Synthetic Control Arms: Making the leap beyond the gold standard

Synthetic Control Arms: Making the leap beyond the gold standard

Synthetic Control Arms: Making the leap beyond the gold standard 558 344 Exploristics

by Exploristics Chief Data Scientific Officer Kimberley Hacquoil

Drug development is difficult and it’s only going to get harder. The cost of drug development continues to increase, the time to get investigational treatments to patients is longer, and the path to registration is more complex and uncertain. I don’t think we will ever fully get rid of the gold standard traditional randomised control trials; however, I do think the future will contain more alternative approaches as we innovate to address some of the issues within the industry.

One approach revolutionising drug development is the use of external data (either from historical trials or real-world data) as a control arm explicitly within the clinical trial setting. This means that instead of recruiting patients to assign them to the control arm in a study, a synthetic control arm can repurpose external controls to accurately match patients in the investigational arm. Thus, saving time, cost and patient burden.

External control arms are applied in certain situations, for example rare diseases where it may be unethical or unfeasible to include patients on a control arm. Robust statistical methodologies have been developed to ensure that the integrity of the data is maintained which allows for an effective and unbiased assessment of the safety and efficacy of new treatments by the regulators.

Leaping over the hurdles

There are several barriers to overcome to unlock the full power of synthetic control arms, these are related to better planning and awareness to increase implementation.

Sponsors and clinical development teams need to consider the bigger picture. Often there is not enough time and resource spent on planning an optimal clinical development program. Starting a study sooner does not mean it will finish sooner nor improve the probability of success. This is especially true for the use of synthetic controls where more time is required up front to ensure the right data, methods and design are used. However, if done correctly, the saving in terms of less patients for the clinical trial recruitment can have a dramatic effect on the time and cost without sacrificing the probability of success.

It’s also vital to engage with the right partners in term of:

  • getting access to relevant and quality data to form the synthetic arms
  • having sufficient background knowledge about the historical data, in order to transform them accurately for endpoint alignment and comparability within the current trial
  • statisticians who have the skills and knowledge to implement the technical aspects to reduce bias and ensure the impacts of using synthetic control arms is quantified
  • early discussions with regulators to ensure methods to address matching and alignment of data are agreed prospectively

Leaping forward to survive

Synthetic control arms can do more than reduce the time, cost and practical and ethical hurdles related to the clinical trial setting. Synthetic data (simulating virtual populations of patients) can also support quantification of a treatments benefit to patients post authorisation through prediction of outcomes in patients who were not studied within a clinical trial. This provides additional evidence to support the impact of treatments within the real-world setting, informing decision-makers in the utility of such treatments.

Going forward, I think the pharma industry must embrace different ways of implementing clinical trials to continue to develop novel treatments. Employing synthetic control arms is one approach which is gaining traction and will continue to provide leaps forward in getting much needed treatments to market quicker and cheaper whilst maintaining the rigor and quality for our highly regulated environment.


Read more:

Are external control arms worth the extra effort?

Are synthetic control arms the future standard in clinical trials?

It’s not rare to have a rare disease

Why thinking big for small populations is transforming rare disease drug development

Going synthetic with real-world data

External control arm review

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