Does difference between label and actual potency of factor VIII concentrate affect pharmacokinetic-guided dosing of replacement therapy in haemophilia A?

BACKGROUND: To account for interindividual variability in the pharmacokinetics (PK) of factor concentrates, PK-guided dosing is increasingly implemented in haemophilia patients. Calculations are based on provided label potency, but legislation allows a potency difference of ±20% between label and actual potency. It is unknown if these differences affect PK guidance. AIM: Explore the effects of potency differences on individual factor VIII (FVIII) PK parameters and the prediction of FVIII trough levels of dosing regimens. METHODS: We analyzed individual preoperative PK profiling data from severe and moderate haemophilia A patients included in the OPTI-CLOT randomized controlled trial. Label and actual potency were compared, with data on potency provided by pharmaceutical companies. For both potencies, individual PK parameters were estimated and concentration-time curves were constructed by nonlinear mixed-effects modelling. Finally, we explored the effect of both the identified and the maximum legislated potency difference on predicted FVIII trough levels infused in a low and high dose regimen. RESULTS: In 45/50 included patients, actual potency was higher than its label potency. The median potency difference was 6.0% (range -9.2% to 18.4%) and resulted in varying individual PK parameter estimates but practically identical FVIII concentration-time curves. As expected, predicted FVIII trough levels were linearly correlated to the actual dose. CONCLUSION: It is not necessary to take potency differences into account when applying PK guidance of FVIII concentrates in haemophilia A patients. However, when the patient is switched to another FVIII batch after PK-guided dosing, trough levels may deviate ±20% from calculations based on label dose.

Eating behavior during dexamethasone treatment in children with acute lymphoblastic leukemia.

BACKGROUND AND AIM: Large prospective studies on dexamethasone-induced changes in eating behavior, energy, and nutrient intake are lacking in pediatric acute lymphoblastic leukemia (ALL). We prospectively studied eating behavior, energy, nutrient intake, and the effect on leptin and adiponectin levels during dexamethasone administration in children with ALL. PATIENTS: Parents of patients with ALL (3-16 years) completed a dietary diary for their child during 4 days of dexamethasone (6 mg/m2 ) administration. Energy intake and nutrient intake (energy percentage = E%) were assessed and compared with the recommended intake. The Dutch Eating Behavior Questionnaire for Children was completed before start and after 4 days of dexamethasone administration by patients of 7-12 years of age. Fasting leptin and adiponectin levels were also measured before start and after 4 days of dexamethasone administration. RESULTS: Energy intake per day(kcal) (N = 44) increased significantly during dexamethasone (median day 1: 1,103 (717-1,572) versus day 4: 1,482 (1,176-1,822), P < 0.01), including an increase in total protein, fat, saturated fat, carbohydrate, and sodium intake. Intake of saturated fat (median day 4: 12 E%) and salt (median day 4: 1.9 g/day) exceeded the healthy range for age and gender. With respect to eating behavior, dexamethasone significantly decreased restrained eating (P = 0.04). Leptin levels as well as adiponectin levels increased significantly during the dexamethasone course. CONCLUSIONS: Four days of dexamethasone treatment significantly increased energy intake, including excessive saturated fat and salt intake, and changed eating behavior in children with ALL. Nutritional and behavioral interventions during dexamethasone treatment are recommended to stimulate a healthy lifestyle.

Subtype prediction in pediatric acute myeloid leukemia: classification using differential network rank conservation revisited.

BACKGROUND: One of the most important application spectrums of transcriptomic data is cancer phenotype classification. Many characteristics of transcriptomic data, such as redundant features and technical artifacts, make over-fitting commonplace. Promising classification results often fail to generalize across datasets with different sources, platforms, or preprocessing. Recently a novel differential network rank conservation (DIRAC) algorithm to characterize cancer phenotypes using transcriptomic data. DIRAC is a member of a family of algorithms that have shown useful for disease classification based on the relative expression of genes. Combining the robustness of this family's simple decision rules with known biological relationships, this systems approach identifies interpretable, yet highly discriminate networks. While DIRAC has been briefly employed for several classification problems in the original paper, the potentials of DIRAC in cancer phenotype classification, and especially robustness against artifacts in transcriptomic data have not been fully characterized yet. RESULTS: In this study we thoroughly investigate the potentials of DIRAC by applying it to multiple datasets, and examine the variations in classification performances when datasets are (i) treated and untreated for batch effect; (ii) preprocessed with different techniques. We also propose the first DIRAC-based classifier to integrate multiple networks. We show that the DIRAC-based classifier is very robust in the examined scenarios. To our surprise, the trained DIRAC-based classifier even translated well to a dataset with different biological characteristics in the presence of substantial batch effects that, as shown here, plagued the standard expression value based classifier. In addition, the DIRAC-based classifier, because of the integrated biological information, also suggests pathways to target in specific subtypes, which may enhance the establishment of personalized therapy in diseases such as pediatric AML. In order to better comprehend the prediction power of the DIRAC-based classifier in general, we also performed classifications using publicly available datasets from breast and lung cancer. Furthermore, multiple well-known classification algorithms were utilized to create an ideal test bed for comparing the DIRAC-based classifier with the standard gene expression value based classifier. We observed that the DIRAC-based classifier greatly outperforms its rival. CONCLUSIONS: Based on our experiments with multiple datasets, we propose that DIRAC is a promising solution to the lack of generalizability in classification efforts that uses transcriptomic data. We believe that superior performances presented in this study may motivate other to initiate a new aline of research to explore the untapped power of DIRAC in a broad range of cancer types.

Age-dependent frequencies of NPM1 mutations and FLT3-ITD in patients with normal karyotype AML (NK-AML).

Prognosis of AML in elderly patients is poor due to adverse patient characteristics and comorbidities. In addition, disease-associated parameters reveal differences between older and younger patients with AML. Survival in normal karyotype AML (NK-AML) is influenced by different clinical and molecular markers. The aim of this work was to investigate the frequencies of molecular markers in patients with NK-AML with a focus on NPM1 mutations and FLT3-ITD in different age groups. In the present study, we analyzed the frequencies of mutations of NPM1 and FLT3-ITD in a cohort of 1,321 adult patients and 148 children with AML treated within the AMLCG99, the AML98, and AML04 trials and their distribution in different age groups. Additionally, the frequencies of mutations in CEBPA genes, FLT3-TKD, and MLL-PTD were analyzed in the cohort with NK-AML (n = 729). Our data show that the presence of mutations of NPM1 (from 60% to 40%) and FLT3-ITD (from 50% to 20%) significantly decreased with age in adult AML. Consequently, the proportion of NPM1-/FLT3-ITD- patients increased with age. The decreasing frequency of NPM1 mutations in elderly patients was paralleled by a reduced complete remission (CR) rate in the elderly of 55% compared to 80% in the younger patients. By contrast, the frequencies of other gene mutations, like FLT3-TKD and MLL-PTD, and mutations in CEBPA were not age-dependent. The decreasing frequency of the favorable NPM1 mutations with increasing age may partially explain the worse outcome in the elderly patients. Furthermore, the increasing amount of elderly patients without NPM1 mutations or FLT3-ITD suggests that other molecular and clinical risk factors may influence prognosis in this age group.

Acute leukemias in children with Down syndrome.

Children with Down syndrome have an increased risk for developing both acute myeloid as well as lymphoblastic leukemia. These leukemias differ in presenting characteristics and underlying biology when compared with leukemias occurring in non-Down syndrome children. Myeloid leukemia in children with Down syndrome is preceded by a preleukemic clone (transient leukemia or transient myeloproliferative disorder), which may disappear spontaneously, but may also need treatment in case of severe symptoms. Twenty percent of children with transient leukemia subsequently develop myeloid leukemia. This transition offers a unique model to study the stepwise development of leukemia, and of gene dosage effects mediated by aneuploidy.