Browse
Submit Data
Databases
API
Help

Dataset Information

27 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Improving the predictive value of interventional animal models data.

ABSTRACT: For many chronic diseases, translational success using the animal model paradigm has reached an impasse. Using Alzheimer's disease as an example, this review employs a networks-based method to assess repeatability of outcomes across species, by intervention and mechanism. Over 75% of animal studies reported an improved outcome. Strain background was a significant potential confounder. Five percent of interventions had been tested across animals and humans, or examined across three or more animal models. Positive outcomes across species emerged for donepezil, memantine and exercise. Repeatable positive outcomes in animals were identified for the amyloid hypothesis and three additional mechanisms. This approach supports in silico reduction of positive outcomes bias in animal studies.

SUBMITTER: Zeiss CJ

PROVIDER: S-EPMC4417064 | biostudies-literature | 2015 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Improving the predictive value of interventional animal models data.

Zeiss Caroline J CJ

Drug discovery today 20141104 4

For many chronic diseases, translational success using the animal model paradigm has reached an impasse. Using Alzheimer's disease as an example, this review employs a networks-based method to assess repeatability of outcomes across species, by intervention and mechanism. Over 75% of animal studies reported an improved outcome. Strain background was a significant potential confounder. Five percent of interventions had been tested across animals and humans, or examined across three or more animal ...[more]

PMID: 25448761

Similar Datasets

Improving the Positive Predictive Value of Non-invasive Prenatal Screening (NIPS)

Project description:Assessment of maternal blood to detect maternal microduplication causing false positive trisomy result in fetus

2017-05-22 | GSE84810 | GEO

Improving predictive models for Alzheimer's disease using GWAS data by incorporating misclassified samples modeling.

Project description:Late-onset Alzheimer's Disease (LOAD) is the most common form of dementia in the elderly. Genome-wide association studies (GWAS) for LOAD have open new avenues to identify genetic causes and to provide diagnostic tools for early detection. Although several predictive models have been proposed using the few detected GWAS markers, there is still a need for improvement and identification of potential markers. Commonly, polygenic risk scores are being used for prediction. Nevertheless, other methods to generate predictive models have been suggested. In this research, we compared three machine learning methods that have been proved to construct powerful predictive models (genetic algorithms, LASSO, and step-wise) and propose the inclusion of markers from misclassified samples to improve overall prediction accuracy. Our results show that the addition of markers from an initial model plus the markers of the model fitted to misclassified samples improves the area under the receiving operative curve by around 5%, reaching ~0.84, which is highly competitive using only genetic information. The computational strategy used here can help to devise better methods to improve classification models for AD. Our results could have a positive impact on the early diagnosis of Alzheimer's disease.

| S-EPMC7179850 | biostudies-literature

IMPROVING PREDICTIVE MODELS OF IN-STREAM PHOSPHORUS CONCENTRATION BASED ON NATIONALLY-AVAILABLE SPATIAL DATA COVERAGES.

Project description:Spatial data are playing an increasingly important role in watershed science and management. Large investments have been made by government agencies to provide nationally-available spatial databases; however, their relevance and suitability for local watershed applications is largely unscrutinized. We investigated how goodness of fit and predictive accuracy of total phosphorus (TP) concentration models developed from nationally-available spatial data could be improved by including local watershed-specific data in the East Fork of the Little Miami River, Ohio, a 1290 km2 watershed. We also determined whether a spatial stream network (SSN) modeling approach improved on multiple linear regression (nonspatial) models. Goodness of fit and predictive accuracy were highest for the SSN model that included local covariates, and lowest for the nonspatial model developed from national data. Septic systems and point source TP loads were significant covariates in the local models. These local data not only improved the models but enabled a more explicit interpretation of the processes affecting TP concentrations than more generic national covariates. The results suggest that SSN modeling greatly improves prediction and should be applied when using national covariates. Including local covariates further increases the accuracy of TP predictions throughout the studied watershed; such variables should be included in future national databases, particularly the locations of septic systems.

| S-EPMC6052460 | biostudies-literature

Medications development for the treatment of alcohol use disorder: insights into the predictive value of animal and human laboratory models.

Project description:Development of effective treatments for alcohol use disorder (AUD) represents an important public health goal. This review provides a summary of completed preclinical and clinical studies testing pharmacotherapies for the treatment of AUD. We discuss opportunities for improving the translation from preclinical findings to clinical trial outcomes, focusing on the validity and predictive value of animal and human laboratory models of AUD. Specifically, while preclinical studies of medications development have offered important insights into the neurobiology of the disorder and alcohol's molecular targets, limitations include the lack of standardized methods and streamlined processes whereby animal studies can readily inform human studies. Behavioral pharmacology studies provide a less expensive and valuable opportunity to assess the feasibility of a pharmacotherapy prior to initiating larger scale clinical trials by providing insights into the mechanism of the drug, which can then inform recruitment, analyses, and assessments. Summary tables are provided to illustrate the wide range of preclinical, human laboratory, and clinical studies of medications development for alcoholism. Taken together, this review highlights the challenges associated with animal paradigms, human laboratory studies, and clinical trials with the overarching goal of advancing treatment development and highlighting opportunities to bridge the gap between preclinical and clinical research.

| S-EPMC4969222 | biostudies-literature

Improving the Positive Predictive Value of Non-Invasive Prenatal Screening (NIPS).

Project description:We evaluated performance characteristics of a laboratory-developed, non-invasive prenatal screening (NIPS) assay for fetal aneuploidies. This assay employs massively parallel shotgun sequencing with full automation. GC sequencing bias correction and statistical smoothing were performed to enhance discrimination of affected and unaffected pregnancies. Maternal plasma samples from pregnancies with known aneuploidy status were used for assay development, verification, and validation. Assay verification studies using 2,085 known samples (1873 unaffected, 69 trisomy 21, 20 trisomy 18, 17 trisomy 13) demonstrated complete discrimination between autosomal trisomy (Z scores >8) and unaffected (Z scores <4) singleton pregnancies. A validation study using 552 known samples (21 trisomy 21, 10 trisomy 18, 1 trisomy 13) confirmed complete discrimination. Twin pregnancies showed similar results. Follow-up of abnormal results from the first 10,000 clinical samples demonstrated PPVs of 98% (41/42) for trisomy 21, 92% (23/25) for trisomy 18, and 69% (9/13) for trisomy 13. Adjustment for causes of false-positive results identified during clinical testing (eg, maternal duplications) improved PPVs to 100% for trisomy 21 and 96% for trisomy 18. This NIPS test demonstrates excellent discrimination between trisomic and unaffected pregnancies. The PPVs obtained in initial clinical testing are substantially higher than previously reported NIPS methods.

| S-EPMC5382935 | biostudies-literature

Predictive animal models of mania: hits, misses and future directions.

Project description:Mania has long been recognized as aberrant behaviour indicative of mental illness. Manic states include a variety of complex and multifaceted symptoms that challenge clear clinical distinctions. Symptoms include over-activity, hypersexuality, irritability and reduced need for sleep, with cognitive deficits recently linked to functional outcome. Current treatments have arisen through serendipity or from other disorders. Hence, treatments are not efficacious for all patients, and there is an urgent need to develop targeted therapeutics. Part of the drug discovery process is the assessment of therapeutics in animal models. Here we review pharmacological, environmental and genetic manipulations developed to test the efficacy of therapeutics in animal models of mania. The merits of these models are discussed in terms of the manipulation used and the facet of mania measured. Moreover, the predictive validity of these models is discussed in the context of differentiating drugs that succeed or fail to meet criteria as approved mania treatments. The multifaceted symptomatology of mania has not been reflected in the majority of animal models, where locomotor activity remains the primary measure. This approach has resulted in numerous false positives for putative treatments. Recent work highlights the need to utilize multivariate strategies to enable comprehensive assessment of affective and cognitive dysfunction. Advances in therapeutic treatment may depend on novel models developed with an integrated approach that includes: (i) a comprehensive battery of tests for different aspects of mania, (ii) utilization of genetic information to establish aetiological validity and (iii) objective quantification of patient behaviour with translational cross-species paradigms.

| S-EPMC3229761 | biostudies-literature

Stem cell transplantation in neurological diseases: improving effectiveness in animal models.

Project description:Neurological diseases afflict a growing proportion of the human population. There are two reasons for this: first, the average age of the population (especially in the industrialized world) is increasing, and second, the diagnostic tools to detect these pathologies are now more sophisticated and can be used on a higher percentage of the population. In many cases, neurological disease has a pharmacological treatment which, as in the case of Alzheimer's disease, Parkinson's disease, Epilepsy, and Multiple Sclerosis can reduce the symptoms and slow down the course of the disease but cannot reverse its effects or heal the patient. In the last two decades the transplantation approach, by means of stem cells of different origin, has been suggested for the treatment of neurological diseases. The choice of slightly different animal models and the differences in methods of stem cell preparation make it difficult to compare the results of transplantation experiments. Moreover, the translation of these results into clinical trials with human subjects is difficult and has so far met with little success. This review seeks to discuss the reasons for these difficulties by considering the differences between human and animal cells (including isolation, handling and transplantation) and between the human disease model and the animal disease model.

| S-EPMC4206985 | biostudies-literature

Data based predictive models for odor perception.

Project description:Machine learning and data analytics are being increasingly used for quantitative structure property relation (QSPR) applications in the chemical domain where the traditional Edisonian approach towards knowledge-discovery have not been fruitful. The perception of odorant stimuli is one such application as olfaction is the least understood among all the other senses. In this study, we employ machine learning based algorithms and data analytics to address the efficacy of using a data-driven approach to predict the perceptual attributes of an odorant namely the odorant characters (OC) of "sweet" and "musky". We first analyze a psychophysical dataset containing perceptual ratings of 55 subjects to reveal patterns in the ratings given by subjects. We then use the data to train several machine learning algorithms such as random forest, gradient boosting and support vector machine for prediction of the odor characters and report the structural features correlating well with the odor characters based on the optimal model. Furthermore, we analyze the impact of the data quality on the performance of the models by comparing the semantic descriptors generally associated with a given odorant to its perception by majority of the subjects. The study presents a methodology for developing models for odor perception and provides insights on the perception of odorants by untrained human subjects and the effect of the inherent bias in the perception data on the model performance. The models and methodology developed here could be used for predicting odor characters of new odorants.

| S-EPMC7553929 | biostudies-literature

Preclinical Development of Orally Inhaled Drugs (OIDs)-Are Animal Models Predictive or Shall We Move Towards In Vitro Non-Animal Models?

Project description:Respiratory diseases constitute a huge burden in our society, and the global respiratory drug market currently grows at an annual rate between 4% and 6%. Inhalation is the preferred administration method for treating respiratory diseases, as it: (i) delivers the drug directly at the site of action, resulting in a rapid onset; (ii) is painless, thus improving patients' compliance; and (iii) avoids first-pass metabolism reducing systemic side effects. Inhalation occurs through the mouth, with the drug generally exerting its therapeutic action in the lungs. In the most recent years, orally inhaled drugs (OIDs) have found application also in the treatment of systemic diseases. OIDs development, however, currently suffers of an overall attrition rate of around 70%, meaning that seven out of 10 new drug candidates fail to reach the clinic. Our commentary focuses on the reasons behind the poor OIDs translation into clinical products for the treatment of respiratory and systemic diseases, with particular emphasis on the parameters affecting the predictive value of animal preclinical tests. We then review the current advances in overcoming the limitation of animal animal-based studies through the development and adoption of in vitro, cell-based new approach methodologies (NAMs).

| S-EPMC7460012 | biostudies-literature

Improving the Predictive Value of Preclinical Studies in Support of Radiotherapy Clinical Trials.

Project description:There is an urgent need to improve reproducibility and translatability of preclinical data to fully exploit opportunities for molecular therapeutics involving radiation and radiochemotherapy. For in vitro research, the clonogenic assay remains the current state-of-the-art of preclinical assays, whereas newer moderate and high-throughput assays offer the potential for rapid initial screening. Studies of radiation response modification by molecularly targeted agents can be improved using more physiologic 3D culture models. Elucidating effects on the cancer stem cells (CSC, and CSC-like) and developing biomarkers for defining targets and measuring responses are also important. In vivo studies are necessary to confirm in vitro findings, further define mechanism of action, and address immunomodulation and treatment-induced modification of the microenvironment. Newer in vivo models include genetically engineered and patient-derived xenograft mouse models and spontaneously occurring cancers in domesticated animals. Selection of appropriate endpoints is important for in vivo studies; for example, regrowth delay measures bulk tumor killing, whereas local tumor control assesses effects on CSCs. The reliability of individual assays requires standardization of procedures and cross-laboratory validation. Radiation modifiers must be tested as part of clinical standard of care, which includes radiochemotherapy for most tumors. Radiation models are compatible with but also differ from those used for drug screening. Furthermore, the mechanism of a drug as a chemotherapeutic agent may be different from its interaction with radiation and/or radiochemotherapy. This provides an opportunity to expand the use of molecular-targeted agents. Clin Cancer Res; 22(13); 3138-47. ©2016 AACR.

| S-EPMC4930691 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data