Electrophysiological and ECG Effects of Perhexiline, a Mixed Cardiac Ion Channel Inhibitor, Evaluated in Nonclinical Assays and in Healthy Subjects

"Emerging clinical approaches in diabetic cardiomyopathy: insights from clinical trials and future directions"

AIM

We aim to explore the potential of diverse treatments, including perhexiline, calcium channel blockers, anti-hypertensives, PDE5 inhibitors, anti-anginal drugs, aldose reductase inhibitors, and SGLT-2 inhibitors, supported by clinical evidence. Additionally, this review seeks to identify novel therapeutic targets and future avenues for improving cardiovascular outcomes in diabetic populations.

METHOD

We performed a comprehensive literature review of English-language studies across multiple electronic databases, such as PubMed, ScienceDirect, Scopus, and Google Scholar, focusing on clinical trials. The search utilized keywords including 'Anti-hyperglycaemic drug,' 'Diabetic cardiomyopathy,' 'DPP-4 inhibitors,' 'GLP-1 receptor agonists,' 'Heart failure,' and 'SGLT-2 inhibitors.'

RESULT

We assessed clinical investigations in the treatment of cardiomyopathy and diabetes mellitus (DM) that are enhancing our understanding through trials evaluating the Polypill, Perhexiline, Eplerenone, IMB-1018972, AT-001, tadalafil, and dapagliflozin inhibitors. The development of new targeted interventions is of paramount importance due to the overlooked early symptoms, the complexity of the cellular and molecular pathways involved, and the absence of effective drug therapies.

CONCLUSION

Pharmacological treatments like GLP-1 agonists, SGLT-2 inhibitors, NHE-1, NHE-3, and PPAR-γ agonists show promise for treating DCM. These treatments improve myocardial glucose absorption, address dysregulated glucose and lipid metabolism, and lower heart failure and cardiovascular events. Further research is needed to confirm effectiveness and safety.

hERG agonists pose challenges to web-based machine learning methods for prediction of drug-hERG channel interaction

Pharmacological blockade of the IKr channel (hERG) by diverse drugs in clinical use is associated with the Long QT Syndrome that can lead to life threatening arrhythmia. Various computational tools including machine learning models (MLM) for the prediction of hERG inhibition have been developed to facilitate the throughput screening of drugs in development and optimise thus the prediction of hERG liabilities. The use of MLM relies on large libraries of training compounds for the quantitative structure-activity relationship (QSAR) modelling of hERG inhibition. The focus on inhibition omits potential effects of hERG channel agonist molecules and their associated QT shortening risk. It is instructive, therefore, to consider how known hERG agonists are handled by MLM. Here, two highly developed online computational tools for the prediction of hERG liability, Pred-hERG and HergSPred were probed for their ability to detect hERG activator drug molecules as hERG interactors. In total, 73 hERG blockers were tested with both computational tools giving overall good predictions for hERG blockers with reported IC50s below Pred-hERG and HergSPred cut-off threshold for hERG inhibition. However, for compounds with reported IC50s above this threshold such as disopyramide or sotalol discrepancies were observed. HergSPred identified all 20 hERG agonists selected as interacting with the hERG channel. Further studies are warranted to improve online MLM prediction of hERG related cardiotoxicity, by explicitly taking into account channel agonism as well as inhibition.

Study of the roles of cytochrome P450 (CYPs) in the metabolism and cytotoxicity of perhexiline

Perhexiline is a prophylactic antianginal agent developed in the 1970s. Although, therapeutically, it remained a success, the concerns of its severe adverse effects including hepatotoxicity caused the restricted use of the drug, and eventually its withdrawal from the market in multiple countries. In the clinical setting, cytochrome P450 (CYP) 2D6 is considered as a possible risk factor for the adverse effects of perhexiline. However, the role of CYP-mediated metabolism in the toxicity of perhexiline, particularly in the intact cells, remains unclear. Using our previously established HepG2 cell lines that individually express 14 CYPs (1A1, 1A2, 1B1, 2A6, 2B6, 2C8, 2C9, 2C18, 2C19, 2D6, 2E1, 3A4, 3A5, and 3A7) and human liver microsomes, we identified that CYP2D6 plays a major role in the hydroxylation of perhexiline. We also determined that CYP1A2, 2C19, and 3A4 contribute to the metabolism of perhexiline. The toxic effect of perhexiline was reduced significantly in CYP2D6-overexpressing HepG2 cells, in comparison to the control cells. In contrast, overexpression of CYP1A2, 2C19, and 3A4 did not show a significant protective effect against the toxicity of perhexiline. Pre-incubation with quinidine, a well-recognized CYP2D6 inhibitor, significantly attenuated the protective effect in CYP2D6-overexpressing HepG2 cells. Furthermore, perhexiline-induced mitochondrial damage, apoptosis, and ER stress were also attenuated in CYP2D6-overexpressing HepG2 cells. These findings suggest that CYP2D6-mediated metabolism protects the cells from perhexiline-induced cytotoxicity and support the clinical observation that CYP2D6 poor metabolizers may have higher risk for perhexiline-induced hepatotoxicity.