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Division of Pharmacometrics


Overview

Drug development and regulatory decisions are driven by information that is compiled primarily from clinical trials and other supportive experiments, but 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 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 and diverse.

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 dis-continuation and adherence. Typical focus of Pharmacometrics has been on drug models, also referred to by terms such as: concentration-effect, dose-response, PKPD relationships. These Pharmacometric analyses are designed, conducted and presented in the context of drug development, therapeutic and regulatory decisions.  The single-most important strength of such analyses is its ability to integrate knowledge across the development program and compounds, and biology.

The Pharmacometrics staff consists of a multidisciplinary team consisting of quantitative clinical pharmacologists, statisticians, engineers and data management experts, and 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 drug dosing for all patients. This has been the primary focus of our group.
  2. Providing advice on trial design decisions by sponsors is a consulting function where the focus is both trial success and rendering dosage regimens likely to be successful in all patients.
  3. Research is conducted to create new knowledge bases on the unique data available at FDA (i.e., prior NDA submissions) and literature both to inform better regulatory and drug development decisions by sponsors. Research is conducted to create or confirm prior models of disease change, placebo effect, drop-outs, and drug effect. Research is also conducted to help determine the value of biomarkers across clinical trials for a given disease or drug class to reflect change in primary disease endpoints. An important component of this research is training future Pharmacometricians.

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A Summary of Pharmacometrics 2020 Goals

The Office of Clinical Pharmacology’s Division of Pharmacometrics (DPM) established a 10-year strategic plan in 2010.[1] Table 1 summarizes the Division’s 10-year achievements under this plan in each strategic goal.

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

Field

Strategic Goals

Achievements

Research and Training

Train 20 pharmacometricians

The division has trained 91 pharmacometricians in the past 10 years.

Develop 5 disease models

The division has developed 14 disease models (See Table 2 below).

Review

Implement 15 standard templates

The 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 Design

Support the implementation of the Design-by-Simulation approach

  • Design-by-simulation is now a common practice in new drug development at the IND stage.

Harmonization

International Harmonization

  • Routine, quarterly pharmacometrics cluster meetings allow for the exchange of scientific information, sharing of experiences, and discussion of review and policy issues with agencies worldwide.

* Guidance documents represent the Agency'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 https://www.fda.gov/regulatory-information/search-fda-guidance-documents.

Table 2: A Summary of Disease Models Developed by DPM

No

Disease Model

Use

1

Non-small cell lung cancer model [2]

Late phase clinical trial design

2

Parkinson’s disease model [3]

Endpoint selection and clinical trial design

3

Alzheimer’s disease model [4]

Endpoint selection and clinical trial design

4

Diabetes disease model [5]

Clinical trial design

5

Huntington’s disease model [6]

Patient enrichment and clinical trial design

6

Duchenne muscular dystrophy disease model [7]

Patient enrichment and clinical trial design

7

Human immunodeficiency virus model [5]

Clinical trial design

8

Schizophrenia model [8]

Pediatric extrapolation

9

Bipolar I disorder model [9]

Pediatric extrapolation

10

Weight loss model [10]

Clinical trial design

11

Bone density model [11]

Clinical trial design

12

Idiopathic pulmonary fibrosis model [12]

Patient enrichment and clinical trial design

13

Rheumatoid arthritis model [13]

Endpoint selection and clinical trial design

14

Pulmonary arterial hypertension model [14]

Endpoint selection and clinical trial design

References:

  1. J Gobburu. Pharmacometrics 2020. J Clin Pharmacol. 2010 Sep;50(9 Suppl):151S-157S. doi: 10.1177/0091270010376977.
     
  2. Y Wang, C Sung, C Dartois, R Ramchandani, B Booth, E Rock, J Gobburu. 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. 2009 Aug;86(2):167-74. doi: 10.1038/clpt.2009.64.
     
  3. V Bhattaram, O Siddiqui, L Kapcala, J Gobburu. Endpoints and analyses to discern disease-modifying drug effects in early Parkinson's disease. AAPS J. 2009 Sep;11(3):456-64. doi: 10.1208/s12248-009-9123-2.
     
  4. D William-Faltaos, Y Chen, Y Wang, J Gobburu, H Zhu. Quantification of disease progression and dropout for Alzheimer's disease. Int J Clin Pharmacol Ther. 2013 Feb;51(2):120-31. doi: 10.5414/CP201787.
     
  5. Y Wang, V Bhattaram, P Jadhav, L Lesko, R Madabushi, J Powell, W Qiu, H Sun, D Yim, J Zheng, J Gobburu. Leveraging prior quantitative knowledge to guide drug development decisions and regulatory science recommendations: Impact of FDA pharmacometrics during 2004-2006. J Clin Pharmacol. 2008 Feb;48(2):146-56. doi: 10.1177/0091270007311111.
     
  6. W Sun, D Zhou, J Warner, D Langbehn, G Hochhaus, Y Wang. Huntington's Disease progression: A population modeling approach to characterization using clinical rating scales. J Clin Pharmacol. 2020 Aug;60(8):1051-1060. doi: 10.1002/jcph.1598.
     
  7. G Haber, K Conway, P Paramsothy, A Roy, H Rogers, X Ling, N Kozauer, N Street, P Romitti, D Fox, H Phan, D Matthews, E Ciafaloni, J Oleszek, Katherine A James, M Galindo, N Whitehead, N Johnson, R Butterfield, S Pandya, S Venkatesh, V Bhattaram. Association of genetic mutations and loss of ambulation in childhood-onset dystrophinopathy. Muscle Nerve. 2021 Feb;63(2):181-191. doi: 10.1002/mus.27113.
     
  8. S Kalaria, H Zhu, T Farchione, M Mathis, M Gopalakrishnan, R Uppoor, M Mehta, Islam Younis. A quantitative justification of similarity in placebo response between adults and adolescents with acute exacerbation of schizophrenia in clinical trials. Clin Pharmacol Ther. 2019 Nov;106(5):1046-1055. doi: 10.1002/cpt.1501.
     
  9. S Kalaria, T Farchione, R Uppoor, M Mehta, Y Wang, H Zhu. 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. 2021 Jun;61 Suppl 1: S117-S124. doi: 10.1002/jcph.1836.
     
  10. Weight loss Model: http://wayback.archive-it.org/7993/20170405065431/https://www.fda.gov/ohrms/dockets/ac/06/briefing/2006-4248B1-04-FDA-topic-3-replacement.pdf. Accessed on 9/25/2021.
     
  11. Y Lien, K Madrasi, S Samant, MJ Kim, F Li, L Li, Y Wang, S Schmidt. Establishment of a disease-drug trial model for postmenopausal osteoporosis: A zoledronic acid case study. J Clin Pharmacol. 2020 Dec;60 Suppl 2:S86-S102. doi: 10.1002/jcph.1748.
     
  12. Y Bi, D Rekić, MO Paterniti, J Chen, A Marathe, B Chowdhury, B Karimi-Shah, Yaning Wang. A disease progression model of longitudinal lung function decline in idiopathic pulmonary fibrosis patients. J Pharmacokinet Pharmacodyn. 2021 Feb;48(1):55-67. doi: 10.1007/s10928-020-09718-9.
     
  13. L Ma, L Zhao, Y Xu, S Yim, S Doddapaneni, C Sahajwalla, Y Wang, P Ji. Clinical endpoint sensitivity in rheumatoid arthritis: modeling and simulation. J Pharmacokinet Pharmacodyn. 2014 Oct;41(5):537-43. doi: 10.1007/s10928-014-9385-x.
     
  14. S Brar. Role of biomarker-clinical outcome relationships in clinical drug development: FDA Experience. Nov 2011. https://static.medicine.iupui.edu/IMG/clinpharm/ctsi/slides/brar.pdf. Accessed on 7/19/2021.

Contact Information

Hao Zhu, Ph.D.
Acting 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

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