| dc.description.abstract | In this thesis, engineering approaches are applied to two major medical research diseases,
namely cancer and coronavirus disease of 2019 (COVID-19).
The first approach (Part A) provides new preclinical cell culture (in vitro) cancer models for
metabolic treatments. In engineering experimental modelling, models are intended to
investigate, improve and/or simulate a practical problem. For this process to be accurate, the
small-scale model should be designed within the bounds of scaling validity. This ensures that
the small-scale model accurately represents the full-scale model.
This engineering experimental modelling principle was applied to in vitro (cell cultures) cancer
models to develop alternative methods for metabolic cancer treatments, i.e., glucose
deprivation (GD). In vitro cancer models do not necessarily focus on aspects that are important
for quantification in a realistic environment. Current microenvironments of in vitro cancer
models are optimised for cell growth and do not mimic physiological conditions.
This results in glucose and glutamine concentrations (the main energy sources for cell growth)
being much higher in cell cultures than in typical cancer patients’ concentrations. In addition,
in vitro glucose concentrations of metabolic treatments are tested at much lower levels than
what is achievable in humans. Furthermore, cancer cells are exposed to GD at much shorter
durations than typical clinical metabolic treatments in humans.
These discrepancies could partly result in untranslatable results and misrepresenting data
used to develop in vivo (human) metabolic cancer treatments. Therefore, novel replicable
metabolic in vitro methods were developed within the bounds of scaling validity, i.e., at
achievable glucose and glutamine concentrations. The results obtained from these new
methods are the following:
• Cancer and non-cancer cells stabilise after 20 days when exposed to physiological
glucose and glutamine concentrations. Therefore, moderate long-term GD should only be
implemented at least 20 days after cells were exposed to physiological conditions.
• Cancer cells were affected more than non-cancer cells after exposure to long-term
moderate GD, with respective minimum cell growth after treatment of 62% and 84%.
• Long-term moderate GD is not sufficiently effective to achieve remission. Therefore,
additional therapies are needed.
• Cancer cells are most vulnerable approximately 26 days after moderate GD. Additional
therapies were implemented at this point.
Cells were exposed to the following extra therapies during metabolic treatments: (i) very low
short-term GD, (ii) very low short-term glucose and glutamine deprivation, and (iii) different
doses of two different chemotherapies. Results of these extra therapies were the following:
(i) Short-term GD decreased cancer cell growth further than long-term moderate GD; the
minimum cancer cell growth after treatment was 15%.
(ii) The addition of short-term glutamine deprivation did not decrease cell growth any
further; the minimum cancer cell growth after treatment was 16%.
(iii) Long-term moderate GD increased the efficacy of chemotherapy on some of the cancer
cell lines.
The insights gained from these tests were further used to develop a hypothetical non-toxic
long-and short-term metabolic treatment for future clinical trials. This hypothetical method
provides an alternative, non-toxic way to decrease circulating blood glucose levels. Most
aspects of this proposed method have been shown to be safe in non-cancer patients.
Therefore, future work should aim to implement such therapies on cancer patients in clinical
trials.
The second approach (Part B) provides a systems engineering approach to medical research,
which provides a holistic view of factors that influence disease severity and therapeutic
insights on COVID-19. Traditionally, medical research employs a reductionist approach, which
entails dividing complex systems into smaller parts and focusing on these smaller parts to
solve the problem. This leads to an in-depth understanding of only the smaller aspects and
not the larger overall problem.
Furthermore, the whole-system interaction and cause-and-effect are not adequately
considered. This reductionistic approach is seen in numerous medical studies of severe
COVID-19 cases and deaths due to COVID-19 in patients with chronic cardiovascular
comorbidities.
Part B of this study aimed to apply a systems-based engineering approach to integrate an
existing systems-based coronary heart disease (CHD) model with the activated pathogenetic
pathways seen in severe COVID-19 complications. This new integraded model was developed
to help explain the mechanisms of interaction of severe COVID-19 on the vascular system.
The new integrated CHD/COVID-19 model provides the following insight:
• This fully integrated model presents a visual explanation of the pathogenetic mechanisms
of interaction between CHD and COVID-19 complications.
• A detailed integrated explanation of a death spiral as a result of interactions between
Inflammation, endothelial cell injury, Hypercoagulability and hypoxia.
• The model also presents how this death spiral is aggravated through the following CHD
hallmarks: Hyperglycaemia/Hyperinsulinaemia, Hypercholesterolaemia, and/or
Hypertension.
• A strong association between CHD and COVID-19 for all the investigated health factors
and pharmaceutical interventions, except for β-blockers, was found.
• The new model shows how different health factors (stress, exercise, smoking, etc.) and
pharmaceutical interventions (statins, salicylates, thrombin inhibitors, etc.) may either
aggravate or suppress COVID-19 severity.
With the insight gained from this new model, recommendations are made for future research
in potential new pharmacotherapeutics and personalised computational analysis to help
assess the risk of a patient with severe COVID-19 vascular complications. | en_US |
