Modeling drug response using network-based personalized treatment prediction (NetPTP) with applications to inflammatory bowel disease

Dietary Zinc Ameliorates TNBS-Induced Colitis in Mice Associated with Regulation of Th1/Th2/Th17 Balance and NF-κB/NLRP3 Signaling Pathway

Inflammatory bowel diseases (IBDs), including Crohn's disease and ulcerative colitis, are chronic relapsing inflammatory gastrointestinal tract diseases of uncertain origin, which are frequently associated with zinc deficiency. Animal models have a considerable value in elucidating the process of IBD. In this study, 50 male C57BL/6 J mice were randomly assigned to five groups: control group (Con), 2,4,6-trinitrobenzenesulfonic acid (TNBS) group, and three zinc supplementation groups, namely 160 ppm group, 400 ppm group, and 1000 ppm group. The results showed that supplementation of dietary zinc with zinc oxide could effectively relieve the severity of ulcerative colitis induced by TNBS in mice. We demonstrate that the protective mechanism involves the immunomodulation of dietary zinc by increasing CD3+, CD3+CD8+, and Th2 cells, suppressing Th1 and Th17 cells, and decreasing the production of serum IL-1β and IL-18. The dietary zinc oxide seems to be able to suppress the NF-κB/NLRP3 signaling pathway by downregulating the mRNA and protein expression of NIK, IKK, NF-κB, and NLRP3. The results suggest that dietary supplementation of zinc oxide may protect against colitis, and proper daily zinc supplementation may reduce the risk of IBD.

BatMan: Mitigating Batch Effects Via Stratification for Survival Outcome Prediction

Reproducible translation of transcriptomics data has been hampered by the ubiquitous presence of batch effects. Statistical methods for managing batch effects were initially developed in the setting of sample group comparison and later borrowed for other settings such as survival outcome prediction. The most notable such method is ComBat, which adjusts for batches by including it as a covariate alongside sample groups in a linear regression. In survival prediction, however, ComBat is used without definable groups for survival outcome and is done sequentially with survival regression for a potentially batch-confounded outcome. To address these issues, we propose a new method called BATch MitigAtion via stratificatioN (BatMan). It adjusts batches as strata in survival regression and uses variable selection methods such as the regularized regression to handle high dimensionality. We assess the performance of BatMan in comparison with ComBat, each used either alone or in conjunction with data normalization, in a resampling-based simulation study under various levels of predictive signal strength and patterns of batch-outcome association. Our simulations show that (1) BatMan outperforms ComBat in nearly all scenarios when there are batch effects in the data and (2) their performance can be worsened by the addition of data normalization. We further evaluate them using microRNA data for ovarian cancer from the Cancer Genome Atlas and find that BatMan outforms ComBat while the addition of data normalization worsens the prediction. Our study thus shows the advantage of BatMan and raises caution about the use of data normalization in the context of developing survival prediction models. The BatMan method and the simulation tool for performance assessment are implemented in R and publicly available at LXQin/PRECISION.survival-GitHub.

"Technology Proficiency" in Medical Education: Worthiness for Worldwide Wonderful Competency and Sophistication

PURPOSE

Advances in bioinformatics, information technology, advanced computing, imaging techniques are changing fundamentally the way physicians define, diagnose, treat, and prevent disease. New disciplines - Artificial Intelligence, Machine Learning, Computational Biology - are improving healthcare. Digital health solutions have immense scope. Education and practice need to keep pace.

METHODS

We aimed at assessment of "Technology proficiency" required by medical graduates and its implementation, if found useful. All this in a conceptual framework of "TP" model, having categories (a) proper assessment (b) pertinent treatment (c) progress monitoring (d) prevention applications (e) professional standards. A search of the literature was performed using MedLine & Cochrane Central Register of Controlled Trials databases, for systematic reviews and meta-analysis articles published in the last five years using keyword "technology". Analysis of those relevant to the role all medical graduates should play. An analysis of worldwide statutory medical institutions guidelines.

RESULTS

Twenty-three systematic studies and meta-analysis were studied. Eighteen show clear evidence for 'Technology proficiency", while 5 recommend further studies. The findings are discussed suiting the roles of doctors in the "TP" model. Medical institutions guidelines worldwide diligence suggests need of including "Technology proficiency" as a definite and distinct strategic plan. Medical Council of India mandates "use information technology for appropriate patient care and continued learning". General Medical Council, UK and Medical Council India have been proactive in technology training. GMC recommends technology use for learning, prescribing, communication, and interpersonal skills. It should be expanding technology proficiency in practice as an essential professional capability.

CONCLUSION

"Technology proficiency" is found pertinently fruitful. It should be included as a definitive requirement and a distinct strategic plan worldwide. Modern curriculum development is proposed (i) Educational goals and objectives as the proposed Conceptual framework "Technology proficiency" model (ii) Instructional strategies 'Five Bs' (iii) Implementation 'Five Ms'.

Prediction of Drug-Drug Interaction Using an Attention-Based Graph Neural Network on Drug Molecular Graphs

The treatment of complex diseases by using multiple drugs has become popular. However, drug-drug interactions (DDI) may give rise to the risk of unanticipated adverse effects and even unknown toxicity. Therefore, for polypharmacy safety it is crucial to identify DDIs and explore their underlying mechanisms. The detection of DDI in the wet lab is expensive and time-consuming, due to the need for experimental research over a large volume of drug combinations. Although many computational methods have been developed to predict DDIs, most of these are incapable of predicting potential DDIs between drugs within the DDI network and new drugs from outside the DDI network. In addition, they are not designed to explore the underlying mechanisms of DDIs and lack interpretative capacity. Thus, here we propose a novel method of GNN-DDI to predict potential DDIs by constructing a five-layer graph attention network to identify k-hops low-dimensional feature representations for each drug from its chemical molecular graph, concatenating all identified features of each drug pair, and inputting them into a MLP predictor to obtain the final DDI prediction score. The experimental results demonstrate that our GNN-DDI is suitable for each of two DDI predicting scenarios, namely the potential DDIs among known drugs in the DDI network and those between drugs within the DDI network and new drugs from outside DDI network. The case study indicates that our method can explore the specific drug substructures that lead to the potential DDIs, which helps to improve interpretability and discover the underlying interaction mechanisms of drug pairs.

Molecular docking and dynamics approach to in silico drug repurposing for inflammatory bowels disease by targeting TNF alpha

Inflammatory bowel disease is a chronic disorder of the large intestine with the prevalence of approximately 400 cases in 100000, and it is rising day by day. However, several drugs like sulfasalazine (composed of sulfapyridine and 5-aminosalicylic acid or 5-ASA), corticosteroids, and immunosuppressants manage the disease. But there are no absolute treatments for the pain and inflammation of the disease. TNFα is an important target, and drugs like infliximab and adalimumab have pharmacological potency but with pronounced toxicity. So, we choose this major target TNFα for the virtual screening of US-FDA-approved drugs for its repurposing using the in silico method. The protein TNFα (PDB ID: 2AZ5) with small molecule inhibitor and the US-FDA-approved drug molecules (from Zinc database) were first imported and prepared using Protein Preparation Wizard and LigPrep, respectively, followed by molecular docking, ADMET analysis and prime MMGBSA. After that, the drugs were shortlisted according to dock score, ADMET parameters and MM GBSA dG binding score. After that, the shortlisted drug molecules were subjected to an induced-fit docking analysis. Two of the most promising molecules, ZINC000003830957 (Iopromide) and ZINC000003830635 (Deferoxamine), were chosen for molecular dynamics simulation. Finally, the bioisosteric replacement was used to improve the ADMET properties of these molecules. This research provides an idea for drug exploration and computational tools for drug discovery in treating inflammatory bowel disease.Communicated by Ramaswamy H. Sarma.