Extraction of pharmacokinetic evidence of drug-drug interactions from the literature

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 F10.93, MCC0.74, iAUC0.99) and sentences (F10.76, MCC0.65, iAUC0.83). 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.National Institutes of Health, National Library of Medicine Program, grant 01LM011945-01 "BLR: Evidence-based Drug-Interaction Discovery: In-Vivo, In-Vitro and Clinical," a grant from the Indiana University Collaborative Research Program 2013, "Drug-Drug Interaction Prediction from Large-scale Mining of Literature and Patient Records," as well as a grant from the joint program between the Fundação Luso-Americana para o Desenvolvimento (Portugal) and National Science Foundation (USA), 2012-2014, "Network Mining For Gene Regulation And Biochemical Signaling.

Evaluation of linear classifiers on articles containing pharmacokinetic evidence of drug-drug interactions

Background. Drug-drug interaction (DDI) is a major cause of morbidity and mortality. [...] Biomedical literature mining can aid DDI research by extracting relevant DDI signals from either the published literature or large clinical databases. However, though drug interaction is an ideal area for translational research, the inclusion of literature mining methodologies in DDI workflows is still very preliminary. One area that can benefit from literature mining is the automatic identification of a large number of potential DDIs, whose pharmacological mechanisms and clinical significance can then be studied via in vitro pharmacology and in populo pharmaco-epidemiology. Experiments. We implemented a set of classifiers for identifying published articles relevant to experimental pharmacokinetic DDI evidence. These documents are important for identifying causal mechanisms behind putative drug-drug interactions, an important step in the extraction of large numbers of potential DDIs. We evaluate performance of several linear classifiers on PubMed abstracts, under different feature transformation and dimensionality reduction methods. In addition, we investigate the performance benefits of including various publicly-available named entity recognition features, as well as a set of internally-developed pharmacokinetic dictionaries. Results. We found that several classifiers performed well in distinguishing relevant and irrelevant abstracts. We found that the combination of unigram and bigram textual features gave better performance than unigram features alone, and also that normalization transforms that adjusted for feature frequency and document length improved classification. For some classifiers, such as linear discriminant analysis (LDA), proper dimensionality reduction had a large impact on performance. Finally, the inclusion of NER features and dictionaries was found not to help classification.Comment: Pacific Symposium on Biocomputing, 201

Evaluation of linear classifiers on articles containing pharmacokinetic evidence of drug-drug interactions

Background. Drug-drug interaction (DDI) is a major cause of morbidity and mortality. DDI research includes the study of different aspects of drug interactions, from in vitro pharmacology, which deals with drug interaction mechanisms, to pharmaco-epidemiology, which investigates the effects of DDI on drug efficacy and adverse drug reactions. Biomedical literature mining can aid both kinds of approaches by extracting relevant DDI signals from either the published literature or large clinical databases. However, though drug interaction is an ideal area for translational research, the inclusion of literature mining methodologies in DDI workflows is still very preliminary. One area that can benefit from literature mining is the automatic identification of a large number of potential DDIs, whose pharmacological mechanisms and clinical significance can then be studied via in vitro pharmacology and in populo pharmaco-epidemiology. Experiments. We implemented a set of classifiers for identifying published articles relevant to experimental pharmacokinetic DDI evidence. These documents are important for identifying causal mechanisms behind putative drug-drug interactions, an important step in the extraction of large numbers of potential DDIs. We evaluate performance of several linear classifiers on PubMed abstracts, under different feature transformation and dimensionality reduction methods. In addition, we investigate the performance benefits of including various publicly-available named entity recognition features, as well as a set of internally-developed pharmacokinetic dictionaries. Results. We found that several classifiers performed well in distinguishing relevant and irrelevant abstracts. We found that the combination of unigram and bigram textual features gave better performance than unigram features alone, and also that normalization transforms that adjusted for feature frequency and document length improved classification. For some classifiers, such as linear discriminant analysis (LDA), proper dimensionality reduction had a large impact on performance. Finally, the inclusion of NER features and dictionaries was found not to help classification.IU -Indiana Universit

Leveraging syntactic and semantic graph kernels to extract pharmacokinetic drug drug interactions from biomedical literature

BACKGROUND: Information about drug-drug interactions (DDIs) supported by scientific evidence is crucial for establishing computational knowledge bases for applications like pharmacovigilance. Since new reports of DDIs are rapidly accumulating in the scientific literature, text-mining techniques for automatic DDI extraction are critical. We propose a novel approach for automated pharmacokinetic (PK) DDI detection that incorporates syntactic and semantic information into graph kernels, to address the problem of sparseness associated with syntactic-structural approaches. First, we used a novel all-path graph kernel using shallow semantic representation of sentences. Next, we statistically integrated fine-granular semantic classes into the dependency and shallow semantic graphs. RESULTS: When evaluated on the PK DDI corpus, our approach significantly outperformed the original all-path graph kernel that is based on dependency structure. Our system that combined dependency graph kernel with semantic classes achieved the best F-scores of 81.94 % for in vivo PK DDIs and 69.34 % for in vitro PK DDIs, respectively. Further, combining shallow semantic graph kernel with semantic classes achieved the highest precisions of 84.88 % for in vivo PK DDIs and 74.83 % for in vitro PK DDIs, respectively. CONCLUSIONS: We presented a graph kernel based approach to combine syntactic and semantic information for extracting pharmacokinetic DDIs from Biomedical Literature. Experimental results showed that our proposed approach could extract PK DDIs from literature effectively, which significantly enhanced the performance of the original all-path graph kernel based on dependency structure

Role of therapeutic drug monitoring in pulmonary infections : use and potential for expanded use of dried blood spot samples

Respiratory tract infections are among the most common infections in men. We reviewed literature to document their pharmacological treatments, and the extent to which therapeutic drug monitoring (TDM) is needed during treatment. We subsequently examined potential use of dried blood spots as sample procedure for TDM. TDM was found to be an important component of clinical care for many (but not all) pulmonary infections. For gentamicin, linezolid, voriconazole and posaconazole dried blood spot methods and their use in TDM were already evident in literature. For glycopeptides, beta-lactam antibiotics and fluoroquinolones it was determined that development of a dried blood spot (DBS) method could be useful. This review identifies specific antibiotics for which development of DBS methods could support the optimization of treatment of pulmonary infections

Pharmacokinetic Herb-Drug Interactions: Insight into Mechanisms and Consequences

Herbal medicines are currently in high demand, and their popularity is steadily increasing. Because of their perceived effectiveness, fewer side effects and relatively low cost, they are being used for the management of numerous medical conditions. However, they are capable of affecting the pharmacokinetics and pharmacodynamics of coadministered conventional drugs. These interactions are particularly of clinically relevance when metabolizing enzymes and xenobiotic transporters, which are responsible for the fate of many drugs, are induced or inhibited, sometimes resulting in unexpected outcomes. This article discusses the general use of herbal medicines in the management of several ailments, their concurrent use with conventional therapy, mechanisms underlying herb-drug interactions (HDIs) as well as the drawbacks of herbal remedy use. The authors also suggest means of surveillance and safety monitoring of herbal medicines. Contrary to popular belief that "herbal medicines are totally safe," we are of the view that they are capable of causing significant toxic effects and altered pharmaceutical outcomes when coadministered with conventional medicines. Due to the paucity of information as well as sometimes conflicting reports on HDIs, much more research in this field is needed. The authors further suggest the need to standardize and better regulate herbal medicines in order to ensure their safety and efficacy when used alone or in combination with conventional drugs

Contributions to an improved phenytoin monitoring and dosing in hospitalized patients

Phenytoin (PHT) is one of the mostly used and well established anticonvulsants for the treatment of epilepsy and a standard in the antiepileptic prophylaxis in adults with severe traumatic brain injuries before and after neurosurgical intervention. Its therapeutic use is challenging as PHT has a narrow therapeutic range and shows non-linear kinetics. It is extensively metabolized by a variety of CYP enzymes. PHT shows 85-95% binding to plasma proteins mostly albumin. This renders PHT also an important drug interaction candidate. Therefore, therapeutic drug monitoring is often required. A rational timing for good interpretation of the lab data translated in optimal individual dosing are necessary. Therapeutic guidance especially in teaching hospitals are needed and have to be implemented. Bayesian Forecasting (BF) versus conventional dosing (CD): a retrospective, long-term, single centre analysis In the hospital, medication management for effective antiepileptic therapy with PHT often needs rapid IV loading and subsequent dose adjustment according to TDM. To investigate PHT performance in reaching therapeutic target serum concentration, a BF regimen was compared to CD, according to the official summary of product characteristics. In a Swiss acute care teaching hospital (Kantonsspital Aarau), a retrospective, single centre, and long-term analysis was assessed by using all PHT serum tests from the central lab from 1997 to 2007. The BF regimen consisted of a guided, body weight-adapted rapid IV PHT loading over five days with pre-defined TDM time points. The CD was applied without written guidance. Assuming non-normally distributed data, non-parametric statistical methods were used. A total of 6’120 PHT serum levels (2’819 BF and 3’301 CD) from 2’589 patients (869 BF and 1’720 CD) were evaluated and compared. 63.6% of the PHT serum levels from the BF group were within the therapeutic range versus only 34.0% in the CD group (p<0.0001). The mean BF serum level was 52.0 ± 22.1 µmol/L (within target range), whereas the mean serum level of the CD was 39.8 ± 28.2 µmol/L (sub-target range). In the BF group, men had small but significantly lower PHT serum levels compared to women (p<0.0001). The CD group showed no significant gender difference (p=0.187). A comparative sub-analysis of age-related groups (children, adolescents, adults, seniors, and elderly) showed significant lower target levels (p<0.0001) for each group in the CD group, compared to BF. Comparing the two groups, BF showed significantly better performance in reaching therapeutic PHT serum levels. Free PHT assessment However, total serum drug levels of difficult-to-dose drugs like PHT are sometimes insufficient. The knowledge of the free fraction is necessary for correct dosing. In a subgroup analysis of the above BF vs. CD study we evaluated the suitability of the Sheiner-Tozer algorithm to calculate the free PHT fraction in hypoalbuminemic patients. Free PHT serum concentrations were calculated from total PHT concentration in hypoalbuminemic patients and compared with the measured free PHT. The patients were separated into two groups (a low albumin group; 35 ≤ albumin ≥ 25 g/L and a very low albumin group; albumin < 25 g/L). These two groups were compared and statistically analysed for the calculated and the measured free PHT concentration. The calculated (1.2 mg/L, SD=0.7) and the measured (1.1 mg/L, SD=0.5) free PHT concentration correlated. The mean difference in the low and the very low albumin group was 0.10 mg/L (SD=1.4, n=11) and 0.13 mg/L (SD=0.24, n=12), respectively. Although the variability of the data could be a bias, no statistically significant difference between the groups was found: t-test (p=0.78), the Passing-Bablok regression, the Spearman’s rank correlation coefficient of r=0.907 and p=0.00, and the Bland-Altman plot including the regression analysis between the calculated and the measured value (M=0.11, SD=0.28). We concluded that in absence of a free PHT serum concentration measurement also in hypoalbuminemic patients, the Sheiner-Tozer algorithm represents a useful tool to assist TDM to calculate or control free PHT by using total PHT and the albumin concentration. GC-MS Analysis of biological PHT samples To correlate PHT blood serum levels, with “brain PHT levels” (the site of action of PHT), extracellular fluid from microdialysates in neurosurgical patients could be analyzed for PHT by an appropriate quantifying analytical method. In this investigation we describe the development and validation of a sensitive gas chromatography–mass spectrometry (GC–MS) method to identify and quantitate PHT in brain microdialysate, saliva and blood from human samples. For sample clean-up a SPE was performed with a nonpolar C8-SCX column. The eluate was evaporated with nitrogen (50°C) and derivatized with trimethylsulfonium hydroxide before GC-MS analysis. 5-(p-methylphenyl)-5-phenylhydantoin was used as internal standard. The MS was run in scan mode and the identification was made with three ion fragment masses. All peaks were identified with MassLib. Spiked PHT samples showed recovery after SPE of ≥ 94%. The calibration curve (PHT 50 to 1’200 ng/ml, n=6 at six concentration levels) showed good linearity and correlation (r2 > 0.998). The limit of detection was 15 ng/mL, the limit of quantification was 50 ng/mL. Dried extracted samples were stable within a 15% deviation range for ≥ 4 weeks at room temperature. The method met International Organization for Standardization standards and was able to detect and quantify PHT in different biological matrices and patient samples. The GC-MS method with SPE is specific, sensitive, robust and well reproducible and therefore, an appropriate candidate for pharmacokinetic assessment of PHT concentrations in different biological samples of treated patients

Xenobiotic metabolism: the effect of acute kidney injury on non-renal drug clearance and hepatic drug metabolism.

Acute kidney injury (AKI) is a common complication of critical illness, and evidence is emerging that suggests AKI disrupts the function of other organs. It is a recognized phenomenon that patients with chronic kidney disease (CKD) have reduced hepatic metabolism of drugs, via the cytochrome P450 (CYP) enzyme group, and drug dosing guidelines in AKI are often extrapolated from data obtained from patients with CKD. This approach, however, is flawed because several confounding factors exist in AKI. The data from animal studies investigating the effects of AKI on CYP activity are conflicting, although the results of the majority do suggest that AKI impairs hepatic CYP activity. More recently, human study data have also demonstrated decreased CYP activity associated with AKI, in particular the CYP3A subtypes. Furthermore, preliminary data suggest that patients expressing the functional allele variant CYP3A5*1 may be protected from the deleterious effects of AKI when compared with patients homozygous for the variant CYP3A5*3, which codes for a non-functional protein. In conclusion, there is a need to individualize drug prescribing, particularly for the more sick and vulnerable patients, but this needs to be explored in greater depth

Human cytochrome P450 2B6 genetic variability in Botswana: a case of haplotype diversity and convergent phenotypes

Identification of inter-individual variability for drug metabolism through cytochrome P450 2B6 (CYP2B6) enzyme is important for understanding the di erences in clinical responses to malaria and HIV. This study evaluates the distribution of CYP2B6 alleles, haplotypes and inferred metabolic phenotypes among subjects with di erent ethnicity in Botswana. A total of 570 subjects were analyzed for CYP2B6 polymorphisms at position 516G>T (rs3745274), 785A>G (rs2279343) and 983T>C (rs28399499). Samples were collected in three districts of Botswana where the population belongs to Bantu (Serowe/Palapye and Chobe) and San-related (Ghanzi) ethnicity. The three districts showed di erent haplotype composition according to the ethnic background but similar metabolic inferred phenotypes, with 59.12%, 34.56%, 2.10% and 4.21% of the subjects having, respectively, an extensive, intermediate, slow and rapid metabolic pro le. The results hint at the possibility of a convergent adaptation of detoxifying metabolic phenotypes despite a di erent haplotype structure due to the di erent genetic background. The main implication is that, while there is substantial homogeneity of metabolic inferred phenotypes among the country, the response to drugs metabolized via CYP2B6 could be individually associated to an increased risk of treatment failure and toxicity. These are important facts since Botswana is facing malaria elimination and a very high HIV prevalence