informatics, information technology, Suresh Rewar, bioinformatics, genome informatics, development, sequence analysis, cancer informatics, drug discovery, biomedical informatics, emerging information technologies, Rajasthan University of Health Sciences, toxicity, pharmacoinformatics, Department of Pharmaceutics, drug interactions, Global Trends, development activities, internet technologies, Cambridge University Press, Helmest D.M., information technologies, National Human Genome Research Institute, Brusic V, technology, biological data, Richard M. Twyman, ImMunoGeneTics database, Trends Biomater, Zeleznikow J. Artificial neural network, J. Howard Parish, Taylor & Francis, Brusic V. Bioinformatic, Curr Opin Chem Biol, Philos Trans, Medical Informatics, Nucleic Acids, David R. Westhead, Journal of Biomedical Informatics, Rachel Tyndale, Van Essen DC, Urs A Meyer, Gasteiger J. Chemoinformatics, Nucleic Acids Res, pharmaceutical development, African National Bioinformatics Institute, Canadian Bioinformatics Resource, Bioinformatics Institute, Bioinformatics databases, Secondary databases, Australian National Genome Information Service, brain mapping, experimental efforts, Computational methods, QSTR, Databases Brain Architecture Management System, toxicological data, statistical approach, Brain Map Surface Management System, Singapore Bioinformatics Centre, Protein database, multiple sequence alignments, Institute of Bioinformatics, Genome database, Sequence databases Genome databases, Prot dbEST GDB Ensembl Pfam Protein interaction, Bimolecular Interaction Network Database, Protein Structure, genetic disorders, biological science, Genetic sequence database, textual databases, protein databases, amino acid sequence, Peking Center of Bioinformatics, Biological Databases, Human Genome Database, management information system
Suresh Rewar et al. / JGTPS / 6(2)-(2015) 2562 2571
ISSN: 2230-7346 (Review Article)
Journal of Global Trends in Pharmaceutical Sciences Journal home page: www.jgtps.com A VITAL ROLE OF PHARMACOINFORMATICS
Suresh Rewar* Department of Pharmaceutics, Rajasthan University of Health Sciences, Jaipur, Rajasthan,
The rapid growth of the internet and the World Wide Web has led to the development of pharmacoinformatics technologies to assist oncology healthcare professionals in delivering optimum pharmaceutical care and health related outcomes. There is an increasing recognition that Information Technology
can be effectively used for drug discovery. The work in pharmacoinformatics can be broadly divided into two categories - scientific aspects and service aspects. The scientific component deals with the drug discovery and development activities, whereas the service oriented aspects are more patient centric. Pharmacoinformatics subject feeds on many emerging information technologies like neuroinformatics, immunoinformatics, biosystem informatics, metabolomics, chemical reaction informatics, toxicoinformatics, cancer informatics, genome informatics, proteome informatics, biomedical informatics, The minimizing the time between a drug`s discovery and its delivery to the marketplace and maintaining high productivity in the manufacturing processes. During a product`s lifecycle many complex decisions must be made to achieve these goals. To better support the development and manufacturing processes at each stage, we have proposed a new epitome to facilitate the management and transfer of data information and knowledge. In future these information technology efforts are expected to grow both in terms of their reliability and scope. Thus, this emerging technology (pharmacoinformatics) is becoming an essential component of pharmaceutical sciences. Key words: Pharmacoinformatics, Immunoinformatics, Chemoinformatics, Bioinformatics
INTRODUCTION Informatics and Internet technologies
are becoming extremely popular in today's health-care system. The emergence of the worldwide web has affected the way in which health-related information is distributed and accessed over cyberspace. The internet is rapidly gaining importance, not just for health-care professionals, but also for patients, by enabling them to search for drug-related and other health-related information . Address for correspondence Mr. Suresh Rewar Research Scholar, Department of Pharmaceutics, Rajasthan University of Health Sciences, Jaipur, Rajasthan, 302033 Mobile No - +919468719912 E-mail ID: [email protected]
Pharmacoinformatics is the study, invention and effectuation of discipline where technology with any aspect of drug delivery, from the basic sciences to the clinical use of medications in individuals and populations. Informatics is commonly defined as the "use of computers to manage data and information" and represents the nexus of people, information, and technology. Includes pharmacy technologies involved in the preparation, delivery, and management of medication use within health care delivery systems [2, 3]. Many applications of pharmacoinformatics currently exist within the health-care sector. These applications have important roles in helping to reduce DRPs in the oncology setting. Studies on the effectiveness of support systems for clinical decisions, e-prescribing, and drug-order entry have shown benefits in reducing medication errors [4-5], and in the prevention and management of chronic diseases .
2562 Suresh Rewar et al, JGTPS, 2015, Vol. 6(2): 2562 - 2571
CLASSIFICATION OF PHARMACOINFORMATICS Pharmacoinformatics is new emerging information technologies like neuroinformatics, immunoinformatics, bioinformatics, Metabolomics, chemo-informatics, toxico-informatics, cancer
informatics, genome informatics, proteome informatics, biomedical informatics are basic tools provided for the purpose of drug discovery . Shows the current statuses of the activities in pharmacoinformatics are given in (fig. 1).
Figure.1: Classification of Pharmacoinformatics
1. BIOINFORMATICS: Bioinformatics is the
Molecular bio-informatics: bioinformatics is
combination of biology and information
conceptualizing biology in terms of molecules
technology. The discipline encompasses any
(in the sense of physical chemistry) and applying
computational tools and methods used to
"informatics techniques" (derived from
manage, analyze and manipulate large sets of
disciplines such as applied maths, computer
biological data. Essentially, bioinformatics has
science and statistics) to understand and organize
the information associated with these molecules,
on a large scale. In short, bioinformatics is a
The creation of databases, allowing the
management information system
storage and management of large
biology and has many Practical applications
biological data sets.
12]. Bioinformatics and medical informatics
(BIOMI) are multidisciplinary fields at the
The development of algorithms and
intersection of computing and informatics,
statistics to determine relationships among members of large data sets.
mathematics and statistics, biology, chemistry, and engineering . Bioinformatics is the
The use of these tools for the analysis and interpretation of various types of biological data, including DNA, RNA and protein sequences, protein structures, gene expression
profiles, and biochemical pathways .
combination of biology and information technology. The discipline encompasses any computational tools and methods used to manage, analyze and manipulate large sets of biological data. The National Center for Biotechnology Information (NCBI 2001) defines Bioinformatics as "Bioinformatics is the field of
The term bioinformatics first came into use in the 1990s and was originally synonymous with the management and analysis of DNA, RNA and protein sequence data. Computational tools for sequence analysis had been available since the 1960s, but this was a minority interest until advances in sequencing technology led to a rapid expansion in the number of stored sequences in databases such as GenBank. Now, the term has expanded to incorporate many other types of biological data, for example protein structures, gene expression profiles and protein interactions. Each of these areas requires its own set of databases, algorithms and statistical methods .
science in which biology, computer science, and information technologies merge into a single discipline [14-15]. There are three important subdisciplines within Bioinformatics: the development of new algorithms and statistics with which to assess relationships among members of large data sets; the analysis and interpretation of various types of data including nucleotide and amino acid sequences, protein domains, and protein structures; and the development and implementation of tools that enable efficient access and management of different types of information ." Basically, bioinformatics has three components: The creation of databases, allowing the storage and
2563 Suresh Rewar et al, JGTPS, 2015, Vol. 6(2): 2562 - 2571
management of large biological data sets. The
General Bioinformatics Web Sites: Many of
development of algorithms and statistics to
the sites are offering the same sorts of links and
determine relationships among members of large
many to other Bioinformatics sites; many have
data sets. The use of these tools for the analysis
links to a Sequence Retrieval System or other
and interpretation of various types of biological
facilities for sequence retrieval. These are
data, including DNA, RNA and protein sequences, protein structures, gene expression profiles, and biochemical pathways [11, 15].
categorized as under: Academic Sites and Corporate/Government Sites . Access to Journals: Providing access to journals
Bioinformatics and internet: Bioinformatics is
such as; Nature, Science, Molecular biology and
largely, although not exclusively, a computer-
Evolution, Nucleic Acids Research,
based discipline. Computers are important in
bioinformatics for two reasons: First, many
As a Centre for Biotechnology Information:
bioinformatics problems require the same task to
One can explore extensive sites of resources and
be repeated millions of times. In such cases, the
including newsletters, Bioinformatics databases,
ability of computers to process information and
and links to the major medical bibliographic
test alternative solutions rapidly is indispensable . Second, computers are required for their
databases. It not only connects to textual databases but also to Protein Structure Servers.
problem-solving power. Typical problems that
These include 3DB browser, biomolecular
might be addressed using bioinformatics could include solving the folding pathways of protein
modeling and structural classification of proteins etc .
given its amino acid sequence, or deducing a
biochemical pathway given a collection of RNA
Types of Biological Databases Accessible:
expression profiles. Internet plays an important
There are many different types of database but
role to retrieve the biological information.
for routine sequence analysis, the following are
Bioinformatics emerging new dimension of
initially the most important. Primary Database
biological science includes computer science,
(Nucleic Acid Protein): EMBL, Genbank, DDBJ,
mathematics and life science. The Computational
SWISS-PROT. Secondary databases: PROSITE,
part of bioinformatics use to optimize the
Pfam. Composite databases: Combine different
biological problems like metabolic disorder, genetic disorders [18-19]. The Internet provides
sources of primary databases. Example: NRDB OWL [18-19].
various facilities for Bioinformatics such as;
Some bioinformatics research and service
World Wide Web (WWW) Virtual Library:
centers: National Center for Biotechnology
This directory, provided by Cato Research Ltd.,
Information (NCBI) in the USA; European
contains over 1000 URLs specific to
Bioinformatics Institute (EBI) in the UK; Swiss
biotechnology, pharmaceutical development, and related fields .
Institute of Bioinformatics (SIB); Australian National Genome Information Service (ANGIS);
Subject Specific Sites: These sites are more
Canadian Bioinformatics Resource (CBR);
likely to concentrate on a particular area of
Peking Center of Bioinformatics (CBI);
Bioinformatics. These sites are further divided
Singapore Bioinformatics Centre (BIC); South-
into the various areas of Bioinformatics e.g. genomic comparisons .
African National Bioinformatics Institute (SANBI) .
Table: 1. Databases Information Contain Type of databases [7, 18-19]
Sequence databases Genome databases Secondary protein databases
GenBank EMBL UniProt dbEST GDB Ensembl Pfam
Protein interaction databases
Genetic sequence database Nucleic acid and protein databases Protein database Expressed Sequence Tags database Human Genome Database Genome database Protein family database with multiple sequence alignments and hidden Markov models Protein family and domain database Bimolecular Interaction Network Database
http://www.ncbi.nlm.nih.gov http://www.ebi.ac.uk/embl/index.html http://www.uniprot.org http://www.ncbi.nlm.nih.gov/dbEST/index.html http://www.gdb.org/ http://www.ensembl.org/index.html http://www.sanger.ac.uk/Software/Pfam/ http://us.expasy.org/prosite/details.html http://www.bind.ca
2564 Suresh Rewar et al, JGTPS, 2015, Vol. 6(2): 2562 - 2571
2. GENOME INFORMATICS:
database, genome SCOUT gene RAGE,
This is a relatively well-known topic being
CoGenT++. In India, Institute of Genomics and
closely related to bioinformatics through
Integrated Biology (IGIB) is one of the leading
sequence analysis. Genome informatics as a
institutes working in the field of genome
field encompasses the various methods and
informatics. Personalized medicine is the
algorithms for analyzing and extracting
idealized medical practice
to give right drugs to
biologically relevant information from the
right patients at right times. Finding SNPs is
rapidly growing biological and essential
considered as a premise for this practice, but it
sequence databases . The Genome
is by no means the sufficient effort. Good
Informatics program supports research in
practice must be supported by well trained
computational biology that will enable the
medical professionals who can easily access
development of tools for sequence analysis,
relevant data and knowledge. Such an
gene mapping, complex trait mapping and
informational environment would be called the
genetic variation. These tools include
infrastructure for personalized medicine .
mathematical and statistical methods for the
identification of functional elements in complex
Immunoinformatics is another major area
genomes; the identification of patterns in large
in biomedical research where computational and
datasets (for example, microarray data); and the
informational technologies are playing a major
mapping of complex traits and genetic
role in the development of drugs and vaccines.
variations (for example, single nucleotide
This field is still in its infancy and it covers both
polymorphisms, or SNPs).The program also
modeling and informatics of the immune system
encourages development and maintenance of
and is the application of informatics technology
databases of genomic and genetic data. This
to the study of immunological macromolecules,
emphasis includes new tools for annotating
addressing important questions in
complex genomes so as to expand their utility.
immunobiology and vaccinology. Data sources
The program also supports the production of
for immunoinformatics include experimental
robust, exportable software that can be widely
approaches and theoretical models, both
shared among different databases in order to
demanding validation at every stage. Major
facilitate database interoperability. These
bioinformatics resources will allow the
immunological databases, sequence analysis,
scientific community efficient access to
structure modeling, modeling of the immune
genomic data, which will enable new types of
system, simulation of laboratory experiments,
analyses. The analyses, in turn, will allow for
statistical support for immunological
the computer model
ing and subsequent
experimentation, and immunogenomics [26-27].experimental validation
of the complex
The field of immunoinformatics has direct
pathways and networks that ultimately
influence in the following areas: (a) improve
determine the phenotype of a cell or the causes
transplantation outcomes (b) identify novel
of many human diseases [20-21]. A number of
genes involved in immunological disorders (c)
online resources and servers are available that
decipher the relationship between antigen
assist in genome informatics research. Few of
presentation pathways and human disease (d)
them are Fly Base , KEGG (Kyoto
predict allergenicity of molecules including
Encyclopedia of Genes and Genomes) , and
drugs (e) personalized medicine (f) vaccine
Ensemble Compara Database , cis RED
Table 2: Some Selected Immunoinformatics Databases and Tools [18, 28-31]
Databases and Tools IMGT, the international ImMunoGeneTics information system HIV Molecular Immunology Database MHCPEP FIMM SYFPEITHI BIMAS
Brief Description A sequence, genome, and structure database for immunogenetics data A database of HIV specific B - cell and T - cell responses Database of MHC - binding peptides Database of Functional Immunology Database and prediction server of MHC ligands Bioinformatics and Molecular Analysis Section (MHC peptide - binding prediction)
URL http://imgt.cines.fr http://www.hiv.lanl.gov/content/imm unology/index.html http://wehih.wehi.edu.au/mhcpep/ http://research.i2r.astar.edu.sg/fimm/ http://www.syfpeithi.de/ http://bimas.dcrt.nih.gov/molbio/hla_ bind/
2565 Suresh Rewar et al, JGTPS, 2015, Vol. 6(2): 2562 - 2571
web-based information management systems
Neuroinformatics may be defined as the
one of the major objectives of neuroinformatics.
organization and analysis of neuroscientific data
Apart from data sharing, computational
using the tools of information technology. The
modeling of ion channels, neurons and neural
information sources in neuroinformatics include
networks, second messenger pathways,
behavioral sciences (psychological description)
morphological features, and biochemical
and medicinal (including drugs and diagnostic
reaction are also often included in
images) and biological (membranes, neurons,
neuroinformatics. The initial ideas on
synapses, genes, etc.) aspects. The aim of
neuroinformatics can be traced to the work of
neuroinformatics is to unravel the complex
Hodgkin and Huxley, who initiated
structure function relationship of the brain in
computational neuronal modeling. Current
an integrative effort. Neuroscientists work at
efforts in the direction include studies related to
multiple levels and are producing enormous
modeling the neuropsychological tests,
amounts of data. Distributed databases are being
neuroimaging, computational neuroscience,
prepared and novel analytical tools are being
brain mapping, molecular neuroimaging, and
generated with the help of informationmagnetic resonance imaging
technology. Producing digital capabilities for
Table: 3. Selected List of Important Neuroinformatics Tools and Databases [18, 32-34]
Databases Brain Architecture Management System (BAMS) Brain Map Surface Management System (SuMS) L - Neuron GENESIS NeuroScholar
Brief Description Repository of brain structure information; contains to date around 40,000 connections For meta - analysis of human functional brain-mapping literature A surface - based database to aid cortical surface reconstruction, visualization and analysis Computational Neuroanatomy Database Neural Simulator MySQL Database frontend with management of bibliography, histological and tracing data
URL http://brancusi.usc.edu/bkms/ http://brainmap.org/ http://sumsdb.wustl.edu/sums/index.jsp http://www.krasnow.gmu.edu/LNeuron http://www.genesissim.org/GENESIS/ http://www.neuroscholar.org
5. TOXIC INFORMATICS: Toxicoinformatics involves the use of information technology and computational science for the prediction of toxicity of chemical molecules in the living systems. Early prediction of toxicological parameters of new chemical entities (NCEs) is an important requirement in the drug discovery strategy today. This is being emphasized in the wake of many drug withdrawals in the recent past. Computational methods for predicting toxicophoric features is a cost effective approach toward saving experimental efforts and saving animal life. Current efforts in Toxicoinformatics are mainly based on QSTR (quantitative structure toxicity relationships) and rule - based mechanistic methods. QSTR is a statistical approach, in which a correlation is developed between structural descriptors of a series of compounds and their toxicological data. In this approach, a model can be trained with the help of a set of known data, validated using many approaches, and then used for the prediction of toxicological parameters.
The only limitation of this approach is that the predictive power of these models gets reduced when chemicals' belonging to a class outside the series of molecules is used for the construction of the model. Toxicity prediction tools using this approach include TOPKAT and CASE/M-CASE. TOPKAT mainly employs electrotopological descriptors based on graph theory for the development of QSTR models. TOPKAT uses linear free - energy relationships in statistical regression analysis of a series of compounds. In this software, the continuous/dichotomous toxicity end points are correlated to the structural features like electronic topological descriptors, shape descriptors, and substructure descriptors. CASE (Computer Automated Structure Evaluation) and M - CASE are Toxicoinformatics software packages that have the capability to automatically generate predictive models. A hybrid QSTR artificial expert system - based methodology is adopted in CASE - based systems [35-38].
2566 Suresh Rewar et al, JGTPS, 2015, Vol. 6(2): 2562 - 2571
(CYP450 enzymes) are being studied [18, 39].
Metabolomics is an emerging new ominous
Many databases and software systems are
science analogous to genomics, transcriptomics,
available in this field for the early prediction of
proteomics, etc. Metabolomics is the lesser-
substrates of CYP450 enzymes. Some of the
known cousin to genomics and proteomics. An
databases and predictive systems for metabolic
understanding of the pharmacokinetics of a drug
information of drugs are given in Table. The
can play a major role in reducing the probability
Human Drug Metabolism Database (hDMdb)
of bringing a new chemical entity (NCE) with
project is a nonprofit, internet database of
inappropriate ADME/Toxicity profile to the
xenobiotic metabolic transformations that are
market. Drug metabolism and toxicity in the
observed in humans . The predictive systems
human body are primarily assessed during
available for metabolism are mainly expertclinical trials
, and preclinical assessment of the
systems based on experimental data
same involves study on in vivo and in vitro
representing the metabolic effects (database)
systems. In silico models for predicting
and/or rules derived from such data (rule -
pharmacokinetic properties based on the
base). The rules may either be induced rules,
experimental results can greatly reduce the cost
which are quantitative, derived from a statistical
and time required for the experiments. These
analysis of the metabolic data, or knowledge -
methods range from modeling approaches such
based rules derived from expert judgment .
as QSARs, to similarity searches as well as
Plant breeding and assessment of crop quality,
informatics methods like ligand-protein docking
Food assessment and safety, Toxicity
and pharmacophore modeling. Metabolic
assessment, Nutrition assessment, Medical
biotransformation of any NCE may profoundly
diagnosis and assessment of disease status,
affect the bioavailability, activity, distribution,
Pharmaceutical drug development
toxicity, and elimination of a compound; the
improvement in crops and fermentation,
effects of probable metabolism are now
Biomarker discovery, Technological advances
considered in the early stages of drug discovery
in analytical chemistry, Genotyping,
with the help of computer - aided methods. In
Environmental adaptations, Gene-function
silico prediction of metabolic biotransformation
elucidation, integrated system
occurring at the liver cytochrome enzymes
Table: 4.Databases and Tools for Metabolism Informatics [18, 39-41]
Databases and Tools
METEOR (LHASA Ltd., Leeds, UK) META (Multicase, Inc.)
Brief Description IUPAC project for a web-based model database for human drug metabolism information Comprised of a database, registration system, and browsing interface Human cytochrome P450 information and predictive system Predictions presented as metabolic trees Uses dictionaries to create metabolic paths of query molecules
URL http://www.iupac.org/projects/2000 /2000 -010-1-700.html http://www.mdl.com/products/predi ctive/metabolite/index.Jsp http://www.fqs.pl/ http://www.lhasalimited.org/ http://www.multicase.com/products /prod05.htm
7. HEALTHCARE INFORMATICS: Biomedical Informatics is an emerging discipline that has been defined as the study, invention, and implementation of structures and algorithms to improve communication, understanding and management of medical information." Medical informatics is more concerned with structures and algorithms for the manipulation of medical data, rather than with the data itself. This suggests that one difference between bioinformatics and medical informatics as disciplines lies with their approaches to the data there are bioinformatics interested in the theory behind the manipulation of that data and there are bioinformatics scientists concerned with the
data itself and its biological implications. Medical informatics, biomedical informatics, clinical informatics, nursing informatics, etc. come under the service-oriented sectors. Other topics like cancer informatics, diabetes informatics are specific therapeutic area based information technology topics. These topics are also related to pharmacoinformatics as a whole because the information obtained from these subjects leads to decision making in pharmaceutical industry. For example, medical informatics deals with medicines and health care. The databases associated with this filed include the feedback received response to a drug. Thus, future designing of the drugs can be made to suit
2567 Suresh Rewar et al, JGTPS, 2015, Vol. 6(2): 2562 - 2571
the needs of the patients. electronic health record
(EHR) systems, Hospital Information Systems (HIS), Decision Support Systems (DSS), etc. are the major components of healthcare informatics . Medical Information Science is the science of using system-analytic tools to develop procedures (algorithms) for management, Process control
, decision making and scientific analysis of medical knowledge - Ted Short life. Medical Informatics comprises the theoretical and practical aspects of information processing and communication, based on knowledge and experience derived from processes in medicine and health care - Jan van Bemm. Medical Informatics (MI) is the study of information processing as it is used in healthcare. It might have been called medical computing, but the French-derived term informatics is more commonly used internationally and probably conveys a broader set of concerns, including the uses and flows of information that may have little to do with computers. Like many engineering fields, MI has scientific aspects that focus on the description, modeling and interpretation of how information is actually generated, disseminated and used, and underlying constraints or natural laws
that govern these activities. MI is also deeply concerned with the design of appropriate medical information processing systems, with tradeoffs in their implementation, and with ways to evaluate their effectiveness . Some have suggested health informatics as a better, broader term, meant to encompass aspects of health care that are not traditionally the focus of medicine, such as preventive care, nutrition, patient education, epidemiology, etc. Related terms include bioinformatics, which is the study of information processing in biological sciences
. Opinion current
ly varies on whether bioinformatics is part of medical informatics, or-if it forms a distinct discipline-how it relates. Most expect that progress in understanding the molecular basis of disease will bring these fields closer together, if not to merger. Telemedicine (or the recent European coinage telematique) focuses on one aspect of MI, access to and use of medical information at a distance. At MIT, in line with our traditions of institutional flexibility, we have no official organization that does medical informatics, but a number of small foci around the research and teaching interests of faculty in different Departments and Laboratories [42-45]. Pharmacoinformatics preventing adverse drug reactions in hospital patients. Health informatics is concerned with the systematic processing of data, information and knowledge in medicine and healthcare, increasingly delivered by a mix of public and private
organisations. Health informatics is delivered by operational health
practitioners, academic research
ers and educators, scientists and technologists in operational, commercial and academic domains. The ultimately focus is to improve patient safety
and organisational effectiveness to achieve better outcomes . Nursing informatics (NI) is a specialty that integrates nursing science, computer science, and information science to manage and communicate data, information, knowledge, and wisdom in nursing practice. NI supports consumers, patients, nurses, and other providers in their decision making in all roles and settings. This support is accomplished through the use of information structures, information processes, and information technology opportunities in Health broadly cover the following facets, sometimes in combination and with grey boundaries between them . 8. CHEMO INFORMATICS: Chemo informatics is the application of informatics methods to solve chemical problems. All areas of chemistry from analytical chemistry to drug design can benefit from chemo informatics methods. And there are still many challenging chemical problems waiting for solutions through the further development of chemo informatics . The term "Chemoinformatics" appeared a few years ago and rapidly gained widespread use. Workshops and symposia are organized that are exclusively devoted to Chemoinformatics, and many job advertisements can be found in journals. The first mention of Chemoinformatics may be attributed to Frank Brown . Chemo informatics is the arrangement of information resources to transform data into information and information into knowledge for the intended purpose of making better decisions faster in the area of drug lead identification and organization. So chemo informatics is helpful in drug design, Greg Paris came up with a much broader definition. Chemical Data Storage in Databases Data Information Data Retrieval Analysis The current schema of chemoinformatics in drug designing is given below: Analysis of predesigned drug structure structural property prediction (QSAR) property prediction by smiles format perform some modification in prior drug again predict the drug property if variation occurs in novel structure save that structure and design a fragment library .Chemoinformatics is a generic term that encompasses the design, creation, organization, management, retrieval, analysis, dissemination, visualization, and use of chemical information. The needs for chemoinformatics recent chemical developments for drug discovery are generating a lot of chemical data. These developments are combinatorial chemistry and high-throughput
2568 Suresh Rewar et al, JGTPS, 2015, Vol. 6(2): 2562 - 2571
screening. Some scientists have described this situation as a chemical information explosion. This has created a demand to effectively collect, organize, and apply the chemical information . Chemo informatics which deals with the information of the molecules, chemical reaction informatics also plays an important role in the field of pharmacoinformatics. Chemical reaction informatics enable a chemist to explore synthetic pathways, quickly design and record completely new experiments from scratch or by beginning with reactions found in the reaction databases. Chemical reaction informatics a database consists of the following information -Reactants and products, Atom mapping, which allows you to tell which atom, becomes which product atom through the reaction, Information regarding reacting center(s), The catalyst used, The atmosphere, including pressure and composition, The solvent used, Product yield, Optical purity, References to literature. The chemical reaction informatics would essentially assist the chemist in giving access to reaction information, in deriving knowledge on chemical reactions, in predicting the course and outcome of chemical reactions, and in designing syntheses. Specifically, the following tasks can be accomplished by analysis tools in chemical reaction informatics- Storing information on chemical reactions, Retrieving information on chemical reactions, Comparing and analyzing sets of reactions, Defining the scope and limitations of a reaction type, Developing models of chemical reactivity, Predicting the course of chemical reactions, Analyzing reaction networks, Developing methods for the design of syntheses, etc. Applications of Chemo informatics: Chemical Information, All fields of chemistry, Analytical Chemistry, Organic Chemistry
, Drug Design and Textile Industry
Drug discovery and development requires
the integration of multiple scientific and
technological disciplines. These include
pharmaceutical technology and extensive use of
information technology. The latter is increasingly
recognised as Pharmacoinformatics. As
discussed above, there is several information
technology efforts related to the pharmaceutical
sciences which are useful for drug discovery. In
future, these efforts are expected to grow both in
terms of their reliability and scope. Thus, this
emerging technology (pharmacoinformatics) is
becoming an essential component of
Improving pharmaceutical care in oncology by
the evolving role of informatics and
internet for drug therapy; Lancet Oncol. 2009;
2. Fox BI, Karcher RB, Flynn A, Mitchell S. Pharmacy informatics syllabi in doctor of pharmacy programs in the US; Am J Pharm Educ. 2008; 72(4):89.
3. American Society of Health-System Pharmacists, ASHP statement on the pharmacist's role in informatics;Am J HealthSyst Pharm. 2007;64(2):200-203.
4. Bates DW, Teich JM, Lee J, et al. The impact of computerized physician order entry on medication error prevention; J Am Med Inform Assoc. 1999; 6: 313-21.
5. Overhage JM, Tierney WM, Zhou XH, McDonald CJ. A randomized trial of "corollary orders" to prevent errors of omission; J Am Med Inform Assoc. 1997; 4: 364-75.
6. Dexter PR, Perkins S, Overhage JM, et al. A computerized reminder system to increase the use of preventive care for hospitalized patients; N Engl J Med. 2001; 345: 965-70.
7. Bharatam, Prasad V.; Khanna, Smriti; Francis, Sandrea M., Modeling and Informatics in Drug Design, in: Gad Shayne Cox (ed.), Preclinical Development Handbook: ADME and Biopharmaceutical Properties; JOHN WILEY & SONS
.2008; pp. 1-46.
8. Anderson, James G, and Kenneth W. Goodman. Ethics and Information Technology: A CaseBased Approach to a health care system
in Transition. Health Informatics. New York: Springer, 2002. BA Call Number: 174.2 A5451 (B4).
9. Temple F. Smith, The Challenges Facing Genomic Informatics, in: Tao Jiang, Ying Xu, Michael Q. Zhang (ed.), Current Topics in Computational Molecular Biology; [electronic book] MIT Press, 2002; pp. 3-8.
A historical perspective on gene/protein functio
nal assignment; Bioinformatics, 2000; 16(1):10-
11. Luscombe NM, Greenbaum D, Gerstein M.
A proposed definition and overview of the field;
Methods Inf Med. 2001; 40(4):346-58.
2569 Suresh Rewar et al, JGTPS, 2015, Vol. 6(2): 2562 - 2571
12. Benton D., Bioinformatics-principles and potential of a new multidisciplinary tool; Trends Biotechnol, 1996; 14(8):261-72.
13. Bioinformatics and Medical Informatics: In the
College of Sciences; Graduate Bulletin, 2014-
Medical%20Informatics.pdf [Accessed: March
14. NCBI, NCBI Science Primer: Bioinformatics:
[Accessed: March 27, 2015].
15. Crick F Central dogma of molecular biology; Nature, 1970; 227(5258):561-3.
25. CSIR-Institute of Genomics & Integrative
http://www.igib.res.in/ [Accessed: March 28,
26. Rammensee HG. Immunoinformatics: bioinformatic strategies for better understanding of immune function
. Introduction; Novartis Found Symp 2003; 254: 1-2.
27. Brusic V, Zeleznikow J, Petrovsky N. Molecular immunology databases and data repositories; J Immunol Methods 2000; 238: 1728.
28. Robinson J, Waller MJ, Parham P, et.al. IMGT/HLA and IMGT/MHC: sequence databases for the study of the major histocompatibility complex; Nucleic Acids Res 2003; 31: 311-314.
16. Nagarajan P. An Over View of Bioinformatics; Trends Biomater. Artif. Organs. 2004; 17(2):48.
29. Lefranc MP. IMGT, the international ImMunoGeneTics database; Nucleic Acids Res 2003; 31: 307-310.
17. David R. Westhead, J. Howard Parish, and Richard M. Twyman, Bioinformatics (Oxford: BIOS, 2002).
18. Tang S.W. and Helmest D.M., WWW bioinformatics resources, in: Werner Kalow., Urs A Meyer, Rachel Tyndale (editors). Pharmacogenomics; (2nd edition) Boca Raton: Taylor & Francis, 2005; pp. 493-514.
19. Luscombe NM, Greenbaum D, Gerstein M, What is bioinformatics? An introduction and overview; Int Med
Inform Assoc Yearbook, 2001; 83-100.
20. National Human Genome Research Institute, Genome Informatics and Computational Biology Program Overview; (Last Updated: Oct. 24, 2014] Available at: http://www.genome.gov/10001735 [Accessed: March 27, 2015].
21. Hocquette JF, Where are we in genomics? J Physiol
Pharmacol. 2005; 56 Suppl 3:37-70
22. Drysdale RA, Crosby MA, Gelbart W, et al. Fly Base: genes and gene models. Nucleic Acids Res.2005; 33:D390-95.
KEGG: Kyoto encyclopedia of genes and geno
mes; Nucleic Acids Res. 2000; 28(1):27-30.
24. Ensembl, Genome Assemblies; Available at: http://asia.ensembl.org/info/genome/genebuild/a ssembly.html [Accessed: March 28, 2015].
30. Petrovsky N, Schonbach C, Brusic V. Bioinformatic strategies for better understanding of immune function; In Silico Biol 2003; 3: 411-416 . 31. Brusic V, Zeleznikow J. Artificial neural network
applications in immunology, in Proceedings of the International Joint Conference on Neural Networks, 1999; 5: 36853689. 32. Dickson J, Drury H, Van Essen DC. The surface management system (SuMS) database: a surface - based database to aid cortical surface reconstruction, visualization and analysis; Philos Trans R Soc Lond B Biol Sci 2001; 356: 1277-1292 . 33. Van Essen DC. Windows on the brain: the emerging role of atlases and databases in neuroscience; Curr Opin Neurobiol 2002; 12: 574-579. 34. Burns GA. Knowledge management of the neuroscientific literature: the data model and underlying strategy of the NeuroScholar system; Philos Trans R Soc Lond B Biol Sci 2001; 356: 1187-1208. 35. Barratt MD, Rodford RA. The computational prediction of toxicity; Curr Opin Chem Biol 2001; 5: 383-388. 36. Pearl GM, Livingston - Carr S, Durham SK. Integration of computational analysis as a sentinel tool in toxicological assessments; Curr Top Med Chem 2001; 1: 247-255.
2570 Suresh Rewar et al, JGTPS, 2015, Vol. 6(2): 2562 - 2571
37. Dearden JC, Barratt MD, Benigni R, et.al. The development and validation of expert systems for predicting toxicity; ATLA 1997; 25: 223252. 38. Schultz TW, Cronin MTD, Walker JD, Aptula AO. Quantitative structure activity relationships (QSARs) in toxicology: a historical perspective; J Mol Structure (Theochem) 2003; 622: 1-22. 39. Korolev D, Balakin KV, Nikolsky Y, Kirillov E , Ivanenkov YA , Savchuk NP , Ivashchenko AA , Nikolskaya T . Modeling of human cytochrome P450 - mediated drug metabolism using unsupervised machine learning approach; J Med Chem 2003; 46: 3631-3643. 40. Erhardt PW. A human drug metabolism database: potential roles in the quantitative predictions of drug metabolism and metabolism - related drug - drug interactions; Curr Drug Metab 2003; 4: 411-422. 41. Langowski J, Long A. Computer systems for the prediction of xenobiotic metabolism; Adv Drug Deliv Rev 2002; 54: 407-415. 42. Maojo V, Iakovidis I, Martin-Sanchez F, Crespo J, Kulikowski C. Medical Informatics and Bioinformatics: European Efforts to Facilitate Synergy; Journal of Biomedical Informatics, 2002; 10:1042. 43. Hanson CW. Healthcare Informatics, New York: McGraw - Hill Professional; 2005. 44. Goodman K. Ethics, Computing, and Medicine: Informatics and the Transformation of Health
Care, 1st ed. Cambridge, UK: Cambridge University Press ; 1998 . 45. Evans RS, Pestotnik SL, Classen DC, Horn DS, Bass SB, Burke JP. Pharmacoinformatics preventing adverse drug reactions in hospital patients. The Annals of Pharmacotherapy, 1994; 8:523. 46. Saba VK, McCormick KA. Essentials of Nursing Informatics; 4th ed. New York: McGraw-Hill Medical; 2005. 47. Gasteiger J. Chem
oinformatics: a new field with long tradiation; Anal bioanal Chem 2006; 384:57-64. 48. BROWN F. Chemoinformatics: What is it and How does it impact drug discovery; Annu Rep Med Chem, 1998; 33:375-84. 49. Ivanciuc O. Chemical graphs, molecular matrices and topological indices in chemoinformatics and quantitative structureactivity relationships; Curr Comput Aided Drug Des, 2013;9(2):153-63. 50. Bajorath, Juergen, ed. Chemoinformatics: Concepts, Methods, and Tools for Drug Discovery. Totowa, N.J.: Humana Press, c2004. 51. Gasteiger, Johann, ed. Handbook of Chemoinformatics: From Data to Knowledge. 4 vol. Weinheim, Germany: Wiley-VCH, 2003. 52. Maldonado AG, Doucet JP, Petitjean M, Fan BT. Molecular similarity and diversity in chemoinformatics: from theory to applications; Mol Divers. 2006; 10(1):39-79.
How to cite this article: Suresh Rewar, A Vital role of Pharmacoinformatics, 6 (2): 2562 2571 (2015) All © 2010 are reserved by Journal of Global Trends in Pharmaceutical Sciences.
2571 Suresh Rewar et al, JGTPS, 2015, Vol. 6(2): 2562 - 2571