Oxypurinol Formulation May Be Adapted for Gout Treatment

XORTX has announced that it will have a Type B meeting with the FDA to review chemistry, manufacturing, pharmacology, toxicology and clinical evidence regarding its XRx-026 program for the treatment of gout.1

The company’s XRx-026 program is developing a formulation of oxypurinol termed XORLO. The meeting will assess the therapy’s readiness for a new drug application (NDA).

“We look forward to FDA feedback the last week in April and advancing the XRx-026 program, thereafter. Many key elements of the XRx-026 program have advanced sufficiently to warrant this robust program review with the FDA to define any additional information needed to complete this marketing approval. We believe that the XRx-026 program provides a much needed therapeutic option for individuals with gout and that advancing with the XRx-026 program will transform XORTX to a revenue positive state,” Allen Davidoff, CEO of XORTX, said in a statement.1

Other recent research into gout found that an interpretable machine learning (ML) model was feasible for predicting gout and may establish a foundation for future applications in supporting gout diagnosis.2

“The integration of ultrasound data into ML algorithms could provide a more comprehensive approach to gout diagnosis, combining the advantages of both imaging diagnostics and artificial intelligence. Furthermore, while existing ML models for gout prediction have shown promise, their clinical applicability has been limited by a lack of interpretability concerning their decision-making processes,” lead investigator Lishan Xiao, Department of Ultrasound, the Affiliated Hospital of Qingdao University, Qingdao, China, and colleagues wrote.

The investigators included data from 609 patients’ first metatarsophalangeal (MTP1) joint ultrasound from 2 institutions. Institution 1 data (n = 571) were split into training cohort (TC) and internal testing cohort (ITC) in an 8:2 ratio, and Institution 2 data (n = 92) served as an external testing cohort (ETC). Key predictors were selected using Random Forest (RF), Least Absolute Shrinkage and Selection Operator (LASSO), and Extreme Gradient Boosting (XGBoost) algorithms. Xiao and colleagues evaluated 6 ML models using standard performance metrics, with SHapley Additive exPlanations (SHAP) analysis for model interpretation.2

Xiao and colleagues identified 5 key predictors of gout: serum uric acid (SUA), deep learning (DL) model predictions, tophus, bone erosion, and double contour sign (DCs). They achieved optimal predictive performance using a logistic regression (LR) model which had an Area Under the Curve (AUC) of 0.870 (95% CI, 0.820–0.920) in ITC and 0.854 (95% CI, 0.804–0.904) in ETC. The model had good calibration, with Brier scores of 0.138 in ITC and 0.159 in ETC.2

“This study developed an interpretable ML model for gout prediction and utilized SHAP to elucidate feature contributions, establishing a foundation for future applications in clinical decision support for gout diagnosis,” Xiao and colleagues wrote.2

REFERENCES

1. XORTX Announces Update for Discussion with the FDA. News release. XORTX. March 19, 2025. https://www.globenewswire.com/news-release/2025/03/19/3045235/0/en/XORTX-Announces-Update-for-Discussion-with-the-FDA.html

2. Xiao L, Zhao Y, LI Y, et al. Developing an interpretable machine learning model for diagnosing gout using clinical and ultrasound features. Eur. J. Radiol. 2025: 184(111959) doi: 10.1016/j.ejrad.2025.111959