Bench to Budget: Integrated Evidence Generation for Medications

“Bench to bedside” research has long driven evidence generation for drug development. However, modern healthcare demands a similar innovation feedback loop between medical science and population-level evidence—what we term “Bench to Budget.”

Population-level healthcare delivery and budget considerations are often neglected in early research discussions. Yet, the ultimate impact and value of any medical intervention depends on access and real-world effectiveness. Clinical trial efficacy data may not always be available for critical medical scenarios such as safety in special populations (e.g., patients with renal failure). Over the past decade, pharmaceutical companies, regulators, and health technology assessment (HTA) agencies have increasingly considered real-world evidence (RWE) to fill these gaps. We advocate for explicit discussions on best practices, barriers to implementation, and methods for generating and integrating evidence along the full bench-to-bedside-to-budget drug development pathway.

Modern medications, and our understanding of how to maximize their benefits and minimize their harms, are remarkable achievements. From bench research to payer budget, decision-making that impacts human health is often required in settings of uncertainty. This Clinical Pharmacology & Therapeutics (CPT; Figure 1) “Bench to Budget” special issue explores the promise and perils of leveraging multiple sources and types of evidence in a holistic approach to developing, using, and paying for new medications.

Grueger and Srikant¹ describe an evidence-planning process that considers the many evidence needs of the stakeholders along a drug's life cycle and how they can be satisfied by using adequate data sources and scientific approaches. The resulting integrated evidence plans help ensure that no unforeseen evidence gaps will appear during the development, launch, and post-marketing process and lead to delayed access to medications. Such plans will identify efficiencies by planning mutually supporting knowledge transfers between research programs and by building pipelines of research studies that build on each other and share resources (see Figure 2 as an illustrative example).

Within the appealing framework of a longitudinally integrated evidence generation, Chen et al.,² Yavuz et al.,³ Jiang et al.,⁴ Baumfeld Andre et al.,⁵ and Bhattacharya et al.⁶ address various conceptual and statistical challenges in managing and synthesizing evidence across scientific approaches during the drug-development lifecycle. Kent et al.⁷ focuses on orchestrating evidence generation after the approval of medications. Bischof et al.⁸ and Wilczok and Zhavoronkov⁹ explore the strengths and limitations of artificial intelligence (AI) tools in supporting drug development.

Much progress has been made with innovative trial designs. Ko et al.¹⁰ describe advances in hybrid trials as a promising tool.

Over the past decade, the impact of evidence generated outside clinical trials, or RWE, has grown. The methodological advances in RWE studies over the past two decades are described and exemplified by Hernandez et al.¹¹; Huybrechts et al.¹² describe study design advances for research on medication effects during pregnancy, and McMahon et al.¹³ describe research and regulatory advances in pediatric RWE studies. Desai et al.¹⁴ illustrate powerfully how methodologically sound RWE helps prioritize large numbers of hypotheses for repositioning marketed medications before embarking on trials in humans. Benedum et al.¹⁵ bring RWE directly to the challenge of evaluating and optimizing representativeness in clinical trials.

All these advances occur in a regulated environment and under cost constraints. Regulatory agencies have made great advances in their understanding of, and decision-making based on, RWE as illustrated by Asano et al.¹⁶ Similarly, Emond et al.¹⁷ describe how HTA organizations have integrated RWE into their decision-making frameworks. Arlett et al.¹⁸ describe how regulators use real-world data networks to support their decision-making, while Pavel et al.⁷ and Lasch et al.¹⁹ take critical looks at how regulators and HTA agencies review and evaluate RWE for their decision-making.

Expanding the aperture of evidence considered for holistic decision-making, as exemplified in many ways in this issue of CPT, holds promise for better health for all.

Dr. Schneeweiss was funded by the National Institutes of Health (NHLBI R01-HL141505, NIAMS R01-AR080194).

Dr. Schneeweiss is a consultant to and holds equity in Aetion, Inc., a software manufacturer. He is the principal investigator of research grants to the Brigham and Women's Hospital from UCB unrelated to the topic of this article. Dr. Miksad is employed by and holds equity in Color.