Evidence map›Paper›PMID 40119341›Full record

ArticleBiology direct2025

Doxorubicin PK/PD modeling in multiple myeloma: towards in silico trials.

Daniele Andrean, Francesco Da Ros, Mario Mazzucato, Morten Gram Pedersen, Roberto Visentin

Abstract read
In one paragraph

Article in Biology direct, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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

5 authors.

Daniele AndreanDepartment of Information Engineering, University of Padova, Via Giovanni Gradenigo 6b, 35131, Padova, Italy.
Francesco Da RosCRO Aviano, National Cancer Institute, IRCCS, Via Franco Gallini 2, 33081, Aviano, Italy.
Mario MazzucatoCRO Aviano, National Cancer Institute, IRCCS, Via Franco Gallini 2, 33081, Aviano, Italy.
Morten Gram PedersenDepartment of Information Engineering, University of Padova, Via Giovanni Gradenigo 6b, 35131, Padova, Italy. mortengram.pedersen@unipd.it.
Roberto VisentinDepartment of Information Engineering, University of Padova, Via Giovanni Gradenigo 6b, 35131, Padova, Italy.

Funding

Department of Information Engineering, University of Padova SID 2020
6 · The paper itself

Abstract

Doxorubicin (DOXO) is a well-known chemotherapy drug, which is widely used in the treatment of Multiple Myeloma (MM), a treatable but not curable type of blood cancer. Here, we propose a pharmacokinetics and pharmacodynamics (PK/PD) simulation environment, aimed at facilitating the optimization of DOXO treatment regimens in MM treatment. The resulting model has a transparent mechanistic structure, which facilitates its use and interpretation. The simulator was developed using a combination of experimental and modeling techniques, starting from in vitro PK/PD experiments conducted on MM cells. In our previous work, we carefully developed a PK model for DOXO in MM cells by fitting experimental data. We now devise a PD model from in vitro data investigating the effect of different concentrations of DOXO on cell growth and death in MM cell populations. The PK model is extended to enable a clear mechanistic link between the PK and the PD models, hence providing a complete PK/PD simulator. We show how the mathematical model can be exploited to simulate different DOXO administration protocols with different dosages, repetitions and exposure times, thus, making it possible to explore the effect of a wide range of treatment protocols easily.

Indexed as

Antibiotics, AntineoplasticDoxorubicinModels, BiologicalMultiple MyelomaCell Line, TumorComputer SimulationHumansAntibiotics, AntineoplasticDoxorubicinin silicoModelingMultiple myelomaPK/PD

Identifiers

PMID40119341
PMCPMC11927236

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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.