Regulatory guidelines do not accurately predict tolvaptan and metabolite interactions at BCRP, OATP1B1, and OAT3 transporters

Pharmacologic Management of Autosomal Dominant Polycystic Kidney Disease

Autosomal dominant polycystic kidney disease (ADPKD) is the most common genetic kidney disorder and the fourth leading cause of end-stage kidney disease. ADPKD encompasses a wide range of morbidity in addition to chronic kidney disease and end-stage kidney disease, and its pathogenesis remains incompletely understood. Progress in the management of this condition includes the 2018 FDA approval of tolvaptan as the only mechanism-specific treatment available for individuals at risk of rapid progression. Assessing the risk of rapid progression is discussed at greater length in a separate article in this special issue. This section will address use and prescription of tolvaptan in more detail and address other therapies that may be considered in the treatment of patients with ADPKD.

Reimagining the Framework Supporting the Static Analysis of Transporter Drug Interaction Risk; Integrated Use of Biomarkers to Generate Pan‐Transporter Inhibition Signatures

Solute carrier (SLC) transporters present as the loci of important drug-drug interactions (DDIs). Therefore, sponsors generate in vitro half-maximal inhibitory concentration (IC₅₀ ) data and apply regulatory agency-guided "static" methods to assess DDI risk and the need for a formal clinical DDI study. Because such methods are conservative and high false-positive rates are likely (e.g., DDI study triggered when liver SLC R value ≥ 1.04 and renal SLC maximal unbound plasma (C_max,u )/IC₅₀ ratio ≥ 0.02), investigators have attempted to deploy plasma- and urine-based SLC biomarkers in phase I studies to de-risk DDI and obviate the need for drug probe-based studies. In this regard, it was possible to generate in-house in vitro SLC IC₅₀ data for various clinically (biomarker)-qualified perpetrator drugs, under standard assay conditions, and then estimate "% inhibition" for each SLC and relate it empirically to published clinical biomarker data (area under the plasma concentration vs. time curve (AUC) ratio (AUCR, AUC_inhibitor /AUC_reference ) and % decrease in renal clearance (ΔCL_renal )). After such a "calibration" exercise, it was determined that only compounds with high R values (> 1.5) and C_max,u /IC₅₀ ratios (> 0.5) are likely to significantly modulate liver (AUCR > 1.25) and renal (ΔCL_renal  > 25%) biomarkers and evoke DDI risk. The % inhibition approach supports integration of liver and renal SLC data and allows one to generate pan-SLC inhibition signatures for different test perpetrators (e.g., SLC % inhibition ranking). In turn, such signatures can guide the selection of the most appropriate individual (or combinations of) biomarkers for testing in phase I studies.