Druggability

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Druggability is a term used in drug discovery to describe a biological target (such as a protein) that is known to or is predicted to bind with high affinity to a drug. Furthermore, by definition, the binding of the drug to a druggable target must alter the function of the target with a therapeutic benefit to the patient. The concept of druggability is most often restricted to small molecules (low molecular weight organic substances) [1] but also has been extended to include biologic medical products such as therapeutic monoclonal antibodies.

Contents

Drug discovery comprises a number of stages that lead from a biological hypothesis to an approved drug. Target identification is typically the starting point of the modern drug discovery process. Candidate targets may be selected based on a variety of experimental criteria. These criteria may include disease linkage (mutations in the protein are known to cause a disease), mechanistic rationale (for example, the protein is part of a regulatory pathway that is involved in the disease process), or genetic screens in model organisms. [2] Disease relevance alone however is insufficient for a protein to become a drug target. In addition, the target must be druggable.

Prediction of druggability

If a drug has already been identified for a target, that target is by definition druggable. If no known drugs bind to a target, then druggability is implied or predicted using different methods that rely on evolutionary relationships, 3D-structural properties or other descriptors. [3]

Precedence-based

A protein is predicted to be "druggable" if it is a member of a protein family [4] for which other members of the family are known to be targeted by drugs (i.e., "guilt" by association). While this is a useful approximation of druggability, this definition has limitations for two main reasons: (1) it highlights only historically successful proteins, ignoring the possibility of a perfectly druggable, but yet undrugged protein family; and (2) assumes that all protein family members are equally druggable.[ citation needed ]

Structure-based

This relies on the availability of experimentally determined 3D structures or high quality homology models. A number of methods exist for this assessment of druggability but all of them consist of three main components: [5] [6] [7] [8]

  1. Identifying cavities or pockets on the structure
  2. Calculating physicochemical and geometric properties of the pocket
  3. Assessing how these properties fit a training set of known druggable targets, typically using machine learning algorithms

Early work on introducing some of the parameters of structure-based druggability came from Abagyan and coworkers [9] and then Fesik and coworkers, [10] the latter by assessing the correlation of certain physicochemical parameters with hits from an NMR-based fragment screen. There has since been a number of publications reporting related methodologies. [5] [11] [12]

There are several commercial tools and databases for structure-based druggability assessment. A publicly available database of pre-calculated druggability assessments for all structural domains within the Protein Data Bank (PDB) is provided through the ChEMBL's DrugEBIlity portal. [13]

Structure-based druggability is usually used to identify suitable binding pocket for a small molecule; however, some studies have assessed 3D structures for the availability of grooves suitable for binding helical mimetics. [14] This is an increasingly popular approach in addressing the druggability of protein-protein interactions. [15]

Predictions based on other properties

As well as using 3D structure and family precedence, it is possible to estimate druggability using other properties of a protein such as features derived from the amino-acid sequence (feature-based druggability) [3] which is applicable to assessing small-molecule based druggability or biotherapeutic-based druggability or the properties of ligands or compounds known to bind the protein (Ligand-based druggability). [16] [17]

The importance of training sets

All methods for assessing druggability are highly dependent on the training sets used to develop them. This highlights an important caveat in all the methods discussed above: which is that they have learned from the successes so far. The training sets are typically either databases of curated drug targets; [18] [19] screened targets databases (ChEMBL, BindingDB, PubChem etc.); or on manually compiled sets of 3D structure known by the developers to be druggable. As training sets improve and expand, the boundaries of druggability may also be expanded.

Undruggable targets

About 3% of human proteins are known to be "mode of action" drug targets, i.e., proteins through which approved drugs act. [20] Another 7% of the human proteins interact with small molecule chemicals. [20] Based on DrugCentral, 1795 human proteins annotated to interact with 2455 approved drugs. [21]

Furthermore, it is estimated that only 10-15% of human proteins are disease modifying while only 10-15% are druggable (there is no correlation between the two), meaning that only between 1 and 2.25% of disease modifying proteins are likely to be druggable. Hence it appears that the number of new undiscovered drug targets is very limited. [22] [23] [24]

A potentially much larger percentage of proteins could be made druggable if protein–protein interactions could be disrupted by small molecules. However the majority of these interactions occur between relatively flat surfaces of the interacting protein partners and it is very difficult for small molecules to bind with high affinity to these surfaces. [25] [26] Hence these types of binding sites on proteins are generally thought to be undruggable but there has been some progress (by 2009) targeting these sites. [27] [28]

Chemoproteomics techniques have recently expanded the scope of what is deemed a druggable target through the identification of covalently modifiable sites across the proteome. [29]

Related Research Articles

<span class="mw-page-title-main">Allosteric regulation</span> Regulation of enzyme activity

In biochemistry, allosteric regulation is the regulation of an enzyme by binding an effector molecule at a site other than the enzyme's active site.

<span class="mw-page-title-main">Binding site</span> Molecule-specific coordinate bonding area in biological systems

In biochemistry and molecular biology, a binding site is a region on a macromolecule such as a protein that binds to another molecule with specificity. The binding partner of the macromolecule is often referred to as a ligand. Ligands may include other proteins, enzyme substrates, second messengers, hormones, or allosteric modulators. The binding event is often, but not always, accompanied by a conformational change that alters the protein's function. Binding to protein binding sites is most often reversible, but can also be covalent reversible or irreversible.

<span class="mw-page-title-main">Drug design</span> Invention of new medications based on knowledge of a biological target

Drug design, often referred to as rational drug design or simply rational design, is the inventive process of finding new medications based on the knowledge of a biological target. The drug is most commonly an organic small molecule that 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 the design of molecules that are complementary in shape and charge to the biomolecular target with which they interact and therefore will bind to it. Drug design frequently but not necessarily relies on computer modeling techniques. This type of modeling is sometimes referred to as computer-aided drug design. Finally, drug design that relies on the knowledge of the three-dimensional structure of the biomolecular target is known as structure-based drug design. In addition to small molecules, biopharmaceuticals including peptides and especially therapeutic antibodies are an increasingly important class of drugs and computational methods for improving the affinity, selectivity, and stability of these protein-based therapeutics have also been developed.

A biological target is anything within a living organism to which some other entity is directed and/or binds, resulting in a change in its behavior or function. Examples of common classes of biological targets are proteins and nucleic acids. The definition is context-dependent, and can refer to the biological target of a pharmacologically active drug compound, the receptor target of a hormone, or some other target of an external stimulus. Biological targets are most commonly proteins such as enzymes, ion channels, and receptors.

<span class="mw-page-title-main">Aptamer</span> Oligonucleotide or peptide molecules that bind specific targets

Aptamers are short sequences of artificial DNA, RNA, XNA, or peptide that bind a specific target molecule, or family of target molecules. They exhibit a range of affinities, with variable levels of off-target binding and are sometimes classified as chemical antibodies. Aptamers and antibodies can be used in many of the same applications, but the nucleic acid-based structure of aptamers, which are mostly oligonucleotides, is very different from the amino acid-based structure of antibodies, which are proteins. This difference can make aptamers a better choice than antibodies for some purposes.

<span class="mw-page-title-main">Ligand (biochemistry)</span> Substance that forms a complex with a biomolecule

In biochemistry and pharmacology, a ligand is a substance that forms a complex with a biomolecule to serve a biological purpose. The etymology stems from Latin ligare, which means 'to bind'. In protein-ligand binding, the ligand is usually a molecule which produces a signal by binding to a site on a target protein. The binding typically results in a change of conformational isomerism (conformation) of the target protein. In DNA-ligand binding studies, the ligand can be a small molecule, ion, or protein which binds to the DNA double helix. The relationship between ligand and binding partner is a function of charge, hydrophobicity, and molecular structure.

<span class="mw-page-title-main">Docking (molecular)</span> Prediction method in molecular modeling

In the field of molecular modeling, docking is a method which predicts the preferred orientation of one molecule to a second when a ligand and a target are bound to each other to form a stable complex. Knowledge of the preferred orientation in turn may be used to predict the strength of association or binding affinity between two molecules using, for example, scoring functions.

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<span class="mw-page-title-main">Virtual screening</span>

Virtual screening (VS) is a computational technique used in drug discovery to search libraries of small molecules in order to identify those structures which are most likely to bind to a drug target, typically a protein receptor or enzyme.

In the fields of computational chemistry and molecular modelling, scoring functions are mathematical functions used to approximately predict the binding affinity between two molecules after they have been docked. Most commonly one of the molecules is a small organic compound such as a drug and the second is the drug's biological target such as a protein receptor. Scoring functions have also been developed to predict the strength of intermolecular interactions between two proteins or between protein and DNA.

Molecular binding is an attractive interaction between two molecules that results in a stable association in which the molecules are in close proximity to each other. It is formed when atoms or molecules bind together by sharing of electrons. It often, but not always, involves some chemical bonding.

Fragment-based lead discovery (FBLD) also known as fragment-based drug discovery (FBDD) is a method used for finding lead compounds as part of the drug discovery process. Fragments are small organic molecules which are small in size and low in molecular weight. It is based on identifying small chemical fragments, which may bind only weakly to the biological target, and then growing them or combining them to produce a lead with a higher affinity. FBLD can be compared with high-throughput screening (HTS). In HTS, libraries with up to millions of compounds, with molecular weights of around 500 Da, are screened, and nanomolar binding affinities are sought. In contrast, in the early phase of FBLD, libraries with a few thousand compounds with molecular weights of around 200 Da may be screened, and millimolar affinities can be considered useful. FBLD is a technique being used in research for discovering novel potent inhibitors. This methodology could help to design multitarget drugs for multiple diseases. The multitarget inhibitor approach is based on designing an inhibitor for the multiple targets. This type of drug design opens up new polypharmacological avenues for discovering innovative and effective therapies. Neurodegenerative diseases like Alzheimer’s (AD) and Parkinson’s, among others, also show rather complex etiopathologies. Multitarget inhibitors are more appropriate for addressing the complexity of AD and may provide new drugs for controlling the multifactorial nature of AD, stopping its progression.

<span class="mw-page-title-main">Stapled peptide</span>

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<span class="mw-page-title-main">Targeted covalent inhibitors</span>

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<span class="mw-page-title-main">Molecular glue</span> Class of chemical compounds

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References

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