Evidence map›Paper›PMID 29725996›Full record

Trial reportClinical pharmacokinetics2019

Population Pharmacokinetic Analysis of Alirocumab in Healthy Volunteers or Hypercholesterolemic Subjects Using a Michaelis-Menten Approximation of a Target-Mediated Drug Disposition Model-Support for a Biologics License Application Submission: Part I.

Jean-Marie Martinez, Aurélie Brunet, Fabrice Hurbin, A Thomas DiCioccio, Clémence Rauch, David Fabre

Abstract readClinical Trial, Phase IClinical Trial, Phase IIClinical Trial, Phase III
In one paragraph

Trial report in Clinical pharmacokinetics, 2019. 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
–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

20 citing papers in PubMed, 1 synthesis or guideline 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

6 authors.

Jean-Marie MartinezTMED/PKDM Department, Sanofi, 371 Rue du Professeur Joseph Blayac, 34184, Montpellier Cedex 04, France. Jean-Marie.Martinez@sanofi.com.
Aurélie BrunetTMED/PKDM Department, Sanofi, 371 Rue du Professeur Joseph Blayac, 34184, Montpellier Cedex 04, France.
Fabrice HurbinTMED/PKDM Department, Sanofi, 371 Rue du Professeur Joseph Blayac, 34184, Montpellier Cedex 04, France.
A Thomas DiCioccioRegeneron Pharmaceuticals, Inc., Tarrytown, NY, USA.
Clémence RauchTMED/PKDM Department, Sanofi, 371 Rue du Professeur Joseph Blayac, 34184, Montpellier Cedex 04, France.
David FabreTMED/PKDM Department, Sanofi, 371 Rue du Professeur Joseph Blayac, 34184, Montpellier Cedex 04, France.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAlirocumab, a human monoclonal antibody, inhibits proprotein convertase subtilisin/kexin type 9 (PCSK9) to significantly reduce low-density lipoprotein cholesterol levels; pharmacokinetics (PK) are governed by non-linear, target-mediated drug disposition (TMDD).

objectivesWe aimed to develop and qualify a population PK (PopPK) model to characterize the PK profile of alirocumab, evaluate the impact of covariates on alirocumab PK and on individual patient exposures, and estimate individual predicted concentrations for a subsequent PK/pharmacodynamic (PD) analysis.

methodsData from 13 phase I-III trials of 2799 healthy volunteers or patients with hypercholesterolemia treated with intravenous or subcutaneous alirocumab (13,717 alirocumab concentrations) were included; a Michaelis-Menten approximation of the TMDD model was used to estimate PK parameters and exposures. The final model comprised two compartments with first-order absorption. Elimination from the central compartment was described by linear (CLL) and non-linear Michaelis-Menten clearance (Vm and Km). The model was validated using visual predictive check and bootstrap methods. Patient exposures to alirocumab were computed using individual PK parameters.

resultsThe PopPK model was well-qualified, with the majority of observed alirocumab concentrations in the 2.5th-97.5th predicted percentiles. Covariates responsible for interindividual variability were identified. Body weight and concomitant statin administration impacted CLL, whereas time-varying free PCSK9 concentrations and age affected Km and peripheral distribution volume (V3), respectively. No covariates were clinically meaningful, therefore no dose adjustments were needed.

conclusionsThe model explained the between-subject variability, quantified the impact of covariates, and, finally, predicted alirocumab concentrations (subsequently used in a PopPK/PD model, see Part II) and individual exposures.

Indexed as

Models, BiologicalAdultAgedAntibodies, Monoclonal, HumanizedAnticholesteremic AgentsBiological ProductsBiological Variation, IndividualFemaleHealthy VolunteersHumansHypercholesterolemiaMaleMiddle AgedalirocumabAntibodies, Monoclonal, HumanizedAnticholesteremic AgentsBiological Products

Identifiers

PMID29725996
PMCPMC6325993

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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.