Evidence map›Paper›PMID 33904970›Full record

Trial reportCancer chemotherapy and pharmacology2021

Time-dependent population PK models of single-agent atezolizumab in patients with cancer.

Mathilde Marchand, Rong Zhang, Phyllis Chan, Valerie Quarmby, Marcus Ballinger, Nitzan Sternheim, Benjamin Wu, Jin Y Jin, René Bruno

Abstract readClinical Trial
PubMed Publisher
In one paragraph

Trial report in Cancer chemotherapy and pharmacology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed, 1 pooled it
2.5field-weighted citation impact, top 11% 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

20 citing papers in PubMed, 1 synthesis or guideline pooled it, 29 citations in OpenAlex.

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  14. Antibody Drug Clearance: An Underexplored Marker of Outcomes with Checkpoint Inhibitors.Clinical cancer research : an official journal of the American Association for Cancer Research · 2024
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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

9 authors at 1 institution in 2 countries.

Mathilde MarchandCertara Strategic Consulting, Certara, 54 Rue de Londres, 75009, Paris, France.
Rong ZhangClinical Pharmacology, Genentech, Inc, 1 DNA Way, South San Francisco, CA, 94080, USA.
Phyllis ChanClinical Pharmacology, Genentech, Inc, 1 DNA Way, South San Francisco, CA, 94080, USA.
Valerie QuarmbyBioAnalytical Sciences, Genentech, Inc, 1 DNA Way, South San Francisco, CA, 94080, USA.
Marcus BallingerProduct Development, Genentech, Inc, 1 DNA Way, South San Francisco, CA, 94080, USA.
Nitzan SternheimProduct Development, Genentech, Inc, 1 DNA Way, South San Francisco, CA, 94080, USA.
Benjamin WuClinical Pharmacology, Genentech, Inc, 1 DNA Way, South San Francisco, CA, 94080, USA.
Jin Y JinClinical Pharmacology, Genentech, Inc, 1 DNA Way, South San Francisco, CA, 94080, USA.
René BrunoClinical Pharmacology, Genentech-Roche, 84 Chemin des Grives, 13013, Marseille, France. rene.bruno@roche.com.ORCID 0000-0003-0200-039X
Roche (Switzerland) · CH

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeThe time-varying clearance (CL) of the PD-L1 inhibitor atezolizumab was assessed on a population of 1519 cancer patients (primarily with non-small-cell lung cancer or metastatic urothelial carcinoma) from three clinical studies.

methodsThe first step was to identify the baseline covariates affecting atezolizumab CL without including time-varying components (stationary covariate model). Two time-varying models were then investigated: (1) a model allowing baseline covariates to vary over time (time-varying covariate model), (2) a model with empirical time-varying Emax CL function.

resultsThe final stationary covariate model included main effects of body weight, albumin levels, tumor size, anti-drug antibodies (ADA) and gender on atezolizumab CL. Both time-varying models resulted in a clear improvement of the data fit and visual predictive checks over the stationary model. The time-varying covariate model provided the best fit of the data. In this model, the main driver for change in CL over time was variations in albumin level with an increase in serum albumin (improvement in a patient's status) mirroring a decrease in CL. Time-varying ADAs had a small impact (9% increase in CL). None of the covariates impacted atezolizumab CL by more than ± 30% from median. The estimated maximum decrease in CL with time was 22% with the Emax model.

conclusionThe overall impact of covariates on atezolizumab CL did not warrant any change in atezolizumab dosing recommendations. The results support the hypothesis that variation in atezolizumab CL over time is associated with patients' disease status, as shown with other checkpoint inhibitors.

Indexed as

AdultAgedAged, 80 and overAntibodies, Monoclonal, HumanizedAntineoplastic AgentsCarcinoma, Non-Small-Cell LungFemaleHumansLung NeoplasmsMaleMiddle AgedUrinary Bladder NeoplasmsUrotheliumYoung AdultAntibodies, Monoclonal, HumanizedAntineoplastic AgentsatezolizumabAtezolizumabCancer patientsCovariate effectsPD-L1Time-varying clearance

Identifiers

PMID33904970
OpenAlexW3158704737

What OpenQuestion holds

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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.