Targeted Photodynamic Therapy of cancer using photoimmunoconjugates based on pyropheophorbide a derivatives

Photodynamic therapy (PDT) utilises light, oxygen and organic macrocycles, called photosensitisers, to produce reactive oxygen species that can kill malignant cells. Conventional PDT is associated with side effects that have stifled its advance and widespread use. These include low tumour selectivity, slow blood clearance and poor formulation. We proposed that an antibody fragment could be used to carry the photosensitiser to the target cells, significantly overcoming these limitations. Pyropheophorbide-a (PPa) was synthetically modified to enhance its water solubility obtaining two compounds, PS1 and PS4 each more water soluble than PPa. The use of Sonogashira coupling and short polyethylene glycol chains gave PS1, whereas the use of Suzuki coupling and a single positive charge gave PS4. The singlet oxygen quantum yields of these were improved compared to PPa with that of PS4 being 1.5 times higher than PPa. The in vitro characterisation of PPa, PS1 and PS4 using cytotoxicity assays did not correlate with their photophysical characterisation. PS4 was significantly less potent than PPa and PS1 on SKOV3 and KB human cancer cell lines. Confocal microscopy aided further characterisation using stains for intracellular organelles. PS1 was found to localise primarily in the ER and Golgi apparatus, similarly to PPa, while PS4 was found to localise mainly in the lysosomes. PS1 was conjugated to C6.5(-k), an anti-HER2 single chain variable fragment (scFv) using lysine coupling, to obtain a photoimmunoconjugate that was characterised in vitro and subsequently in vivo. In vitro characterisation showed increased potency and specificity but non-specific cell death attributed to the non-covalently bound photosensitiser was observed. However, in vivo therapy studies showed that the C6.5(-k)-PS1 photoimmunoconjugate could be used to cure SKOV3 subcutaneous tumours in nude mice, validating the use of targeted PDT as a successful targeted therapy with the potential to lower the effective drug dose and minimise side effects

Optimal Experimental Design for Discriminating between Microbial Growth Models as a Function of Suboptimal Temperature

In the field of predictive microbiology, mathematical models play an important role for describing microbial growth, survival and inactivation. Microbial dynamics are often described likewise by different models. However, the model that describes the system in the best way is desired. Optimal experimental design for model discrimination (OED/MD) is an efficient tool for discriminating among rival models. This dissertation focuses on the use of methods for optimal experiment design for model discrimination between microbial kinetic models, with particular emphasis to secondary models describing microbial kinetics in the suboptimal temperature range. On the one hand, different methods have been considered and tested for their applicability on the current application domain, i.e., secondary models describing microbial kinetics in the suboptimal temperature range. It appears that the method proposed by Schwaab et al. (2008) and Donckels et al. (2009) would behave better in a real life application. On the other hand, a thorough study has been performed as to define the possibilities of this particular selected method and get an indication of the expected experimental burden, when applied on the discrimination between the models under interest. Given the above aspects the last step includes the smooth and efficient transition from the in silico to the in vivo environment.nrpages: 154status: publishe

Targeted photodynamic therapy of cancer using photoimmunoconjugates based on pyropheophorbide a derivatives

Photodynamic therapy (PDT) utilises light, oxygen and organic macrocycles, called photosensitisers, to produce reactive oxygen species that can kill malignant cells. Conventional PDT is associated with side effects that have stifled its advance and widespread use. These include low tumour selectivity, slow blood clearance and poor formulation. We proposed that an antibody fragment could be used to carry the photosensitiser to the target cells, significantly overcoming these limitations. Pyropheophorbide-a (PPa) was synthetically modified to enhance its water solubility obtaining two compounds, PS1 and PS4 each more water soluble than PPa. The use of Sonogashira coupling and short polyethylene glycol chains gave PS1, whereas the use of Suzuki coupling and a single positive charge gave PS4. The singlet oxygen quantum yields of these were improved compared to PPa with that of PS4 being 1.5 times higher than PPa. The in vitro characterisation of PPa, PS1 and PS4 using cytotoxicity assays did not correlate with their photophysical characterisation. PS4 was significantly less potent than PPa and PS1 on SKOV3 and KB human cancer cell lines. Confocal microscopy aided further characterisation using stains for intracellular organelles. PS1 was found to localise primarily in the ER and Golgi apparatus, similarly to PPa, while PS4 was found to localise mainly in the lysosomes. PS1 was conjugated to C6.5(-k), an anti-HER2 single chain variable fragment (scFv) using lysine coupling, to obtain a photoimmunoconjugate that was characterised in vitro and subsequently in vivo. In vitro characterisation showed increased potency and specificity but non-specific cell death attributed to the non-covalently bound photosensitiser was observed. However, in vivo therapy studies showed that the C6.5(-k)-PS1 photoimmunoconjugate could be used to cure SKOV3 subcutaneous tumours in nude mice, validating the use of targeted PDT as a successful targeted therapy with the potential to lower the effective drug dose and minimise side effects.EThOS - Electronic Theses Online ServiceEU Framework 6 ProgrammeGBUnited Kingdo

Optimal experiment design for discrimination in predictive microbiology models

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Optimal experimental design for discriminating between microbial growth models as function of suboptimal temperature: From in silico to in vivo

Temperature is an important food preservation factor, affecting microbial growth. Secondary predictive models can be used for describing the impact of this factor on microbial growth. In other words, the microbial behavior can be described in a dynamic environment with the use of a primary and secondary model. Two models for describing the effect of temperature on the microbial growth rate are the cardinal temperature model with inflection (CTMI) (Rosso et al., 1993) and its adapted version (aCTMI) (Le Marc et al., 2002). Although Escherichia coli is commonly modeled using CTMI, there are indications that aCTMI may be more appropriate (Van Derlinden and Van Impe, 2012a). For clarifying this, the method of Optimal experiment design for model discrimination (OED/MD) will be used in this work (Donckels et al., 2009; Schwaab et al., 2008). Results from an in silico study point out the required direction. Whereas the results of the in vivo study give a more realistic answer to the research question. Finally, discrimination unravelled the appropriate model for the needed use.publisher: Elsevier articletitle: Optimal experimental design for discriminating between microbial growth models as function of suboptimal temperature: From in silico to in vivo journaltitle: Food Research International articlelink: http://dx.doi.org/10.1016/j.foodres.2016.08.001 content_type: article copyright: © 2016 Published by Elsevier Ltd.status: publishe

Critical assessment of the Time-To-Detection method’s performance to estimate microbial growth parameters

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On the effect of sampling rate and experimental noise in the discrimination between microbial growth models in the suboptimal temperature range

© 2015 . Biochemical and microbial processes benefit from mathematical models. Often microbial kinetics are described as a function of environmental conditions in models exploited in predictive microbiology. Based on the organism different model structures are available. However, the aim is to determine the model that describes the system best.This work deals with secondary models describing microbial kinetics in the suboptimal temperature range and their possibility to be discriminated. The used models are the cardinal temperature model with inflection and its adapted version. The method of Optimal Experiment Design for Model Discrimination is used to investigate the practical (in)feasibility of model discrimination given different noise and sampling frequency values.Results point out the required steps and the possibilities of the method for model discrimination. It has been observed that discrimination is possible at various noise and sampling frequency levels. Moreover, also the corresponding increase in required experimental effort has been obtained.publisher: Elsevier articletitle: On the effect of sampling rate and experimental noise in the discrimination between microbial growth models in the suboptimal temperature range journaltitle: Computers & Chemical Engineering articlelink: http://dx.doi.org/10.1016/j.compchemeng.2015.10.005 content_type: article copyright: Copyright © 2015 Published by Elsevier Ltd.status: publishe

Critical Assessment of the Time-to-Detection Method for Accurate Estimation of Microbial Growth Parameters

The time-to-detection (TTD) method is a rapid and high throughput approach for the estimation of microbial growth parameters (maximum specific growth rate μmax and lag phase duration λ), which relies on optical density (OD) measurements. The performance of this method depends on several factors that are often selected in an arbitrary way. In this work, a sensitivity analysis was performed to assess the effect of several key factors on the resulting output data of this method with Listeria monocytogenes. The factors showing higher influence on the results include (1) the calibration curve relating viable plate counts and OD data; (2) the approach to estimate TTD values; (3) the detection limit of OD measurements; and (4) the range of the initial cell concentrations considered (Ni). In general, lag phase (λ) estimates were more sensitive than maximum specific growth rate (μmax) estimates. The approach to estimate TTD values and the OD detection limit was the most influential factors for the μmax and λ estimation. This work has illustrated that, despite all the advantages of the TTD method, there are crucial steps in experimental design and data processing that significantly influence its output in terms of lag phase duration and maximum specific growth rate.status: publishe

Optimal experiment design for discriminating between microbial growth models as function of suboptimal temperature

In the field of predictive microbiology, mathematical models play an important role for describing microbial growth, survival and inactivation. Often different models are available for describing the microbial dynamics in a similar way. However, the model that describes the system in the best way is desired. Optimal experimental design for model discrimination (OED-MD) is an efficient tool for discriminating among rival models. In this work the T12-criterion proposed by Atkinson and Fedorov (1975) [1] and applied efficiently by Ucinski and Bogacka (2005) [2] and the Schwaab-approach proposed by Schwaab et al. (2008) [3] and Donckels et al. (2009) [4] will be applied for discriminating among rival models for the microbial growth rate as a function of temperature. The two methods will be tested in silico and their performances will be compared. Results from a simulation study indicate that it is possible to validate the case that one of the proposed models is more accurate for describing the temperature effect on the microbial growth rate. Both methods are able to design inputs with a sufficient discrimination potential. However, it has been observed that the Schwaab-approach provides inputs with a higher discrimination potential in combination with more accurate parameter estimates.publisher: Elsevier articletitle: Optimal experimental design for discriminating between microbial growth models as function of suboptimal temperature journaltitle: Mathematical Biosciences articlelink: http://dx.doi.org/10.1016/j.mbs.2014.01.006 content_type: article copyright: Copyright © 2014 Published by Elsevier B.V.status: publishe