Implementing pharmacogenomic testing in clinical practice can sig­nificantly improve drug therapy management. It enables healthcare pro­viders to select the most appropriate medications and dosages for individual patients based on their genetic profiles, thus reducing the trial­and-error approach often associated with medication prescribing.

However, NGS also brings challenges in data management and inter­pretation, requiring sophisticated computational resources and expertise in genetics and bioinformatics.79 As NGS technology evolves, it is set to enhance medical diagnostics and personalized medicine further.

In SNP genotyping, microarrays identify variations at single nucleo­tide positions in the genome. Microarray technology simultaneously ena­bles the analysis of thousands of SNPs, facilitating large-scale genetic association studies, population genetics research, and personalized medi­cine approaches.

In chronic diseases such as diabetes and hypertension, genomics can help in predicting which patients are likely to benefit from certain medications, thus avoiding the trial-and-error approach often asso­ciated with these conditions.89 This not only improves patient outcomes but also reduces the time and cost associated with finding the right medication.

Adverse drug reactions are unintended, harmful events attributed to the use of medicines. They have become a frequent cause of morbidity and mortal­ity93 and an increasing challenge in modern healthcare, particularly given the increasing complexity of therapeutics, an aging population, and rising multimorbi dity.94 They can occur due to various mechanisms, often influ­enced by genetic factors. These mechanisms include altered drug metabo­lism, hypersensitivity reactions, and genetically predisposed toxicities. For example, some individuals have genetic variations that affect the function of liver enzymes responsible for drug metabolism. These variations can lead to either an accumulation of the drug to toxic levels or insufficient drug levels to be effective. Another example is the hypersensitivity to abacavir, a drug used in HIV treatment, which is strongly associated with the HLA­B*57:01 allele. Patients with this genetic variant are at a significantly increased risk of developing a severe hypersensitivity reaction to the drug.

Genomic predictors of drug response, often identified through pharma­cogenomic testing, are genetic variations that influence an individual’s response to a drug. These predictors can indicate whether a patient is likely to experience an adverse reaction or whether they will respond well to the treatment. For instance, variations in the TPMT gene can affect the response to thiopurine drugs, used in certain types of cancer and autoim­mune diseases. Patients with certain TPMT gene mutations are at increased risk of toxicity from thiopurines due to reduced enzyme activity leading to drug accumulation.

The methodology of personalized treatment typically involves collect­ing and analyzing genetic data through methods such as genomic sequenc­ing or SNP genotyping. This information is then integrated with clinical data, including the patient’s medical history, current health status, and lifestyle factors. The combination of this data allows healthcare providers to develop a treatment plan that is more likely to be effective and less likely to cause side effects.