A computational approach is described that can predict the VD(ss) of new compounds in humans, with an accuracy of within 2-fold of the actual value. A dataset of VD values for 384 drugs in humans was used to train a hybrid mixture discriminant analysis-random forest (MDA-RF) model using 31 computed descriptors. Descriptors included terms describing lipophilicity, ionization, molecular volume, and various molecular fragments. For a test set of 23 proprietary compounds not used in model construction, the geometric mean fold-error (GMFE) was 1.78-fold (+/-11.4%). The model was also tested using a leave-class out approach wherein subsets of drugs based on therapeutic class were removed from the training set of 384, the model was recast, and the VD(ss) values for each of the subsets were predicted. GMFE values ranged from 1.46 to 2.94-fold, depending on the subset. Finally, for an additional set of 74 compounds, VD(ss) predictions made using the computational model were compared to predictions made using previously described methods dependent on animal pharmacokinetic data. Computational VD(ss) predictions were, on average, 2.13-fold different from the VD(ss) predictions from animal data. The computational model described can predict human VD(ss) with an accuracy comparable to predictions requiring substantially greater effort and can be applied in place of animal experimentation.

The relatively weak Raman enhanced factors of semiconductor-based substrate limit its further application in surface-enhanced Raman scattering (SERS). Here, a kind of two-dimensional (2D) semimetal material, molybdenum carbide (Mo2C) film, is prepared via a chemical vapor deposition (CVD) method, and the origin of SERS is investigated for the first time. The detection limits of the prepared Mo2C films for crystal violet (CV) and rhodamine 6G (R6G) molecules are low at 10–6 M and 10–8 M, respectively. Our detailed theoretical analysis, based on density functional theory and the finite element method, demonstrates that the enhancement of the 2D Mo2C film is indeed CM in nature rather than the EM effects. Besides, the basic doping strategies are proposed to further optimize the SERS sensitivity of Mo2C for Fermi level regulation. We believe this work will provide a helpful guide for developing a highly sensitive semimetal SERS substrate.

In this paper, we propose a novel approach to model spatial heterogeneity for epidemic spreading, which combines the relevance of transport proximity in human movement and the excellent estimation accuracy of deep neural network. We apply this model to investigate the effects of various transportation networks on the heterogeneous propagation of COVID-19 in China. We further apply it to predict the development of COVID-19 in China in two scenarios, i.e., i) assuming that different types of traffic restriction policies are conducted and ii) assuming that the epicenter of the COVID-19 outbreak is in Beijing, so as to illustrate the potential usage of the model in generating various policy insights to help the containment of the further spread of COVID-19. We find that the most effective way to prevent the coronavirus from spreading quickly and extensively is to control the routes linked to the epicenter at the beginning of the pandemic. But if the virus has been widely spread, setting restrictions on hub cities would be much more efficient than imposing the same travel ban across the whole country. We also show that a comprehensive consideration of the epicenter location is necessary for disease control.

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Background Evidence supports therapeutic drug monitoring of polymyxin B, but clinical data for establishing an area under the concentration–time curve across 24 h at steady state (AUCss,24 h) threshold are still limited. This study aimed to examine exposure–response/toxicity relationship for polymyxin B to establish an AUCss,24 h threshold in a real-world cohort of patients. Methods Using a validated Bayesian approach to estimate AUCss,24 h from two samples, AUCss,24 h threshold that impacted the risk of polymyxin B-related nephrotoxicity and clinical response were derived by classification and regression tree (CART) analysis and validated by Cox regression analysis and logical regression analysis. Results A total of 393 patients were included; acute kidney injury (AKI) was 29.0%, clinical response was 63.4%, and 30-day all-cause mortality was 35.4%. AUCss,24 h thresholds for AKI of > 99.4 mg h/L and clinical response of > 45.7 mg h/L were derived by CART analysis. Cox and logical regression analyses showed that AUCss,24 h of > 100 mg h/L was a significant predictor of AKI (HR 16.29, 95% CI 8.16–30.25, P < 0.001) and AUCss,24 h of ≥ 50 mg h/L (OR 4.39, 95% CI 2.56–7.47, P < 0.001) was independently associated with clinical response. However, these exposures were not associated with mortality. In addition, the correlation between trough concentration (1.2–2.8 mg/L) with outcomes was similar to AUCss,24 h. Conclusions For critically ill patients, AUCss,24 h threshold of 50–100 mg h/L was associated with decreased nephrotoxicity while assuring clinical efficacy. Therapeutic drug monitoring is recommended for individualizing polymyxin B dosing.