New target DDR1: A "double-edged sword" in solid tumors

Chemical Derivatization Leads to the Discovery Of Novel Analogs of Azotochelin, a Natural Siderophore, as Promising Anticancer Agents

Siderophores are structurally unique medicinal natural products and exhibit considerable therapeutic potential. Herein, we report the design and synthesis of azotochelin, a natural siderophore, and an extensive library of azotochelin analogs and their anticancer properties. We modified the carboxylic acid and the aromatic ring of azotochelin using various chemical motifs. We evaluated the cytotoxicity of the compounds against six different cancer cell lines (KB-3-1, SNB-19, MCF-7, K-562, SW-620, and NCI-H460) and a non-cancerous cell line (HEK-293). Among the twenty compounds tested, the IC50 values of nine compounds (14, 32, 35-40, and 54) were between 0.7 and 2.0 μM against a lung cancer cell line (NCI-H460). Moreover, several compounds showed good cytotoxicity profile (IC50 <10 μM) against the tested cancer cell lines. The flow cytometry analysis showed that compounds 36 and 38 induced apoptosis in NCI-H460 in a dose-dependent manner. The cell cycle analysis indicated that compounds 36 and 38 significantly arrested the cell cycle at the S phase to block cancer cell proliferation in the NCI-H460 cell line. The study has produced various novel azotochelin analogs that are potentially effective anticancer agents and lead compounds for further synthetic and medicinal chemistry exploration.

Research progress of DDR1 inhibitors in the treatment of multiple human diseases

Discoidin domain receptor 1 (DDR1) is a collagen-activated receptor tyrosine kinase (RTK) and plays pivotal roles in regulating cellular functions such as proliferation, differentiation, invasion, migration, and matrix remodeling. DDR1 is involved in the occurrence and progression of many human diseases, including cancer, fibrosis, and inflammation. Therefore, DDR1 represents a highly promising therapeutic target. Although no selective small-molecule inhibitors have reached clinical trials to date, many molecules have shown therapeutic effects in preclinical studies. For example, BK40143 has demonstrated significant promise in the therapy of neurodegenerative diseases. In this context, our perspective aims to provide an in-depth exploration of DDR1, encompassing its structure characteristics, biological functions, and disease relevance. Furthermore, we emphasize the importance of understanding the structure-activity relationship of DDR1 inhibitors and highlight the unique advantages of dual-target or multitarget inhibitors. We anticipate offering valuable insights into the development of more efficacious DDR1-targeted drugs.

Altered Treg Infiltration after Discoidin Domain Receptor 1 (DDR1) Inhibition and Knockout Promotes Tumor Growth in Lung Adenocarcinoma

Lung cancer is the leading cause of cancer-related death worldwide. Discoidin domain receptor 1 (DDR1), a tyrosine kinase receptor, has been associated with poor prognosis in patients with non-small cell lung cancer (NSCLC). However, its role in tumorigenesis remains poorly understood. This work aimed to explore the impact of DDR1 expression on immune cell infiltration in lung adenocarcinoma. Pharmacological inhibition and knockout of DDR1 were used in an immunocompetent mouse model of KRAS/p53-driven lung adenocarcinoma (LUAD). Tumor cells were engrafted subcutaneously, after which tumors were harvested for investigation of immune cell composition via flow cytometry. The Cancer Genome Atlas (TCGA) cohort was used to perform gene expression analysis of 509 patients with LUAD. Pharmacological inhibition and knockout of DDR1 increased the tumor burden, with DDR1 knockout tumors showing a decrease in CD8+ cytotoxic T cells and an increase in CD4+ helper T cells and regulatory T cells. TCGA analysis revealed that low-DDR1-expressing tumors showed higher FoxP3 (regulatory T-cell marker) expression than high-DDR1-expressing tumors. Our study showed that under certain conditions, the inhibition of DDR1, a potential therapeutic target in cancer treatment, might have negative effects, such as inducing a pro-tumorigenic tumor microenvironment. As such, further investigations are necessary.

Improving drug discovery with a hybrid deep generative model using reinforcement learning trained on a Bayesian docking approximation

Generative approaches to molecular design are an area of intense study in recent years as a method to generate new pharmaceuticals with desired properties. Often though, these types of efforts are constrained by limited experimental activity data, resulting in either models that generate molecules with poor performance or models that are overfit and produce close analogs of known molecules. In this paper, we reduce this data dependency for the generation of new chemotypes by incorporating docking scores of known and de novo molecules to expand the applicability domain of the reward function and diversify the compounds generated during reinforcement learning. Our approach employs a deep generative model initially trained using a combination of limited known drug activity and an approximate docking score provided by a second machine learned Bayes regression model, with final evaluation of high scoring compounds by a full docking simulation. This strategy results in molecules with docking scores improved by 10-20% compared to molecules of similar size, while being 130 × faster than a docking only approach on a typical GPU workstation. We also show that the increased docking scores correlate with (1) docking poses with interactions similar to known inhibitors and (2) result in higher MM-GBSA binding energies comparable to the energies of known DDR1 inhibitors, demonstrating that the Bayesian model contains sufficient information for the network to learn to efficiently interact with the binding pocket during reinforcement learning. This outcome shows that the combination of the learned latent molecular representation along with the feature-based docking regression is sufficient for reinforcement learning to infer the relationship between the molecules and the receptor binding site, which suggest that our method can be a powerful tool for the discovery of new chemotypes with potential therapeutic applications.