Drug design

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Drug design , often referred to as rational drug design or just rational design, is an inventive process for finding new drugs based on biological target knowledge. This drug is most often a small organic molecule that activates or inhibits the function of biomolecules such as proteins, which in turn generate therapeutic benefits for patients. In the most basic sense, drug design involves the design of complementary molecules in form and filling into the biomolecular targets by which they interact and therefore bind them. Drug design is often but not necessarily dependent on computer modeling techniques. This type of modeling is sometimes referred to as computer-assisted drug design . Finally, drug designs that rely on knowledge of the three-dimensional structure of biomolecular targets are known as drug designs based on structure . In addition to small molecules, biopharmaceuticals and especially therapeutic antibodies are an increasingly important class of drugs and computational methods for increasing the affinity, selectivity, and stability of these protein-based therapies have also been developed.

The phrase "drug design" to some extent is wrong. A more accurate term is the ligand design (ie, the design of the molecule that will bind tightly to its target). Although design techniques for predicting binding affinities are quite successful, there are many other traits, such as bioavailability, metabolic beak, side effects, etc., The first must be optimized before ligands can be a safe and efficacious drug. These other characteristics are often difficult to predict with rational design techniques. However, due to the high rate of erosion, especially during the clinical phases of drug development, more attention is focused on the beginning of the drug design process on the selection of drug candidates whose physicochemical properties are expected to produce fewer complications during development and are therefore more likely to cause approved drugs and marketed. Furthermore, in vitro experiments were complemented by an increasingly used calculation method in the early drug discovery to select compounds with more favorable ADME (absorption, distribution, metabolism, and excretion) and toxicological profiles.

Video Drug design

Target obat

Biomolecular targets (most commonly proteins or nucleic acids) are key molecules involved in certain metabolic pathways or signals associated with specific disease conditions or pathology or with the infectivity or survival of pathogenic microbes. Potential drug targets do not always cause disease but must by definition be modifying the disease. In some cases, small molecules will be designed to increase or inhibit the target function in the path of modifying a particular disease. Small molecules (eg receptor agonists, antagonists, inverse agonists, or modulators, enzyme activators or inhibitors, or ion or blocker openers) will be designed that complement the target binding sites. Small molecules (drugs) can be designed in such a way that they do not affect other important "off-target" molecules (often referred to as antitarget) because drug interactions with off-target molecules can cause undesirable side effects. Because of the similarity in binding sites, closely related targets identified through the homology sequence have the highest chance of cross reactivity and hence the highest potential side effects.

Most commonly, drugs are small organic molecules produced by chemical synthesis, but biopolymer-based drugs (also known as biopharmaceuticals) produced through biological processes are becoming increasingly common. In addition, mRNA gene muting technology may have therapeutic applications.

Maps Drug design

Rational drug discovery

In contrast to traditional methods of drug discovery (known as forward pharmacology), which relies on chemical trial-and-error on cultured cells or animals, and matching tangible effects with care, rational drug design (also called reverse pharmacology) begins with the hypothesis that a particular biological target modulation may have therapeutic value. In order for a biomolecule to be selected as a drug target, two important pieces of information are needed. The first is evidence that target modulation will modify the disease. This knowledge can be derived from, for example, the study of disease relationships showing the relationship between mutations in biological targets and specific disease states. The second is the target is "druggable". This means that it is able to bind small molecules and that its activity can be modulated by small molecules.

Once a suitable target has been identified, the target is usually cloned and produced and refined. The purified protein is then used to prepare a screening test. In addition, the three-dimensional structure of the target can be determined.

The search for small molecules that bind the target begins with the filtering of potential drug compound libraries. This can be done using a screening check ("wet screen"). In addition, if the target structure is available, a virtual screen can be performed from a drug candidate. Ideally the candidate drug compounds should be "like drugs", ie they should have predictable properties leading to oral bioavailability, adequate chemical stability and metabolism, and minimal toxic effects. Several methods are available to estimate drug similarities such as Lipinski's Rule of Five and various scoring methods such as lipophilic efficiency. Several methods for predicting drug metabolism have also been proposed in scientific literature.

Because of the many drug properties that must be optimized simultaneously during the design process, multi-objective optimization techniques are sometimes used. Finally due to limitations in current methods for activity prediction, drug design still relies heavily on serendipity and limited rationality.

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Computer-assisted computer design

The most fundamental goal in drug design is to predict whether a molecule will be bound to the target and if so how strong. Molecular mechanics or molecular dynamics are most commonly used to estimate the strength of intermolecular interactions between small molecules and their biological targets. This method is also used to predict small molecule conformations and to model conformational changes in targets that may occur when small molecules bind them. Semi-empirical quantum chemistry methods, ab initio, or density functional theory are often used to provide parameters that are optimized for molecular mechanical calculations and also provide estimates of electronic properties (electrostatic potential, polarizability, etc.) of drug candidates that will affect binding affinities.

Molecular mechanical methods can also be used to provide semi-quantitative predictions of binding affinities. Also, a knowledge-based valuation function can be used to provide binding affinity estimates. This method uses linear regression, machine learning, neural nets or other statistical techniques to obtain predictive binding affinity equations by adjusting experimental affinity to the computed interaction energy between small molecules and targets.

Ideally, computational methods will be able to predict affinity before the compound is synthesized and therefore theoretically only one compound needs to be synthesized, saving enormous time and cost. The reality is that the current computational method is not perfect and only provides a qualitative accurate estimate of affinity. In practice it still requires some design iteration, synthesis, and testing before the optimal drug is found. Computational methods have accelerated discovery by reducing the number of iterations required and often giving new structures.

Computer-assisted drug design can be used in any of the following phases of drug discovery:

1. hit identification using virtual filtering (ligand-based structure or design)
2. optimization and hit-to-lead selectivity (structure-based design, QSAR, etc.)
3. optimization of other pharmaceutical property prospects while maintaining affinity

To address the inadequate prediction of affinity binding calculated by the recent assessment function, protein-ligand interactions and 3D structural compound information were used for analysis. For drug-based structural design, several post-screening analyzes focused on protein-ligand interactions have been developed to enhance enrichment and effectively mine potential candidates:

• Consensus score
  • Selects candidates by selecting multiple scoring functions
  • May lose the link between protein-ligand structural information and assessment criteria
• Cluster analysis
  • Represents and groups candidates based on 3D ligand protein information
  • Needs a meaningful representation of protein-ligand interactions.

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Type

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

Ligand based

The design of ligand-based drugs (or indirect drug designs ) depends on knowledge of other molecules that bind the desired biological target. These other molecules can be used to derive the pharmacofora model that defines the minimum required structural characteristics that a molecule must possess to bind the target. In other words, the biological target model can be built on the knowledge of what it binds, and this model can in turn be used to design new molecular entities that interact with the target. Alternatively, the quantitative activity-quantitative relationship (QSAR), in which the correlations between the properties calculated from the molecules and their experimental biological activity can be derived. This QSAR relationship can in turn be used to predict new analog activity.

Structure based

Drug-based drug design (or direct drug design ) depends on knowledge of the three-dimensional structure of biological targets obtained by methods such as X-ray crystallography or NMR spectroscopy. If the target experimental structure is not available, it is possible to create a target homology model based on the experimental structure of the associated protein. Using a biological target structure, drug candidates predicted to be bound by affinity and high selectivity against targets can be designed using interactive graphics and intuition from medical chemists. Alternatively, various automated computing procedures can be used to suggest new drug candidates.

Current methods for drug design based on structure can be roughly divided into three main categories. The first method is the identification of new ligands for the given receptors by searching large databases of 3D structures from small molecules to find those that fit the receptor binding pockets using a fast approximate docking program. This method is known as virtual screening. The second category is the new de novo ligand design. In this method, the ligand molecule is built within the boundary of the binding bag by assembling small pieces gradually. These pieces may be individual atoms or molecular fragments. The main advantage of such a method is that the novel structure, which is not contained in any database, can be suggested. A third method is ligand optimization known by evaluating the proposed analog in a binding cavity.

Identify binder sites

Identification of binder sites is the first step in structural-based design. If a sufficiently similar target or homologous structure is determined by the presence of a bonded ligand, then the ligand should be observable in the structure in which the case location of the binding site is trivial. However, there may be an allosteric binding site that does not exist that may be of interest. Furthermore, it is possible that only apoprotein structures (available proteins without ligands) are available and reliable identification of empty spots that have the potential to bind ligands with high affinity is not trivial. In short, the binding of a binding site typically depends on the identification of a concave surface on a protein that can accommodate drug-sized molecules that also have an appropriate "hotspot" (hydrophobic surface, hydrogen bonding, etc.) that encourages ligand binding.

Scoring function

Drug-based drug design attempts to use protein structures as a basis for designing new ligands by applying the principles of molecular recognition. Selective high affinity binding to the target is generally desirable as it leads to a more efficacious drug with fewer side effects. Thus, one of the most important principles for designing or acquiring potential new ligands is predicting the affinity of binding a particular ligand to its target (and known antitarget) and using the predicted affinity as the criterion for selection.

One of the earliest general empirical assessment functions to describe the ligand binding energy to the receptor was developed by BÃÆ'¶hm. This empirical assessment function takes the form of:

${\ displaystyle \ Delta G _ {\ text {bind}} = \ Delta G _ {\ text {0}} \ Delta G _ {\ text {hb}} \ Sigma _ {h-obligasi} \ Delta G _ {\ text {ionic}} \ Sigma _ {ionic-int} \ Delta G _ {\ text {lipophilic}} \ left \ vert A \ right \ vert \ Delta G _ {\ teks {rot}} {\ mathit {NROT}}}  ÂÂ$

Where:

• ? G ₀ - offsets empirically partially corresponding to the overall loss of translation and the rotation of the ligand entropy on binding.
• ? G _hb - the contribution of the hydrogen bond
• ? G _ionic - the contribution of ionic interactions
• ? G _lip - the contribution of lipophilic interactions where | A _lipo | is the lipophilic contact surface area between the ligand and the receptor
• ? G _rotted - entropy penalty because freezing can be rotated in ligand bonds at binding time

The more general term "master" of thermodynamics is as follows:

${\ displaystyle {\ begin {array} {lll} \ Delta G _ {\ text {bind}} = - RT \ ln K _ {\ text {d}} \\ [ 1.3ex] K_ {\ text {d}} = {\ dfrac {[{\ text {Ligand}}] [{\ text {Receptor}}]} {[{\ text {Complex}}]}} \\ [ 1.3ex] \ Delta G _ {\ text {bind}} = \ Delta G _ {\ text {desolvation}} \ Delta G _ {\ text {motion}} \ Delta G _ {\ text {configuration}} \ Delta G_ {\ text {interaction}} \ end {array}}}  ÂÂ$

Where:

• desolvation - the enthalpic penalty for removing the ligand from the solvent
• entropic motions to reduce the degree of freedom when the ligand binds to its receptor
• configuration - the conformational strain energy required to place the ligand in its "active" conformation
• interaction - enthalpic gain to "solve" ligand with its receptor

The basic idea is that the overall binding free energy can be decomposed into an independent component known to be important for the binding process. Each component reflects a particular type of free energy during the binding process between the ligand and its target receptor. The Master equation is a linear combination of these components. According to the Gibbs free energy equation, the relationship between dissociation equilibrium constants, K _d , and the free energy component is constructed.

Various computational methods are used to estimate each component of the master equation. For example, changes in the polar surface area of ​​the ligand binding can be used to estimate the desolved energy. The amount of playable bonds that are frozen on the ligand binding is proportional to the term motion. The configuration or strain energy can be estimated using molecular mechanical calculations. Finally the interaction energy can be estimated using methods such as changes in non-polar surfaces, statistically average potential strength, number of hydrogen bonds formed, etc. In practice, the components of the master equation are suitable for experimental data using some linear regression. This can be done with diverse training sets including many different types of ligands and receptors to produce less accurate but more general "global" models or a more limited set of ligands and receptors to produce more accurate but less common "local" models.

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Example

Specific examples of rational drug design involve the use of three-dimensional information about biomolecules obtained from techniques such as X-ray crystallography and NMR spectroscopy. Computer-aided drug design in particular becomes more manageable when there is a high-resolution structure of target proteins tied to strong ligands. This approach to drug discovery is sometimes referred to as a drug design based on structure. The first rigorous example of the application of a structural based drug design leading to an approved drug is dorzolamide inhibitor carbonate anhydrase, which was approved in 1995.

Another important case study in rational drug design is imatinib, a tyrosine kinase inhibitor specially designed for the typical bcr-abl protein fusion typical for Philadelphia's chromosomal-positive leukemia (chronic myelogenous leukemia and occasionally acute lymphocytic leukemia).. Imatinib is substantially different from previous drugs for cancer, since most chemotherapy agents target only rapidly dividing cells, not distinguishing between cancer cells and other tissues.

Additional examples include:

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Case Study

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Criticism

It has been argued that the rigid and focused nature of rational drug designs suppress serendipity in drug discovery. Because many of the most significant medical discoveries are unintentional, the last focus on rational drug design can limit the progress of drug discovery. In addition, the rational design of the drug may be limited by a rough or incomplete understanding of the underlying molecular process of a disease intended to be treated.

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See also

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References

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External links

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

Source of the article : Wikipedia