A chemical modification of a peroxisome proliferator-activated receptor pan agonist produced a shift to a new dual alpha/gamma partial agonist endowed with mitochondrial pyruvate carrier inhibition and antidiabetic properties.

New analogs of the PPAR pan agonist AL29-26 encompassed ligand (S)-7 showing potent activation of PPARα and -γ subtypes as a partial agonist. In vitro experiments and docking studies in the presence of PPAR antagonists were performed to help interpretation of biological data and investigate the main interactions at the binding sites. Further in vitro experiments showed that (S)-7 induced anti-steatotic effects and enhancement of the glucose uptake. This latter effect could be partially ascribed to a significant inhibition of the mitochondrial pyruvate carrier demonstrating that (S)-7 also acted through insulin-independent mechanisms. In vivo experiments showed that this compound reduced blood glucose and lipid levels in a diabetic mice model displaying no toxicity on bone, kidney, and liver. To our knowledge, this is the first example of dual PPARα/γ partial agonist showing these combined effects representing, therefore, the potential lead of new drugs for treatment of dyslipidemic type 2 diabetes.

Synthesis and in vitro cytotoxicity of benzoxazole-based PPARα/γ antagonists in colorectal cancer cell lines.

A series of benzoxazole-based amides and sulfonamides were synthesized and evaluated for their human peroxisome proliferator-activated receptor (PPAR)α and PPARγ activity. All tested compounds showed a dual antagonist profile on both PPAR subtypes; based on transactivation results, seven compounds were selected to test their in vitro antiproliferative activity in a panel of eight cancer cell lines with different expression rates of PPARα and PPARγ. 3f was identified as the most cytotoxic compound, with higher potency in the colorectal cancer cell lines HT-29 and HCT116. Compound 3f induced a concentration-dependent activation of caspases and cell-cycle arrest in both colorectal cancer models. Docking experiments were also performed to shed light on the putative binding mode of this novel class of dual PPARα/γ antagonists.

AMALPHI: A Machine Learning Platform for Predicting Drug-Induced PhospholIpidosis.

Drug-induced phospholipidosis (PLD) involves the accumulation of phospholipids in cells of multiple tissues, particularly within lysosomes, and it is associated with prolonged exposure to druglike compounds, predominantly cationic amphiphilic drugs (CADs). PLD affects a significant portion of drugs currently in development and has recently been proven to be responsible for confounding antiviral data during drug repurposing for SARS-CoV-2. In these scenarios, it has become crucial to identify potential safe drug candidates in advance and distinguish them from those that may lead to false in vitro antiviral activity. In this work, we developed a series of machine learning classifiers with the aim of predicting the PLD-inducing potential of drug candidates. The models were built on a high-quality chemical collection comprising 545 curated small molecules extracted from ChEMBL v30. The most effective model, obtained using the balanced random forest algorithm, achieved high performance, including an AUC value computed in validation as high as 0.90. The model was made freely available through a user-friendly web platform named AMALPHI (https://www.ba.ic.cnr.it/softwareic/amalphiportal/), which can represent a valuable tool for medicinal chemists interested in conducting an early evaluation of PLD inducer potential.