FDA Computational Biology and Pharmacology Fellowship
Oak Ridge Institute for Science and Education, Silver Spring, MD, United States
Organization
U.S. Food and Drug Administration (FDA) – Center for Drug Evaluation and Research (CDER), Office of Clinical Pharmacology (OCP), Office of Translational Sciences (OTS), Silver Spring, Maryland. Research Project
The Office of Clinical Pharmacology (OCP) and Office of New Drugs (OND) are creating new and updating existing toxicity databases based on available nonclinical animal data (and available clinical data where applicable) that could be used in validation of human‑based in vitro and computational methods submitted to FDA for regulatory use. The developed models will predict various toxicity endpoints and drug exposure to help provide foundational knowledge and experience enabling FDA staff to integrate new alternative methods (NAMs) into drug evaluation. Under the guidance of a mentor, you will assist in developing comprehensive toxicology databases by analyzing proprietary nonclinical data from drug applications, focusing on endpoints where NAMs can provide significant regulatory value. Your research activities will include collaborating with multidisciplinary teams to curate large‑scale datasets, investigating patterns in toxicological data using statistical methods, contributing to the development of reference compound libraries for predictive model validation, and developing AI, statistical, or mechanistic models to translate nonclinical data into safety endpoints such as cardiotoxicity and immunogenicity. Responsibilities
Analyze proprietary nonclinical data from drug applications, focusing on endpoints where NAMs can provide significant regulatory value. Collaborate with multidisciplinary teams to curate large‑scale datasets and investigate patterns in toxicological data using statistical methods. Contribute to the development of reference compound libraries for predictive model validation. Develop AI, statistical, or mechanistic models to translate nonclinical data into safety endpoints such as cardiotoxicity, immunogenicity, and others. Participate in developing standardized computational frameworks for regulatory assessment and data management systems. Learning Objectives
As a participant, you will have structured learning opportunities in regulatory science, computational toxicology, and artificial intelligence applications in drug development. You will gain experience researching advanced in silico prediction algorithms, analyzing machine learning approaches for toxicity pattern recognition, and participating in the development of standardized computational frameworks for regulatory assessment. You will also develop competencies in data curation and analysis, understand validation criteria for alternative testing methods, and acquire knowledge of regulatory frameworks for NAM acceptance. Training components include mentorship from senior CDER scientists, engagement with industry and academic partners, and hands‑on experience with data management systems and quality control procedures. Qualifications
Master's or doctoral degree in one of the following fields received within the past five (60) months: Chemistry and Materials Sciences, Computer, Information, and Data Sciences, Engineering, Life Health and Medical Sciences, Mathematics and Statistics, Physics. Experience with toxicological data analysis, statistical modeling, or AI/ML approaches. Strong analytical skills and ability to collaborate across disciplines. Eligibility Requirements
Applicants must be U.S. citizens, lawful permanent residents (LPR), or foreign nationals who have resided in the United States for at least 36 of the past 60 months and who have read and understand the FDA Ethics Requirements. Appointment Details
Start date: Anticipated in 2026 (flexible). Length: Initial appointment of one year, renewal possible on recommendation and contingent on funds. Position type: Full‑time. Stipend: Monthly stipend commensurate with educational level and experience. Contact Information
Mentors: Jeffry Florian
and Zhihua Li
.
#J-18808-Ljbffr
U.S. Food and Drug Administration (FDA) – Center for Drug Evaluation and Research (CDER), Office of Clinical Pharmacology (OCP), Office of Translational Sciences (OTS), Silver Spring, Maryland. Research Project
The Office of Clinical Pharmacology (OCP) and Office of New Drugs (OND) are creating new and updating existing toxicity databases based on available nonclinical animal data (and available clinical data where applicable) that could be used in validation of human‑based in vitro and computational methods submitted to FDA for regulatory use. The developed models will predict various toxicity endpoints and drug exposure to help provide foundational knowledge and experience enabling FDA staff to integrate new alternative methods (NAMs) into drug evaluation. Under the guidance of a mentor, you will assist in developing comprehensive toxicology databases by analyzing proprietary nonclinical data from drug applications, focusing on endpoints where NAMs can provide significant regulatory value. Your research activities will include collaborating with multidisciplinary teams to curate large‑scale datasets, investigating patterns in toxicological data using statistical methods, contributing to the development of reference compound libraries for predictive model validation, and developing AI, statistical, or mechanistic models to translate nonclinical data into safety endpoints such as cardiotoxicity and immunogenicity. Responsibilities
Analyze proprietary nonclinical data from drug applications, focusing on endpoints where NAMs can provide significant regulatory value. Collaborate with multidisciplinary teams to curate large‑scale datasets and investigate patterns in toxicological data using statistical methods. Contribute to the development of reference compound libraries for predictive model validation. Develop AI, statistical, or mechanistic models to translate nonclinical data into safety endpoints such as cardiotoxicity, immunogenicity, and others. Participate in developing standardized computational frameworks for regulatory assessment and data management systems. Learning Objectives
As a participant, you will have structured learning opportunities in regulatory science, computational toxicology, and artificial intelligence applications in drug development. You will gain experience researching advanced in silico prediction algorithms, analyzing machine learning approaches for toxicity pattern recognition, and participating in the development of standardized computational frameworks for regulatory assessment. You will also develop competencies in data curation and analysis, understand validation criteria for alternative testing methods, and acquire knowledge of regulatory frameworks for NAM acceptance. Training components include mentorship from senior CDER scientists, engagement with industry and academic partners, and hands‑on experience with data management systems and quality control procedures. Qualifications
Master's or doctoral degree in one of the following fields received within the past five (60) months: Chemistry and Materials Sciences, Computer, Information, and Data Sciences, Engineering, Life Health and Medical Sciences, Mathematics and Statistics, Physics. Experience with toxicological data analysis, statistical modeling, or AI/ML approaches. Strong analytical skills and ability to collaborate across disciplines. Eligibility Requirements
Applicants must be U.S. citizens, lawful permanent residents (LPR), or foreign nationals who have resided in the United States for at least 36 of the past 60 months and who have read and understand the FDA Ethics Requirements. Appointment Details
Start date: Anticipated in 2026 (flexible). Length: Initial appointment of one year, renewal possible on recommendation and contingent on funds. Position type: Full‑time. Stipend: Monthly stipend commensurate with educational level and experience. Contact Information
Mentors: Jeffry Florian
and Zhihua Li
.
#J-18808-Ljbffr