2D-Quantitative structure-activity relationship modeling for risk assessment of pharmacotherapy applied during pregnancy.

The risk evaluation for pharmacological therapy during pregnancy is critical for maternal and fetal health. The initial risk assessment stage, the risk measurement, begins with pregnancy-labeling categories (A, B, C, D, and X) for pharmaceuticals defined by the US Food and Drug Administration (FDA). Recently, in silico methods have been preferred in toxicology studies to eliminate ethical issues before conducting clinical toxicology studies and animal experiments. Quantitative structure-activity relationship (QSAR) modeling is one of the in silico methodologies. The research focuses on creating a QSAR model that predicts the five FDA pregnancy categories of medications. Our dataset included 868 pharmaceuticals, containing nearly every pharmacological group collected from the FDA. 2D-molecular descriptors were calculated using PaDEL software. Twenty-four QSAR models were developed, and the best four models were discussed. The results of the models were compared according to sensitivity, accuracy, F-score, specificity, receiver operating characteristic (ROC) values, and Matthews correlation coefficient. Considering the statistical results, random forest is the best model for determining the pregnancy risk category of drugs. The accuracy of the model was 76.49% for internal and 93.58% for external validation. According to the kappa statistics, there is an average agreement of 0.583 for internal validation and a perfect agreement of 0.893 for external validation. Because the error rates of the model are very close to 0, the model is highly accurate. Consequently, our novel QSAR model gives guidance on the safe use of pharmaceuticals during pregnancy without requiring animal tests or clinical trials on pregnant women.

In silico QSAR modeling to predict the safe use of antibiotics during pregnancy.

The use of medicines during pregnancy is a growing public health concern due to the risk of developmental toxicity. Healthcare providers heavily rely on the FDA pregnancy risk categories (A, B, C, D, and X). Antibiotics are among the most prescribed drugs during pregnancy and are often listed under category B or C. However, the risk-benefit assessment may be lacking due to challenges in the clinical toxicology studies on pregnant women, such as ethical concerns. The primary focus of this study is to generate a model that predicts the safe use of antibiotics during pregnancy by using in silico approaches. Thus, a QSAR model was created to assess the FDA pregnancy category (B or C) of antibiotics. The dataset consisted of 97 antibiotics obtained from the FDA. A total of 6420 molecular descriptors were determined via multiple software and various machine learning algorithms were utilized. The performance of the models was measured using internal and external validation. The accuracy (ACC) values of the most successful model were 83.82% for the internal and 94.11% for the external validation. Sensitivity (SE), specificity (SP), MCC, and ROC values were 0.878, 0.778, 0.68, and 0.892 for the internal validation and 0.9, 1, 0.887, and 0.936 for the external validation, respectively. Kappa statistics also indicate that there was a substantial agreement for internal validation with 0.6765 and an almost perfect agreement for external validation with 0.8811. In conclusion, our model can be used as an initial step before pre-clinical and clinical studies to predict the safe use of antibiotics in pregnancy.

Inappropriate Prescribing of Proton Pump Inhibitors in Outpatient Clinics.

Proton pump inhibitors are the commonly prescribed drugs for acid-related disorders. However, many of those prescriptions are inappropriate in inpatient and outpatient settings according to the recommended guidelines. Many studies have been conducted in inpatient clinics, but data about the appropriateness of proton pump inhibitor prescribing in outpatient clinics are scarce. Therefore, the aim of this study was to determine inappropriate proton pump inhibitor prescribing rates among patients admitted to a tertiary hospital family medicine outpatient clinic. A total of 259 patients (median age = 59 years; 72.6% women) were enrolled into the study and 35.9% of them had no proper indications to utilize proton pump inhibitors. Inappropriate proton pump inhibitor usage rate was significantly higher in patients older than 60 years compared with their younger counterparts (62.4% vs. 37.6%; p = .001). The most frequent reason to use a proton pump inhibitor with nonapproved indications was polypharmacy (41.9%). Despite endoscopic evaluation, 41.9% of the patients received a proton pump inhibitor without an approved indication. A significant proportion of nonindicated prescriptions were a consequence of continued prescribing without re-evaluating patients in outpatient clinics. Consideration of proton pump inhibitor indications according to the guidelines in every admission may prevent inappropriate prescriptions.

Towards safer pesticide management: A quantitative structure-activity relationship based hazard prediction model.

Pesticides are recognized as common environmental contaminants. The potential pesticide hazard to non-target organisms, including various mammal species, is a global concern. The global problem requires a comprehensive risk assessment. To assess the toxic effects of pesticides at the early stage, a toxicological risk analysis is conducted to determine pesticide hazard levels. World Health Organization (WHO) has established five pesticide hazard classes based on lethal dose (LD50) values to perform these assessments. In this paper, we have developed one-vs-all quantitative structure-activity relationship (OvA-QSAR) models using five machine-learning techniques with the selected optimum molecular descriptors. Descriptor selection was conducted based on correlation to evaluate the relevance and significance of individual features in our dataset. Our OvA-QSAR model was built using a dataset obtained from the WHO, covering a wide range of chemical pesticides. These models can predict the hazard category for a pesticide within the five available categories. Notably, our experiments demonstrate the outstanding performance and robustness of the Random Forest (RF) model in addressing the challenge of multi-class classification with the selected descriptors.

Chronic anemia is associated with systemic endothelial dysfunction.

Background: In acute myocardial infarction and heart failure, anemia is associated with adverse clinical outcomes. Endothelial dysfunction (ED) is characterized by attenuated nitric oxide (NO)-mediated relaxation responses which is poorly studied in chronic anemia (CA). We hypothesized that CA is associated with ED due to increased oxidative stress in the endothelium. Methods: CA was induced by repeated blood withdrawal in male C57BL/6J mice. Flow-Mediated Dilation (FMD) responses were assessed in CA mice using ultrasound-guided femoral transient ischemia model. Tissue organ bath was used to assess vascular responsiveness of aortic rings from CA mice, and in aortic rings incubated with red blood cells (RBCs) from anemic patients. In the aortic rings from anemic mice, the role of arginases was assessed using either an arginase inhibitor (Nor-NOHA) or genetic ablation of arginase 1 in the endothelium. Inflammatory changes in plasma of CA mice were examined by ELISA. Expression of endothelial NO synthase (eNOS), inducible NO synthase (iNOS), myeloperoxidase (MPO), 3-Nitrotyrosine levels, and 4-Hydroxynonenal (4-HNE) were assessed either by Western blotting or immunohistochemistry. The role of reactive oxygen species (ROS) in ED was assessed in the anemic mice either supplemented with N-Acetyl cysteine (NAC) or by in vitro pharmacological inhibition of MPO. Results: The FMD responses were diminished with a correlation to the duration of anemia. Aortic rings from CA mice showed reduced NO-dependent relaxation compared to non-anemic mice. RBCs from anemic patients attenuated NO-dependent relaxation responses in murine aortic rings compared to non-anemic controls. CA results in increased plasma VCAM-1, ICAM-1 levels, and an increased iNOS expression in aortic vascular smooth muscle cells. Arginases inhibition or arginase1 deletion did not improve ED in anemic mice. Increased expression of MPO and 4-HNE observed in endothelial cells of aortic sections from CA mice. NAC supplementation or inhibition of MPO improved relaxation responses in CA mice. Conclusion: Chronic anemia is associated with progressive endothelial dysfunction evidenced by activation of the endothelium mediated by systemic inflammation, increased iNOS activity, and ROS production in the arterial wall. ROS scavenger (NAC) supplementation or MPO inhibition are potential therapeutic options to reverse the devastating endothelial dysfunction in chronic anemia.

Circulating levels of leptin, nesfatin-1 and kisspeptin in postmenopausal obese women.

The roles of leptin, nesfatin-1 and kisspeptin in the regulation of food intake and/or reproduction are well known; however, the interactions between these hormones remain unclear, especially in humans. The aim of this study was to determine the roles of leptin, nesfatin-1 and kisspeptin in pre- and postmenopausal obese and non-obese women. The study included 83 women who were divided into four groups based on menopausal status and body mass index. The leptin level was significantly higher in the obese women than in the non-obese women (p < 0.05), but did not differ significantly between pre- and postmenopausal women (p > 0.05). The nesfatin-1 and kisspeptin-1 levels did not differ significantly between any of the study groups (p > 0.05). The present findings show that nesfatin-1 and kisspeptin levels are not affected by obesity or menopausal status.