Computational Techniques in the Drug Design Process
Cytoclonal Pharmaceutics Inc.
The purpose of this document is to outline the drug design
process and specifically the role of computational modeling techniques.
This is not meant to be a comprehensive review. It is meant to list
the most important techniques currently in use.
The process of designing a new drug and bringing it to market
is very complex. According to a 1997 government report, it takes 12 years
and 350 million dollars for the average new drug to go from the research
laboratory to patient use. Pieces of this process
are often repeated to create successively better drugs for the same
condition. In the case of antibiotics, drugs loose effectiveness as
an immunity is built up, thus leading to a continuing "arms race". The
major steps in the drug design process "from scratch" are.
FIND WHAT IS KNOWN
Find out all that is known about the disease and existing or traditional
remedies. It is also important to look at very similar afflictions and their
DEVELOP AN ASSAY
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.
At worst, there must be a way to test the drug on a laboratory animal
that is susceptible to the disease. If the only way to test the
effectiveness of a trial compound is to inject an untested compound into
a human subject then there is no way to proceed in finding a pharmaceutical
Steps 4 and 5 of this procedure are often performed simultaneously.
CONSIDER FINANCIAL ISSUES
The next step is to make a financial decision about whether to proceed
with the development process. The assay technique will determine the
cost of testing compounds. If there are existing chemical treatments, it
will be a refinement effort which saves the expense of finding lead compounds.
All drugs must go through extensive testing so this is a fairly fixed cost.
There may be governmental grants or tax incentives associated with certain
diseases. The number of patients requiring treatment and merits of existing
treatments will determine the long term profitability of producing a drug.
FIND LEAD COMPOUNDS
Lead compounds are compounds that have some activity against a disease.
These may be only marginally useful and may have severe side effects.
However, the lead compounds provide a starting point for refinement of
the chemical structures. Lead compounds may come from many sources, including
- The isolation of active compounds from traditional remedies.
- The testing of natural materials followed by an isolation
- Drugs effective against similar diseases.
- 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
- 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.
ISOLATE THE MOLECULAR BASIS FOR THE DISEASE
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.
More recently a set of techniques, called rational drug design techniques
or De Novo techniques have been used. These techniques attempt to reproduce
the researchers' understanding of how to choose likely compounds built into a
software package that is capable of modeling a very large number of
compounds in an automated way. Many different algorithms have been used for
this type of testing, many of which were adapted from artificial
intelligence applications. No clear standard has yet emerged in this area
so it is impossible to say what is best the best technique at this time.
These techniques have seen quite a bit of active development in recent
years. Unfortunately, the complexity of biological systems makes it very
difficult to determine the structures of large biomolecules. Ideally a
x-ray chrystallography structure is desired, but biomolecules are very
difficult to chrystalize. Another very useful technique, called "distance
geometry" is to find some of the internuclear distances using
NMR Nuclear Overhauser Effect experiments then find molecular geometries that
have these distances. If only a protein sequence is known, there are many
techniques for predicting how that protein will fold, but none has yet been
shown to be 100% reliable. Even once a structure has been determined,
identifying the site where a drug must bind is not a trivial task.
The difficulty in find geometries makes it possible to bring first generation
drugs to market by refinement of lead compounds without ever knowing the
target site for the drug in the body. As such, these techniques are being
used primarily for designing improved treatments for diseases that have
already been characterized extensively.
REFINE DRUG ACTIVITY
Once a number of lead compounds have been found, computational and laboratory
techniques have been very successful in refining the molecular structures to
give a greater drug activity and fewer side effects. This is done both
in the laboratory and computationally by examining the molecular structures to
determine which aspects are responsible for both the drug activity and the
Synthetically, functional groups are removed in order to find out which must
be present to give a useful drug and which are not necessary. The back bone
of the structure is made more flexible or more rigid. A rigid back bone may
hold the functional groups in the exact alignment necessary for the drug to
bind. A flexible back bone may be necessary to allow the drug to get into
the binding site. Adding bulky groups at other points on the molecule is
often done in the hopes that these new groups may hinder the molecule from
binding at unwanted sites which are responsible for the side effects.
Computationally, the technique used is known as QSAR (Quantitative
Structure Activity Relationships). It consists of computing every possible
number that can describe a molecule then doing an enormous curve fit to
find out which aspects of the molecule correlate well with the drug activity
or side effect severity. This information can then be used to suggest new
chemical modifications for synthesis and testing.
Another important aspect of the molecular structure is its solubility.
Whether the molecule is water soluble or readily soluble in fatty tissue
will affect what part of the body it becomes concentrated in. The ability
to get a drug to the correct part of the body is an important factor in
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.
Once a drug has been shown to be effective by an initial assay technique,
much more testing must be done before it can be given to human patients.
Animal testing is the primary type of testing at this stage. The scientists
doing the testing must be particularly observant of many little details since
this is where unexpected side effects can be found. Another question to be
answered is whether the drug will work well or poorly with other drugs.
This is also where initial data necessary to determine correct dosages
Eventually, the compounds which are deemed suitable at this stage are
sent on to clinical trials. In the clinical trials, additional side
effects may be found and human dosages are determined. The typical testing
process goes like this.
- Preclinical testing in animals and test tubes. This takes an average
of 6.5 years. Only one compound in 1000 is sent on to clinical testing.
- Phase I clinical trials in a few human volunteers. This typically
takes a year and a half. Seventy percent of the compounds are sent on
to the next step. This is primarily a safety test.
- Phase II clinical trials in a few hundred patients. This takes two years
and a third of the compounds are passed on to the next step. This is further
safety testing and an initial examination of the ability of the drug to
have the intended effect in humans.
- Phase III clinical trials in a few thousand patients. This step collects
more data on safety, dosage, drug activity and side effects. About a quarter
of the compounds pass this phase.
- An advisory panel of doctors reviews the data and makes recommendations
to the FDA.
- FDA approval or rejection.
- The FDA continues to monitor drug performance long after approval has
Before a drug can be produced, there must be a means to administer it.
Ideally, a tasteless or bland tablet can be created. Alternatively,
an oral liquid, intravenous injection or directly applied cream may be
Tablets are created by adding other compounds to minimize stomach upset and
control timed release of the drug. A tablet may also have a compound which
is a matrix that helps it hold it's shape without crumbling into a powder.
Oral liquids are often combined with strong flavors and alcohol to mask
the taste of the drug and prevent throat irritation.
A cream may have to be thickened or have a component that the skin will
The large scale production of complex molecules can be very difficult.
Compounds originally isolated from natural products may continue to
be harvested. Often natural products are found in nature only in extremely
small quantities necessitating a complex synthesis. One route that has been
under development more recently is to have compounds produced by genetically
engineered micro-organisms or plants.
Drugs have a high value per gram. As such production techniques can be
viable even though they are far more inefficient than those used by bulk
chemical producers. Often all possible production techniques are researched
even though only one will be put into practice. This is done so that there
are no openings for competing corporations to get around a manufacturers
patents by using a different technique.
Manufacturing regulations have become much more stringent in recent years.
It is now also important to determine what by-products will result from
production and what environmental impact there will be. It is possible
to have a case in which a less efficient manufacturing process is more
profitable due to the value of side products and reduced waste disposal
If there is only one available treatment for a disease, it is only necessary
to see that physicians know about it. If there are several competing
treatments, there may be quite a bit of marketing done so that physicians
will understand the relative merits of each.
After a large amount of experience under a physicians supervision, a
drug may be approved for over-the-counter sales. This is often the
biggest profit making end of the pharmaceutical industry.
Once the chemical patents have expired, a drug can be produced by any
manufacturer. Generic drugs are often less expensive for the consumer
and yield a low profit margin for the producer. The production of generic
drugs favors the most cost effective production process.
A good book over all, and chapter 7 in particular, is
G. L. Patrick "An Introduction to Medicinal Chemistry" Oxford (1995)
A recent review is
L. M. Balbes, S. W. Mascarella and D. B. Boyd, in
"Reviews in Computational Chemistry, Vol. 5" K. B. Lipkowitz, D. B. Boyd,
Eds., VCH, 337 (1994)
An introduction to computational techniques is
G. H. Grant, W. G. Richards "Computational Chemistry" Oxford (1995)
A more detailed description of computational techniques is
A. R. Leach "Molecular Modelling Principles and Applications" Longman (1996)
L. Balbes' "Guide to Rational (Computer-aided) Drug Design" is at
There are many links on Soaring Bear's web page at
An introduction to structure-based techniques is
I. D. Kuntz, E. C. Meng, B. K. Shoichet Acct. Chem. Res. 27 (5), 117 (1994)
An introduction to De Novo techniques is
S. Borman Chemical and Engineering News 70 (12), 18 (1992)
There is more information about clinical testing at
An expanded version of this article will be published in
"Computational Chemistry: A Practical Guide for Applying Techniques
to Real World Problems" by David Young, which will be available from
John Wiley & Sons in the spring of 2001.
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