The neutral CB1 antagonist AM6527 reduces ethanol seeking, binge-like consumption, reinforcing, and withdrawal effects in male and female mice.

RATIONALE: Alcohol use disorder (AUD) is a debilitating physiological and psychiatric disorder which affects individuals globally. The current pharmacological interventions to treat AUD are limited, and hence there is an urgent need for a novel pharmacological therapy which can be effective and safe across the population. OBJECTIVE: We aimed to investigate a novel neutral cannabinoid receptor-1 (CB1R) antagonist, AM6527, in several preclinical models of ethanol consumption using male and female C57BL6/J mice. METHODS: Independent groups of male and female mice were subjected to repeated cycles of drinking in the dark (DID), or intermittent access to alcohol (IAA) procedures. Twenty minutes prior to ethanol access in each procedure, animals were treated with intraperitoneal injections of either 1, 3, and 10 mg/kg of AM6527 or its respective vehicle. Acamprosate (100, 200, 300, and 400 mg/kg) or its respective vehicle was used as a positive control. Separate groups of male mice were subjected to a chain schedule of ethanol reinforcement to gain access to ethanol wherein completion of a fixed interval (FI; 5 min) schedule (link 1: "Seeking") was reinforced with continuous access to ethanol (fixed ratio; FR1) for up to 1.8 g/kg (link 2: "consumption"). All the animals were treated with 1, 3, and 10 mg/kg of AM6527 or its respective vehicle 20 mins prior to the start of the FI chain of the procedure. Separately, AM6527 was also evaluated in male and female mice undergoing acute ethanol withdrawal following 8 weeks of intermittent or continuous access to 20% ethanol drinking. RESULTS: In both DID and IAA procedures, AM6527 reduced ethanol consumption in a dose-related manner in both male and female mice. AM6527 produced no tolerance in the DID procedure; mice treated with 3 mg/kg of AM6527 for 3 weeks continuously drank significantly smaller amounts of ethanol as compared to vehicle-treated mice over a period of three DID cycles. Moreover, in the IAA procedure, AM6527 caused an increase in water intake over the 24-h period. Acamprosate transiently reduced ethanol intake in male mice in both the DID and the IAA procedures but failed to produce any significant effect in female mice. AM6527 also produced a decrease in the FI responding ("ethanol seeking") in animals trained to self-administer ethanol. Lastly, AM6527 mitigated neurological withdrawal signs, i.e., handling induced convulsions (HIC) in mice undergoing acute ethanol withdrawal. CONCLUSIONS: Current findings support previous studies with CB1R neutral antagonist in reducing voluntary ethanol intake and seeking behavior. Based on results shown in this work, AM6527 can be developed as a first in class CB1R neutral antagonist to treat AUD in both males and females.

Molecule generation toward target protein (SARS-CoV-2) using reinforcement learning-based graph neural network via knowledge graph.

AI-driven approaches are widely used in drug discovery, where candidate molecules are generated and tested on a target protein for binding affinity prediction. However, generating new compounds with desirable molecular properties such as Quantitative Estimate of Drug-likeness (QED) and Dopamine Receptor D2 activity (DRD2) while adhering to distinct chemical laws is challenging. To address these challenges, we proposed a graph-based deep learning framework to generate potential therapeutic drugs targeting the SARS-CoV-2 protein. Our proposed framework consists of two modules: a novel reinforcement learning (RL)-based graph generative module with knowledge graph (KG) and a graph early fusion approach (GEFA) for binding affinity prediction. The first module uses a gated graph neural network (GGNN) model under the RL environment for generating novel molecular compounds with desired properties and a custom-made KG for molecule screening. The second module uses GEFA to predict binding affinity scores between the generated compounds and target proteins. Experiments show how fine-tuning the GGNN model under the RL environment enhances the molecules with desired properties to generate 100 % valid and 100 % unique compounds using different scoring functions. Additionally, KG-based screening reduces the search space of generated candidate molecules by 96.64 % while retaining 95.38 % of promising binding molecules against SARS-CoV-2 protein, i.e., 3C-like protease (3CLpro). We achieved a binding affinity score of 8.185 from the top rank of generated compound. In addition, we compared top-ranked generated compounds to Indinavir on different parameters, including drug-likeness and medicinal chemistry, for qualitative analysis from a drug development perspective. Supplementary Information: The online version contains supplementary material available at 10.1007/s13721-023-00409-2.

Immune Checkpoint Inhibitor-Mediated Cancer Theranostics with Radiolabeled Anti-Granzyme B Peptide.

Although immune checkpoint inhibitors (ICI) have revolutionized cancer management, patient response can be heterogeneous, and the development of ICI resistance is increasingly reported. Novel treatment strategies are necessary not only to expand the use of ICI to previously unresponsive tumor types but also to overcome resistance. Targeted radionuclide therapy may synergize well with ICIs since it can promote a pro-inflammatory tumor microenvironment. We investigated the use of a granzyme B targeted peptide (GZP) as a cancer theranostic agent, radiolabeled with 68Ga (68Ga-GZP) as a PET imaging agent and radiolabeled with 90Y (90Y-GZP) as a targeted radionuclide therapy agent for combinational therapy with ICI in murine models of colon cancer. Our results demonstrate that GZP increasingly accumulates in tumor tissue after ICI and that the combination of ICI with 90Y-GZP promotes a dose-dependent response, achieving curative response in some settings and increased overall survival.