Recent studies suggest that excess dietary fructose contributes to metabolic dysfunction by promoting insulin resistance, de novo lipogenesis (DNL), and hepatic steatosis, thereby increasing the risk of obesity, type 2 diabetes (T2D), non-alcoholic steatohepatitis (NASH), and related comorbidities. Whether this metabolic dysfunction is driven by the excess dietary calories contained in fructose or whether fructose catabolism itself is uniquely pathogenic remains controversial. We sought to test whether a small molecule inhibitor of the primary fructose metabolizing enzyme ketohexokinase (KHK) can ameliorate the metabolic effects of fructose. The KHK inhibitor PF-06835919 was used to block fructose metabolism in primary hepatocytes and Sprague Dawley rats fed either a high-fructose diet (30% fructose kcal/g) or a diet reflecting the average macronutrient dietary content of an American diet (AD) (7.5% fructose kcal/g). The effects of fructose consumption and KHK inhibition on hepatic steatosis, insulin resistance, and hyperlipidemia were evaluated, along with the activation of DNL and the enzymes that regulate lipid synthesis. A metabolomic analysis was performed to confirm KHK inhibition and understand metabolite changes in response to fructose metabolism in vitro and in vivo. Additionally, the effects of administering a single ascending dose of PF-06835919 on fructose metabolism markers in healthy human study participants were assessed in a randomized placebo-controlled phase 1 study. Inhibition of KHK in rats prevented hyperinsulinemia and hypertriglyceridemia from fructose feeding. Supraphysiologic levels of dietary fructose were not necessary to cause metabolic dysfunction as rats fed the American diet developed hyperinsulinemia, hypertriglyceridemia, and hepatic steatosis, which were all reversed by KHK inhibition. Reversal of the metabolic effects of fructose coincided with reductions in DNL and inactivation of the lipogenic transcription factor carbohydrate response element-binding protein (ChREBP). We report that administering single oral doses of PF-06835919 was safe and well tolerated in healthy study participants and dose-dependently increased plasma fructose indicative of KHK inhibition. Fructose consumption in rats promoted features of metabolic dysfunction seen in metabolic diseases such as T2D and NASH, including insulin resistance, hypertriglyceridemia, and hepatic steatosis, which were reversed by KHK inhibition.

Hydroxysteroid 17-beta-dehydrogenase 13 (HSD17B13) is a hepatic lipid droplet-associated enzyme that is upregulated in patients with non-alcoholic fatty liver disease. Recently, there have been several reports that predicted loss of function variants in HSD17B13 protect against the progression of steatosis to non-alcoholic steatohepatitis with fibrosis and hepatocellular carcinoma. Here we report crystal structures of full length HSD17B13 in complex with its NAD⁺ cofactor, and with lipid/detergent molecules and small molecule inhibitors from two distinct series in the ligand binding pocket. These structures provide insights into a mechanism for lipid droplet-associated proteins anchoring to membranes as well as a basis for HSD17B13 variants disrupting function. Two series of inhibitors interact with the active site residues and the bound cofactor similarly, yet they occupy different paths leading to the active site. These structures provide ideas for structure-based design of inhibitors that may be used in the treatment of liver disease.

Several hydroxysteroid dehydrogenase 17-beta 13 variants have previously been identified as protective against metabolic dysfunction-associated steatohepatitis (MASH) fibrosis, ballooning and inflammation, and as such this target holds significant therapeutic potential. However, over 5 years later, the function of 17B-HSD13 remains unknown. Structure-aided design enables the development of potent and selective sulfonamide-based 17B-HSD13 inhibitors. In order to probe their inhibitory potency in endogenous expression systems like primary human hepatocytes, inhibitors are transformed into synthetic surrogate substrates with distinct selectivity advantages over substrates previously published. Their application to cells endogenously expressing 17B-HSD13 enables quantitative measures of enzymatic inhibition in primary human hepatocytes which has never been reported to date. Application to multiple cellular systems expressing the protective human variants reveals that the most prevalent IsoD variant maintains NAD-dependent catalytic activity towards some but not all substrates, contradicting reports that the truncation results in loss-of-function.

A key unmet need in metabolomics continues to be the specific, selective, accurate detection of traditionally difficult to retain molecules including simple sugars, sugar phosphates, carboxylic acids, and related amino acids. Designed to retain the metabolites of central carbon metabolism, this Mixed Mode (MM) chromatography applies varied pH, salt concentration and organic content to a positively charged quaternary amine polyvinyl alcohol stationary phase. This MM method is capable of separating glucose from fructose, and four hexose monophosphates a single chromatographic run. Coupled to a QExactive Orbitrap Mass Spectrometer with negative ESI, linearity, LLOD, %CV, and mass accuracy were assessed using 33 metabolite standards. The standards were linear on average >3 orders of magnitude (R² > 0.98 for 30/33) with LLOD < 1 pmole (26/33), median CV of 12% over two weeks, and median mass accuracy of 0.49 ppm. To assess the breadth of metabolome coverage and better define the structural elements dictating elution, we injected 607 unique metabolites and determined that 398 are well retained. We then split the dataset of 398 documented RTs into training and test sets and trained a message-passing neural network (MPNN) to predict RT from a featurized heavy atom connectivity graph. Unlike traditional QSAR methods that utilize hand-crafted descriptors or pre-defined structural keys, the MPNN aggregates atomic features across the molecular graph and learns to identify molecular subgraphs that are correlated with variations in RTs. For sugars, sugar phosphates, carboxylic acids, and isomers, the model achieves a predictive RT error of <2 min on 91%, 50%, 77%, and 72% of held-out compounds from these subsets, with overall root mean square errors of 0.11, 0.34, 0.18, and 0.53 min, respectively. The model was then applied to rank order metabolite IDs for molecular features altered by GLS2 knockout in mouse primary hepatocytes.