Digital calculation – PharmaTimes

As research advances, allowing scientists to more clearly pinpoint the underlying causes of disease, new drugs are becoming more precise and highly targeted.

At the same time, regulatory requirements in clinical development have become more stringent, with new therapies having to meet more specific criteria before they are approved. 

Keeping pace with this evolving landscape has proven a challenge for the clinical development sector.

Sponsors are facing obstacles in finding and recruiting patients for trials and identifying the right investigator sites, leading to multiple costly protocol amendments per trial.

And as a result, failure rates are rising. According to Phesi’s annual global analysis of clinical development, nearly one third of phase 2 clinical trials were terminated in 2023, a substantial increase on pre-pandemic levels of 20%.

Increased attrition rates have a compounding effect on the industry at large, increasing sunk costs and contributing to fewer therapies reaching the market. 

Sponsors are eager to modernise and overhaul the clinical trial process and tackle these issues, but lack the data and insights to confidently do so.

A data-led approach to trial design allows sponsors to decrease cycle times, improve patient-centricity and get life-saving therapies to those who need them faster.

Historical inefficiencies persist

Clinical trials account for nearly 40% of the $100bn+ spent on US pharma research annually, and with delays to trials costing sponsors up to $8m each day, time and money are equally of the essence.

Therefore, developing a more effective protocol for a clinical trial before it begins can be the difference between a drug making it to market, and a sponsor incurring millions in losses with a failed clinical development candidate.

Investigator site selection is a pivotal element of clinical trial planning and a key factor in a trial’s success, with a 2023 Phesi analysis of 173 cancer clinical trials finding that one in five investigator sites recruited just a single patient.

The financial impact is substantial – a single-patient site has an average cost-per-patient that is ten times higher than a better performing site ($130,000 per patient vs. $14,167).

Yet for too long, the industry has struggled with recruitment in the absence of comprehensive data on the target patient for an indication.

Research shows that around 80% of studies fail to meet enrolment within the intended timeline often because of a lack of knowledge into the characteristics of the patient populations.

This further leads to escalating amendments, increased chance of trial attrition, higher costs and increased patient burden. 

As therapies become more targeted, we will see even more narrowing of patient eligibility criteria, requiring sponsors to take a new approach to selecting patients.

By designing trials based on a granular, comprehensive view of the target patient– and knowing which investigator sites have access to the patient population – sets a clinical trial up for success. 

‘Meeting’ patients before the clinical trial 

Finding the right patients for a trial can often be like finding a needle in a haystack. But if the search area were narrowed down to the equivalent of just a square inch, it would be far easier to find the right individuals.

This is how Digital Patient Profiles (DPPs) are helping to shift clinical development from being perception-led to becoming data-led, using real world data to inform future studies.

DPPs provide a granular breakdown of the patient population using attributes such as sex, ethnicity, comorbidities and age, and any other details relevant to the disease being studied that can help sponsors eliminate non-active, non-enrolling investigator sites.

DPPs can also be used for predictive analytics – accurately simulating ‘what if’ scenarios in a trial.

This comprehensive view results in optimised alignment between targeted patient population and protocol design and increases the likelihood of an investigator site being able to recruit patients. 

The data in a DPP can further be applied to create a Digital Twin that is used to model safety and efficacy outcomes for a control arm.

This allows sponsors to model patient responses and even look to reduce the number of patients assigned to a control arm or eliminate the need for a control arm altogether via the creation of a Digital Trial Arm.

A digital trial arm collates data from similar or identical trials using the same agent, with real-world patient data, to accurately model comparator outcomes while accelerating development.

As well as running trials in silico, Digital Trial Arms can determine the potential side effects of a therapy, and even predict clinical outcomes of a treatment. 

Not only can digital trial arms help clinical development professionals move toward the industry-wide goal of eliminating the ethically questionable and outdated practice of placebo arms to reduce patient burden, to also greatly reduces costs, eliminates avoidable amendments, and accelerates outcomes. 

Futureproofing with data

The emerging trend of increased Phase II cancellations should serve as a canary in the coalmine for the clinical development industry. It is reasonable to expect that increased attrition could spill over into Phase III trials, which will be even more costly for the industry at large. 

We live in a world abundant with data, the challenge has always been on how we can meaningfully use it. The clinical development industry has the tools to no longer be guided by gut feeling.

By taking a data-led approach to clinical trial design, sponsors can minimize overall operational costs and derisk studies from the millions of dollars that can be lost from poorly designed protocols. 

Dr Gen Li is President and Founder at Phesi

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