Dataset Information

β-Lactam TRPM8 Antagonist RGM8-51 Displays Antinociceptive Activity in Different Animal Models.

ABSTRACT: Transient receptor potential melastatin subtype 8 (TRPM8) is a cation channel extensively expressed in sensory neurons and implicated in different painful states. However, the effectiveness of TRPM8 modulators for pain relief is still a matter of discussion, since structurally diverse modulators lead to different results, depending on the animal pain model. In this work, we described the antinociceptive activity of a β-lactam derivative, RGM8-51, showing good TRPM8 antagonist activity, and selectivity against related thermoTRP channels and other pain-mediating receptors. In primary cultures of rat dorsal root ganglion (DRG) neurons, RGM8-51 potently reduced menthol-evoked neuronal firing without affecting the major ion conductances responsible for action potential generation. This compound has in vivo antinociceptive activity in response to cold, in a mouse model of oxaliplatin-induced peripheral neuropathy. In addition, it reduces cold, mechanical and heat hypersensitivity in a rat model of neuropathic pain arising after chronic constriction of the sciatic nerve. Furthermore, RGM8-51 exhibits mechanical hypersensitivity-relieving activity, in a mouse model of NTG-induced hyperesthesia. Taken together, these preclinical results substantiate that this TRPM8 antagonist is a promising pharmacological tool to study TRPM8-related diseases.

SUBMITTER: Martin-Escura C

PROVIDER: S-EPMC8910920 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature

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β-Lactam TRPM8 Antagonist RGM8-51 Displays Antinociceptive Activity in Different Animal Models.

Martín-Escura Cristina C Medina-Peris Alicia A Spear Luke A LA de la Torre Martínez Roberto R Olivos-Oré Luis A LA Barahona María Victoria MV González-Rodríguez Sara S Fernández-Ballester Gregorio G Fernández-Carvajal Asia A Artalejo Antonio R AR Ferrer-Montiel Antonio A González-Muñiz Rosario R

International journal of molecular sciences 20220228 5

Transient receptor potential melastatin subtype 8 (TRPM8) is a cation channel extensively expressed in sensory neurons and implicated in different painful states. However, the effectiveness of TRPM8 modulators for pain relief is still a matter of discussion, since structurally diverse modulators lead to different results, depending on the animal pain model. In this work, we described the antinociceptive activity of a β-lactam derivative, RGM8-51, showing good TRPM8 antagonist activity, and selec ...[more]

PMID: 35269831

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Project description:The protein transient receptor potential melastatin type 8 (TRPM8), a non-selective, calcium (Ca2+)-permeable ion channel is implicated in several pathological conditions, including neuropathic pain states. In our previous research endeavors, we have identified β-lactam derivatives with high hydrophobic character that exhibit potent and selective TRPM8 antagonist activity. This work describes the synthesis of novel derivatives featuring C-terminal amides and diversely substituted N'-terminal monobenzyl groups in an attempt to increase the total polar surface area (TPSA) in this family of compounds. The primary goal was to assess the influence of these substituents on the inhibition of menthol-induced cellular Ca2+ entry, thereby establishing critical structure-activity relationships. While the substitution of the tert-butyl ester by isobutyl amide moieties improved the antagonist activity, none of the N'-monobencyl derivatives, regardless of the substituent on the phenyl ring, achieved the activity of the model dibenzyl compound. The antagonist potency of the most effective compounds was subsequently verified using Patch-Clamp electrophysiology experiments. Furthermore, we evaluated the selectivity of one of these compounds against other members of the transient receptor potential (TRP) ion channel family and some receptors connected to peripheral pain pathways. This compound demonstrated specificity for TRPM8 channels. To better comprehend the potential mode of interaction, we conducted docking experiments to uncover plausible binding sites on the functionally active tetrameric protein. While the four main populated poses are located by the pore zone, a similar location to that described for the N-(3-aminopropyl)-2-[(3-methylphenyl)methoxy]-N-(2-thienylmethyl)-benzamide (AMTB) antagonist cannot be discarded. Finally, in vivo experiments, involving a couple of selected compounds, revealed significant antinociceptive activity within a mice model of cold allodynia induced by oxaliplatin (OXA).

Project description:The β-Lactam antibiotics represent a widely used class of antibiotics, yet the latent and often overlooked risk of coagulation dysfunction associated with their use underscores the need for proactive assessment. Machine learning methodologies can offer valuable insights into evaluating the risk of coagulation dysfunction associated with β-lactam antibiotics. This study aims to identify the risk factors associated with coagulation dysfunction related to β-lactam antibiotics and to develop machine learning models for estimating the risk of coagulation dysfunction with real-world data. A retrospective study was performed using machine learning modeling analysis on electronic health record data, employing five distinct machine learning methods. The study focused on adult inpatients discharged from 1 January 2018, to 31 December 2021, at the First Affiliated Hospital of Shandong First Medical University. The models were developed for estimating the risk of coagulation dysfunction associated with various β-lactam antibiotics based on electronic health record feasibility. The dataset was divided into training and test sets to assess model performance using metrics such as total accuracy and area under the curve. The study encompassed risk-factor analysis and machine learning model development for coagulation dysfunction in inpatients administered different β-lactam antibiotics. A total of 45,179 participants were included in the study. The incidence of coagulation disorders related to cefazolin sodium, cefoperazone/sulbactam sodium, cefminol sodium, amoxicillin/sulbactam sodium, and piperacillin/tazobactam sodium was 2.4%, 5.4%, 1.5%, 5.5%, and 4.8%, respectively. Machine learning models for estimating coagulation dysfunction associated with each β-lactam antibiotic underwent validation with 5-fold cross-validation and test sets. On the test set, the optimal models for cefazolin sodium, cefoperazone/sulbactam sodium, cefminol sodium, amoxicillin/sulbactam sodium, and piperacillin/tazobactam sodium yielded AUC values of 0.798, 0.768, 0.919, 0.783, and 0.867, respectively. The study findings suggest that machine learning classifiers can serve as valuable tools for identifying patients at risk of coagulation dysfunction associated with β-lactam antibiotics and intervening based on high-risk predictions. Enhanced access to administrative and clinical data could further enhance the predictive performance of machine learning models, thereby expanding pharmacovigilance efforts.

Dataset Information

β-Lactam TRPM8 Antagonist RGM8-51 Displays Antinociceptive Activity in Different Animal Models.

Publications

β-Lactam TRPM8 Antagonist RGM8-51 Displays Antinociceptive Activity in Different Animal Models.

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