Supply Chain Planning and Clinical Trials Adaptive Design

2 min read

It is time for cheaper, faster, more granular, and more precise clinical trials

Discovering new drugs, repurposing or repositioning existing ones, and reducing healthcare costs require cheaper, faster, more granular, and more precise clinical trials without compromising safety.

While advances in digital technology hold promise, life science organizations are contingent on rethinking research designs in light of new patient-centric characteristics.

Why Adaptive Design

Resizing the sample, discontinuing treatments or doses, adjusting the ratio of patients to study arms, focusing recruitment efforts on patients most likely to benefit, and stopping the trial early for success or lack of efficacy. All of these adjustments may be built into the original study ahead of time, saving money and improving efficiency.

Known as adaptive design, this method of anticipating changes that could yield extra information while retaining the trial’s validity and integrity has been around since 1989. While adopted in medical device development, it has yet to become standard practice in the life sciences, aside from occasional papers reporting advantages.

Adoption is stifled, in large part, because researchers are forced to grapple with exploitation vs. exploration challenges (how to allocate resources among potential improvements for maximum effect).

The tides have turned.

Reinforcement learning algorithms are an effective tool to deal with resource allocation. The focus on costs and time, as well as the externalization and diversity of the trials, is making it possible to standardize the patient’s record. 

Altogether, these advances give a bust to adaptive design in drug development, enforcing a patient-centric demand-driven approach.

Removing remaining barriers 

To get to the next level of end-to-end clinical trial process orchestration, though, clinical trials organizations must employ cutting-edge digital technologies to overcome three barriers:

  • Timely response: IRT systems are essentially obsolete today, and most communication between patients and clinics is not even close to real-time. By provisioning more time, planners manage the risk caused by changes in regulations and other signals that could affect the whole process. A redesigned architecture for clinical trials should include faster, near-real-time connection with patient data. Adding wearable medical devices to the monitoring can also help this progress.
  • Recruiting data: Introducing new data and analysis into a design needs to become an opportunity rather than a challenge. With a mechanism for rewarding internal and external research input, structuring the clinical record by data protection rules would incentivize collaboration. Current clinical trial externalization will facilitate the infrastructure, and life science consortia should foster collaboration between research organizations.
  • Overcoming fragmentation: Only an integrated architecture will enable a more effective and efficient clinical trial process. Any patient feedback, new evidence, or regulatory change will nourish a concurrent supply planning exercise linked in a closed loop with enterprise and supplier resource management. Data scientists, subject matter experts, and business stakeholders will make objective decisions based on shared knowledge. The recent experience of one of the leading life science organizations paves the way forward.

Conclusions

Enabling data scientists, subject matter experts, and business stakeholders with the ability to make objective decisions based on shared knowledge and collective intelligence will result in cheaper, faster, more granular, and much more precise clinical trials.

Only an integrated architecture will enable a more effective and efficient clinical trial process.

With the latest advances in digital technologies, it is now possible to have near-real-time communication with sites and patients, achieve complete network visibility, and have a closed loop between supply planning and management of enterprise resources.

At the healthcare ecosystem level, supporting the above infrastructure upgrade with a proper incentive for collaboration will foster research to new heights.

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Flavio Aliberti Flavio Aliberti brings with him a 22-year track record in consulting around business intelligence, change management, strategy, M&A transformation, IT and SOX auditing for high regulated domains, like Insurance, Airlines, Trade Associations, Automotive, and Pharma. He holds an MSc in Space Aeronautic Engineering from the University of Naples and an MSc in Advanced Information Technology and Business Management from the University of Wales.

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