This Pune Based Startup Is Helping Pharma Companies Leverage Analytics Across Various Drug Development Processes

Founders–Gaurav Tripathi (L) Dr. Gunjan Bharadwaj (R)

This 2011 founded company is one of the few that caters to life sciences industry offering Data as a Service (DaaS) and Continuous Analytics as a Service (CaaS) products. Headquartered in Frankfurt and with office in Pune, this startup calls itself “The Intelligent Machine”, and leverages AI and advanced analytics to help global life sciences and pharmaceutical organisations expedite drug development process across all stages— preclinical, clinical, regulatory and commercial.

What makes Innoplexus unique is its strong focus on IP and at the same time on life sciences. “We don’t claim to know it all, and nor are we domain experts who claim to know our customer’s business better than them. What we do really well is provide a platform to facilitate self serviced problem solving. We have crawled and indexed over a 300 Terabytes of scientific data. Curated data resides in a single repository and includes 365k+ clinical trial databases, 200+ biological databases, all major patent offices, regulatory agencies, patient forums which includes 27 million publications & 20 million patents”, said Gaurav Tripathi, Co-founder, Innoplexus, that flaunts a strong investor backup. It serves to 20 global pharma giants providing AI assistance along with financial consulting services.

The idea behind Innoplexus

After realising that in the current scenario of big data, analytics and machine learning, companies are still curating data manually and that data providers who perform this task sell manually curated data to customers at a very high price, the founders decided to extend decision support to small and mid-sized companies enabling them with access to insights in real-time, in a comprehensive and cost effective manner. “We’re helping organisations move to continuous decision making by generating insights from structured and unstructured private and public data. Innoplexus aims to transform decision making in enterprises into a continuum”, he said.

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Artificial intelligence plays a central role here helping in discovering the ‘usefulness’ of TBs of data as it cannot be done manually. “Computer vision and NLP helps us in ‘extracting’ the information from all different types of data sources e.g. posters, images, pdf files, web pages etc. and helps in discovering different types of entities and classifying them correctly”, he said.

Currently with a team of 140 and a presence in Europe, USA, India and UK, the foundation of Innoplexus was laid at IIT Mumbai Gaurav Tripathi and Gunjan Bharadwaj, who became friends while setting up a Hindi newspaper at the institute. After their graduation in 2005, the two worked in consulting and various startups before meeting again in the end of 2010 to discuss how best to merge consulting with machine learning. “The idea was to focus on building products and real IP instead of making money the easier way by taking up all kinds of projects”, shared Tripathi.

In initial two years, they worked on client consulting and realised that collating real-time

data was the future. By the third year, they observed that the pharma industry was in need of understanding public data. They got a big break with a customer and started building more products targeting specific business functions in pharma. Today, Innoplexus has clients across Europe and the United States. The company offers analytics as a service and the business model is to charge customers on a “payasyougo model”.

Adoption of AI in pharma and life sciences

The team believes that one of the most compelling, and beneficial applications of AI and machine learning technology is in medical research. Tripathi explains that currently researchers and medical practitioners are confined to knowledge they possess personally, or that their organisation has paid for on unwieldy and outdated systems. “As a result, many turn to more generic search solutions like Google to try and find relevant data. These outlets fall short, as their algorithms don’t take into account the complexity involved with medical and life sciences research”, he said. This is where they face competition from generic search engines during drug development, and others such as Thomson Reuters, OpenQ, informa, Bloomberg, FACTSET, MDC Partners, IBM, Palantir, MicroStrategy, Bioxcel, Mu Sigma, Fractal, Apple etc.

The team believes that platforms leveraging AI and machine learning specifically tailored to the needs of researchers are an ideal solution, as these tools can help create search alternatives that match the right kind of data to research queries. They can also expand a researcher’s information resources to any credible data that’s ever been published.

“As an incredibly complex and context based industry, healthcare can benefit from the use of AI for everything from research to the development of new drugs. The data challenges in life sciences are complex and multidimensional, and therefore require more intelligent systems to support decision making. As more intelligent systems emerge, problems that used to be unsolvable now seem far less daunting”, says Tripathi.

Healthcare industry has witnessed significant growth in data resources, with estimations of a growth in global healthcare analytics segment to $34.27 billion by 2022. With more pharma companies planning to make analytics solutions a priority in the coming year, the startup aims to take raw unstructured data and give it a form, making it more useful.

“Making data and insights available instantly takes the pain out of data collection, curation and analysis, which means companies can make more informed decisions faster. Healthcare and life sciences are currently entering a wave of innovation thanks to disruptive, computer based technologies. Paradoxically, the evolution of machine learning, which raises the threshold of intelligent analysis beyond that of the human brain, can teach us more about what it means to be human”, he said.

Products by Innoplexus with a focus on AI, ML and Analytics

The products by the startup use machine learning and interactive visualisations for generating intelligence and displaying insights in an intuitive way, and they are— iPlexusTM and kPlexusTM. While prior is an end to end platform for life sciences to generate intelligence and insights across preclinical, clinical, regulatory and commercial stages of drug across therapeutic areas and indications, kPlexus is the world’s first self-service platform for management of Key Opinion Leaders (KOLs) designed for Pharmaceutical and Life Sciences industry which will help its users in effectively scaling and accelerating KOL engagement efforts. It leverages iPlexusTM platform for processing millions of global profiles from public and proprietary data using proprietary algorithms for generating the right set of KOLs for any given indications.

iPlexus is an innovative and intuitive semantic search facility which understands biomedical concepts and allows users to see relevant results in context across multiple data sources like publications, clinical trials, congresses, theses and much more. It helps users get quick, crisp and summarized snapshots of the current landscape in any given context, be it important deals and mergers, fast-tracked clinical trials and much more helping them make informed and data backed decisions.

The founders believe that information is scattered, making it difficult for consumption and not readily available for a researcher. “Our product, aims at bridging this gap by collecting, cleaning, categorizing and arranging various kinds of information readily for the user. It offers confidence that the researcher will not miss any significant information that might impact his/her research later”, he said.

The kPlexus platform on the other hand facilitates KOL discovery and management. Finding top people in any given domain is hard. Usually companies rely on personal knowledge or peer surveys which are biased on multiple levels. It is difficult and time consuming to prepare reports on work done by each KOL for finding the most suited ones. This platform by Innoplexus overcomes these shortfalls.

The success story

The increased use of artificial intelligence and machine learning is steadily shifting the paradigm of medical research and treatment, providing researchers real-time access to every white paper and clinical case study conducted on a genetic disorder. Being able to develop such an elaborate database of information allows researchers to not only understand the full scope of a medical condition, but further shorten the amount of time it takes to develop a cure. Innoplexus is helping pharma companies achieve just that.

Whether a drug developer is seeking existing research, a medical researcher is searching for alternative treatments, or a practitioner is attempting to find data on a particular disease increasing access to relevant information removes roadblocks to discovery and fuels rapid growth. “The diversity of experience in our leadership team has been an asset in creating solutions that meet the industry’s needs, while keeping our business model sustainable”, he shared.

With a team that is passionate about their clients, the startup has been keeping the customers engaged with the product offerings that suits them well in their overall development.

Getting the right data can be a daunting task

Being a startup in the AI space, Tripathi shares that getting the right data, nature of data, crossing the belief barrier and using the right tools can be some of the major challenges that they face being an AI startup. “Data in life sciences is deep, dense and diverse where conventional NLP has always failed to deliver, so we needed to develop new methods. It is also challenging to make clients believe that the results generated by machines can be better at times than those generated by humans”, he said on a concluding note.

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Srishti Deoras
Srishti currently works as Associate Editor at Analytics India Magazine. When not covering the analytics news, editing and writing articles, she could be found reading or capturing thoughts into pictures.

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