Drug design

Overview[edit | edit source]

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.^[1] The drug is most commonly a 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.^[2] This type of modeling 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 becomes a safe and efficacious drug. These other characteristics are often difficult to optimize using rational drug design techniques.

Types[edit | edit source]

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

Structure based[edit | edit source]

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.^[3] 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.

Ligand based[edit | edit source]

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.^[4] 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.

Background[edit | edit source]

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 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.

Most commonly, drugs are organic small molecules but protein based drugs (also known as biologics) are becoming increasing more common. In addition mRNA based gene silencing technologies may have therapeutic applications.

Rational drug discovery[edit | edit source]

In contrast to traditional methods of drug discovery which rely on trial-and-error testing of chemical substances on cultured cells or animals, and matching the apparent effects to treatments, rational drug design begins with a hypothesis that modulation of a specific biological target may have therapeutic value. In order for a biomolecule to be selected as a drug target, two essential pieces of information are required. The first is evidence that modulation of the target will have therapeutic value. This knowledge may come from, for example, disease linkage studies that show an association between mutations in the biological target and certain disease states. The second is that the target is "drugable". This means that it is capable of binding to a small molecule and that its activity can be modulated by the small molecule.

Once a suitable target has been identified, the target is normally cloned and expressed. The expressed target is then used to establish a screening assay. In addition, the three-dimensional structure of the target may be determined.

The search for small molecules that bind to the target is begun by screening libraries of potential drug compounds. This may be done by using the screening assay (a "wet screen"). In addition, if the structure of the target is available, a virtual screen may be preformed of candidate drugs. Ideally the candidate drug compounds should be "drug-like", that is they should possess properties that are predicted to lead to oral bioavailability, adequate chemical and metabolic stability, and minimal toxic effects. One way of estimating druglikeness is Lipinski's Rule of Five. Several methods for predicting drug metabolism have been proposed in the scientific literature, and a recent example is SPORCalc.^[5] Due to the complexity of the drug design process, two terms of interest are still serendipity and bounded rationality. Those challenges are caused by the large chemical space describing potential new drugs without side-effects.

Computer-assisted drug design[edit | edit source]

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

Examples[edit | edit source]

A particular example of rational drug design involves the use of three-dimensional information about biomolecules obtained from such techniques as x-ray crystallography and NMR spectroscopy. This approach to drug discovery is sometimes referred to as structure-based drug design. The first unequivocal example of the application of structure-based drug design leading to an approved drug is the carbonic anhydrase inhibitor dorzolamide which was approved in 1995.^[6][7]

Another important case study in rational drug design is imatinib, a tyrosine kinase inhibitor designed specifically for the bcr-abl fusion protein that is characteristic for Philadelphia chromosome-positive leukemias (chronic myelogenous leukemia and occasionally acute lymphocytic leukemia). Imatinib is substantially different from previous drugs for cancer, as most agents of chemotherapy simply target rapidly dividing cells, not differentiating between cancer cells and other tissues.

Additional examples include:

• Cimetidine, the prototypical H₂-receptor antagonist from which the later members of the class were developed
• dorzolamide, a carbonic anhydrase inhibitor used to treat glaucoma
• Many of the atypical antipsychotics
• Selective COX-2 inhibitor NSAIDs
• SSRIs (selective serotonin reuptake inhibitors), a class of antidepressants
• Zanamivir, an antiviral drug
• Enfuvirtide, a peptide HIV entry inhibitor
• Probenecid
• nonbenzodiazepines like Zolpidem and Zopiclone

See also[edit | edit source]

• Drug development
• Drug discovery
• Drug metabolism
• Xenobiotic metabolism
• Medicinal chemistry
• Designer drug
• Enzyme inhibitors
• Bioinformatics
• Cheminformatics
• Biomedical informatics
• Pharmaceutical company
• Polyanhydrides
• Software for molecular modeling
• Molecular Design software
• International Journal of Computational Biology and Drug Design (IJCBDD)
• International Journal of Functional Informatics and Personalized Medicine (IJFIPM)
• International Society of Intelligent Biological Medicine (ISIBM)

References[edit | edit source]

External links[edit | edit source]

• Drug+Design at the US National Library of Medicine Medical Subject Headings (MeSH)

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