Extending the vision of adaptive point-of-care platform trials to improve targeted use of drug therapy regimens: An agile approach in the learning healthcare system toolkit.

OBJECTIVES: Improving the targeted use of drug regimens requires robust real-world evidence (RWE) to address the uncertainties that remain regarding their real-world performance following market entry. However, challenges in the current state of RWE production limit its impact on clinical decisions, as well as its operational scalability and sustainability. We propose an adaptive point-of-care (APoC) platform trial as an approach to RWE production that improves both clinical and operational efficiencies. METHODS AND FINDINGS: We explored design innovations, operational challenges, and infrastructure needs within a multi-stakeholder consortium to evaluate the potential of an APoC platform trial for studying chronic disease treatment regimens using rheumatoid arthritis as a case study. The concept integrates elements from adaptive clinical trials (dynamic treatment regimen strategies) and point-of-care trials (research embedded into routine clinical care) under a perpetual platform infrastructure. The necessary components to implement an APoC platform trial within outpatient settings exist, and present an opportunity for a cross-disciplinary, multi-stakeholder approach. Effective engagement of key stakeholders involved in and impacted by the platform is critical to success. Our collaborative design process identified three high-impact stakeholder-engagement areas: (1) focus on research question(s), (2) design and implementation planning such that it is feasible and fit-for-purpose, and (3) measurement, or meaningful metrics for both clinical (patient outcomes) and system (operational efficiencies) impact. CONCLUSIONS: An APoC platform trial for rheumatoid arthritis integrating innovative design elements in a scalable infrastructure has the potential to reduce important uncertainties about the real-world performance of biomedical innovations and improve clinical decisions.

Examining Endpoint Concordance in Clinical Trials and Real-World Clinical Practice to Advance Real-World Evidence Utilization.

Real-world evidence (RWE) is increasingly contributing to more informed decisions regarding the optimal access to and use of therapeutics to improve patient outcomes. However, in many cases, a disconnect between evidence derived from clinical trials and the RWE that follows market approval impedes the potential value and widespread adoption of RWE to optimize patient care. Collaborators with the Learning Ecosystems Accelerator for Patient-centered, Sustainable innovation (LEAPS), a major project of the Tufts Medical Center [formally Massachusetts Institute of Technology (MIT)] NEW Drug Development ParadIGmS (NEWDIGS) initiative, propose assessing the relationship between efficacy endpoints used in randomized controlled trials (RCTs) and effectiveness measures that inform treatment decisions within real-world clinical settings as one way to bridge this divide and further leverage RWE to improve care and patient outcomes. This commentary outlines elements of an endpoint concordance study using Rheumatoid Arthritis as a case study. The authors describe the ways in which better understanding of the relationship between effectiveness and RCT endpoints could improve the confidence in and adoption of RWE by both contextualizing existing RWE as well as identifying ways in which to improve the value of RWE in improving care and outcomes.

Precision Reimbursement for Precision Medicine: Using Real-World Evidence to Evolve From Trial-and-Project to Track-and-Pay to Learn-and-Predict.

Basic scientists and drug developers are accelerating innovations toward the goal of precision medicine. Regulators create pathways for timely patient access to precision medicines, including individualized therapies. Healthcare payors acknowledge the need for change but downstream innovation for coverage and reimbursement is only haltingly occurring. Performance uncertainty, high price-tags, payment timing, and actuarial risk issues associated with precision medicines present novel financial challenges for payors. With traditional drug reimbursement frameworks, payment is based on an assumed randomized controlled trial (RCT) projection of real-world effectiveness, a "trial-and-project" strategy; the clinical benefit realized for patients is not usually ascertained ex post by collection of real-world data (RWD). To mitigate financial risks resulting from clinical performance uncertainty, manufacturers and payors devised "track-and-pay" frameworks (i.e., the tracking of a pre-agreed treatment outcome which is linked to financial consequences). Whereas some track-and-pay arrangements have been successful, inherent weaknesses include the potential for misalignment of incentives, the risk of channeling of patients, and a failure to use the RWD generated to enable continuous learning about treatments. "Precision reimbursement" (PR) intends to overcome inherent weaknesses of simple track-and-pay schemes. In combining the collection of RWD with advanced analytics (e.g., artificial intelligence and machine learning) to generate actionable real-world evidence, with prospective alignment of incentives across all stakeholders (including providers and patients), and with pre-agreed use and dissemination of information generated, PR becomes a "learn-and-predict" model of payment for performance. We here describe in detail the concept of PR and lay out the next steps to make it a reality.

Dynamic Dossier in the Cloud: A Sociotechnical Architecture for a Real-Time and Metrics-Based Data Tracking System with Gene and Cell Therapies as a Case Study.

BACKGROUND: Data sharing among stakeholders in the development, access, and use of drug therapies is critical but the current system and process are inefficient. METHODS: We take a Systems Engineering approach with a realistic use case to propose a scalable design for multi-stakeholder data sharing. RESULTS: We make three major contributions to the drug development and healthcare communities: first, a methodology for developing a multi-stakeholder data sharing system, with its focus on high-level requirements that influence the design of the system architecture and technology choice; second, the development of a realistic use case for long-term patient and therapy data tracking and sharing in the use of potentially curative and durable gene and cell therapies. Further, a bridge for the 'awareness gap' was found between the payer (Payer is organization which takes care of financial and operational aspects (which include insurance plans, provider network) of providing health care to US citizens. Or refer to health care insurers.) and the regulator communities by illustrating the common data tracking needs, which highlights the need for coordinated data activities; and third, a proposed system architecture for scalable, multi-stakeholder data sharing. Next steps are briefly discussed. CONCLUSION: We present a system design for multiple stakeholders such as the payer, the regulator, the developer (drug manufacturer), and the healthcare provider to share data for their decision-making. The stakeholder community would benefit from collaboratively moving the system development proposal forward for efficient and cost-effective data sharing.

Recognizing that Evidence is Made, not Born.

Therapeutic product development, licensing and reimbursement may seem a well-oiled machine, but continuing high attrition rates, regulatory refusals, and patients' access issues suggest otherwise; despite serious efforts, gaps persist between stakeholders' stated evidence requirements and actual evidence supplied. Evidentiary deficiencies and/or human tendencies resulting in avoidable inefficiencies might be further reduced with fresh institutional cultures/mindsets, combined with a context-adaptable practices framework that integrates emerging innovations. Here, Structured Evidence Planning, Production, and Evaluation (SEPPE) posits that evidence be treated as something produced, much like other manufactured goods, for which "built-in quality" (i.e., "people" and "process") approaches have been successfully implemented globally. Incorporating proactive, iterative feedback-and-adjust loops involving key decision-makers at critical points could curtail avoidable evidence quality and decision hazards-pulling needed therapeutic products with high quality evidence of beneficial performance through to approvals. Critical for success, however, is dedicated, long-term commitment to systemic transformation.

Medicines Adaptive Pathways to Patients (MAPPs): A Story of International Collaboration Leading to Implementation.

After nearly a decade of discussion, analysis, and development, the Medicines Adaptive Pathways to Patients (MAPPs) initiative is beginning to see acceptance from regulators, industry, patients, and payers, with the first live pilot project initiated under the guidance of the European Medicines Agency in 2014. Although it is a significant achievement to see the first asset being placed into human trials under an adaptive pathway, there is much to be learned regarding the multinational and multi-stakeholder effort that has driven the growing acceptance of MAPPs as a methodology and concept, as well as the need for continued and increasing international collaboration to foster the wider adoption of MAPPs. Changes in available science and technology, as well as a number of challenges in the current system, outlined in this paper, are transforming approaches to medicines development and approval. It is these challenges that have led directly to the groundbreaking MAPPs collaboration between the Massachusetts Institute of Technology Center for Biomedical Innovation's New Drug Development Paradigms Initiative, the EMA, patient, payer and health technology assessment groups, the European Federation of Pharmaceutical Industries and Associations, and the Innovative Medicines Initiative-a European public-private partnership. This article examines the development of MAPPs, from inception of the concept, to the establishment of this trans-Atlantic initiative, and examines challenges for the future.

Curing consortium fatigue.

The complex pathology of consortium fatigue provides diagnostic data on how to improve collaboration in biomedical innovation.

Comparison of Stakeholder Metrics for Traditional and Adaptive Development and Licensing Approaches to Drug Development.

This study evaluates whether an adaptive development and licensing approach to drug development, compared with approaches widely used today, might have tangible advantages across stakeholder groups, thereby facilitating the future adoption. Details involving actual and modeled clinical development and licensing programs for 3 case studies were used as inputs into a discounted cash flow spreadsheet model. Outputs included net present value and expected net present value, which are metrics considered as key incentives for pharmaceutical developers, and change in patient access over the product life and numbers of appropriately and inappropriately treated patients, which are metrics considered as key incentives for regulators, patients, and prescribers. Actual and modeled development programs were compared using an "adaptiveness" scoring algorithm. Generally, the more adaptive programs correlated with more favorable stakeholder outcomes. However, favorable outcomes may be overwhelmed in some cases, and the causative conditions and stakeholder reactions need to be defined.

The future of drug development: advancing clinical trial design.

Declining pharmaceutical industry productivity is well recognized by drug developers, regulatory authorities and patient groups. A key part of the problem is that clinical studies are increasingly expensive, driven by the rising costs of conducting Phase II and III trials. It is therefore crucial to ensure that these phases of drug development are conducted more efficiently and cost-effectively, and that attrition rates are reduced. In this article, we argue that moving from the traditional clinical development approach based on sequential, distinct phases towards a more integrated view that uses adaptive design tools to increase flexibility and maximize the use of accumulated knowledge could have an important role in achieving these goals. Applications and examples of the use of these tools--such as Bayesian methodologies--in early- and late-stage drug development are discussed, as well as the advantages, challenges and barriers to their more widespread implementation.