Evidence map›Paper›PMID 31140642›Full record

ArticleBritish journal of clinical pharmacology2019

Evaluation of covariate effects on pharmacokinetics of monoclonal antibodies in oncology.

Gaurav Bajaj, Satyendra Suryawanshi, Amit Roy, Manish Gupta

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Article in British journal of clinical pharmacology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
45citing papers in PubMed, 4 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.

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3 · Its place in the literature

Who cites it

45 citing papers in PubMed, 4 syntheses or guidelines pooled it.

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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

4 authors.

Gaurav BajajBristol-Myers Squibb, Princeton, NJ, USA.ORCID 0000-0003-0298-4659
Satyendra SuryawanshiBristol-Myers Squibb, Princeton, NJ, USA.
Amit RoyBristol-Myers Squibb, Princeton, NJ, USA.ORCID 0000-0003-2140-1455
Manish GuptaBristol-Myers Squibb, Princeton, NJ, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

aimsThe development of monoclonal antibodies (mAbs) requires an understanding of the interindividual variability (IIV) in pharmacokinetics (PK) at the population level facilitated by population PK (PopPK) modelling. However, there is no clear rationale for selecting which covariates to screen during PopPK model development. Here, we compare the effect of covariates on PK parameters for mAbs in oncology and identify the most commonly used covariates affecting PK parameters.

methodsAll 25 mAbs approved for therapeutic use in oncology until December 2017 by the Food and Drug Administration and the European Medicines Agency were selected for study. Literature searches revealed 23 available PopPK models for these mAbs. To understand the magnitude and types of covariate effect on PK parameters, all covariates included in the final PopPK model for each mAb were summarized.

resultsThe most commonly identified covariates were baseline body weight (BW; 17 mAbs), baseline serum albumin (8 mAbs), and sex (7 mAbs) on clearance; and BW (16 mAbs) and sex (12 mAbs) on central volume of distribution. A reduced PopPK model was developed for nivolumab and ipilimumab using these covariates, and the percentage of explained IIV from the reduced model (20.3% and 16.8%, respectively) was compared with that from the full model (24.5% and 27.9%, respectively).

conclusionsThis analysis provides a uniform platform for selecting covariates and suggests that the effect of BW, albumin and sex should be included during the development of PopPK models for mAbs in oncology. The reduced model was able to explain IIV to a similar extent as the full model.

Indexed as

Biological Variation, PopulationModels, BiologicalAntibodies, MonoclonalAntineoplastic Agents, ImmunologicalBody WeightFemaleHumansIpilimumabMaleNeoplasmsNivolumabSex FactorsAntibodies, MonoclonalAntineoplastic Agents, ImmunologicalIpilimumabNivolumabcancermonoclonal antibodiespharmacokineticspopulation analysis

Identifiers

PMID31140642
PMCPMC6710515

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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.