A Phase 3, Randomized, Open-label, Noninferiority Trial Evaluating Anti-Rabies Monoclonal Antibody Cocktail (TwinrabTM) Against Human Rabies Immunoglobulin (HRIG).

BACKGROUND: Limited supply, cost and potential for severe adverse effects observed with the blood derived rabies immunoglobulin products has led to search for alternative therapies. This issue has been addressed by developing an anti-rabies monoclonal antibody cocktail. METHODS: This is a phase 3, randomized, open-label, noninferiority trial conducted in patients with World Health Organization (WHO) category III exposure with suspected rabid animal. Eligible patients were assigned to either the test arm, TwinrabTM (docaravimab and miromavimab) or the reference arm, human rabies immunoglobulin (HRIG; Imogam® Rabies-HT), in a ratio of 1:1. The primary endpoint was the comparison of responder rates between the 2 arms assessed as percentage of those with rabies virus neutralizing antibodies titers ≥0.5 IU/mL on day 14. RESULTS: A total of 308 patients were equally randomized into the 2 arms. In the per-protocol (PP) population, there were 90.21% responders in the TwinrabTM arm and 94.37% in the HRIG arm. The geometric mean of rapid fluorescent foci inhibition test titers in the PP on day 14 were 4.38 and 4.85 IU/mL, for the TwinrabTM and HRIG arms, respectively. There were no deaths or serious adverse events reported. CONCLUSIONS: This study confirmed that TwinrabTM is noninferior to HRIG in terms of providing an unbroken window of protection up to day 84. This trial in healthy adults with WHO category III exposure from suspected rabid animal also establishes the safety of TwinrabTM in patients with 1 WHO approved vaccine regimen (Essen). CLINICAL TRIALS REGISTRATION: CTRI/2017/07/009038.

Outcomes of Desidustat Treatment in People with Anemia and Chronic Kidney Disease: A Phase 2 Study.

BACKGROUND: Desidustat (ZYAN1) is an oral hypoxia-inducible factor prolyl hydroxylase inhibitor (HIF-PHI) that stimulates erythropoiesis. Stabilizing HIF via PHI is developing as a new therapeutic approach to treat anemia secondary to chronic kidney disease (CKD). This trial evaluated the safety, tolerability, and efficacy of Desidustat in adult CKD patients with anemia, who were not on dialysis. METHODS: This was a Phase 2, randomized, double-blind, 6-week, placebo-controlled, dose-ranging, safety and efficacy study. A total of 117 eligible patients were randomized to 4 arms: 100, 150, 200 mg, or placebo. The investigational product was administered every alternate day for 6 weeks in fasting conditions. The primary endpoint was change in hemoglobin (Hb) from baseline to week 6. RESULTS: Baseline demographics were well balanced among all the treatment arms. In the modified intent-to-treat (mITT) population, a mean Hb increase of 1.57, 2.22, and 2.92 g/dL in Desidustat 100, 150, and 200 mg arms, respectively, was observed post 6 weeks treatment. The responder rate (≥1 g/dL increase) was 66% in 100 mg, 75% in 150 mg, and 83% in 200 mg treatment arms, in the mITT population. Eighteen patients had at least one treatment emergent adverse event (TEAE), and 5 patients reported at least one drug-related mild TEAE. No death or serious adverse event was reported during the trial. CONCLUSION: There was dose-related increase in Hb across all doses compared to placebo in mITT and per-protocol populations. Desidustat also increased pharmacokinetic parameters Cmax and AUC in dose-related manner. There was no significant change in vital signs, electrocardiographic parameters, or safety laboratory values. Clinical Trial Registration Number CTRI/2017/05/008534 (registered on May 11, 2017).

Limited Sampling Strategy for Accurate Prediction of Pharmacokinetics of Saroglitazar: A 3-point Linear Regression Model Development and Successful Prediction of Human Exposure.

PURPOSE: Our aim was to develop and validate the extrapolative performance of a regression model using a limited sampling strategy for accurate estimation of the area under the plasma concentration versus time curve for saroglitazar. METHODS: Healthy subject pharmacokinetic data from a well-powered food-effect study (fasted vs fed treatments; n = 50) was used in this work. The first 25 subjects' serial plasma concentration data up to 72 hours and corresponding AUC0-t (ie, 72 hours) from the fasting group comprised a training dataset to develop the limited sampling model. The internal datasets for prediction included the remaining 25 subjects from the fasting group and all 50 subjects from the fed condition of the same study. The external datasets included pharmacokinetic data for saroglitazar from previous single-dose clinical studies. Limited sampling models were composed of 1-, 2-, and 3-concentration-time points' correlation with AUC0-t of saroglitazar. Only models with regression coefficients (R2) >0.90 were screened for further evaluation. The best R2 model was validated for its utility based on mean prediction error, mean absolute prediction error, and root mean square error. Both correlations between predicted and observed AUC0-t of saroglitazar and verification of precision and bias using Bland-Altman plot were carried out. FINDINGS: None of the evaluated 1- and 2-concentration-time points models achieved R2 > 0.90. Among the various 3-concentration-time points models, only 4 equations passed the predefined criterion of R2 > 0.90. Limited sampling models with time points 0.5, 2, and 8 hours (R2 = 0.9323) and 0.75, 2, and 8 hours (R2 = 0.9375) were validated. Mean prediction error, mean absolute prediction error, and root mean square error were <30% (predefined criterion) and correlation (r) was at least 0.7950 for the consolidated internal and external datasets of 102 healthy subjects for the AUC0-t prediction of saroglitazar. The same models, when applied to the AUC0-t prediction of saroglitazar sulfoxide, showed mean prediction error, mean absolute prediction error, and root mean square error <30% and correlation (r) was at least 0.9339 in the same pool of healthy subjects. IMPLICATIONS: A 3-concentration-time points limited sampling model predicts the exposure of saroglitazar (ie, AUC0-t) within predefined acceptable bias and imprecision limit. Same model was also used to predict AUC0-∞. The same limited sampling model was found to predict the exposure of saroglitazar sulfoxide within predefined criteria. This model can find utility during late-phase clinical development of saroglitazar in the patient population.