5 thoughts on clinical trials

Aiden Flynn, founder of analytical solutions company Exploristics answers some of the most pressing questions relating to clinical trials.

What are some of the major barriers currently facing clinical trials? 

The clinical trials industry suffers from a very poor record of success. One of the major barriers facing clinical trials today is poor trial design, which can lead to incomplete data sets, an inefficient or costly trial process and ultimately contribute to a trial’s failure.

The increasingly complex clinical questions that are being asked in trials that focus on precision medicine or rare diseases demand an optimised study design from the outset. Statisticians should be brought in at the design stage to ensure that studies are designed to deliver statistically significant and clinically meaningful answers.

Many case studies are not planned like this and statisticians are only involved at the end of the process to recover value from incomplete data sets, which isn’t always possible. 

How digitised is the clinical trials industry? 

The industry has been reluctant to employ new approaches and software but is beginning to recognise the benefits. Cloud computing, for example, can facilitate the electronic data capture (EDC) of the large volumes of data routinely collected as part of a clinical trial. 

We are also seeing the emergence of cloud-based solutions in other stages of a clinical trial.  Researchers are starting to employ simulation software to improve their design with the advent of new data types, such as biomarkers and wearable technology. 

With server-based software, statisticians are constrained by processing power and the time required to generate and repeatedly amend programming code to change parameters and run multiple simulations. Cloud-based simulation tools allow the user to investigate the design and analysis options through a web browser and run hundreds of parallel processes at one time. 

Clinical development teams and regulators are starting to acknowledge the advantages of cloud-based software. As such, we are starting to see growth in requests and the application of simulation to optimise clinical trials and programmes. 

How can patients benefit from the use of tech within clinical trials? 

Software technologies that facilitate the clinical trial process will enable drug developers to deliver medicines faster to the patients who will receive a marketed drug following approval. As the clinical trial process becomes more efficient and effective at evaluating a treatment, it means that a treatment is more likely to be approved by regulators and be accepted as a marketed medicine. 

The better we get at designing clinical trials, the better we will understand which patients are more likely to respond to new treatments. The result will be the right drug to the right patient first time, meaning huge cost savings to organisations like the NHS, which can avoid paying for drugs that aren’t very effective. 

How can clinical trials use cloud-software to improve study time and results?

Failure to optimise a clinical trial design often results in unnecessarily lengthy timescales, inefficient recruitment strategies and ultimately inadequate results, which could lead to failure.

The ability of cloud software to use sophisticated statistical algorithms and synthetic data sets to reveal probable clinical trial outcomes before any time or money is spent in the clinic has the potential to revolutionise clinical research and the rate at which life-saving innovation is enjoyed by patients.

Does the emergence of cloud-software coincide well with the rise of wearables in the healthcare sector and what does this mean for clinical trials? 

Wearable devices have proven invaluable in some clinical studies, particularly those on neuro diseases, as they allow key patient measurements such as heart rate, gait and sleep pattern to be taken 24 hours a day and provide a more detailed picture on why some people may respond to a treatment and others don’t. Wearable technology also allows huge numbers of subjects, not enrolled on specific clinical trials, to generate data relating to their baseline activity and other simple physiological measurements. If we harness these data intelligently in the future, we have the possibility of understanding patient behaviour and disease progression in greater detail enabling more effective trial designs having understood more about the subjects.