Metabolic dysfunction-associated steatotic liver disease (MASLD) represents a progressive spectrum ranging from simple hepatic steatosis to steatohepatitis and fibrosis. Although insulin resistance (IR) plays a central role in metabolic diseases, in the liver, insulin- or substrate-driven de novo lipogenesis (DNL) promotes triglyceride accumulation through multiple complex regulatory mechanisms, including specific transcription factors, regardless of whether IR is primary or not. Elevated free fatty acids, resulting from increased adipose lipolysis, further augment hepatic lipid storage and contribute to IR and the progression of MASLD through lipotoxic intermediates such as diacylglycerols and ceramides, as well as other pathways. Numerous studies have identified DNL as a major, yet modifiable, contributor to MASLD. In addition, zonal differences in hepatic insulin signaling, non-classical insulin signaling pathways, and activation of the mechanistic target of rapamycin complex 1 and protein kinase C pathways appear to be involved in the development of selective hepatic IR. Recently, new pharmacologic agents, including resmetirom, have shown promise in improving steatohepatitis and fibrosis in MASLD. Nevertheless, sustained weight loss through lifestyle modification remains the cornerstone of MASLD prevention and therapy. Further mechanistic understanding of how IR and substrate overload promote DNL and hepatic fat accumulation is critical for developing effective treatments for MASLD.

A cell surface serine protease, dipeptidyl peptidase 4 (DPP-4), cleaves dipeptide from peptides containing proline or alanine in the N-terminal penultimate position. Two important incretin hormones, glucagon-like peptide-1 (GLP-1) and glucose-dependent insulinotropic peptide (GIP), enhance meal-stimulated insulin secretion from pancreaticβ-cells, but are inactivated by DPP-4. Diabetes and hyperglycemia increase the DPP-4 protein level and enzymatic activity in blood and tissues. In addition, multiple other functions of DPP-4 suggest that DPP-4 inhibitor, a new class of antidiabetic agents, may have pleiotropic effects. Studies have shown that DPP-4 itself is involved in the inflammatory signaling pathway, the stimulation of vascular smooth cell proliferation, and the stimulation of oxidative stress in various cells. DPP-4 inhibitor ameliorates these pathophysiologic processes and has been shown to have cardiovascular protective effects in bothin vitroandin vivoexperiments. However, in recent randomized clinical trials, DPP-4 inhibitor therapy in high risk patients with type 2 diabetes did not show cardiovascular protective effects. Some concerns on the actions of DPP-4 inhibitor include sympathetic activation and neuropeptide Y-mediated vascular responses. Further studies are required to fully characterize the cardiovascular effects of DPP-4 inhibitor.

Lee BW, et al. Diabetes Metab J. 2020 Jan;44:e24. https://doi.org/10.4093/dmj.2020.0010

Heterogeneity in intratumoral cancers leads to discrepancies in drug responsiveness, due to diverse genomics profiles. Thus, prediction of drug responsiveness is critical in precision medicine. So far, in drug responsiveness prediction, drugs’ molecular “fingerprints”, along with mutation statuses, have not been considered. Here, we constructed a 1-dimensional convolution neural network model, DeepIC50, to predict three drug responsiveness classes, based on 27,756 features including mutation statuses and various drug molecular fingerprints. As a result, DeepIC50 showed better cell viability IC50 prediction accuracy in pan-cancer cell lines over two independent cancer cell line datasets. Gastric cancer (GC) is not only one of the lethal cancer types in East Asia, but also a heterogeneous cancer type. Currently approved targeted therapies in GC are only trastuzumab and ramucirumab. Responsive GC patients for the drugs are limited, and more drugs should be developed in GC. Due to the importance of GC, we applied DeepIC50 to a real GC patient dataset. Drug responsiveness prediction in the patient dataset by DeepIC50, when compared to the other models, were comparable to responsiveness observed in GC cell lines. DeepIC50 could possibly accurately predict drug responsiveness, to new compounds, in diverse cancer cell lines, in the drug discovery process.