The revolution which is underway regarding effective cancer treatment is linked to a new drug, ZL105, derived from the metal iridium and could be a perfect replacement for the anticancer drugs which are currently under use. However, the drug has not been completely certified to justify the aspects of safety when induced in the human system. Therefore, to approve its dispatch and applicability in conducting human treatment, I would suggest the integration of computer simulation models because over the years, the drugs regarded as essential in the battle against cancer reacted exactly the way they do in real life, but while under simulation in the computer-generated model belonging to one of the cell’s key molecular pump (the protein P-glycoprotein), their behavior is quite different since they act according to predictions (Dror et al., 2012). Therefore, the reliability of the computer simulation procedure is geared towards eliminating the overreliance on static images for the structure of P-glycoprotein. The model enables the docking of drug ZL105 in P-gp to determine its behavior in P-gp’s pump.
Protein glycoprotein is the cellular pump that guards cells by pumping out toxins. The problem arises during chemotherapy when the pump consider drugs meant for cancer treatment as toxic and bar them from fighting cancer cells (Iwamoto, 2013). Thus, to stall the protein’s pumping action and enable drugs to work, several inhibitors are incorporated and this may be accompanied by several side effects on cells’ normal functioning. Therefore, the advantage of integrating computer simulation in testing the viability of induction of drug ZL105 is the ideology on clarity concerning how the drug works by binding with the cell protein while keeping up with quick proliferation and invasion (Iwamoto, 2013). It is also easy to note how cancer cells respond to the transformation taking place within the cell ‘power-house’ as the drug pushes them over the limit and reduces their quick division without affecting the normal cells unlike the other procedures where patients get exposed to rays which kill cells. Hence, while significant advances have been made in statistical methods, systems biology, machine learning and data science on both clinical and basic biomedical research levels, computer simulation and mathematical modeling still serves as the best approach because they aid in the optimization of clinical tools for cancer treatment and in the establishment computer-aided diagnosis.
Delegate your assignment to our experts and they will do the rest.
In determining the safety and effectiveness of Drug ZL105, it is pertinent to consider factors such as toxicity, its efficacy, and lethality. The drug’s prospects regarding these factors is sampled based on impacts and meted on the normal cells and the consequent alteration of the normal functioning of the human body. The current cancer cures regularly become less effective after the first course because cancer cells often mutate upon learning there is an attack, but ZL105 counters such cases due to its catalytic nature and is active at low doses hence minimizing the issue of toxicity because it is induced in minimal amounts (Zemzemi, 2013). The drug is effective as it attacks cancer cells in multiple ways at the same time unlike the platinum-based medications normally used in chemotherapeutic regimens. The current drugs are linked to DNA damage because they cannot distinguish between cancerous and non-cancerous cells leading to renal failure, nausea, ototoxicity, and neurotoxicity (Mak, Evaniew & Ghert, 2014). The iridium-based drug does not attack DNA but slows down and stops cancer growth while equally minimizing side effects encountered by patients.
The tests harbor profound information in the field of Cancer Informatics which represent a hybrid disciplines ranging from statistics, bioinformatics, computational biology, computer science, quantitative epidemiology, and oncology among others (Mielczarek, & Uziałko-Mydlikowska, 2012). Therefore, the future information on cancer should have its basis on these fields with the appropriate background for leveraging the tenets on clinical, computational, and the basic science resources which provide comprehensive data for research on various products for clinical validation and simulations to determine their efficacy and safety on humans, and in designing better measures to work on them through computer-aided predictive tools.
References
Dror, R. O., Dirks, R. M., Grossman, J. P., Xu, H., & Shaw, D. E. (2012). Biomolecular simulation: a computational microscope for molecular biology. Annual review of biophysics , 41 , 429-452.
Iwamoto, T. (2013). Clinical application of drug delivery systems in cancer chemotherapy: review of the efficacy and side effects of approved drugs. Biological and Pharmaceutical Bulletin , 36 (5), 715-718.
Mak, I. W., Evaniew, N., & Ghert, M. (2014). Lost in translation: animal models and clinical trials in cancer treatment. American journal of translational research , 6 (2), 114.
Mielczarek, B., & Uziałko-Mydlikowska, J. (2012). Application of computer simulation modeling in the health care sector: a survey. Simulation , 88 (2), 197-216.
Zemzemi, N., Bernabeu, M. O., Saiz, J., Cooper, J., Pathmanathan, P., Mirams, G. R., & Rodriguez, B. (2013). Computational assessment of drug‐induced effects on the electrocardiogram: from ion channel to body surface potentials. British journal of pharmacology , 168 (3), 718-733.
