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Clincal_Trials_Pharma_Intelligence

 

Ken Getz, director of sponsored research and associate professor at Tufts University School of Medicine’s Center for the Study of Drug Development (CSDD), discusses how information and technology advances, stakeholder education, and system reforms focused on service integration are finally transforming the patient experience in clinical trials. Combined, these three trends carry the potential for improving the bottom-line performance of this most costly component of the new drug product cycle. 

Making a 20th century trial model fit-for-purpose into the third decade of the 21st century is a strategic necessity as drug discovery attracts a more diverse set of players and expands to complex areas of science with vastly higher price tags. According to Tufts CSDD research, these biotech products, often developed by smaller companies with fewer resources than the typical big pharma, now comprise a third of all US novel drug approvals.   

In Vivo: Clinical trials are central to the mission of biopharma in developing new drugs to treat disease. Because humans are involved, the process is imperfect.  How is the industry progressing in its task to raise the efficiency of trials by delivering results that are relevant to regulators and productive for patients?

Ken Getz: Reducing the time and cost of clinical trials is the endless, unresolved challenge facing every biopharma company. In 2019, however, we see three broad trends that are game changers with the potential to reverse this stalemate as we move forward into the next decade.

The first relates to use of data and analytics: companies are getting better at integrating information that historically has been very siloed. Data is emerging as a tool to drive cross-functional decision-making. To see operating functions – procurement, site management and patient recruitment – aggregating and sharing summary data with clinical teams represents real progress. Its creating that direct line of sight into protocol performance. It may be a baby step, but it paves the way for artificial intelligence, machine learning and other cutting-edge technologies that exist today but cannot be applied effectively to trials due to the hidebound organizational culture of most big pharma today. Before you can leverage the high volume of diverse data, you must integrate the data. That is the stage we are at now.

The second trend is the patient engagement movement. This is also transformative because it is forcing companies to revise their standard procedures and practices in developing a drug for registration and launch. Companies are soliciting input from patients and patient advocacy groups when building their development plan. And patients are helping sponsors to determine clinically relevant protocol design endpoints, and improve trial feasibility and participant convenience.  The US Food and Drug Administration and the European Medicines Agency are looking to encourage these collaborations by requiring companies to report clinical trial results in language understandable to patients. I am optimistic this will soon be standard practice in every clinical trial. In fact, engagement is extending beyond the patient to include outreach to payers and providers too. Taken together, this has a positive influence on the overall delivery of care while simultaneously focusing resources to better execute against plan.

The third trend is linked to the two I’ve just cited: the convergence of clinical research and care delivered at the clinic setting. If you harvest the data and analytics, integrate the information in the trial protocol, and then use it to more effectively engage the patient, then the result is trials that are easier to recruit for and less expensive to conduct. The National Academy of Sciences is promoting an initiative called Learning Health Systems, which uses data from patient responses both to investigational and commercial medical therapies to incentivize broader changes in the way care is delivered to patients in the real-world setting. Essentially clinical research data contributes to the patient’s medical record and informs clinical care throughout the patient’s lifetime. Learning Health Systems represent a shift in thinking whereby research and accessible medical data will become embedded in a much leaner operating model supported by wearable and mobile technologies, flexible personnel staffing like telemedicine and other virtual communications. It is all about having infrastructure that reaches out to meet the patients, where they can most easily share their experiences and data, rather than the reverse.  Embedding those contacts should address a stark statistic explaining why the trial status quo needs to change: we estimate that upwards of 80% of patients in a clinical trial never learn if their participation resulted in an approved drug. 

In Vivo: What about disruptive changes in the trial space that could slow or complicate these three positive transitions you’ve just outlined?  
Ken Getz:There are several – though instead of disruptions I’d prefer to describe them as curveballs. One issue that stands out is the predicament of many smaller companies whose innovations now tend to dominate the development landscape in biopharma. What the FDA calls the “emerging sponsors” group accounts for nearly two-thirds of all active drugs and biologics in R&D today. Their economic footprint is different, most notably because these innovators cannot fund the sizable trials typical of a big pharma. Another issue is the continued proliferation of rare and stratified disease drug candidates in company pipelines. This is a major curveball, because to progress a trial in this space you have to work with relatively more complex protocols and a smaller number of patients. Sponsors must look far and wide for eligible trial participants, all while educating a provider community that often has no exposure to clinical research for the literally thousands of rare conditions vying for attention. And you don’t have the scale efficiencies that you get when working with the much larger population groups in chronic disease.
Taken together, the concern is that such curveballs will delay the positive structural changes I have laid out that promise to enhance patient experience and engagement. The importance of the science coming from these two segments of the biopharma business demands we make the transition to a leaner, more efficient operating model for drug development, reducing the cost of trials with more focus on patient-centric care. We all want to realize the promise of transformative, precision medicine as quickly as possible.

 

 
In Vivo: Are internal company cultures, standards and practices “fit for purpose” in accelerating the operational transformation you reference?
Ken Getz:The industry is continuing to pursue multiple and often inconsistent strategies on policy issues like open sourcing of data and trial transparency.  At the company level, we see a disconnect between what senior management identifies as a strategic opportunity and the execution of that opportunity down through the ranks. An example is a corporate decision that is commonly made to choose a single or limited  number of preferred external providers for contract services on clinical trials. But that decision gets upended when it is time for individual clinical teams to select who they want to work with. It is all mix and match – some part of the operation adheres to management’s preference for a single service provider, while others, for various reasons, choose to go their own way. The result just perpetuates inefficiency and inconsistent performance. There are countless additional examples of this type of behavior.  Fixing organizational incoherence is a priority for big pharma, but change is hard.  It takes time to adapt.


In Vivo: What specifically are you predicting for the clinical trial environment in the coming year?
Ken Getz:Developments in this space tend to unfold slowly.  The most important thing to expect in 2019 is that the CRO community will prioritize the introduction of advanced data and analytics to drive efficiencies in trial design, recruitment and execution. They are not going to leave it to the companies to muddle through any longer. We will see some compelling new examples of how that data is being integrated across the entire drug development spectrum. Specifically, information will be applied more robustly to identify and recruit investigators with the right credentials as well as to accurately target the best study volunteers.

In Vivo: Tufts CSDD has a robust research program covering all aspects of the drug development cycle. What’s on tap for 2019? 
Ken Getz: Tufts CSDD typically works on 15 or so research projects simultaneously each year; some of it is sponsored by individual organizations (e.g. foundations, industry, and government agencies), with others done on a multi-sponsor basis. We work closely with most of the top 50 biopharmaceutical companies and the top 10 CROs, whom we survey frequently to capture emerging trends in policy and practice. My current work centers on a number of areas, including the evaluation of protocol design practices and their impact on drug development performance and cost; and benchmarks and trends in functional area practices such as outsourcing, site management, data management and patient recruitment. My team also looks at the impact of new operating solutions and innovations – patient engagement, single-source manufacturing, predictive analytics – on the expected net present value of drug development programs.

One example of projects we are currently working on is evaluating the use of machine learning and other forms of AI in clinical trial management, where we identify major areas this technology could be applied for performance improvement and demonstrable cost savings.  Another is assessing study volunteers’ participation in clinical trials to better understand the burden borne by patients. Specifically, we are developing a methodology to routinely measure the burdens that patients face from procedures as well as the simple cumulative inconveniences of patient participation. The results will show companies where they need to modify protocol design to make participation more convenient for enrollees, enhancing study feasibility and volunteer retention rates -- ultimately delivering a better cohort of patients and a higher quality of clinical research data to support registration.

An upcoming project for 2019 will quantify study volunteer diversity (e.g. gender, race and ethnicity) in new drug and biologic approvals. Hard and credible baseline measures are lacking right now. If you were to ask me for the number of black patients enrolled in trials for new diabetes drugs last year, I could not tell you. We have received a large grant to conduct this benchmark assignment based on evidence from clinical trials conducted over the past several years. Clearly, there is no shortage of drug development inefficiencies and improvement opportunities ripe for scholarly research. Our goal must be to apply this work to actual changes in the way trials are conducted – to establish precedents for patients, backed by evidence and astute observation.

 

Other 2019 Predictions: Prep For The Value Wave

Information is the currency of value in biopharma, and, as data analytics capabilities explode, competitive advantage will accrue to those companies able to master the logistics and operational challenges in making this bounty of knowledge useful to the business. Julie Locklear, In Vivo editorial advisory board (EAB) member and managing director of the end-to-end evidence consultancy Genesis Research, weighed in on the topic of managing and utilizing data for value-based contracting success.

“Making the pursuit of value part of your business model doesn’t just happen. We’ve found that successful companies recognize early on that the transition to value contracting can’t be accomplished entirely in-house. It’s not about setting a metric like recruiting a fixed number of additional FTE’s. Instead, a flexible, hybrid approach – relying on a mix of internal and external resources – is required to address the new realities of budget limitations, the growing diversity of payer expectations, and real time access to vast amounts of data to speed decision-making. Bottom line: to prove its mettle in commercial negotiations, a value-based approach depends on partnering.  Strategic external engagement is critical, especially in getting the analytics behind the transaction right. With multiple stakeholders in play, the approach to data has to be agnostic, so it pays to cast the net widely.

The second mandate we see coming in 2019 is more focus on the details of value-based transactions. The most important is to address the auditing of contracts contingent on specific outcomes like pay for performance.  Discounts demanded by payers are going to increase, in turn raising the bar on the scope, reliability and integrity of the audit role –clearly, this function is vital as a confidence-builder for both biopharma and the payer community.  In some therapeutic areas, like oncology, where verifiable data on progression-free survival is hard to come by, especially at the point of care, a consensus on auditing terms will be difficult, and, due to these data constraints, expensive too.

One possible solution to variant perceptions of data reliability is the emergence of a new class of vendors offering data management as an independent, third-party service, to work on behalf of payers who lack the internal expertise or resources to register outcomes.

The third mandate biopharma would be wise to follow in the months ahead is to recognize the growing institutional clout of third-party HTA review bodies, especially the Institute of Clinical and Economic Review (ICER), which now has a formal advisory role in the formulary listing decisions of high-profile pharmacy payers like CVS Heath Corp. and the US Veterans Administration Health Service. One practical step is for drug makers to take the initiative in modeling the ICER QALY-based methodology as part of their early-stage work to establish a compound’s value proposition with payers and other key stakeholders.”   

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