The Drug-Target Interaction Heatmap

The Drug-Target Interaction Heatmap

A heatmap is a two-dimensional data visualization approach that displays the magnitude of a phenomenon as color. The color shift might be via hue or intensity, giving the reader clear visual indications about how the occurrence is clustered or evolves over space. Heatmaps are classified into two types: cluster heatmaps and spatial heatmaps. The sorting of rows and columns is intentional and somewhat arbitrary in a clustered heatmap, and the magnitudes are laid out into a matrix of fixed cell size whose rows and columns are discrete phenomena and categories, to suggest clusters or portray them as discovered via statistical analysis. The cell size is arbitrary, but it must be large enough to be seen. The position of a magnitude on a spatial heatmap, on the other hand, is determined by its location in that space, and there is no concept of cells; the phenomena are assumed to change continuously.

Data scientists and data analysts examine and determine essential links and characteristics among different points in a dataset, as well as aspects of those data points when working with small and large datasets. Heatmaps depict these data points and their interactions in a high-dimensional context without becoming excessively compressed and visually unpleasant. In data analysis, heatmaps enable specific variables of rows and/or columns to be plotted on the axes.

The drug-target interaction heatmap facilitates decision-making about the potential off-target activity of drug candidates early in the new drug development and drug repurposing workflows. In terms of important factors, a heatmap provides a clear picture of the interactions between drugs and their targets. This allows for the quick identification of the most important interactions.

GOSTAR presents these findings in a visually intuitive manner, allowing end-users to easily interpret the data and draw conclusions.


  1. Heat map. In: Google Arts and Culture. Accessed 21 April 2022.
  2. Exploratory Data Analysis. In: IBM Cloud Learn Hub. Accessed 21 April 2022.

Matched Molecular Pair Analysis

Matched Molecular Pair Analysis

The complexity in molecular design is selecting what to do next based on existing data, medicinal chemistry knowledge, experience, and intuition. In small compound sets, a skilled chemist can discern trends and correlations by eye. As the number of molecules increases, more methodical procedures are required.

The Matched Molecular Pair (MMP) analysis, which compares closely related chemical structures pairwise across a big dataset, is one method in the medicinal chemist’s toolbox for accomplishing this. Since the structures of the two molecules in question differ very slightly, any change in a physical or biological feature between the matched molecular pair can be more easily interpreted.

In 2004, Kenny and Sadowski coined the term Molecular Matched Pair (MMP) for a subset of QSAR; it is now a widely used concept in drug design processes [1]. Matched molecular pairs differ only in small single-point alterations, which are referred to as chemical transformations. As the structural difference between the two molecules is minimal, any differences in physical properties or observed biological effects can simply be linked to it. In 2010, Hussain and Rea published an approach to find matched molecular pairs and relate them to the distribution of value differences for each transformation, and it has since become a popular tool for analyzing huge chemistry datasets.

MMP is typically used to describe a pair of compounds that differ structurally at a single site because of a well-defined transformation accompanied by a change in a property value. To rationalize observed structure-property relationships (SPR) and compound optimization, the relationship between structural and property change is used. Aside from assisting in hypothesis creation and testing, MMP can also be used to find outliers, such as a pair of compounds that have a sudden change in a property, known as an activity cliff. These compounds are typically the most intriguing to investigate in the development of compounds aimed at increasing the property that exhibits this change.

GOSTAR provides tools for determining the matched molecular pairs and analyzing activity landscapes across compound datasets.


  1. Kenny P.W., Sadowski J. Structure modification in chemical databases. In: Oprea T., editor. Cheminformatics in drug discovery. Wiley-VCH Weinheim; Germany, 2004, 271.
  2. Hussain J, Rea C. Computationally efficient algorithm to identify matched molecular pairs (MMPs) in large data sets. J Chem Inf Model. 2010, 50(3), 339-348.

Interactive Property Space Exploration

Interactive Property Space Exploration

Lipophilicity plays a significant role in small molecule drug design and discovery. A partition coefficient, logP, can be used to describe the lipophilicity of an organic compound. It is expressed as the ratio of the unionized compound’s concentration in the organic and aqueous phases at equilibrium. The distribution of species in compounds containing ionizable groups is influenced by pH and the lipophilicity of a molecule is affected by its ionization state. As a result, the distribution coefficient (logD) of a compound is defined, which considers the dissociation of weak acids and bases. In aqueous conditions, highly lipophilic substances are often less soluble. Lipophilic compounds, on the other hand, may have good solubility in oils and lipids, making them good candidates for lipid-based formulations.

Lipophilicity influences potency, selectivity, permeability, absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties. High lipophilicity, with logP greater than five, is associated with limited solubility, increased clearance, and poor oral absorption. Furthermore, highly lipophilic drugs have a predisposition for interacting with hydrophobic targets other than the primary target, thereby enhancing promiscuity and toxicity. Low lipophilicity can reduce permeability and potency, resulting in lower bioavailability and overall efficacy. Compounds with logP greater than one or less than four are thought to have better physicochemical and ADME properties for oral drugs.

Lipophilicity is often regarded as a key indicator of potential promiscuity, with many property–promiscuity studies indicating that drug promiscuity rises with the increase in lipophilicity. This tendency is concerning since increasing a molecule’s lipophilicity can improve its efficacy at the primary target; however, this can be counterbalanced by an increase in off-target promiscuity [2]. Lipophilicity is a key element in determining a drug’s affinity for protein targets and in modulating ADMET characteristics. As a result, the combination of high target potency and high lipophilicity may increase the likelihood of ADMET-related attrition. 

Therefore, medicinal chemistry optimization needs to be balanced and multidimensional. GOSTAR empowers medicinal chemists to efficiently explore the property space against a variety of bioactivity endpoints.


  1. Gao Y, Gesenberg C, Zheng W. Oral Formulations for Preclinical Studies: Principle, Design, and Development Considerations, Developing Solid Oral Dosage Forms (Second Edition), Academic Press. 2017, 455-495.
  2. Armstrong D, Li S, Frieauff W, Martus H.J, Reilly J, Mikhailov D, Whitebread S, Urban L. Predictive Toxicology: Latest Scientific Developments and Their Application in Safety Assessment, Comprehensive Medicinal Chemistry III, Elsevier. 2017, 94-115.

X-Chem and Excelra’s GOSTAR Join Forces to Advance Drug Discovery for Challenging Targets

X-Chem and Excelra’s GOSTAR Join Forces to Advance Drug Discovery for Challenging Targets

London, United Kingdom and Hyderabad, India: February 9, 2022 – A new collaboration between data science and analytics leader, Excelra, and artificial intelligence pioneer X-Chem will accelerate preclinical drug discovery and aid scientists to find new drug candidates for currently hard-to-drug targets.

Machine learning and artificial intelligence are reshaping discovery and optimization of drug candidates. This synergistic new partnership between Excelra’s GOSTAR and X-Chem’s RosalindAI  will enable unique and powerful tools to predict small molecules, chemical, biological, and physical properties, accelerating time and resource-intensive stages of drug discovery from  hit identification to preclinical candidate selection.

“This is a perfect match between two of the best solutions for some hard challenges in drug discovery,” said Norman Azoulay, Director, Scientific Products. “We’re convinced this partnership will immediately help drug developers fuel their pipelines better.”

GOSTAR’s proprietary data set underwent rigorous analysis and large-scale ML model building to predict drug solubility in a recent joint study. X-Chem’s RosalindAI delivered superior and actionable results than other similar analyses using  well-known publically available datasets. The results confirmed that RosalindAI’s proprietary models are designed specifically to address challenges in chemical datasets, and when trained on the larger, more diverse GOSTAR data, yielded models twice as better than models trained on other datasets.

X-Chem SVP Noor Shaker says: “AI is revolutionizing drug discovery in ways never thought possible before, and RosalindAI is leading the way with AI tools that are accurate, scalable, and robust, enabling a transformation in preclinical drug discovery. Our collaboration with Excelra will enable us to leverage GOSTAR data and enable AI in ways never possible before.”

GOSTAR provides a unique 360⁰ view of over 8 million small molecules. The content in GOSTAR is meticulously curated with a proprietary QMS-ISO certified process. It captures the most up-to-date view of the chemical space with information on chemical structures and their biological properties, including binding, in-vitro, in-vivo, ADME, Tox, and physicochemical properties.

X-Chem’s RosalindAI is a leading AI platform for preclinical drug discovery. It provides a seamless interface to build best-in-class AI models for chemical design and optimization. It has also been successfully applied to the design of a novel chemotype for challenging targets and for the accurate prediction of chemical activities and properties. 

About Excelra and GOSTAR

Excelra’s data and analytics solutions empower innovation in life sciences from molecule to market. The Excelra Edge comes from harmonizing heterogeneous data sets, applying innovative bioinformatics know-how & technologies to accelerate drug discovery and development with reliable and result-oriented insights. Excelra’s GOSTAR is an application available for users to seek, find, and discover compounds. In addition, it is offered via APIs and as a downloadable dataset to power in-house libraries and machine learning models.

For more information about GOSTAR, visit 

About X-Chem

X-Chem is a leader in small molecule drug discovery services for pharmaceutical and biotech companies. As pioneers of DNA-encoded chemical library (DEL) technology, the company leverages its market-leading DEL platform to discover novel small molecule leads against challenging, high-value therapeutic targets. With industry-leading expertise in medicinal chemistry, custom synthesis and scale-up process chemistry and a proprietary AI platform to support and accelerate all aspects of drug discovery, X-Chem empowers its partners to effectively build drug pipelines from target to clinical candidate.

For further information, please visit 

For media inquiries, contact:

Jigesh Shah –

Noor Shaker  –


XtalPi Uses GOSTAR to Enhance its Intelligent Digital Drug Discovery Platform

XtalPi Partners with Excelra for GOSTAR to Enhance its Intelligent Digital Drug Discovery and Development Platform

Hyderabad, India/ Shenzhen, China: January 19, 2022 – Excelra, a leading global Data & Analytics organization, today announced the partnership for its Global Online Structure Activity Relationship Database (GOSTAR) with XtalPi Inc., an AI-based pharmaceutical biotechnology company reinventing the industry’s approach to drug research and development with its Intelligent Digital Drug Discovery and Development platform.

Excelra will provide ADMET datasets in the GOSTAR database to XtalPi Inc. as part of the partnership. GOSTAR’s ADMET data will power XtalPi’s predictive models. The data helps XtalPi with high precision and predictability to confidently tackle clinical failures of new chemical entities. The well-annotated, high-quality ADMET datasets of GOSTAR are built with a proprietary QMS-ISO certified curation process powered by NLP and human intelligence.

GOSTAR provides comprehensive information encompassing over 8 million compounds, manually curated from a variety of sources including patents and journal articles. The database contains over 29 million SAR associated data points. The well-structured relational database can be utilized for diverse applications across various stages of drug discovery and development lifecycle and aids in target validation, hit identification, early lead identification, and optimization.

Min Xu, Senior Scientist, Research Manager, XtalPi Inc., said, “In XtalPi Inc., we develop advanced AI-based algorithms to tackle the challenges in the drug design process. The size and quality of datasets are always a big concern for us to build high-accuracy predictive models. That is why we consider GOSTAR as a unique and precious resource. It has millions of data points covering different compounds’ ADMET properties and is also trustful, structured, and updated. We highly recommend GOSTAR to whoever is involved in the innovation of drug design methodologies.”

Norman Azoulay, Product Leader, Excelra, said, “Artificial Intelligence and Machine Learning is bringing a paradigm shift to drug discovery and development. This partnership will help train XtalPi’s models to accurately predict efficacy and safety parameters and to ultimately increase the success rate of drug design.”

About XtalPi:

XtalPi is a pharmaceutical technology company that is reinventing the industry’s approach to drug research and development with its Intelligent Digital Drug Discovery and Development platform. With tightly interwoven quantum physics, artificial intelligence, and high-performance cloud computing algorithms, XtalPi’s platform provides accurate predictions on the physiochemical and pharmaceutical properties of small-molecule candidates for drug design, solid-form selection, and other critical aspects of drug development. XtalPi is dedicated to improving the efficiency, accuracy, and success rate of drug research and development, and contributing to a healthier society worldwide. To know more, visit

About Excelra and GOSTAR

Excelra’s data and analytics solutions empower innovation in life sciences from molecule to market. The Excelra Edge comes from harmonizing heterogeneous data sets, applying innovative bioinformatics know-how and technologies to accelerate your drug discovery & development with reliable and result-oriented insights. Excelra’s GOSTAR is available as an application for users to seek, find, and discover compounds. In addition, it is offered via APIs and as a downloadable dataset to power in-house libraries and machine learning models.

For more information about GOSTAR, visit

For media inquiries, contact:

Jigesh Shah –


Silexon Deploys GOSTAR to Strengthen its AI-driven Drug Discovery Platform

Silexon Partners with Excelra, Deploying GOSTAR to Strengthen its AI-driven Drug Discovery and Biopharmaceutical Research Platform

Hyderabad, India and Nanjing, China: December 1, 2021 – Excelra, a leading global data & analytics provider, today announced its partnership with Silexon AI Technology, a next-generation biotech company with a scalable AI/ML (Artificial Intelligence and Machine Learning) platform for drug discovery and biopharmaceutical research.

Under the agreement, Excelra will provide ADMET and Binding Assays datasets from its GOSTAR database to Silexon. GOSTAR provides comprehensive information encompassing over 8 million compoundsand contains more than 28 million SAR associated data points. The well-structured relational database can be utilized for diverse applications across different phases of drug discovery and development and aids in target validation, hit identification, early lead Identification, and optimization.

Silexon’s AI/ML platform uses both high-quality, real-world biological data and outstanding machine learning capabilities to aid in de novo drug design, drug discovery, and biopharmaceutical research through its disruptive predictive tools that help streamline the discovery and design process, thus shortening the time to market for new life-saving drugs. Silexon focuses on innovative biomedicines with unique target product profiles for undruggable, difficult-to-drug, and novel targets in multiple therapeutic areas.

Anandbir Singh Brar, CEO of Excelra, said, “We are excited that GOSTAR has become the gold standard dataset for Biopharma and AI/ML companies. Our partnership with Silexon underscores GOSTAR’s ability to provide large, normalized, quality-controlled datasets that empower a wide range of predictive models. We are delighted to partner with Silexon and to be part of their journey in developing the AI4D platform for drug discovery.” 

Hainian ZENG, CEO of Silexon, said, “We look forward to combining the well-annotated GOSTAR data with data accumulated and generated by Silexon’s proprietary AI/ML models and wet-lab experiments. Using our AI/ML platform, we aim to address bottlenecks in drug discovery and biopharmaceutical research to ultimately deliver innovative therapeutic candidates with higher overall success rates. Our preliminary assessment gives us the confidence that GOSTAR’s database would be an important addition to our current training dataset for next-generation modeling.”

About Silexon:

Silexon is an emerging AI-empowered technology company that aims to create an open AI platform for strategic collaboration to facilitate data-driven biopharma research, empower drug R&D processes, and ultimately provide patients with greater access to innovative drugs for their unmet medical needs. Silexon stands for the integration of Silicon and Exon, and hence machine plus life. To know more, visit

About Excelra and GOSTAR

Excelra’s data and analytics solutions empower innovation in life sciences from molecule to market. The Excelra Edge comes from a seamless amalgamation of proprietary data assets, domain expertise, and data sciences to accelerate drug discovery and development.

Excelra’s Global Online Structure Activity Relationship Database (GOSTAR) is the largest repository for structured SAR content. Available as a one-stop data source, GOSTAR allows researchers to thoroughly explore the known chemical space around a target of interest. The database is manually curated by scientific teams who excerpt and enrich datasets from functional assays, in vitro, and in vivo studies. A variety of small molecule activities encompassing SAR, physicochemical, metabolic, ADME, and toxicological profiles are captured in GOSTAR

To know more, visit:

For media inquiries, contact:

Jigesh Shah –


Drug Discovery Databases

Types of Drug Discovery Databases

GOSTAR is an integrated platform of various standalone databases with cross-indexing of chemical compounds of interest with SAR, ADME, Toxicity, Preclinical/Clinical, Biological Targets, Structural information, Developmental pipeline, etc., along with extensive cross-references within the database as well as with external public databases and open-access repositories.

Medicinal Chemistry Database

A largest Reference centric database with data annotated from most referred mainstream Medicinal Chemistry Journals and enriches database with Pharmacodynamics, Pharmacokinetics, Efficacy, safety, metabolite and toxicity data for millions of small molecules tested in various In vitro, In vivo and ex vivo assays which are in early discovery in Drug development.

GOSTAR Med Chem knowledge base enables researchers to quickly and confidently identify the most promising compounds to take forward in the drug discovery process by covering the chemical, biological and Pharmacology space. Physiochemical properties like experimental LogP, LogD, solubility, etc, of small molecules, were captured for discovery compounds which enable scientists to generate new drug ideas by exploring the chemical space of new compounds.

Robust SAR activity against a target protein for compound(s) of interest and relative sphere of influence across other target space to measure Compound-Target selectivity could be achieved for discovery compounds through GOSTAR Med Chem Database.

Targets Database

A huge Biological Target centric database of chemical molecules excerpted from pharmacological Patents and various highly referred Medicinal Chemistry Journals, mapped with SAR data against various Biological Targets. This database can be further customized to suit any specific requirements or additional data. The following are various databases categorized based on Target Superfamilies.

  • Kinase Database
  • Protease Database
  • GPCR Database
  • Nuclear Hormone Receptor Database
  • Ion-Channel Database
  • Transporters Database
  • Transferases Database
  • Protease Database
  • Phosphatase Database

Clinical Candidate Database

Database of chemical compounds in different phases of clinical development globally, mapped with Pharmacological data, ADME, Phase Details, Biological Activities, Chemical space, Developmental pipeline, Approved/Suspended/Discontinued info, etc. Various features of include,

  • Compounds in the database include Preclinical and IND-filed to NDA-filed compounds (Pre-registration)
    The database also includes compounds whose development is suspended or discontinued
  • Data information includes developmental pipeline across the top to small scale pharma companies
  • Other drug-related information including Approval dates, Suspended/Discontinued reasons, Therapeutic Indications & Adverse events (mapped with standard nomenclature like ICD10, MedDra, etc.)
  • Biological activities are annotated from subscribed and freely-accessible journals, News releases, Conferences, Abstracts and Meetings besides others

Mechanism-Based Toxicity Database

Toxicity Related Database detailing toxic effects induced by chemical compounds (Discovery, Developmental or Drug-like Compounds) or their metabolites, mapped with their mechanism of action. Various features of Mechanism-Based Toxicity Database include,

  • Detailed information on toxic effects induced by compounds and/or their metabolites
  • Toxicity information includes carcinogenicity, tumorigenicity, mutagenicity, teratogenicity, neurotoxicity, cytotoxicity, etc.
  • Organo-specific Toxicity information specific to species, organ detailed mechanism behind the toxic effect of compound and/or metabolites
  • Diagrammatic representation/schema of metabolism
  • Biological activities are annotated from subscribed and freely accessible journals, Conferences, Abstracts, and Meetings among others.

Drug Approvals 2021

Drug Approvals 2021

Published on 13-Sept-2021

In 2021, the FDA has approved many novel products that serve previously unmet medical needs or significantly help to advance patient quality of life. The broad indication wise distribution of all CDER’s 2021 drug approvals indicates notable advances in drug discovery1,2.

New Drug Approvals & Drugs in Pipeline (FDA) for 2021*

Table 1. Approved Drug List
Table 2. Drugs in Pipeline

*This information is updated as July 31, 2021; listed alphabetically by trade name.

Significant drug launches of 2021

  • Verquvo (Vericiguat, MERCK SHARP DOHME, 01/19/2021)
    Mitigates the risk of cardiovascular death and hospitalization for chronic heart failure 
  • Cabenuva (Cabotegravir and Rilpivirine (Co-Packaged), VIIV HLTHCARE, 01/21/2021)
    Treats HIV 
  • Lupkynis (Voclosporin, AURINIA, 01/22/2021)
    Treats lupus nephritis 
  • Tepmetko (Tepotinib, EMD SERONO INC, 02/03/2021)
    Treats non-small cell lung cancer 
  • Ukoniq (Umbralisib Tosylate, TG THERAPS, 02/05/2021) 
    Treats marginal zone lymphoma and follicular lymphoma 
  • Evkeeza (Evinacumab-Dgnb, REGENERON PHARMACEUTICALS, 02/11/2021) 
    Treats homozygous familial hypercholesterolemia
  • Cosela (Trilacicilib Dihydrochloride, G1 THERAP, 02/12/2021), 
    Mitigates chemotherapy-induced myelosuppression in small cell lung cancer
  • Amondys 45 (Casimersen, SAREPTA THERAPS INC, 02/25/2021)
    Treats Duchenne muscular dystrophy
  • Nulibry (Fosdenopterin Hydrobromide, ORIGIN, 02/26/2021) 
    Reduces the risk of mortality in molybdenum cofactor deficiency Type A 
  • Pepaxto (Melphalan Flufenamide Hydrochloride, ONCOPEPTIDES AB, 02/26/2021)
    Treats relapsed or refractory multiple myeloma 
  • Azstarys (Serdexmethylphenidate Hydrochloride; Dexmethylphenidate Chloride, COMMAVE THERAP, 03/02/2021)
    Treats attention deficit hyperactivity disorder 
  • Fotivda (Tivozanib Hydrochloride, AVEO PHARMS, 03/10/2021)
    Treats renal cell carcinoma 
  • Ponvory (Ponesimod, JANSSEN PHARMS, 03/18/2021)
    Treats relapsing forms of multiple sclerosis
  • Zegalogue (Dasiglucagon Hydrochloride, ZEALAND PHARMA, 03/22/2021)
    Treats severe hypoglycemia 
  • Qelbree (Viloxazine Hydrochloride, SUPERNUS PHARMS, 04/02/2021)
    Treats attention deficit hyperactivity disorder 
  • Nextstellis (Drospirenone; Estetrol, MAYNE PHARMA, 04/15/2021)
    Prevents pregnancy 
  • Jemperli (Dostarlimab-Gxly, GLAXOSMITHKLINE, 04/22/2021)
    Treats endometrial cancer 
  • Zynlonta (Loncastuximab Tesirine-Lpyl, ADC Therapeutics SA, 04/23/2021)
    Treats certain types of relapsed or refractory large B-cell lymphoma 
  • Empaveli (Pegcetacoplan, APELLIS PHARMS, 05/14/2021)
    Treats paroxysmal nocturnal hemoglobinuria 
  • Rybrevant (Amivantamab-Vmjw, JANSSEN BIOTECH, 05/21/2021)
    Treats a subset of non-small cell lung cancer 
  • Pylarify (Piflufolastat F-18, PROGENICS PHARMS INC, 05/26/2021)
    Identifies prostate-specific membrane antigen-positive lesions in prostate cancer 
  • Lumakras (Sotorasib SIB, AMGEN INC, 05/28/2021)
    Treats types of non-small cell lung cancer 
  • Truseltiq (Infigratinib Phosphate, QED THERAP, 05/28/2021)
    Treats cholangiocarcinoma whose disease meets certain criteria
  • Lybalvi (Olanzapine; Samidorphan L-Malate, ALKERMES INC, 05/28/2021)
    Treats schizophrenia and certain aspects of bipolar I disorder
  • Brexafemme (Ibrexafungerp Citrate, SCYNEXIS, 06/01/2021)
    Treats vulvovaginal candidiasis
  • Aduhelm (Aducanumab-Avwa, BIOGEN INC, 06/07/2021)
    Treats Alzheimer’s disease 
  • Rylaze (Asparaginase Erwinia Chrysanthemi (Recombinant)-Rywn, JAZZ PHARMS, 06/30/2021)
    Treats acute lymphoblastic leukemia and lymphoblastic lymphoma in patients who are allergic to E. coli-derived asparaginase products, as a component of a chemotherapy regimen 
  • Kerendia (Finerenone, BAYER HEALTHCARE PHARMACEUTICALS INC, 07/09/2021)
    Reduces the risk of kidney and heart complications in chronic kidney disease associated with type 2 diabetes 
  • Fexinidazole (Fexinidazole, DNDI, 07/16/2021)
    Treats human African trypanosomiasis caused by the parasite Trypanosoma brucei gambiense 
  • Rezurock (Belumosudil, KADMON PHARMS LLC, 07/16/2021)
    Treats chronic graft-versus-host disease after failure of at least two prior lines of systemic therapy
  • Bylvay (Odevixibat, ALBIREO PHARMA INC, 07/20/2021)
    Treats pruritus 
  • Twyneo (Tretinoin and benzoyl peroxide, SOL-GEL TECHNOLOGIES LTD, 07/26/2021)
    It is a topical retinoid and antibacterial fixed-dose combination for the treatment of acne vulgaris in adults and children 9 years of age and older
  • Saphnelo (Anifrolumab, AstraZeneca, 07/30/2021)
    It is a type I interferon (IFN) receptor antagonist indicated for the treatment of adult patients with moderate to severe systemic lupus erythematosus (SLE), who are receiving standard therapy 

Significant Drug launches in Pipeline for 2021

  • Oteseconazole (VT-1161, MYCOVIA PHARMACEUTICALS INC)
    It is an investigational oral antifungal in development for the treatment of recurrent vulvovaginal candidiasis (RVVC)
    It is an orally bioavailable, broad-spectrum penem β-lactam antibiotic in development for the treatment of infections caused by multi-drug resistant bacteria
  • Brixadi (Buprenorphine, BRAEBURN INC)
    It is a long-acting partial opioid agonist injection formulation in development for the treatment of opioid use disorder
  • Tenapanor (ARDELYX INC)
    It is a sodium/hydrogen exchanger 3 (NHE3) inhibitor in development for the control of serum phosphorus in adult patients with chronic kidney disease (CKD) on dialysis or Hyperphosphatemia of Renal Failure
  • Libervant (Diazepam, AQUESTIVE THERAPEUTICS INC)
    It is a buccal film formulation of the approved benzodiazepine diazepam in development for the management of seizure clusters
  • Roxadustat (FG-4592, FIBROGEN INC)
    It is a first-in-class, orally administered small molecule hypoxia-inducible factor prolyl hydroxylase (HIF-PH) inhibitor in development for the treatment of anaemia of chronic kidney disease (CKD)
    It is an investigational, potential first-in-class anti-thymic stromal lymphopoietin (TSLP) monoclonal antibody in development for the treatment of severe asthma
  • LV-101 (Carbetocin intranasal, LEVO THERAPEUTICS INC)
    It is an oxytocin analog in development as a treatment for hyperphagia and behavioral distress associated with Prader-Willi syndrome (PWS)
  • Teplizumab (PROVENTION BIO INC)
    It is an investigational anti-CD3 monoclonal antibody (mAb) in development for the delay or prevention of clinical type 1 diabetes (T1D) in at-risk individuals
    It is a novel, oral angio-immuno kinase inhibitor in development for the treatment of pancreatic and non-pancreatic neuroendocrine tumors (“NET”)
  • Lenacapavir (GILEAD SCIENCES INC)
    It is an investigational, long-acting HIV-1 capsid inhibitor in development for the treatment of HIV-1 infection in heavily treatment-experienced (HTE) people with multi-drug resistant (MDR) HIV-1 infection
    It is an investigational RNAi therapeutic in development for the treatment of the polyneuropathy of hereditary transthyretin-mediated (hATTR) amyloidosis in adults
  • Pedmark (Sodium thiosulfate, FENNEC PHARMACEUTICALS INC)
    It is a cisplatin neutralizing agent in development for the protection against hearing loss in pediatric patients receiving cisplatin chemotherapy
    It is a protein kinase-R (PKR) activator in development for the treatment of adults with pyruvate kinase (PK) deficiency
  • Arimoclomol (ORPHAZYME A/S)
    It is an investigational Heat-Shock Protein amplifier in development for the treatment of Niemann-Pick disease Type C (NPC)
  • Ruxolitinib (INCYTE DERMATOLOGY)
    It is a JAK1/JAK2 inhibitor formulated for topical application in development for the treatment of atopic dermatitis and vitiligo
  • Zimhi (Naloxone hydrochloride, ADAMIS PHARMACEUTICALS CORPORATION)
    It is a high-dose formulation of the approved opioid antagonist naloxone in development for the treatment of opioid overdose
    It is a topical aryl hydrocarbon receptor (AhR) modulating agent in development for the treatment of plaque psoriasis and atopic dermatitis
  • Plinabulin (BEYONDSPRING INC)
    It is a selective immunomodulating microtubule-binding agent (SIMBA) in development for use in combination with granulocyte colony-stimulating factor (G-CSF) for the prevention of chemotherapy-induced neutropenia (CIN)


  1. US FDA


GOSTAR Content Updates - 2021

Published on 13-Sept-2021

GOSTAR is the largest manually annotated structure-activity relationships (SAR) database of small molecules published in mainstream medicinal chemistry journals and patents. Compounds from both discovery and development stages targeting all target families are covered. Along with SAR, key properties like ADME, and Toxicity are captured. This relational database enables users to navigate and analyze the massive content of small molecules to derive insightful decisions in the design and discovery of novel compounds.

Content Coverage

The GOSTAR database is composed of many different types of content, from the scientific literature to publicly available material.

  • MedChem Journals
  • Patents
  • FDA/EMEA/PMDA Reports
  • Clinical Trial Registries
  • Scientific Reviews
  • Company websites
  • Books
  • Conferences
  • Public Sources
Fig 1. A quick view of content covered and sources of the content

Preclinical Candidates Covered in 2021 (until July’2021)

In the year 2021, the GOSTAR database is enriched with various preclinical compounds acting against various indications like COVID-19, Non-alcoholic steatohepatitis (NASH), Hepatitis virus infections, HIV infections, Cardiovascular diseases, and various cancers.

Few significant drug inclusions until July 31, 2021: 

  • Synflorix
  • AZD1222
  • Benaglutide
  • GSK-1557484A
  • MRNA-1273

Target Space Covered in 2021 Updates


New content is updated for more than 2400 protein targets into the GOSTAR database until July 31, 2021.

Table 2: List of top 20 targets covered

Type of Content

Further deep analysis of the content covered in 2021 is shown in figure 2. Of the 1.2 million SAR rows added to GOSTAR, functional in-vitro and in-vivo contribute 41% to data, binding constitutes 33%, and 5% of content consists of ADME properties. 2% of content covers toxicity properties of compounds covered in 2021 and the rest 19% represents other property types including physicochemical properties.

Fig 2. Assay wise distribution of SAR content
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Biologics – The Biotech Drugs Transforming Medicine

Biologics - The Biotech Drugs Transforming Medicine

Published on 13-Sept-2021

Biologics, also known as biological products, are any type of medicines derived from living organisms such as humans, animals, or microorganisms via highly complex manufacturing processes and administered under closely monitored conditions. This is in contrasts to traditional non-biologic pharmaceutical drugs, which are synthesized in a laboratory through chemical processes without the use of components of living matter. Cancer, infectious diseases, autoimmune disease are among the ailments for which biologics are used to prevent, treat, or cure (Fig. 1).

Figure 1. Biologic medicines in development by therapeutic category [1].

Note: Some medicines are being explored in more than one therapeutic category.

Biologics include a wide variety of products such as monoclonal antibodies, vaccines, gene and cell therapies, and recombinant proteins (Fig. 2).

Figure 2. Biologic medicines in development by product category [1].

Monoclonal antibodies are by far the most researched category of biologics with at least 338 therapeutic mAbs currently being developed by pharmaceutical companies [1].

Monoclonal antibody (mAb) – the bestselling category of biological products.

Antibody engineering has significantly advanced ever since the approval of the first monoclonal antibody by the United States Food and Drug Administration (US FDA) in 1986 [2]. Therapeutic antibodies currently available in the market are safe with fewer adverse effects owing to their high specificity. Consequently, antibody drugs have become the leading class of newly developed drugs in recent years. Eight of the top ten bestselling drugs worldwide in 2018 were biologics. In 2018, the global therapeutic monoclonal antibody was worth roughly US$115.2 billion, with revenues expected to reach $300 billion by 2025 (Fig. 3) [3].

Figure 3. Timeline from 1975 showing the successful development of therapeutic antibodies and their applications [3].

As of December 2019, US FDA had approved 79 therapeutic mAbs, including 30 for cancer treatment [4].

Best-selling biotech drugs worldwide

AbbVie’s Humira and Merck’s Keytruda are among the top-selling biotechnology drugs in the world, generating 19.6 billion and 11.1 billion U.S. dollars, respectively, in 2019 (Fig. 4) [5]. Oncology, autoimmune/immunology, hematology, ophthalmology, and dermatology are among the top five therapy areas in 2019. Oncologic treatments account for six of the top-selling drugs in 2019, making oncology the most targeted field [6].

Figure 4. Top selling biotech drugs worldwide in 2019 [6].

Bristol Myers Squibb, AbbVie, Pfizer, and Roche are four pharmaceutical companies with more than one best-selling drug of 2019. Bristol Myers Squibb had the most top-selling drugs (Eliquis, Opdivo, and Revlimid) in 2019, accounting for 63% of the company’s total revenue. Whereas AbbVie’s revenues in 2019 were significantly reliant on its main products (Humira and Imbruvica), which accounted for 72% of the company’s total revenues [6]. The United States spent approximately 45 billion U.S. dollars on biotechnology research and development. In addition, the United States had approximately 34% of the world’s share of biotechnology patents filed in 2014, while Germany filed 8% of global biotech patents [5].

The Rise of Biosimilars

A biosimilar is a biologic that is similar to another biologic medicine (known as a reference product) that has already been approved by the FDA in the United States. In terms of safety, purity, and potency, biosimilars are very similar to the reference product, but there may be minor differences in clinically inactive components. The biologics and biosimilars industry in the United States is fast expanding, and as new medications are introduced, the benefits for patient access and cost management will continue to grow. There are 18 biosimilars on the market in the United States as of November 2020, competing against seven reference biologics, with ten more FDA-approved biosimilars expected to hit the market in the coming years [7].

Biosimilars save money in the long run, with higher savings coming from newer launches competing against more expensive drugs. The gap between the originator and the mean Average Sales Price (ASP) of their biosimilars ranged from 8.1 percent to 45.1 percent lower than the originator products as of July 2020 (including insulins) [7]. Biosimilars saved 6.5 billion U.S. dollars annually in the second quarter of 2020, and savings are expected to exceed 100 billion U.S. dollars over the next five years [8].

A biopharmaceutical product knowledge base is the need of the hour

Antibodies are the most successful class of biotherapeutics because of their binding versatility [9]. With the rapid growth of therapeutic antibody research, the chances of a specific antibody being the only one against a certain antigen are decreasing. Understanding the methods used to produce competing antibodies, as well as their pros and cons, can be extremely helpful in moving therapeutic antibodies forward. Data from clinical trials dominate the scientific literature on therapeutic antibodies, rather than the details of pre-clinical development that is underway for nearly two-thirds of all therapeutic antibodies. The information on the latter could only be obtained from patents. Many researchers are put off by patents’ opaque and archaic language but hidden in the text of these files are details about antibody sequences, assay techniques, epitopes, and much more. Patent applications are usually the first public disclosure of novel antibodies, often months or even years before conference papers or clinical trials. Researchers can identify novel antibodies in early stages of development months or years before they are formally announced by mining the patent literature.

There are very few databases that harvest this information. The IMGT Monoclonal Antibody Database and WHOINNIG are two non-commercial resources for antibody research. Other databases that aren’t unique to antibodies, such as ChEMBL, DrugBank, and KEGG DRUG, also capture WHO data. Most databases deliver additional metadata for their therapeutic entries, such as clinical trial status, companies involved in the development, target specificity, and alternative names. While these archives include sequence information, it is currently not possible to query them by sequence or to bulk-download relevant collections of therapeutic sequences for direct bioinformatic analysis.

Excelra is strongly positioned to deliver tailor-made curation on chemically defined antibodies (i.e., antibodies with a known primary amino-acid sequence) connected with their antigenic target, which can be either a protein or a chemical entity.

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  1. PhRMA [Pharmaceutical Research and Manufacturers of America] (2013). Medicines in Development: Biologics. 2013 Report. Accessed 10 Jul 2021.
  2. Ecker, D. M., Jones, S. D., Levine, H. L. The therapeutic monoclonal antibody market. MAbs2015, 7, 9–14.
  3. Lu, R. M., Hwang, Y. C., Liu, I. J. et al.Development of therapeutic antibodies for the treatment of diseases. J Biomed Sci2020, 27 (1), 1-30.
  4. The Antibody Society (2019). In: Approved antibodies. Accessed 10 Jul 2021.
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  6. PharmaIntelligence (2020). Top 10 Best-Selling Drugs of 2019. Accessed 10 Jul 2021.
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