Pfizer Inc. recently joined the TriNetX global health research network. As a member, Pfizer will have access to clinical data from TriNetX’s network of healthcare organizations to support clinical study and protocol design, site identification, and patient recruitment for clinical trials across a range of therapeutic areas. 

TriNetX is a global health research network that connects healthcare organizations, biopharmaceutical companies, and CROs to collaborate to enhance trial design, accelerate patient recruitment, conduct in-depth research, and help bring new therapies to market faster. TriNetX’s cloud-based platform offers researchers the ability to analyze patient populations and perform “what-if” analyses in real-time. Users of the platform are presented with aggregate views, but each data point in the TriNetX network can be traced to healthcare organizations who have the ability to identify patients, allowing clinical researchers to develop virtual patient cohorts for potential recruitment into a clinical trial.

“Pfizer joined TriNetX to harness real world data for clinical trial optimization, with the goal of accelerating our ability to bring new therapies to market,” said Dr. Mohanish Anand, head of Study Optimization at Pfizer. “Pfizer will use the real-time access to clinical, genomic and oncology data to design clinical trial protocols with greater efficiency. For example, we hope to reduce avoidable amendments by identifying and correcting overly restrictive inclusion and exclusion criteria early in the design process.”

Contract Pharma spoke with Manfred Stapff, senior vice president and chief medical officer at TriNetX about the challenges and delays associated with conducting clinical trials, as well as opportunities to improve protocol design and make more data-driven decisions. – KB
 
 
Contract Pharma: What are the major challenges pharma/biopharma companies face conducting clinical trials?

Manfred Stapff: One major challenge is that the complexity of protocols is still rising despite its proven negative impact on enrollment, timeline and budget. Ultimately this is not only causing delays in bringing therapies to market, but also questions whether the study results can be applied in a representative way to the population in routine medical practice.

CP: Where do you see the greatest opportunities to improve the clinical trial process?

MS: Pharmaceutical researchers need access to real world data, which enables them to design clinical trial protocols with realistic eligibility criteria. Before the protocol is finalized, the planned study population (according to eligibility criteria) should be tested against real world data for representivity and to identify potential enrollment hurdles. Overall, we need more data-driven decision making in early design phases of studies.
 
CP: What is and isn’t working well with clinical trial data?
 
MS: Clinical trials have been conducted for more than 25 years basically the same way, with few minor tweaks through the use of technology. This means that the operational aspects, the compliance with laws and regulations, the respect of patients’ rights, the validity of the collected data, and the robustness of the analyses work relatively well. But, what actually does not work well, and I would say is even worsening: the selection of patients and the conduct of the trial has less and less to do with medical reality. It starts with the protocol which often has to be amended due to enrollment difficulties, which delays the process and is costly. Furthermore, clinical trials are conducted on a highly selective patient population, and the visit schedules and treatment protocols often represent a level of care and attention which no patient in real medical practice would ever receive. This leads to a situation in which these study data barely can be generalized, and the knowledge gained during Phase II and III has to be complemented by large real world observations after launch. Fortunately, pharmaceutical companies are realizing that significant downstream costs can be avoided by an improved protocol design process using the TriNetX platform.

CP: How can challenges and delays associated with the growing volume of data in clinical trials and expanded use of real world evidence be overcome?

MS: It is not enough to provide access to a large volume of data. The data also must contain the appropriate information, clinical data types, and longitudinal history. In addition, a researcher should have intuitive self-service access to these data to analyze them in real time, modify the protocol, if necessary, and see the consequences immediately.

CP: What are the challenges and potential benefits around establishing open technology standards?

MS: A widely adopted standard generally serves the greater public good, providing the right information at the right time. In digitizing healthcare and life sciences, standardization is faced with a number of common challenges, some of which include the proliferation of too many standards, over-complexity inhibiting implementation, and an abundance of funding for standards development without mandates or incentives to adopt the end result.