First-in-class MKK4 inhibitors enhance liver regeneration and prevent liver failure.

Diminished hepatocyte regeneration is a key feature of acute and chronic liver diseases and after extended liver resections, resulting in the inability to maintain or restore a sufficient functional liver mass. Therapies to restore hepatocyte regeneration are lacking, making liver transplantation the only curative option for end-stage liver disease. Here, we report on the structure-based development and characterization (nuclear magnetic resonance [NMR] spectroscopy) of first-in-class small molecule inhibitors of the dual-specificity kinase MKK4 (MKK4i). MKK4i increased liver regeneration upon hepatectomy in murine and porcine models, allowed for survival of pigs in a lethal 85% hepatectomy model, and showed antisteatotic and antifibrotic effects in liver disease mouse models. A first-in-human phase I trial (European Union Drug Regulating Authorities Clinical Trials [EudraCT] 2021-000193-28) with the clinical candidate HRX215 was conducted and revealed excellent safety and pharmacokinetics. Clinical trials to probe HRX215 for prevention/treatment of liver failure after extensive oncological liver resections or after transplantation of small grafts are warranted.

Deep Learning Reveals Cancer Metastasis and Therapeutic Antibody Targeting in the Entire Body.

Reliable detection of disseminated tumor cells and of the biodistribution of tumor-targeting therapeutic antibodies within the entire body has long been needed to better understand and treat cancer metastasis. Here, we developed an integrated pipeline for automated quantification of cancer metastases and therapeutic antibody targeting, named DeepMACT. First, we enhanced the fluorescent signal of cancer cells more than 100-fold by applying the vDISCO method to image metastasis in transparent mice. Second, we developed deep learning algorithms for automated quantification of metastases with an accuracy matching human expert manual annotation. Deep learning-based quantification in 5 different metastatic cancer models including breast, lung, and pancreatic cancer with distinct organotropisms allowed us to systematically analyze features such as size, shape, spatial distribution, and the degree to which metastases are targeted by a therapeutic monoclonal antibody in entire mice. DeepMACT can thus considerably improve the discovery of effective antibody-based therapeutics at the pre-clinical stage. VIDEO ABSTRACT.

Paraneoplastic syndrome in mice bearing high-angiogenic variant of Lewis lung carcinoma: relations with tumor derived VEGF.

BACKGROUND: It is well-known that tumor exerts nonmetastatic systemic effect on organism caused the development of paraneoplastic syndrome (PNS). Recent findings point to relationships between development of PNS and tumor-derived vascular endothelial growth factor (VEGF). AIM: Comparative study of PNS manifestations in mice with transplanted two variants of Lewis lung carcinoma with different angiogenic potential. METHODS: Plasma VEGF level was determined by immunoenzyme method, hematological indices were estimated with the use of hematological analyzer, the weight and cellularity of spleen and thymus were registered and histological analysis of tissue section of these organs was performed. RESULTS: Manifestations of anemia, extramedullary hemopoiesis and tumor-associated inflammatory disease was observed in animals with high angiogenic LLC/R9 variant and was not registered in low angiogenic LLC. The emergence of PNS symptoms correlated with elevated level of circulating VEGF at the early stages of LLC/R9 growth. CONCLUSION: Manifestation of the paraneoplastic hematological syndrome most likely is conditioned on the ability of cancer cell to secrete VEGF in a high rate.