It follows that a pharmacodynamic/pharmacogenomic (PD/PG) model, which encapsulates both signature chemoresistance miRNAs and/or lncRNAs and the corresponding signal transduction pathways, would be a useful value-addition in combating chemoresistance and augmenting the therapeutic success of cancer chemotherapy. We have also described long non-coding RNA (lncRNA)-miRNA regulatory interactions in cancer translational medicine. This mechanism may be partly explained by stress constraints in the cancer cell and/or by temporal or spatial differences in the tumour milieu. We have previously described the architecture of signature microRNA (miRNA) regulatory networks comprising novel bifunctional cancer miRNAs that may play dual roles in the chemoprevention against, and chemoresistance to, various cancers. We have reviewed elsewhere the applicability of pharmacogenetics to personalised medicine in the Latin American population, which exhibits large genetic variability, as well as the evidence for personalising therapy in the pharmacogenetics of pain and nociceptors.
We also described the accurate frequency of CYP2D6 ultrarapid metabolisers in the Spanish population to be 5.34% when considering only the active alleles instead of counting just the multiplicated alleles. We recently reported that the CYP2D6 genotype is not a robust predictor of the CYP2D6 ultrarapid metaboliser status in a comparative clinical pharmacogenomics study in the Cuban population. This will also translate into significant cost benefits and time savings. In this way, treatment can be personalised for the candidate subpopulation who will benefit the most from it without exposing them to the undue risks of serious adverse events.
#Sims 4 cancer mod 2018 trial#
The stratification of the clinical trial population into non-responders, responders, and hyper-responders can lead to a better designed trial that will include or exclude appropriate subpopulations for improved safety and better targeted efficacy. Population pharmacogenomicsĪt the intermediate or late discovery stages of the drug development pipeline, population pharmacogenetics can help in optimising clinical trial design by stratifying populations, in optimising drug dosage for the appropriate patients, and in identifying patients at risk for adverse drug events. Nonetheless, significant limitations remain in clinical practice, and these adversely affect the best options available to the cancer patient who also has to bear the rising costs of cancer care.
However, advances in modern diagnostic techniques and next-generation sequencing methodologies have provided some opportunities to mitigate the challenges in better informing the clinician regarding the best therapeutic strategies for a particular cancer type or patient. However, the road to success in personalised cancer care is beset with many difficulties in the practical realm, including the lack of reliable biomarkers, the inability to identify an accurate signature biomarker panel for each cancer type, the variability between patients diagnosed with the same cancer, etc. This knowledge enables the clinician to tailor the right dose of oncology therapeutic to the right subpopulation of cancer patients who will likely respond to the intervention, thereby resulting in greater therapeutic success and decreased ‘financial toxicity’ to cancer patients, families and caregivers. When this epistemologic knowledge is applied to clinical oncology care, the exciting field is known as personalised cancer medicine – a field that has now evolved to ‘precision medicine’. Personalised medicine takes into consideration the inter-individual differences amongst people, which may be responsible for the variability seen in response to a therapeutic intervention in any disease.