The era of genomic medicine, starting with the first draft sequence of the human genome, stimulated a move from the model of one size fits all towards a more customised and personalised treatment model. Personalised medicine is not only about selecting the right treatment for a patient, but also about individual differences in terms of drug metabolism and predisposition to drug side effects. Pharmacogenomics (PGx) covers this latter aspect and PGx testing is used to pinpoint these individual differences in responses before administering a drug.
The main goal of PGx is to preemptively identify adverse reactions such as too slow or too fast metabolisation of a drug or even life threatening side effects. Thus PGx could be an important complement in clinical decision making. Despite this, PGx has not been integrated into routine clinical settings. This article will try to understand the reasons for this gap between the perceived utility of PGx and its practical implementation.
PGx has already improved the drug discovery pipeline (Table 1). The ability of PGx to identify potentially slow and fast metabolisers, as well as bad or non-responders to investigational drugs has improved test cohort assembly. Pharmacogenomics prevents attrition of the drugs from the market due to adverse effects of the drug not being identified in time. In some cases it can also put back the drugs abandoned in the development phase.
Conventional methods of monitoring drug effects on patients have so far mostly been reactive; focusing on the drug effect and its side effects during and after administration. PGx is a proactive method that allows for the minimisation of toxicity and other adverse side effects ahead of drug usage. It also allows for the prediction of optimal drug dosage based on the patient’s genomic data. Notwithstanding this potential impact on patient care, its adoption in the healthcare sector has been sluggish.
| Stage | Application of PGx in different drug development stages |
| Drug target Identification | PGx is used for identification and characterisation of polymorphism associated with responders, non responders, slow and fast metabolisers of drug target which could later lead to variation in the clinical study |
| Clinical trial | Use of PGx information for:Patient selection – Inclusion/Exclusion criteria in case of adverse effects due to certain polymorphism.Dose range selection and modification – For identification of slow and fast metabolisers of the drug based on the polymorphism.Adverse drug effects – Reporting and analysing adverse drug effects based on PGx information |
| Regulatory issues | PGx data is required to be submitted during development to national drug administration bodies such as FDA and sometimes also to be included in the label |
Clinical adoption of PGx depends heavily on the cost and efficiency of genomic data detection, pharmacogenomic knowledge, and clinical relevance. The progress of array and sequencing technologies, along with their price drop has solved the detection aspect quite some time ago and is not a reason for the gap in adopting PGx in the healthcare sector. How does one get from genomic medicine in a research context to the usage at large of PGx for the benefit of patients in a healthcare context?
Pharmacogenomic knowledge is continuously growing as relationships between genomic variants and drug metabolism are observed, evaluated, and documented. Even with this already quite considerable amount of knowledge around, it is still only the tip of the iceberg. The increase in the genomic data has made it possible to carry out genome-wide association studies (GWAS) and shed light on multiple correlations between genetic variation and drug response, although the understanding is still lacking. GWAS has contributed to the discovery of genetic markers, many of which can be used for PGx(2).
A lack of understanding of genetic impact on drug outcome is detrimental to clinical adoption as it limits the confidence of physicians. Furthermore, even when a sufficient understanding of the genetic impact is available, the cost and the time taken for the genetic tests needs to be at least on par with conventional methods. The time and money invested in genetic testing should be justified over conventional methods since in most cases patients pay out of pocket. Currently, the reimbursement coverage for PGx tests is low and based on evidence of the clinical utility. As a new mode of testing, there is not very much data yet that can be used to strengthen the value of PGx.
PGx relies on, and generates a considerable amount of data, setting high requirements on the underlying healthcare IT systems from a point of view of security, management, and storage. Also, the data formalism used for PGx requires upgrading of most electronic patient health record systems.
Currently, any molecular genetics diagnostic labs could perform PGx tests. However, only a handful of hospitals are performing these tests on a routine basis (for a few examples, see (3)). Those hospitals usually have positive experiences to share on how PGx testing has been beneficial for patients. A typical practical example would go as follows: A patient, Eden, who was diagnosed with acute lymphoblastic leukemia had a gene test done for ALL treatment. No mutations which would have required a change in her treatment plan were found. However, an incidental finding revealed that she would have trouble processing simvastatin, a drug for high cholesterol, due to a specific mutation in the SLCO1B1 gene, which is involved in the processing of simvastatin. Giving this drug to a patient that cannot process the drug can lead to life-threatening muscle damage. Therefore, Eden needed to be advised to avoid that particular drug. When this information is available in Eden’s health record, her treating physician can be notified. By systematically applying PGx, this result would appear as a main observation, and not serendipitously, as an incidental finding. Systematic PGx would also identify putatively relevant interactions for other drugs.
Clearly, multiple and various efforts are still required to close the gap between the perceived utility of PGx and its practical implementation. Regulatory bodies like EMA and FDA and other players in the diagnostic technologies field are contributing to bridging the gap. Expert bodies such as the Clinical Pharmacogenetics Implementation Consortium (CPIC) (4) have also been formed to buttress these efforts. CPIC, for example, provides as of today 33 detailed guidelines for unique drug-gene associations that help clinicians optimise drug dosage and avoid adverse side effects. Reimbursement modalities are similarly evolving. They cover the tests that have gained high clinical relevance, which leads to more evidence of positive impact on patient treatment, eventually removing obstacles and resistance to the progression of PGx.
References:
- Surendiran A, Pradhan SC, Adithan C. Role of pharmacogenomics in drug discovery and development. Indian J Pharmacol. 2008;40:137–143. doi: 10.4103/0253-7613.43158
- MacArthur et.al, The new NHGRI-EBI Catalog of published genome-wide association studies (GWAS Catalog), Nucleic Acids Research, 2017, Vol. 45, Published online 28 November 2016 doi: 10.1093/nar/gkw1133
- Dunnenberger HM, Crews KR, Hoffman JM, et al. Preemptive clinical pharmacogenetics implementation: current programs in five US medical centers. Annu Rev Pharmacol Toxicol. 2015;55:89–106.
- https://cpicpgx.or
