The Next Step in Psoriasis Care: Predicting Biologic Response

In this episode of Topical Conversations, Tina Bhutani, MD, and Adrian Rodriguez, MD, discuss the growing role of precision medicine in psoriasis management and how predictive testing may help guide biologic selection earlier in the treatment journey.  

With an expanding range of biologic therapies available for psoriasis, clinicians now have more therapeutic flexibility than ever before. However, as Dr Bhutani notes, one of the persistent challenges in practice is determining which patient is most likely to respond to which therapy before treatment is initiated.  

The discussion centers on Mind.Px, a predictive test that uses a dermal biomarker patch to help identify the likelihood of response to biologic classes including TNF inhibitors, IL-17 inhibitors, and IL-23 inhibitors.  

Moving toward precision medicine in psoriasis

Dr Rodriguez frames Mind.Px within the broader evolution of precision medicine across medicine, noting that while other specialties have increasingly incorporated predictive tools into treatment decision-making, dermatology has historically lacked similar guidance for psoriasis biologic selection.  

He describes the test as a practical tool to help clinicians identify which biologic class may be the best fit for an individual patient, potentially reducing the need for therapeutic switching and minimizing delays associated with insurance approvals and treatment interruptions.  

They emphasize that this type of approach may be particularly valuable given the growing number of available biologics and the reality that not every therapy will be effective for every patient.

Supporting patient confidence and treatment buy-in

Beyond biologic selection itself, the conversation highlights how predictive testing may influence patient engagement and confidence in treatment decisions.

Dr Bhutani shares that she often finds the test useful in patients who are hesitant to initiate biologic therapy, including individuals with needlephobia or concerns about long-term treatment commitment. Presenting patients with data suggesting a higher likelihood of response may help reinforce confidence in the treatment plan.  

Dr Rodriguez discusses a patient case involving a needlephobic frequent traveler who was concerned about treatment selection and dosing logistics. In that scenario, the test supported selection of an IL-23 inhibitor, which aligned both with predicted response and the patient’s lifestyle needs due to its dosing schedule.  

The speakers also note that the report format itself is intentionally straightforward, categorizing biologic classes as likely responder (R) or nonresponder (NR), making interpretation relatively simple for both clinicians and patients.  

Applications in biologic-experienced patients

The conversation also explores how predictive testing may be useful in biologic-experienced patients, particularly in situations involving loss of coverage, waning efficacy, or declining patient confidence after prior treatment challenges.  

Dr Rodriguez notes that the test can help rebuild patient confidence when prior therapeutic choices have not produced satisfactory outcomes. Similarly, Dr Bhutani explains that she often finds the test especially valuable in experienced patients because she wants to avoid another unsuccessful treatment selection that could further reduce patient engagement.  

Operational and access considerations

The discussion next shifts to practical considerations surrounding biologic access and office workflow.

Dr Rodriguez highlights the administrative burden associated with biologic switching, including repeat prior authorizations, reassessments, and additional staff workload. Selecting the most appropriate therapy earlier in the process may help reduce some of these operational challenges while also potentially lowering costs associated with cycling through multiple biologics. 

From a workflow standpoint, Dr Rodriguez notes that implementation in clinic has been relatively streamlined. The test involves application of a dermal patch for several minutes, with a kit provided for return shipment and relatively quick turnaround times.  

Limitations and future directions

The speakers also discuss several limitations of the current test.

One limitation is the binary responder/nonresponder output. If a patient is predicted to respond to multiple biologic classes, the report does not currently rank therapies or indicate which option may provide the strongest response.  

Dr Rodriguez also notes that current response categorizations were based on PASI75 thresholds, whereas many clinicians now aim for PASI90-level outcomes in practice. He points to ongoing work evaluating PASI90 data as an important future development.  

Dr Bhutani further explains that earlier studies suggested stronger predictive performance in patients with more severe disease, although ongoing real-world analyses are evaluating performance in patients with lower disease severity. This may become increasingly relevant as dermatologists consider earlier biologic intervention for patients with lower BSA involvement but high-impact disease in special sites such as the scalp, hands, feet, and genital regions.  

Key takeaways

• Predictive testing may help support biologic selection in psoriasis by identifying likely responders and nonresponders across biologic classes  
• Mind.Px uses a dermal biomarker patch and provides a simplified responder/nonresponder report format for TNF, IL-17, and IL-23 inhibitor categories  
• There are potential benefits for both biologic-naïve and biologic-experienced patients, including improved patient confidence and reduced therapeutic switching  
• Practical advantages may include reduced administrative burden associated with repeated prior authorizations and treatment changes  
• Current limitations include lack of ranked therapeutic recommendations and reliance on PASI75-based response thresholds  
• Ongoing research is evaluating predictive performance in lower-severity psoriasis and incorporating PASI90-level outcomes into future analyses