Abstract:Objectives
As new, effective therapies emerge for acute lymphoblastic leukaemia (ALL), the results of clinical trials need to relate to standard of care.
Methods
We used the population‐based Swedish ALL Registry to evaluate characteristics, treatment and long‐term outcome in 933 patients with diagnosis between 1997 and 2015.
Results
The median age was 53 years. The frequency of Philadelphia (Ph)‐positive leukaemia was 34% of examined B‐ALL with a peak incidence at 50‐59 years. Five‐year overall survival (OS) i… Show more
“… 3 , 4 Dasatinib exhibited clinical efficacy in adult patients, associated with 5-year overall survival of 18%–64%. 5 In children, the 5-year overall survival was 86% under the treatment of dasatinib. 6 Figure 1 Mass spectra and structures of dasatinib ( A ) and posaconazole ( B ) in present study.…”
Background and Aim: Dasatinib is approved for the treatment of leukaemia worldwide.Triazole agents such as posaconazole may be used for the control of secondary fungal infection with leukaemia. This work aimed to develop a bioanalytical method to study the potential interaction between dasatinib and posaconazole. Methods: An ultrahigh-performance liquid chromatography-tandem mass spectrometry method was established to measure the plasma concentrations of dasatinib and posaconazole in rats simultaneously. Simple protein precipitation with acetonitrile was applied to extract dasatinib and posaconazole in samples. The chromatographic separation of analytes was conducted on an UPLC BEH C18 column using a mobile phase consisting of 0.1% aqueous formic acid and acetonitrile. Dasatinib and posaconazole were monitored in positive ion mode with the following mass transition pairs: m/z 488.2→401.1 for dasatinib and m/z 701.3→683.4 for posaconazole. The method was successfully applied for pharmacokinetic interaction between dasatinib and posaconazole. Results: The established method expressed good linearity in 1-1000 ng/mL of dasatinib and 5-5000 ng/mL of posaconazole, with limit of detection was 1 ng/mL and 5 ng/mL, respectively. Methodology validations, including accuracy, precision, matrix effect, recovery, and stability, met the US Food and Drug Administration (FDA) acceptance criteria for bioanalytical method validation. Dasatinib strongly inhibited the clearance of posaconazole in vivo, while posaconazole expressed no significant effect on the pharmacokinetics of dasatinib. Conclusion: Dasatinib alters the pharmacokinetics of posaconazole. Attention should be paid to the unexpected risk of adverse clinical outcomes when posaconazole is coadministered with dasatinib.
“… 3 , 4 Dasatinib exhibited clinical efficacy in adult patients, associated with 5-year overall survival of 18%–64%. 5 In children, the 5-year overall survival was 86% under the treatment of dasatinib. 6 Figure 1 Mass spectra and structures of dasatinib ( A ) and posaconazole ( B ) in present study.…”
Background and Aim: Dasatinib is approved for the treatment of leukaemia worldwide.Triazole agents such as posaconazole may be used for the control of secondary fungal infection with leukaemia. This work aimed to develop a bioanalytical method to study the potential interaction between dasatinib and posaconazole. Methods: An ultrahigh-performance liquid chromatography-tandem mass spectrometry method was established to measure the plasma concentrations of dasatinib and posaconazole in rats simultaneously. Simple protein precipitation with acetonitrile was applied to extract dasatinib and posaconazole in samples. The chromatographic separation of analytes was conducted on an UPLC BEH C18 column using a mobile phase consisting of 0.1% aqueous formic acid and acetonitrile. Dasatinib and posaconazole were monitored in positive ion mode with the following mass transition pairs: m/z 488.2→401.1 for dasatinib and m/z 701.3→683.4 for posaconazole. The method was successfully applied for pharmacokinetic interaction between dasatinib and posaconazole. Results: The established method expressed good linearity in 1-1000 ng/mL of dasatinib and 5-5000 ng/mL of posaconazole, with limit of detection was 1 ng/mL and 5 ng/mL, respectively. Methodology validations, including accuracy, precision, matrix effect, recovery, and stability, met the US Food and Drug Administration (FDA) acceptance criteria for bioanalytical method validation. Dasatinib strongly inhibited the clearance of posaconazole in vivo, while posaconazole expressed no significant effect on the pharmacokinetics of dasatinib. Conclusion: Dasatinib alters the pharmacokinetics of posaconazole. Attention should be paid to the unexpected risk of adverse clinical outcomes when posaconazole is coadministered with dasatinib.
“…While Lennmyr et al [57] showed that all adult patients affected by lymphoblastic leukemia was successfully treated with allogeneic hematopoietic stem cell transplantation (alloHSCT) with an increased overall survival rate of more than 10% in 5 years [57]. Meanwhile, Schlenk et al [58] evaluated alloHSCT among patients having acute myeloid leukemia (AML) showed a significant beneficial impact after treatment.…”
The applicability of machine learning-based analysis in the field of biomedical field has been very beneficial in determining the biological mechanism and validation for a wide range of biological scenarios. This approach is also gaining momentum in variou s stem cells research activities, specifically for stem cells characterization and differentiation pattern. The adoption of similar computational approaches to study and assess biosafety and bioefficacy risks of stem cells for clinical application is the n ext progression. In particular where tumorigenicity has been one of the major concerns in stem cells therapy. There are many factors influencing tumorigenicity in stem cells which may be difficult to capture under conventional laboratory settings. In addition, given the possible multifactorial etiology of tumorigenicity, defining a onesize-fits-all strategy to test such risk in stem cells might not be feasible and may compromise stem cells safety and effectiveness in therapy. Given the increase in biological datasets (which is no longer limited to genomic data) and the advancement of health informatics powered by state-of-the-art machine learning algorithms, there exists a potential for practical application in biosafety and bioefficacy of stem cells therap y. Here, we identified relevant machine learning approaches and suggested protocols intended for stem cells research focusing on the possibility of its usage for stem cells biosafety and bioefficacy assessment. Ultimately, generating models that may assist healthcare professionals to make a better-informed decision in stem cell therapy.
“…AL has become a global health concern due to its increasing incidence over the past decade (3). The worldwide incidence of AML has gradually increased from 63.84x10 3 cases in 1990 to 119.57x10 3 cases in 2017, showing an increase of 87.3% (4). The USA has diagnosed approximately 5,960 new cases of ALL and reported 1,470 ALL-associated mortalities in 2018 (5).…”
Section: Introductionmentioning
confidence: 99%
“…AL primarily comprises of acute lymphoblastic (ALL), myeloblastic (AML), undifferentiated (AUL) and mixed-lineage leukemia (AMLL) ( 2 ). AL has become a global health concern due to its increasing incidence over the past decade ( 3 ). The worldwide incidence of AML has gradually increased from 63.84×10 3 cases in 1990 to 119.57×10 3 cases in 2017, showing an increase of 87.3% ( 4 ).…”
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