How we establish pharmacogenomic factors related to medications
Ruslan is the Chief Scientific Officer of GeneYouIn, the operators of the industry-leading Pillcheck Pharmacogenomics service based in Canada working with the Medications Data Interoperability Project.
Introduction
Pharmacogenomics – also known with the acronym 'PGx' - can reduce the overall cost of patient care by reducing adverse events, increasing adherence to treatment, optimizing patients' overall therapeutic regimen and helping to achieve a faster therapeutic response. However, sourcing the relevant data to establish PGx factors is quite complex work. Here I describe the main steps in our approach.
Sourcing authoritative and accurate pharmacogenetic information
The drug-gene annotations are present in the two main types of documents:
- Drug labelling information is obtained from regulatory bodies, including EMA, FDA, Health Canada, and other authorities that regulate drug approval. These are primarily predicated on the data from different phases of clinical drug trials.
- Clinical consortia guidelines which are derived from the findings academic clinical experts in pharmacology and genetics who study peer-reviewed publications and evidence, particularly Real World Evidence (RWE) as opposed to the artificial cohorts in clinical drug trials.
Both of these sources broadly provide the main evidence for PGx factors, and they complement each other through the combination of trial and real world data.
Drug Labelling Information
Drug labeling may contain information on genomic biomarkers and can describe:
- Drug exposure and clinical response variability
- Risk for adverse events
- Genotype-specific dosing
- Mechanisms of drug action
- Polymorphic drug target and disposition genes
- Trial design features
- Drug label and guidelines aggregator databases
The above data are largely sourced from the information gathering during Clinical Trials as well as additional labelling updates mandated by some medications regulators. The primary data sources included in the drug labelling are primarily based on the drug’s pharmacokinetics obtained in the different stages of trials, the first 3 stages of which we describe below.
| Clinical Trial Phase | Normal Objectives of Phase |
|---|---|
| Phase I trials are concerned primarily with establishing a new drug's safety and dose range in about 20-100 healthy volunteers. How a drug is absorbed, distributed, metabolized and excreted by the human body is called Pharmacokinetics. This is determined through frequent blood draws (usually in an inpatient environment) to check for the level of drug in the blood plasma. | Establishing safety and dose Observing how the drug is absorbed distributed, metabolised and excreted by the human body. |
| Phase II studies determine the effectiveness of an experimental drug on a particular disease or condition in approximately 100 to 300 volunteers. This phase may last from several months to two years. | A Phase II trial seeks to answer the question of efficacy of a drug, i.e. the ability of the drug to alleviate or cure a medical condition. |
| Phase III studies are conducted at multiple centers with several hundred to several thousand patients for whom the drug is intended. Massive testing of a drug provides continued generation of data on a drug's safety and efficacy. | Phase III trials provide the bulk of information needed for the package insert and labeling of a medicine, after approval by a regulator. Phase III also continues to gather the efficacy data examined in Phase II. |
Some issues with data gathering at these stages are that pharmacokinetic analyses are frequently not published, and only limited genetic variants are included in the limited clinical trial study population. Additionally, artificially high ethnic and genetic homogeneity in clinical trials during Phases 1 to 3 may also be a problem.
In some instances, following real-world post-approval drug utilization, the contribution of genetic variations and drug-drug interactions is uncovered, and the health authority updates the drug label. In one example, new biological medications for Alzheimer's disease received a warning about the higher risk of brain swelling and other complications for patients.
Additionally, a biomarker-driven indication for a drug is added when the medication is designed to treat a specific subpopulation with a rare disease, cancer, or other niche indications. For many of these types of drugs, a companion genetic diagnostic is required before prescribing and using them. The FDA's Table of Pharmacogenetic Associations classifies different drug-gene pairs into three sections/classes where the pharmacogenomic factors are examined:
- PGx Associations for which the Data Support Therapeutic Management Recommendations
- PGx Associations for which the Data Indicate a Potential Impact on Safety or Response
- PGx Associations for which the Data Demonstrate a Potential Impact on Pharmacokinetic Properties Only
In addition, Health Canada, EMA, Swissmedic, the Italian Agency of Medicines (AIFA), and the Japanese PMDA provide independent pharmacogenetic annotations on drug labels. However, other regulators do not offer clear classifications of drug-gene interactions.
Furthermore, some of the potentially actionable PGx interactions can be derived from drug-drug interaction annotations mentioned in older drug labels. However, most drug labels fail to mention the drug-drug interaction potential in people with specific genetic profiles.
Aggregator databases, primarily PharmGKB, collate and rank pharmacogenetic annotations from different regulatory bodies, peer-reviewed academic publications, and clinical guidelines. The PharmGKB highlights regional differences in reported biomarkers, dosing recommendations, and alerts. The Chinese pharmacogenomics knowledge base (CNPKB) is another large-scale database of gene-drug correlation studies in the Chinese population. While aggregator database can flag drug-gene pairs identified in various drug labels, they do not provide systematic evidence reviews. It is also important to note that aggregator updates are episodic and may miss annotations for newly approved drugs and the most recent pharmacokinetic studies.
In summary, drug labelling information can be valuable and clinically actionable in most cases. However, direct mining of drug labels requires very careful curation.
Clinical Consortia Guidelines
Clinical consortia in the domain of pharmacogenomics refer to groups of academic clinical experts in pharmacology and genetics interested who are facilitating pharmacogenetic tests for patient care. One such example is the Clinical Pharmacogenetics Implementation Consortium (CPIC®) which is funded by NIH grants. Other groups include the Dutch Pharmacogenetics Working Group (DPWG, funded by the Royal Dutch Pharmacist's Association), the French‐Speaking Network of Pharmacogenetics (RNPGx) and the Canadian Pharmacogenomics Network for Drug Safety (CPNDS).
These consortia recommendations are primarily based on published peer-reviewed genetic association studies. These publications are based on more ethnically diverse patent populations and represent Real-World Evidence rather than on artificially selected cohorts in clinical drug trials.
These groups of specialists in each clinical area evaluate the published data regarding the clinical actionability of different genetic variations performed in broader populations. These meta-analyses assess potential confounding factors and biases in published studies. Therefore, the consortia-based recommendations are more refined, provide more specific quantitative dosing recommendations, and describe more genetic variants than those mentioned on drug labels.
The consortia guidelines are much more informative than pharmacogenetic drug annotations and should be used whenever available. Yet, CPIC and DPWG have significant discrepancies in drug-gene pair selections and dosing recommendations. Unfortunately, the consortia lack sufficient coordination (due to the voluntary nature of the consortia and limited grant funding) to develop consensus guidelines. Ongoing guideline updates aim to further enhance the accuracy of dosing recommendations by evaluating new pharmacokinetic and drug response genome analysis, offering an opportunity to better align recommendations between different expert groups.