Current Pharmaceutical Design - Volume 22, Issue 23, 2016

Background: Histone Deacetylase (HDAC) enzymes in the human body play an important role in the transcriptional regulation of gene expression. In the last decade, HDAC inhibitors and activators have been explored and have become known as therapeutic agents for many diseases such as osteodystrophy, neurogenerative disorders, cardiomyopathy, cancer, and diabetes. In recent years, the development of HDAC inhibitors or activators to obtain new potent lead compounds has been conducted using in vitro, in vivo, and in silico methods. Some HDAC family inhibitors and activators have been discovered. But some compounds have limitations such as not selectively binding to one of the HDAC variants. Methods: At present, through bioinformation, HDAC family sequences have been revealed, and some in silico methods such as molecular modelling (homology modelling and pharmacophore modelling), virtual screening, and molecular dynamics are widely used to find and develop new potent and selective compounds. Results: The main utilization of molecular modelling in this work is intended to complete the HDAC structure that partially lacks data regarding its amino acid monomer. Virtual screening methods are helpful in finding the best binding affinity of the test compounds. By molecular dynamic simulation, the temperature, time, and pressure can be adjusted to analyze the hydrogen bond. Conclusion: Combining these in silico approaches will be a more effective and efficient solution in finding new lead compounds for HDAC drug discovery research in the future.

Background: Many factors that directly or indirectly cause adverse drug reaction (ADRs) varying from pharmacological, immunological and genetic factors to ethnic, age, gender, social factors as well as drug and disease related ones. On the other hand, advanced methods of statistics, machine learning and data mining allow the users to more effectively analyze the data for descriptive and predictive purposes. The fast changes in this field make it difficult to follow the research progress and context on ADR detection and prediction. Methods: A large amount of articles on ADRs in the last twenty years is collected. These articles are grouped by recent data types used to study ADRs: omics, social media and electronic medical records (EMRs), and reviewed in terms of the problem addressed, the datasets used and methods. Results: Corresponding three tables are established providing brief information on the research for ADRs detection and prediction. Conclusion: The data-driven approach has shown to be powerful in ADRs detection and prediction. The review helps researchers and pharmacists to have a quick overview on the current status of ADRs detection and prediction.

Background: A major class of chemotherapy drugs targets the genome of cancer cells. These DNA damaging agents induce damage to the DNA helix, resulting in the programmed death of cancer cells. An overactivated DNA repair mechanism in cancer cells can reduce the efficacy of these drugs, thereby eliminating their therapeutic benefit and developing an acquired resistance to these otherwise effective drugs. A promising approach to enhance the therapeutic window of DNA damaging agents is to target the DNA repair pathways causing this type of resistance. Methods: Computational approaches have been applied successfully to study many of these DNA repair mechanisms at different scales and focusing on various aspects. The ultimate goal of these studies has been to identify the key players in developing resistance to DNA damaging agents and to design regulators for their activities. This review covers the most important and recent computational efforts toward this goal. This includes modelling the mechanisms involved in DNA repair and identifying novel pharmacological inhibitors for their activities. Results: We focus here mainly on the pathways associated with an acquired drug resistance to DNA damaging agents, concentrating on the recent advances in modelling the key mechanisms and foreseeing the future directions in this field. Conclusion: We hope that this short, yet comprehensive review can help in discovering novel strategies to overcome the resistance effects inherent in various cancer treatments.

Background: The influenza virus is one of the most critical threats to public health with major economic impact. Though annual influenza vaccination is currently the most effective prevention strategy against flu epidemics and pandemics, the mutational evolution of the influenza virus tends to reduce the effectiveness of strain-specific vaccines. Methods: For past decades, a broad spectrum of potentially universal influenza vaccines has been thoroughly investigated to suppress different strains and subtypes of influenza virus concomitantly. Universal influenza vaccines were attempted to be designed to target conserved regions of surface receptors to provide the necessary preventive strategy against new influenza outbreaks. Conclusion: Notably, the influenza hemagglutinin (HA) receptor has evolutionary conserved domains that can serve as basis for the rational design of a universal influenza vaccine. In this review, we examine recent studies on HA-based universal influenza vaccines and address their molecular mechanism.

Background: Computer-aided drug design is still a state-of-the-art process in medicinal chemistry, and the main topics in this field have been extensively studied and well reviewed. These topics include compound databases, ligand-binding pocket prediction, protein-compound docking, virtual screening, target/off-target prediction, physical property prediction, molecular simulation and pharmacokinetics/pharmacodynamics (PK/PD) prediction. Message and Conclusion: However, there are also a number of secondary or miscellaneous topics that have been less well covered. For example, methods for synthesizing and predicting the synthetic accessibility (SA) of designed compounds are important in practical drug development, and hardware/software resources for performing the computations in computer-aided drug design are crucial. Cloud computing and general purpose graphics processing unit (GPGPU) computing have been used in virtual screening and molecular dynamics simulations. Not surprisingly, there is a growing demand for computer systems which combine these resources. In the present review, we summarize and discuss these various topics of drug design.

Background: Drug discovery is one important issue in medicine and pharmacology area. Traditional methods using target-based approach are usually time-consuming and ineffective. Recently, the problems are approached in a system-level view and therefore it is called systems pharmacology. This research field deals with the problems in drug discovery by integrating various kinds of biomedical and pharmacological data and using advanced computational methods. Ultimately, the problems are more effectively solved. One of the most important problem in systems pharmacology is prediction of drug-target interactions. Methods: In this review, we are going to summarize various computational methods for this problem. Results: More importantly, we formed a unified framework for the problem. In addition, to study human health and disease in a more systematically and effectively, we also presented an integrated scheme for a wider problem of prediction of disease-gene-drug associations. Conclusion: By presenting the unified framework and the integrated scheme, underlying computational methods for problems in systems pharmacology can be understood and complex relationships among diseases, genes and drugs can be identified effectively.

The drug discovery process in general is a very resource intensive undertaking that has existed for a very long time. In the last two decades, performing molecular simulations that determine the level of interaction between a protein and ligand have been refined to the point where they are now an essential part of the drug discovery process. These simulations serve to reduce the time to discovery and improve the positive “hit” rates when screening for molecule with biological activity. As a result, the chemical search space is greatly reduced in silico, prior to any in vitro experiments that validate the results. Recently, there have been many advances in computer science technologies that have improved the virtual screening process. This paper will give a brief overview of the virtual screening process and then summarize the current state-of-the-art technologies applied to virtual screenings. Both biomedical researchers and computer scientists can use this review as a guide to the implementation requirements for computational resources of virtual screening.

Discovery of new therapeutics is a very challenging, expensive and time-consuming process. With the number of approved drugs declining steadily, combined with increasing costs, a rational approach is needed to facilitate, expedite and streamline the drug discovery process. In silico methods are playing key roles in the discovery of a growing number of marketed drugs. The use of computational approaches, particularly molecular dynamics, in drug design is rapidly gaining momentum and acceptance as an essential part of the toolkit for modern drug discovery. From analysing atomistic details for explaining experimentally observed phenomena, to designing drugs with increased efficacy and specificity, the insight that such simulations can provide is generating new ideas and applications that have previously been unexplored. Here we discuss physics-based simulation methodologies and applications in drug design: from locating pockets to designing novel lead compounds, from small molecules to peptides. With developments in hardware, software and theory, the improved predictive abilities of in silico efforts are becoming an essential part of efficient, economic and accurate drug development strategies.

Background: The human islet amyloid polypeptide (hIAPP) can form insoluble fibrillar aggregates in the pancreas of patients with type 2 diabetes. However, increasing evidence suggests that, rather than the fibrils themselves, the hIAPP oligomers that appear on the fibrillation pathway are the toxic species for the pancreatic β -cells. In the perspective of designing therapeutic inhibitors of hIAPP aggregation, it is thus crucial to better understand the mechanism of formation and to characterize the structures of these intermediate species. Methods: However, it still remains a great challenge to experimentally study the hIAPP conformations, due to its intrinsically disordered characteristic and its fast aggregation propensity. Therefore, theoretical and computational approaches were used by many groups as complementary methods to investigate the hIAPP structural features involved in its oligomerization process. Conclusion: In this review, we examine the results provided by the hIAPP molecular simulations, in order to identify convergent insights into its conformational ensemble. Since hIAPP aggregation was shown to be modulated by the presence of lipid membranes, we survey molecular modeling studies of the peptide both in solution and membrane environment.

Background: Metabolic syndromes such as insulin resistance, type 2 diabetes and obesity share common pathogenic pathways with some age-related neurodegenerative disorders. Impaired insulin signaling, inflammation, mitochondrial dysfunction and ER stress can be both causatives and consequences in both groups of the diseases. Patients with chronic metabolic disorders therefore have potential risks to develop neurological diseases in late-age phase and vice versa those who with neurodegenerative diseases also have impairments in metabolic signaling. Method: In this review, we summarize about the interrelation between pathogenic pathways, common drug targets as well as known and developing therapeutics for these “modern” diseases. Results: There are conventional medicines for insulin resistance associated metabolic disorders such as insulin analogues, insulin sensitizers and ER stress releasers which have been suggested in the treatments of some neurodegenerative diseases. Some used or tested therapeutics such as bromocriptine, memantine and α-2A adrenergic antagonists for Parkinson’s and Alzheimer’s diseases, vice versa, were promisingly shown as alternative or complementary drugs for metabolic syndromes. Conclusion: Therefore, it is important and possible to consider contemporary control and intervention for both diseases.