Habilidades de interacción entre niños de edad pre-escolar y sus docentes

In vitro tumour cell models are informative insofar as they can be physiologically

relevant, but in certain respects, their utility is limited since they cannot adequately replicate the complete nature of actual tumours (Katt et al. 2016). For example, in vitro cell models are static models that rely on passive diffusion for drugs to reach and permeate tumour cells or spheroids, which does not account for transport across the vascular endothelium as happens in vivo. Notably for nanoparticle testing, in vitro models cannot reproduce the complex vascular network, hypoxia, interstitial fluid pressure and fluid shear observed in the in vivo tumour environment (Velasco-Velazquez et al. 2011). Additionally, in order to understand the impact of the complexity of the tumour microenvironment, including the extracellular matrix, stromal cells and immune cells, on the performance of new drugs, models as similar to the in vivo situation as possible are required. While ex vivo tumour models have been reported, such as microfluidic-based platforms that recapitulate circulation, extravasation and drug delivery to tumours across the interstitial space (Tang et al. 2017), evaluation of new nanotherapies and other drugs in whole animals remains the standard for preclinical testing.

In vivo, the EPR effect enables the passive accumulation of liposomes to tumour sites,

92 is not observed for all solid tumours (Hansen et al. 2015; Wang 2015). Generally, the nanoparticle targeting of haematological and lymphoid tumours, particularly for ligand- directed liposomes, has shown greater success in in vivo tumour models since tumour cells in circulation are more directly accessible to liposomes than large solid tumours immersed in complex microenvironments (Buxton 2009; Cho & Lee 2014). However, the application of nanoparticles in the treatment of some solid tumours may have greater potential for use in the adjuvant setting to target vascularised micrometastases rather than (or in addition to) the primary tumour (Zhao, M et al. 2017).

Currently, metastatic disease is the leading cause of cancer mortality, accounting for approximately 90% of cancer-related deaths, and remains untreatable (Sledge 2016). Metastatic disease cannot be adequately modelled and studied without the use of whole organisms. Modelling metastatic disease using mice is necessary to evaluate the potential effect of a nanotherapeutic on the spread of cancer and growth rates of secondary tumours (Fantozzi & Christofori 2006). The generation of such models usually involves the injection of human cells into an immunocompromised mouse, forming a xenograft, and after primary tumour formation, those human cells may metastasise. Many established human cancer cell lines have a low metastatic potential, with the likelihood of spontaneous metastasis in an animal being dependent on the model used. For example, the standard MDA-MB-231 breast cancer cell line routinely used in vitro has been shown to metastasise in an intraductal NODScidIL2gamma-/- (NSG) mouse model (Young et al. 2016) but remains poorly metastatic in a mammary fat pad BALB/c nude mouse model (Stutchbury et al. 2007). This highlights the importance of selecting appropriate tumour models for the evaluation of nanotherapies in vivo.

While mouse models are the most frequently used animal models of tumours and other disease states, given the high degree of similarity between mice and humans, there are several known differences between mouse tumour models and the human context. For example, the rate of mouse model tumour growth and resultant angiogenesis is much greater than the formation of a tumour in humans, which tends to increase the EPR effect (Maeda 2015). Additionally, the lack of an adaptive immune system in immunocompromised xenograft mouse models used to study nanotherapies means that known immune system effects on tumour growth and metastasis are absent from testing

93 (Budhu et al. 2014). Despite their limitations, mouse models enable the determination of key characteristics of new nanotherapies, such as potential toxicity and off-target effects. Importantly, mouse models allow the evaluation of pharmacokinetics to determine how quickly a nanoparticle formulation is cleared from the bloodstream (plasma half-life), which is an important indicator of how likely it is that the nanoparticle will successfully reach the site of the tumour. Similarly, biodistribution studies provide information about where the nanoparticle localises in the body and how the nanoparticle is cleared. These characteristics can be measured by radiolabelling the nanoparticles and then detecting the presence of radiolabel in plasma and tissues at various time points post-injection (Vine et al. 2014). This information can then guide dosing for efficacy experiments, which can help elucidate the effects of repeated nanoparticle treatments on primary tumour growth rate and tumour metastases to major organs, giving an indication of potential therapeutic effect in a clinical setting.

4.1.2 Experimental rationale

To better understand the behaviour and potential anti-tumour effects of PAI-2- functionalised liposomes containing N-AI (N-AI PAI-2 liposomes) in vivo, it is necessary to evaluate the properties of the liposomes in animal models of breast cancer. Toxicology studies previously performed by our laboratory have shown that N-AI-loaded liposomes are non-toxic in mice, with up to 100 mg/kg total dose of liposomal N-AI showing no adverse effects (Appendix D). PAI-2 has also been shown to be safe and non-toxic in mice when administered intravenously (Hang et al. 1998; Vine et al. 2012). The biodistribution and pharmacokinetic properties of N-AI PAI-2 liposomes need to be determined to guide treatment schedules for efficacy experiments in mouse breast tumour models, which can be used to evaluate the potential therapeutic effect of N-AI PAI-2 liposomes in uPAR-positive breast cancer.

4.1.3 Aims

This chapter tested the hypothesis that N-AI-loaded PAI-2-functionalised liposomes (N- AI PAI-2 liposomes) enhance the tumour cell uptake into and cytotoxic effect of N-AI against uPAR-positive breast tumours. Therefore, the overall aim of this chapter was to determine the pharmacokinetics, biodistribution and anti-tumour efficacy of N-AI PAI-2

94 liposomes in human xenograft models of primary and metastatic uPAR-positive triple- negative breast cancer (TNBC) in order to evaluate the scope for future preclinical analysis and clinical application of N-AI PAI-2 liposomes. The specific aims of this chapter were to:

1. Determine the pharmacokinetic and biodistribution profiles of N-AI PAI-2 liposomes in female BALB/c nude mice bearing MDA-MB-231 orthotopic breast tumour xenografts;

2. Determine the anti-tumour (primary tumour) efficacy of multiple doses of N-AI PAI-2 liposomes in female BALB/c nude mice bearing MDA-MB-231

orthotopic breast tumour xenografts; and

3. Determine the anti-tumour (primary tumour and metastatic tumour) efficacy of multiple doses of N-AI PAI-2 liposomes in NOD-SCID-IL2gamma-/- mice bearing MDA-MB-231 intraductal breast tumour xenografts.

4.2 Methods