In medicinal chemistry, the relationship between molecular structure of a compound and its biological activity is referred to as Structure Activity Relationship (SAR). Medicinal chemists modify biomedical molecules by inserting new chemical groups into the compound and test those modifications for their biological effects. Determining and identifying SARs is key to many aspects of the drug discovery process, ranging from hit identification to lead optimization.

Although information on millions of compounds and their bio-activities e.g. reaction ability, solubility, target activity etc., is freely available to the public, it is very challenging to infer a meaningful and novel SAR from that information. The underlying problem in here is the un-structured and heterogeneous nature of these datasets contributed by the scientific & research community in journals, scientific articles, patents, regulatory documents and various secondary sources. Owing to the increasing structural diversity among hit compounds and their potency distribution, it is becoming a challenge to analyze the SAR information. If these relationships are properly extracted, associated and analyzed, they provide valuable information that would support drug discovery and development. To this end, there has been an increasing need and interest in mining and structuring SAR information from bioactivity data available in the public domain.

Global Online Structure Activity Relationship Database (GOSTAR)

Excelra, a leading global biopharma data and analytics company, has responded to this pertinent need by developing a knowledge repository, Global Online Structure Activity Relationship Database (GOSTAR), which provides a 360-degree view of millions of compounds linking their chemical structure to the biological, pharmacological and therapeutic information. GOSTAR contains high-quality, manually annotated and very well-structured SAR data captured from various primary sources (patents and top journals of medicinal chemistry) and secondary sources (conference meetings & abstracts, company drug development pipelines, company annual reports, clinical registries and drug approval reports).

Who can use GOSTAR and how?

The main objective for creating GOSTAR is to assist medicinal chemists, computational chemists and cheminformaticians in their quest for identifying potential small molecules that have decent biological effect and could be of a specific therapeutic use. GOSTAR enables users to quickly visualize, explore, analyze and evaluate SAR data based on their project requirements. The users can explore various SAR associations by searching various identifiers like drug names, chemical structures, bibliography, compound development stage and activity endpoints.

What are the applications of GOSTAR?

Better understanding of SAR data will enable the users to take correct decisions in exploring the chemical space while designing a drug.

Following are the applications of GOSTAR:

• Target profiling – GOSTAR enables a holistic exploration of the chemical space around a target of interest & enables the users to understand the pathways and indications in which a given target is implicated
• Structure based drug design – GOSTAR can be used as a compound library to perform virtual screening and hit identification in traditional structure-based drug design methodologies
• Lead optimization – GOSTAR enables lead optimization by suggesting the structure activity relationships with improved potency, reduced off-target activities, and physiochemical/metabolic properties
• Assay validation – GOSTAR suggests the right functional assays for secondary validation for the chemical modifications while involved in the tuning of the hit molecule
• Drug repurposing and Translational science – GOSTAR data can be mined to interrogate diverse targets with a compound of interest to understand the feasibility and viability for drug rescue or for label expansion
• Competitive intelligence and Novelty analysis – GOSTAR captures drug lifecycle information such as indication, phase of development, sponsor and recruitment/approval status including suspended trials along with the reason for discontinuation that can be used for building the competitive landscape around the drug/target/indication.

Why GOSTAR?

Currently, there are hundreds and thousands of chemical classes, and it often becomes daunting task to identify potential candidates for therapeutic use. In such cases, using knowledge repositories like GOSTAR, we can rapidly characterize data points that can help to efficiently capture and encode specific SAR. Below are the key features that showcase why GOSTAR is the ideal and simplistic solution for the complex task of gathering SAR data.

• Reachability – Easy content accessibility to a wide and diverse user community
• Utility – Maximize the utilization of content to create insights/concepts
• Applicability – Selective utilization of content in diverse early discovery programs targeting unmet medical needs
• Reliability – Standardized and normalized content to support traditional as well as AI/ML driven discovery programs

Try GOSTAR today. To schedule a free demo, write to us at: marketing@excelra.com

For more information on GOSTAR, visit: https://www.gostardb.com/gostar/

A brief review of GPCR family: The largest family of druggable targets

G protein-coupled receptors (GPCRs) have become a hot frontier in basic research of life sciences and therapeutic discovery of translational medicines and is widely pursued by both academic and industrial research for drug discovery. They represent an important opportunity for both small molecule-based and antibody-based therapeutics and are the largest family of targets for approved drugs. The discovery of a diverse set of molecules targeting this family could become valuable assets, by solving unexploited horizons like establishing target biological functions and disease relevance.

GPCR structures and families

GPCRs are the largest family of proteins involved in membrane signal transduction and are also the most intensively studied drug targets, largely due to their substantial involvement in human pathophysiology. The pharmacological modulation of GPCRs provides leverage for treatment of diseases of central nervous system (CNS), cancer, viral infections, inflammatory disorders, metabolic disorders, etc.

The superfamily is classified into six classes based on amino acid sequence similarities namely, Class A (rhodopsin-like family); Class B (secretin receptor family); Class C (glutamate receptor family); Class D (fungal mating pheromone receptors); Class E (cAMP receptors) and Class F (frizzled or smoothened receptors), of which only four (A, B, C and F) are found in humans.

GPCRs are involved in various biological processes and disease indications and they make excellent drug targets (Fig 2). Some GPCRs have been linked to cancer development and progression, based on their overexpression and/or up-regulation by diverse factors. A higher expression of GPR49 was found to be involved in the formation and proliferation of basal cell carcinoma, the glycine receptor GPR18 was found to be associated with melanoma metastases, and high levels of GPR87 were found to be associated with lung, cervix, skin, urinary bladder, testis, head and neck squamous cell carcinomas.

Recently, orphan GPCRs have become a potentially novel targets for treatment of diverse set of indications, such as GPR119 for treatment of diabetes, leucine-rich repeat-containing G protein-coupled receptors 4 & 5 (LGR4/5) for treatment of gastrointestinal disease, GPR35 for treatment of an allergic inflammatory condition, GPR55 as an antispasmodic target, proto-oncogene Mas for treatment of thrombocytopenia, and GPR84 for of ulcerative colitis.

Landscape of GPCR research and drug development

GPCRs are the largest ‘target’ class of the ‘druggable genome’ representing approximately 19% of the currently available drug targets. In humans, the GPCR superfamily consists of 827 distinct members, of which 406 are non-olfactory. However, current therapeutics in humans target only 25% of potentially druggable GPCRs, 103 out of possible 403 GPCR targets, for which there is at least one marketed drug in practice.

Current literature analysis shows that GPCRs have traditionally been regarded as the domain for small-molecule drugs and very few targets are well studied. More than 30% of the US Food and Drug Administration (FDA) approved drugs target GPCRs, which makes them the largest druggable class of biomolecules (Fig 4).

Enormous efforts have been expended to find relevant and potent GPCR ligands as lead compounds. Non-olfactory GPCRs constitute more than half of the human genome encoded targets that are not yet exploited for any therapeutic use and the knowledge is disproportionately focused in the scientific literature. Preliminary studies highlight that these receptors have functions in genetic and immune system disorders.

While the drugs that currently target GPCRs are primarily small molecules and peptides, GPCRs also recognize diverse ligands, including inorganic ions, amino acids, proteins, steroids, lipids, nucleosides, nucleotides, and small molecules (Fig 5).

The latest trends in GPCR research indicates that modalities other than small molecules are becoming more popular as GPCR targeting agents with the entry of monoclonal antibodies, peptide drugs and allosteric modulators into early-stage clinical trials. For instance, GLP1 receptor targeting biologics like exenatide, liraglutide, and dulaglutide have been approved for type 2 diabetes, and CGRP receptor targeting erenumab in the treatment of chronic migraine and so many other peptide drugs targeting various GPCRs are also in development.

Current trends in GPCR research

In recent years, there is a significant increase in information available about the sequences, structures and signaling networks of GPCRs and the G proteins, due to breakthroughs in X-ray crystallography and cryo-electron microscopy (cryo-EM), leading to great understanding of GPCR-G protein interactions. This significant increase in information of GPCR-G protein interactions is being explored using several bioinformatics and software tools, including protein data bank , GPCRdb , gpDB , human gpDB  and many more.

Due to limited spatial and high cost of experimental studies, computational modeling techniques such as bioinformatics, protein-protein docking and molecular dynamics simulations are playing an important role in exploring the GPCR-G protein interactions. Determining the 3-dimensional structural features of various unexplored orphan receptors and their ligand-associated complexes has become an exciting avenue in the GPCRs research in understanding on the molecular recognition and activation mechanisms and help the pharmaceutical investigation of new diseases in variety of therapeutic areas.

As the current human therapeutics cover only 25% of potentially druggable GPCRs, a relatively large extent of GPCRs still remain ‘orphan’ and therapeutically unexploited. This prediction and identification of GPCR ligands for these orphan receptors is an active area of research and interest to pharmaceutical industry.

References

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