A drug’s journey from initial conceptualisation to hitting the market place remains a costly, complex and time-consuming process with no guarantee of success, informs Swetabh Pathak, Co-founder and CEO, Elucidata to Akanki Sharma, while elaborating more on the process of drug discovery and the role data science plays in making the process easier
What led to the origin of Elucidata and what products and services does it offer?
Advancement in technology coupled with reducing costs, has shifted the focus in drug discovery from data generation to data analysis. With biological data being produced at an unprecedented rate today, pharmaceutical companies require faster data analytics to gather relevant insights in time. This growing need for fast data mining spurred the origin of Elucidata, a data analytics startup that helps in drug discovery. Our mission is to use data analytics and to transform decision-making processes in R&D labs in biotechnology and pharma companies. We build algorithms and software to process, analyse and visualise large omics datasets across metabolomics, genomics, transcriptomics and proteomics, among others. We build our own products and provide customised solutions for our partners and analyse datasets to answer specific questions. Elucidata has already raised a funding of $1.7 million and is working with some of the leading global pharma companies.
Our products include
- Polly: A cloud-based, integrative omics data analytics platform, that can drastically transform the end-to-end drug discovery process and allow for more rapid data turnaround and analysis. Polly can easily adapt to several types of data workflows and features an array of applications that can process, analyse, integrate, and visualise biological data.
- El-MAVEN: An LC-MS data-processing engine for large-scale metabolomic experiments that can handle large datasets (tens of GBs), with an interface built for ease of processing and visualising hundreds of samples.
How important is Elucidata’s role in pharma companies? Do you target both domestic as well as international pharma companies?
In the past, researchers and scientists were hesitant in using data science to further advance the development of new pharma drugs and other medical therapies. However, they soon realised that traditional analytical tools were inefficient in analysing large sets of data, and how dependent these two fields are on each other. The need for better data analysis platforms has been felt acutely in recent times. This is where Elucidata’s role becomes crucial. With the use of data science and cloud-based metabolomics data analytics platforms like Polly, the rate at which new medicines are being approved is changing. We are seeing an explosion on data for the pharma industry and the ability to integrate data sets has moved forward massively due to companies like ours, helping in the advancement of drug discovery. At present, we are working with leading global companies with presence across markets, including India and we hope to expand our presence across both domestic and international pharma companies in the coming years.
Kindly elaborate on the role of data science in drug discovery.
There are four key areas in pharma where big data can play a crucial role:
- Omics data management The success of genomic sequencing techniques and their affordability has increased their importance in clinical diagnosis and in research. However, analysing them and extracting salient features will help in diagnosis is a job for big data analytics.
- Clinical trial management Conducting large population trials with enough diversity and across multiple study sites is an odious task. Conventional data management and analytical tools may prove insufficient to scale up studies and to make sense of diverse streams of data. Data analytics and management platforms can help by enabling rapid turnaround. Allowing scientists and doctors to respond in real-time should help increase the chances of success of clinical trials.
- Drug candidate selection and pipeline development Using complex algorithms to screen large databases containing biological, chemical and clinical information can help whittle down the right candidates amongst thousands, probable for testing. Advanced analytics is helping analyse clinical data and candidate profiles.
- Orphan drugs, rare diseases and drug repurposing
With big data analytics, companies can also identify specific subpopulations for which a ‘failed’ drug can still be a success. This practice of drug repurposing is especially beneficial to patients suffering from rare diseases that may not be commercially attractive for dedicated product development.
What bottlenecks does the pharma industry face in the drug discovery process in India? How can Elucidata help eliminate them?
As a developing market, India is figuring out the best standards for itself. But there are certain global challenges that everyone, including the Indian pharma industry is grappling with. Firstly, it usually takes years of effort, money and data mining to bring a drug from bench to bedside. A drug’s journey from initial conceptualisation to hitting the marketplace remains a costly, complex and time-consuming process with no guarantee of success. According to a recent report by Pharmaceutical Research and Manufacturers of America, it takes an average at least 10 years for a drug to make the journey from discovery to the marketplace at an average cost of $2.6 billion. Data science aims to thereby streamline the process involved in selecting a candidate drug, developing it, getting it into the clinic and then to the public. Our main goal is to shorten the drug discovery process. Another challenge in the drug discovery process is the analysis of large sets of data. Traditional analytical tools fall short in analysing huge amounts of data. Here, data science plays a pivotal role. Using data analysis in pharma simplifies comprehensive datasets that are troublesome to understand with conventional programming, equipment and techniques. By finding affiliations and understanding examples and patterns inside these datasets, we can possibly improve care, lower expenses, and spare lives. Data science can help make informed and educated choice in research and development helping in the creation of new drug therapies within the pharma industry.
How does Elucidata aim to enable advancements for pharma brands in India? Can you share a brief about your partners?
We are keen to work with India biopharma to bring us to the forefront of global drug discovery. We have also been in active conversations with a few labs, hospitals and pharma companies in India. We believe our technology will be useful to companies working on India-specific problems as well. For instance, diabetes is an epidemic in India. We have run a lot of analysis related to metabolic diseases. This kind of technology will be useful to any company working on a better cure for diabetes. We are currently working with global brands like Pfizer, Aruon, Yale School of Medicine, Labs at Princeton, and UCLA to name a few. We hope to expand our presence globally in the coming future.
What opportunities do you see for new age start-ups in the healthcare technology space?
Quick advances and innovation in big data and Artificial Intelligence (AI) are effectively changing the life science space — thanks to technology and numerous online healthcare products. There is a lot of information driven by patient profiling, consistency, administrative prerequisites, and logical research. Startups are using this information to support a wide scope of healthcare capacities around clinical choice help, illness observation, and clinical investigation. There is a tremendous opportunity in big data, gene-based studies, omics research and improving patient care via AI. We believe this is just the beginning. We are moving towards a technology-enabled, better-managed future of healthcare and pharma services.
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