Limitations of Current Treatments
Current cancer treatments are as heterogeneous as cancer is, and none of the current strategies are foolproof. Currently, most patients will receive a mix of radiation, surgery and pharmaceuticals to attack the cancer from all angles, and ensure the patient has the best chance (1). However, due to this large scope, there are significant gaps in the research that significantly affect the success rate of these treatments
Firstly, and most importantly, cancer treatments have limited scope. Targeted cancer treatments have the side effect of being effective in limited numbers of patients, and without the ability to test patients for their susceptibility to a certain drug, cancer treatment becomes an iterative process for patients (2). Although there is already significant research on biomarkers of cancer for diagnosis and prognosis, as well as those representing the pharmacogenomics of a tumour, there is still a lot of room for this to be researched. This can be done with ’omics studies and would allow for a much more in depth analysis of tumour, as well as identification of novel drug targets (3). A lot of proteins that are complicit in oncogenesis have already been targeted or discounted, so discovering novel proteins to target is a key part of the future of cancer treatment success. However, ’omics research is expensive and can be rather slow. As such, utilising current research in algorithm development and artificial intelligence to discover promising targets is an interesting new field that is rapidly expanding (4).
Other limitations of current cancer pharmaceuticals include the drug resistance and off target-effects that are implicit in most cancer treatments. Most cancer cells have upregulated waste systems, specifically to export toxic chemicals that make it past their membranes. This normally takes the form of upregulated ATP-Binding Cassettes (ABCs), which will expel toxic chemicals almost as soon as they reach their target (3). Additionally, cancer cells can mutate themselves in order to overexpress the target or create point mutations that interrupt drug binding. All of these adaptations ensure that drug molecules become available to target and kill healthy cells instead. However, these effects are theoretically due to our current occupancy-driven model of pharmacology. This model means that our designed drugs rely on high affinity compounds, as the drug must be bound to the target for a long time in order to exert its inhibitory effect, which means these drugs are very vulnerable to point mutations (5). Ultimately, this model means that drugs that are effective with low occupancy of their targets would be able to evade many of the side effects of cancer treatment.
In conclusion, if low-occupancy driven drugs can then be optimised to a range of novel targets, and directed towards tumours that would be particularly vulnerable to them through ’omics research or mathematical prediction, cancer treatment research would be revolutionised.
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