Current Pharmaceutical Design - Volume 24, Issue 32, 2018

Backgrounds: Targeted therapy is the foundation of personalized medicine in cancer, which is often understood as the right patient using the right drug. Thinking from the viewpoint of determinants during personalized drug treatment, the genetics, epigenetics and metagenomics would provide individual-specific biological elements to characterize the personalized responses for therapy. Methods: Such personalized determinants should be not only understood as specific to one person, while they should have certain replicate observations in a group of individuals but not all, which actually provide more credible and reproducible personalized biological features. The requirement of detecting personalized determinants is well supported by novel high-throughput sequencing technologies and newly temporal-spatial experimental protocols, which quickly produce the omics big data. Results: In this mini-review, we would like to give a brief introduction firstly on the advanced drug or drug-like therapy with genetics, epigenetics and metagenomics, respectively, from the viewpoint of personalized determinants; then summarize the computational methods helpful to analyze the corresponding omics data under the consideration of personalized biological context; and particularly focus on metagenomics to discuss current data, method, and opportunity for personalized medicine. Conclusion: Totally, detecting personalized determinants during drug treatment from omics big data will bring the precision medicine or personalized medicine from concept to application. More and more inspiring biotechnologies, data resources, and analytic approaches will benefit All of US in the near future.

Nucleic acid is the key unit and a predominant genetic material for interpreting the fundamental basis of genetic information in an organism and now it is used for the evolution of a novel group of therapeutics. To identify the potential impact on the biological science, it receives high recognition in therapeutic applications. Due to its selective recognition of molecular targets and pathways, DNA significantly imparts tremendous specificity of action. Examining the properties of DNA holds numerous advantages in assembly, interconnects, computational elements, along with potential applications of DNA self-assembly and scaffolding include nanoelectronics, biosensors, and programmable/autonomous molecular machines. The interaction of low molecular weight, small molecules with DNA is a significant feature in pharmacology. Based on the mode of binding mechanisms, small molecules are categorized as intercalators and groove binders having a significant role in target-based drug development. The understanding mechanism of drug-DNA interaction plays an important role in the development of novel drug molecules with more effective and lesser side effects. This article attempts to outline those interactions of drug-DNA with both experimental and computational advances, including ultraviolet (UV) -visible spectroscopy, fluorescent spectroscopy, circular dichroism, nuclear magnetic resonance (NMR), molecular docking and dynamics, and quantum mechanical applications.

Background: Cancer is one of the most debilitating diseases worldwide; even though advances in molecular and cellular biology have contributed to the decline of mortality associated with cancer, the procedure of drug discovery and development of cancer are time-consuming and expensive. However, with computer-aided drug discovery (CADD) techniques, pharmaceutical firms can save production costs and reduce the time of introducing effective anticancer drugs for clinical trials. CADD strategies like structure-based drug designing, ligandbased drug designing, and combined structure-based and ligand-based approaches also have the advantage of identifying target sites and discovering active compounds with high affinity for the target sites. In this article, research carried out on cancer biology aspect of the computational approaches in drug discovery technology have been reviewed. Objective: The main objective of the study is to identify the potential causes and the development of the cancer. In addition to this, its recovery has been discussed briefly. Conclusion: Our findings indicate that only a few studies have been carried out regarding this area. Hence, it is recommended that further researches should be conducted on the computational methods for identifying candidate drugs for breast, pancreatic, colon, prostate, and other types of cancer.

Role of Shp2: The dysregulation of cell signaling cascades associated with the cell differentiation and growth, due to the deletion, insertion or point mutation in specific amino acids which alters the intrinsic conformation of the protein, can ultimately lead to a fatal cancer disease. The protein tyrosine phosphatase has been recognized as a key regulator of extracellular stimuli such as cytokine receptor and receptor tyrosine kinase signaling. In the last era, the PTPN11 gene (encode a Shp2 protein) and its association with acute myeloid, juvenile myelomonocytic, and B-cell acute lymphoblastic leukemia, Noonan syndrome, and myelodysplastic have been recognized as the cause of such deadly disease due to the occurrence of germline mutations in the interface of PTP and SH2 domain. Conclusion: The current study was designed to focus on the allosteric regulation (autoinhibition) of the of Shp2 protein. Subsequently, it will cover the last 10-year recap of Shp2 protein, their role in cancer, and regulation in numerous ways (allosteric regulation).

A complex framework of interacting partners including genetic, proteomic, and metabolic networks that cooperate to mediate specific functional phenotypes drives human biological processes. Recent technological and analytical advances in “omic” sciences allow the identification and elucidation of reprogramming biological functions in response to perturbations in cells and tissues. To understand such a complex system, biological networks are generated to reduce the complexity into relatively simple models, and the integration of these molecular networks from different perspectives is implemented for a holistic interpretation of the entire system. Ultimately, network-based methods will effectively facilitate the development and improvement of precision medicine by directing therapies based on the underlying biology of a given patient's disease. The goal of precision medicine is to identify novel therapeutic strategies that can be optimized for each disease type or each patient based on the underlying genetic, environmental, and lifestyle factors. Pharmaco-omics analyses based on an integration of pharmacology and various “omics” data types can be employed to develop effective treatment strategies using particular drugs and doses that are tailored to each individual. In the current review, we first present the core elements of network-based systems biology in the context of pharmaco-omics followed by integration of multi-omics data using various biological networks. Next, we provide an opening into precise medicine and drug targeting based on network approaches. Lastly, we review the current significant efforts as well as the accomplishments and limitations in precise drug targeting with the utility of network-based guided drug discovery methods for effective treatment of breast cancer.

Cancer immunoinformatics have led to new directions towards vaccine design from predicted potential epitope candidates, which are able to stimulate the correct cellular or humoral immune responses. They were employed to accomplish an advanced vaccine design through reverse vaccinology by replacing the whole organisms. In this review, computational tools play an essential role in evaluating multiple proteomes to identify and select the potential targeted epitopes or combinations of distinct epitopes, so that candidates may afford a rationale design competent for obtaining suitable cytotoxic T lymphocytes (CTL) or B cell-mediated immune responses. This review is a complete collection of the most beneficial online and user-friendly immunological tools, servers, and databases for the design and development of the peptide vaccine. The mechanism of major histocompatability (MHC)-restricted peptide presentation and how these tools support the vaccine development are also presented. The human papillomavirus (HPV) is taken as a model microbial strain for peptide vaccine design and its sensitization against HPV-induced cervical cancer is discussed.

Cancer genome sequencing is useful for diagnosis and personalized treatment. Analysis of the sequencing data involves integration of computation, statistics and system biology methods. The amalgam of such methods which help study interaction of cancer cells with immune system, harnessing immune system for cancer therapy or its prevention through vaccines has led to the foundation of cancer immunomics. It is, therefore, a combinatorial science which merges diverse techniques from genomics and proteomics for diagnosis and drug design/treatment. There has been a gradual increase in establishment of cancer immune focused start-ups, research facilities and pharma giants working on state-of-the-art methods for improving diagnostics, treatment and prevention or minimizing side effects, applying immunomics. However, we are still far away from making precise, quick and reliable diagnostic and treatment predictions. We need decision support systems to facilitate diagnosis, tumor evaluation prediction and assessment of individual profile for making personalized therapy a reality. The future is centered not only on data management but wise decision aided by artificially intelligent algorithms. In this review, we provide an overall picture and focus on immune biomarkers and relevant softwares that aid in diagnostics and analysis of cancer.

Worldwide, colorectal cancer takes up the third position in commonly detected cancer and fourth in cancer mortality. Recent progress in molecular modeling studies has led to significant success in drug discovery using structure and ligand-based methods. This study highlights aspects of the anticancer drug design. The structure and ligand-based drug design are discussed to investigate the molecular and quantum mechanics in anti-cancer drugs. Recent advances in anticancer agent identification driven by structural and molecular insights are presented. As a result, the recent advances in the field and the current scenario in drug designing of cancer drugs are discussed. This review provides information on how cancer drugs were formulated and identified using computational power by the drug discovery society.

Background: Due to its ability to provide quantitative and dynamic information on tumor genesis and development by directly profiling protein expression, the proteomics has become intensely popular for characterizing the functional proteins driving the transformation of malignancy, tracing the large-scale protein alterations induced by anticancer drug, and discovering the innovative targets and first-in-class drugs for oncologic disorders. Objective: To quantify cancer proteomics data, the label-free quantification (LFQ) is frequently employed. However, low precision, poor reproducibility and inaccuracy of the LFQ of proteomics data have been recognized as the key “technical challenge” in the discovery of anticancer targets and drugs. In this paper, the recent advances and development in the computational perspective of LFQ in cancer proteomics were therefore systematically reviewed and analyzed. Methods: PubMed and Web of Science database were searched for label-free quantification approaches, cancer proteomics and computational advances. Results: First, a variety of popular acquisition techniques and state-of-the-art quantification tools are systematically discussed and critically assessed. Then, many processing approaches including transformation, normalization, filtering and imputation are subsequently discussed, and their impacts on improving LFQ performance of cancer proteomics are evaluated. Finally, the future direction for enhancing the computation-based quantification technique for cancer proteomics are also proposed. Conclusion: There is a dramatic increase in LFQ approaches in recent year, which significantly enhance the diversity of the possible quantification strategies for studying cancer proteomics.

Biopolymers are ubiquitous in biomedical and healthcare application. Its nanocomposites have gained more attention as antimicrobials, a drug carrier, sensors, disease diagnosis, tissue engineering, wound healing, and cancer therapy. These biopolymer composites are efficient in holding, protecting and releasing bioactive drugs such as pharmaceutics, nutraceuticals, enzymes, and probiotics. Several studies revealed a polymer-based drug delivery system in cancer therapy and microbial treatments. However, this mini-review emphasized the source, extraction, and characterizations of the biopolymers and their use in the fabrication of various drug or metals based nanocomposites followed by its utilization as drug carrier or drug to treat the various diseases such as antimicrobial infections and cancer.