Unlock the power of pharmacogenomic prescribing by leveraging clinical decision support

Dr Simon Hendricks, Product Innovation Manager at FDB, discusses how pharmacogenomic prescribing is transforming the healthcare landscape and how clinical decision support can amplify its impact.

A physician by background, I’ve worked in the Health Technology industry for over 15 years. I’ve dedicated my career to amplifying technology’s positive impact on the provision of optimal healthcare resulting in improved clinical outcomes and patient experience.

Like all doctors, I want to ensure that patients receive the most appropriate prescribed medications to achieve optimal medical outcomes. Ultimately, I want to know:

  • is this the correct medication for this patient?
  • is this the right time to prescribe the medication for this patient?
  • is this the correct dose for this patient?

The monumental impact of pharmacogenomic-informed decision support

The journey into pharmacogenomics began back in 2012 with the 100,000 genomes project, which aimed to understand the genes and their variations in the UK population and included examining the effect of these variations on medications.

Genomic medicine initially focussed on cancer care and rare diseases, so pharmacogenomic-informed prescribing was primarily seen within the realm of tertiary care – specialist centres treating rare conditions with uncommonly prescribed medications.

Fast forward to today however, and the impact that pharmacogenomic-informed decision support will have across the entire healthcare landscape is monumental. Did you know, for example, that:

  • Over 99% of us have at least one pharmacogenetic variant that influences how medications are metabolised, many of which are commonly prescribed drugs?
  • On average, we have four gene variants that affect the efficacy of commonly prescribed medications?
  • A recent study showed that patients who received medications informed by pharmacogenomic results had a 30% lower risk of experiencing adverse drug reactions (ADRs)?

We are seeing a shift away from pharmacogenomic-informed decision-making being considered a tool for specialist settings and towards recognising its transformational potential in other healthcare environments. In other words, pharmacogenomic-decision support is not only relevant for a select number of rare drugs in a specialist setting for a small number of patients. It's for everyone, even for commonly prescribed drugs that are regularly prescribed in primary care.

Since the most common intervention in primary care for disease modifiers is medicine, and 99% have gene variants that would influence how commonly prescribed medicines interact with us, these variations may well explain some of the ADRs and sub-optimal clinical outcomes many patients experience.

Instead of going through a ‘trial and error’ approach where the optimal treatment is eventually reached via a tortuous path, I believe prescribers should be empowered with the relevant genetic information to prescribe the right drug at the right dose first time.

But how do we implement that at scale?

Integrating pharmacogenomics into the clinical workflow

Essentially what’s required is pharmacogenomic-informed decision support that’s fully integrated into the existing prescribing workflow, so that it’s readily available to clinicians. This would enable pharmacogenomic prescribing at scale and would transform the outcomes of interventions across different settings.

Consider a ‘pre-test’ scenario, where a patient would benefit from a genome test before the prescribing intervention. We know that a third of people can’t metabolise clopidogrel very well for instance, just imagine the improvements to patient safety (not to mention time and resource efficiencies) if the clinician was notified of this fact during the prescribing workflow. Rather than proceed with prescribing a drug that isn’t metabolised well in 1 out of 3 cases, the clinician would instead be prompted to initiate a pharmacogenomic test to determine whether this is the optimal treatment for this patient.1 

Alternatively, consider a ‘post-test’ scenario in which a clinician is made aware of existing genomic test results prior to initiating a prescription. So, if a patient has completed a test and the results are in their record, when prescribing a medication which pairs with a known gene variant in the patient's record, a prescriber could receive pharmacogenomic-guided prescribing support to achieve optimal pharmaceutical benefits.

The role of clinical decision support in supporting pharmacogenomic-informed prescribing at scale is vital. It is about ‘democratising’ the information and flagging relevant insights to empower the prescriber. It’s about giving clinicians access to the results so they can intervene at an appropriate time, to explain these results and provide context to the prescriber, and to suggest what options they have at their disposal.

Envisioning the future

The field of genomics in medicine is rapidly evolving, and we’re already witnessing the impact that pharmacogenomic-informed decision making would have for large cohorts of patients in primary care. I’ve participated in some of the exciting trials and pilots aiming to further demonstrate the benefits of pre-emptive pharmacogenomic-decision support in the UK.

I attended the UK Pharmacogenetics and Stratified Medicine event back in June, a network of inspiring intellectuals determined to accelerate the adoption of personalised medicine in the UK. Being in a room full of advocates at the vanguard of personalised medicine was inspiring. And Pharmacogenomics is just one of many feeds into precision medicine - the event showcased streams dedicated to nutritional and economic influencers, as well as proteomics and epigenetics to name a few areas.

When it comes to precision medicine, we are genuinely at a turning point with a huge opportunity to transform patient safety, reduce avoidable ADRs and reduce wasted resource. The utopia would be personalised treatment plans for every individual, moving away from the current paradigm of trial and error to prescribe the right medication, at the right time, at the right dose for each patient.

The potential for pharmacogenomics in prescribing is vast, and leveraging clinical decision support solutions can help unlock its full potential at scale for improved patient outcomes. The technology, research and solutions are already out there to support this – it’s just a question of joining up the dots!


1 NICE recently published draft guidance in May 2023 recommending genotype testing for those patients considered for clopidogrel following a TIA or stroke. The guidance is expected to be published following consultation at the end of the year.