Editorial theme | Precision versus personalized medicine in psoriasis and PsA clinical practice

*Data from Zarika, et al.¹

Why is precision medicine necessary?

Treating psoriasis without a precision medicine approach can lead to trial-and-error, with multiple follow-up visits necessary to ensure treatment efficacy.¹ Response rates seen in clinical trials may not be reflected in the real world, meaning treatment switching is needed for 10–75% of patients within the first year.¹ A prospective study showed that within the first year of treatment with a biologic, 23% of patients discontinued due to a lack of efficacy.⁴ Another study revealed that ~87% of treatment switching occurred within the first two years of treatment.⁵ Using a precision medicine approach could reduce treatment switching or discontinuation by identifying the optimal treatment, leading to quicker disease clearance.¹ Additionally, it could avoid the possible adverse effects of multiple medications and reduce the financial burden on patients and the healthcare system.¹

Current perspectives

Treat-to-target in PsA⁶

Approximately 30–40% of patients with PsA will not reach an adequate treatment response. Currently, there are no biomarkers that can predict treatment response in PsA. The suggested strategy is to use a treat-to-target approach to treat the prevalent domains. Noviani et al.⁶ summarized existing published trials of efficacies of different classes of therapeutic agents for various PsA domains, which can act as a guide for treatment selection (Table 1). Ideally, treatments should be individualized based on the most affected domains for each patient. However, treatment selection is unlikely to be  straightforward in real-world scenarios, and there is a lack of comparative studies of treatments in patients with different affected domains. Therefore, each patient should be considered on an individual basis. In addition, the management of PsA will be greatly enhanced by the availability biomarkers that could predict responses to therapies.

footer

 Biomarkers and genetics³

There have been many studies into predictive biomarkers, yet none of these have made it to clinical practice yet. Associations between drug responses and genetic polymorphisms differ between populations. Therefore, there may be population-specific genetic biomarkers that could predict drug responses. The TNF-α and TNFRSF1B genes have been widely studied, with results suggesting that some polymorphisms in these genes have been associated with a better response to anti-TNF-α drugs. In particular, the HLA-Cw*06 allele has been investigated for an effect on response to adalimumab, infliximab, and etanercept. A study by Coto-Segura⁷ showed that HLA-Cw*06 positive patients had an improved response to adalimumab compared to those without the allele. However, in a study by Gallo et al.,⁸ it was found that HLA-Cw*06 positive patients were less responsive to treatment with adalimumab, infliximab, and etanercept.

The effect of HLA-Cw*06 on the ustekinumab response is more consistent. Multiple studies have suggested that patients who are positive for the allele achieve higher response rates than those who are negative. However, ustekinumab generally has a high response rate regardless of the presence of the allele, which may reduce the usefulness of HLA-Cw*06 as a biomarker.

Future perspectives

Guidelines¹

There is a lack of guidance available on which biomarkers can be used to help clinicians decide on an appropriate biologic for each patient. Therefore, for the long-term feasibility of precision medicine, additional guidelines on how genetics and biomarkers can be used to manage psoriasis and PsA are needed.

Conclusion

Precision medicine is an evolving field that will allow for continuous improvement in the effectiveness of patient management. Utilizing biomarkers and genetics can assist in the selection of appropriate biologics for each patient, reducing the need for treatment switching. By moving from a trial-and-error approach to precision medicine, patients will experience quicker clinical improvement with reduced adverse events, ultimately improving patient quality of life.