discovery, small molecules got around 76% of US-FDA (Food and Drug Administration-US) approval in the last decade. This encouraged us to review information regarding clinically relevant small molecule inhibitors of the NF-κB pathway from cell surface receptor stimulation to nuclear signaling. We have also highlighted the underexplored targets in this pathway that have potential to succeed in clinic.

Computational design of syntheses leading to compound libraries or isotopically labelled targets

Although computer programs for retrosynthetic planning have shown improved and in some cases quite satisfactory performance in designing routes leading to specific, individual targets, no algorithms capable of planning syntheses of entire target libraries - important in modern drug discovery - have yet been reported. This study describes how network-search routines underlying existing retrosynthetic programs can be adapted and extended to multi-target design operating on one common search graph, benefitting from the use of common intermediates and reducing the overall synthetic cost. Implementation in the Chematica platform illustrates the usefulness of such algorithms in the syntheses of either (i) all members of a user-defined library, or (ii) the most synthetically accessible members of this library. In the latter case, algorithms are also readily adapted to the identification of the most facile syntheses of isotopically labelled targets. These examples are industrially relevant in the context of hit-to-lead optimization and syntheses of isotopomers of various bioactive molecules.