Total therapeutic product value: The integration of approaches and incorporation of real-world data to optimize trial design

Accenture’s Jeff Elton explores the benefits of real-world data in clinical trial design.

When developing new medicines, pharmaceutical companies must place a focus on total product value, health, and quality of life outcomes. By doing so, pharmaceutical companies can best determine which medicines to develop and commercialize – those which are not only efficacious, but also which deliver value to health care systems as well.

The increasing emphasis on total therapeutic product value is requiring pharmaceutical companies to make changes in their clinical development and management processes, including optimizing their clinical trial designs to more effectively identify medicines that offer value.

“…pharmaceutical companies need to revise their approach to developing clinical trial plans to more effectively identify true therapeutic and value beneficiaries…”

But pharmaceutical R&D has always been challenging. The likelihood of success from the earliest points is often assessed as being less than 10 percent. Once in the clinic the probabilities remain less than 50 percent until the very latest phases.1

We observe that we are now entering a period where the number of new candidate medicines and even the number of new and forecasted approvals are increasing. At the same time, we are seeing newly approved medicines failing to meet their sponsoring company’s expectations.

Failure can be the result of inadequate efficacy in the clinical setting relative to existing alternatives, or care-provider defined approaches that may involve multiple alternative medicines used in combination. This is creating new criteria for adoption that differ from the criteria for approval alone – value in use.

We believe this is a ‘predictable’ source of failure or failure to meet expectations. To address this failure, pharmaceutical companies need to revise their approach to developing clinical trial plans to more effectively identify true therapeutic and value beneficiaries and those for whom value is less.

Partners in trial designs

We see that early phase collaborations can aid in improving clinical trial designs. Diagnostics companies or groups should work with the therapeutics team to identify clinical practical biomarkers or tests. In addition, payer and provider analytics groups can build a perspective on ‘value’ that can be integrated into the design of both early and late phase trials to support a practical assessment of value to patients and the system versus alternative treatment approaches.

“Real-world data offer valuable insight into the effectiveness of different medicines.”

This creates new pressures to assure that the diagnostic or other patient stratification approaches can be practically deployed in the clinical contexts where the new medicines will be used, such as in community non-acute settings versus sophisticated academic medical centers. Even now, reimbursement for many diagnostic technologies and approaches is no easier than for some medicines. But the combined approach of therapeutic with practical diagnostics aids regulators and private and public health payers by giving them stronger data in support of both.

Pharmaceutical companies, diagnostic companies, and academic or government-based research centers can become partners in designing new clinical trials, sharing their knowledge and expertise. For instance, Novartis is using Foundation Medicine’s genome interpretation technology as part of its clinical trial enrollment process for cancer drug testing.2 This tumor genome analysis technique involves seeking potential drug targets in the genetic sequence of tumors. With genomic sequencing, treatments can be tailored to the genetic anomalies of an individual’s tumor, which may be quite different from those of another patient’s tumor.

Incorporate Real-World Data

As part of ongoing efforts to optimize clinical trials and bring the greatest therapeutic value for patients, pharmaceutical companies should look to incorporate new real-world data which We define as data acquired from electronic medical records, laboratory information systems, etc. Real-world data are collected in the actual clinical care environments where patients are treated as opposed through clinical trials, such as data from personal electronic medical records (EMRs). It is different than data collected from controlled environments, such as controlled clinical trials. Real-world data offer valuable insight into the effectiveness of different medicines. It facilitates coverage and reimbursement decisions.

Real-world data and comparative effectiveness research can provide noteworthy evidence on the efficacy, benefits and possible negative effects of treatment options under consideration. Real-world data are increasingly needed by pharmaceutical companies to determine and validate how value is being realized from their therapeutics and associated treatment approaches.

Real-word data and associated analytics can come from several sources:

• Academic centers that provide rich insights into specific disease states and targeted patient populations;

• A growing number of health payer enterprises that collect and analyze claims-derived information;

• Third party firms aggregating EMR derived data across multiple health delivery systems.

In each of these cases, payers, third-party aggregators, and the leading academic medical centers are providing a consistent data model and analytic tools that make possible insights into the use and value of current medicines, which, in turn, can guide the design and endpoints for a new medicine’s value plan.

“Pharmaceutical companies, diagnostic companies, and academic or government-based research centers can become partners in designing new clinical trials, sharing their knowledge and expertise.”

We find that increasingly, clinical trial designs must:

• Anticipate that such real-world data will be collected on an ongoing basis, showing for whom the medicine works, and for whom it is less effective;

• Include approaches that incorporate the medicine in a real clinical context;

• Build the foundation for value.

The integration of approaches to optimize trial design

We purport that the trial designs and practical therapeutic approaches of the future will increasingly bring together a variety of tools and technologies– next generation sequencing for clinical diagnosis, other diagnostic modalities, real-world evidence from patient populations over time. It is the integration of these approaches that will drive value and enable clinicians and clinical care institutions to realize value consistent with their clinical trials designs and beyond what they are realizing with their current approaches.

Today it is clear that many of these approaches are still being validated for utility. Policies for reimbursement are still being established. Yet, this is not altogether different from any new therapeutic approach being considered, approved or reimbursed.

What is novel is how these approaches are partnered, who is engaged early on and throughout the process, and who is provided access to the data. It is in the partnering across the development and early commercial life cycle that will engage critical parties, lower risk, and accelerate needed new innovations. This is the same process that will allow pharmaceutical companies to prioritize, kill certain programs earlier, and increase the value they can realize from their R&D investments.


In this manner, pharmaceutical companies can best determine which therapies to develop and commercialize – those that deliver value to patients and health care systems.


1. 2012 R&D Performance Success Rates and Cycle Time; Pharmaceutical Benchmarking Forum; KMR Group



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About the author:

Jeff Elton is Managing Director in Life Sciences in Accenture. Jeff has over 20 years of experience as a global executive and consultant in the biopharmaceutical and healthcare sectors. Within Accenture he has broad responsibility for Accenture’s strategy and partnerships for new solutions enabled by clinical data and analytics.

How can pharma companies best decide which therapies to develop and commercialise?