Research Interests

Pediatrics, rare diseases, regulatory science, innovative clinical trial designs, Bayesian statistics, Real World Evidence/Big data, biomarker development

Proposed Research Project for FDA Commissioner's Fellow

Despite an increasing understanding of potential differences in pharmacokinetics, pharmacodynamics, and clinical conditions, clinical trials in children continue to pose practical and ethical challenges. The limited availability of patients, challenges with patient recruitment, trial design considerations including the ethics of placebo use, blood sampling volume limits, etc. underscore the need to optimize the collection, analysis, and interpretation of data on safety and efficacy in children. From a drug development perspective, extrapolation has evolved as an approach to minimize the burden associated with conducting full development programs in children.

Extrapolation relies on the use of inferences about efficacy in children based on data in adults when a predefined set of requirements are met. First, the disease pathophysiology and progression must be similar in adults and children. Second, the expected response to the treatment has to be comparable across the two populations. Lastly, for full extrapolation, exposure-response relationships will need to be similar.

Known experience with extrapolation has been previously reviewed by the FDA. Among 366 studies submitted to the FDA between 1998 and 2008, one or more confirmatory pivotal phase III studies were required in almost 60% of the cases reviewed, suggesting that there were limitations in the available data to support further extrapolation. The FDA review also revealed that a rigorous and consistent approach to defining and establishing disease and response similarity (or lack thereof) is still missing. Sponsors, for lack of data to support extrapolation, often must conduct larger trials than would otherwise be necessary. As a result, on average, it takes almost 9 years from the time of a drug product's approval for use in adults until the label is updated to include pediatric data. During this time, children who can benefit from timely interventions are affected, and clinicians are forced to use products off-label. Many studies have shown that off-label drug use in pediatrics is associated with significantly increased risk for developing adverse drug reactions.

The critical need to systematically assess extrapolation assumptions is well recognized in pediatric drug development. The FDA has access to data from approximately 1,200 pediatric clinical trials of which data from 600 trials spanning over 300 pediatric development programs will allow disease-focused systematic review. These data can provide the basis for a statistical modeling for Bayesian extrapolation of adult clinical trial information in pediatric drug evaluation.
The proposed project is aimed at analyzing adult and pediatric clinical trials to verify assumptions underlying extrapolation of efficacy using Bayesian approaches.

The specific aim of the study is to conduct a systematic review of adult and pediatric clinical trials in 1-2 disease areas to assess:

• Similarity of disease between adults and children
• Understand expected treatment response differences in children, if any
• Assess possibility of borrowing information from previous studies (adult and pediatric), and specify the proper extent of this borrowing (e.g. determined by study quality or similarity of the various data sources and expert opinion)
• Provide a framework for optimizing trial sample size by leveraging prior data considering study power and Type I error rate for future pediatric drug development

Bayesian methods are encouraged in medical device trials by CDRH in its 2016 Guidance to Industry on Pediatric Extrapolation. To date, there has been limited application of Bayesian methods in pediatric product development. The results of this project can inform the use of Bayesian approaches in pediatric product development and can provide a quantitative definition of disease and response similarity between adults and pediatric patients. This project will be conducted in collaboration with OND review divisions and the Offices of Biostatistics and Clinical Pharmacology.

Applicant Requirements

• Ph.D., Pharm.D. or M.D. with knowledge or willingness to learn about Bayesian modeling.
• Strong analytical skills
• Strong verbal and written communication skills
• Familiarity with drug development principles is desirable.

Selected Recent Publications

1. Penov D, Tomasi P, Eichler I, Murphy D, Yao L, Temeck J, Pediatric Drug Development: Pre-Registration Regulatory Interactions in the European Union and United States, Ther Innov Regul Sci, Accepted for publication
2. Mulugeta Y, Barrett JS, Nelson R, Eshete AT, Mushtag A, Yao L, Glasgow N, Mulberg AE, Gonzalez D, Green D, Florian J, Krudys K, Seo S, Kim I, Chilukuri D, Burckart GJ, Exposure matching for extrapolation of efficacy in pediatric drug development, J Clin Pharmacol, 2016, 0(0)1-9 [Epub ahead of print]
3. Momper JD, Karesh A, Green DJ, Hirsh M, Khurana M, Lee J, Kim MJ, Mulugeta Y, Sach HC, Yao L, Burckart GJ, Drug development for pediatric neurogenic bladder dysfunction: dosing, endpoints, and study design, J Clin Pharmacol, 2014, 54(11)1239-1246.
4. Bai JP, Barrett JS, Burckart GJ, Meibohm B, Sachs HC, Yao L, Strategic biomarkers for drug development in treating rare diseases and diseases in neonates and infants, AAPS J, 2013, Apr;15(2):447-54.
5. Dunne J, Rodriguez WJ, Murphy D, Beasley BN, Burckhart GJ, Filie JD, Lewis LL, Sachs HC, Sheridan PH, Starke P, Yao LP, Extrapolation of Adult Data and Other Data in Pediatric Drug-Development Programs, Pediatr, 2011, 128:e1242-1249.