Evidence map›Paper›PMID 40364448›Full record

ArticleCPT: pharmacometrics & systems pharmacology2025

An Integrative Mechanistic Model of Type 1 IFN-Mediated Inflammation in Systemic Lupus Erythematosus.

Alina Volkova, Victor Sokolov, Florencia Tettamanti, Meghna Verma, Yaroslav Ugolkov, Kirill Peskov, Weifeng Tang, Holly Kimko

Abstract read
In one paragraph

Article in CPT: pharmacometrics & systems pharmacology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Article
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

8 authors.

Alina VolkovaModeling and Simulation Decisions FZ - LLC, Dubai, UAE.ORCID https://orcid.org/0000-0003-2342-2495
Victor SokolovModeling and Simulation Decisions FZ - LLC, Dubai, UAE.ORCID https://orcid.org/0000-0001-7066-0811
Florencia TettamantiSystems Medicine, Clinical Pharmacology and Quantitative Pharmacology, R&D BioPharmaceuticals, AstraZeneca, Cambridge, UK.ORCID https://orcid.org/0000-0002-4050-0064
Meghna VermaSystems Medicine, Clinical Pharmacology and Quantitative Pharmacology, R&D BioPharmaceuticals, AstraZeneca, Gaithersburg, Maryland, USA.ORCID https://orcid.org/0000-0002-7776-0433
Yaroslav UgolkovMarchuk Institute of Numerical Mathematics of the Russian Academy of Sciences, Moscow, Russia.ORCID https://orcid.org/0000-0003-3590-7860
Kirill PeskovModeling and Simulation Decisions FZ - LLC, Dubai, UAE.ORCID https://orcid.org/0000-0003-0678-5729
Weifeng TangSystems Medicine, Clinical Pharmacology and Quantitative Pharmacology, R&D BioPharmaceuticals, AstraZeneca, Gaithersburg, Maryland, USA.ORCID https://orcid.org/0000-0002-8261-8116
Holly KimkoSystems Medicine, Clinical Pharmacology and Quantitative Pharmacology, R&D BioPharmaceuticals, AstraZeneca, Gaithersburg, Maryland, USA.ORCID https://orcid.org/0000-0003-1456-4862

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Type I interferon (IFN1) pathway-targeting therapies represent a highly promising class of remedies for the treatment of systemic lupus erythematosus. However, the overall clinical benefit of these compounds is afflicted by marked variability. In this study, we developed a quantitative systems pharmacology model of type I IFN-mediated inflammation and applied it for an indirect comparison of anifrolumab, sifalimumab, daxdilimab, and litifilimab pharmacodynamic response, represented in the model by the change in IFN1 gene signature (IFNGS). The model consists of 20 ordinary differential equations and 68 parameters, among which four systemic parameters (including one random effect) were estimated using patient-level data from Phase IIb anifrolumab clinical trial. Within-target and within-pathway validation was performed using study-level pharmacokinetics, IFNα, and/or IFNGS data from five anifrolumab, four sifalimumab, one daxdilimab, and one litifilimab trials. The model successfully captured overall trends in IFNGS at clinically relevant doses of these compounds and discriminated IFNGS response between patients with low (< 2.75) and high (≥ 2.75) baseline IFNGS. Overprediction of treatment benefit was observed for the low range of anifrolumab doses (100-150 mg every 4 weeks). In contrast, IFNGS response under 150 mg of daxdilimab was underpredicted, despite the accurate description of plasmacytoid dendritic cells and IFNα biomarkers. Results of the global sensitivity analysis revealed baseline IFNGS, IFNα, and IFNα fraction as key factors affecting treatment benefit the most. In terms of maximum IFNGS reduction, anifrolumab showed superior potential compared to sifalimumab, daxdilimab, and litifilimab (ΔIFNGS~25%), which was further enhanced in patients with high baseline IFNGS or IFNα (ΔIFNGS~50%-60%).

Indexed as

InflammationInterferon Type ILupus Erythematosus, SystemicModels, BiologicalAntibodies, MonoclonalAntibodies, Monoclonal, HumanizedHumansInterferon-alphaanifrolumabAntibodies, MonoclonalAntibodies, Monoclonal, HumanizedInterferon-alphaInterferon Type Isifalimumabautoimmune diseaseslupusmathematical modelingquantitative systems pharmacologytype 1 interferon

Identifiers

PMID40364448
PMCPMC12256560

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.