Conversations on genomics: How genomics could transform prescribing

Genomics, the study of DNA and RNA and how genetic variation influences health and disease, can help explain why a medicine works, has little effect, or causes harmful side effects. Despite this, its role in prescribing remains poorly understood across health and care.

In the second of four conversations, Health Innovation Network South London Medical Director Dr Natasha Curran asks Professor Sean Whittaker, Medical Director of the South East Genomic Medicine Service, to examine how the national genomic medicine service was built, what implementation challenges remain and what success could look like over the next five years.

Natasha: What is pharmacogenomics?

Sean: Pharmacogenomics looks at how genetic variation influences a person’s response to medicines. Sometimes it helps us predict toxicity and adverse effects. Perhaps even more importantly, it can help us predict whether a medicine is likely to work in the first place.

Natasha: Are there examples of this already being used in practice?

Sean: A well-known example is clopidogrel. Around one in four patients carry a genetic variant that makes the drug less effective. Following a thrombotic stroke, those patients may derive less benefit from clopidogrel and may be better suited to an alternative such as aspirin. The only way to identify this group is through genetic testing.

Natasha: Could pharmacogenomics become a routine part of prescribing?

Sean: I think it is inevitable, but the challenge is complexity. Some prescribing decisions are relatively straightforward because they are linked to a single gene variant. Many medicines, however, are affected by multiple genes, environmental factors and individual patient characteristics. Understanding all of those relationships requires very large studies and significant amounts of research data.

Natasha: That takes me back to something I expected when I first trained as a doctor. Early in my career as a pain specialist, I imagined a future in which a patient could provide a cheek swab or DNA sample, and we could immediately identify the analgesic likely to provide the greatest benefit with the fewest side effects. Rather than relying on the familiar process of trial and error, we would tailor treatment from the beginning. Why aren’t we there yet?

Sean: The answer is probably complexity. Unlike clopidogrel, where there is a relatively simple genetic relationship, pain pathways involve multiple receptors and biological systems. The genetics are likely to be polygenic rather than driven by a single variant, which makes the research considerably more challenging.

Natasha: Are there other areas where pharmacogenomics could make a real difference?

Sean: Absolutely. Antidepressants are one important example. Evidence is emerging that genetic information can help predict which patients are more likely to benefit from particular treatments.  Pharmacogenomics can also improve equity of access to medicines. Clozapine is one example: genetic information can help distinguish patients who have a low neutrophil count due to a side effect of clozapine treatment from those who have a low neutrophil count as a result of a benign genetic cause. Previously, those patients may have been excluded from taking clozapine because this distinction could not be made.

Natasha: Beyond prescribing, how might genomics support prevention?

Sean: It may help us identify people at increased risk of common conditions such as diabetes and cardiovascular disease. Rather than relying only on traditional risk factors, future assessments may combine clinical information with genomic data to create a more sophisticated picture of someone’s overall risk.

Natasha: What is the main challenge with using genomic information in that way?

Sean: The biggest challenge is not detecting risk; it is knowing what to do with the information. There is little value in identifying an increased genetic risk if we cannot meaningfully reduce it. Any population screening programme must demonstrate that knowing someone’s risk leads to interventions that genuinely improve outcomes.

Genetics is only one part of the picture. Behaviour, lifestyle, environment and the wider social determinants of health remain hugely important.

Natasha: AI and genomics are increasingly discussed together. Why is that?

Sean: AI is already helping specialists interpret the enormous amount of data generated by genomic testing. Modern laboratory systems can process vast numbers of samples, with robots preparing the DNA for sequencing, but the real challenge comes at the interpretation stage.

AI tools can help identify which genetic variants are likely to be clinically significant and which represent normal background variation. That dramatically reduces the amount of manual analysis required, although expert human oversight remains essential. The final clinical interpretation still needs to be reviewed and signed off by experienced clinical scientists and health professionals.

Natasha: One of the most intriguing things about pharmacogenomics is that it challenges one of medicine’s oldest traditions: prescribing first and learning later. We may not yet be at the point where every patient receives a personalised genetic prescribing profile, but from stroke prevention to antidepressants, and potentially even pain management, the direction of travel is clear. The question is no longer whether genomics will influence prescribing, but how quickly that influence will become part of everyday clinical practice.

The next conversation moves from those future possibilities to two areas where genomics is already changing care. In the third blog, Natasha and Sean look at its use in cancer and rare disease.