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Alterations of chemotherapeutic pharmacokinetic profiles by drug-drug interactions.

ABSTRACT: Drug interactions in oncology are common place and largely ignored as we tolerate high thresholds of 'toxic' drug responses in these patients. However, in the era of 'targeted' or seemingly 'less toxic' therapy, these interactions are more commonly flagged and contribute significantly towards poor 'quality of life' and medical fatalities.This review and opinion article focuses on alteration of chemotherapeutic pharmacokinetic profiles by drug interactions in the setting of polypharmacy. The assumption is that the drugs, with changes in their pharmacokinetics, will contribute towards changes in their pharmacodynamics.The examples cited for such drug-drug interactions are culled from published literature with an emphasis on those interactions that have been well characterized at the molecular level.Although very few drug interaction studies have been performed on approved oncology based drugs, it is clear that drugs whose pharmacokinetics profiles are closely related to their pharmacodynamics will indeed result in clinically important drug interactions. Some newer mechanisms are described that involve interactions at the level of gene transcription, whereby, drug metabolism is significantly altered. However, for any given drug interaction, there does not seem to be a comprehensive model describing interactions.Mechanisms based drug interactions are plentiful in oncology; however, there is an absolute lack of a comprehensive model that would predict drug-drug interactions.

SUBMITTER: Mani S

PROVIDER: S-EPMC3533254 | biostudies-other | 2009 Feb

REPOSITORIES: biostudies-other

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Alterations of chemotherapeutic pharmacokinetic profiles by drug-drug interactions.

Mani Sridhar S Ghalib Mohammed M Chaudhary Imran I Goel Sanjay S

Expert opinion on drug metabolism & toxicology 20090201 2

<h4>Background</h4>Drug interactions in oncology are common place and largely ignored as we tolerate high thresholds of 'toxic' drug responses in these patients. However, in the era of 'targeted' or seemingly 'less toxic' therapy, these interactions are more commonly flagged and contribute significantly towards poor 'quality of life' and medical fatalities.<h4>Objective</h4>This review and opinion article focuses on alteration of chemotherapeutic pharmacokinetic profiles by drug interactions in ...[more]

PMID: 19239394

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Project description:Drug-drug interaction (DDI) is a major cause of morbidity and mortality and a subject of intense scientific interest. Biomedical literature mining can aid DDI research by extracting evidence for large numbers of potential interactions from published literature and clinical databases. Though DDI is investigated in domains ranging in scale from intracellular biochemistry to human populations, literature mining has not been used to extract specific types of experimental evidence, which are reported differently for distinct experimental goals. We focus on pharmacokinetic evidence for DDI, essential for identifying causal mechanisms of putative interactions and as input for further pharmacological and pharmacoepidemiology investigations. We used manually curated corpora of PubMed abstracts and annotated sentences to evaluate the efficacy of literature mining on two tasks: first, identifying PubMed abstracts containing pharmacokinetic evidence of DDIs; second, extracting sentences containing such evidence from abstracts. We implemented a text mining pipeline and evaluated it using several linear classifiers and a variety of feature transforms. The most important textual features in the abstract and sentence classification tasks were analyzed. We also investigated the performance benefits of using features derived from PubMed metadata fields, various publicly available named entity recognizers, and pharmacokinetic dictionaries. Several classifiers performed very well in distinguishing relevant and irrelevant abstracts (reaching F1≈0.93, MCC≈0.74, iAUC≈0.99) and sentences (F1≈0.76, MCC≈0.65, iAUC≈0.83). We found that word bigram features were important for achieving optimal classifier performance and that features derived from Medical Subject Headings (MeSH) terms significantly improved abstract classification. We also found that some drug-related named entity recognition tools and dictionaries led to slight but significant improvements, especially in classification of evidence sentences. Based on our thorough analysis of classifiers and feature transforms and the high classification performance achieved, we demonstrate that literature mining can aid DDI discovery by supporting automatic extraction of specific types of experimental evidence.

Project description:BackgroundPharmacokinetic natural product-drug interactions (NPDIs) occur when botanical or other natural products are co-consumed with pharmaceutical drugs. With the growing use of natural products, the risk for potential NPDIs and consequent adverse events has increased. Understanding mechanisms of NPDIs is key to preventing or minimizing adverse events. Although biomedical knowledge graphs (KGs) have been widely used for drug-drug interaction applications, computational investigation of NPDIs is novel. We constructed NP-KG as a first step toward computational discovery of plausible mechanistic explanations for pharmacokinetic NPDIs that can be used to guide scientific research.MethodsWe developed a large-scale, heterogeneous KG with biomedical ontologies, linked data, and full texts of the scientific literature. To construct the KG, biomedical ontologies and drug databases were integrated with the Phenotype Knowledge Translator framework. The semantic relation extraction systems, SemRep and Integrated Network and Dynamic Reasoning Assembler, were used to extract semantic predications (subject-relation-object triples) from full texts of the scientific literature related to the exemplar natural products green tea and kratom. A literature-based graph constructed from the predications was integrated into the ontology-grounded KG to create NP-KG. NP-KG was evaluated with case studies of pharmacokinetic green tea- and kratom-drug interactions through KG path searches and meta-path discovery to determine congruent and contradictory information in NP-KG compared to ground truth data. We also conducted an error analysis to identify knowledge gaps and incorrect predications in the KG.ResultsThe fully integrated NP-KG consisted of 745,512 nodes and 7,249,576 edges. Evaluation of NP-KG resulted in congruent (38.98% for green tea, 50% for kratom), contradictory (15.25% for green tea, 21.43% for kratom), and both congruent and contradictory (15.25% for green tea, 21.43% for kratom) information compared to ground truth data. Potential pharmacokinetic mechanisms for several purported NPDIs, including the green tea-raloxifene, green tea-nadolol, kratom-midazolam, kratom-quetiapine, and kratom-venlafaxine interactions were congruent with the published literature.ConclusionNP-KG is the first KG to integrate biomedical ontologies with full texts of the scientific literature focused on natural products. We demonstrate the application of NP-KG to identify known pharmacokinetic interactions between natural products and pharmaceutical drugs mediated by drug metabolizing enzymes and transporters. Future work will incorporate context, contradiction analysis, and embedding-based methods to enrich NP-KG. NP-KG is publicly available at https://doi.org/10.5281/zenodo.6814507. The code for relation extraction, KG construction, and hypothesis generation is available at https://github.com/sanyabt/np-kg.

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Alterations of chemotherapeutic pharmacokinetic profiles by drug-drug interactions.

Publications

Alterations of chemotherapeutic pharmacokinetic profiles by drug-drug interactions.

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