Why can't we simplify our studies, and therefore our lives?

Complexity: it's not just for breakfast anymore. Think about the evolution of species, the advancement of technology since the industrial revolution (1800's), horse and buggies to self-driving Teslas. Almost anyone could fix a wagon wheel back in the day; now you need a computer science degree to fix a car (and now AI has replaced computer science degrees). Complexity is a thing, and it will continue to increase as we expand our desire to do less manually and consume more for our own enjoyment. Think about it: if we decided to simplify our lives, goals, desires, interests and diets, and focus on basic needs, we could rid ourselves of a lot of complexity. Queue the off-the-grid folks!

Clinical trials: needlessly complex? Like the world, clinical trials have become more complex over time. Complex biomarkers, more technology, more data, faster data... But the real driver is, as always, money. Sponsors need to make better decisions, faster, for drugs that are more nuanced, specialized (personalized) and risky. Large molecule compounds of a bygone era focused on high incident diseases; think Metformin, Lipitor, Viagra... These diseases were relatively well understood and the biomarkers and treatment profiles were as well.

Today's treatments, like targeted therapies for oncology, are less well understood, require assessment of complex endpoints and biomarkers and simply cost more to research. To support these new complex treatments we have developed creative, complex clinical trials. Trials that gather and analyze new types of information, adapt to changes, and can support earlier go/no-go decisions so that precious research effort and money is not wasted.

So complex treatments breed complex trials? Yes, but there's more to it...

Here are some factors driving complexity in clinical trials:

Study design elements:

• Biomarkers are plentiful and have grown in number as the ability to identify and properly measure them has evolved, often done through very involved processes and technology. They are not only becoming more complex to capture and process, but also to analyze and understand.

• Technology can be great and support a lot of innovative approaches, but every additional implementation, training module, dashboard login and source of data bring with them more complexity to manage.

• FDA requirements have grown over time, quite naturally as the safety profiles for certain therapies and mechanisms-of-action have become better understood (if we learn that a certain drug type can cause heart palpitations, we need to test for it). Additional assessments, technologies and even entire protocols are now requested as part of ensuring proper safety monitoring and proof of efficacy for a treatment.

• Complex Innovative Designs (CID) like hybrid studies, adaptive trials, basket trials and umbrella trials are all great ways to deliver faster, higher quality outcomes for investigational therapies. But they are more complex to setup and manage than traditional clinical trials.

Patient elements:

• Multifaceted diseases could include challenges with mobility, fine motor skills, cognition and motivation; as Sponsors navigate new indications and genetic subtypes it becomes important to understand and plan for these aspects.

• Patient knowledge is powerful, and important. But it also means that patients are no longer simply relying on the expert guidance of their healthcare provider. It is important to meet patients where they are and provide the information and support they deserve.

• Patient privacy and security is critical. With the advent of more technology and more cloud-based data sources (e.g. EHRs) this becomes yet another important factor to consider, and manage.

• Patient availability for trials is becoming more challenging. It is harder to locate patients with more specific types of diseases, or rare diseases. It is also hard to make trials work for them when the demands of studies has increased and patients' time and resources are strained, just like you and me.

Site elements:

• Site resources and expertise may be less robust than in the past. Why? Instead of being very focused on a prevalent disease, many larger sites have evolved to support a wide array of diseases. And there is a growing number of smaller sites that now help support specialized diseases. Additionally, sites lack resources, have high turnover rates and need to deal with ever increasingly complex and technology-heavy trials.

• Global trials can be very useful when you need to recruit for diseases with geographically diverse incidence rates or to find treatment naïve patients. But disparate regulatory requirements, geopolitical unrest and continued challenges with coordinating operations across the globe all add to complexity.

• Recruitment and retention are longstanding challenges, but layer on top the need to locate patients with rarer diseases, who are busy and may live hours from an investigator site, and suddenly recruitment and retention become even more critical, and tricky!

What Tufts and others say:

It's no surprise that Ken Getz and his super-smart team have honed in on complexity as a major factor in clinical research. They have certainly highlighted a number of the factors I mentioned above, but please read up on their analysis here. Here is a nice summary of the Tufts research on this topic.

Some other good resources covering the topic of complexity:

• WCG's 2024 Clinical Research Site Challenges Report

• PubMed article on Complex Innovative Trial Design

• Clinical Trials Arena article on Patient Centric Design

So how do we manage protocol complexity?

The principles of Quality by Design (QbD) are meant to help address these challenges of complexity. Outside of clinical trials this method has existed for some time in the product and manufacturing space, but in the last several years it has become priority for the FDA and EMA when it comes to clinical trials. Important aspects include a focus on quality, ensuring safety and reducing study burden for sites and participants.

Risk-Based Quality Management (RBQM) is a natural extension of QbD into the operational phase for trials. While QbD focuses designing the study to maximize quality, RBQM focuses on the identification of risks to quality and proactively monitoring and taking action in order to effectively manage those risks.

Finally, Clinical Data Science (CDS) is another tool to help manage complexity. CDS looks at novel, insightful new types of data to understand patterns across patients and sites, critical outliers that need attention, and further comprehension of the treatment-disease paradigm. AI has been a big enabler of CDS approaches.

What I try to do in this blog site is cover some practical approaches to implementing QbD, RBQM and CDS for trials. Because, while it is important to talk about the benefits of these approaches, we also need to realize the fruits of implementing them.

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