Anandbir Singh Brar, CEO
In an attempt to fend off the tide of declining success rates and stagnant pipelines, pharmaceutical companies are sailing into alternative realms of R&D innovation. A successful traverse, however, dictates a requirement: A novel approach to drug discovery and development while navigating through a time and resource intensive data-driven methodology across the value chain. While life sciences organizations struggle to find a comprehensive solution to this triage, Anandbir Singh Brar—a serial entrepreneur—sought an end to this intricate challenge and with that vision laid the cornerstone of Excelra Knowledge Solutions. This Hyderabad based company adopts a data-driven approach by integrating scientific, clinical, medical, and outcomes data from across the drug discovery and develop-ment value chain. It further adds a layer of machine learning and predictive analytics to come up with insightful solutions. Today, the company boasts of doing business with nearly all top 20 Pharma and Biotech organizations across the globe.
Excelra’s Goal: To help find the right drug for the right patient for the right disease.
It’s in Excelra’s DNA: the prowess to adopt a techno-human approach, which is a combination of automation and manual intervention, to assist life sciences organizations to structure huge data-sets and drive decision-making insights. The company’s proprietary algorithms and demonstrable analytical know-how span across pre-clinical, clinical, and post-clinical phases of the drug discovery and development life cycle.
Excelra’s subscription-based knowledge platform allows gleaning insights from its large data repository, constantly updated to keep up with the latest research and development.
Excelra GOSTAR is a comprehensive Structure Activity Relationship (SAR) database that houses 7 million compounds, 24 million SAR data-points, screened from a wide array of biological and chemical literature that includes 2.2 million patents, 331,000 journals, and more. GOSTAR is instrumental in furnishing information on SAR, ADME, toxicity, preclinical, clinical, and structural data.
GOBIOM is the second significant piece of the data platform. It is a repository of biomarkers reported in preclinical, clinical, and exploratory phases. Each biomarker is lined with its therapeutic area, target, and drug, which contains information about biochemical, genomic, imaging, metabolite data along with more than 18,000 therapeutic indications. Complementing the aforementioned data repositories, Clinical Trial Outcome Database (CTOD) gives a definitive edge to Excelra’s knowledgebase platform. It consolidates valuable information from premium medical literature and research papers in a controlled and standardized fashion. With all these data skills, the company in the more recent years has developed a compelling analytics service offering—essentially the insight on the data. These analytics are focused on computational biology, computational chemistry, in-silico analysis, portfolio maximization services, and commercial analytics.
Recently, Excelra assisted a North America based large life sciences organization that struggled to rescue a failing clinical program due to safety issues. The company had spent millions of dollars and wanted to revive the program. Excelra came with a predictive repurposing solution. By consolidating the data collected from the organization, Excelra drew a relationship between the nature of the molecule, its design, target, and therapeutic indication. “With our proprietary platforms and algorithms, we were able to draw insights and repurpose the molecule for an alternative indication. Today, we can confidently say the molecule has a different clinical program showing very good results based on Excelra hypothesis,” adds Brar, CEO, Excelra.
Going forward, Excelra endeavors to focus on niche therapies, bringing in their expertise to neglected diseases and to clinical drug discovery realms. One of the key areas that the firm wants to capitalize on is evidence synthesis and communication strategies, which is essential for pharma companies to demonstrate the value proposition of newer technologies. In addition, Excelra visions to continue focusing on data sciences and leverage new machine learning technologies to help generate more ways of solving R&D challenges.