With the biopharmaceutical industry expected to grow by 47% in the next 5 years, data is more important than ever. Data on its own, while crucial for improved processes and efficiency, must be analysed and utilised in the right way to maximise its potential.
In this article we will look at the impact of Big Data and Machine Learning on the biopharmaceutical industry and how these technologies can transform and shape the future of pharmaceutical production.
What is Big Data and why is it important?
While data itself has its own inherent worth, Big Data is the foundation of good analytics, which ultimately provides helpful and meaningful insights to improve processes, efficiency and productivity. Big Data is a combination of structured and unstructured data usually collected in large amounts which can then be mined for information and used in Machine Learning projects, predictive modeling and other advanced analytics applications.
What separates Big Data from its data counterparts is usually its;
- Velocity (refers to how fast data can be generated, gathered and analysed)
The amount of data captured can be calculated in terabytes of information and memory. The analysis of such large amounts of data can reveal patterns and trends in the past, identify changes in real time and even make meaningful forecasts for the future.
Big Data can be split into:
- Structured data: Consists of data that is searchable and in a predetermined and defined format. It is usually organized and can be used directly without needed to be “cleaned up” for interpretation.
- Unstructured Data: Comprises data from scattered sources, for example general economic and business forecasts, social media posts or traffic data. This data usually needs to be cleaned and optimised for interpretation.
The significance and impact of Big Data can be best seen in its analytics, which involves data mining to draw out patterns and relationships by identifying a change in the data’s status quo, and predictive analytics which uses historical data to make predictions about the future – enabling us to identify any risks and opportunities. The sources of data in the biopharmaceutical industry are typically more varied and complex and can come from anyone along the supply chain from manufacturers to distributors and even patients.
What is Machine Learning and why is it important?
Machine Learning is a subset of artificial intelligence and involves the use of computer systems that can self-adapt and learn using algorithms and statistical models, with the ability to draw inferences from patterns in data sets. In other words, it is defined as having the capability to imitate the way the human brain learns.
A foundational aspect of Machine Learning is gaining sufficient data, which can take almost any form including phone numbers, sales reports, data from sensors, bank transactions, or in the case of the biopharmaceutical industry, data like patient records or clinical trial participant information.
Machine Learning can be split into three different functions:
- Descriptive: explains what happens
- Predictive: uses data to predict future outcomes
- Prescriptive: uses data to make suggestions about what action to take next
The significance of Machine Learning is the biopharmaceutical sphere will be seen in its ability to speed up processes, both during the drug discovery and manufacturing stages respectively.
How Will Big Data and Machine Learning Help the Biopharma Industry?
The biopharmaceutical industry is complex and has many sources of data, from drug developers to manufacturers and distributors. This complexity makes the need for coherent data even more important. Big Data analytics helps with the management of data, the optimization of inventory processes, tracking trends and deviations, and providing helpful market insights.
Managing data is a key part of improving the efficiency of the biopharma market, as data comes from all parts of the pharmaceutical supply chain from manufacturers to pharmacies and patients. This complex business environment is especially suitable to utilise Big Data to speed up processes, efficiency and accuracy of the processes in the industry.
Big Data and Machine Learning are not mutually exclusive, but can actually be complementary. If Big Data is managed correctly, it can improve Machine Learning by providing higher quality relevant data for analytics. Machine Learning and Big Data if used together, have the potential to:
- Accelerate the drug discovery process
- Improve experimental design methods
- Analyse and improve data insights
- Optimize production and manufacturing processes
- Improve data storage and management
As mentioned above, Machine Learning technology essentially means that computers are able to learn the way humans do. Using Machine Learning provides scientists the ability to create automated experiments to predict how drug molecules will behave and even to predict drug-protein interactions. Being able to predict how molecules will react is crucial for the manufacturing of a new drug or treatment. Historically, it has been trial-and-error process that can be expensive and time-consuming. This is where Machine Learning models are helpful. A recent study saw the development of a data-driven approach where experiments are combined with Machine Learning models to understand chemical reactivity which makes the process of creating pharmaceuticals much faster.
In biopharma, clinical trial data collected throughout the trial process covers information such as protocols, patient information, lab test results and more. The global clinical trials outsourcing market was valued at USD 40.77 billion in 2022, estimated to reach USD 74.38 billion by 2031. The end goal of a clinical trial is to determine whether a drug is safe and effective for humans, which is a process that can be tedious and lengthy.
One of the challenges faced is at the recruitment stage, particularly finding enough individuals to recruit for the control group. Typically, control groups would comprise patients who can sometimes have rare conditions, making the process of finding consenting patients lengthy and not always successful. Leveraging on Big Data, we can remove the need for an initial control group altogether by leveraging Big Data. The data is reused from previous trials to create “external control arms”, which is a group that has the same function as a traditional clinical trial but can be used when control group participants are particularly difficult to recruit or if the illness requires immediate treatment, like cancer.
Possibly the most relevant immediate use of external control arms is in conducting preliminary trials which can provide the basis for evaluation of whether or not a treatment is worth conducting a full clinical trial on.
Challenges to Implementation of Big Data and Machine Learning in Biopharmaceutical Operations
Implementing Big Data into pharmaceutical operations is no easy feat. Company-wide implementation would require budget for storage of the data, analytics tools, cybersecurity and solid data governance programmes. Particularly with the biopharmaceutical industry, there are swathes of data that need to be tracked and recorded, which could weigh even more heavily on costs.
Difficulty in securing useful data
Not all data is equal. Securing the right kind of data will have a huge impact on the quality of insights that Big Data analytics can obtain. Understandably, not all data will be useful or helpful but the cost of storage, integration and analysis of this poor-quality data will likely rack up further costs than using data that is free of errors. The complexity and volume of data along the biopharmaceutical supply chain might make securing quality data a bigger challenge, which is a factor to take into account during implementation. Duplicate records, inaccurate information and formatting errors are some of the common issues with data quality, which companies would need to rectify. While not all data will be free of errors, companies would need to be proactive in ensuring that data is audited and validated regularly to ensure the most meaningful analytics outcome.
Implementing Big Data across operations will also require specialised skills, which means having to upskill or hire new employees to fill the gap. As the market becomes more competitive for skilled professionals, it may be difficult to source the right talent and it may cost the company a significant amount to do so. Working with Big Data will require a change in processes perhaps from more manual tasks to more virtual and digital handling of information and processes. This would require further upskilling across the organisation because employees would need to be trained on handling new processes and be equipped with the correct knowledge to ensure that operations run smoothly and efficiently.
Big Data and Machine Learning will eventually revolutionise the biopharmaceutical industry through improving processes, shortening lead times, improving experimental design and improving data management. Implementing these new technologies into daily operations has its challenges including high costs, talent-sourcing and difficulty in securing high-quality data, however their benefits will outweigh the challenges in the long-run, in the form of improved efficiency for the industry and more accurate and effective pharmaceutical treatments.
- Mordor Intelligence
- National Library of Medicine – Machine Learning in Drug Discovery: A Review
- Straits Research
- MIT Technology Review: Clinical trials are better, faster, cheaper with big data
- McKinsey & Company: How big data can revolutionize pharmaceutical R&D
- AJMC: How AI and Machine Learning Can Bring Quality Improvements in Biopharma
- Nature Chemistry: Probing the chemical ‘reactome’ with high-throughput experimentation data