Ontosight® – Biweekly NewsletterJune 17th – June 30th, 2024 –Read More
Biotech’s Future Success Lies in Innovative Technology and Real-Time Data
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.
Featured Blogs
Machine learning as an indispensable tool for Biopharma
The cost of developing a new drug roughly doubles every nine years (inflation-adjusted) aka Eroom’s law. As the volume of data…
Find biological associations between ‘never thought before to be linked’
There was a time when science depended on manual efforts by scientists and researchers. Then, came an avalanche of data…
Find key opinion leaders and influencers to drive your therapy’s
Collaboration with key opinion leaders and influencers becomes crucial at various stages of the drug development chain. When a pharmaceutical…
Impact of AI and Digitalization on R&D in Biopharmaceutical Industry
Data are not the new gold – but the ability to put them together in a relevant and analyzable way…
Why AI Is a Practical Solution for Pharma
Artificial intelligence, or AI, is gaining more attention in the pharma space these days. At one time evoking images from…
How can AI help in Transforming the Drug Development Cycle?
Artificial intelligence (AI) is transforming the pharmaceutical industry with extraordinary innovations that are automating processes at every stage of drug…
How Will AI Disrupt the Pharma Industry?
There is a lot of buzz these days about how artificial intelligence (AI) is going to disrupt the pharmaceutical industry….
Revolutionizing Drug Discovery with AI-Powered Solutions
Drug discovery plays a key role in the pharma and biotech industries. Discovering unmet needs, pinpointing the target, identifying the…
Leveraging the Role of AI for More Successful Clinical Trials
The pharmaceutical industry spends billions on R&D each year. Clinical trials require tremendous amounts of effort, from identifying sites and…
Understanding the Language of Life Sciences
Training algorithms to identify and extract Life Sciences-specific data The English dictionary is full of words and definitions that can be…
Understanding the Computer Vision Technology
The early 1970s introduced the world to the idea of computer vision, a promising technology automating tasks that would otherwise…
AI Is All Hype If We Don’t Have Access to
Summary: AI could potentially speed drug discovery and save time in rejecting treatments that are unlikely to yield worthwhile resultsAI has…