1. Home
  2. About FDA
  3. FDA Organization
  4. Center for Drug Evaluation and Research (CDER)
  5. CDER Offices and Divisions
  6. Division of Pharmacometrics
  1. CDER Offices and Divisions

Division of Pharmacometrics

Overview

The Division of Pharmacometrics (DPM) is part of the Office of Clinical Pharmacology (OCP) that is in the Office of Translational Sciences (OTS).

Drug development and regulatory decisions are driven by information that is compiled primarily from clinical trials and other supportive experiments—and also through clinical experience in the post-market period. The wisdom of these decisions determines the efficiency of drug development, the decision to approve the drug, and the resultant drug product quality, including guidance on how to use the product, which is known as the label. While the decisions are usually simple in nature (e.g., trial design and project progression at the company, product and labeling approval at FDA), the data informing the decision are complex.

Pharmacometrics is an emerging science defined as the science that quantifies drug, disease, and trial information to aid efficient drug development and/or regulatory decisions. Drug models describe the relationship between exposure (or pharmacokinetics), response (or pharmacodynamics) for both desired and undesired effects, and individual patient characteristics. Disease models describe the relationship between biomarkers and clinical outcomes, time course of disease, and placebo effects. The trial models describe the inclusion/exclusion criteria, patient’s discontinuation from the trial, and adherence to the trial. Pharmacometrics analyses are issue-driven and can be used to support endpoint selection, identify evidence of effectiveness, optimize dosing and administration, select patients, bridge efficacy and safety findings, and streamline trial design and drug development. Typical analysis tools include, but are not limited to, population pharmacokinetics, physiologically based pharmacokinetics, exposure (dose)-response (e.g., pharmacokinetic-pharmacodynamic, quantitative system pharmacology, quantitative system toxicology models), and artificial intelligence/machine learning models. These pharmacometric analyses are designed, conducted, and presented in the context of drug development, therapeutic and regulatory decisions. The analyses allow the integration of knowledge/information across development program(s), real-world evidence/data, and compounds, and biology.

The DPM staff consists of quantitative clinical pharmacologists, statisticians, mathematicians, artificial intelligence scientists, engineers, and data management experts who work closely with clinicians and statisticians.

At FDA, pharmacometric work is conducted with three objectives:

  1. Most important is the decision to approve and label the drug product with particular attention to ensure safe and effective use of the drug for all patients..
  2. Providing guidance on trial design to increase trial/development success rate, enhance information generation, and improve appropriate patient care.
  3. Research is conducted to create new knowledge to inform regulatory and drug development decisions. DPM’s research focus is to understand disease progression, placebo effect, dropouts, and drug effects. Additionally, research is also conducted to help determine the value of biomarkers across clinical trials for a given disease or drug class. An important component of this research is training future pharmacometricians.

A Summary of Achievements

OCP’s DPM established a 10-year strategic plan in 2010.[1] Table 1 summarizes the division’s 10-year achievements under this plan for each strategic goal.

Table 1: A Summary of Achievements Based on DPM’s 2020 Strategic Goals

FieldStrategic GoalsAchievements
Research and TrainingTrain 20 pharmacometriciansThe division has trained 91 pharmacometricians in the past 10 years.
Develop 5 disease modelsThe division has developed 14 disease models (See Table 2 below).
ReviewImplement 15 standard templatesThe division has developed internal templates.
Integrate quantitative clinical pharmacology summaries
  • NDA/BLA submissions and reviews frequently contain population pharmacokinetic (PopPK), physiologically based pharmacokinetic (PBPK), and/or exposure-response (ER) analysis components.
  • A standardized format for PopPK, PBPK, and ER reports has been developed to facilitate regulatory review.
Trial DesignSupport the implementation of the design-by-simulation approach
HarmonizationInternational harmonization
  • Routine, quarterly cluster meetings on pharmacometrics allow for the exchange of scientific information, sharing of experiences, and discussion of review and policy issues with agencies worldwide.
  • DPM is actively involved in pharmacometrics-related guidance development with the International Council for Harmonisation (ICH).

* Guidance documents represent FDA’s current thinking on a particular subject. They do not create or confer any rights for or on any person and do not operate to bind FDA or the public. An alternative approach may be used if such approach satisfies the requirements of the applicable statute, regulations, or both. For information on a specific guidance document, please use the contact information provided in that guidance. We update guidances periodically. For the most recent version of a guidance, check the FDA guidance web page. 

Table 2: A Summary of Disease Models Developed by DPM

No

Disease Model

Use

1Non-small cell lung cancer model [2]Late phase clinical trial design
2Parkinson’s disease model [3]Endpoint selection and clinical trial design
3Alzheimer’s disease model [4]Endpoint selection and clinical trial design
4Diabetes disease model [5]Clinical trial design
5Huntington’s disease model [6]Patient enrichment and clinical trial design
6Duchenne muscular dystrophy disease model [7]Patient enrichment and clinical trial design
7Human immunodeficiency virus model [5]Clinical trial design
8Schizophrenia model [8]Pediatric extrapolation
9Bipolar I disorder model [9]Pediatric extrapolation
10Weight loss model [10]Clinical trial design
11Bone density model [11]Clinical trial design
12Idiopathic pulmonary fibrosis model [12]Patient enrichment and clinical trial design
13Rheumatoid arthritis model [13]Endpoint selection and clinical trial design
14Pulmonary arterial hypertension model [14]Endpoint selection and clinical trial design

References:

  1. Gobburu JV, 2010, Pharmacometrics 2020, J Clin Pharmacol, 50(9 Suppl):151S-157S. doi: 10.1177/0091270010376977.
  2. Wang Y, Sung C, Dartois C, Ramchandani R, Booth B, Rock E, and JV Gobburu, 2009, Elucidation of relationship between tumor size and survival in non-small-cell lung cancer patients can aid early decision making in clinical drug development, Clin Pharmacol Ther, 86(2):167–74. doi: 10.1038/clpt.2009.64.
  3. Bhattaram VA, Siddiqui O, Kapcala LP, and JV Gobburu, 2009, Endpoints and analyses to discern disease-modifying drug effects in early Parkinson's disease, AAPS J, 11(3):456–64. doi: 10.1208/s12248-009-9123-2.
  4. William-Faltaos D, Chen Y, Wang Y, Gobburu, JV, and H Zhu, 2013, Quantification of disease progression and dropout for Alzheimer's disease, Int J Clin Pharmacol Ther, 51(2):120-31. doi: 10.5414/CP201787.
  5. Wang Y, Bhattaram AV, Jadhav PR, Lesko LJ, Madabushi R, Powell JR, Qiu W, Sun H, Yim DS, Zheng J, and JV Gobburu, 2008, Leveraging prior quantitative knowledge to guide drug development decisions and regulatory science recommendations: Impact of FDA pharmacometrics during 2004-2006, J Clin Pharmacol, 48(2):146–56. doi: 10.1177/0091270007311111.
  6. Sun W, Zhou D, Warner JH, Langbehn DR, Hochhaus G, and Y Wang, 2020, Huntington's Disease progression: A population modeling approach to characterization using clinical rating scales, J Clin Pharmacol, 60(8):1051–1060. doi: 10.1002/jcph.1598.
  7. Haber G, Conway KM, Paramsothy P, Roy A, Rogers H, Ling X, Kozauer N, Street N, Romitti PA, Fox DJ, Phan HC, Matthews D, Ciafaloni E, Oleszek J, James KA, Galindo M, Whitehead N, Johnson N, Butterfield RJ, Pandya S, Venkatesh S, and V Bhattaram, 2021, Association of genetic mutations and loss of ambulation in childhood-onset dystrophinopathy, Muscle Nerve, 63(2):181–191. doi: 10.1002/mus.27113.
  8. Kalaria SN, Zhu H, Farchione TR, Mathis MV, Gopalakrishnan M, Uppoor R, Mehta M, and I Younis, 2019, A quantitative justification of similarity in placebo response between adults and adolescents with acute exacerbation of schizophrenia in clinical trials, Clin Pharmacol Ther, 106(5):1046–1055. doi: 10.1002/cpt.1501.
  9. Kalaria SN, Farchione TR, Uppoor R, Mehta M, Wang Y, and H Zhu, 2021, Extrapolation of efficacy and dose selection in pediatrics: A case example of atypical antipsychotics in adolescents with schizophrenia and Bipolar I Disorder, J Clin Pharmacol, 61 Suppl 1: S117–S124. doi: 10.1002/jcph.1836.
  10. U.S. Food and Drug Administration (FDA), 2006, Weight loss model, FDA, accessed September 25, 2021, http://wayback.archive-it.org/7993/20170405065431/https://www.fda.gov/ohrms/dockets/ac/06/briefing/2006-4248B1-04-FDA-topic-3-replacement.pdf.
  11. Lien YT, Madrasi K, Samant S, Kim MJ, Li F, Li L, Wang Y, and S Schmidt, 2020, Establishment of a disease-drug trial model for postmenopausal osteoporosis: A zoledronic acid case study, J Clin Pharmacol, 60:S86–S102. doi: 10.1002/jcph.1748.
  12. Bi Y, Rekić D, Paterniti MO, Chen J, Marathe A, Chowdhury BA, Karimi-Shah BA, and Y Wang, 2021, A disease progression model of longitudinal lung function decline in idiopathic pulmonary fibrosis patients, J Pharmacokinet Pharmacodyn, 48(1):55–67. doi: 10.1007/s10928-020-09718-9.
  13. Ma L, Zhao L, Xu Y, Yim S, Doddapaneni S, Sahajwalla CG, Wang Y, and P Ji, 2014, Clinical endpoint sensitivity in rheumatoid arthritis: modeling and simulation, J Pharmacokinet Pharmacodyn, 41(5):537–43. doi: 10.1007/s10928-014-9385-x.
  14. Brar S, 2011, Role of biomarker-clinical outcome relationships in clinical drug development: FDA Experience, Indiana CTSI Symposium on Disease and Therapeutic Response Modeling, accessed July 19, 2021, https://static.medicine.iupui.edu/IMG/clinpharm/ctsi/slides/brar.pdf.

Contact Information

Hao Zhu, Ph.D.
Director, Division of Pharmacometrics
OTS/OCP/DPM
U.S. Food and Drug Administration
Center for Drug Evaluation and Research
E-mail: Hao.Zhu@fda.hhs.gov
Telephone: (301) 796-2772



Back to Top