Quizartinib versus salvage chemotherapy in relapsed or refractory FLT3-ITD acute myeloid leukaemia (QuANTUM-R): a multicentre, randomised, controlled, open-label, phase 3 trial.

BACKGROUND: Patients with relapsed or refractory FLT3 internal tandem duplication (FLT3-ITD)-positive acute myeloid leukaemia have a poor prognosis, including high frequency of relapse, poorer response to salvage therapy, and shorter overall survival than those with FLT3 wild-type disease. We aimed to assess whether single-agent quizartinib, an oral, highly potent and selective type II FLT3 inhibitor, improves overall survival versus salvage chemotherapy. METHODS: QuANTUM-R is a randomised, controlled, phase 3 trial done at 152 hospitals and cancer centres in 19 countries. Eligible patients aged 18 years or older with ECOG performance status 0-2 with relapsed or refractory (duration of first composite complete remission ≤6 months) FLT3-ITD acute myeloid leukaemia after standard therapy with or without allogeneic haemopoietic stem-cell transplantation were randomly assigned (2:1; permuted block size of 6; stratified by response to previous therapy and choice of chemotherapy via a phone-based and web-based interactive response system) to quizartinib (60 mg [30 mg lead-in] orally once daily) or investigator's choice of preselected chemotherapy: subcutaneous low-dose cytarabine (subcutaneous injection of cytarabine 20 mg twice daily on days 1-10 of 28-day cycles); intravenous infusions of mitoxantrone (8 mg/m2 per day), etoposide (100 mg/m2 per day), and cytarabine (1000 mg/m2 per day on days 1-5 of up to two 28-day cycles); or intravenous granulocyte colony-stimulating factor (300 µg/m2 per day or 5 µg/kg per day subcutaneously on days 1-5), fludarabine (intravenous infusion 30 mg/m2 per day on days 2-6), cytarabine (intravenous infusion 2000 mg/m2 per day on days 2-6), and idarubicin (intravenous infusion 10 mg/m2 per day on days 2-4 in up to two 28-day cycles). Patients proceeding to haemopoietic stem-cell transplantation after quizartinib could resume quizartinib after haemopoietic stem-cell transplantation. The primary endpoint was overall survival in the intention-to-treat population. This trial is registered with ClinicalTrials.gov, number NCT02039726, and follow-up is ongoing. FINDINGS: Between May 7, 2014, and Sept 13, 2017, 367 patients were enrolled, of whom 245 were randomly allocated to quizartinib and 122 to chemotherapy. Four patients in the quizartinib group and 28 in the chemotherapy group were not treated. Median follow-up was 23·5 months (IQR 15·4-32·3). Overall survival was longer for quizartinib than for chemotherapy (hazard ratio 0·76 [95% CI 0·58-0·98; p=0·02]). Median overall survival was 6·2 months (5·3-7·2) in the quizartinib group and 4·7 months (4·0-5·5) in the chemotherapy group. The most common non-haematological grade 3-5 treatment-emergent adverse events (within ≤30 days of last dose or >30 days if suspected to be a treatment-related event) for quizartinib (241 patients) and chemotherapy (94 patients) were sepsis or septic shock (46 patients [19%] for quizartinib vs 18 [19%] for chemotherapy), pneumonia (29 [12%] vs eight [9%]), and hypokalaemia (28 [12%] vs eight [9%]). The most frequent treatment-related serious adverse events were febrile neutropenia (18 patients [7%]), sepsis or septic shock (11 [5%]), QT prolongation (five [2%]), and nausea (five [2%]) in the quizartinib group, and febrile neutropenia (five [5%]), sepsis or septic shock (four [4%]), pneumonia (two [2%]), and pyrexia (two [2%]) in the chemotherapy group. Grade 3 QT prolongation in the quizartinib group was uncommon (eight [3%] by central reading, ten [4%] by investigator report); no grade 4 events occurred. There were 80 (33%) treatment-emergent deaths in the quizartinib group (31 [13%] of which were due to adverse events) and 16 (17%) in the chemotherapy group (nine [10%] of which were due to adverse events). INTERPRETATION: Treatment with quizartinib had a survival benefit versus salvage chemotherapy and had a manageable safety profile in patients with rapidly proliferative disease and very poor prognosis. Quizartinib could be considered a new standard of care. Given that there are only a few available treatment options, this study highlights the value of targeting the FLT3-ITD driver mutation with a highly potent and selective FLT3 inhibitor. FUNDING: Daiichi Sankyo.

Lack of effect of DX-619, a novel des-fluoro(6)-quinolone, on glomerular filtration rate measured by serum clearance of cold iohexol.

DX-619 is a novel des-fluoro(6)-quinolone with activity against a broad range of bacterial strains, including methicillin-resistant Staphylococcus aureus. The effects of DX-619 on the glomerular filtration rate (GFR) were evaluated because drug-related increases in serum creatinine levels were observed in studies with healthy volunteers. Forty-one healthy subjects were randomized to receive intravenous DX-619 at 800 mg or placebo once daily for 4 days, and the GFR was directly measured by determination of the clearance of a bolus iohexol injection in 33 subjects who completed the study per protocol. DX-619 was noninferior to placebo for the GFR on the basis of a criterion for a clinically significant difference of -12 ml/min/1.73 m(2). The mean GFRs on day 4 were 101.1 +/- 14.2 ml/min/1.73 m(2) and 100.2 +/- 15.6 ml/min/1.73 m(2) for the volunteers receiving placebo and DX-619, respectively. On day 4 the mean serum creatinine concentration for volunteers receiving DX-619 increased by 30 to 40%, with a corresponding decrease in mean creatinine clearance. Both parameters normalized within 7 days after the cessation of DX-619 treatment. Nonclinical studies suggest that DX-619 increases the serum creatinine concentration by inhibiting excretory tubular transporters. In conclusion, DX-619 administered intravenously at 800 mg once a day for 4 days did not affect the GFR in healthy volunteers. Glomerular toxicity is not expected to present a risk to patients receiving DX-619 in clinical trials, but monitoring of the renal function, with an emphasis on the serum creatinine concentration, is still warranted.

Analysis and prediction of combinatorial chemistry synthesis and screening data.

The goal of combinatorial chemistry is to simultaneously synthesize sets of compounds possessing properties that are then distinguished through screening. As the size of a compound set increases, data analysis becomes more challenging. Analysis of Variance (ANOVA) is an accepted statistical method that offers a straightforward solution to this problem. Two steps encountered by combinatorial scientists appear well suited to ANOVA: the prediction of synthetic outcomes (purity and yield) of set members and the analysis of screening data to identify combinations of reagent inputs that result in molecules with a desired property. To illustrate, a subset of a combinatorial array, referred to as a reaction rehearsal set, is evaluated to create a model predictive of the individual synthetic outcomes of the full matrix. In a second exercise, the biochemical screening data obtained from a combinatorial library is analyzed to identify reagent interactions that result in molecules possessing the sought activity.

Recursive partitioning analysis of complex disease pharmacogenetic studies. I. Motivation and overview.

Identifying genetic variation predictive of important phenotypes, including disease susceptibility, drug efficacy, and adverse events, is a challenging task, and theory and computer science work is being carried out in an attempt to tackle this issue. For many important diseases, such as diabetes, schizophrenia, and depression, the etiology is complex; either the disease is a result of several multiple mechanisms or is caused by an interaction among multiple genes or gene-environment interactions, or both. There is a need for statistical methods to deal with the large, complex data sets that will be used to disentangle these diseases. Each putative genetic polymorphism can be tested for association sequentially. The most difficult problem, however, is the identification of combinations of polymorphisms or genetic markers with increased predictive characteristics. Data from clinical trials, where patients with a particular disease are treated with certain drugs, can be retrospectively assembled using a case-control design. Such data will typically include treatment assignment, demographics, medical history, and genotypes for a large number of genetic markers. The number of variables in such data is expected to be much larger than the number of subjects. This report focuses on some of the methods being employed to deal with this complex data and covers, in some detail, a data-mining method--recursive partitioning--to analyze such data. The methods are demonstrated using a complex simulated data set, as there are few available public data sets. This explication of recursive partitioning should provide researchers with a better idea of the current available analysis techniques, in order to allow them to plan their experiments more effectively.

Prediction of IFN-gamma regulated gene transcription.

IFN-gamma, a cytokine promoting cell-mediated immunity and antiviral effects, regulates the expression of a large set of genes involved in the immune response. Based on logistic regression, an in silico model for predicting IFN-gamma regulated transcription has been developed by scoring the transcription factor binding sites on the putative promoters of regulated versus not regulated genes derived from the microarray data of IFN-gamma treated human macrophages. The model effectively discriminates the transcription factor binding sites that confer responsiveness to IFN-gamma from those that do not. The model has 65% true positive and 22% false positive rates when evaluated on a small validation set. In order to identify potential IFN-gamma regulated genes in the whole genome, the model has been used to screen 13,668 promoter pairs of human-mouse orthologs/homologs from Ensembl, and 1,387 of them were predicted to be potentially regulated by IFN-gamma. In the pilot experiment, the regulation pattern of a subset of predicted genes that were not detected by microarray approach was evaluated by quantitative PCR. The results for the four novel genes, which are up regulated by IFN-gamma in human macrophages and identified by this approach, are described in the present communication.

Design of diversity and focused combinatorial libraries in drug discovery.

Using well-characterized chemical reactions and readily available monomers, chemists are able to create sets of compounds, termed libraries, which are useful in drug discovery processes. The design of combinatorial chemical libraries can be complex and there has been much information recently published offering suggestions on how the design process can be carried out. This review focuses on literature with the goal of organizing current thinking. At this point in time, it is clear that benchmarking of current suggested methods is required as opposed to further new methods.

Comparing gene discovery from Affymetrix GeneChip microarrays and Clontech PCR-select cDNA subtraction: a case study.

BACKGROUND: Several high throughput technologies have been employed to identify differentially regulated genes that may be molecular targets for drug discovery. Here we compared the sets of differentially regulated genes discovered using two experimental approaches: a subtracted suppressive hybridization (SSH) cDNA library methodology and Affymetrix GeneChip technology. In this "case study" we explored the transcriptional pattern changes during the in vitro differentiation of human monocytes to myeloid dendritic cells (DC), and evaluated the potential for novel gene discovery using the SSH methodology. RESULTS: The same RNA samples isolated from peripheral blood monocyte precursors and immature DC (iDC) were used for GeneChip microarray probing and SSH cDNA library construction. 10,000 clones from each of the two-way SSH libraries (iDC-monocytes and monocytes-iDC) were picked for sequencing. About 2000 transcripts were identified for each library from 8000 successful sequences. Only 70% to 75% of these transcripts were represented on the U95 series GeneChip microarrays, implying that 25% to 30% of these transcripts might not have been identified in a study based only on GeneChip microarrays. In addition, about 10% of these transcripts appeared to be "novel", although these have not yet been closely examined. Among the transcripts that are also represented on the chips, about a third were concordantly discovered as differentially regulated between iDC and monocytes by GeneChip microarray transcript profiling. The remaining two thirds were either not inferred as differentially regulated from GeneChip microarray data, or were called differentially regulated but in the opposite direction. This underscores the importance both of generating reciprocal pairs of SSH libraries, and of real-time RT-PCR confirmation of the results. CONCLUSIONS: This study suggests that SSH could be used as an alternative and complementary transcript profiling tool to GeneChip microarrays, especially in identifying novel genes and transcripts of low abundance.

An empirical Bayesian significance test of cDNA library data.

Automated high-throughput sequencing of cDNA clones from numerous libraries has generated a wealth of information about both genome sequence and relative transcript abundances. A common statistical challenge in the analysis of library sequences is to infer whether there is differential expression for the same transcript under two different conditions, such as normal and diseased tissue. In contrast to the continuously variable intensity measurements from microarray experiments, data from cDNA library sequencing presents itself as a discrete count of the incidence of some clone or transcript in a finite sample. In this paper, we first propose a statistical model for data generated from cDNA library sequencing efforts. The model is based on the Poisson mixed with generalized inverse Gaussian (PGIG), introduced by Sichel (1971, 1975). PGIG has been used in modeling population abundance, ecological studies, word frequencies in publications, etc. Using data from the literature, we show that the proposed model provides a good fit to the observed data. Using this new model for cDNA library data, we developed an empirical Bayesian significance test (EBST) for inferring the statistical significance of differential gene expression from discrete data.