Through a series of blogs, we are taking a deep dive into the E suite of Accenture Life Sciences ecosystem partners. This provides clients with the ability to harness the collective power of a harmonised partner community and how it can enable companies to adopt a new, innovative R&D model. Many of these ecosystem partners are enabled through our INTIENT platform to drive innovative solutions for clients and patients.

E is for Efficiency

With drawn-out timelines and billions in investment, traditional clinical trial methods are increasingly a barrier to cost-efficient and timely drug development. By harnessing a wealth of historical clinical trial and real-world data we are making trials more efficient and ethical through the use of external control arms (ECAs).

So, how does it work? A traditional randomized control trial (RCT) can require a large number of participants, many of whom will be part of the control arm of the trial and who will receive the current standard of care treatment, or depending on the indication and research objective, a placebo.

This means not only higher investment in patient recruitment and retention, but also creates a situation where some patients who wished to receive a particular treatment through the trial will instead receive standard of care treatment. In the case of diseases like certain cancers, the current standard of care treatment may be associated with poor outcomes and is unacceptable to patients. In these diseases, such as recurrent glioblastoma, many patients would not want to participate in a clinical trial in which there is a chance they would receive standard of care rather than the investigational therapy. If there is a way to conduct clinical trials in a more patient centric manner that is also cheaper and faster, should the industry not at least explore the possibility?

The challenge of using standards of care associated with poor outcomes in clinical trials is not only an ethical question, but extremely emotive.

ECAs not only save site and operational costs, my team at Accenture estimated that by including an ECA, you can save on average of $9M-13M per oncology trial depending on the phase of the study.

"The challenge of using placebos in clinical trials is not only an ethical question, but extremely emotive. If there is a way to conduct trials in a smarter, cheaper, faster way, one that is truly patient centric, shouldn’t we explore the possibility?"

By tapping into existing data, the drug development process can become more efficient. For starters, you may be able to reduce the number of patients in the trial, therefore reducing the overall patient burden and mitigating the ethical issues related to standards of care associated with poor outcomes.

It’s exciting to see big names in the industry start to harness ECA technologies and put them into practice. Two compelling examples of this are from 1) Roche having used an external control arm for the accelerated FDA approval of Alecensa (alectinib) for a specific form of lung cancer treatment, and 2) Amgen’s use of an external control arm to accelerate the approval of Blincyto (blinatumomab) for the treatment of a rare form of leukaemia.

Of course, there is still a long way to go, and there are many obstacles to further the adoption, so I am sharing a few of those hurdles related to data:

Variety— Pharmaceutical companies typically do not have broad external data and have been hesitant to look beyond their own clinical data to sources such as industry data from other clinical trials and real-world data from electronic medical records (EMRs).

Volume— A pharmaceutical company’s own data is usually not sufficient for rigorous statistical analysis and is just a fraction of the potential data available for use at an indication level.

Velocity— Pharmaceutical companies are often utilizing historical and distinct data sets, without the processes set up to interpret and incorporate incoming data. Companies need to build the capabilities to intake and digest data as it arrives, to inform trial design and implementation.

These challenges are already starting to be addressed in the external data ecosystem. Vendors are developing data sources that can be tapped to generate ECA, and our partner ecosystem opens the possibilities and opportunities for companies to tap into these.

One of the most exciting approaches are those built upon historical clinical trial data (HCTD). Medidata, a Dassault Systemes company, has been working with customers for several years to provide Synthetic Control Arms® (SCA®.) Medidata’s SCA offers “regulatory grade” data, comprised of traditional clinical trial style endpoints and complete covariate information, as they were designed in the clinical protocol, and captured, monitored and validated in the Medidata Rave electronic data capture (EDC) platform. HCTD enables data-driven decision making by providing patient-level data in the common domains and complete covariates. Our Medidata partners shared, three examples of where a Medidata Synthetic Control Arm can help pharmaceutical development teams:

  1. Early development to help with go / no-go decisions such as enhancing the interpretation of a singular arm or increase efficiency of RCT.
  2. Late-stage research such as enhancing the interpretation of a confirmatory single-arm trial in a life threatening, rare disease area where there really isn't a good standard of care. Or augmenting a randomised controlled trial in situations where long term assignment to the standard of care therapy is undesirable.
  3. Health Authority (HA) discussions to demonstrate value with comparative or real-world effectiveness or burden of illness.

What are some of my suggestions for pharmaceutical companies to benefit from existing efficiencies and planning a synthetic data approach?

Pilot projects— Create a bold vision for leveraging external control arms in clinical trials and prioritise assets with which to pilot the new approach. Partner with professionals who are extremely experienced in designing trials.

Enrich your data— Supplement internal data with external data sources to achieve a robust validated data set. Find a partner such as Medidata Acorn AI, which ensures data is regulatory grade.

Deploy smart algorithms for “what-if” analysis— Integrate and automate algorithms into analytics platforms to evaluate and predict potential outcomes.

Evolve your capability— New governance, skills, and processes are needed so that predictive data analytics can inform clinical deployment plans.

As more success stories emerge of external control arms in clinical trials, awareness and consideration of their role in clinical development uptake will continue to improve. There is already a wealth of data out there, and companies would do well to boost their own capabilities to identify, harness, and utilise that data to transform their own clinical trial design. In short, Accenture Life Sciences ecosystem is about bringing the best technology and value add services to clients that solve their challenges, through the ecosystem, companies can start making use of existing efficiencies.

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Dr. Sanjay Jaiswal

Managing Director, R&D Analytics Lead – NA

Kevin Nikitczuk

INTIENT Network Lead – Life Sciences

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