An application of CIFAP for predicting the binding affinity of Chk1 inhibitors derived from 2‐aminothiazole‐4‐carboxamide

Investigation of protein‐ligand interactions obtained from experiments has a crucial part in the design of newly discovered and effective drugs. Analyzing the data extracted from known interactions could help scientists to predict the binding affinities of promising ligands before conducting experiments. The objective of this study is to advance the CIFAP (compressed images for affinity prediction) method, which is relevant to a protein‐ligand model, identifying 2D electrostatic potential images by separating the binding site of protein‐ligand complexes and using the images for predicting the computational affinity information represented by pIC₅₀ values. The CIFAP method has 2 phases, namely, data modeling and prediction. In data modeling phase, the separated 3D structure of the binding pocket with the ligand inside is fitted into an electrostatic potential grid box, which is then compressed through 3 orthogonal directions into three 2D images for each protein‐ligand complex. Sequential floating forward selection technique is performed for acquiring prediction patterns from the images. In the prediction phase, support vector regression (SVR) and partial least squares regression are used for testing the quality of the CIFAP method for predicting the binding affinity of 45 CHK1 inhibitors derived from 2‐aminothiazole‐4‐carboxamide. The results show that the CIFAP method using both support vector regression and partial least squares regression is very effective for predicting the binding affinities of CHK1‐ligand complexes with low‐error values and high correlation. As a future work, the results could be improved by working on the pose of the ligands inside the grid.     

Efficient approximate k‐fold and leave‐one‐out cross‐validation for ridge regression

In model building and model evaluation, cross‐validation is a frequently used resampling method. Unfortunately, this method can be quite time consuming. In this article, we discuss an approximation method that is much faster and can be used in generalized linear models and Cox’ proportional hazards model with a ridge penalty term. Our approximation method is based on a Taylor expansion around the estimate of the full model. In this way, all cross‐validated estimates are approximated without refitting the model. The tuning parameter can now be chosen based on these approximations and can be optimized in less time. The method is most accurate when approximating leave‐one‐out cross‐validation results for large data sets which is originally the most computationally demanding situation. In order to demonstrate the method's performance, it will be applied to several microarray data sets. An R package penalized, which implements the method, is available on CRAN.     

Risk‐Group‐Specific Dose Finding Based on an Average Toxicity Score

Summary We propose a Bayesian dose‐finding design that accounts for two important factors, the severity of toxicity and heterogeneity in patients' susceptibility to toxicity. We consider toxicity outcomes with various levels of severity and define appropriate scores for these severity levels. We then use a multinomial‐likelihood function and a Dirichlet prior to model the probabilities of these toxicity scores at each dose, and characterize the overall toxicity using an average toxicity score (ATS) parameter. To address the issue of heterogeneity in patients' susceptibility to toxicity, we categorize patients into different risk groups based on their susceptibility. A Bayesian isotonic transformation is applied to induce an order‐restricted posterior inference on the ATS. We demonstrate the performance of the proposed dose‐finding design using simulations based on a clinical trial in multiple myeloma.     

Non‐Parametric Ecological Regression and Spatial Variation

Ecological studies aim to analyse the variation of disease risk in relation to exposure variables that are measured at an area unit level. In practice it is rarely possible to use the exposure variables themselves, either because the corresponding data are not available or because the causes of the disease are not fully understood. It is therefore quite common to use crude proxies of the real exposure to the disease in question. These proxies are rarely able to explain the disease variation and hence additional area level random effects are introduced to account for the residual variation. In this paper we investigate the possibility to model the effect of ecological covariates non‐parametrically, with and without additional random effects for the residual spatial variation. We illustrate the issues arising through analyses of simulated and real data on larynx cancer mortality in Germany, during the years of 1986 to 1990, where we use the corresponding lung cancer rates as a proxy for smoking consumption.     

Eribulin regresses a doxorubicin‐resistant Ewing's sarcoma with a FUS‐ERG fusion and CDKN2A‐deletion in a patient‐derived orthotopic xenograft (PDOX) nude mouse model

Ewing's sarcoma is a recalcitrant tumor greatly in need of more effective therapy. The aim of this study was to determine the efficacy of eribulin on a doxorubicin (DOX)‐resistant Ewing's sarcoma patient derived orthotopic xenograft (PDOX) model. The Ewing's sarcoma PDOX model was previously established in the right chest wall of nude mice from tumor resected form the patient's right chest wall. In the previous study, the Ewing's sarcoma PDOX was resistant to doxorubicin (DOX) and sensitive to palbociclib and linsitinib. In the present study, the PDOX models were randomized into three groups when the tumor volume reached 60 mm³: G1, untreated control (n = 6); G2, DOX treated (n = 6), intraperitoneal (i.p.) injection, weekly, for 2 weeks); G3, Eribulin treated (n = 6, intravenous (i.v.) injection, weekly for 2 weeks). All mice were sacrificed on day 15. Changes in body weight and tumor volume were assessed two times per week. Tumor weight was measured after sacrifice. DOX did not suppress tumor growth compared to the control group (P = 0.589), consistent with the previous results in the patient and PDOX. Eribulin regressed tumor size significantly compared to G1 and G2 (P = 0.006, P = 0.017) respectively. No significant difference was observed in body weight among any group. Our results demonstrate that eribulin is a promising novel therapeutic agent for Ewing's sarcoma.     

Protective role of melatonin in early‐stage and end‐stage liver cirrhosis

The liver is composed of hepatocytes, cholangiocytes, Kupffer cells, sinusoidal endothelial cells, hepatic stellate cells (HSCs) and dendritic cells; all these functional and interstitial cells contribute to the synthesis and secretion functions of liver tissue. However, various hepatotoxic factors including infection, chemicals, high‐fat diet consumption, surgical procedures and genetic mutations, as well as biliary tract diseases such as sclerosing cholangitis and bile duct ligation, ultimately progress into liver cirrhosis after activation of fibrogenesis. Melatonin (MT), a special hormone isolated from the pineal gland, participates in regulating multiple physiological functions including sleep promotion, circadian rhythms and neuroendocrine processes. Current evidence shows that MT protects against liver injury by inhibiting oxidation, inflammation, HSC proliferation and hepatocyte apoptosis, thereby inhibiting the progression of liver cirrhosis. In this review, we summarize the circadian rhythm of liver cirrhosis and its potential mechanisms as well as the therapeutic effects of MT on liver cirrhosis and earlier‐stage liver diseases including liver steatosis, nonalcoholic fatty liver disease and liver fibrosis. Given that MT is an antioxidative and anti‐inflammatory agent that is effective in eliminating liver injury, it is a potential agent with which to reverse liver cirrhosis in its early stage.     

Inverse‐probability‐of‐treatment weighted estimation of causal parameters in the presence of error‐contaminated and time‐dependent confounders

Inverse‐probability‐of‐treatment weighted (IPTW) estimation has been widely used to consistently estimate the causal parameters in marginal structural models, with time‐dependent confounding effects adjusted for. Just like other causal inference methods, the validity of IPTW estimation typically requires the crucial condition that all variables are precisely measured. However, this condition, is often violated in practice due to various reasons. It has been well documented that ignoring measurement error often leads to biased inference results. In this paper, we consider the IPTW estimation of the causal parameters in marginal structural models in the presence of error‐contaminated and time‐dependent confounders. We explore several methods to correct for the effects of measurement error on the estimation of causal parameters. Numerical studies are reported to assess the finite sample performance of the proposed methods.     

A matrix‐based method of moments for fitting the multivariate random effects model for meta‐analysis and meta‐regression

Multivariate meta‐analysis is becoming more commonly used. Methods for fitting the multivariate random effects model include maximum likelihood, restricted maximum likelihood, Bayesian estimation and multivariate generalisations of the standard univariate method of moments. Here, we provide a new multivariate method of moments for estimating the between‐study covariance matrix with the properties that (1) it allows for either complete or incomplete outcomes and (2) it allows for covariates through meta‐regression. Further, for complete data, it is invariant to linear transformations. Our method reduces to the usual univariate method of moments, proposed by DerSimonian and Laird, in a single dimension. We illustrate our method and compare it with some of the alternatives using a simulation study and a real example.     

Bayesian propensity scores for high‐dimensional causal inference: A comparison of drug‐eluting to bare‐metal coronary stents

High‐dimensional data provide many potential confounders that may bolster the plausibility of the ignorability assumption in causal inference problems. Propensity score methods are powerful causal inference tools, which are popular in health care research and are particularly useful for high‐dimensional data. Recent interest has surrounded a Bayesian treatment of propensity scores in order to flexibly model the treatment assignment mechanism and summarize posterior quantities while incorporating variance from the treatment model. We discuss methods for Bayesian propensity score analysis of binary treatments, focusing on modern methods for high‐dimensional Bayesian regression and the propagation of uncertainty. We introduce a novel and simple estimator for the average treatment effect that capitalizes on conjugacy of the beta and binomial distributions. Through simulations, we show the utility of horseshoe priors and Bayesian additive regression trees paired with our new estimator, while demonstrating the importance of including variance from the treatment regression model. An application to cardiac stent data with almost 500 confounders and 9000 patients illustrates approaches and facilitates comparison with existing alternatives. As measured by a falsifiability endpoint, we improved confounder adjustment compared with past observational research of the same problem.     

Semiparametric Bayesian Survival Analysis using Models with Log‐linear Median

Summary We present a novel semiparametric survival model with a log‐linear median regression function. As a useful alternative to existing semiparametric models, our large model class has many important practical advantages, including interpretation of the regression parameters via the median and the ability to address heteroscedasticity. We demonstrate that our modeling technique facilitates the ease of prior elicitation and computation for both parametric and semiparametric Bayesian analysis of survival data. We illustrate the advantages of our modeling, as well as model diagnostics, via a reanalysis of a small‐cell lung cancer study. Results of our simulation study provide further support for our model in practice.     

SIZE‐CORRECTION AND PRINCIPAL COMPONENTS FOR INTERSPECIFIC COMPARATIVE STUDIES

Phylogenetic methods for the analysis of species data are widely used in evolutionary studies. However, preliminary data transformations and data reduction procedures (such as a size‐correction and principal components analysis, PCA) are often performed without first correcting for nonindependence among the observations for species. In the present short comment and attached R and MATLAB code, I provide an overview of statistically correct procedures for phylogenetic size‐correction and PCA. I also show that ignoring phylogeny in preliminary transformations can result in significantly elevated variance and type I error in our statistical estimators, even if subsequent analysis of the transformed data is performed using phylogenetic methods. This means that ignoring phylogeny during preliminary data transformations can possibly lead to spurious results in phylogenetic statistical analyses of species data.