INTRODUCTION:

To discover and development of new drugs has been made responsible to the medicinal chemist to prevent disease or decreasing morbidity. The newer developed product or drug from continuous research have dramatically improved quality of life, moreover modern drugs are highly cost effective from of the treatment. Often many of the new drugs are found by accidental discoveries. For development and developed for to discovered new ways to treat specific diseases. For development and to discovered drug it takes many millions and 12-15 years and even then only one third of those reaching the market pay back. To be successful at least four new drugs must be generated, where as development of new techniques and different specific approach in discovery of new drug is known as “drug design”. Drug design, also sometimes referred to as rational drug design, is the inventive process of finding new medications based on the knowledge of the biological target. The drug is most commonly an organic small molecule which activates or inhibits the function of a biomolecule such as a protein which in turn results in a therapeutic benefit to the patient. In the most basic sense, drug design involves design of small molecules that are complementary in shape and charge to the biomolecular target to which they interact and therefore will bind to it. Drug design frequently but not necessarily relies on computer modeling techniques. This type of modeling is often referred to as computer-aided drug design.

The phrase '"drug design" is to some extent a misnomer. What is really meant by drug design is ligand design. Modeling techniques for prediction of binding affinity are reasonably successful. However there are many other properties such as bioavailability, metabolic half life, lack of side effects, etc. that first must be optimized before a ligand can become a safe and efficacious drug. These other characteristics are often difficult to optimize using rational drug design techniques.

NEED OF STUDY : ^1,3,6

It is considered that with new technologies in drug design involving combinatorial libraries, high througput screening and use of molecular targets based on genomics and proteomics will reduce the time and costs involved for new drug development. The main advantage of these new techniques is that drug discovered would be more target and disease specific.Typically a drug target is a key molecule involved in a particular metabolic or signaling pathway that is specific to a disease condition or pathology, or to the infectivity or survival of a microbial pathogen. Some approaches attempt to inhibit the functioning of the pathway in the diseased state by causing a key molecule to stop functioning. Drugs may be designed that bind to the active region and inhibit this key molecule. Another approach may be to enhance the normal pathway by promoting specific molecules in the normal pathways that may have been affected in the diseased state. In addition, these drugs should also be designed in such a way as not to affect any other important "off-target" molecules or antitargets that may be similar in appearance to the target molecule, since drug interactions with off-target molecules may lead to undesirable side effects. Sequence homology is often used to identify such risks.

TYPES OF DRUG DESIGN: ^6-10

Flow charts of two strategies of structure-based drug design.

There are two major types of drug design. The first is referred to as ligand-based drug design and the second, structure-based drug design.

1. Ligand based:

Ligand-based drug design (or indirect drug design) relies on knowledge of other molecules that bind to the biological target of interest. These other molecules may be used to derive a pharmacophore which defines the minimum necessary structural characteristics a molecule must possess in order to bind to the target.^[3] In other words, a model of the biological target may be built based on the knowledge of what binds to it and this model in turn may be used to design new molecular entities that interact with the target.

2. Structure based:

Structure-based drug design (or direct drug design) relies on knowledge of the three dimensional structure of the biological target obtained through methods such as x-ray crystallography or NMR spectroscopy.If an experimental structure of a target is not available, it may be possible to create a homology model of the target based on the experimental structure of a related protein. Using the structure of the biological target, candidate drugs that are predicted to bind with high affinity and selectivity to the target may be designed using interactive graphics and the intuition of a medicinal chemist. Alternatively various automated computational procedures may be used to suggest new drug candidates.As experimental methods such as X-ray crystallography and NMR develop, the amount of information concerning 3D structures of biomolecular targets has increased dramatically. In parallel, information about the structural dynamics and electronic properties about ligands has also increased. This has encouraged the rapid development of the structure-based drug design. Current methods for structure-based drug design can be

divided roughly into two categories. The first category is about “finding” ligands for a given receptor, which is usually referred as database searching. In this case, a large number of potential ligand molecules are screened to find those fitting the binding pocket of the receptor. This method is usually referred as ligand-based drug design. The key advantage of database searching is that it saves synthetic effort to obtain new lead compounds. Another category of structure-based drug design methods is about “building” ligands, which is usually referred as receptor-based drug design. In this case, ligand molecules are built up within the constraints of the binding pocket by assembling small pieces in a stepwise manner. These pieces can be either individual atoms or molecular fragments. The key advantage of such a method is that novel structures, not contained in any database, can be suggested. These techniques are raising much excitement to the drug design community.

COMPUTER AIDED DRUG DESIGN: (CADD) ^11,12

Computer-assisted drug design (CADD), also called computer-assisted molecular design (CAMD), and represents more recent applications of computers as tools in the drug design process in considering this topic, it is important to emphasize that computers cannot substitute for a clear understanding of the system being studied. That is, a computer is only an additional tool to gain better insight into the chemistry and biology of the problem at hand. Computer-assisted drug design uses computational chemistry to discover, enhance, or study drugs and related biologically active molecules. The most fundamental goal is to predict whether a given molecule will bind to a target and if so how strongly. Molecular mechanics or molecular dynamics are most often used to predict the conformation of the small molecule and to model conformational changes in the biological target that may occur when the small molecule binds to it. Semi-empirical, ab initio quantum chemistry methods, or density functional theory are often used to provide optimized parameters for the molecular mechanics calculations and also provide an estimate of the electronic properties (electrostatic potential, polarizability, etc.) of the drug candidate which will influence binding affinity.Molecular mechanics methods may also be used to provide semi-quantitative prediction of the binding affinity. Alternatively knowledge based scoring function may be used to provide binding affinity estimates. These methods use linear regression, machine learning, neural nets or other statistical techniques to derive predictive binding affinity equations by fitting experimental affinities to computationally derived interaction energies between the small molecule and the target.Ideally the computational method should be able to predict affinity before a compound is synthesized and hence in theory only one compound needs to be synthesized. The reality however is that present computational methods provide at best only qualitative accurate estimates of affinity. Therefore in practice it still takes several iterations of design, synthesis, and testing before an optimal molecule is discovered. On the other hand, computational methods have accelerated discovery by reducing the number of iterations required and in addition have often provided more novel small molecule structures.

Drug design with the help of computers may be used at any of the following stages of drug discovery:

1. Hit identification using virtual screening (structure- or ligand-based design)

2. Hit-to-lead optimization of affinity and selectivity (structure-based design, QSAR, etc.)

3. Lead optimization optimization of other pharmaceutical properties while maintaining affinity

Computational chemistry or CADD may be divided in to following categories

QUALITATIVE STRUCTURE ACTIVITY RELATIONSHIP (QSAR): ³

QSAR technique employs powerful computer, molecular graphics and sophisticated softwares. Studies amied at broadening the quantitative understanding of correlation between intrinsic, physical, chemical or biological or the molecular properties that gives QSAR studies.

MOLECULAR MODELLING: ¹³

Drug design is an iterative process, which begins when a chemist identifies a compound that displays an interesting biological profile and ends when both the activity profile and the chemical synthesis of the new chemical entity are optimized. Traditional approaches to drug discovery rely on a step-wise synthesis and screening program for large numbers of compounds to optimize activity profiles. Over the past ten to twenty years, scientists have used computer models of new chemical entities to help define activity profiles, geometries and reactivities. This article introduces the concepts of molecular modeling and contains references for further reading. One of the basic tenets of medicinal chemistry is that biological activity is dependent on the three-dimensional placement of specific functional groups (the pharmacophore). Over the past few years, advances in the development of new mathematical models which describe chemical phenomena and development of more intuitive program interfaces coupled with the availability of faster, smaller and affordable computer hardware have provided experimental scientists with a new set of computational tools. These tools are being successfully used, in conjunction with traditional research techniques, to examine the structural properties of existing compounds, develop and quantify a hypothesis which relates these properties to observed activity and utilize these "rules" to predict properties and activities for new chemical entities. The development of molecular modeling programs and their application in pharmaceutical research has been formalized as a field of study known as computer assisted drug design (CADD) or computer assisted molecular design (CAMD).

CHEMOINFORMATICS: ^14,15

Chemoinformatics is the use of computer and ifformational techniques, applied to a range of problems in the field of chemistry. Also known as chemo informatics and chemical informatics. The chemoinformatics was defined by F.K.Brown in 1998.

Chemoinformatics is the mixing of those 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 optimization. Since this, the term has evolved to be established as chemoinformatics.

BIOINFORMATICS:

The term bioinformatics and computational biology are often used interchangeably. However bioinformatics more properly refers to the certain and advancement of algorithms, computational and stastical techniques, and theory to solve formal and practical problems posed by or inspired from the management and analysis of biological data. Bioinformatics and computational biology involve the use of techniques from applied mathematics, informations, statics, and computer science and chemistry especially biochemistry to solve biological problems usually on the molecular level. Research in computational biology often overlaps with systems biology. Major research efforts in the field include sequence alignment, gene fielding, genome assembly, protein structure alignment, protein structure prediction, prediction of gene expression and protein-protein interactions and the modeling of evolution.

CONCLUSION:

The process of drug discovery and development is long and difficult one and the cost of developing new theraputic agents are increasing rapidely. The use of new CADD technology has the ability to accomplished both of there goals and to improve the efficiency of the process as well, thus reducing cost. Similarly these new technique in drug designing can improve the lead optimization process. Finally more and more drug research understand and become familier with the concept and methods of CADD, new appications of the integration of these techniques will emerge and will have a major impact both on a basic science and or discovering new drugs for the future.

ACKNOWLEDGEMENT:

The author are very thankful to the Principal, Staff members and management of K.T. Patil College of Pharmacy Siddharth Nagar,Osmanabad for their valuable guidance and advice.

REFERENCES:

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Received on 30.12.2009 Modified on 15.02.2010

Asian J. Research Chem. 3(2): April- June 2010; Page 261-264