(T-073) A PK/PD Model to Evaluate Uricosuric Clinical Efficacy versus Therapeutic Window for Treatment of Gout

Julia Schulz Pauly – Amgen Inc., South San Francisco, CA, USA; Cindy Yanfei Li – Amgen Inc., South San Francisco, CA, USA; Afroz Mohammad – Amgen Inc., South San Francisco, CA, USA; Cody Peer – Amgen Inc., Rockville, MD, USA; Yang Song – Amgen Inc., South San Francisco, CA, USA; Vijay Upreti – Amgen Inc., South San Francisco, CA, USA; M. Scott Bowers – Amgen Inc., Deerfield, IL, USA; Annie Lumen – Amgen Inc., South San Francisco, CA, USA

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Objectives: Gout is a common form of inflammatory arthritis often caused by limited renal uric acid excretion resulting in high serum uric acid and uric acid crystallization in distal joints and other soft tissues [1]. Uricosuric agents are a promising treatment option for gout patients due to their ability to decrease serum uric acid levels by increasing renal elimination. However, this drug class may increase risk for uric acid kidney stone formation. Previous uricosuric models describe relative changes in serum and urine uric acid and thus cannot capture kidney stone risk associated with increases in the absolute urine uric acid levels [2, 3]. Here, we apply a dynamic platform model to describe changes in absolute uric acid levels following treatment of gout patients with uricosurics to predict the therapeutic window.

Methods: A dynamic model of uric acid homeostasis was developed based on published models [2, 3]. Lesinurad, an FDA approved uricosuric with publicly available clinical data, was selected as a case study to assess efficacy and safety for a range of doses (50-600 mg) [2, 4]. Lesinurad was considered efficacious if serum uric acid concentrations decreased below 6 mg/dL. The on-target risk of urinary uric acid precipitation was assessed with the modeled urine concentration of uric acid and established pH-dependent solubility limits [5, 6]. Local sensitivity analysis was conducted to identify factors that affect key PD endpoints, including fraction excreted, serum and urine uric acid concentration.

Results: The model was verified using clinically observed Lesinurad PK/PD data in healthy volunteers and gout patients [2, 4]. Applying the model predicted absolute serum and urine uric acid concentrations with PD thresholds outlined in methods, we were able to predict and confirm the narrow therapeutic index of Lesinurad ( < 2-fold) [2, 4]. Findings from sensitivity analysis (normalized sensitivity coefficient ≥0.2) identified drug potency (Imax, IC50) as the major driver of drug effect. PK parameters, like clearance or volume of distribution, had greater impact on the duration than the maximal extent of PD effect.

Conclusions: We developed and verified a dynamic platform uricosuric model using Lesinurad as a case study. We demonstrated that modeling changes in absolute serum and urine uric acid concentrations rather than relative changes is more interpretable for clinical efficacy and safety assessment of gout treatments. We identified drug-related parameters that are key determinants of clinical efficacy and safety assessment. Future work could extend these findings to enable a discovery-to-clinic platform tool for other gout treatments. Such modeling efforts can also guide a priori experimental design of preclinical and clinical studies by identifying optimal sampling timepoints and dosing regimens and may aid patient stratification. Together this work describes a consolidated Model Informed Drug Development (MIDD) workflow spanning all stages of development, from discovery through translation into the clinic, potentially bringing therapeutics to patients faster.

Citations: 1. Fleischmann, R., et al., Pharmacodynamic, pharmacokinetic and tolerability evaluation of concomitant administration of lesinurad and febuxostat in gout patients with hyperuricaemia. Rheumatology (Oxford), 2014. 53(12): p. 2167-74.
2. Aksenov, S., et al., Individualized treatment strategies for hyperuricemia informed by a semi-mechanistic exposure-response model of uric acid dynamics. Physiol Rep, 2018. 6(5).
3. Leander, J., et al., A semi-mechanistic exposure-response model to assess the effects of verinurad, a potent URAT1 inhibitor, on serum and urine uric acid in patients with hyperuricemia-associated diseases. J Pharmacokinet Pharmacodyn, 2021. 48(4): p. 525-541.
4. FDA, Clinical Pharmacology Review - Zurampic (Lesinurad). 2014.
5. Wiederkehr, M.R. and O.W. Moe, Uric Acid Nephrolithiasis: A Systemic Metabolic Disorder. Clin Rev Bone Miner Metab, 2011. 9(3-4): p. 207-217.
6. Iwata, H., et al., Solubility of uric acid and supersaturation of monosodium urate: why is uric acid so highly soluble in urine? J Urol, 1989. 142(4): p. 1095-8.

Keywords: Mechanistic PKPD Modeling, Gout, Lesinurad