[Construction and validation of a decision tree based on biomarkers for predicting severe acute kidney injury in critically ill patients].

OBJECTIVE: To construct and evaluate a decision tree based on biomarkers for predicting severe acute kidney injury (AKI) in critical patients. METHODS: A prospectively study was conducted. Critical patients who had been admitted to the department of critical care medicine of Xiaolan Hospital of Southern Medical University from January 2017 to June 2018 were enrolled. The clinical data of the patients were recorded, and the biomarkers, including serum cystatin C (sCys C) and urinary N-acetyl-ß-D-glucosaminidase (uNAG) were established immediately after admission to intensive care unit (ICU), and the end points were recorded. The test cohort was established with patient data from January to December 2017. The decision tree classification and regression tree (CART) algorithm was used, and the best cut-off values of biomarkers were used as the decision node to construct a biomarker decision tree model for predicting severe AKI. The accuracy of the decision tree model was evaluated by the overall accuracy and the receiver operating characteristic (ROC) curve. The validation cohort, established on patient data from January to June 2018, was used to further validate the accuracy and predictive ability of the decision tree. RESULTS: In test cohort, 263 patients were enrolled, of whom 57 developed severe AKI [defined as phase 2 and 3 of Kidney Disease: Improving Global Outcomes (KDIGO) criterion]. Compared with patients without severe AKI, severe AKI patients were older [years old: 64 (49, 74) vs. 52 (41, 66)], acute physiology and chronic health evaluation II (APACHE II) score were higher [23 (19, 27) vs. 15 (11, 20)], the incidence of hypertension, diabetes and other basic diseases and sepsis were higher (64.9% vs. 40.3%, 28.1% vs. 10.7%, 63.2% vs. 29.6%), the levels of sCys C and uNAG were higher [sCys C (mg/L): 1.38 (1.12, 2.02) vs. 0.79 (0.67, 0.98), uNAG (U/mmol Cr): 5.91 (2.43, 10.68) vs. 2.72 (1.60, 3.90)], hospital mortality and 90-day mortality were higher (21.1% vs. 4.4%, 52.6% vs. 13.1%), the length of ICU stay was longer [days: 6.0 (4.0, 9.5) vs. 3.0 (1.0, 6.0)], and renal replacement therapy requirement was higher (22.8% vs. 1.9%), with statistically significant differences (all P < 0.05). ROC curve analysis showed that the areas under ROC curve (AUC) of sCys C and uNAG in predicting severe AKI were 0.857 [95% confidence interval (95%CI) was 0.809-0.897)] and 0.735 (95%CI was 0.678-0.788), and the best cut-off values were 1.05 mg/L and 5.39 U/mmol Cr, respectively. The structure of the biomarker decision tree model constructed by biomarkers were intuitive. The overall accuracy in predicting severe AKI was 86.0%, and AUC was 0.905 (95%CI was 0.863-0.937), the sensitivity was 0.912, and the specificity was 0.796. In validation cohort of 130 patients, this decision tree yielded an excellent AUC of 0.909 (95%CI was 0.846-0.952), the sensitivity was 0.906, and the specificity was 0.816, with an overall accuracy of 81.0%. CONCLUSIONS: The decision tree model based on biomarkers for predicting severe AKI in critical patients is highly accurate, intuitive and executable, which is helpful for clinical judgment and decision.

Utilization of Stable Isotope Labeling to Facilitate the Identification of Polar Metabolites of KAF156, an Antimalarial Agent.

Identification of polar metabolites of drug candidates during development is often challenging. Several prominent polar metabolites of 2-amino-1-(2-(4-fluorophenyl)-3-((4-fluorophenyl)amino)-8,8-dimethyl-5,6-dihydroimidazo[1,2-a]pyrazin-7(8H)-yl)ethanone ([(14)C]KAF156), an antimalarial agent, were detected in rat urine from an absorption, distribution, metabolism, and excretion study but could not be characterized by liquid chromatography-tandem mass spectrometry (LC-MS/MS) because of low ionization efficiency. In such instances, a strategy often chosen by investigators is to use a radiolabeled compound with high specific activity, having an isotopic mass ratio (i.e., [(12)C]/[(14)C]) and mass difference that serve as the basis for a mass filter using accurate mass spectrometry. Unfortunately, [(14)C]KAF156-1 was uniformly labeled (n = 1-6) with the mass ratio of ∼0.1. This ratio was insufficient to be useful as a mass filter despite the high specific activity (120 µCi/mg). At this stage in development, stable isotope labeled [(13)C6]KAF156-1 was available as the internal standard for the quantification of KAF156. We were thus able to design an oral dose as a mixture of [(14)C]KAF156-1 (specific activity 3.65 µCi/mg) and [(13)C6]KAF156-1 with a mass ratio of [(12)C]/[(13)C6] as 0.9 and the mass difference as 6.0202. By using this mass filter strategy, four polar metabolites were successfully identified in rat urine. Subsequently, using a similar dual labeling approach, [(14)C]KAF156-2 and [(13)C2]KAF156-2 were synthesized to allow the detection of any putative polar metabolites that may have lost labeling during biotransformations using the previous [(14)C]KAF156-1. Three polar metabolites were thereby identified and M43, a less polar metabolite, was proposed as the key intermediate metabolite leading to the formation of a total of seven polar metabolites. Overall this dual labeling approach proved practical and valuable for the identification of polar metabolites by LC-MS/MS.

KAE609 (Cipargamin), a New Spiroindolone Agent for the Treatment of Malaria: Evaluation of the Absorption, Distribution, Metabolism, and Excretion of a Single Oral 300-mg Dose of [14C]KAE609 in Healthy Male Subjects.

KAE609 [(1'R,3'S)-5,7'-dichloro-6'-fluoro-3'-methyl-2',3',4',9'-tetrahydrospiro[indoline-3,1'-pyridol[3,4-b]indol]-2-one] is a potent, fast-acting, schizonticidal agent in clinical development for the treatment of malaria. This study investigated the absorption, distribution, metabolism, and excretion of KAE609 after oral administration of [(14)C]KAE609 in healthy subjects. After oral administration to human subjects, KAE609 was the major radioactive component (approximately 76% of the total radioactivity in plasma); M23 was the major circulating oxidative metabolite (approximately 12% of the total radioactivity in plasma). Several minor oxidative metabolites (M14, M16, M18, and M23.5B) were also identified, each accounting for approximately 3%-8% of the total radioactivity in plasma. KAE609 was well absorbed and extensively metabolized, such that KAE609 accounted for approximately 32% of the dose in feces. The elimination of KAE609 and metabolites was primarily mediated via biliary pathways. M23 was the major metabolite in feces. Subjects reported semen discoloration after dosing in prior studies; therefore, semen samples were collected once from each subject to further evaluate this clinical observation. Radioactivity excreted in semen was negligible, but the major component in semen was M23, supporting the rationale that this yellow-colored metabolite was the main source of semen discoloration. In this study, a new metabolite, M16, was identified in all biologic matrices albeit at low levels. All 19 recombinant human cytochrome P450 enzymes were capable of catalyzing the hydroxylation of M23 to form M16 even though the extent of turnover was very low. Thus, electrochemistry was used to generate a sufficient quantity of M16 for structural elucidation. Metabolic pathways of KAE609 in humans are summarized herein and M23 is the major metabolite in plasma and excreta.

Proton exchange reactions in isotope chemistry (II) synthesis of stable isotope-labeled LCQ908.

The proton exchange reaction was applied to the preparation of stable isotope-labeled LCQ908. For this synthesis, a suitable intermediate with protons alpha to a carbonyl group was first subjected to the H-D exchange reaction; subsequent coupling of a carbonyl group with [(13)C2 ]triethyl phosphonoacetate, followed by hydrogenation and hydrolysis, led to the stable labeled compound. Incorporation of two carbon-13 atoms in the molecule eliminated the presence of undesired M+0.