Lysophosphatidic Acid Receptor 1 (LPA1) Antagonists as Potential Migrastatics for Triple Negative Breast Cancer.

Metastasis is responsible for about 90 % of cancer deaths. Anti-metastatic drugs, termed as migrastatics, offer a distinctive therapeutic approach to address cancer migration and invasion. However, therapeutic exploitation of metastasis-specific targets remains limited, and the effective prevention and suppression of metastatic cancer continue to be elusive. Lysophosphatidic acid receptor 1 (LPA1) is activated by an endogenous lipid molecule LPA, leading to a diverse array of cellular activities. Previous studies have shown that the LPA/LPA1 axis supports the progression of metastasis for many types of cancer. In this study, we report the synthesis and biological evaluation of fluorine-containing triazole derivatives as potent LPA1 antagonists, offering potential as migrastatic drugs for triple negative breast cancer (TNBC). In particular, compound 12 f, the most potent and highly selective in this series with an IC50 value of 16.0 nM in the cAMP assay and 18.4 nM in the calcium mobilization assay, inhibited cell survival, migration, and invasion in the TNBC cell line. Interestingly, the compound did not induce apoptosis in TNBC cells and demonstrated no cytotoxic effects. These results highlight the potential of LPA1 as a migrastatic target. Consequently, the LPA1 antagonists developed in this study hold promise as potential migrastatic candidates for TNBC.

Can machine learning 'transform' peptides/peptidomimetics into small molecules? A case study with ghrelin receptor ligands.

There has been considerable interest in transforming peptides into small molecules as peptide-based molecules often present poorer bioavailability and lower metabolic stability. Our studies looked into building machine learning (ML) models to investigate if ML is able to identify the 'bioactive' features of peptides and use the features to accurately discriminate between binding and non-binding small molecules. The ghrelin receptor (GR), a receptor that is implicated in various diseases, was used as an example to demonstrate whether ML models derived from a peptide library can be used to predict small molecule binders. ML models based on three different algorithms, namely random forest, support vector machine, and extreme gradient boosting, were built based on a carefully curated dataset of peptide/peptidomimetic and small molecule GR ligands. The results indicated that ML models trained with a dataset exclusively composed of peptides/peptidomimetics provide limited predictive power for small molecules, but that ML models trained with a diverse dataset composed of an array of both peptides/peptidomimetics and small molecules displayed exceptional results in terms of accuracy and false rates. The diversified models can accurately differentiate the binding small molecules from non-binding small molecules using an external validation set with new small molecules that we synthesized previously. Structural features that are the most critical contributors to binding activity were extracted and are remarkably consistent with the crystallography and mutagenesis studies.

The development of modulators for lysophosphatidic acid receptors: A comprehensive review.

Lysophosphatidic acids (LPAs) are bioactive phospholipids implicated in a wide range of cellular activities that regulate a diverse array of biological functions. They recognize two types of G protein-coupled receptors (LPARs): LPA1-3 receptors and LPA4-6 receptors that belong to the endothelial gene (EDG) family and non-endothelial gene family, respectively. In recent years, the LPA signaling pathway has captured an increasing amount of attention because of its involvement in various diseases, such as idiopathic pulmonary fibrosis, cancers, cardiovascular diseases and neuropathic pain, making it a promising target for drug development. While no drugs targeting LPARs have been approved by the FDA thus far, at least three antagonists have entered phase Ⅱ clinical trials for idiopathic pulmonary fibrosis (BMS-986020 and BMS-986278) and systemic sclerosis (SAR100842), and one radioligand (BMT-136088/18F-BMS-986327) has entered phase Ⅰ clinical trials for positron emission tomography (PET) imaging of idiopathic pulmonary fibrosis. This article provides an extensive review on the current status of ligand development targeting LPA receptors to modulate LPA signaling and their therapeutic potential in various diseases.

Capnocytophaga bacteremia precipitating severe thrombocytopenia and preterm labor in an asplenic host.

Capnocytophaga species are gram-negative bacilli that inhabit mammalian oral surfaces and can cause opportunistic infection, especially in asplenic patients. The species Capnocytophaga canimorsus is particularly associated with dog bites and is known to cause endocarditis, meningitis, and sepsis in the general population. In pregnant patients, infections tied to Capnocytophaga species from human flora have been associated with preterm labor, chorioamnionitis, and neonatal septicemia. There is little known about the effects of zoonotically-acquired Capnocytophaga infection in pregnant patients. In this case report, we present a patient with Capnocytophaga bacteremia acquired after a dog bite associated with profound thrombocytopenia and preterm labor. Dog bites are common in the United States, and we present basic recommendations for management of dog bites in pregnant patients in order to avoid morbidity associated with delay in time to antibiotic treatment of infection as described in this case.