Modelling tools in bioinformatics

ARISg provides an integrated system for pharmacovigilance and risk management, thereby enabling pharmaceutical companies to monitor and evaluate their products for safety risk.

Oracle Argus is software for pharmacovigilance application which enables pharmaceutical companies to infer fast and better safety decisions, optimize global compliance, and with integrate risk management system. Argus provides customizable end-to-end safety process with automated case processing, periodic reporting, E2B intake and submission, detailed analytics, and safety operations integrated into a single system. Clinical trial is the key link between advances in medical research technology and improved health care.

It is an integral part of medical health research dedicated to elucidate human disease, its prevention, treatment, and promoting health.

Clinical trial of a novel drug candidate is increasingly highly complex, time—consuming, and very expensive process. The pharmaceutical marketplace is very competitive, and the demand for rapid access to approval of new drug is high.

Hence, pharmaceutical companies face huge pressure to increase the efficiency and efficacy of the drug discovery and development. The technology initiative is considered as the only way to achieve this goal. With the advent of electronic clinical trials and computer-aided drug design research, there was a revolution in the drug discovery and development processes.

The software companies developed various tools for analysis of genomic, sequence analysis, genetic algorithms, phylogenetic inference, genome database organization and mining, optical computation and holographic memory, pattern recognition, and image analysis. In addition, various stages of clinical trials such as target identification, target validation, randomization, data collection, data integration; trial management and pharmacovigilance became streamlined, efficient, and manageable.

The adoption of the technologies by the pharmaceutical companies and regulators not only improved the efficiency but also the evaluation of clinical data i. Speculated advantages of bioinformatics in clinical research are e stablishing and using huge data strategy for clinical trials, leveraging the new techniques to provide the patient-centric approach in trials, bringing evolution in existing processes and systems with new techniques, provide help in case studies from existing data sources for advanced trials, easier data sharing, and would combat data privacy issues [ Figure 3 ].

Thus, the drive to innovate has prompted researchers and the regulators to explore novel and more complicated ways to investigate promising new products with the aid of bioinformatics and software tools yielding trial design that are faster, more flexible, and more targeted.

Looking forward, we can only anticipate that the future will bring more rapid technological changes for the new level of drug discovery that would have never been achieved through traditional review of the raw data.

National Center for Biotechnology Information , U. Journal List Perspect Clin Res v. Perspect Clin Res. Author information Copyright and License information Disclaimer. Address for correspondence: Dr. E-mail: moc. This article has been cited by other articles in PMC. Abstract Clinical research is making toiling efforts for promotion and wellbeing of the health status of the people. Open in a separate window. Figure 1.

Illustrating the role of bioinformatics and software tools in clinical research. Figure 2. Target identification can be done by computational methods Molecular docking This technique predicts the structure of intermolecular complex found between two molecules and to find the best orientation of ligand which would form a complex with overall minimum energy.

Molecular dynamics simulation This computer simulation method calculates the time-dependent behavior of a molecular system and provides the information about the structure or the microscopic interaction between the molecules.

Proteomics Proteomics are studies on the structure and function of proteins. Target validation With the technological advancement in drug discovery, the availability of potential target is not rate limiting rather the problem is in the selection of the most potent drug target.

Databases and computational tools used for target validation are:. Gene logic Gene logic is a leading integrated genomics company providing comprehensive genomic reference databases and life science laboratory information management solutions. Immusol Immusol San Diego recently launched a proprietary technology that allows fast and efficient in vivo target validation for efficacy and safety in multiple disease models using siRNA vectors.

Aptamers Nascacell works with aptamers, the synthetic nucleic acid ligands for target validation as well as screening. Drug bank It is a database which associates the chemical and the pharmacological data with various drug targets and provides comprehensive information about the sequence, structure, and pathway information.

PharmaGKB It is a computational tool which predicts the response of a drug with respect to the variation in the human genetics. Table 1 Software and bioinformatics tools used in clinical research drug discovery. Software and bioinformatics tools in clinical trials Besides, expanding exponentially in volume, clinical trials are becoming more complex too. Predixion Software uses cloud-based predictive analytic software to explain patterns in hospital datasets Health fidelity is using natural language processing to turn unstructured data e.

The Informatica Platform provides all the capabilities; the pharmaceutical industry needs to ensure that it can integrate and manage ever-growing volumes of data while using that data to innovate faster and achieve optimal results MIM cloud: It makes data collection and distribution easy.

Data management in clinical research Clinical Data Management CDM is an integral part of clinical research as it efficiently gathers the data of trial subjects at the investigator site and ensures the validity, quality, and integrity of the same. Few of the currently available off-the-shelf software for clinical trials are: e-Clinical: Innovative e-Clinical technologies are now becoming essential to make clinical data acquisition, aggregation, analysis, and decision-making for the new product.

The workflows developed in e-Clinical enables clinical operations to effectively plan each stage of a trial Oracle clinical and oracle remote data capture: It is also known as relational database management system. The Oracle clinical application allows electronic data to be created, modified, maintained, and transmitted without compromising the authenticity, integrity, and confidentiality of data Electronic case report form: Electronic case report form is an electronic tool replica of paper CRF where the clinical trial subject data are captured in an electronic format.

Importance of database in clinical research Databases are created in clinical research for recording and propagating scientific information. Software and bioinformatics tools in pharmacovigilance Drug safety software used by pharmaceutical companies ARISg It is leading platform for both pharmacovigilance and clinical safety system.

Argus Oracle Argus is software for pharmacovigilance application which enables pharmaceutical companies to infer fast and better safety decisions, optimize global compliance, and with integrate risk management system.

Figure 3. Financial support and sponsorship Nil. Conflicts of interest There are no conflicts of interest. Zerhouni EA.

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Bioinformatics tools are thus based on multiarray technology for transforming complicated genomic data into valuable insights.

Paid and open-source bioinformatics tools further support the sequencing data analysis for aligning and assembling DNA fragments. You can do variant calling, data visualizations, RNA expression profiling, gene fusion detections and data mining.

Molecular biology- Bioinformatics software offers gRNA design tools for studying:. Bioinformatics tools with their genomic testing abilities have been helpful in finding genetic alternations that have strong link to serious disorders and diseases.

Test genomic tools has various applications:. Popular applications for bioinformatics are best for sequence analysis and curations. The best solutions in the field have key inbuilt computational and big data analysis tools for genome sequencing. Let us have a look at what else these applications are comprised of in the following list. The bioinformatics tool also supports various plug-ins for genomics and gene integration.

The open-source and free bioinformatics tool can also be used for basic statistical analysis and transcription factor analysis. BioPerl bioinformatics tool for Linux is most deployed for computational molecular biology.

The standardized CPAN style is the unique selling point of this bioinformatics platform. The Linux bioinformatics software offers Perl modules for peptide and nucleotide sequence data. UGENE open source and free bioinformatics tool Linux offers a common UI for integrating the platform with other bioinformatics applications.

The software supports multiple formats for biological data available and you can retrieve it from remote locations. Biojava Bioinformatics tool for Linux, Windows and Solaris is best known for its various java tools ideal for processing biological data.

The genome sequencing software supports a range of datasets and parsers for the common file format. Biojava test genomic tool can also be used for managing statistical and analytical routines. Biopython genome sequencing tool is most deployed for doing biological computation. Inbuilt into Biopython are various python modules for making interactive and integrated sequences. InterMine bioinformatics tools is used for analyzing and integrating biological data.

The open source and free bioinformatics platform supports dynamic tables for data filtering and drilling it down. The software functions by working with either a single object or multiple lists in multiple languages. IGV genomic sequencing application offers genomics viewing for interactive genomics and effective visualization. IGV has been built on next-generation sequencing technology for genomic annotation.

IGV bioinformatics software also lets you navigate through extensive data set and zoom it as per your requirement. Taverna Workbench open source bioinformatics tool Linux is deployed for streamlining bioinformatics workflows. You can also use this bioinformatics tools for nucleotide sequence pattern analysis.

Clustal Omega in bioinformatics is a next generation sequencing tool designed for doing multi sequence alignments. The genome testing software for Linux supports different input sequence types such as HMM profile and aligning the sequence. The genome testing tool for Linux supports finding match between protein and nucleotide sequences. BLAST in bioinformatics tool also helps with structuring query sequences and mapping such data sets. Bedtool bioinformatics platform is used for genomic testing and analysis purposes.

The genome sequencing tool is based on plugin architecture for semantic web functioning. Bioclipse is best used for representing molecular structures.

Bioconductor is an open source bioinformatics tool that makes use of R programming for analyzing data like oligonucleotide arrays and flow cytometer. You can also deploy these solutions for generating powerful statistical and graphical database. Bioconductor Tutorial: Access Bioconductor tutorial. Sequencing helps understand mutations and variations in human genes to identify disorders and serious diseases like cancers.

Bioinformatics databases with their advanced sequencing techniques for genomic testing have proven critical for screening deadly diseases.

Genomic Testing is a type of test done to study mutations or alterations in genes to identify diseases, food-borne bacteria, and infections. For organizing vast molecular biological data, Developing tools required for analyzing the data and Accurate interpretation of results.

Bioinformatics tools help perform genomic tests or next-generation sequencing for studying mutations in genes. These genes help predict the nature of a disease. Profiling gene expression of protein target: Examining gene or protein expression profiles in disease and drug treatment using genomics and proteomics techniques offers a basis for detecting drug targets.

In the past few years, researchers have identified protein targets for many diseases using genomic and proteomic approaches [33]. Microarrays or DNA chips are powerful tools for genome analysis. The genomes of hundreds of organisms are being sequenced, and the genomes of approximately 30 model organisms have been revealed. These genomic data are helpful for functional predictions of specific genes, particularly for studying the biological effect and potency of a designed drug targeting such genes.

Data obtained can be used to screen race-specific genes, virulence genes, and conserved genes of pathogenic organisms, specific bacterial or viral enzyme genes, and bacterial membrane-translocation proteins [34].

Gene microarray can be used to evaluate the expression levels of certain genes in different patients or different tissues of the same patient and analyze gene function and regulation during differentiation periods and pathological change conditions [35].

Specific binding of cDNA or oligonucleotide sequences of interest with extracted nucleic acid labeled with fluorescent dye enables researchers to profile the gene expression patterns of tens of thousands of genes in a single experiment. The labeled isolated RNA binds quantitatively to its complementary sequences and the difference in fluorescence intensities between the test samples and control reflects the level of expression of the gene, which can be measured with a laser scanner [13].

Expression of the test sample is compared with that of a steadily expressed gene known as a housekeeping gene. RT-PCR can be used to analyze the expression of many genes in a quantitative manner during treatment or in different tissues of the same organism [13,36].

Two-dimensional electrophoresis 2DE : 2DE separates a mixture of proteins from different sources based on their isoelectric points and molecular weights. Using this technique, we can analyze proteomes of cell, tissue, and serum, and detect posttranslational modifications after gene expression, such as phosphorylation, glycosylation, hydroxylation, etc. In MALDI-TOF, expressed proteins are differentially digested with a protease to produce peptides and then these peptides are ionized at high sensitivity by a laser coupled and the ion mass to charge ratio is detected with a mass analyzer.

This technique is informative because it provides information about both peptide masses and the amino acid sequence [13]. In vitro assay of drug-target interactions: In vitro assays are required to generate preliminary results which can be used to gain ethical approval for in vivo studies. In vitro studies are conducted using isolated and purified proteins, enzymes, cells, or tissues. These processes are advantageous because they are inexpensive, simple, noncontroversial, and can be automated.

In vivo assay of drug-target interactions: In vivo assays represent the next step. First, the drug is tested for its biological effect, toxicity, immunological reactions, etc.

One of the main limitations of this method is the genetic differences between experimental animals and humans in their responses to certain drugs. Scientists have overcome this problem by generating a series of transgenic animal models in which their genetic material is altered, and certain genes are replaced with human genes to produce the desired human target to be studied. Alternatively, mouse genes can be mutated to become susceptible to certain diseases.

The most commonly used models are carcinogenic rats [22]. Identification of the binding site in the target molecule by NMR: After purification and characterization of the target, the next step is to identify the key residues in the binding site that determine the space and chemical environment around it. The binding site can be identified by X-ray crystallography [37] or NMR [22] as a concave pocket on the surface of a protein target that accommodates drug molecules through hydrophobic interactions, hydrogen bonding, among others, to drive drug binding [38,39].

NMR can be used to detect whether a drug binds to its protein target by exposing it to radiation waves. This energy excites the nuclei of specific atoms such as hydrogen, carbon, or nitrogen. The relaxation time needed for each atom to release the excess energy to return to the ground state depends on the type of atom and environment and space around it, providing information about the structure of the ligand and how it binds to the target [22].

Many computational methods are used to determine the various components of ligands and target proteins. These methods predict the changes in polar and non-polar areas upon ligand binding and estimate the desolvation energy, number of rotatable bonds, configurational or strain energy, and number of hydrogen bonds formed [40].

Once the crystal structure of the target protein is known, molecular modeling software can be used to identify, predict, and design a ligand and study its binding to the target. Protein-ligand docking is a computer-based technique used to predict the position and orientation of a ligand in its binding site in the protein target. Moreover, by measuring the distance between different atoms in the binding site and ligand, it became simple to in silico predict the binding interaction and make changes in the design of the drug to improve binding.

A cluster value of 0 means that the best FF score has been reached, while a greater negative FF score means that a more favorable binding mode with a better fit has been reached.

However, not all targets can be crystalized after binding to the drug to study their active sites by X-ray or NMR. Hence, molecular modeling can be used to predict such interactions [44]. Further chemical experiments are needed to ensure the expected binding and biological activity of such drugs [22]. Recognizing certain functional groups is essential for properly understanding the mechanism of drug-target interactions.

It is known that the chemical structure of the drug is complementary to the binding region in the target. Thus, previous knowledge of the structure of the target or ligand using certain software tools and data from specialized databases can help predict the structure of the binding site of the target or ligand and modify the structure to alter their characteristics. For example, replacing a specific chemical group of a drug with another group may improve both the biological activity of the drug and its binding to the target, as well as reveal chemical nature of the binding site [22].

Amines can form also strong ionic bonds with the carboxylate group of aspartate or glutamate. Such compounds can interact with their targets through a variety of bonding forces such as van der Waal forces, hydrophobic interactions, and hydrogen and ionic bonding. Various chemical groups can be added to a drug structure to modify its biological or physical activity. Optimization of the chemical structure of the drug to improve activity: Using molecular docking and bioinformatics programs, the structure of a well-known drug can be modified to overcome various limitations such as low activity, poor selectivity, side effects, or even difficulty in the synthesis of such compounds.

Identifying drug analogs with improved properties depends on optimizing its interaction with the binding site in the target molecule. Many different strategies can be used, as follows:.

Optimization of drug for better physical properties: Variation in the structure of a drug may affect its access to the target by affecting its solubility, stability, and toxicity. The drug absorption can be improved by changing alkyl, acyl, N-acetyl group, etc. Drug stability and resistance to chemical and enzymatic degradation and decreasing its metabolism can be modified by introducing an ester group. Also the drug targeting to certain tissues such as by targeting tumor cells via a monoclonal antibody and targeting gastrointestinal tract infections can be optimized by fully ionized drug; e.

Finally, drug toxicity can be reduced by improving membrane permeability [22]. The unraveling of human genome sequence enabled many applications and ideas to discover new drugs, study their effect and determine their main target. Using bioinformatics and the knowledge in the fields of genomics and proteomics have led to build new powerful strategies to design new drugs.

The accumulated knowledge in these fields helps in DNA and genome annotation, predicting protein structure, and understanding the genetics of disease. As a result, a new trend in pharmaceutical research has evolved to illustrate the mechanism of drug action, prediction of drug resistance, and discovery of biomarkers for many diseases.

This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Article Information Abstract Article References PDF Abstract Unraveling of human genome sequences had enabled the discovery of new drugs, studies of their effects, and determination of their main targets.

Keywords Ligand-based drug design; Structure-based drug design; Target structure; Molecular docking; Mass spectrometry. Introduction Drug design is the development of new medications that interact with biological targets. Drug Design and Discovery Drug discovery is a multi-process that involves genomics, proteomics, and bioinformatics studies, which lead to the discovery of a new drug entity with a novel mechanism of action.

Types of drug design Two major strategies were used for drug design: ligand-based and structure-based techniques. Ligand-based drug design relies on knowledge of a known chemical typically a drug, inhibitor, or cofactor that binds to the target of interest and uses this compound as a model for drug to examine its binding to a known target [10].

Structure-based drug design depends on the X-ray crystallographic structure of a known protein target or homology model of an unstudied protein compared to a known structure [7,11]. This type is divided into three methods [12]: » Identification of a drug that fits into the binding pocket of a given receptor by searching large databases of 3D structures using fast approximate docking programs. Relations of Genomics, Proteimics and Bioinformatics to Drug Design The genome of bacteriophage lambda was the first genome sequenced, which opened a new era of research to obtain information related to open reading frames sequences that are translated into protein.