Biotech’s Future Success Lies in Innovative Technology and Real-Time Data

Biotech’s Future Success Lies in Innovative Technology and Real-Time Data

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Businesses that adapt to current innovations in technology are in a much better position to realize success than those that don’t. No company has ever been able to withhold innovation and thrive. Today, the biotechnology industry faces tremendous challenges, including unsuccessful projects, unexpected terminations, missed deadlines, and high expenditures. Those companies that embrace technological innovations that make it easy to collect information from the ever-increasing wealth of life sciences data will be able to overcome these challenges and reap the benefits that this data has to offer.

 

While the biotech industry is faced with uncertainty and a steep learning curve for startups in the United States and Europe, it is not only because of growing competition from big pharma. Often, biotech companies lack the resources and tech skills required to flourish in today’s market. While there has been a strong debate about democratizing life sciences data, these companies often find themselves looking for a needle in a part of the haystack. That is, most of them have access only to a small percentage of the available life sciences data ocean and often are analyzing it through slow manual efforts because they lack an in-house team of data analysts.

 

Staying ahead of the competition does not only require faster R&D and more successful trials, it also requires alignment of company goals to unmet needs of patients. Analyzing the market, the competition, and the sentiment around a brand or a therapy will require unparalleled support via technology and data. Incorporating emerging digital technologies such as AI-based data analysis and machine learning into research, discovery, and the development cycle of a drug can help leverage the true potential of biotechnology for better therapies and personalized medicine.

 

How lack of technology and access to data lead to increased rate of failure

 

The challenges faced by biotechs are often similar to those faced by the pharma industry in general. Many companies believe that the most common reasons for clinical trial failure—including missed deadlines, lower enrollment than expected, site exhaustion, overspending of budget, and unwanted toxicity level—are beyond their control. However, successful startups and major companies have controlled these factors through strategic manipulation of resources, data, and technology. This leads to the question: Can adapting to emerging technologies and access to better quality digital data improve success rates?

 

Every day, researchers scroll through publications and scientific papers searching for information that will help them reach a potential hypothesis. This process is very time-consuming, and while they are searching, more data is being added to the internet. The toughest challenge is, therefore, not creating trials that do not fail but generating relevant, real-time insights that allow informed decision-making.

 

What is needed is innovative technologies that address these challenges. Imagine having a reliable estimate as to how many patients will enroll at a site or identifying and knowing where to find the most suitable KOL for your therapeutic area. Imagine being able to predict how successful a clinical trial will be or the factors that should be taken into account for success. Imagine having all the life sciences data at your fingertips, from biological connections and KOL networks to patient sentiment and real-time guidelines and patent updates. Technologies in existence today have achieved this, but so far only a few companies have adopted it. Artificial intelligence (AI) is one of these technologies that can offer unprecedented support to grow in an ever-challenging environment by helping biotech companies stay up to date every moment.

 

Data holds the key to the answers only if it is centralized and analyzed in real time. The rate that the volume of data is growing is no longer a concern for many industries, as advanced big data analytics tools now make it easier to navigate through hundreds of pages in minutes by collecting only the important details. In fact, when it comes to the life sciences industry, it already has increased its reach to more medical data through e-health apps. Dr. Peter Densen estimated in 2011 that medical knowledge will double every 73 days by 2020.1 Access to this data, aggregating what’s relevant and important for specific uses, and analyzing it in real time are essential for avoiding stale decision-making.

 

The emerging need for advanced, automatic tools

 

Today, pharma is flourishing in areas where the use of automated dashboards and advanced tools that leverage machine learning, neural networks, and deep learning algorithms to enhance daily productivity and overall success rate have been adopted. Research is a key component of a biotech startup. Analyzing known and unknown biological connections between entities such as drugs, pathways, proteins, and diseases can lead to improving the efficacy of research and more potential hypotheses. But research is not the only area of focus. Clinical trials account for the most significant expenses. Digitizing trial data can radically transform and enhance the efficiency of clinical development through all phases.

 

Data-driven processes and AI-prediction tools in use today have achieved the pride of anticipating what a successful trial would look like. Leveraging life sciences data to its optimal capacity using artificial intelligence can greatly accelerate processes, thereby reducing time and effort and taking the biotechnology industry to new heights of success.

 

Reference: 1. Densen P. Challenges and opportunities facing medical education. Trans Am Clin Climatol Assoc. 2011;122:48-58.

 

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Author

Lawrence Ganti

Lawrence Ganti is the Global President of Life Sciences, CEO of  Americas, and Member of the Global Executive Community. 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.

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