title: Combined mathematical modelling and experimentation to predict polymersome uptake by oral cancer cells.
abstract: This study is motivated by understanding and controlling the key physical properties underlying internalisation of nano drug delivery. We consider the internalisation of specific nanometre size delivery vehicles, comprised of self-assembling amphiphilic block copolymers, called polymersomes that have the potential to specifically deliver anticancer therapeutics to tumour cells. The possible benefits of targeted polymersome drug delivery include reduced off-target toxic effects in healthy tissue and increased drug uptake by diseased tissue. Through a combination of in vitro experimentation and mathematical modelling, we develop a validated model of nanoparticle uptake by cells via the clathrin-mediated endocytotic pathway, incorporating receptor binding, clustering and recycling. The model predicts how the characteristics of receptor targeting, and the size and concentration of polymersomes alter uptake by tumour cells. The number of receptors per cell was identified as being the dominant mechanism accounting for the difference between cell types in polymersome uptake rate.
  
  
  BACKGROUND
  This article reports on a validated model developed through a combination of in vitro experimentation and mathematical modeling of nanoparticle uptake by cells via the clathrin-mediated endocytotic pathway. The model incorporates receptor binding, clustering, and recycling and predicts how the characteristics of receptor targeting, the size and concentration alter polymersome uptake by cancer cells.
authors.affiliation: Department of Molecular and Clinical Pharmacology, MRC Centre for Drug Safety Science, University of Liverpool, Liverpool, UK.
authors.forename: Ian
authors.initials: I
authors.lastname: Sorrell
authors.affiliation: Department of Mechanical Engineering, University College London, Torrington Place, London, UK.
authors.forename: Rebecca J
authors.initials: RJ
authors.lastname: Shipley
authors.affiliation: School of Clinical Dentistry, University of Sheffield, Claremont Crescent, Sheffield, UK.
authors.forename: Vanessa
authors.initials: V
authors.lastname: Hearnden
authors.affiliation: School of Clinical Dentistry, University of Sheffield, Claremont Crescent, Sheffield, UK.
authors.forename: Helen E
authors.initials: HE
authors.lastname: Colley
authors.affiliation: School of Clinical Dentistry, University of Sheffield, Claremont Crescent, Sheffield, UK.
authors.forename: Martin H
authors.initials: MH
authors.lastname: Thornhill
authors.affiliation: School of Clinical Dentistry, University of Sheffield, Claremont Crescent, Sheffield, UK.
authors.forename: Craig
authors.initials: C
authors.lastname: Murdoch
authors.affiliation: Department of Molecular and Clinical Pharmacology, MRC Centre for Drug Safety Science, University of Liverpool, Liverpool, UK. Electronic address: steven.webb@liverpool.ac.uk.
authors.forename: Steven D
authors.initials: SD
authors.lastname: Webb
mesh_terms: D045744:Cell Line, Tumor; D002966:Clathrin / Q000378:metabolism; D016503:Drug Delivery Systems; D004705:Endocytosis; D006258:Head and Neck Neoplasms / Q000188:drug therapy*; D006801:Humans; D007700:Kinetics; D008962:Models, Theoretical; D009062:Mouth Neoplasms / Q000378:metabolism*; D050997:Nanomedicine; D053758:Nanoparticles / Q000378:metabolism; D011108:Polymers / Q000378:metabolism*; D012235:Rhodamines / Q000378:metabolism; D013269:Stochastic Processes
chemical_list: D002966:Clathrin; D011108:Polymers; D012235:Rhodamines
keywords: Data fitting; Endocytosis; Mathematical model; Polymersome; Stochastic model