With drastically decreasing costs of genetic sequencing, it has become feasible to use individual genetic markers to optimize treatment selection in cancer therapy. However, it is still difficult for medical practitioners to integrate these new kinds of data into clinical routine, since available information is growing rapidly. Pharmacogenomics (PGx) is one of the core elements of personalized medicine. PGx information reduces the likelihood of adverse drug reactions (ADRs) and optimizes therapeutic efficacy. The advancement of new technologies, including SNP genotyping and microarray/biochips, have been among the key drivers for the implementation of personalized medicine in practice.
Figure 1 Pharmacogenomics Clinical Decision Support(CDS) Service
Researchers already have genomic test where they can look at slight differences within patients' genes that will help doctors tailor the therapy. Dosage adjustments according to the patient's genotype for certain drugs and drug classes have already been implemented into relevant clinical guidelines. The rise in 'omics' studies and exploration in recent years has generated more data whose interpretation requires methods well beyond the grasp of traditional statistical techniques. An increasing amount of data relevant to disease symptoms, diagnostics, biomarkers, therapy, adverse effects, as well as sociometric data, demographic data and heterogeneous biomedical data, has been collected over time. Thus, there is an obvious need for AI-powered platforms that allow the analysis and discovery of patterns in heterogeneous clinical data sets to improve overall patient care and ensure more prompt diagnostics. New data on genomic variations, drug–gene, drug–drug interactions and more could emerge numerously to make the technology development process faster. The powerful merge between Big Data analytics and PGx will allow us to analyze, catalog, differentiate and utilize factors that contribute to a patient's drug response.
AI models in PGx implementation are utilized in different stages. Our scientists use AI methods for the stratification of patients which requires high-complexity data analytics for the categorization of patients into subpopulations. Deep learning (DL) algorithms play a significant role in overcoming the challenges associated with the implementation of PGx data. This will address the high dimensionality of the electronic health record (EHR) data structure, noise, heterogeneity, sparseness, incomplete data fields, random error, systematic biases and the extraction of relevant clinical phenotypes among others. Our pharmacogenomics service investigates how variations in genes affect response to medications, thereby using a patient's genetic profile to predict a drug's efficacy, guide dosage and improve patient safety. If you need pharmacogenomics service to develop better predictive risk models and harness the data to inform clinically actionable measures, please feel free to contact us.
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