Innoplexus’s New North American Affiliate is Bringing AI Innovation to the US Pharma Industry
It’s no secret that the American pharma industry is facing its fair share of challenges. From rising drug prices to corruption charges, the issues that American pharmaceutical companies face today demand innovative solutions. Fortunately, a leading pharmaceutical innovation company is hoping to deliver these solutions with the launch of their North American affiliate.
Innoplexus AG has been bringing AI- and Big Data-based innovation to the global pharmaceutical industry since 2011. Just last week, they announced the opening of Innoplexus Americas, which will focus on empowering pharmaceutical companies in the United States and Latin America to achieve new heights of creativity and innovation.
We spoke with Lawrence Ganti, a former Merck executive who is now serving as Innoplexus America’s inaugural CEO, about the company’s plans to solve some of the pharmaceutical industry’s most urgent challenges.
Why is now the right time for Innoplexus to expand its presence in North America? What big opportunities do you see for innovation in pharma?
LG: We took our time to build the technology and had the luxury of working with early adopters in Europe. This is key because, in Europe, data privacy standards are more strict, so we were able to learn and adapt our technology to those standards prior to entering the US Market. Most companies focus on the US market first and then go east. We took an opposite approach, again to ensure our products were “ready” and more “robust” for the US Market.
While much of the innovation comes from the US, most of the US Pharmas are more risk averse when it comes to new technology. Timing is good now, as now, I can go to potential clients in the US with a proven product being used at commercial scale. From an opportunity perspective, margins and costs are getting tighter and tighter for pharmaceuticals and biotechs. These companies need to find new business models- models which help them do more with less. I believe that Innoplexus can help with that.
You said in the announcement that data analytics has become a core competency for Pharma. Can you expand on what you mean by that?
LG: Pharma needs to differentiate more and more. Product competition is getting fiercer and payers are getting more picky about what to pay for and what not to pay for. This means that pharma needs to not only innovate, but they need to innovate quicker and cheaper. The two go hand in hand. If a company can shorten development times by just a few months, that brings significant cost savings. If a company can kill a research project earlier because they found someone else doing the same thing but is more advanced, then that will allow the company to refocus their investments on areas of more unmet need. Data is central to all of this.
There is no lack of data. Pharma captures a ton of data. The challenge is that the data is disparate and unstructured. This is where we come in. We help to bring the disparate data together, regardless of the structure or lack thereof, and us AI and Machine Learning to pull insights and connections. Ultimately, we aim to provide more “Ah ha!” moments to the pharma industry.
We’ve heard a lot of hype around AI in recent years. How does Innoplexus go beyond the hype? What practical problems do you help pharma companies solve?
LG: Our leadership, including myself, have spent the majority of our careers in the Pharma space, grappling with real problems. So, our mission is to go beyond the hype and deliver practical ecosystems which empower our clients in terms of data analytics and decision making. We have practical use cases across the entire pharma value chain. We have done work with clients to help them shape their scientific story in preparation of a new product launch; we have helped clients automate processes; we have helped resize fieldforce based on KOL network centrality; we have helped to improve the productivity of clinical research; the list goes on. On a high level, what we do is work with unstructured and structured data, use AI and Machine Learning to analyze the data and provide predictive insights, and visualize the insights into real-time automated dashboards.
Based on your experience in the pharmaceutical industry, what trends or developments should we expect in the industry over the next year?
LG: Other than the trend of consolidation, I believe there will be a focus on going beyond the hype. Meaning, companies will start to ask for practical applications of AI. Today, I see too many companies making promises and not delivering. I think people are already getting tired of the letters AI and ML and are going to start re-focusing on data analytics. AI and ML are technologies which are a means to an end. We at Innoplexus focus on the problems we solve for clients, and not only on showcasing our technology. Yes, we have the cool tech. Yes, we have the vast ocean of relevant data for life sciences. But really what sets us apart are the actual problems we can and have started to solve for clients.
The original article was published on Disruptordaily.
About The Author:
Prior to joining Innoplexus, Lawrence spent 15 years leading teams across functions and geographies in the Pharmaceutical industry. He also worked as a research associate for McKinsey & Company and holds an MBA from IMD in Switzerland and a Bachelors from Babson College in the United States.
The original article was published on Disruptor.