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FDA’s PDUFA VI Goals Highlight Model-informed Drug Development

On July 15, the US FDA published its goals and commitment letters for the re-authorization of its Prescription Drug User Fee Act (PDUFA) for fiscal years 2018-2022, known as PDUFA VI. The document reflects the agency’s performance and procedural goals to expedite bringing safer therapies to patients.  It also reflects and incorporates the advances in regulatory science and decision-support tools to support drug development and decision-making. To that end, PDUFA VI addresses three key elements at the heart of our work with sponsors that I will discuss in this blog post: promotion of early consultation with the agency, enhancing the use of real world evidence, and advancing model-informed drug development.

Promoting innovation through regulatory communication

A key principle in the publication is “building on the success of the FDA’s regulatory science program that includes advancing the science of meta-analysis methodologies, advancing the use of biomarkers and pharmacogenomics, enhancing communication between FDA and sponsors on the use of new surrogate endpoints as the primary basis for product approval, advancing rare disease drug development, advancing the development of combination products, and exploring the use of real world evidence for use in regulatory decision-making.”

  • The Agency has designated the Type C meeting to provide advice regarding a proposed biomarker as a new surrogate endpoint. The sponsor needs to include preliminary human data indicating the impact of the drug on the biomarker at a dose that appears to be generally tolerable.  This information needs to be submitted 47 calendar days before the date of the meeting.
  • Building on the success of the Agency’s Rare Disease Program (RDP), review teams will provide their “unique expertise on flexible and feasible approaches to studying and reviewing drugs.” Their expertise includes the use of biomarkers, new clinical study approaches such as adaptive design, evaluation of novel endpoints, new statistical approaches, and the leveraging of other FDA programs such as Breakthrough, Priority Review and Accelerated Approval.  Recognizing the unique challenges in rare disease development, the RDP staff will reach out to industry and patient groups to collaborate on decision-support tools and technologies.

Integrating real world evidence into regulatory decision-making

According to the June 2016 report, Using Real-World Evidence to Accelerate Safe and Effective Cures, from the Bipartisan Policy Center, data gathered from sources outside of randomized controlled trials reflecting the actual experiences of patients during routine patient care is often referred to as real-world evidence (RWE) or data. Sources of real-world data include electronic health records (EHRs) used within provider settings, laboratory information systems, pharmacy and radiology systems, administrative claims systems and registries, and patient-generated data captured on home-based and wearable monitoring devices, as well as patient information-sharing networks and social media.

Today, the FDA uses RWE in post-marketing surveillance under the Agency’s Sentinel Initiative.  Our scientists leverage summary level real-world data in our outcomes and comparative effectiveness consulting (model-based meta-analysis) to compare current health care interventions and determine which work best for which patients.

The report continues by stating that RWE can support many activities during the clinical trials phase of drug development. It can expedite the generation of hypotheses to inform the design of clinical studies and enable identification of subpopulations with higher risk-benefit ratios to target development efforts. RWE can enable more efficient and targeted recruitment of patients for clinical trials. It can also reduce the burden of data collection and reporting and enable the collection of patient-reported outcomes. The use of real-world evidence can improve the generalizability of trials by augmenting trials with data from a broader, more diverse group of patients in different practice settings than is currently gained through targeted, tightly controlled populations, to gain better insights on safety and effectiveness. Real-world evidence can make studies and their findings more relevant to patients and provide information on long-term outcomes. Finally, earlier generation of effectiveness data can help inform decision-making regarding value and reimbursement sooner, which is a goal of both sponsors and payers.

PDUFA VI is committed to fully evaluating RWE into its regulatory decision-making via education, pilot studies, and methodology development projects.

Advancing model-informed drug development

Model-Informed Drug Development (also called Modeling & Simulation) has already played a sizeable role in drug development, with more than 90 percent of all drugs and biologics approved in 2015 incorporating one or more of these quantitative approaches. M&S has been written into more than ten FDA guidance documents. A 2015 paper written by Dr. Janet Woodcock and others at FDA, called “Catalyzing the Critical Path Initiative: FDA’s Progress in Drug Development Activities,” offered the following:

Modeling and simulation (M&S) tools for drug exposure and its response have been useful in both pre- and postmarket settings when questions related to safety and efficacy of therapeutic products arise. Some recent examples where M&S has served as a useful predictive tool include:

  • Dose selection for pivotal trials
  • Dosing in select populations such as pediatrics
  • Optimization of dose and dosing regimen in patient population subsets
  • Prediction of efficacy and dosing in an unstudied patient population in clinical trials
  • Characterizing exposure and dose-related QT interval prolongation
  • Using PBPK modeling in predicting drug–drug interactions.

Under PDUFA VI, FDA will develop its regulatory science and review expertise and capacity in MIDD approaches.  The Agency will conduct workshops to identify best practices, including

  • Physiologically-based pharmacokinetic modeling (PBPK),
  • Design analysis and inferences from dose-exposure-response studies,
  • Disease progression model development, including natural history and trial simulation,
  • Immunogenicity and correlates of protection for evaluating biological products.

The FDA will conduct pilot programs, publish additional guidance documents, and develop or revise SOPs for incorporating guidelines for the evaluation of MIDD approaches.

As the leading provider of MIDD technology and services, we have published “Best Practices in Drug Development Modeling & Simulation.” Do you agree or disagree with our proposed best practices? Let me know in the comments section!

About the author

Ellen Leinfuss
By: Ellen Leinfuss

Ellen Leinfuss serves as SVP and Chief Commercial Officer for Certara’s Simcyp Division. Simcyp is the global leader in mechanistic modeling, which includes the renowned Simcyp Simulator for physiologically based pharmacokinetics (PBPK) and its Quantitative Systems Pharmacology (QSP) immunogenicity, immune-oncology, vaccine and gene therapy modeling & simulation platforms. Ellen has been at Certara for the past seven years, serving for many years as Chief Marketing Officer for the corporation. With a degree in chemistry and MBA in marketing, Ellen has provided management, business development and marketing leadership to organizations in the biopharmaceutical, environmental and energy fields. She is a published author and speaks regularly on topics related to model-informed drug development.

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