Design and Discovery of a Potent and Selective Inhibitor of Integrin αvß1.

Selective inhibition of the RGD (Arg-Gly-Asp) integrin αvß1 has been recently identified as an attractive therapeutic approach for the treatment of liver fibrosis given its function, target expression, and safety profile. Our identification of a non-RGD small molecule lead followed by focused, systematic changes to the core structure utilizing a crystal structure, in silico modeling, and a tractable synthetic approach resulted in the identification of a potent small molecule exhibiting a remarkable affinity for αvß1 relative to several other integrin isoforms measured. Azabenzimidazolone 25 demonstrated antifibrotic efficacy in an in vivo rat liver fibrosis model and represents a tool compound capable of further exploring the biological consequences of selective αvß1 inhibition.

Direct Comparison of the Prediction of the Unbound Brain-to-Plasma Partitioning Utilizing Machine Learning Approach and Mechanistic Neuropharmacokinetic Model.

The mechanistic neuropharmacokinetic (neuroPK) model was established to predict unbound brain-to-plasma partitioning (Kp,uu,brain) by considering in vitro efflux activities of multiple drug resistance 1 (MDR1) and breast cancer resistance protein (BCRP). Herein, we directly compare this model to a computational machine learning approach utilizing physicochemical descriptors and efflux ratios of MDR1 and BCRP-expressing cells for predicting Kp,uu,brain in rats. Two different types of machine learning techniques, Gaussian processes (GP) and random forest regression (RF), were assessed by the time and cluster-split validation methods using 640 internal compounds. The predictivity of machine learning models based on only molecular descriptors in the time-split dataset performed worse than the cluster-split dataset, whereas the models incorporating MDR1 and BCRP efflux ratios showed similar predictivity between time and cluster-split datasets. The GP incorporating MDR1 and BCRP in the time-split dataset achieved the highest correlation (R2 = 0.602). These results suggested that incorporation of MDR1 and BCRP in machine learning is beneficial for robust and accurate prediction. Kp,uu,brain prediction utilizing the neuroPK model was significantly worse compared to machine learning approaches for the same dataset. We also investigated the predictivity of Kp,uu,brain using an external independent test set of 34 marketed drugs. Compared to machine learning models, the neuroPK model showed better predictive performance with R2 of 0.577. This work demonstrates that the machine learning model for Kp,uu,brain achieves maximum predictive performance within the chemical applicability domain, whereas the neuroPK model is applicable more widely beyond the chemical space covered in the training dataset.

Improving prediction of surgery duration using operational and temporal factors.

Inherent uncertainties in surgery durations impact many critical metrics about the performance of an operating room (OR) environment. OR schedules that are robust to natural variability in surgery durations require surgery duration estimates that are unbiased, with high accuracy, and with few cases with large absolute errors. Earlier studies have shown that factors such as patient severity, personnel, and procedure type greatly affect the accuracy of such estimations. In this paper we investigate whether operational and temporal factors can be used to improve these estimates further. We present an adjustment method based on a combination of these operational and temporal factors. We validate our method with two years of detailed operational data from an electronic medical record. We conclude that while improving estimates of surgery durations is possible, the inherent variability in such estimates remains high, necessitating caution in their use when optimizing OR schedules.