Predictive Prescribing

Patient genotypes are usually categorized into the following predicted phenotypes:

  • Extensive Metabolizer: Normal metabolic activity
  • Intermediate Metabolizer: Patients with reduced metabolic activity
  • Poor Metabolizer: Patients with little to no functional metabolic activity
  • Ultra-Rapid Metabolizer: Patients with substantially increased metabolic activity

The two extremes of this spectrum are the Poor Metabolizers and Ultra-Rapid Metabolizers. Efficacy of a medication is not only based on the above metabolic statuses, but also the type of drug consumed. Drugs can be classified into two main groups: active drugs and pro-drugs. Active drugs refer to drugs that are inactivated during metabolism, and Pro-Drugs are inactive until they are metabolized.

An overall process of how pharmacogenomics functions in a clinical practice. From the raw genotype results, this is then translated to the physical trait, the phenotype. Based on these observations, optimal dosing is evaluated.

For example, we have two patients who are taking codeine for pain relief. Codeine is a pro-drug, so it requires conversion from its inactive form to its active form. The active form of codeine is morphine, which provides the therapeutic effect of pain relief. If person A receives one *1 allele each from mother and father to code for the CYP2D6 gene, then that person is considered to have an extensive metabolizer (EM) phenotype, as allele *1 is considered to have a normal-function (this would be represented as CYP2D6 *1/*1). If person B on the other hand had received one *1 allele from the mother and a *4 allele from the father, that individual would be an Intermediate Metabolizer (IM) (the genotype would be CYP2D6 *1/*4). Although both individuals are taking the same dose of codeine, person B could potentially lack the therapeutic benefits of codeine due to the decreased conversion rate of codeine to its active counterpart morphine.

Each phenotype is based upon the allelic variation within the individual genotype. However, several genetic events can influence a same phenotypic trait, and establishing genotype-to-phenotype relationships can thus be far from consensual with many enzymatic patterns. For instance, the influence of the CYP2D6*1/*4 allelic variant on the clinical outcome in patients treated with Tamoxifen remains debated today. In oncology, genes coding for DPD, UGT1A1, TPMT, CDA involved in the pharmacokinetics of 5-FU/capecitabine, irinotecan, 6-mercaptopurine and gemcitabine/cytarabine, respectively, have all been described as being highly polymorphic. A strong body of evidence suggests that patients affected by these genetic polymorphisms will experience severe/lethal toxicities upon drug intake, and that pre-therapeutic screening does help to reduce the risk of treatment-related toxicities through adaptive dosing strategies.

Identification of the genetic basis for polymorphic expression of a gene is done through intronic or exomic SNPs which abolishes the need for different mechanisms for explaining the variability in drug metabolism. The SNPs based variations in membrane receptors lead to multidrug resistance (MDR) and the drug–drug interactions. Even drug induced toxicity and many adverse effects can be explained by genome-wide association studies (GWAS).

The list below provides a few more commonly known applications of pharmacogenomics:

  • Improve drug safety, and reduce ADRs
  • Tailor treatments to meet patients unique genetic pre-disposition, identifying optimal dosing
  • Improve drug discovery targeted to human disease
  • Improve proof of principle for efficacy trials