Current Pharmaceutical Design - Volume 24, Issue 34, 2018

Background: Diabetes mellitus (DM) is a disease of systemic metabolic disorders caused by the decrease of secretion or sensitivity of insulin. In recent years, the study of insulin-related drug targets and the development of new drugs have become the popular topic of current medical research, and studies have shown that multiple signaling pathways are associated with diabetes treatment. At present, some new drugs based on the new target design have been listed on the market and have achieved good hypoglycemic effect. However, most of the drugs are still in the clinical or pre-clinical stage. The efficacy and safety of the drugs need further clinical validation. Objective: This article will introduce the advancements of targets and drugs to promote insulin secretion.

Background: On a tide of big data, machine learning is coming to its day. Referring to huge amounts of epigenetic data coming from biological experiments and clinic, machine learning can help in detecting epigenetic features in genome, finding correlations between phenotypes and modifications in histone or genes, accelerating the screen of lead compounds targeting epigenetics diseases and many other aspects around the study on epigenetics, which consequently realizes the hope of precision medicine. Methods: In this minireview, we will focus on reviewing the fundamentals and applications of machine learning methods which are regularly used in epigenetics filed and explain their features. Their advantages and disadvantages will also be discussed. Results: Machine learning algorithms have accelerated studies in precision medicine targeting epigenetics diseases. Conclusion: In order to make full use of machine learning algorithms, one should get familiar with the pros and cons of them, which will benefit from big data by choosing the most suitable method(s).

Background: Anatomical Therapeutic Chemical (ATC) classification of unknown compound has raised high significance for both drug development and basic research. The ATC system is a multi-label classification system proposed by the World Health Organization (WHO), which categorizes drugs into classes according to their therapeutic effects and characteristics. This system comprises five levels and includes several classes in each level; the first level includes 14 main overlapping classes. The ATC classification system simultaneously considers anatomical distribution, therapeutic effects, and chemical characteristics, the prediction for an unknown compound of its ATC classes is an essential problem, since such a prediction could be used to deduce not only a compound’s possible active ingredients but also its therapeutic, pharmacological, and chemical properties. Nevertheless, the problem of automatic prediction is very challenging due to the high variability of the samples and the presence of overlapping among classes, resulting in multiple predictions and making machine learning extremely difficult. Methods: In this paper, we propose a multi-label classifier system based on deep learned features to infer the ATC classification. The system is based on a 2D representation of the samples: first a 1D feature vector is obtained extracting information about a compound’s chemical-chemical interaction and its structural and fingerprint similarities to other compounds belonging to the different ATC classes, then the original 1D feature vector is reshaped to obtain a 2D matrix representation of the compound. Finally, a convolutional neural network (CNN) is trained and used as a feature extractor. Two general purpose classifiers designed for multi-label classification are trained using the deep learned features and resulting scores are fused by the average rule. Results: Experimental evaluation based on rigorous cross-validation demonstrates the superior prediction quality of this method compared to other state-of-the-art approaches developed for this problem. Conclusion: Extensive experiments demonstrate that the new predictor, based on CNN, outperforms other existing predictors in the literature in almost all the five metrics used to examine the performance for multi-label systems, particularly in the “absolute true” rate and the “absolute false” rate, the two most significant indexes. Matlab code will be available at https://github.com/LorisNanni.

Knowledge of protein subcellular localization is vitally important for both basic research and drug development. With the avalanche of protein sequences emerging in the post-genomic age, it is highly desired to develop computational tools for timely and effectively identifying their subcellular localization based on the sequence information alone. Recently, a predictor called “pLoc-mPlant” was developed for identifying the subcellular localization of plant proteins. Its performance is overwhelmingly better than that of the other predictors for the same purpose, particularly in dealing with multi-label systems in which some proteins, called “multiplex proteins”, may simultaneously occur in two or more subcellular locations. Although it is indeed a very powerful predictor, more efforts are definitely needed to further improve it. This is because pLoc-mPlant was trained by an extremely skewed dataset in which some subsets (i.e., the protein numbers for some subcellular locations) were more than 10 times larger than the others. Accordingly, it cannot avoid the biased consequence caused by such an uneven training dataset. To overcome such biased consequence, we have developed a new and bias-free predictor called pLoc_bal-mPlant by balancing the training dataset. Cross-validation tests on exactly the same experimentconfirmed dataset have indicated that the proposed new predictor is remarkably superior to pLoc-mPlant, the existing state-of-the-art predictor in identifying the subcellular localization of plant proteins. To maximize the convenience for the majority of experimental scientists, a user-friendly web-server for the new predictor has been established at http://www.jci-bioinfo.cn/pLoc_bal-mPlant/, by which users can easily get their desired results without the need to go through the detailed mathematics.

Background: The relationship between protein structure and its bioactivity is one of the fundamental problems for protein engineering and pharmaceutical design. Method: A new method, called SPTD (Simulated Protein Thermal Detection), was proposed for studying and improving the thermal stability of enzymes. The method was based on the evidence observed by conducting the MD (Molecular Dynamics) simulation for all the atoms of an enzyme vibrating from the velocity at a room temperature (e.g., 25°C) to the desired working temperature (e.g., 65°C). According to the recorded MD trajectories and the coordinate deviations of the constituent residues under the two different temperatures, some new strategies have been found that are useful for both drug delivery and starch industry. Conclusion: The SPTD technique presented in this paper may become a very useful tool for pharmaceutical design and protein engineering.

Background: Closely related to causes of various diseases such as rheumatoid arthritis, septic shock, and coeliac disease; tyrosine nitration is considered as one of the most important post-translational modification in proteins. Inside a cell, protein modifications occur accurately by the action of sophisticated cellular machinery. Specific enzymes present in endoplasmic reticulum accomplish this task. The identification of potential tyrosine residues in a protein primary sequence, which can be nitrated, is a challenging task. Methods: To counter the prevailing, laborious and time-consuming experimental approaches, a novel computational model is introduced in the present study. Based on data collected from experimentally verified tyrosine nitration sites feature vectors are formed. Later, an adaptive training algorithm is used to train a back propagation neural network for prediction purposes. To objectively measure the accuracy of the proposed model, rigorous verification and validation tests are carried out. Results: Through verification and validation, a promising accuracy of 88%, a sensitivity of 85%, a specificity of 89.18% and Mathew’s Correlation Coefficient of 0.627 is achieved. Conclusion: It is concluded that the proposed computational model provides the foundation for further investigation and be used for the identification of nitrotyrosine sites in proteins.

Background: Atorvastatin is the best-selling statin in the market. However, some patients have to reduce drug doses or discontinue atorvastatin therapy mainly due to adverse drug reactions (ADRs). Genetic factors play an important role in the occurrence of ADRs. Aim: This study aimed to investigate the association between SLCO1B1 polymorphisms (c.521T>C or c.388A>G) and atorvastatin safety and efficacy. Methods: We systematically searched PubMed, Web of Science and Embase to screen relevant studies published before Sep 2018. This meta-analysis was performed to identify the relationship between SLCO1B1 c.521T>C or c.388A>G polymorphisms and atorvastatin-related ADRs by the odds ratios (ORs) and 95% confidence intervals (CIs). The relationship of SLCO1B1 polymorphisms and lipid-lowering effects [low-density lipoprotein cholesterol (LDL-C), total cholesterol (TC)] was assessed in pooled data by calculating the mean difference (MD) with 95% CIs. All statistical tests were performed by the Review Manager 5.3 software. Results: A total of 13 studies involving 1,550 atorvastatin users were included in this analysis. There was a significant association between the SLCO1B1 c.521T>C polymorphism and atorvastatin-related ADRs associated with risk allele C (dominant model: OR=1.57, P=0.01). Allele C is associated with increased lipid-lowering efficacy in people with Hyperlipidemias as compared to allele T (LDL-C/dominant model: MD=6.19, P<0.00001 and (TC)/dominant model: MD=2.07, P=0.008). No association between the SLCO1B1 c.388A>G polymorphism and ADRs or efficacy was observed (P>0.05). Conclusion: SLCO1B1 c.521T>C polymorphism is a valuable biomarker for the evaluation of atorvastatin safety and efficacy.

The studying of synaptic plasticity, the ability of synaptic connections between neurons to be weakened or strengthened and specifically long-term potentiation (LTP) and long-term depression (LTD), is one of the most active areas of research in neuroscience. The process of synaptic connections playing a crucial role in improving cognitive processes is important to the processing of information in brain. In general, the dysfunction of synaptic plasticity was involved in a wide spectrum of central nervous system (CNS) disorders, including some neurodegenerative disorders. Thus, synaptic plasticity which is a dysfunction reported in neurodegenerative disorders may also be involved in posttraumatic stress disorder (PTSD), an anxiety and/or memory disorder developed after experiencing natural disasters, domestic violence or combat-related trauma. In this review, we mainly focus on discussing the biological function and mechanism for diagnostics and therapy of synaptic plasticity in PTSD and associated comorbidities, such as schizophrenia, depression, sleep disturbances and alcohol dependence, and further studying the molecular mechanisms of PTSD with a particular focus on the LTP/LTD, glutamatergic ligand-receptor systems, voltage-gated calcium channels (VGCCs) and brain-derived neurotrophic factor (BDNF)-tyrosine kinase B (TrkB). The summarized function and mechanism of synaptic plasticity in PTSD and its comorbidities may help us further understand PTSD and provide insight into novel neuroplasticity modifying for diagnostics and treatment for PTSD.

Anticancer treatment has become a research highlight in recent years. Despite several techniques have been developed and applied in the clinic, this area still meets great challenges in the construction of smart anticancer devices with accurate targeting, controlled release and microenvironment response properties. Most of the carbon-based materials are biocompatible, possessing abundant and tunable pore structures and particularly large surface areas. These properties make them suitable materials as drug carriers. In addition, some carbon-based materials are capable of absorbing near-infrared radiation (NIR) and have highly efficient photothermal effects. The generated heat in situ can be used to kill cancer cells in short time on the position. This review describes the recent and significant application of four kinds of carbon materials including carbon nanotubes, graphene, carbon dots and mesoporous carbon for drug delivery and photothermal therapy. After a short introduction of the structures and properties of these materials, the construction and application of these nanoplatforms in drug delivery, photothermal therapy or their combination will be summarized and discussed in depth. In addition, other carbon allotropes as drug carriers will be introduced briefly. Finally, the risk assessments and the perspectives and challenges of these materials used in cancer therapies are enclosed.

Cardiovascular diseases, such as stroke and myocardial infarction (MI) remain the major cause of death and disability worldwide. However, the mortality of MI has declined dramatically over the past several decades because of advances in medicines (thrombolytic agents, antiplatelet drugs, beta blockers, and angiotensin converting enzyme inhibitors) and approaches to restore tissue perfusion (percutaneous coronary intervention and cardiopulmonary bypass). Animal studies have been shown that these treatments have been effective in reducing acute myocardial ischemic injury and limiting MI size. The paradox is that the process of reperfusion can itself amplify cell injury and death, known as myocardial ischemia-reperfusion injury (I/R). Intensive research has uncovered several complex mechanisms of cardiomyocyte damage after reperfusion,and potential therapeutic targets for preventing I/R. Importantly, it is now recognized that excessive elevation of intracellular and mitochondrial Ca2+during reperfusion predisposes the cells to hypercontracture, proteolysis and mitochondrial failure and eventually to necrotic or apoptotic death. These enormous alterations in cytosolic Ca2+ levels are induced by the Ca2+ channels of the sarcolemma(L-Type Ca2+channels, sodium/calcium exchanger), the endoplasmic/ sarcoplasmic reticulum (SERCA ATPase) and ryanodine receptors, SOCE(store-operated calcium entry), lysosomes and others, which are modified by I/R injury. The overall goal of this review is to describe the different pathways that lead to I/R injury via Ca2+ overload, focus on recent discoveries and highlight prospects for therapeutic strategies for clinical benefit.