Per a global report by Statista, in 2020, R&D spending in the pharmaceutical industry totaled nearly $200B globally. For comparison, R&D expenditures totaled $137B in 2012.
As any research and development team will tell you, trial-and-error is a vital part of the job. However, continuous trial-and-error is highly time-consuming. In the medical field, especially when developing vaccines in response to a worldwide pandemic or medications for life-threatening illnesses, R&D teams do not have the luxury of time.
R&D teams working to develop medications, therapies, remedies, or vaccines need tools that will prevent them from wasting time duplicating research or tests, and enable them to get to clinical trials and desired approval for faster distribution. At the same time, they must ensure that what they produce is safe. A holistic view of past research and data collected on developing similar medications can significantly shorten the research process, prevent teams from trying something that has already proven ineffective, and allow teams to shift their focus on the actual development and real-world testing of the drug.
What Does a "Holistic View" Mean?
A holistic view refers to the complete picture of a company's data. Gaining this comprehensive view requires looking at the data throughout its entire lifecycle – from the moment a piece of work comes into fruition up until the current moment someone needs access to the information.
As touched on, in pharmaceuticals, the accuracy of the research is so crucial, and no details on a given product can go unaccounted for. Achieving a holistic view considers every piece of intelligence surrounding a specific drug or vaccine, making the lives of hardworking R&D teams much more manageable and productive.
Before discussing how a business can accelerate its R&D process with holistic views of data, it is essential to have a brief understanding of the technicalities on how you get there.
Achieving a Holistic View of Your Data
Achieving this view requires the work of a system powered by artificial intelligence (AI). AI has taken the world by storm with its revolutionary advances across a multitude of industries and markets. Life sciences have actively been looking to adapt to digital transformation and embrace new automation technology to speed up processes and approvals. The adaptation towards the digital age has not only been adopted by major pharma corporations but by research institutes, medical facilities, and even regulatory authorities.
Using an automated AI system allows you to connect data from every corner of your business with applicable data from sources outside your company to give you a detailed image of any product in the research and development phase. These AI systems extract data from action letters, information requests, review recommendations, specialized applications, medical studies, and research databases. With machine learning capabilities, every piece of the data extracted from the above data sources is indexed and consolidated into a single application – preventing R&D teams from going on witch hunts for much-needed knowledge.
With the explanation of the technology side now in the rearview, we can look at how this holistic view, in fact, accelerates and optimizes the entire R&D process.
Benefits and the Acceleration of the Entire Research & Development Process
The best way to understand how holistic views can accelerate the intense R&D process is to look at it through the users' lenses. On one side of the coin, you have R&D teams working on drug approvals and all the information gathering that goes into it. On the other side of the coin, you have regulatory authorities responsible for reviewing and making sound decisions on the numerous applications that hit their desk. Both sides require consistency and accuracy in a timely manner.
With connected data, an R&D team can search for a subject and find every document, file, video, or image on the topic in a matter of seconds. This subject could be as simple as the name of a drug, a side effect, a medical condition, symptom, or ingredient. The search can also be as complex as how a specific medication reacts with men aged 60-65 with high blood pressure. No matter the search term, the user gets the most relevant information placed at their fingertips quicker than they ever would.
In addition to the quick information, the researcher is provided with diagrams and graphs that show the relationship between different entities, often referred to as knowledge graphs. Innovative AI allows the R&D department to visualize and conceptualize the relationship between two entities in the form of a knowledge graph. For example, what is the relationship between COVID-19 and diabetes? A knowledge graph will show you because all the data on those topics are already connected and consolidated from a wide variety of trusted data sources.
These capabilities accelerate the entire process from beginning to end. A researcher preparing an application for a drug modification doesn't need to fish through multiple databases for worthy intelligence while worrying about hitting their deadlines for approval submission. Another way a researcher without the proper tools may try and retrieve information is by contacting a colleague or a group of colleagues. The right colleague to contact is often unknown or may take ample time to get back to you. A holistic view of your data identifies experts within your company on any given topic. You can see which experts have authored or co-authored the most documents in the past and know exactly who to go to with a specific question.
Once any modifications of formulas, updates to medications, or new applications are submitted, there, of course, is a review process by regulatory authorities. Holistic views of data help the regulatory side of the operation just as much as the other to expedite the process. Clinical reviewers need quick access to past applications, so their rejection and approvals remain consistent, along with their reasons for making such decisions.
With an excess amount of information at their disposal and applications involving similar or identical active ingredients, clinical reviewers need a solution to support their day-to-day work. The number one priority is sound decisions based on facts and error-free intelligence. Conquering this reduces complaints from organizations on inconsistent rulings and helps streamline their way of operating.
Three Key Points: An Overview
Anybody who has been a part of the pharmaceutical R&D process is aware of its intensity and the toll it can take. Using a solution on the market to accelerate and help the workers involved be efficient and timely is crucial to the success of their organization and the medical world as a whole.
1. Saving Valuable Time for Researchers and Reviewers – Fast information gathering from data sources within your company and trusted databases outside your organization allows R&D teams to find wide-ranging intelligence in the most effective and timely manner without exhausting their resources. Doing so avoids continuous trial and error and re-works in the development stage.
2. Finding Insights and Patterns in your Data – Connected information opens the door to groundbreaking findings. A holistic view shows correlations and patterns that could change how a team or review board approaches a certain medication, vaccine, or therapy.
3. Consistent Decision-Making Based on Data – There is not much of an excuse for in- consistency when you have access to everything. Seeing the 360-degree view of previous requests and applications permits reviewers to do their job fairly and quickly. People in need can obtain possible life-saving medication and vaccines faster and not be held back by slow or inefficient review processes, the common goal for all.
About the Author
Daniel Fallmann founded Mindbreeze in 2005, and as its CEO, he is a living example of high quality and innovation standards. From the company's very beginning, Fallmann, together with his team, laid the foundation for the highly scalable and intelligent Mindbreeze InSpire appliance. His passion for enterprise search and machine learning in a big data environment fascinated not only the Mindbreeze employees but also their customers.
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