15 new pubmed citations were retrieved for your search. Click on the search hyperlink below to display the complete search results:

"Cystic Fibrosis"

These pubmed results were generated on 2016/08/16

PubMed comprises more than 24 million citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites.

15 new pubmed citations were retrieved for your search. Click on the search hyperlink below to display the complete search results:

"Systems Biology"[Title/Abstract] AND ("2005/01/01"[PDAT] : "3000"[PDAT])

These pubmed results were generated on 2016/08/16

PubMed comprises more than 24 million citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites.

Pharmacodynamics and Systems Pharmacology Approaches to Repurposing Drugs in the Wake of Global Health Burden.

J Pharm Sci. 2016 Aug 11;

Authors: Bai JP

Abstract
There are emergent needs for cost-effective treatment worldwide, for which repurposing to develop a drug with existing marketing approval of disease(s) for new disease(s) is a valid option. Although strategic mining of electronic health records has produced real-world evidences to inform drug repurposing, using omics data (drug and disease), knowledge base of protein interactions, and database of transcription factors have been explored. Structured integration of all the existing data under the framework of drug repurposing will facilitate decision making. The ability to foresee the need to integrate new data types produced by emergent technologies and to enable data connectivity in the context of human biology and targeted diseases, as well as to use the existing crucial quality data of all approved drugs will catapult the number of drugs being successfully repurposed. However, translational pharmacodynamics databases for modeling information across human biology in the context of host factors are lacking and are critically needed for drug repurposing to improve global public health, especially for the efforts to combat neglected tropic diseases as well as emergent infectious diseases such as Zika or Ebola virus.

PMID: 27522921 [PubMed - as supplied by publisher]

Poly lactic-co-glycolic acid controlled delivery of Disulfiram to target liver cancer stem-like cells.

Nanomedicine. 2016 Aug 10;

Authors: Wang Z, Tan J, McConville C, Kannappan V, Tawari PE, Brown J, Ding J, Armesilla AL, Irache JM, Mei QB, Tan Y, Liu Y, Jiang W, Bian X, Wang W

Abstract
Disulfiram (DS), an anti-alcoholism drug, shows very strong cytotoxicity in many cancer types. However its clinical application in cancer treatment is limited by the very short half-life in the bloodstream. In this study, we developed a poly lactic-co-glycolic acid (PLGA)-encapsulated DS protecting DS from the degradation in the bloodstream. The newly developed DS-PLGA was characterized. The DS-PLGA has very satisfactory encapsulation efficiency, drug-loading content and controlled release rate in vitro. PLGA encapsulation extended the half-life of DS from shorter than 2minutes to 7hours in serum. In combination with copper, DS-PLGA significantly inhibited the liver cancer stem cell population. CI-isobologram showed a remarkable synergistic cytotoxicity between DS-PLGA and 5-FU or Sorafenib. It also demonstrated very promising anticancer efficacy and antimetastatic effect in liver cancer mouse model. Both DS and PLGA are FDA approved products for clinical application. Our study may lead to repositioning of DS into liver cancer treatment.

PMID: 27521693 [PubMed - as supplied by publisher]

GITIRBio: A Semantic and Distributed Service Oriented-Architecture for Bioinformatics Pipeline.

J Integr Bioinform. 2015;12(1):255

Authors: Castillo LF, López-Gartner G, Isaza GA, Sánchez M, Arango J, Agudelo-Valencia D, Castaño S

Abstract
The need to process large quantities of data generated from genomic sequencing has resulted in a difficult task for life scientists who are not familiar with the use of command-line operations or developments in high performance computing and parallelization. This knowledge gap, along with unfamiliarity with necessary processes, can hinder the execution of data processing tasks. Furthermore, many of the commonly used bioinformatics tools for the scientific community are presented as isolated, unrelated entities that do not provide an integrated, guided, and assisted interaction with the scheduling facilities of computational resources or distribution, processing and mapping with runtime analysis. This paper presents the first approximation of a Web Services platform-based architecture (GITIRBio) that acts as a distributed front-end system for autonomous and assisted processing of parallel bioinformatics pipelines that has been validated using multiple sequences. Additionally, this platform allows integration with semantic repositories of genes for search annotations. GITIRBio is available at: http://c-head.ucaldas.edu.co:8080/gitirbio.

PMID: 26527189 [PubMed - indexed for MEDLINE]

Adjuvant Therapy of Resected Non-small Cell Lung Cancer: can We Move Forward?

Curr Treat Options Oncol. 2016 Oct;17(10):54

Authors: Buffoni L, Vavalà T, Novello S

Abstract
OPINION STATEMENT: Twenty years ago, an individual patient data meta-analysis of eight cisplatin-based adjuvant chemotherapy (AC) studies in completely resected early stage non-small cell lung cancer (NSCLC) demonstrated a 13 % reduction of the risk of death favoring chemotherapy that was of borderline statistical significance (p = 0.08). This marginal benefit boosted a new generation of randomized trials to evaluate the role of modern platinum-based regimens in resectable stages of NSCLC and, although individual studies generated conflicting results, overall they contributed to confirm the role of AC which is now recommended for completely resected stage II and III NSCLC, mostly 4 cycles, while subset analyses suggested a benefit in patients with large IB tumors. Cisplatin-based therapy was the core regimen of those adjuvant clinical trials and even if a substitution with other platinum-derived was also suggested, mainly based on extrapolated data from studies in advanced disease, cisplatin was confirmed to be slightly superior to carboplatin and is still the drug of choice in the adjuvant setting. Currently, any attempt to improve efficacy of cisplatin-based chemotherapy through antiangiogenic drugs association or pharmacogenomics approaches have failed, while results of additional studies are eagerly awaited. In the context of promising targeted therapies, even if several randomized trials in the advanced setting evaluated tyrosine kinase inhibitors (TKis) versus platinum-based chemotherapy and showed impressive results, clinical experience with TKIs in the adjuvant setting is still limited and most of the trials have not required patients to be molecularly tested for the drug-specific molecular predictive factor. At the present time, the role of targeted agents as adjuvant approaches remains largely not investigated. Finally, with the negative experience of the use of vaccines in this setting, the integration of immunotherapy (mainly immunocheckpoint inhibitors) in platinum-based schedules has just started to be evaluated, representing a potential future clinical option, but still far from clinical practice.

PMID: 27523606 [PubMed - as supplied by publisher]

Pharmacometabolomics informs Pharmacogenomics.

Metabolomics. 2016 Jul;12(7)

Authors: Neavin D, Kaddurah-Daouk R, Weinshilboum R

Abstract
INTRODUCTION: The initial decades of the 21(st) century have witnessed striking technical advances that have made it possible to detect, identify and quantitatively measure large numbers of plasma or tissue metabolites. In parallel, similar advances have taken place in our ability to sequence DNA and RNA. Those advances have moved us beyond studies of single metabolites and single genetic polymorphisms to the study of hundreds or thousands of metabolites and millions of genomic variants in a single cell or subject. It is now possible to merge and integrate large data sets generated by the use of different "-omics" techniques to increase our understanding of the molecular basis for variation in disease risk and/or drug response phenotypes.
OBJECTIVES: This "Brief Review" will outline some of the challenges and opportunities associated with studies in which metabolomic data have been merged with genomics in an attempt to gain novel insight into mechanisms associated with variation in drug response phenotypes, with an emphasis on the application of a pharmacometabolomics-informed pharmacogenomic research strategy and with selected examples of the application of that strategy.
METHODS: Studies that used pharmacometabolomics to inform and guide pharmacogenomics were reviewed. Clinical studies that were used as the basis for pharmacometabolomics-informed pharmacogenomic studies, published in five independent manuscripts, are described briefly.
RESULTS: Within these five manuscripts, both pharmacokinetic and pharmacodynamic metabolomics approaches were used. Candidate gene and genome-wide approaches that were used in concert with these metabolomic data identified novel metabolite-gene relationships that were associated with drug response phenotypes in these pharmacometabolomics-informed pharmacogenomics studies.
CONCLUSION: This "Brief Review" outlines the emerging discipline of pharmacometabolomics-informed pharmacogenomics in which metabolic profiles are associated with both clinical phenotypes and genetic variants to identify novel genetic variants associated with drug response phenotypes based on metabolic profiles.

PMID: 27516730 [PubMed - as supplied by publisher]

Pharmacogenomics in Psychiatric Practice.

Clin Lab Med. 2016 Sep;36(3):507-23

Authors: El-Mallakh RS, Roberts RJ, El-Mallakh PL, Findlay LJ, Reynolds KK

Abstract
Pharmacogenomic testing in psychiatry is becoming an established clinical procedure. Several vendors provide clinical interpretation of combinatorial pharmacogenomic testing of gene variants that have documented predictive implications regarding either pharmacologic response or adverse effects in depression and other psychiatric conditions. Such gene profiles have demonstrated improvements in outcome in depression, and reduction of cost of care of patients with inadequate clinical response. Additionally, several new gene variants are being studied to predict specific response in individuals. Many of these genes have demonstrated a role in the pathophysiology of depression or specific depressive symptoms. This article reviews the current state-of-the-art application of psychiatric pharmacogenomics.

PMID: 27514465 [PubMed - in process]

Pharmacogenetics in Oral Antithrombotic Therapy.

Clin Lab Med. 2016 Sep;36(3):461-72

Authors: Maier CL, Duncan A, Hill CE

Abstract
Certain antithrombotic drugs exhibit high patient-to-patient variability that significantly impacts the safety and efficacy of therapy. Pharmacogenetics offers the possibility of tailoring drug treatment to patients based on individual genotypes, and this type of testing has been recommended for 2 oral antithrombotic agents, warfarin and clopidogrel, to influence use and guide dosing. Limited studies have identified polymorphisms that affect the metabolism and activity of newer oral antithrombotic drugs, without clear evidence of the clinical relevance of such polymorphisms. This article provides an overview of the current status of pharmacogenetics in oral antithrombotic therapy.

PMID: 27514462 [PubMed - in process]

Fundamentals of Pharmacogenetics in Personalized, Precision Medicine.

Clin Lab Med. 2016 Sep;36(3):447-59

Authors: Valdes R, Yin DT

Abstract
This article introduces fundamental principles of pharmacogenetics as applied to personalized and precision medicine. Pharmacogenetics establishes relationships between pharmacology and genetics by connecting phenotypes and genotypes in predicting the response of therapeutics in individual patients. We describe differences between precision and personalized medicine and relate principles of pharmacokinetics and pharmacodynamics to applications in laboratory medicine. We also review basic principles of pharmacogenetics, including its evolution, how it enables the practice of personalized therapeutics, and the role of the clinical laboratory. These fundamentals are a segue for understanding specific clinical applications of pharmacogenetics described in subsequent articles in this issue.

PMID: 27514461 [PubMed - in process]

PIPE: a protein-protein interaction passage extraction module for BioCreative challenge.

Database (Oxford). 2016;2016

Authors: Chang YC, Chu CH, Su YC, Chen CC, Hsu WL

Abstract
Identifying the interactions between proteins mentioned in biomedical literatures is one of the frequently discussed topics of text mining in the life science field. In this article, we propose PIPE, an interaction pattern generation module used in the Collaborative Biocurator Assistant Task at BioCreative V (http://www.biocreative.org/) to capture frequent protein-protein interaction (PPI) patterns within text. We also present an interaction pattern tree (IPT) kernel method that integrates the PPI patterns with convolution tree kernel (CTK) to extract PPIs. Methods were evaluated on LLL, IEPA, HPRD50, AIMed and BioInfer corpora using cross-validation, cross-learning and cross-corpus evaluation. Empirical evaluations demonstrate that our method is effective and outperforms several well-known PPI extraction methods. DATABASE URL.

PMID: 27524807 [PubMed - as supplied by publisher]

A Knowledge Map for Hospital Performance Concept: Extraction and Analysis: A Narrative Review Article.

Iran J Public Health. 2016 Jul;45(7):843-54

Authors: Markazi-Moghaddam N, Arab M, Ravaghi H, Rashidian A, Khatibi T, Zargar Balaye Jame S

Abstract
BACKGROUND: Performance is a multi-dimensional and dynamic concept. During the past 2 decades, considerable studies were performed in developing the hospital performance concept. To know literature key concepts on hospital performance, the knowledge visualization based on co-word analysis and social network analysis has been used.
METHODS: Documents were identified through "PubMed" searching from 1945 to 2014 and 2350 papers entered the study after omitting unrelated articles, the duplicates, and articles without abstract. After pre-processing and preparing articles, the key words were extracted and terms were weighted by TF-IDF weighting schema. Support as an interestingness measure, which considers the co-occurrence of the extracted keywords and "hospital performance" phrase was calculated. Keywords having high support with "hospital performance" are selected. Term-term matrix of these selected keywords is calculated and the graph is extracted.
RESULTS: The most high frequency words after "Hospital Performance" were "mortality" and "efficiency". The major knowledge structure of hospital performance literature during these years shows that the keyword "mortality" had the highest support with hospital performance followed by "quality of care", "quality improvement", "discharge", "length of stay" and "clinical outcome". The strongest relationship is seen between "electronic medical record" and "readmission rate".
CONCLUSION: Some dimensions of hospital performance are more important such as "efficiency", "effectiveness", "quality" and "safety" and some indicators are more highlighted such as "mortality", "length of stay", "readmission rate" and "patient satisfaction". In the last decade, some concepts became more significant in hospital performance literature such as "mortality", "quality of care" and "quality improvement".

PMID: 27516990 [PubMed]

BioC viewer: a web-based tool for displaying and merging annotations in BioC.

Database (Oxford). 2016;2016

Authors: Shin SY, Kim S, Wilbur WJ, Kwon D

Abstract
BioC is an XML-based format designed to provide interoperability for text mining tools and manual curation results. A challenge of BioC as a standard format is to align annotations from multiple systems. Ideally, this should not be a major problem if users follow guidelines given by BioC key files. Nevertheless, the misalignment between text and annotations happens quite often because different systems tend to use different software development environments, e.g. ASCII vs. Unicode. We first implemented the BioC Viewer to assist BioGRID curators as a part of the BioCreative V BioC track (Collaborative Biocurator Assistant Task). For the BioC track, the BioC Viewer helped curate protein-protein interaction and genetic interaction pairs appearing in full-text articles. Here, we describe the BioC Viewer itself as well as improvements made to the BioC Viewer since the BioCreative V Workshop to address the misalignment issue of BioC annotations. While uploading BioC files, a BioC merge process is offered when there are files from the same full-text article. If there is a mismatch between an annotated offset and text, the BioC Viewer adjusts the offset to correctly align with the text. The BioC Viewer has a user-friendly interface, where most operations can be performed within a few mouse clicks. The feedback from BioGRID curators has been positive for the web interface, particularly for its usability and learnability.Database URL: http://viewer.bioqrator.org.

PMID: 27515823 [PubMed - in process]

24 new pubmed citations were retrieved for your search. Click on the search hyperlink below to display the complete search results:

("orphan disease" OR "rare disease" OR "orphan diseases" OR "rare diseases")

These pubmed results were generated on 2016/08/12

PubMed comprises more than 24 million citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites.

11 new pubmed citations were retrieved for your search. Click on the search hyperlink below to display the complete search results:

"Cystic Fibrosis"

These pubmed results were generated on 2016/08/12

PubMed comprises more than 24 million citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites.

Connection Map for Compounds (CMC): A Server for Combinatorial Drug Toxicity and Efficacy Analysis.

J Chem Inf Model. 2016 Aug 10;

Authors: Liu L, Tsompana M, Wang Y, Wu D, Zhu L, Zhu R

Abstract
Drug discovery and development is a costly and time-consuming process with a high risk for failure resulting primarily from a drug's associated clinical safety and efficacy potential. Identifying and eliminating inapt candidate drugs as early as possible is an effective way for reducing unnecessary costs, but limited analytical tools are currently available for this purpose. Recent growth in the area of toxicogenomics and pharmacogenomics has provided with a vast amount of drug expression microarray data. Web servers such as CMap and LTMap have used this information to evaluate drug toxicity and mechanisms of action independently, however their wider applicability has been limited by the lack of a combinatorial drug-safety type of analysis. Using available genome-wide drug transcriptional expression profiles, we developed the first web server for combinatorial evaluation of toxicity and efficacy of candidate drugs named "Connection Map for Compounds" (CMC). Using CMC, researchers can initially compare their query drug gene signatures with prebuilt gene profiles generated from two large-scale toxicogenomics databases, and subsequently perform a drug efficacy analysis for identification of known mechanisms of drug action or generation of new predictions. CMC provides a novel approach for drug repositioning and early evaluation in drug discovery with its unique combination of toxicity and efficacy analyses, expansibility of data and algorithms, and customization of reference gene profiles. CMC can be freely accessed at http://cadd.tongji.edu.cn/webserver/CMCbp.jsp.

PMID: 27508329 [PubMed - as supplied by publisher]

Identification of Trypanocidal Activity for Known Clinical Compounds Using a New Trypanosoma cruzi Hit-Discovery Screening Cascade.

PLoS Negl Trop Dis. 2016 Apr;10(4):e0004584

Authors: De Rycker M, Thomas J, Riley J, Brough SJ, Miles TJ, Gray DW

Abstract
Chagas disease is a significant health problem in Latin America and the available treatments have significant issues in terms of toxicity and efficacy. There is thus an urgent need to develop new treatments either via a repurposing strategy or through the development of new chemical entities. A key first step is the identification of compounds with anti-Trypanosoma cruzi activity from compound libraries. Here we describe a hit discovery screening cascade designed to specifically identify hits that have the appropriate anti-parasitic properties to warrant further development. The cascade consists of a primary imaging-based assay followed by newly developed and appropriately scaled secondary assays to predict the cidality and rate-of-kill of the compounds. Finally, we incorporated a cytochrome P450 CYP51 biochemical assay to remove compounds that owe their phenotypic response to inhibition of this enzyme. We report the use of the cascade in profiling two small libraries containing clinically tested compounds and identify Clemastine, Azelastine, Ifenprodil, Ziprasidone and Clofibrate as molecules having appropriate profiles. Analysis of clinical derived pharmacokinetic and toxicity data indicates that none of these are appropriate for repurposing but they may represent suitable start points for further optimisation for the treatment of Chagas disease.

PMID: 27082760 [PubMed - indexed for MEDLINE]

Meeting report: 28th International Conference on Antiviral Research in Rome, Italy.

Antiviral Res. 2015 Nov;123:172-87

Authors: Vere Hodge RA

Abstract
The 28th International Conference on Antiviral Research (ICAR) was held in Rome, Italy from May 11 to 15, 2015. This article summarizes the principal invited lectures. Phillip Furman, the Elion award recipient, described the research leading to sofosbuvir. Dennis Liotta, who received the Holý award, described how an investigation into HIV entry inhibitors led to a new therapy for cancer patients. Erica Ollmann Saphire, winner of the Prusoff Young Investigator award, explored the world of viral proteins and how they remodel to perform different essential roles in viral replication. The keynote addresses, by Raffaele De Francesco and Michael Manns, reported on the remarkable progress made in the therapy of chronic HCV infections. A third keynote address, by Armand Sprecher, related the difficulties and successes of Médicins Sans Frontières in West Africa ravaged by the Ebola outbreak. There were three mini-symposia on RNA Viruses, Antiviral Chemistry and Emerging Viruses. There was a good collection of talks on RNA viruses (norovirus, rabies, dengue, HEV, HCV, and RSV). A highlight of the chemistry was the preparation of prodrugs for nucleotide triphosphates as this opens a door to new options. The third mini-symposium emphasized how research work in the antiviral area is continuing to expand and needs to do so with a sense of urgency. Although this meeting report covers only a few of the presentations, it aims to illustrate the great diversity of topics discussed at ICAR, bringing together knowledge and expertise from the whole spectrum of antiviral research.

PMID: 26431686 [PubMed - indexed for MEDLINE]

DL-ADR: a novel deep learning model for classifying genomic variants into adverse drug reactions.

BMC Med Genomics. 2016;9 Suppl 2:48

Authors: Liang Z, Huang JX, Zeng X, Zhang G

Abstract
BACKGROUND: Genomic variations are associated with the metabolism and the occurrence of adverse reactions of many therapeutic agents. The polymorphisms on over 2000 locations of cytochrome P450 enzymes (CYP) due to many factors such as ethnicity, mutations, and inheritance attribute to the diversity of response and side effects of various drugs. The associations of the single nucleotide polymorphisms (SNPs), the internal pharmacokinetic patterns and the vulnerability of specific adverse reactions become one of the research interests of pharmacogenomics. The conventional genomewide association studies (GWAS) mainly focuses on the relation of single or multiple SNPs to a specific risk factors which are a one-to-many relation. However, there are no robust methods to establish a many-to-many network which can combine the direct and indirect associations between multiple SNPs and a serial of events (e.g. adverse reactions, metabolic patterns, prognostic factors etc.). In this paper, we present a novel deep learning model based on generative stochastic networks and hidden Markov chain to classify the observed samples with SNPs on five loci of two genes (CYP2D6 and CYP1A2) respectively to the vulnerable population of 14 types of adverse reactions.
METHODS: A supervised deep learning model is proposed in this study. The revised generative stochastic networks (GSN) model with transited by the hidden Markov chain is used. The data of the training set are collected from clinical observation. The training set is composed of 83 observations of blood samples with the genotypes respectively on CYP2D6*2, *10, *14 and CYP1A2*1C, *1 F. The samples are genotyped by the polymerase chain reaction (PCR) method. A hidden Markov chain is used as the transition operator to simulate the probabilistic distribution. The model can perform learning at lower cost compared to the conventional maximal likelihood method because the transition distribution is conditional on the previous state of the hidden Markov chain. A least square loss (LASSO) algorithm and a k-Nearest Neighbors (kNN) algorithm are used as the baselines for comparison and to evaluate the performance of our proposed deep learning model.
RESULTS: There are 53 adverse reactions reported during the observation. They are assigned to 14 categories. In the comparison of classification accuracy, the deep learning model shows superiority over the LASSO and kNN model with a rate over 80 %. In the comparison of reliability, the deep learning model shows the best stability among the three models.
CONCLUSIONS: Machine learning provides a new method to explore the complex associations among genomic variations and multiple events in pharmacogenomics studies. The new deep learning algorithm is capable of classifying various SNPs to the corresponding adverse reactions. We expect that as more genomic variations are added as features and more observations are made, the deep learning model can improve its performance and can act as a black-box but reliable verifier for other GWAS studies.

PMID: 27510822 [PubMed - in process]

Predicting Clearance Mechanism in Drug Discovery: Extended Clearance Classification System (ECCS).

Pharm Res. 2015 Dec;32(12):3785-802

Authors: Varma MV, Steyn SJ, Allerton C, El-Kattan AF

Abstract
Early prediction of clearance mechanisms allows for the rapid progression of drug discovery and development programs, and facilitates risk assessment of the pharmacokinetic variability associated with drug interactions and pharmacogenomics. Here we propose a scientific framework--Extended Clearance Classification System (ECCS)--which can be used to predict the predominant clearance mechanism (rate-determining process) based on physicochemical properties and passive membrane permeability. Compounds are classified as: Class 1A--metabolism as primary systemic clearance mechanism (high permeability acids/zwitterions with molecular weight (MW) ≤400 Da), Class 1B--transporter-mediated hepatic uptake as primary systemic clearance mechanism (high permeability acids/zwitterions with MW >400 Da), Class 2--metabolism as primary clearance mechanism (high permeability bases/neutrals), Class 3A--renal clearance (low permeability acids/zwitterions with MW ≤400 Da), Class 3B--transporter mediated hepatic uptake or renal clearance (low permeability acids/zwitterions with MW >400 Da), and Class 4--renal clearance (low permeability bases/neutrals). The performance of the ECCS framework was validated using 307 compounds with single clearance mechanism contributing to ≥70% of systemic clearance. The apparent permeability across clonal cell line of Madin - Darby canine kidney cells, selected for low endogenous efflux transporter expression, with a cut-off of 5 × 10(-6) cm/s was used for permeability classification, and the ionization (at pH7) was assigned based on calculated pKa. The proposed scheme correctly predicted the rate-determining clearance mechanism to be either metabolism, hepatic uptake or renal for ~92% of total compounds. We discuss the general characteristics of each ECCS class, as well as compare and contrast the framework with the biopharmaceutics classification system (BCS) and the biopharmaceutics drug disposition classification system (BDDCS). Collectively, the ECCS framework is valuable in early prediction of clearance mechanism and can aid in choosing the right preclinical tool kit and strategy for optimizing drug exposure and evaluating clinical risk of pharmacokinetic variability caused by drug interactions and pharmacogenomics.

PMID: 26155985 [PubMed - indexed for MEDLINE]