Recent
initiatives to sequence the human genome and those of pathogenic microorganisms
have provided a plethora of information, which hold considerable promise for
drug discovery and development. Technological
advances in biological sciences allowing for the rapid development of new
diagnostic methods and drugs based on biological molecules, including proteins
and nucleic acids. The key to the maximal exploitation of these data for therapeutic
purposes lies in accurately identifying the structure and biological function of
the protein coded by a given gene. Analyses of genomic data to characterise
proteins and predict their form and function has thus become an integral part of
the drug design cycle.
Advances
in macromolecular structure determination, directed combinatorial chemistry
and biocomputing have further extended the boundaries of the structure-based
drug design technique. Chemical
information technology helps us to appreciate the richness and variety of
chemical structural complexity. Computer Aided Drug Design(CADD)
is a combination of computational chemistry and information technology
tools that help us to discover new and useful compounds. These technologies are changing the traditional approaches to the drug
discovery process.
The process of designing a new drug and bringing it
to market is very complex. It takes
5-7 years and 350-500 million
dollars for the average new drug to go from the research laboratory to patient
use. Develop an assay technique to test drug effectiveness. An ideal assay is
one in which a compound can be added to tissue samples or micro-organism
colonies and there will be a visible indication of an effective treatment. If it
is known that a drug must bind to a particular spot on a particular protein or
nucleotide then a drug can be tailor made to bind at that site.
This is often modeled computationally using any of
several different techniques. Traditionally, the primary way of determining what
compounds would be tested computationally was provided by the researchers'
understanding of molecular interactions. A second method is the brute force
testing of large numbers of compounds from a database of available structures.
Lead compounds are compounds that have some activity against a disease. However,
the lead compounds provide a starting point for refinement of the chemical
structures.
Use of combinatorial chemistry techniques, which
produce large numbers of related chemical compounds. This allows testing a large
number of compounds at once. When a mixture that is useful is found, a
separation must be done to determine which of the related structures has some
drug activity. This has been one of the most promising and rapidly growing
techniques in recent years. Many chemical/drug databases as well as chemical
information systems are developed by pharmaceutical companies as well as
molecular modeling software vendors. Searching
chemical databases to find compounds similar to those found by the above means.
This is the only part of the lead finding process that is considered to be a
computational technique. There are many different measures of molecular
similarity and ways of efficiently handling large databases, so this is not yet
a trivial step.
Computer Aided Drug Design is to find ligands that
are predicted to interact strongly with a host (Receptor) . Alternatively, this
procedure can be reversed to search for hosts that will interact strongly with a
given ligand. Computer aided Drug Design is playing a major role in
rational drug design process and also to understand molecular recognision
processes involving interactions between protein-protein or protein-DNA, protein
or DNA binding with substrates.
CADD can be done in two ways: ligand based or
receptor based. Receptor based design starts with a known receptor, such as a
protein-binding site or supramolecular host. Ligand based design uses a known
set of ligands, but an unknown receptor site. Both approaches are actually very
similar. Even once a structure has been determined, identifying the site where a
drug/ligand must bind is not a trivial task.
The first phase is to determine the three
dimensional structure of the protein (Receptor) either by X-ray or NMR and
identify the binding site (Drug target) using standard structural analysis from
X-ray diffraction, NMR. In the
absence of structural information, homology of the unkown receptor sequence with
known structures that have been identified through database searches may be a
good starting point. Thus increased availability of X-ray crystal structures of
the receptors, and the increased reliability of homology models, are an
important incentive for direct drug design.
The current emphasis in CADD is on lead
development, which contrasts with early efforts that concentrated on lead
optimization. Techniques for the latter are well established, while techniques
for lead development are still under development and generally involve either
(a) using computer technology to propose a new structure to fit a putative or
known receptor (de novo design), or
searching a database of known structures for those with a desired activity or
similarity to active compounds.
The discovery of new natural products help us
explore structures and functionality that we would never guess are importanty
availability of chemical structure databases is playing an important role in
enhancing the drug discovery approach. Characterizing the biological activity
and properties of all the known compounds is impossible.
It is important to develop predicative tools for
understanding structure-function relationships and these techniques enhances our
ability to predict chemical reactivity and design useful compounds.
Computationally, the technique used is known as QSAR (Quantitative Structure
Activity Relationships. Quantitative structure activity relationships (QSAR),
Quantitative structure property relationships (QSPR), and 3D-database mining
play a central role in this effort. Analytical chemists have developed new
chemometric techniques that allow the rapid retrieval and prediction of
molecular and biological properties. Hydrophobic properties express the ability
of a molecule to be transported in the environment and in an organism, to
interact with biological membranes, and to be bound to a receptor by hydrophobic
forces. Hydrophobic properties calculated currently
are;
logP
- logarithm of the octanol-water partition coefficient
MR
- molar refractivity
log(1/WS)
- water solubility; log(VP) - vapor pressure
Multi-variate and artificial intelligence
techniques are necessary to efficiently use our wealth of information. This
information can then be used to suggest new chemical modifications for synthesis
and testing. Ideally there is a continual exchange of information between the
researchers doing QSAR studies, synthesis and testing. These techniques are
frequently used and often very successful since they do not rely on knowning the
biological basis of the disease which can be very difficult to determine.
The second phase is to generate a query for
database searching. This model may be based on a pharmacophore( Functional group
types (e.g. hbond donors, acceptors, hydrophobic regions) and the spatial
arrangement of those groups on a molecule that interact with the receptor and
are responsible for binding and biological activity). Ideally separate the
binding pharmacophore from the activity pharmacophore to design a compound that
binds but does not cause the biological activity (antagonist). Thus
pharmacophore identifies a few
specific interactions that are responsible for the binding.
The query is generated by building a simplified
model of the receptor site .
The next phase is to search databases for ligands
that may bind to the chosen receptor. The 3D-pharmacophore is used in
conformationally flexible searches for ligands that match the spatial
distribution of the receptor. Alternatively, the receptor pocket can be used
with auto-docking to find ligands that avoid close-contacts. The
3D-pharmacophore approach and the binding pocket approach are actually very
similar, and queries can be fashioned that incorporate aspects of both
approaches. Pharmacophores emphasize a few specific and varied types of
interactions, while binding pockets emphasize steric interactions over the
entire ligand. Some of the docking programmes that are described below are widely
used in the rational structure based drug design.
Docking can be accomplished by either geometric
matching of the ligand and its receptor or by minimising the energy of
interaction. The geometric matching algorithms form the majority of approaches
because of their relative speed. A subdivision can be made in geometry matching
based on descriptors and geometry matching based on fragments. One of the oldest
docking programmes, DOCK
(Kuntz et al.) is based on a description of the negative image of a spacefilling
representation of the receptor that should be filled by the ligand. Matching of
the structures in the Cambridge Structural Database with the DOCK images offered
some of the early successes.
CAVEAT(Barret et al)
suggests ligands to a particular receptor, not based on the matching
atoms, but rather based on the desired bond vectors. The concept of
electrostatic complementarity is not addressed by Caveat, nor in the original
versions of DOCK. Nowadays, recent improvements to DOCK are the addition of a
force-field for energy evaluation, limited conformational flexibility and the
inclusion of a hydrophobic term in the energy evaluation.
Based on fragments, instead of a descriptors, are
the GROW (Moon et al.), HOOK (Eisen et al.)
Programmes. By slowly growing functional groups to a peptide, GROW
proposes peptidic ligands for a given macromolecule. Which are easy to
synthesise and evaluate pharmacologically. The HOOK programme proposes docking
sites by using multiple copies of functional groups in simultaneous searches,
followed by molecular mechanics or dynamics with the CHARm programme.
LUDI
proposes somewhat larger fragments to match with
the interaction sites of a macromolecule and scores its hits based on geometry
criteria taken from the CSD, the PDB and on criteria based on binding data. The
most recent improvement is the incorporation of hydrophobic terms and the loss
of binding energy because of freezing of the internal ligand energy or torsional
and translational rotors.
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