Evidence map›Paper›PMID 35552984›Full record

ReviewPharmaceutical research2022

Review: Role of Model-Informed Drug Development Approaches in the Lifecycle of Drug Development and Regulatory Decision-Making.

Rajanikanth Madabushi, Paul Seo, Liang Zhao, Million Tegenge, Hao Zhu

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Pharmaceutical research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 109 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
109citing papers in PubMed, 3 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

109 citing papers in PubMed, 3 syntheses or guidelines pooled it.

  1. Pooled it
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  5. Population pharmacokinetic and exposure-response study of a novel anti-tuberculosis drug to inform its dosage design in phase III clinical trial.European journal of pharmaceutical sciences : official journal of the European Federation for Pharmaceutical Sciences · 2025
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49 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Rajanikanth MadabushiOffice of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland, USA. Rajanikanth.Madabushi@fda.hhs.gov.ORCID http://orcid.org/0000-0001-8686-9652
Paul SeoDivision of Biopharmaceutics, Office of New Drug Products, Office of Pharmaceutical Quality, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland, USA.
Liang ZhaoDivision of Quantitative Methods and Modeling, Office of Research and Standards, Office of Generic Drugs, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland, USA.
Million TegengeDivision of Clinical Evaluation and Pharmacology/Toxicology, Office of Tissue and Advanced Therapies, Center for Biologics Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, USA.
Hao ZhuOffice of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland, USA. Hao.Zhu@fda.hhs.gov.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Model-informed drug development (MIDD) is a powerful approach to support drug development and regulatory review. There is a rich history of MIDD applications at the U.S. Food and Drug Administration (FDA). MIDD applications span across the life cycle of the development of new drugs, generics, and biologic products. In new drug development, MIDD approaches are often applied to inform clinical trial design including dose selection/optimization, aid in the evaluation of critical regulatory review questions such as evidence of effectiveness, and development of policy. In the biopharmaceutics space, we see a trend for increasing role of computational modeling to inform formulation development and help strategize future in vivo studies or lifecycle plans in the post approval setting. As more information and knowledge becomes available pre-approval, quantitative mathematical models are becoming indispensable in supporting generic drug development and approval including complex generic drug products and are expected to help reduce overall time and cost. While the application of MIDD to inform the development of cell and gene therapy products is at an early stage, the potential for future application of MIDD include understanding and quantitative evaluation of information related to biological activity/pharmacodynamics, cell expansion/persistence, transgene expression, immune response, safety, and efficacy. With exciting innovations on the horizon, broader adoption of MIDD is poised to revolutionize drug development for greater patient and societal benefit.

Indexed as

Drug DevelopmentDrugs, GenericAnimalsDrug ApprovalHumansLife Cycle StagesPharmaceutical PreparationsUnited StatesUnited States Food and Drug AdministrationDrugs, GenericPharmaceutical Preparationsbiopharmaceuticsgeneric drugsgene therapymodel-informed drug developmentnew drugs

Identifiers

PMID35552984
PMCPMC9097888

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.