An Overview on Pharmacophore:

Their significance and importance for the activity of Drug Design

 

Anil Kumar Sahdev¹, Priya Gupta¹, Kanika Manral¹, Preeti Rana¹, Anita Singh²*

¹Research Scholar, Department of Pharmaceutical Sciences, Faculty of Technology Sir J.C. Bose Technical Campus, Bhimtal, Kumaun University Nainital, Uttarakhand, India.

²Head and Professor, Department of Pharmaceutical Sciences, Faculty of Technology Sir J.C. Bose Technical Campus, Bhimtal, Kumaun University Nainital, Uttarakhand, India.

 

ABSTRACT:

The pharmacophore concept was first put forward as a useful picture of drug interactions almost a century ago, and with the rise in computational power over the last few decades, has become a well-established CADD method with numerous different applications in drug discovery. Depending on the prior knowledge of the system, pharmacophores can be used to identify derivatives of compounds, change the scaffold to new compounds with a similar target, virtual screen for novel inhibitors, profile compounds for ADME-tox, investigate possible off-targets, or just complement other molecular methods “chemical groups” or functions in a molecule were responsible for a biological effect, and molecules with similar effect had similar functions in common. The word pharmacophore was coined much later, by Schueler in his 1960 book Chemobiodynamics and Drug Design, and was defined as “a molecular framework that carries (phoros) the essential features responsible for a drug’s (Pharmacon) biological activity.

 

 

 

INTRODUCTION: 

Pharmacophore Modeling:

Paul Ehrlich was the scientist who gave the original concept of the pharmacophore was during the late 1800s.¹ He states that certain “chemical groups” or functions in a molecule were responsible for a biological effect, and molecules with similar effect had similar functions in common. The word pharmacophore was coined much later, by Schueler in his 1960 book Chemobiodynamics and Drug Design, and was defined as “a molecular framework that carries (phoros) the essential features responsible for a drug’s (Pharmacon) biological activity.²

 

The definition of a pharmacophore was therefore no longer concerned with “chemical groups” but “patterns of abstract features.”Since 1997, a pharmacophore has been defined by the International Union of Pure and Applied Chemistry as A pharmacophore is the ensemble of stearic and electronic features. that is necessary to ensure the optimal supramolecular interactions with a specific biological target and to trigger (or block) its biological response³.

 

Pharmacophore fingerprint:

Pharmacophore fingerprints attempt to model binding related structural or chemical properties of chemical compounds with the use of simple statistics of chemical features. In the case of pharmacophore fingerprints generated these features are always assigned to individual atoms of the molecule thus these fingerprints are Atom based pharmacophore fingerprints.

 

Atom-Pair Based 2D Pharmacophore Fingerprints:

are defined as “The collection of all atom-atom pharmacophore feature pairs along with their topological distances”⁴.

Pharmacophore Model:

Pharmacophore models assemble the set of pharmacophore points along with their relative arrangement, which, in the simplest case, is the three-dimensional Euclidean distance between each point pair.

 

 

Figure 1: A pharmacophore fingerprint is representation the of a small molecule ligand    

 

However, in the two-dimensional case no spatial information is available, thus topological relations are used to represent the relative position of pharmacophore points. The most apparent counterpart of Euclidean distance in the three dimensional space is the topological distance in the topological space of the chemical graphs. This distance is equal to the length of the shortest path between two nodes (atoms) of the chemical graph, that is the smallest number of graph edges (bonds) connecting the two atoms. A further choice in constructing pharmacophore models is how these relative positions are built into the model. Common approaches consider either all pharmacophore point pairs or all pharmacophore point triplets (triangles). In the approach taken by Generate Maximum distance (MD) only pharmacophore pairs are used^5-7.

 

All pharmacophore feature pairs of Captopril, and the shortest paths between its pharmacophore points⁸.

 

Figure 2: Pharmacophore feature pairs of Captopril.

 

Figure 3: Pharmacophore query

 

Such pharmacophore features are typically used as queries to screen small molecule libraries of compounds⁹. In these libraries all the compounds are present in their low-energy bio relevant conformations. Each of these conformations is fitted to the pharmacophore query by aligning the pharmacophore features of the molecule and the query is composed. molecule can be fitted inside the spheres representing

the query features it is considered a hit molecule.

 

In such cases only certain features considered essential for activity are matched. Additional uses of such models are to align molecules or facilitate molecular docking simulations.^10–12.

 

Applications:

1.     Pharmacophore in Drug Design and Discovery-

2.     Pharmacophore Concepts in Computer Added Drug Designing (CADD):

While the pharmacophore concept predates any form of electronic computer, it has nevertheless become an important tool in CADD. Every type of atom or group in a molecule that exhibits certain properties related to molecular recognition can be reduced to a pharmacophore feature. These molecular patterns can be labeled as hydrogen bond donors or acceptors, cationic, anionic, aromatic, or hydrophobic, and any possible combinations¹³. Different molecules can be compared at the pharmacophore level; this usage is often described as “pharmacophore fingerprints.” When only a few pharmacophore features are considered in a 3D model the pharmacophore is sometimes described as a “query.”

 

Pharmacophore modeling in virtual screening:

Pharmacophore modeling is most often applied to virtual screening in order to identify molecules triggering the desired biological effect. For this purpose, researchers create a pharmacophore model In general, it is good practice to divide the ligand data into two sets, a training and an evaluation set to validate the generated pharmacophore query, when multiple active ligands (and inactive derivatives) are known.¹³While in all these cases pharmacophore queries are considered positive filters to identify compounds, they may in fact also be used as negative filters to avoid side effects as well.^14,15

 

No protein structure and no ligand structure is known:

If the target structure and all its ligands are unknown, pharmacophore modeling is impossible. The only option to employ the pharmacophore principle would be to design a diverse library Four employing a diversity metric based on different.

 

Pharmacophore fingerprints to ensure optimal diversity of the library, containing a wide variety of molecules with different pharmacophore feature composition. Indeed, considering the large number of available and potential compounds, the trend is to design libraries very carefully in order to cover chemical space efficiently in any search process.^16,17

 

No protein structure, but active ligand structures are known:

The other scenario is that the structure of the receptor (and any Complex with the ligand) is unknown. This is frequently the case in drug discovery.

 

 

Figure 4: Situations for the pharmacophore

 

If only a single active molecule is known, then it is impossible to map the key contributing pharmacophore features onto the molecule, and the only option may be to use similarity searches (such as using pharmacophore fingerprints) to retrieve similar molecules¹⁸.  When a set of active ligands of known structure, with similar or different scaffolds, is available, then it is possible to use ligand-based pharmacophore modeling¹⁹. The elucidation of the putative pharmacophore involves two steps. First, the conformational space of the flexible molecules needs to be covered extensively since the bioactive conformations are unknown. Second, the molecules need to be aligned by common pharmacophore features, which can be retained in a 3D model. Using inactive derivatives, the essence of the features as well as the permitted steric arrangement of the ligands can be mapped as well. The Catalyst-HypoGen algorithm in particular stands out from the variety of tools available for this purpose²⁰.

 

Protein and ligand structures are known:

In the third case, structural information is present for both ligands and the receptor protein. Usually a pharmacophore model represents the key features of a small molecule that allow it to bind to some receptor molecule, but this idea can be reversed and pharmacophore queries built from features of a protein active site²¹. These features describe the principle interactions between the protein and its ligands, and can be mapped onto the bioactive conformation of the ligand. Ideally the structural model is derived from crystallographic or nuclear magnetic resonance data, but homology models or other structural data can be used as well. Although a structure for one ligand may be enough, it is beneficial to have 3D information for multiple ligands to identify the common interactions. While this approach is compatible with the majority of pharmacophore modeling methods, Ligand Scout is notable as the first software package able to construct automatically a query from one or more Protein Data Bank (PDB) files based on protein–ligand interactions²².

 

Only the protein structure is known:

For structural information for the protein receptor, but no active ligands, is known. In this case, a putative pharmacophore model can be constructed by analyzing the chemical properties of the binding site of interest. There are several different computational approaches that can directly convert 3D atomic structures of protein binding sites into queries^23-25. HS-Pharm is a knowledge-based method that uses machine-learning algorithms to prioritize the most interesting interacting atoms and to generate an interaction map within the binding site^26-29. Subsequently, the interaction map is converted into pharmacophore features. The GRID package is another approach to analyze the pocket in order to identify the key interactions³⁰.

 

Pharmacophore methods in docking simulations:

As indicated in the previous section, pharmacophore models are very suitable as queries for virtual screening of databases. Nevertheless, one of the more common approaches in virtual screening is a so-called hierarchical approach in which different methods are combined consecutively. This is also known as the funnel principle, where at each consecutive step the compounds most unlikely to be active are removed, leaving the most promising compounds for virtual screening.³¹ Molecular docking simulations are computational methods that aim to predict the binding mode of a compound for a given receptor as well as the quality of the interaction, often by attempting to predict the affinity (free energy of binding) using a scoring function³². Several options are available for combining docking-based virtual screening with pharmacophore-based virtual screening software such as Sanjeevani, autodock vina, schrodinger etc.

·       The database of ligands can be pre-filtered using a pharmacophore query, prior to evaluation using docking simulations.³³

·       The docking simulations can be post-filtered using a pharmacophore query to remove any compounds that fail to bind according to the pharmacophore query. The method can also discard compounds that would have scored well in a pure pharmacophore search, but that fail to bind according to some hypothesis taking more information into account, such as incompatibility of the overall ligand structure with the receptor site. In such a case, the ligands are evaluated in absolute conformation and should not be allowed to align with the pharmacophore features.³⁴

·       Another alternative is to use the pharmacophore alignment to guide the placement during the docking simulations.^{35, 36} 

 

Applications of pharmacophores in ADME-tox:

Poor ADME-tox is a major contributing factor to failures during drug development and clinical trials.^37,38 It is, therefore, widely accepted that the ADME-tox properties should be profiled early during the drug discovery process, and pharmacophore modeling approaches are often used for such ADME-tox predictions.³⁹ The pharmacophore models can be used to identify possible interactions of drugs with drug-metabolizing enzymes by matching the equivalent chemical groups of test molecules to those of drug molecules with a well-known ADME-tox profile.⁴⁰

 

The enzymes of major importance for observed ADME-tox profile are the cytochrome P450s (CYP) that initiate drug breakdown⁴¹. It has been estimated that only six CYP isoenzymes (1A2, 2C9, 2C19, 2D6, 2E1, and 3A4) are responsible for over 90% of drug metabolism.⁴² Based on the observed interactions of known drugs with the CYP enzymes, receptor-based pharmacophore models have been generated that are able to predict the binding of a drug-like compound to a certain CYP and assess the possibility of degradation by this enzyme.⁴³

 

Pharmacophore-guided drug target identification:

While typically the aim of CADD is to identify and optimize drug-like molecules for a given target, the opposite situation also exists. Often drug molecules are known, but the mechanism of action is unclear. These compounds are often derived from herbal medicine, or phenotypically developed drugs. In such cases, CADD may help identify the target. Chemoinformatical fingerprint-based similarity tools are employed to identify close analogue compounds with a known mechanism of action. The molecule itself may become the query and the aim is to identify the most likely pharmacophore model that fits the molecule. Such collections of pharmacophore models may be constructed manually or automatically generated from the PDB database.⁴⁴ Experimental testing of the compounds for the given targets validated the applicability of this method. It may be expected that pharmacophore models will play a significant role in the future, as polypharmacology or drug repositioning become more widespread.^45,47  

 

Limitations of pharmacophore methods:

Despite the abundance of successful cases of drug design relying on pharmacophore modeling, as with any method, it is not failsafe and one should be cautious about the limitations of this technique^47-48. The major limitation in virtual screening by pharmacophore is the absence of good scoring metrics. Whereas docking simulations are based on scoring functions trying to predict the affinity, and similarity searches utilize similarity metrics such as the Tanimoto score, pharmacophore queries do not have a reliable, general scoring metric⁴⁹. Most commonly, the quality of fitting the ligand into a pharmacophore query is expressed by the root mean square deviation between the features of the query and atoms of the molecule.⁵⁰ This metric, however, is unable to take any similarity into account with known inhibitors, and also is unable to predict the overall compatibility with the receptor protein, and thus molecules that hit a pharmacophore query may be very different from other inhibitors and have functional groups which are not complementary with the receptor binding site, rendering them inactive despite being a perfect match. Christ et al versus De Luca et al, where for a similar target, a similar yet slightly different pharmacophore was created.^51,52 Pharmacophore approaches aimed at identifying kinase inhibitors would without any doubt identify kinase-inhibitor-like molecules; nevertheless, there would not be a clear guarantee that these molecules would be active for the targeted kinase.⁵³

 

Future perspectives on pharmacophore modeling:

Pharmacophore modeling has been around since the beginning of CADD and has evolved from a basic concept into a well-established CADD method with applications including similarity metrics, virtual screening, ligand optimization, scaffold hopping, target identification, and so on. Given the simplicity and versatility of the pharmacophore concept, it can be anticipated that further developments will be made in the future for different applications⁵⁴.

 

Fragment-based drug design:

Over the last two decades, fragment-based drug design has become a well-established method for the rational development of novel drugs^55-57.Rather than screening drug-like molecules (with molecular weights of around 500 Da), smaller molecules with a molecular weight up to 350 Da (referred to as fragments) are being screened for affinity with a receptor using highly sensitive biophysical methods. Fragments showing some affinity for the target are grown into bigger and more potent compounds, and fragments binding to adjacent areas can be linked as well^58-61.

 

Since the diversity of small molecule fragments can easily be sampled with a few hundred compounds, in silico screening methods are highly suitable for fragment-based design. CADD methods such as docking and pharmacophore modeling have therefore also been used to identify fragment-like compounds in silico prior to testing in vitro; subsequent fragment recombination can be used for the de novo design of inhibitors.^62,63

 

Protein–protein interaction (PPI) inhibition:

The PPI interface are being mimicked by the ligand.⁶⁰ SMPPII are found to copy the natural interaction not only in terms of shape and chemistry, but even at the electrostatic potential level.⁶¹ This mimicry suggests that the pharmacophore queries created from PPI complex structures can be used to identify SMPPII via virtual screening^64-65.

 

PPIs are especially promising targets for controlling inappropriate signaling, as found in diseases such as cancer. The usefulness of pharmacophore modeling to create queries encoding the key interactions at the PPI interface will probably strongly stimulate the discovery of novel SMPPII using pharmacophores, both as a stand-alone virtual screening tool and incorporated into pipelines with other methods^66-70.

 

A potential role in protein design:

Although pharmacophore modeling originated as a drug design concept and, as indicated earlier, is nowadays a key element of CADD, pharmacophore modeling shows promise in the currently burgeoning field of computational protein design. In many cases, this may involve protein–small molecule ligand interactions,^71-72 and for these it can easily be imagined that pharmacophores may be used simply by reversing the process of small molecule drug design for a known protein structure. The ligand of interest could serve as a query to try to identify possible binding proteins, which can then later be redesigned to give optimum complementarity to the ligand. During the virtual protein design process, often multiple rotamers of different amino acids are sampled to identify the most desirable ones.^73-75

 

CONCLUSION:

The pharmacophore concept was first put forward as a useful picture of drug interactions almost a century ago, and with the rise in computational power over the last few decades, has become a well-established CADD method with numerous different applications in drug discovery. Depending on the prior knowledge of the system, pharmacophores can be used to identify derivatives of compounds, change the scaffold to new compounds with a similar target, virtual screen for novel inhibitors, profile compounds for ADME-tox, investigate possible off-targets, or just complement other molecular methods. While there are limitations to the pharmacophore concept, multiple remedies are available at any time to counter them. Given this versatility, it is expected that pharmacophore modeling will maintain a dominant role in CADD for the foreseeable future, and any medicinal chemist should be aware of its benefits and possibilities.

 

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Accepted on 10.05.2022           © RJPT All right reserved

Research J. Pharm. and Tech 2023; 16(3):1496-1502.