Discovery of novel indoleamine 2,3-dioxygenase 1 (IDO1) inhibitors by virtual screening

Virtual screening based on pharmacophore model for developing novel HPPD inhibitors

4-Hydroxyphenylpyruvate dioxygenase (HPPD) is an important target for herbicide design. A multilayered virtual screening workflow was constructed by combining two pharmacophore models based on ligand and crystal complexes, molecular docking, molecular dynamics (MD), and biological activity determination to identify novel small-molecule inhibitors of HPPD. About 110, 000 compounds of Bailingwei and traditional Chinese medicine databases were screened. Of these, 333 were analyzed through docking experiments. Five compounds were selected by analyzing the binding pattern of inhibitors with amino acid residues in the active pocket. All five compounds could produce stable coordination with cobalt ion, and form favorable π-π interactions. MD simulation demonstrated that Phe381 and Phe424 made large contributions to the strength of binding. The enzyme activity experiment verified that compound-139 displayed excellent potency against AtHPPD (IC₅₀ = 0.742 μM), however, compound-5222 had inhibitory effect on human HPPD (IC₅₀ = 6 nM). Compound-139 exhibited herbicidal activity to some extent on different gramineous weeds. This work provided a strong insight into the design and development of novel HPPD inhibitor using in silico techniques.

A theranostic probe of indoleamine 2,3-dioxygenase 1 (IDO1) for small molecule cancer immunotherapy

Discovering novel small molecules for cancer immunotherapy represents a promising but challenging strategy in future cancer treatment. Herein, we designed the first theranostic fluorescent probes to efficiently detect and inhibit the enzymatic activity of 2,3-dioxygenase 1 (IDO1). Probe 6b is a highly active IDO1 inhibitor (IC₅₀ = 12 nM, Cellular IC₅₀ = 10 nM), which can sensitively and specifically detect endogenous IDO1 in living cells. Furthermore, as a theranostic probe, 6b showed excellent in vivo antitumor efficacy in the CT26 xenograft mouse model as well. Therefore, it can be applied as a valuable chemical tool for better understanding the immunotherapy mechanism of IDO1 and improving the therapeutic efficiency.

Machine and deep learning approaches for cancer drug repurposing

Knowledge of the underpinnings of cancer initiation, progression and metastasis has increased exponentially in recent years. Advanced "omics" coupled with machine learning and artificial intelligence (deep learning) methods have helped elucidate targets and pathways critical to those processes that may be amenable to pharmacologic modulation. However, the current anti-cancer therapeutic armamentarium continues to lag behind. As the cost of developing a new drug remains prohibitively expensive, repurposing of existing approved and investigational drugs is sought after given known safety profiles and reduction in the cost barrier. Notably, successes in oncologic drug repurposing have been infrequent. Computational in-silico strategies have been developed to aid in modeling biological processes to find new disease-relevant targets and discovering novel drug-target and drug-phenotype associations. Machine and deep learning methods have especially enabled leaps in those successes. This review will discuss these methods as they pertain to cancer biology as well as immunomodulation for drug repurposing opportunities in oncologic diseases.

Combined inhibition of JAK1/2 and DNMT1 by newly identified small-molecule compounds synergistically suppresses the survival and proliferation of cervical cancer cells

Despite substantial advances in treating cervical cancer (CC) with surgery, radiation and chemotherapy, patients with advanced CC still have poor prognosis and significantly variable clinical outcomes due to tumor recurrence and metastasis. Therefore, to develop more efficacious and specific treatments for CC remains an unmet clinical need. In this study, by virtual screening the SPECS database, we identified multiple novel JAK inhibitor candidates and validated their antitumor drug efficacies that were particularly high against CC cell lines. AH057, the best JAK inhibitor identified, effectively blocked the JAK/STAT pathways by directly inhibiting JAK1/2 kinase activities, and led to compromised cell proliferation and invasion, increased apoptosis, arrested cell cycles, and impaired tumor progression in vitro and in vivo. Next, by screening the Selleck chemical library, we identified SGI-1027, a DNMT1 inhibitor, as the compound that displayed the highest synergy with AH057. By acting on a same set of downstream effector molecules that are dually controlled by JAK1/2 and DNMT1, the combination of AH057 with SGI-1027 potently and synergistically impaired CC cell propagation via dramatically increasing apoptotic cell death and cell-cycle arrest. These findings establish a preclinical proof of concept for combating CC by dual targeting of JAK1/2 and DNMT1, and provide support for launching a clinical trial to evaluate the efficacy and safety of this drug combination in patients with CC and other malignant tumors.

New Insights from Crystallographic Data: Diversity of Structural Motifs and Molecular Recognition Properties between Groups of IDO1 Structures

A large number of crystallographic structures of IDO1 in different ligand-bound and -unbound states have been disclosed over the last decade. Yet, only a few of them have been exploited for structure-based drug design (SBDD) campaigns. In this study, we analyzed the structural motifs and molecular-recognition properties of three groups of IDO1 structures: 1) structures containing the heme group and inhibitors in the catalytic site; 2) heme-free structures of IDO1; 3) substrate-bound structures of IDO1. The results suggest that unrelated conformations of the enzyme have been solved with different ligand-induced changes of secondary motifs that localize even in regions remote from the catalytic site. Moreover, the study identified an uncharted region of molecular-recognition space covered by IDO1 binding sites that could guide the selection of diverse structures for additional SBDD studies aimed at the identification of novel lead compounds with differentiated chemical scaffolds.

Use of QSAR Global Models and Molecular Docking for Developing New Inhibitors of c-src Tyrosine Kinase

A prototype of a family of at least nine members, cellular Src tyrosine kinase is a therapeutically interesting target because its inhibition might be of interest not only in a number of malignancies, but also in a diverse array of conditions, from neurodegenerative pathologies to certain viral infections. Computational methods in drug discovery are considerably cheaper than conventional methods and offer opportunities of screening very large numbers of compounds in conditions that would be simply impossible within the wet lab experimental settings. We explored the use of global quantitative structure-activity relationship (QSAR) models and molecular ligand docking in the discovery of new c-src tyrosine kinase inhibitors. Using a dataset of 1038 compounds from ChEMBL database, we developed over 350 QSAR classification models. A total of 49 models with reasonably good performance were selected and the models were assembled by stacking with a simple majority vote and used for the virtual screening of over 100,000 compounds. A total of 744 compounds were predicted by at least 50% of the QSAR models as active, 147 compounds were within the applicability domain and predicted by at least 75% of the models to be active. The latter 147 compounds were submitted to molecular ligand docking using AutoDock Vina and LeDock, and 89 were predicted to be active based on the energy of binding.

In silico identification of natural products with anticancer activity using a chemo-structural database of Brazilian biodiversity

Cancer is one of the leading causes of death worldwide, and the number of patients has only increased each year, despite the considerable efforts and investments in scientific research. Since natural products (NPs) may serve as suitable sources for drug development, the cytotoxicity against cancer cells of 2221 compounds from the Nuclei of Bioassays, Ecophysiology, and Biosynthesis of Natural Products Database (NuBBE_DB) was predicted using CDRUG algorithm. Molecular modeling, chemoinformatics, and chemometric tools were then used to analyze the structural and physicochemical properties of these compounds. We compared the positive NPs with FDA-approved anticancer drugs and predicted the molecular targets involved in the anticancer activity. In the present study, 46 families comprising potential anticancer compounds and at least 19 molecular targets involved in oncogenesis. To the best of our knowledge, this is the first large-scale study conducted to evaluate the potentiality of NPs sourced from Brazilian biodiversity as anticancer agents, using in silico approaches. Our results provided interesting insights about the mechanism of action of these compounds, and also suggested that their structural diversity may aid structure-based optimization strategies for developing novel drugs for cancer therapy.

Synthesis and in vivo antitumor evaluation of an orally active potent phosphonamidate derivative targeting IDO1/IDO2/TDO

Targeting Trp-Kyn pathways has been identified as an attractive approach for the cancer immunotherapies. In this study, a novel phosphonamidate containing compound was designed, synthesized and evaluated for its inhibitory activity against key dioxygenases in Trp-Kyn pathway, including IDO1, IDO2 and TDO. This compound showed potent IDO1 inhibitory activity with an IC₅₀ value of 94 nM in an enzymatic assay and 12.6 nM in HeLa cells. In addition, this compound showed promising IDO2 inhibition and TDO inhibition with IC₅₀ values of 310 nM and 2.6 μM, respectively, in enzyme assay. Based on the promising enzyme inhibitory activity toward IDO/TDO, compound F04 was evaluated of its antitumor effects in two tumor models. Further evaluation of mechanism demonstrated compound F04 with the remarkable capacity of reducing kynurenine level in plasma/TME and restoring anti-tumor immune response. F04 could be further developed as a potential immunotherapeutic agent combined with immune checkpoint inhibitors or chemotherapeutic drugs for cancer treatment.