Comparison of clinical efficacy of metoprolol combined with irbesartan and hydrochlorothiazide and non-invasive ventilator in the emergency treatment of patients with severe heart failure

Identification of energy metabolism-related biomarkers for risk prediction of heart failure patients using random forest algorithm

Objective

Energy metabolism plays a crucial role in the improvement of heart dysfunction as well as the development of heart failure (HF). The current study is designed to identify energy metabolism-related diagnostic biomarkers for predicting the risk of HF due to myocardial infarction.

Methods

Transcriptome sequencing data of HF patients and non-heart failure (NF) people (GSE66360 and GSE59867) were obtained from gene expression omnibus (GEO) database. Energy metabolism-related differentially expressed genes (DEGs) were screened between HF and NF samples. The subtyping consistency analysis was performed to enable the samples to be grouped. The immune infiltration level among subtypes was assessed by single sample gene set enrichment analysis (ssGSEA). Random forest algorithm (RF) and support vector machine (SVM) were applied to identify diagnostic biomarkers, and the receiver operating characteristic curves (ROC) was plotted to validate the accuracy. Predictive nomogram was constructed and validated based on the result of the RF. Drug screening and gene-miRNA network were analyzed to predict the energy metabolism-related drugs and potential molecular mechanism.

Results

A total of 22 energy metabolism-related DEGs were identified between HF and NF patients. The clustering analysis showed that HF patients could be classified into two subtypes based on the energy metabolism-related genes, and functional analyses demonstrated that the identified DEGs among two clusters were mainly involved in immune response regulating signaling pathway and lipid and atherosclerosis. ssGSEA analysis revealed that there were significant differences in the infiltration levels of immune cells between two subtypes of HF patients. Random-forest and support vector machine algorithm eventually identified ten diagnostic markers (MEF2D, RXRA, PPARA, FOXO1, PPARD, PPP3CB, MAPK14, CREB1, MEF2A, PRMT1) for risk prediction of HF patients, and the proposed nomogram resulted in good predictive performance (GSE66360, AUC = 0.91; GSE59867, AUC = 0.84) and the clinical usefulness in HF patients. More importantly, 10 drugs and 15 miRNA were predicted as drug target and hub miRNA that associated with energy metabolism-related genes, providing further information on clinical HF treatment.

Conclusion

This study identified ten energy metabolism-related diagnostic markers using random forest algorithm, which may help optimize risk stratification and clinical treatment in HF patients.

Angiotensin Type 1 Receptor Blockers in Heart Failure

Homeostasis in the cardiovascular system is maintained by physiological functions of the Renin Angiotensin Aldosterone System (RAAS). In pathophysiological conditions, over activation of RAAS leads to an increase in the concentration of Angiotensin II (AngII) and over activation of Angiotensin Type 1 Receptor (AT1R), resulting in vasoconstriction, sodium retention and change in myocyte growth. It causes cardiac remodeling in the heart which results in left ventricular hypertrophy, dilation and dysfunction, eventually leading to Heart Failure (HF). Inhibition of RAAS using angiotensin converting enzyme inhibitors (ACEi) or angiotensin receptor blockers (ARBs) has shown to significantly reduce morbidity and mortality due to HF. ACEi have been shown to have higher drug withdrawal rates due to discomfort when compared to ARBs; therefore, ARBs are the preferred choice of physicians for the treatment of HF in combination with other anti-hypertensive agents. Currently, eight ARBs have been approved by FDA and are clinically used. Even though they bind to the same site of AT1R displacing AngII binding but clinical outcomes are significantly different. In this review, we described the clinical significance of each ARB in the treatment of HF and their clinical outcome.

Molecular Dynamics Protocols for the Study of Cyclodextrin Drug Delivery Systems

Hypertension treatment is a current therapeutic priority as there is a constantly increasing part of the population that suffers from this risk factor, which may lead to cardiovascular and encephalic episodes and eventually to death. A number of marketed medicines consist of active ingredients that may be relatively potent; however, there is plenty of room to enhance their pharmacological profile and therapeutic index by improving specific physicochemical properties. In this work, we focus on a class of blood pressure regulators, called sartans, and we present the computational scheme for the pharmacological improvement of irbesartan (IRB) as a representative example. IRB has been shown to exert increased pharmacological action compared with other sartans, but it appears to be highly lipophilic and violates Lipinski rule (MLogP >4.15). To circumvent this drawback, proper hydrophilic molecules, such as cyclodextrins, can be used as drug carriers. This chapter describes the combinatory use of computational methods, namely molecular docking, quantum mechanics, molecular dynamics, and free energy calculations, to study the interactions and the energetic contributions that govern the IRB:cyclodextrin association. We provide a detailed computational protocol, which aims to assist the improvement of the pharmacological properties of sartans. This protocol can also be applied to any other drug molecule with diminished hydrophilic character.

On the Rational Drug Design for Hypertension through NMR Spectroscopy

Antagonists of the AT1receptor (AT1R) are beneficial molecules that can prevent the peptide hormone angiotensin II from binding and activating the specific receptor causing hypertension in pathological states. This review article summarizes the multifaced applications of solid and liquid state high resolution nuclear magnetic resonance (NMR) spectroscopy in antihypertensive commercial drugs that act as AT1R antagonists. The 3D architecture of these compounds is explored through 2D NOESY spectroscopy and their interactions with micelles and lipid bilayers are described using solid state 13CP/MAS, 31P and 2H static solid state NMR spectroscopy. Due to their hydrophobic character, AT1R antagonists do not exert their optimum profile on the AT1R. Therefore, various vehicles are explored so as to effectively deliver these molecules to the site of action and to enhance their pharmaceutical efficacy. Cyclodextrins and polymers comprise successful examples of effective drug delivery vehicles, widely used for the delivery of hydrophobic drugs to the active site of the receptor. High resolution NMR spectroscopy provides valuable information on the physical-chemical forces that govern these drug:vehicle interactions, knowledge required to get a deeper understanding on the stability of the formed complexes and therefore the appropriateness and usefulness of the drug delivery system. In addition, it provides valuable information on the rational design towards the synthesis of more stable and efficient drug formulations.