Evidence map›Paper›PMID 39955407›Full record

ArticleDrug delivery and translational research2025

Bridging membrane fluidity studies with a predictive model of drug encapsulation to address industrial challenges of liposomal injectables manufacturing.

Mariana Biscaia-Caleiras, Nuno Fonseca, Ana Sofia Lourenço, António Nunes, Abel Ferreira, João Nuno Moreira, Sérgio Simões

Abstract read
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In one paragraph

Article in Drug delivery and translational research, 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

7 authors.

Mariana Biscaia-CaleirasCNC-UC, Center for Neurosciences and Cell Biology, Center for Innovative Biomedicine and Biotechnology (CIBB), Faculty of Medicine (Polo 1), University of Coimbra, Rua Larga, Coimbra, 3004-504, Portugal.ORCID 0000-0003-4208-5527
Nuno FonsecaBluepharma - Indústria Farmacêutica, S.A, São Martinho do Bispo, Coimbra, 3045-016, Portugal.ORCID 0000-0003-4970-7288
Ana Sofia LourençoBluepharma - Indústria Farmacêutica, S.A, São Martinho do Bispo, Coimbra, 3045-016, Portugal.ORCID 0000-0001-9459-3561
António NunesBluepharma - Indústria Farmacêutica, S.A, São Martinho do Bispo, Coimbra, 3045-016, Portugal.ORCID 0000-0002-9014-9030
Abel FerreiraDepartment of Chemical Engineering, University of Coimbra, CERES, Polo II, Rua Silvio Lima, Coimbra, 3030-970, Portugal.ORCID 0000-0002-8316-200X
João Nuno MoreiraCNC-UC, Center for Neurosciences and Cell Biology, Center for Innovative Biomedicine and Biotechnology (CIBB), Faculty of Medicine (Polo 1), University of Coimbra, Rua Larga, Coimbra, 3004-504, Portugal.ORCID 0000-0003-3449-0522
Sérgio SimõesCNC-UC, Center for Neurosciences and Cell Biology, Center for Innovative Biomedicine and Biotechnology (CIBB), Faculty of Medicine (Polo 1), University of Coimbra, Rua Larga, Coimbra, 3004-504, Portugal. ssimoes@bluepharmagroup.com.ORCID 0000-0002-8898-7625

Funding

CInTech-Technological Hub for Innovation, Translation and Industrialization of Complex Injectable Drugs,co-funded by Component C5 - Capitalisation and Business Innovation under the Portuguese Resilience and Recovery Plan, through the NextGenerationEU Fun C644865576-00000005,Fundação para a Ciência e a Tecnologia https://doi.org/10.54499/PD/BDE/150664/2020Fundação para a Ciência e a Tecnologia LA/P/0058/2020Fundação para a Ciência e a Tecnologia UIDB/04539/2020Fundação para a Ciência e a Tecnologia UIDP/04539/2020
6 · The paper itself

Abstract

Industrial manufacturing of liposomal drugs, often involves high-temperature processes, resulting in increased energy consumption, prolonged process times, and elevated costs, while posing risks of phospholipid and drug degradation. The current study addresses these challenges by exploring remote loading of doxorubicin into liposomes, at temperatures below the phase transition temperature (PTT) of the primary phospholipid (DSPC, 55 °C). Drug loading efficiencies exceeding 90% at 45 °C were achieved, while efficiencies dropped significantly (6-fold and 23-fold) at 37 °C and 25 °C, respectively. This prompted the hypothesis that efficient drug loading might be attained below the PTT, when a minimal threshold for liposomal membrane fluidity is overcome. Using design of experiments (DoE), key factors influencing fluidity were identified: temperature, cholesterol content and surface tension (dependent on the isotonic agent). A full factorial DoE confirmed that membrane fluidity increased with lower surface tension, and high cholesterol content. A predictive model was also generated establishing a correlation between drug loading efficiency, membrane fluidity, and drug partitioning coefficient (logP). This model revealed that doxorubicin (logP = 1.5) requires a fluidity threshold of 4.41 for efficient loading (≥ 90%), whereas daunorubicin (logP = 2.32) needs a lower threshold of 3.85, suggesting that drugs with higher logP values demand lower fluidity thresholds for effective loading. The model's applicability was validated across various lipid formulations, enabling effective drug loading at temperatures as low as 25 °C, potentially reducing degradation risks and energy costs. Overall, these findings highlight the relevance of liposomal membrane fluidity studies as a potential tool for enabling more effective industrial processes.

Indexed as

DoxorubicinMembrane FluidityAntibiotics, AntineoplasticCholesterolLiposomesPhosphatidylcholinesSurface TensionTemperature1,2-distearoyllecithinAntibiotics, AntineoplasticCholesterolDoxorubicinLiposomesPhosphatidylcholinesIndustrial manufacturingLiposomal formulationsMembrane fluidityPredictive modelQuality-by-designRemote drug loading

Identifiers

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Registered trials

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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.