Overall and inter-individual effect of four different drug classes on soluble urokinase plasminogen activator receptor in type 1 and type 2 diabetes.

AIM: To evaluate the effect of four different drug classes on soluble urokinase plasminogen activator receptor (suPAR), a biomarker active in multiple inflammatory processes and a risk factor for complications, in people with type 1 and type 2 diabetes. METHODS: We conducted post hoc analyses of a randomized, open-label, crossover trial including 26 adults with type 1 and 40 with type 2 diabetes with urinary albumin-creatinine ratio ≥30 and ≤500 mg/g assigned to 4-week treatments with telmisartan 80 mg, empagliflozin 10 mg, linagliptin 5 mg and baricitinib 2 mg, separated by 4-week washouts. Plasma suPAR was measured before and after each treatment. SuPAR change after each treatment was calculated and, for each individual, the best suPAR-reducing drug was identified. Subsequently, the effect of the best individual drug was compared against the mean of the other three drugs. Repeated-measures linear mixed-effects models were employed. RESULTS: The baseline median (interquartile range) plasma suPAR was 3.5 (2.9, 4.3) ng/mL. No overall effect on suPAR levels was observed for any one drug. The individual best-performing drug varied, with baricitinib being selected for 20 participants (30%), followed by empagliflozin for 19 (29%), linagliptin for 16 (24%) and telmisartan for 11 (17%). The individual best-performing drug reduced suPAR by 13.3% (95% confidence interval [CI] 3.7, 22.8; P = 0.007). The difference in suPAR response between the individual best-performing drug and the other three was -19.7% (95% CI -23.1, -16.3; P < 0.001). CONCLUSIONS: We demonstrated no overall effect of 4-week treatment with telmisartan, empagliflozin, linagliptin or baricitinib on suPAR. However, individualization of treatment might significantly reduce suPAR levels.

Optimization of Albuminuria-Lowering Treatment in Diabetes by Crossover Rotation to Four Different Drug Classes: A Randomized Crossover Trial.

OBJECTIVE: Renin-angiotensin system (RAS) inhibitors decrease the urinary albumin to creatinine ratio (UACR) but are ineffective in up to 40% of patients. We hypothesized that rotation through different drug classes overcomes RAS inhibitor resistance and tested this in a randomized crossover trial. RESEARCH DESIGN AND METHODS: We assigned 26 adults with type 1 diabetes and 37 with type 2 diabetes and UACR between 30 and 500 mg/g and estimated glomerular filtration rate >45 mL/min/1.73 m2 to 4-week treatment periods with telmisartan 80 mg, empagliflozin 10 mg, linagliptin 5 mg, and baricitinib 2 mg in random order, separated by 4-week washout periods. Each participant was then re-exposed for 4 weeks to the drug that induced that individual's largest UACR reduction. Primary outcome was the difference in UACR response to the best-performing drug during the confirmation period versus UACR response to the other three drugs. RESULTS: There was substantial variation in the best-performing drug. Telmisartan was best performing for 33 participants (52%), empagliflozin and linagliptin in 11 (17%), and baricitinib in 8 participants (13%). The individuals' best-performing drug changed UACR from baseline during the first and confirmatory exposures by a mean of -39.6% (95% CI -44.8, -33.8; P < 0.001) and -22.4% (95% CI -29.7, -12.5; P < 0.001), respectively. The Pearson correlation for first versus confirmatory exposure was 0.39 (P = 0.017). The mean change in UACR with the other three drugs was +1.6% (95% CI -4.3%, 8.0%; P = 0.593 versus baseline; difference versus individuals' best-performing drug at confirmation, 30.9% [95% CI 18.0, 45.3]; P < 0.001). CONCLUSIONS: We demonstrated a large and reproducible variation in participants' responses to different UACR-lowering drug classes. These data support systematic rotation through different drug classes to overcome therapy resistance to RAS inhibition.

Exposure-response relationships for the sodium-glucose co-transporter-2 inhibitor dapagliflozin with regard to renal risk markers.

AIMS: To quantitate the consistency of an individual's plasma exposure to dapagliflozin upon re-exposure, and to investigate whether the individual's systemic exposure to dapagliflozin explains inter-individual variation in response to dapagliflozin with regard to multiple renal risk markers. METHODS: Data were used from a crossover randomized clinical trial that assessed the albuminuria-lowering effect of dapagliflozin in 33 people with type 2 diabetes and elevated albuminuria. Fifteen participants were exposed twice to dapagliflozin. Trough plasma concentrations of dapagliflozin were measured for each participant at steady state. Dapagliflozin plasma concentrations were measured by liquid chromatography tandem mass spectrometry, and pharmacokinetic characteristics were simulated based on a population pharmacokinetic model. Linear mixed-effects models were used to quantify the exposure-response relationships. RESULTS: The median plasma concentration after first and second exposure to dapagliflozin was 5.3 ng/mL vs 4.6 ng/mL, respectively (P = 0.78). Lin's concordance correlation coefficient between occasions was 0.73 (P < 0.0021). Every 100 ng.h/mL increment in area under the dapagliflozin plasma concentration curve was associated with a decrease in log-transformed urinary albumin:creatinine ratio (ß = -5.9, P < 0.01), body weight (ß = -0.3, P < 0.01) and estimated glomerular filtration rate (ß = -0.7, P = 0.01) and an increase in urinary glucose excretion (ß = 17.0, P < 0.001). CONCLUSION: An individual's exposure to dapagliflozin is consistent upon re-exposure and correlates with pharmacodynamic response in renal risk markers.

Association between individual cholesterol and proteinuria response and exposure to atorvastatin or rosuvastatin.

AIM: The PLANET trials showed that atorvastatin 80 mg but not rosuvastatin at either 10 or 40 mg reduced urinary protein to creatinine ratio (UPCR) at similar effects on LDL-cholesterol. However, individual changes in both UPCR and LDL-cholesterol during treatment with these statins varied widely between patients. This inter-individual variability could not be explained by patients' physical or biochemical characteristics. We assessed whether the plasma concentrations of both statins were associated with LDL-cholesterol and UPCR response. MATERIALS AND METHODS: The PLANET trials randomized patients with a UPCR of 500-5000 mg/g and fasting LDL-cholesterol >2.33 mmol/L to a 52-week treatment with atorvastatin 80 mg, rosuvastatin 10 mg or 40 mg. For the current analysis, patients with available samples at week 52 and treatment compliance >80% by pill count were included (N = 295). The main outcome measurements were percentage change in UPCR and absolute change in LDL-cholesterol (delta LDL) from baseline to week 52. RESULTS: Median (interquartile range) plasma concentration at week 52 for atorvastatin 80 mg was 3.9 ng/mL (IQR: 2.1 to 8.7), for rosuvastatin 10 mg 1.0 ng/mL (IQR: 0.7 to 2.0) and for rosuvastatin 40 mg 3.5 ng/mL (IQR: 2.0 to 6.8). Higher plasma concentration of statin was associated with larger LDL-cholesterol reductions at week 52 [rosuvastatin r = -0.40 (P < .001); atorvastatin r = -0.28 (P = .006)]. The plasma concentration of both statins did not correlate with UPCR change [rosuvastatin r = 0.07 (P = .30); atorvastatin r = 0.16 (P = .13)]. CONCLUSIONS: Individual variation in plasma concentrations of rosuvastatin and atorvastatin was associated with LDL-cholesterol changes in patients. The individual variation in UPCR change was not associated with the plasma concentration of both statins.