Drug Designing

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Chapter: Medicinal Chemistry : Introduction to Medicinal Chemistry

It is an approach of finding drugs by designing, based on their biological targets.


DRUG DESIGNING

It is an approach of finding drugs by designing, based on their biological targets. Typically, a drug target is a key molecule that is involved in a particular metabolic or signalling 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 so designed that they bind to the active region and inhibit this key molecule. However, these drugs would also have to be designed in such a way as not to affect any other important molecules that may be similar in appearance to the key molecules. Sequence homologies are often used to identify such risks. Other approaches may be to enhance the normal pathway by promoting specific molecules, which are affected in the diseased state.

Using computational tools the structure of the drug molecule that can specifically interacted with the biomolecules can be modelled. These tools can allow a drug molecule to be constructed within the biomolecule, using the knowledge of its structure and the nature of its active site. Depending on whether the core or the R-groups are chosen first, construction of the drug molecule can be made inside or outside. However, many of these approaches are plagued by the practical problems of chemical synthesis. Newer approaches have also suggested the use of drug molecules that are large and proteinaceous in nature, rather than small molecules. There have also been suggestions to make these using messenger ribonucleic acid (mRNA). Gene silencing may also have therapeutical applications.

Rational Drug Design

Rational drug design begins with a knowledge of the specific chemical responses in the body or the target organism, and tailoring the combinations to fit a treatment profile in contrast to the historical method of drug discovery, by trial-and-error testing of chemical substances on cultured cells or animals and matching the apparent effects to treatments, In drug metabolism, knowledge of the stability and the reactivity of libraries of potential drug compounds, the predicted metabolic and toxicological outcomes, and rational redesign of possible drug candidates is essential. Due to the complexity of the drug design process, two terms of interest are still serendipity and bounded rationality. These challenges are caused by the large chemical space describing potential new drugs without side-effects.

Typical 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 called as structure-based drug design. The first example of the application of structurebased drug design leading to an approved drug is the carbonic anhydrase inhibitor dorzolamide, which was approved in 1995.

Another important candidate discovered in rational drug design is a tyrosine kinase inhibitor imatinib, designed specifically for the bcr-abl fusion protein that is characteristic for Philadelphia chromosomepositive leukaemias (chronic myelogenous leukaemia and occasionally, acute lymphocytic leukaemia). Imatinib is substantially different from the earlier drugs for cancer, as most of the agents of chemotherapy simply target on the rapidly dividing cells not differentiating between cancer cells and other tissues.

The activity of a drug at its binding site is only one part of its design. Another part to be taken into account is the molecule’s drug likeness, which summarizes the necessary physical properties for effective absorption. One way of estimating drug likeness is by Lipinski’s rule of five.

Computer-Assisted Drug Design

Computational chemistry is used to discover, enhance, and study drugs and related biologically active molecules in computer-assisted drug design. Methods used typically include molecular modelling and simple methods from machine learning and statistics. In QSAR of candidate drugs regression is heavily used. Molecular mechanics, molecular dynamics, semiempirical quantum chemistry methods, ab initio quantum chemistry methods, and density functional theory are also used. The purpose is to reduce the cost and time necessary for the development of a new drug.

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