Evaluation of soluble ST2 as a novel cardiovascular biomarker in patients with acute myocardial infarction

Background: Soluble ST-2 has considerable prognostic value and is used as an aid for risk stratification in identifying patients who are at high risk of cardiovascular disease. The main objective of the study was to analyze the level of soluble ST-2 biomarker in patients with acute myocardial infarction and chronic stable angina patients and secondly to evaluate the cardiovascular outcomes after 30 days.Methods: A total of 71 patients were enrolled into the study, patients were divided into two groups of which 50 patients were in test group (AMI patients) and the remaining 21 patients were in the control group (chronic stable angina). Then, 5ml of blood was collected from the patients and plasma soluble ST-2 was estimated from the sample using ELISA technique. Patients were then followed up to 30 days to ascertain the development of major adverse cardiovascular outcomes.Results: The median concentration of soluble ST-2 in test group was found to be 213.46pg/ml and in control group was found to be 124.53 pg/ml. Soluble ST-2 correlated significantly with left ventricular ejection fraction (LVEF) between the two groups (P value=0.01). Measurement of soluble ST-2 early after MI assists in the prediction of adverse cardiovascular events. In this study, soluble ST-2 was found to be higher in patients with acute myocardial infarction and also in patients with poor ejection fraction.Conclusions: Soluble ST-2 is a novel cardiovascular biomarker that is elevated in patients with acute myocardial infarction

The read-across hypothesis and environmental risk assessment of pharmaceuticals

This article is made available through the Brunel Open Access Publishing Fund. Copyright © 2013 American Chemical Society.Pharmaceuticals in the environment have received increased attention over the past decade, as they are ubiquitous in rivers and waterways. Concentrations are in sub-ng to low μg/L, well below acute toxic levels, but there are uncertainties regarding the effects of chronic exposures and there is a need to prioritise which pharmaceuticals may be of concern. The read-across hypothesis stipulates that a drug will have an effect in non-target organisms only if the molecular targets such as receptors and enzymes have been conserved, resulting in a (specific) pharmacological effect only if plasma concentrations are similar to human therapeutic concentrations. If this holds true for different classes of pharmaceuticals, it should be possible to predict the potential environmental impact from information obtained during the drug development process. This paper critically reviews the evidence for read-across, and finds that few studies include plasma concentrations and mode of action based effects. Thus, despite a large number of apparently relevant papers and a general acceptance of the hypothesis, there is an absence of documented evidence. There is a need for large-scale studies to generate robust data for testing the read-across hypothesis and developing predictive models, the only feasible approach to protecting the environment.BBSRC Industrial Partnership Award BB/ I00646X/1 and BBSRC Industrial CASE Partnership Studentship BB/I53257X/1 with AstraZeneca Safety Health and Environment Research Programme

CASE REPOCASE REPORT ON METHOTREXATE-INDUCED ANGIOEDEMA, PANCYTOPENIA, AND STEVENS-JOHNSON SYNDROMERT ON METHOTREXATE INDUCED ANGIOEDEMA, PANCYTOPENIA AND STEVENS - JOHNSON SYNDROME

ABSTRACTA 52-year-old female patient was presented with the complaints of swelling of lips, difficulty in breathing, difficulty in swallowing of food, itching,and facial puffiness and also showed the history of a skin lesion in the neck, forearm, and genitalia. Medical history of the patient showed that shehad been prescribed with methotrexate for rheumatoid arthritis. Within 10 days of treatment, the patient developed above mentioned signs andsymptoms. Further upon patient's systemic and physical examinations were done and then a diagnosis of pancytopenia, angioedema, and StevensJohnsonsyndromeweremade. AccordingtoNaranjoadversedrugreactioncausality assessmentscale,the association ofangioedema,pancytopenia,andStevens-Johnsonsyndromedue tomethotrexatewasprobable.Methotrexatewaswithdrawnfromthe patient,and the patient wastreatedwithmethotrexateantagonist Leucovorinfor3dayswith afrequencyof thrice dailyand injection Avil(Pheniraminemaleate)with afrequencyof twicedailywhichresolvedthe complications of the patient.Keywords: Methotrexate, Angioedema, Pancytopenia, Stevens-Johnson syndrome

Modeling Epoxidation of Drug-like Molecules with a Deep Machine Learning Network

[Image: see text] Drug toxicity is frequently caused by electrophilic reactive metabolites that covalently bind to proteins. Epoxides comprise a large class of three-membered cyclic ethers. These molecules are electrophilic and typically highly reactive due to ring tension and polarized carbon–oxygen bonds. Epoxides are metabolites often formed by cytochromes P450 acting on aromatic or double bonds. The specific location on a molecule that undergoes epoxidation is its site of epoxidation (SOE). Identifying a molecule’s SOE can aid in interpreting adverse events related to reactive metabolites and direct modification to prevent epoxidation for safer drugs. This study utilized a database of 702 epoxidation reactions to build a model that accurately predicted sites of epoxidation. The foundation for this model was an algorithm originally designed to model sites of cytochromes P450 metabolism (called XenoSite) that was recently applied to model the intrinsic reactivity of diverse molecules with glutathione. This modeling algorithm systematically and quantitatively summarizes the knowledge from hundreds of epoxidation reactions with a deep convolution network. This network makes predictions at both an atom and molecule level. The final epoxidation model constructed with this approach identified SOEs with 94.9% area under the curve (AUC) performance and separated epoxidized and non-epoxidized molecules with 79.3% AUC. Moreover, within epoxidized molecules, the model separated aromatic or double bond SOEs from all other aromatic or double bonds with AUCs of 92.5% and 95.1%, respectively. Finally, the model separated SOEs from sites of sp(2) hydroxylation with 83.2% AUC. Our model is the first of its kind and may be useful for the development of safer drugs. The epoxidation model is available at http://swami.wustl.edu/xenosite