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Data · dataset · 2026

Utilisation of in silico tools and a design of experiment approach for the design of novel peptides to cross the blood brain barrier

Listed in ZivaHub and Deakin Research Online and DMU Figshare — shown once because both records carry DOI 10.17034/32639754.v1

The blood brain barrier (BBB) is a major protective physical and biological barrier that surrounds the brain and is composed of brain capillary endothelial cells (BCECs) connected by tight junctions (TJ), astrocytes, pericytes and microglia.

Description

Matrix metalloproteinases and tissue inhibitors further limit transport by regulating BBB integrity. These structures limit 98% of all small molecules from crossing the BBB into the brain preventing unwanted harm.

However, this mechanism poses as a major challenge in the treatment of aggressive brain pathologies such as glioblastoma. This is because current treatment methods invoving Temozolomide and concomitant Temozolomide cannot cross the BBB with high efficacy. Glioblastoma has a 5% survival rate at 5-years and modern treatments have a high failure rate with a 60% chance of relapse and 78% mortality.

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Exploiting the transferrin receptor (TfR) has been proposed as a method to cross the BBB due to its high abundance within all regions of the brain. This thesis aimed to design a drug delivery system using the RALA cell penetrating peptide conjugated to a Tf sequence which could be capable of crossing the BBB. RALA would operate as the delivery system which would condense the nucleic acid payload and transport it across the BBB as a therapeutic strategy to treat glioblastoma.

This was achieved through the attachment of a linker sequence which was designed using in silico tools. This thesis also aimed to perform a Design of Experiments (DoE) to optimise the conditions of transfection efficiency and cell viability in the brain cell lines U87MG, A172 and HBEC-5i for future transfection assays. Results from RT-qPCR and western blot analysissuggested brain cell lines: A172, U87MG and HBEC-5i expressed TfR.

The DoE approach also determined the optimised conditions for U87MG and A172 was N:P 12, 10,000 cells per well, 0.5 µg of cargo and a 2h incubation period. These optimised conditions resulted in a maximised transfection efficiency of 54.8% ± 4.1 and 18.1% ± 4.6 respectfully.<br><br> In contrast, HBEC-5i was found to have the optimised conditions of N:P 12, 10,000 cells per well, 0.5 µg of cargo and a 5 h incubation period which resulted in the maximised transfection efficiency of 34.1%±2.6.

The DoE technique was advantageous as it allowed for a more thorough interrogation of conditions and how each condition impacted each other compared to one-factor-at-a-time (OFAT) approaches. Mass of cargo, N:P ratio and incubation time were found to have a collective impact on transfection efficiency and cell viability for U87MG cells. Furthermore, these conditions had a collective impact on A172 cell viability and HBEC-5i transfection efficiency. 2 A series of RALA-Tf peptides were also designed in this thesis and screened using in silico tools.<br><br> This research interrogated previously designed linker sequences which had demonstrated increased stability in fusion peptides whilst preserving functionality.

Of these linkers, 4 peptide sequences (‘SGSGS’,’SGSG’, ‘GFLG’, ‘RLLRDL’) were predicted to have high cellular uptake, cell-penetrating features (96%, 97%, 97%, 95%), stability (34.54, 35.74, 33.96, 39.62), low haemo and cyto toxicity and did not interfere with the helical structure of RALA. This thesis utilised statistical techniques and in silico tools which reduced time and resource with improved accessibility. Future work should aim to build upon this work through in vitro and in vivo assessment of the designed peptides.

This work should include: a DoE approach for the finalised RALA-Tf peptide, characterisation of the RALA-Tf peptide and assessing the RALA-Tf peptides ability to utilise the TfR. Future work should also aim to build reliable BBB models such as transwell, organoid or microfluidic devices. <br><br>

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Catalogue records · 1

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Provenance · 3 source records, 19 field assertions
SourceKeyLast seenRaw
ZivaHuboai:figshare.com:article/326397545 d agoJSON v1
Deakin Research Onlineoai:figshare.com:article/326397545 d agoJSON v1
DMU Figshareoai:figshare.com:article/326397545 d agoJSON v1
FieldAssertionExtractorEvidence
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concepts[field].anzsrc:field:490304mapping · figshare dmu ac ukvocabulary-mapper@1.0.0keywords['Optimisation']
concepts[field].anzsrc:field:490304mapping · dro deakin edu auvocabulary-mapper@1.0.0keywords['Optimisation']
concepts[field].anzsrc:group:4602mapping · zivahub uct ac zavocabulary-mapper@1.0.0keywords['Artificial intelligence']
concepts[field].anzsrc:group:4602mapping · dro deakin edu auvocabulary-mapper@1.0.0keywords['Artificial intelligence']
concepts[field].anzsrc:group:4602mapping · figshare dmu ac ukvocabulary-mapper@1.0.0keywords['Artificial intelligence']
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concepts[field].local:field:computer-science-aimapping · zivahub uct ac zaconnector:zivahub_uct_ac_za@1.0.0
concepts[field].local:field:computer-science-aimapping · dro deakin edu auconnector:dro_deakin_edu_au@1.0.0
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concepts[field].local:field:life-sciencesmapping · figshare dmu ac ukconnector:figshare_dmu_ac_uk@1.0.0
concepts[field].local:field:life-sciencesmapping · zivahub uct ac zaconnector:zivahub_uct_ac_za@1.0.0
concepts[field].local:field:life-sciencesmapping · dro deakin edu auconnector:dro_deakin_edu_au@1.0.0
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