Fundamental to a pharmaceutical company’s success is the ability to successfully launch their drugs to market.
However, the clinical trial process of identifying potential drugs, developing and testing them, then designing a trial, patient recruitment and retention, capturing the results, and then gaining regulatory approval is complex and time-consuming.
The whole process can take a decade or more, and with the average cost of a successful drug launch being around $2.6 billion, pharmaceutical companies invest significant resource, time and money into bringing new drugs to market. The potential to use data and analytics to reduce time and cost is very attractive.
The mammoth cost of running clinical trials is due to a variety of factors. The initial development and manufacture of the trial drug is costly, as is the sourcing of participants in various demographics.
Then there are the costs associated with designing and setting up the trial protocol, the logistics of delivering the drugs to the right location, clinical procedure costs, administrative staffing expense, site monitoring and the costs of incorporating check-in appointments with trial participants. This all contributes to the huge expense of running a clinical trial.
Deep complexity
As well as the huge financial commitment, running trials is an incredibly complex process. Pharmaceutical companies need to design a trial and then recruit appropriate participants across multiple demographics and ethnicities.
As well as this, is the important role of compliance in keeping patient information safe. Patient recruitment, screening and managing their information can itself be a mammoth task and incredibly time-consuming.
Once the trial is up and running, the operational components are vast. There can be up to 40 processes that all must be undertaken either concurrently or in a specific sequence, which again must be captured and logged to track the progress of the trial, but also as evidence when submitting to the relevant governing bodies for approval.
Capturing outcomes of a trial is important. This includes the monitoring of adverse effects, the outcome of drug administration as well as cross-referencing patient experience based on their background and health profile.
Using data to find patterns in patient findings as quickly and accurately as possible can help to speed up results which can then be acted upon. Often, as the trial progresses, the number of patients increase, which needs to be carefully managed and accurately documented.
It is the same for placebos. Tracking which patients are on placebos and which are on the trial drug is central to clinical trials. Using advanced data and automation, double-blind tests are more straightforward.
In a double-blind test, neither the patients nor clinicians know who is taking the placebo or the trial drug; this can be automated and coded drugs then sent to patients thus reducing the risk of bias entering into the process.
Trials and era
Data and intelligent analytics can help pharmaceutical companies to streamline and improve the clinical trial process. By using the data to track the various stages of a trial, identify areas for improvements, and help companies make better decisions, these businesses can save millions of pounds.
The sheer volume of information, processes and components in a large-scale clinical trial is immense. The major challenge that all businesses face, first and foremost, is capturing the data.
This might seem a simple enough task as everything generates data, but capturing all the data and extracting the parts that are useful is key, and having the right transaction systems, tools and processes to extract data in the first place is vital.
First and foremost, information can be captured, stored and sorted digitally. This enables a streamlined process and easier analysis. This also proves crucial for multi-site or even international trials where language and regional differences need to be accounted for.
The appropriate technology to seamlessly capture the data is vital, without this in place, digitisation is sure to fall short of all expectations.
Streamlined process
The ability to have a comprehensive overview of clinical trials is vital to ensuring an understanding of how trials are progressing, to understanding any blockers, and to ensure processes are streamlined. CEOs or those running the trials must have clear visibility and be able to report back to stakeholders on progress and any issues identified.
This effectively creates a single pane of glass where all data is analysed and used to produce transparency across the entire trial. Stakeholders can zoom in to specifics or zoom out to get a holistic overview.
They can have a complete overview of resources, where the trial is being deployed and how it is performing. This enables them to make real-time decisions based on real-time findings.
Another benefit to data that has been digitised is the speed in which findings can be collated, issues identified, and changes implemented. Rather than those running the trials having to sift through records manually, these findings can be presented in a complete and easily accessible report.
This helps those running the trial to have a holistic overview whilst interpreting the findings more quickly and accurately.
This information can also better form the basis of the final submissions that are submitted to the governing body when applying for final approval and sign-off on the new drug. Having all the information sequenced, collated and analysed throughout the trial not only speeds up the trial itself but also the final approval process.
Utilising data does not simply have operational benefits. Saving time on clinical trials does not simply create financial savings. It has the potential to save lives. Rolling medicines out into the market quickly, such as the COVID vaccines, can be the difference between saving a few people’s lives to saving thousands of people’s lives.
From a human perspective, being able to draw accurate conclusions from trials through complex, but immediate, analysis means outcomes can be quickly reviewed and any required changes implemented straight away.
Reviewing and amending clinical trials in real-time, if appropriate, can also aid not only the trial itself but also the participants.
Data informs artificial intelligence which is increasingly being used within clinical trials. AI can be used to predict which groups or demographics will have the best outcomes and success ratios.
This can also monitor curveballs, such as if participants contract COVID or other conditions that may impact the trial, and help pharma companies assess the most effective next steps.
The role of AI is fast becoming fundamental to the successful and efficient execution of clinical trials and the digitalisation of data is a crucial element of this for the successful use of AI.
The recent advances in Generative AI are a game changer – particularly in the area of submissions. The organisations that are the most data-savvy and willing to evolve quickly will benefit from such advances the most.
Final analysis
In clinical trials, the ability to review the information concisely and in a way that communicates findings clearly is critical. Big data analytics spending in healthcare is set to exceed $101 billion by 2031.
To maximise their data’s value, pharmaceutical companies must ensure their data management is well structured. Ensuring the data can be analysed and understood is fundamental; both for the safety of the drugs being tested and for the outcome of the trial.
Digitisation and data are moving at a rapid pace and pharmaceutical companies that are willing to harness these innovations and apply them to clinical trials are likely to have the competitive edge over their peers in the market.
Ultimately, it is crucial for pharma companies to review their data and analytics now to avoid being stranded.
Ramji Vasudevan is Senior Engineering Leader at Altimetrik
