Evidence map›Paper›PMID 30415351›Full record

ArticleJournal of pharmacokinetics and pharmacodynamics2018

MPBPK-TMDD models for mAbs: alternative models, comparison, and identifiability issues.

Silvia Maria Lavezzi, Enrica Mezzalana, Stefano Zamuner, Giuseppe De Nicolao, Peiming Ma, Monica Simeoni

Abstract readComparative Study
PubMed Publisher
In one paragraph

Article in Journal of pharmacokinetics and pharmacodynamics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. PBPK Modeling: Empowering Drug Development and Precision Dosing in China.CPT: pharmacometrics & systems pharmacology · 2025
    Review
  2. Review
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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

6 authors.

Silvia Maria LavezziDipartimento di Ingegneria Industriale e dell'Informazione, Università degli Studi di Pavia, via Ferrata 5, 27100, Pavia, Italy.
Enrica MezzalanaDipartimento di Ingegneria Industriale e dell'Informazione, Università degli Studi di Pavia, via Ferrata 5, 27100, Pavia, Italy.
Stefano ZamunerClinical Pharmacology Modelling and Simulation, GlaxoSmithKline, Stevenage, UK.
Giuseppe De NicolaoDipartimento di Ingegneria Industriale e dell'Informazione, Università degli Studi di Pavia, via Ferrata 5, 27100, Pavia, Italy.
Peiming MaClinical Pharmacology Modelling and Simulation, GlaxoSmithKline, Shanghai, China.
Monica SimeoniClinical Pharmacology Modelling and Simulation, GlaxoSmithKline, Stockley Park, UK. monica.2.simeoni@gsk.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The aim of the present study was to evaluate model identifiability when minimal physiologically-based pharmacokinetic (mPBPK) models are integrated with target mediated drug disposition (TMDD) models in the tissue compartment. Three quasi-steady-state (QSS) approximations of TMDD dynamics were explored: on (a) antibody-target complex, (b) free target, and (c) free antibody concentrations in tissue. The effects of the QSS approximations were assessed via simulations, taking as reference the mPBPK-TMDD model with no simplifications. Approximation (a) did not affect model-derived concentrations, while with the inclusion of approximation (b) or (c), target concentration profiles alone, or both drug and target concentration profiles respectively deviated from the reference model profiles. A local sensitivity analysis was performed, highlighting the potential importance of sampling in the terminal pharmacokinetic phase and of collecting target concentration data. The a priori and a posteriori identifiability of the mPBPK-TMDD models were investigated under different experimental scenarios and designs. The reference model and QSS approximation (a) on antibody-target complex were both found to be a priori identifiable in all scenarios, while under the further inclusion of QSS approximation (b) target concentration data were needed for a priori identifiability to be preserved. The property could not be assessed for the model including all three QSS approximations. A posteriori identifiability issues were detected for all models, although improvement was observed when appropriate sampling and dose range were selected. In conclusion, this work provides a theoretical framework for the assessment of key properties of mathematical models before their experimental application. Attention should be paid when applying integrated mPBPK-TMDD models, as identifiability issues do exist, especially when rich study designs are not feasible.

Indexed as

Models, BiologicalAntibodies, MonoclonalComputer SimulationTissue DistributionAntibodies, MonoclonalIdentifiabilityMinimal physiologically-based pharmacokineticsMonoclonal antibodiesStudy designTarget mediated drug disposition

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Registered trials

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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.