Evidence map›Paper›PMID 21336535›Full record

ArticleThe AAPS journal2011

From target selection to the minimum acceptable biological effect level for human study: use of mechanism-based PK/PD modeling to design safe and efficacious biologics.

Jing Yu, Helene Karcher, Adam L Feire, Philip J Lowe

Abstract read
In one paragraph

Article in The AAPS journal, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
–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

9 citing papers in PubMed.

  1. Trial
  2. Article
  3. A Tutorial on Target-Mediated Drug Disposition (TMDD) Models.CPT: pharmacometrics & systems pharmacology · 2015
    Article
  4. Article
  5. Article
  6. Review
  7. Article
  8. Article
  9. Review
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

4 authors.

Jing YuNovartis Institutes for Biomedical Research, Cambridge, Massachusetts, USA. jing-1.yu@novartis.com
Helene Karcher
Adam L Feire
Philip J Lowe

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In this paper, two applications of mechanism-based modeling are presented with their utility from candidate selection to first-in-human dosage selection. The first example is for a monoclonal antibody against a cytomegalovirus glycoprotein complex, which involves an antibody binding model and a viral load model. The model was used as part of a feasibility analysis prior to antibody generation, setting the specifications for the affinity needed to achieve a desired level of clinical efficacy. The second example is a pharmacokinetic-pharmacodynamic model based on a single-dose pharmacology study in cynomolgus monkey using data on pharmacokinetics, receptor occupancy, and the dynamics of target cell depletion and recovery. The model was used to estimate the MABEL, here defined as the minimum acceptable biological effect level against which a dose is selected for a first-in-human study. From these applications, we demonstrate that mechanism-based PK/PD binding models are useful for predicting human response to biologics compounds. Especially, such models have the ability to integrate preclinical and clinical, in vitro and in vivo information and facilitate rational decision making during various stages of drug discovery and translational research.

Indexed as

Drug DesignModels, BiologicalAnimalsAntibodies, MonoclonalCytomegalovirusDose-Response Relationship, DrugDrug Delivery SystemsFeasibility StudiesGlycoproteinsHumansImmunologic FactorsMacaca fascicularisSpecies SpecificityTranslational Research, BiomedicalAntibodies, MonoclonalGlycoproteinsImmunologic Factors

Identifiers

PMID21336535
PMCPMC3085698

What OpenQuestion holds

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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.