Fosinopril Sodium

There is insufficient evidence that angiotensin-converting enzyme inhibitors (ACEIs) can reduce pneumonia by inducing a dry cough that confers a protective effect on the airway. To increase the evidence base on the clinical use of ACEIs for pneumonia prevention, this retrospective cohort study aimed to comparatively examine the risk of pneumonia-related hospitalization between ACEI initiators and angiotensin II receptor blocker (ARB) initiators using claims data from two Japanese municipalities. We identified persons who were newly prescribed any ACEI or ARB as their first antihypertensive agent between April 2016 and March 2020. The Fine-Gray method was applied to a Cox proportional hazards model to estimate the subdistribution hazard ratio (HR) of ACEI use (reference: ARB use) for pneumonia-related hospitalization, with death treated as a competing risk. Sex, age, comorbidities, medications, and pneumococcal immunization were included as covariates. The analysis was conducted on 1421 ACEI initiators and 9040 ARB initiators, and the adjusted subdistribution HR of ACEI use was estimated to be 1.21 (95% confidence interval: 0.89-1.65; P = 0.22). ACEI initiation did not demonstrate any significant preventive effect against pneumonia-related hospitalization relative to ARB initiation. There remains a lack of strong evidence on the protective effects of ACEIs, and further research is needed to ascertain the benefits of their use in preventing pneumonia. We conducted a large-scale retrospective cohort study using real-world healthcare data from a Japanese population. In this study, ACEI initiation did not indicate a significant preventive effect against pneumonia-related hospitalization.

This dataset demonstrates the use of computational fragmentation-based and machine learning-aided drug discovery to generate new lead molecules for the treatment of hypertension. Specifically, the focus is on agents targeting the renin-angiotensin-aldosterone system (RAAS), commonly classified as Angiotensin-Converting Enzyme Inhibitors (ACEIs) and Angiotensin II Receptor Blockers (ARBs). The preliminary dataset was a target-specific, user-generated fragment library of 63 molecular fragments of the 26 approved ACEI and ARB molecules obtained from the ChEMBL and DrugBank molecular databases. This fragment library provided the primary input dataset to generate the new lead molecules presented in the dataset. The newly generated molecules were screened to check whether they met the criteria for oral drugs and comprised the ACEI or ARB core functional group criterion. Using unsupervised machine learning, the molecules that met the criterion were divided into clusters of drug classes based on their functional group allocation. This process led to three final output datasets, one containing the new ACEI molecules, another for the new ARB molecules, and the last for the new unassigned class molecules. This data can aid in the timely and efficient design of novel antihypertensive drugs. It can also be used in precision hypertension medicine for patients with treatment resistance, non-response or co-morbidities. Although this dataset is specific to antihypertensive agents, the model can be reused with minimal changes to produce new lead molecules for other health conditions.