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Genetic factors influencing cytarabine therapy.

ABSTRACT: The mainstay of acute myeloid leukemia chemotherapy is the nucleoside analog cytarabine (ara-C). Numerous studies suggest that the intracellular concentrations of the ara-C active metabolite, ara-CTP, vary widely among patients and, in turn, are associated with variability in clinical response to acute myeloid leukemia treatment. Thus, genetic variation in key genes in the ara-C metabolic pathway--specifically, deoxycytidine kinase (a rate-limiting activating enzyme), 5 nucleotidase, cytidine deaminase and deoxycytidylate deaminase (all three are inactivating enzymes), human equilibrative nucleoside transporter (ara-C uptake transporter) and ribonucleotide reductase (RRM1 and RRM2--enzymes regulating intracellular deoxycytidine triphosphate pools)--form the molecular basis of the interpatient variability observed in intracellular ara-CTP concentrations and response to ara-C. Understanding genetic variants in the key candidate genes involved in the metabolic activation of ara-C, as well as the pharmacodynamic targets of ara-C, will provide an opportunity to identify patients at an increased risk of adverse reactions or decreased likelihood of response, based upon their genetic profile, which in future could help in dose optimization to reduce drug toxicity without compromising efficacy. The pharmacogenetic studies on ara-C would also be equally applicable to other nucleoside analogs, such as gemcitabine, decitabine, clofarabine and so on, which are metabolized by the same pathway.

SUBMITTER: Lamba JK

PROVIDER: S-EPMC2828057 | biostudies-literature | 2009 Oct

REPOSITORIES: biostudies-literature

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Genetic factors influencing cytarabine therapy.

Lamba Jatinder K JK

Pharmacogenomics 20091001 10

The mainstay of acute myeloid leukemia chemotherapy is the nucleoside analog cytarabine (ara-C). Numerous studies suggest that the intracellular concentrations of the ara-C active metabolite, ara-CTP, vary widely among patients and, in turn, are associated with variability in clinical response to acute myeloid leukemia treatment. Thus, genetic variation in key genes in the ara-C metabolic pathway--specifically, deoxycytidine kinase (a rate-limiting activating enzyme), 5 nucleotidase, cytidine de ...[more]

PMID: 19842938

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Project description:BackgroundAdherence and motivation are key factors for successful treatment of patients with chronic diseases, especially in long-term care processes like rehabilitation. However, only a few patients achieve good treatment adherence. The causes are manifold. Adherence-influencing factors vary depending on indications, therapies, and individuals. Positive and negative effects are rarely confirmed or even contradictory. An ontology seems to be convenient to represent existing knowledge in this domain and to make it available for information retrieval.MethodsFirst, a manual data extraction of current knowledge in the domain of treatment adherence in rehabilitation was conducted. Data was retrieved from various sources, including basic literature, scientific publications, and health behavior models. Second, all adherence and motivation factors identified were formalized according to the ontology development methodology METHONTOLOGY. This comprises the specification, conceptualization, formalization, and implementation of the ontology "Ontology for factors influencing therapy adherence to rehabilitation" (OnTARi) in Protégé. A taxonomy-oriented evaluation was conducted by two domain experts.ResultsOnTARi includes 281 classes implemented in ontology web language, ten object properties, 22 data properties, 1440 logical axioms, 244 individuals, and 1023 annotations. Six higher-level classes are differentiated: (1) Adherence, (2) AdherenceFactors, (3) AdherenceFactorCategory, (4) Rehabilitation, (5) RehabilitationForm, and (6) RehabilitationType. By means of the class AdherenceFactors 227 adherence factors, thereof 49 hard factors, are represented. Each factor involves a proper description, synonyms, possibly existing acronyms, and a German translation. OnTARi illustrates links between adherence factors through 160 influences-relations. Description logic queries implemented in Protégé allow multiple targeted requests, e.g., for the extraction of adherence factors in a specific rehabilitation area.ConclusionsWith OnTARi, a generic reference model was built to represent potential adherence and motivation factors and their interrelations in rehabilitation of patients with chronic diseases. In terms of information retrieval, this formalization can serve as a basis for implementation and adaptation of conventional rehabilitative measures, taking into account (patient-specific) adherence factors. OnTARi also enables the development of medical assistance systems to increase motivation and adherence in rehabilitation processes.

Dataset Information

Genetic factors influencing cytarabine therapy.

Publications

Genetic factors influencing cytarabine therapy.

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