Increasing costs of drug development and reduced number of new chemical entities have been a growing concern for new drug development in recent years. A number of potential reasons for this outcome have been considered. One of them is a general perception that applied sciences have not kept pace with the advances of basic sciences. Therefore, there is a need for the use of alternative tools to get answers on efficacy and safety, with more certainty and at lower cost. One such alternative tool is the in silico drug design or the computer aided drug design (CADD). In Silico drug designing is a form of computer-based modeling whose technologies are applied in drug discovery processes. This approach has given tremendous opportunity of pharmaceutical industry to identify many new potential drugs than the conventional approaches. It emphasizes on how we can develop better and competitive drugs with the use of software and wet lab synchronization and the hope of developing better tools to facilitate human life the comfort and disease competitive
Keywords: Drug Designing; In Silico; Molecular Modeling; Drug Discovery
Drugs are essential for the prevention and treatment of disease. Human life is constantly threatened by many diseases.
Therefore, ideal drugs are always in great demand. To meet the challenges of ideal drugs, an efficient method of drug development is demanding. But the process of drug design, development and commercialization is a tedious, timeconsuming and cost-intensive process [1].
To fulfil these challenges, several multidisciplinary approaches are required © for the process of drug development; collectively these approaches would form the basis of In-Silico approach in drug design [2].
CADD were established in the early 1970s with the use of structural biology to modify the biological activity of insulin and to guide the synthesis of human haemoglobin ligands. At that time, X-ray crystallography was expensive and timeconsuming, rendering it infeasible for large-scale screening in industrial laboratories [3]. Over the years, new technologies such as comparative modeling based on natural structural homologues have emerged and began to be exploited in lead design [4]. These, together with advances in combinatorial chemistry, high-throughput screening technologies and computational infrastructures, have rapidly bridged the gap between theoretical modeling and medicinal chemistry. Numerous successes of designed drugs were reported, including Dorzolamide for the treatment of cystoid macular edema, Zanamivir for therapeutic or prophylactic treatment of influenza infection, Sildenafil for the treatment of male erectile dysfunction, and Amprenavir for the treatment of HIV. In Silico approach can be classified mainly in two different categories viz. a) Structure based drug design (SBDD) and b) Ligand based drug design (LBDD)[5].
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.
In structure, based drug design for a particular target has been developed on the basis of known structural information of the drug target like receptor structure (mostly protein). If the structure of receptor is not available, the receptor structure can be predicted by homology modeling. Homology modeling usually refers to as comparative modeling in which on the basis of known amino acid sequences of a protein, a model of protein can be constructed and the structure is comparable with the 3D-structure of similar homologous protein (template) [6].
Molecular docking is one of the in - silco methods (computational technique) to study the configuration of intermolecular complexes of one smaller molecule (ligands or drug) with a larger molecule (receptor or enzyme), and a certain score (usually referred to as ‘docking score’) has been given to each orientation a ligand docked in the active site. This score can then be used to evaluate the potential of ligand-protein affinity, which ultimately leads to prediction of biological effectiveness of a ligand against the
On the basis of flexibility of proteins and ligands, the molecular docking can be classified in the following categories [8-9].
a) Rigid body docking in which both receptor and ligand are rigid.
96 evaluation ( 2 b) Flexible ligand docking in which receptor is rigid while ligand is flexible.
c) Flexible docking in which both receptor and ligand are flexible and it is most commonly used docking method.
There are a number of computational programs available to do this such as M- ZDOCK, AutoDock, GLIDE, GOLD, and SLIDE etc. Each of these programs has distinct advantages. The main issue of these programs address is what conformations and orientations of ligands screened are most likely to bind to a specific receptor. An important factor in determining a program’s success is its ability to duplicate the experimentally determined interactions between a
5 Glide Schrödinger Inc. Commercial Commercial Cambridge free for 6 GOLD Crystallographic Data center month) 7 Ligand Fit Accelrys Commercial protein and its ligand. A prediction is generally found acceptable if the RMSD between the docked ligand and the experimentally determined ligand in under 2À.
Different algorithms used in the process of docking are Monte Carlo, genetic algorithm, fragment-based, molecular dynamics etc and on the basis of these algorithms, different programs were developed (free and commercial purpose)[10-11].
Protein data bank (PDB) files may have a variety of problems that need to be corrected before they can be used in AutoDock. These potential problems include missing atoms, added waters; remove water, more than one molecule, and chain breaks etc. [12-13].
Monte Carlo Unix, Linux Glide Score Sampling Gold score Unix, Linux, Genetic Chem score Windows algorithm User defined Monte Carlo Linux, IBM Lig Score Sampling 97
The ligand must be prepared before starting the docking experiment, like energy minimization of ligand, protonation state of ligand etc. The protonation of ligand depends upon the pH of receptor environment.
b) Receptor Preparation Structures of protein/biomolecule evaluated by X-ray crystallographic technique are available on protein data bank and it could easily be downloaded in text format from their website [http://www.rcsb.org]. The selected chains of the biomolecules were prepared for docking by using following steps:
Adding Gasteiger charges Adding polar hydrogen atoms Checking whether total charge per residue is integer Choosing flexible residues Removal of water and/or ions molecules as per requirement Minimizing the receptor, if necessary
From the prepared protein/biomolecule, the co-crystallized ligand was separated from its active site. The active site is generally represented as an enclosing box at the centroid of work space ligand. Following this protocol, a grid centered on the ligand was generated using the default settings of desired software. All ligands were docked into this grid structure.
On a defined receptor grid, flexible docking was performed using appropriate module of desired software. The module analysis the protein ligand interaction on the basis of different interactions between them like vander waals, hydrogen bonding and electrostatic interactions.
Validation is the process by which we can predict the reliability of docking method. A number of validation techniques can be used to simulate the predicative ability of docking experiment.
a) Alignment Method In this method the docked structure of the molecules is superimposed on the reference molecule by using software’s TM TM like MMP , Field Align b) Chemical reasonableness It is another approach of validation technique in which the amino acids, which show the binding interaction with ligand are mutated and further predict their binding affinity.
Much work has been invested in the making of better docking programs and scoring functions over the past years and, although much progress has been made but enhancement of docking program is still necessary [14-15].
The most challenging problems in docking and scoring is the treatment of flexible receptors. Numerous examples have become known where the same protein adopts different conformations depending on nature of ligands. In flexible docking both ligand and receptor are considered flexible. However, there are still some limitations such as only side chains are set flexible while backbone is rigid in nature.
b) Water Interaction Water molecules frequently play a key role in drug-receptor interactions; if one ignores water-mediated interactions during docking then the calculated 98 interaction energy of a given ligand conformation may be too low. It is notoriously difficult to treat water adequately, as first one need to identify possible positions for water molecules where they could interact with the protein and ligand, and subsequently one must be able to predict whether a water molecule is indeed present at that position.
c) Tautomers formation of ligands Another challenge of docking is the formation of various tautomeric and protomeric states of the molecules that can adopt during drug-receptor interaction. A molecule such as acids or amines are stored in their neutral forms but they are ionized under physiological conditions means it is necessary to ionize them earlier to docking experiment. One approach to this would be to generate all possible forms, subsequently to dock all of them, and to choose the relevant form based on the scores. However, it remains to be seen whether such an approach would be beneficial or just generate a large number of tautomers.
Ligand-based drug design (or indirect drug design) relies on knowledge of other molecules that bind to the biological target of interest . This approach is particularly useful when 3D structure of the receptor is not available and it relies on the knowledge of ligands that bind to the desired target[16]. The most prominent techniques used in this approach is quantitative structure activity relationships (QSAR).
In quantitative structure activity relationship (QSAR), a correlation between experimentally determined biological activity and calculated properties of molecules is derived. These exhibit a particular squared predictive correlation 2 2 coefficient (r ); and model with r value close to 1 will be designated as best model. These QSAR models relationships can then be used to predict the activity of new analogous.
Some basic requirements are essential for the development of best QSAR model to predict the biological activity [17]. Out of which some of them are mentioned below All analogues belong to a congeneric series (classical QSAR studies) exerting the same mechanism of action The set of compounds have same mechanism of action Biological response should be distributed over a wide range Biological activity should be in specific units (concentration in molar units or IC or percentage 50 inhibition).
There are different widely used approaches in QSAR studies. Following are the commonly used ones[18].
Hansch analysis (linear free energy
It is one of the most promising approaches to the quantification of the interaction of drug molecules with biological system given by Corwin Hanschin 1969. It is also known as linear free energy (LFER) or extra thermodynamic method, which assumes additive effect of various substituents inelectronic, steric, hydrophobic, and dispersion data in the non-covalent interaction of a drug and macromolecules. Hansch analysis relates the biological activity within a homologous series of compounds to a set of theoretical molecular parameters, which describe essential properties of the drug molecules. Hansch 99 proposed that the action of a drug is depending on two processes.
Journey from point of entry in the body to the site of action which involves passage of series of membranes and therefore it is related to partition coefficient log P (lipophilic) and can be explained by random walk theory.
Interaction with the receptor site depends on, a) Bulk of substituent groups (steric) b) Electron density on attachment group (electronic) This approach was originally coined as Linear Free Energy Relationships (LFER) and later changed, more appropriately, to extra thermodynamic approach and expressed by the following equation.
2 log 1/C = a log P + b(log P) + c Where, a and b are the coefficients of the log 2 P and (log P) terms, respectively, and c is a constant term.
Free and Wilson analysis The Free-Wilson approach is truly a structure-activity based methodology because it incorporates the contribution made by various structural fragments to the overall biological activity. Indicator variables are used to denote the presence or absence of a particular structural feature. It is represented by equation BA = Σ ai xi + μ Where, BA is the biological activity, μ is the overall activity, ai is the contribution of each structural feature, xi denotes the presence (xi = 1) or absence (xi = 0) of particular structural fragment.
Quantum mechanical methods The information provided by QM is more accurate than Free and Wilson analysis therefore more robust QSAR models and/or QSPR models are expected with QM descriptors. Partial charges are the most common descriptors in QSAR/QSPR models due to their simplicity and informative content.
The QSAR is based on structure activity relation (SAR) approach. It uses physicochemical properties (parameters) to represent drug properties that are believed to have a major influence on drug action. Some of the common pharmacophoric features include hydrophobic, aromatic, hydrogen bond acceptor, hydrogen bond donor, positive ionizable, and negative ionizable groups. These parameters are properties that are capable of being represented by a numerical value which are used to produce a general equation correlating activity with relevant physicochemical properties.
Descriptors can be defined as a numerical representation of chemical information encoded within a molecular structure via mathematical procedure. Descriptor can be classified in following categories [19-20].
Partition coefficient (P) and the lipophilic substituent Constant (p) are the two most important lipophilic parameter use in QSAR analysis. The former parameter refers to the whole molecule whilst the latter is related to substituent groups. A drug has to pass through a number of biological membranes in order to reach its site of action. Partition coefficients were the obvious parameter to use as a measure of the movement of the drug through these membranes.
The distribution of the electrons in a drug molecule will have an influence the activity 100 of a drug. When the the drug reaches its target site the distribution of electrons in its structure will control the type of bonds it forms with that target, which in turn affects its biological activity. In other words, the electron distribution in a drug molecule will have an effect on how strongly that drug binds to its target site, which in turn affects its activity. The distribution of electrons within a molecule depends on the nature of the electron withdrawing and donating groups found in that structure.
Drug to bind effectively to its target site the dimensions of the pharmacophore of the drug must be complementary to those of the target site. The Taft steric parameter (Es) was the first attempt to show the relationship between a measurable parameter related to the shape and size (bulk) of a drug and the dimensions of the target site and a drug’s activity. This has been followed by Charton’s steric parameter, Verloop’s steric parameters and the molar refractivity (MR), amongst others. The most used of these additional parameters is probably the molar refractivity. However, in all cases the required parameter is calculated for a set of related analogues and correlated with their activity using a suitable statistical method such as regression analysis.
Refinement of synthetic targets Reduction or replacement of animal tests, thus reducing animal use To predict the biological activities of untested and sometimes yet unavailable compounds To developed new model for a biological systems To optimized the existing leads so as to improve their biological activities QSAR models act virtual screening tools for predicting ADME and toxicity studies To elucidate the phenomena and nature of drugs and receptor interactions
Despite the fact that number of successful QSAR application, there are several pitfalls in their proper application like [21]
Drug action is based on a sequence of complicated physiochemical events (delivery, targeting, metabolism, and excretion) that are either still unknown or not fully understood on a molecular level. For this reason and because of hardware and software limitations in silico studies can only fragmentally reproduce real world observations. QSAR and QSPR are used to describe quantitatively ADMET processes in living cells, e.g. protein binding (plasma enzymes etc.).
An important prerequisite of QSAR is the use of a series of congeners with a common target structure. Chemical similarity is not a guaranty for a common action mechanism of all congeners. A complication is the occurrence of multiple binding modes (MBM) of the very same ligand to its target molecule. QSAR is conducted under the silent assumption that no MBM is present when comparing molecular similarities with ligand binding analysis (LBA) or protein binding analysis (PBA) techniques.
QSAR mainly work with cell-free data is not affected by drug binding to multiple targets. Such multi-potencies occur in vivo when a molecule in lower doses binds to a biomolecule with higher affinity, while in higher doses the same ligand may bind to other targets with lower affinity.
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The molecules considered in QSAR studies are not necessarily the ones responsible for the biological response as in case of Prodrug.
In QSAR studies, over fitting occurs if too many independent variables relative to the number of data points are included in a regression equation. In such cases, regression equations tend to fit the “noise” or errors in the data and, in general, do not yield robust predictions.
Various compounds have occupied researchers in recent years and numerous computational models have been drawn up. Many of these models have been generated by means of Ligand-based approaches, mainly QSAR studies. Such models were capable to predict a potent drug for new drug discovery. Most of these models have has also been successfully applied to the design of new ligands or to the optimization of known active compounds. It is generally recognized that drug discovery and development are very time and resources consuming processes. There is an ever growing effort to apply computational power to the combined chemical and biological space in order to streamline drug discovery, design, development and optimization. In biomedical arena, computer-aided or in silico design is being utilized to expedite and facilitate hit identification, hit-to-lead selection, optimize the absorption, distribution, metabolism, excretion and toxicity profile and avoid safety issues. The development of any potential drug begins with years of scientific study to determine the biochemistry behind a disease, for which pharmaceutical intervention is possible. The result is the determination of specific receptors (targets). In the post genomic era, computer-aided drug design (CADD) has considerably extended its range of applications, spanning almost all stages in the drug discovery pipeline, from target identification to lead discovery, from lead optimization to preclinical or clinical trials.
We gratefully acknowledge the financial support from the Department of Biotechnology (DBT) government of India to work on different drug deigning softwares, our principal, Department faculty, for the source of inspiration in the review of the paper done. We hope to have near advancements in this field of study to make our community better in health care and disease competitive. We are also thankful to the library of university to provide us the full help in studying different aspects of drug discovery.