Evidence map›Paper›PMID 33555032›Full record

ReviewClinical pharmacology and therapeutics2021

Model-Informed Drug Development for Anti-Infectives: State of the Art and Future.

Craig R Rayner, Patrick F Smith, David Andes, Kayla Andrews, Hartmut Derendorf, Lena E Friberg, Debra Hanna, Alex Lepak, Edward Mills, Thomas M Polasek and 5 more

Open access · bronzeAbstract readReview
In one paragraph

Review in Clinical pharmacology and therapeutics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers.

0numbers the graph read from it
0cells of the map it votes in
47citing papers in PubMed
9.2field-weighted citation impact, top 1% of its field
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

47 citing papers in PubMed, 73 citations in OpenAlex.

  1. 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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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

15 authors at 10 institutions in 5 countries.

Craig R RaynerCertara, Princeton, New Jersey, USA.
Patrick F SmithCertara, Princeton, New Jersey, USA.
David AndesUniversity of Wisconsin-Madison, Madison, Wisconsin, USA.
Kayla AndrewsBill & Melinda Gates Medical Research Institute, Cambridge, Massachusetts, USA.
Hartmut DerendorfUniversity of Florida, Gainesville, Florida, USA.
Lena E FribergDepartment of Pharmacy, Uppsala University, Uppsala, Sweden.
Debra HannaBill & Melinda Gates Foundation, Seattle, Washington, USA.
Alex LepakUniversity of Wisconsin-Madison, Madison, Wisconsin, USA.
Edward MillsMcMaster University, Hamilton, Ontario, Canada.
Thomas M PolasekCertara, Princeton, New Jersey, USA.
Jason A RobertsFaculty of Medicine, University of Queensland Centre for Clinical Research, The University of Queensland, Brisbane, Queensland, Australia.
Virna SchuckCertara, Princeton, New Jersey, USA.
Mark J SheltonCertara, Princeton, New Jersey, USA.
David WescheCertara, Princeton, New Jersey, USA.
Karen Rowland-YeoCertara, Princeton, New Jersey, USA.
Certara (United States) · USBill & Melinda Gates Foundation · USIntegra LifeSciences (United States) · USUniversity of Wisconsin–Madison · USMcMaster University · CAMonash University · AURoyal Adelaide Hospital · AUUniversité de Montpellier · FRUniversity of Florida · USUppsala University · SE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Model-informed drug development (MIDD) has a long and rich history in infectious diseases. This review describes foundational principles of translational anti-infective pharmacology, including choice of appropriate measures of exposure and pharmacodynamic (PD) measures, patient subpopulations, and drug-drug interactions. Examples are presented for state-of-the-art, empiric, mechanistic, interdisciplinary, and real-world evidence MIDD applications in the development of antibacterials (review of minimum inhibitory concentration-based models, mechanism-based pharmacokinetic/PD (PK/PD) models, PK/PD models of resistance, and immune response), antifungals, antivirals, drugs for the treatment of global health infectious diseases, and medical countermeasures. The degree of adoption of MIDD practices across the infectious diseases field is also summarized. The future application of MIDD in infectious diseases will progress along two planes; "depth" and "breadth" of MIDD methods. "MIDD depth" refers to deeper incorporation of the specific pathogen biology and intrinsic and acquired-resistance mechanisms; host factors, such as immunologic response and infection site, to enable deeper interrogation of pharmacological impact on pathogen clearance; clinical outcome and emergence of resistance from a pathogen; and patient and population perspective. In particular, improved early assessment of the emergence of resistance potential will become a greater focus in MIDD, as this is poorly mitigated by current development approaches. "MIDD breadth" refers to greater adoption of model-centered approaches to anti-infective development. Specifically, this means how various MIDD approaches and translational tools can be integrated or connected in a systematic way that supports decision making by key stakeholders (sponsors, regulators, and payers) across the entire development pathway.

Indexed as

Models, BiologicalAnti-Bacterial AgentsAntifungal AgentsAnti-Infective AgentsAntimalarialsAntitubercular AgentsAntiviral AgentsBody WeightDose-Response Relationship, DrugDrug ApprovalDrug DevelopmentDrug DiscoveryDrug Resistance, MicrobialHumansImmunityIvermectinAnti-Bacterial AgentsAntifungal AgentsAnti-Infective AgentsAntimalarialsAntitubercular AgentsAntiviral AgentsIvermectin

Identifiers

PMID33555032
PMCPMC8014105
OpenAlexW3126879320

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.