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Drug and disease model resources: a consortium to create standards and tools to enhance model-based drug development.

ABSTRACT: Model-based drug development (MBDD) is accepted as a vital approach in understanding patients' drug-related benefit and risk by integrating quantitative information integration from diverse sources collected throughout drug development.(1) This perspective introduces the activities of the Drug and Disease Model Resources (DDMoRe) consortium, founded in 2011 through the Innovative Medicines Initiative Joint Undertaking (IMI-JU)(2) as a European public-private partnership to address a lack of common tools, languages, and standards for modeling and simulation (M&S) to improve model-based knowledge integration.CPT: Pharmacometrics & Systems Pharmacology (2013) 2, e34; doi:10.1038/psp.2013.10; advance online publication 20 March 2013.

SUBMITTER: Harnisch L

PROVIDER: S-EPMC3615532 | biostudies-literature | 2013 Mar

REPOSITORIES: biostudies-literature

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Drug and disease model resources: a consortium to create standards and tools to enhance model-based drug development.

Harnisch L L Matthews I I Chard J J Karlsson M O MO

CPT: pharmacometrics & systems pharmacology 20130320

Model-based drug development (MBDD) is accepted as a vital approach in understanding patients' drug-related benefit and risk by integrating quantitative information integration from diverse sources collected throughout drug development.(1) This perspective introduces the activities of the Drug and Disease Model Resources (DDMoRe) consortium, founded in 2011 through the Innovative Medicines Initiative Joint Undertaking (IMI-JU)(2) as a European public-private partnership to address a lack of comm ...[more]

PMID: 23887647

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Project description:ObjectiveDisease-specific vocabularies are fundamental to many knowledge-based intelligent systems and applications like text annotation, cohort selection, disease diagnostic modeling, and therapy recommendation. Reference standards are critical in the development and validation of automated methods for disease-specific vocabularies. The goal of the present study is to design and test a generalizable method for the development of vocabulary reference standards from expert-curated, disease-specific biomedical literature resources.MethodsWe formed disease-specific corpora from literature resources like textbooks, evidence-based synthesized online sources, clinical practice guidelines, and journal articles. Medical experts annotated and adjudicated disease-specific terms in four classes (i.e., causes or risk factors, signs or symptoms, diagnostic tests or results, and treatment). Annotations were mapped to UMLS concepts. We assessed source variation, the contribution of each source to build disease-specific vocabularies, the saturation of the vocabularies with respect to the number of used sources, and the generalizability of the method with different diseases.ResultsThe study resulted in 2588 string-unique annotations for heart failure in four classes, and 193 and 425 respectively for pulmonary embolism and rheumatoid arthritis in treatment class. Approximately 80% of the annotations were mapped to UMLS concepts. The agreement among heart failure sources ranged between 0.28 and 0.46. The contribution of these sources to the final vocabulary ranged between 18% and 49%. With the sources explored, the heart failure vocabulary reached near saturation in all four classes with the inclusion of minimal six sources (or between four to seven sources if only counting terms occurred in two or more sources). It took fewer sources to reach near saturation for the other two diseases in terms of the treatment class.ConclusionsWe developed a method for the development of disease-specific reference vocabularies. Expert-curated biomedical literature resources are substantial for acquiring disease-specific medical knowledge. It is feasible to reach near saturation in a disease-specific vocabulary using a relatively small number of literature sources.

Project description:Background: A critical step in tuberculosis (TB) drug development is the Phase 2a early bactericidal activity (EBA) study which informs if a new drug or treatment has short-term activity in humans. The aim of this work was to present a standardized pharmacometric model-based early bactericidal activity analysis workflow and determine sample sizes needed to detect early bactericidal activity or a difference between treatment arms. Methods: Seven different steps were identified and developed for a standardized pharmacometric model-based early bactericidal activity analysis approach. Non-linear mixed effects modeling was applied and different scenarios were explored for the sample size calculations. The sample sizes needed to detect early bactericidal activity given different TTP slopes and associated variability was assessed. In addition, the sample sizes needed to detect effect differences between two treatments given the impact of different TTP slopes, variability in TTP slope and effect differences were evaluated. Results: The presented early bactericidal activity analysis approach incorporates estimate of early bactericidal activity with uncertainty through the model-based estimate of TTP slope, variability in TTP slope, impact of covariates and pharmacokinetics on drug efficacy. Further it allows for treatment comparison or dose optimization in Phase 2a. To detect early bactericidal activity with 80% power and at a 5% significance level, 13 and 8 participants/arm were required for a treatment with a TTP-EBA0-14 as low as 11 h when accounting for variability in pharmacokinetics and when variability in TTP slope was 104% [coefficient of variation (CV)] and 22%, respectively. Higher sample sizes are required for smaller early bactericidal activity and when pharmacokinetics is not accounted for. Based on sample size determinations to detect a difference between two groups, TTP slope, variability in TTP slope and effect difference between two treatment arms needs to be considered. Conclusion: In conclusion, a robust standardized pharmacometric model-based EBA analysis approach was established in close collaboration between microbiologists, clinicians and pharmacometricians. The work illustrates the importance of accounting for covariates and drug exposure in EBA analysis in order to increase the power of detecting early bactericidal activity for a single treatment arm as well as differences in EBA between treatments arms in Phase 2a trials of TB drug development.

Project description:BackgroundOnline health information, if evidence-based and unbiased, can improve patients' and caregivers' health knowledge and assist them in disease management and health care decision-making.ObjectiveTo identify standards for the development of health information resources on the internet for patients.MethodsWe searched in MEDLINE, CINAHL, Scopus, Web of Science, and Google Scholar for publications describing evaluation instruments for websites providing health information. Eligible instruments were identified by three independent reviewers and disagreements resolved by consensus. Items reported were extracted and categorized into seven domains (accuracy, completeness and comprehensiveness, technical elements, design and aesthetics, usability, accessibility, and readability) that were previously thought to be a minimum requirement for websites.ResultsOne hundred eleven articles met inclusion criteria, reporting 92 evaluation instruments (1609 items). We found 74 unique items that we grouped into the seven domains. For the accuracy domain, one item evaluated information provided in concordance with current guidelines. For completeness and comprehensiveness, 18 items described the disease with respect to various topics such as etiology or therapy, among others. For technical elements, 27 items evaluated disclosure of authorship, sponsorship, affiliation, editorial process, feedback process, privacy, and data protection. For design and aesthetics, 10 items evaluated consistent layout and relevant graphics and images. For usability, 10 items evaluated ease of navigation and functionality of internal search engines. For accessibility, five items evaluated the availability of websites to people with audiovisual disabilities. For readability, three items evaluated conversational writing style and use of a readability tool to determine the reading level of the text.ConclusionWe identified standards for the development of online patient health information. This proposed instrument can serve as a guideline to develop and improve how health information is presented on the internet.

Dataset Information

Drug and disease model resources: a consortium to create standards and tools to enhance model-based drug development.

Publications

Drug and disease model resources: a consortium to create standards and tools to enhance model-based drug development.

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