Drug-like Properties and Fraction Lipophilicity Index as a combined metric

Fraction Lipophicity Index (FLI) has been developed as a composite drug-like metric combining logP and logD in a weighted manner. In the present study an extended data set confirmed the previously established drug-like FLI range 0-8 using two calculation systems for logP /logD assessment, the freeware MedChem Designer and ClogP. The dataset was split into two classes according to percentage of fraction absorbed (%FA) - class 1 including drugs with high to medium absorption levels and class 2 including poorly absorbed drugs. The FLI and FLI© (ClogP based FLI) drug-like range covers 93% and 90 % of class 1 drugs, respectively. The dependence of the degree of ionization to intrinsic lipophilicity within the FLI (FLI©) drug-like range as well as the inter-relation between the other Ro5 properties (Mw, HD, HA) was explored, so as to define drug-like / non drug-like combinations as a safer alternative to single properties for drug candidates’ prioritization. In this sense we propose a combined metric of Mw and number of polar atoms (Mw/NO) to account for both size and polarity. Setting the value 50 as cut off, a distinct differentiation between class 1 and class 2 drugs was obtained with Mw/NO>50 for more than 70% of class 1 drugs, while the opposite was observed for class 2 drugs

The value of diastolic function parameters in the prediction of left atrial appendage thrombus in patients with nonvalvular atrial fibrillation

BACKGROUND: Left ventricular diastolic impairment and consequently elevated filling pressure may contribute to stasis leading to left atrial appendage thrombus (LAAT) in nonvalvular atrial fibrillation (AF). We investigated whether transthoracic echocardiographic parameters can predict LAAT independent of traditional clinical predictors. METHODS: We conducted a retrospective cohort study of 297 consecutive nonvalvular AF patients who underwent transthoracic echocardiogram followed by a transesophageal echocardiogram within one year. Multivariate logistic regression analysis models were used to determine factors independently associated with LAAT. RESULTS: Nineteen subjects (6.4%) were demonstrated to have LAAT by transesophageal echocardiography. These patients had higher mean CHADS(2) scores [2.6 ± 1.2 vs. 1.9 ± 1.3, P = 0.009], higher E:e’ ratios [16.6 ± 6.1 vs. 12.0 ± 5.4, P = 0.001], and lower mean e’ velocities [6.5 ± 2.1 cm/sec vs. 9.1 ± 3.2 cm/sec, P = 0.001]. Both E:e’ and e’ velocity were associated with LAAT formation independent of the CHADS(2) score, warfarin therapy, left ventricular ejection fraction (LVEF), and left atrial volume index (LAVI) [E:e’ odds-ratio = 1.14 (95% confidence interval = 1.03 – 1.3), P = 0.009; e’ velocity odds-ratio = 0.68 (95% confidence interval = 0.5 – 0.9), P = 0.007]. Similarly, diastolic function parameters were independently associated with spontaneous echo contrast. CONCLUSION: The diastolic function indices E:e’ and e’ velocity are independently associated with LAAT in nonvalvular AF patients and may help identify patients at risk for LAAT

A regression analysis method for the prediction of olive oil sensory attributes

This article documents an approach to predicting positive sensory attributes – fruitiness, bitterness, pungency-of virgin olive oil from its chemical characteristics, using machine learning methods. The dataset used in this study included forty-nine olive oil samples of the Koroneiki variety from nine selected olive mills, evenly distributed in the region of Messinia, Greece. The samples were analyzed for free acidity, peroxide value, the UV absorption for the determination of the extinction coefficients, phenolic compounds (total secoiridoid phenols, oleocanthal, oleacein, oleuropein aglycon and ligstroside aglycon) and sterol compounds (total sterols, cholesterol, campesterol, stigmasterol, d7-stigmasterol, erythrodiol, uvaol, b-sitosterol). Sensory analysis of the samples took place 20–30 days after their sampling date and the intensity of three positive attributes (fruitiness, bitterness and pungency) was measured. The authors used the least absolute shrinkage and selection operator (Lasso) for feature selection and then applied ordinary least squares (OLS) methods to build the final models. Three Python-based forecasting machine learning models for each sensory characteristic (fruitiness, bitterness, and pungency) were built and evaluated in comparison to one another in terms of the performance metrics of root mean squared error (RMSE) and mean absolute percentage error (MAPE), using repeated 5-fold cross-validation. The interacting effects among the sensory features were also considered for developing the two regression models, while the third model was only based on chemical attributes. The results obtained, revealed a significant relationship between each sensory attribute and the intensity of the other two, with the respective prediction models demonstrating a highly satisfactory level of performance. Furthermore, models that employed only chemical indices as predictors provided strong evidence that chemical indices alone were sufficient to predict the intensities of the sensory attributes. The findings of this study establish the predictive value of the constructed models, which might be utilized to support panels in training and calibration

Steatosis and steatohepatitis in postmortem material from Northwestern Greece

AIM: To determine the prevalence of steatosis and steatohepatitis in a series of autopsies in Northwestern Greece