Current Drug Targets - Volume 21, Issue 1, 2020

Drug discovery is important in cancer therapy and precision medicines. Traditional approaches of drug discovery are mainly based on in vivo animal experiments and in vitro drug screening, but these methods are usually expensive and laborious. In the last decade, omics data explosion provides an opportunity for computational prediction of anti-cancer drugs, improving the efficiency of drug discovery. High-throughput transcriptome data were widely used in biomarkers’ identification and drug prediction by integrating with drug-response data. Moreover, biological network theory and methodology were also successfully applied to the anti-cancer drug discovery, such as studies based on protein-protein interaction network, drug-target network and disease-gene network. In this review, we summarized and discussed the bioinformatics approaches for predicting anti-cancer drugs and drug combinations based on the multi-omic data, including transcriptomics, toxicogenomics, functional genomics and biological network. We believe that the general overview of available databases and current computational methods will be helpful for the development of novel cancer therapy strategies.

Background: Hypertension is a prevalent cardiovascular complication caused by genetic and nongenetic factors. Blood pressure (BP) management is difficult because most patients become resistant to monotherapy soon after treatment initiation. Although many antihypertensive drugs are available, some patients do not respond to multiple drugs. Identification of personalized antihypertensive treatments is a key for better BP management. Objective: This review aimed to elucidate aspects of rational drug design and other methods to develop better hypertension management. Results: Among hypertension-related signaling mechanisms, the renin-angiotensin-aldosterone system is the leading genetic target for hypertension treatment. Identifying a single drug that acts on multiple targets is an emerging strategy for hypertension treatment, and could be achieved by discovering new drug targets with less mutated and highly conserved regions. Extending pharmacogenomics research to include patients with hypertension receiving multiple antihypertensive drugs could help identify the genetic markers of hypertension. However, available evidence on the role of pharmacogenomics in hypertension is limited and primarily focused on candidate genes. Studies on hypertension pharmacogenomics aim to identify the genetic causes of response variations to antihypertensive drugs. Genetic association studies have identified single nucleotide polymorphisms affecting drug responses. To understand how genetic traits alter drug responses, computational screening of mutagenesis can be utilized to observe drug response variations at the protein level, which can help identify new inhibitors and drug targets to manage hypertension. Conclusion: Rational drug design facilitates the discovery and design of potent inhibitors. However, further research and clinical validation are required before novel inhibitors can be clinically used as antihypertensive therapies.

Background: Due to its prevalence and negative impacts on both the economy and society, the diabetes mellitus (DM) has emerged as a worldwide concern. In light of this, the label-free quantification (LFQ) proteomics and diabetic marker selection methods have been applied to elucidate the underlying mechanisms associated with insulin resistance, explore novel protein biomarkers, and discover innovative therapeutic protein targets. Objective: The purpose of this manuscript is to review and analyze the recent computational advances and development of label-free quantification and diabetic marker selection in diabetes proteomics. Methods: Web of Science database, PubMed database and Google Scholar were utilized for searching label-free quantification, computational advances, feature selection and diabetes proteomics. Results: In this study, we systematically review the computational advances of label-free quantification and diabetic marker selection methods which were applied to get the understanding of DM pathological mechanisms. Firstly, different popular quantification measurements and proteomic quantification software tools which have been applied to the diabetes studies are comprehensively discussed. Secondly, a number of popular manipulation methods including transformation, pretreatment (centering, scaling, and normalization), missing value imputation methods and a variety of popular feature selection techniques applied to diabetes proteomic data are overviewed with objective evaluation on their advantages and disadvantages. Finally, the guidelines for the efficient use of the computationbased LFQ technology and feature selection methods in diabetes proteomics are proposed. Conclusion: In summary, this review provides guidelines for researchers who will engage in proteomics biomarker discovery and by properly applying these proteomic computational advances, more reliable therapeutic targets will be found in the field of diabetes mellitus.

Background: Calcium (Ca2+) ion is a major intracellular signaling messenger, controlling a diverse array of cellular functions like gene expression, secretion, cell growth, proliferation, and apoptosis. The major mechanism controlling this Ca2+ homeostasis is store-operated Ca2+ release-activated Ca2+ (CRAC) channels. CRAC channels are integral membrane protein majorly constituted via two proteins, the stromal interaction molecule (STIM) and ORAI. Following Ca2+ depletion in the Endoplasmic reticulum (ER) store, STIM1 interacts with ORAI1 and leads to the opening of the CRAC channel gate and consequently allows the influx of Ca2+ ions. A plethora of studies report that aberrant CRAC channel activity due to Loss- or gain-of-function mutations in ORAI1 and STIM1 disturbs this Ca2+ homeostasis and causes several autoimmune disorders. Hence, it clearly indicates that the therapeutic target of CRAC channels provides the space for a new approach to treat autoimmune disorders. Objective: This review aims to provide the key structural and mechanical insights of STIM1, ORAI1 and other molecular modulators involved in CRAC channel regulation. Results and Conclusion: Understanding the structure and function of the protein is the foremost step towards improving the effective target specificity by limiting their potential side effects. Herein, the review mainly focusses on the structural underpinnings of the CRAC channel gating mechanism along with its biophysical properties that would provide the solid foundation to aid the development of novel targeted drugs for an autoimmune disorder. Finally, the immune deficiencies caused due to mutations in CRAC channel and currently used pharmacological blockers with their limitation are briefly summarized.

Background: Poor selectivity of drug candidates may lead to toxicity and side effects accounting for as high as 60% failure rate, thus, the selectivity is consistently significant and challenging for drug discovery. Objective: To find highly specific small molecules towards very similar protein targets, multiple strategies are always employed, including (1) To make use of the diverse shape of binding pocket to avoid steric bump; (2) To increase binding affinities for favorite residues; (3) To achieve selectivity through allosteric regulation of target; (4) To stabalize the inactive conformation of protein target and (5) To occupy dual binding pockets of single target. Conclusion: In this review, we summarize computational strategies along with examples of their successful applications in designing selective ligands, with the aim to provide insights into everdiversifying drug development practice and inspire medicinal chemists to utilize computational strategies to avoid potential side effects due to low selectivity of ligands.

Stroke is an acute neurologic disorder which can be life-threatening if left untreated or diagnosed late. Various detecting techniques including neurologic imaging of the brain by computed tomography or magnetic resonance imaging can facilitate diagnosis of stroke. However, according to the recent advances in molecular detection techniques, new diagnostic and prognostic markers have emerged. Exosomes as an extra cellar particle are one of these markers which can have useful diagnostic, prognostic, and even therapeutic impact after stroke. We have previously discussed the role of exosomes in cardiovascular disease and in the present review we focus on the most common cerebrovascular disease. The aim of the present review is summarizing the recent diagnostic role of exosomes which are specifically secreted during a stroke and can guide clinicians to better diagnosis of stroke.

Cyclin-dependent kinase inhibitors (CDKIs) are important cell cycle regulators. The CDKI family is composed of the INK4 family and the CIP/KIP family. p19INK4d belongs to the INK4 gene family and is involved in a series of normal physiological activities and the pathogenesis of diseases. Many factors play regulatory roles in the p19INK4d gene expression at the transcriptional and posttranscriptional levels. p19INK4d not only regulates the cell cycle but also plays regulatory roles in apoptosis, DNA damage repair, cell differentiation of hematopoietic cells, and cellular senescence. In this review, the regulatory network of the p19INK4d gene expression and its biological functions are summarized, which provides a basis for further study of p19INK4d as a drug target for disease treatment.