Safety and efficacy of ivacaftor in infants aged 1 to less than 4 months with cystic fibrosis.

BACKGROUND: Ivacaftor (IVA) has been shown to be safe and efficacious in children aged ≥4 months with cystic fibrosis (CF) and CFTR gating variants. We evaluated safety, pharmacokinetics (PK), and efficacy of IVA in a small cohort of infants aged 1 to <4 months with CF. METHODS: In this phase 3, open-label study, infants 1 to <4 months with CF and an IVA-responsive CFTR variant received an initial low dose of IVA based on age and weight. Because IVA is a sensitive CYP3A substrate and CYP3A maturation is uncertain in infants, doses were adjusted at day 15 to better match median adult exposures based on individual PK measurements taken on day 4. Primary endpoints were safety and PK measurements. RESULTS: Seven infants (residual function CFTR variants [n=5]; minimal function CFTR variants [n=2]) received ≥1 dose of IVA. Six infants had doses adjusted at day 15 and one infant did not require dose adjustment; subsequent PK analyses showed mean trough concentrations for IVA and metabolites were within range of prior clinical experience. Four infants (57.1%) had adverse events (AEs); no serious AEs were noted. One infant discontinued study drug due to a non-serious AE of elevated alanine aminotransferase >8x the upper limit of normal. Mean sweat chloride concentration decreased (-40.3 mmol/L [SD: 29.2]) through week 24. Improvements in biomarkers of pancreatic function and intestinal inflammation, as well as growth parameters, were observed. CONCLUSIONS: In this small, open-label study, IVA dosing in infants achieved exposures previously shown to be safe and efficacious. Because PK was predictable, a dosing regimen based on age and weight is proposed. IVA was generally safe and well tolerated, and led to improvements in CFTR function, markers of pancreatic function and intestinal inflammation, and growth parameters, supporting use in infants as young as 1 month of age.

Application of machine learning to predict reduction in total PANSS score and enrich enrollment in schizophrenia clinical trials.

Clinical trial efficiency, defined as facilitating patient enrollment, and reducing the time to reach safety and efficacy decision points, is a critical driving factor for making improvements in therapeutic development. The present work evaluated a machine learning (ML) approach to improve phase II or proof-of-concept trials designed to address unmet medical needs in treating schizophrenia. Diagnostic data from the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) trial were used to develop a binary classification ML model predicting individual patient response as either "improvement," defined as greater than 20% reduction in total Positive and Negative Syndrome Scale (PANSS) score, or "no improvement," defined as an inadequate treatment response (<20% reduction in total PANSS). A random forest algorithm performed best relative to other tree-based approaches in model ability to classify patients after 6 months of treatment. Although model ability to identify true positives, a measure of model sensitivity, was poor (<0.2), its specificity, true negative rate, was high (0.948). A second model, adapted from the first, was subsequently applied as a proof-of-concept for the ML approach to supplement trial enrollment by identifying patients not expected to improve based on their baseline diagnostic scores. In three virtual trials applying this screening approach, the percentage of patients predicted to improve ranged from 46% to 48%, consistently approximately double the CATIE response rate of 22%. These results show the promising application of ML to improve clinical trial efficiency and, as such, ML models merit further consideration and development.

Prediction of Nephropathy in Type 2 Diabetes: An Analysis of the ACCORD Trial Applying Machine Learning Techniques.

Applying data mining and machine learning (ML) techniques to clinical data might identify predictive biomarkers for diabetic nephropathy (DN), a common complication of type 2 diabetes mellitus (T2DM). A retrospective analysis of the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial was intended to identify such factors using ML. The longitudinal data were stratified by time after patient enrollment to differentiate early and late predictors. Our results showed that Random Forest and Simple Logistic Regression methods exhibited the best performance among the evaluated algorithms. Baseline values for glomerular filtration rate (GFR), urinary creatinine, urinary albumin, potassium, cholesterol, low-density lipoprotein, and urinary albumin to creatinine ratio were identified as DN predictors. Early predictors were the baseline values of GFR, systolic blood pressure, as well as fasting plasma glucose (FPG) and potassium at month 4. Changes per year in GFR, FPG, and triglycerides were recognized as predictors of late development. In conclusion, ML-based methods successfully identified predictive factors for DN among patients with T2DM.

Early Systemic Alterations in Severe Spinal Cord Injury: An Experimental Study on the Impact of Injury Level on Renal Function.

STUDY DESIGN: An experimental model of spinal cord injury (SCI) intended to characterize changes in renal function. OBJECTIVE: The aim of this study was to evaluate the possible influence of SCI level on renal function during spinal shock. SUMMARY OF BACKGROUND DATA: SCI triggers multiple systemic and metabolic alterations. Among them, renal dysfunction stands out. Although several variables have been related to its extent, the impact of the cord injury level on renal function has not been clearly stated, particularly during the spinal shock. METHODS: Anesthetized adult Sprague-Dawley rats were subjected to severe spinal cord contusion at low (T8) and high (T1) thoracic levels using the weight-drop method. Glomerular filtration rate (GFR) and tubular secretion (TS) were estimated 24 hours after injury, using a validated method based on the determination of plasma concentrations of iopamidol and p-aminohippuric acid by high-performance liquid chromatography. RESULTS: GFR, fell to 33% (95% CI [24%, 43%]) and 10% (8%, 13%) of the sham-injured controls, whereas TS, decreased to 59% (95% CI [47%, 71%]), and 25% (18%, 32%) of the sham-injured controls, in T8 and T1 injury levels, respectively. Comparisons between cords injured and control rats, as well as between low and high-injured levels, were statistically significant (P < 0.01). CONCLUSION: Renal dysfunction occurs early after severe SCI. The damage is greater in high compared to low injuries. These findings could have important implications in the acute management of patients with high thoracic and cervical injuries, especially in pharmacotherapy using drugs eliminated by the kidney. LEVEL OF EVIDENCE: N/A.

A novel, simple and inexpensive procedure for the simultaneous determination of iopamidol and p-aminohippuric acid for renal function assessment from plasma samples in awake rats.

The purpose of the current study was to design, validate and implement a novel analytical method for the simultaneous plasma measurement of iopamidol and p-aminohippuric acid (PAH) to estimate renal function in awake rats. A reverse-phase high performance liquid chromatographic (RP-HPLC) method for the simultaneous measurement of iopamidol (for glomerular filtration rate estimation, GFR) and PAH (for tubular secretion determination, TS) was designed and validated using a C-18 column, 0.1M acetic acid-10% acetonitrile (90:10, v/v) as mobile phase, at a flow rate of 0.3 ml/min, and UV detection at 270 nm. Iopamidol (244.8 mg/kg) was administered intravenously followed immediately by sodium PAH (100 mg/kg) to healthy female Sprague-Dawley rats. Plasma samples obtained at 2.5, 5, 10, 15, 20, 30, 45, 60, 90, and 120 min after drug administration were deproteinized with 2.5% trichloroacetic acid containing p-aminobenzoic acid as internal standard, and separated by the validated RP-HPLC method described above. The iopamidol and PAH chromatographic data were analyzed using a non-compartmental model. The results demonstrated that the RP-HPLC method was linear in ranges between 15-120 µg/ml and 2.5-120 µg/ml for iopamidol and PAH, respectively. Precision and accuracy were within 15% for both drugs. Recovery of iopamidol and PAH was 92% and 100%, respectively. Plasma iopamidol and PAH clearances in awake rats, estimates for GFR and TS, respectively, were 1.49±0.20 ml/min and 3.73±0.38 ml/min. In conclusion, the method here described is a simple and reliable procedure, for the simultaneous and time-saving determination of GFR and TS from plasma samples in the conscious rat.