Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Biomarker method validation in anticancer drug development.

ABSTRACT: Over recent years the role of biomarkers in anticancer drug development has expanded across a spectrum of applications ranging from research tool during early discovery to surrogate endpoint in the clinic. However, in Europe when biomarker measurements are performed on samples collected from subjects entered into clinical trials of new investigational agents, laboratories conducting these analyses become subject to the Clinical Trials Regulations. While these regulations are not specific in their requirements of research laboratories, quality assurance and in particular assay validation are essential. This review, therefore, focuses on a discussion of current thinking in biomarker assay validation. Five categories define the majority of biomarker assays from 'absolute quantitation' to 'categorical'. Validation must therefore take account of both the position of the biomarker in the spectrum towards clinical end point and the level of quantitation inherent in the methodology. Biomarker assay validation should be performed ideally in stages on 'a fit for purpose' basis avoiding unnecessarily dogmatic adherence to rigid guidelines but with careful monitoring of progress at the end of each stage. These principles are illustrated with two specific examples: (a) absolute quantitation of protein biomarkers by mass spectrometry and (b) the M30 and M65 ELISA assays as surrogate end points of cell death.

SUBMITTER: Cummings J

PROVIDER: S-EPMC2259203 | biostudies-other | 2008 Feb

REPOSITORIES: biostudies-other

ACCESS DATA

Json Xml

Similar Datasets

CLIP2C method development and validation

Project description:In this experiment we test our recently developed CLIP2C method for the identification of the RNA molecules bound by specific RNA binding proteins. CLIP2C, involves a first round of 2C, the treatment of the eluates with DNase I and an RNA fragmentation step. After this, a small aliquot is separated to be sequenced as an input, while the rest is subjected for immunoprecipitation of the protein of interest. Libraries from the input and the RNA isolated from the immunoprecipitation are generated and sequenced. In this particular experiment we used Pab1-TAP and Tdh3-Protein A tagged strains to be used in IgG-based pull-downs. An untagged WT strain was also included in the experiment as a negative control. Sequencing data from the inputs was used to address the variability in gene expression between strains and sequencing data from the IPs to detect enriched target genes or regions.

2021-05-28 | E-MTAB-9263 | biostudies-arrayexpress

Comparison and validation of genomic predictors for anticancer drug sensitivity.

Project description:BACKGROUND:An enduring challenge in personalized medicine lies in selecting the right drug for each individual patient. While testing of drugs on patients in large trials is the only way to assess their clinical efficacy and toxicity, we dramatically lack resources to test the hundreds of drugs currently under development. Therefore the use of preclinical model systems has been intensively investigated as this approach enables response to hundreds of drugs to be tested in multiple cell lines in parallel. METHODS:Two large-scale pharmacogenomic studies recently screened multiple anticancer drugs on over 1000 cell lines. We propose to combine these datasets to build and robustly validate genomic predictors of drug response. We compared five different approaches for building predictors of increasing complexity. We assessed their performance in cross-validation and in two large validation sets, one containing the same cell lines present in the training set and another dataset composed of cell lines that have never been used during the training phase. RESULTS:Sixteen drugs were found in common between the datasets. We were able to validate multivariate predictors for three out of the 16 tested drugs, namely irinotecan, PD-0325901, and PLX4720. Moreover, we observed that response to 17-AAG, an inhibitor of Hsp90, could be efficiently predicted by the expression level of a single gene, NQO1. CONCLUSION:These results suggest that genomic predictors could be robustly validated for specific drugs. If successfully validated in patients' tumor cells, and subsequently in clinical trials, they could act as companion tests for the corresponding drugs and play an important role in personalized medicine.

| S-EPMC3721163 | biostudies-literature

A network flow-based method to predict anticancer drug sensitivity.

Project description:Predicting anticancer drug sensitivity can enhance the ability to individualize patient treatment, thus making development of cancer therapies more effective and safe. In this paper, we present a new network flow-based method, which utilizes the topological structure of pathways, for predicting anticancer drug sensitivities. Mutations and copy number alterations of cancer-related genes are assumed to change the pathway activity, and pathway activity difference before and after drug treatment is used as a measure of drug response. In our model, Contributions from different genetic alterations are considered as free parameters, which are optimized by the drug response data from the Cancer Genome Project (CGP). 10-fold cross validation on CGP data set showed that our model achieved comparable prediction results with existing elastic net model using much less input features.

| S-EPMC4436355 | biostudies-literature

In Vitro Release Study of the Polymeric Drug Nanoparticles: Development and Validation of a Novel Method.

Project description:The in vitro release study is a critical test to assess the safety, efficacy, and quality of nanoparticle-based drug delivery systems, but there is no compendial or regulatory standard. The variety of testing methods makes direct comparison among different systems difficult. We herein proposed a novel sample and separate (SS) method by combining the United States Pharmacopeia (USP) apparatus II (paddle) with well-validated centrifugal ultrafiltration (CU) technique that efficiently separated the free drug from nanoparticles. Polymeric drug nanoparticles were prepared by using a four-stream multi-inlet vortex mixer with d-α-tocopheryl polyethylene glycol 1000 succinate as a stabilizer. Itraconazole, cholecalciferol, and flurbiprofen were selected to produce three different nanoparticles with particle size <100 nm. By comparing with the dialysis membrane (DM) method and the SS methods using syringe filters, this novel SS + CU technique was considered the most appropriate in terms of the accuracy and repeatability to provide the in vitro release kinetics of nanoparticles. Interestingly, the DM method appeared to misestimate the release kinetics of nanoparticles through separate mechanisms. This work offers a superior analytical technique for studying in vitro drug release from polymeric nanoparticles, which could benefit the future development of in vitro-in vivo correlation of polymeric nanoparticles.

| S-EPMC7465254 | biostudies-literature

Autoencoder Based Feature Selection Method for Classification of Anticancer Drug Response.

Project description:Anticancer drug responses can be varied for individual patients. This difference is mainly caused by genetic reasons, like mutations and RNA expression. Thus, these genetic features are often used to construct classification models to predict the drug response. This research focuses on the feature selection issue for the classification models. Because of the vast dimensions of the feature space for predicting drug response, the autoencoder network was first built, and a subset of inputs with the important contribution was selected. Then by using the Boruta algorithm, a further small set of features was determined for the random forest, which was used to predict drug response. Two datasets, GDSC and CCLE, were used to illustrate the efficiency of the proposed method.

| S-EPMC6445890 | biostudies-literature

Tuberculosis biomarker validation cohort

Project description:Tuberculosis biomarker validation cohort

| PRJNA616117 | ENA

Tuberculosis biomarker validation cohort

Project description:Sample collection and identification of biomarker for personalized therapy in tuberculosis patients

2020-03-30 | GSE147689 | GEO

Predicting Antituberculosis Drug-Induced Liver Injury Using an Interpretable Machine Learning Method: Model Development and Validation Study.

Project description:BackgroundTuberculosis (TB) is a pandemic, being one of the top 10 causes of death and the main cause of death from a single source of infection. Drug-induced liver injury (DILI) is the most common and serious side effect during the treatment of TB.ObjectiveWe aim to predict the status of liver injury in patients with TB at the clinical treatment stage.MethodsWe designed an interpretable prediction model based on the XGBoost algorithm and identified the most robust and meaningful predictors of the risk of TB-DILI on the basis of clinical data extracted from the Hospital Information System of Shenzhen Nanshan Center for Chronic Disease Control from 2014 to 2019.ResultsIn total, 757 patients were included, and 287 (38%) had developed TB-DILI. Based on values of relative importance and area under the receiver operating characteristic curve, machine learning tools selected patients' most recent alanine transaminase levels, average rate of change of patients' last 2 measures of alanine transaminase levels, cumulative dose of pyrazinamide, and cumulative dose of ethambutol as the best predictors for assessing the risk of TB-DILI. In the validation data set, the model had a precision of 90%, recall of 74%, classification accuracy of 76%, and balanced error rate of 77% in predicting cases of TB-DILI. The area under the receiver operating characteristic curve score upon 10-fold cross-validation was 0.912 (95% CI 0.890-0.935). In addition, the model provided warnings of high risk for patients in advance of DILI onset for a median of 15 (IQR 7.3-27.5) days.ConclusionsOur model shows high accuracy and interpretability in predicting cases of TB-DILI, which can provide useful information to clinicians to adjust the medication regimen and avoid more serious liver injury in patients.

| S-EPMC8335604 | biostudies-literature

Development and validation of novel biomarker assays for osteoarthritis.

Project description:BackgroundOsteoarthritis (OA) is the most common chronic joint disease usually diagnosed at relatively advanced stages when there is irreparable damage to the joint(s). Recently, we have identified two novel biomarkers C3f and V65 which appear to be OA-specific and therefore potential markers of early disease. We report the development of immunoassays for quantitative measure of these two novel biomarkers.MethodMonoclonal and polyclonal antibodies were generated by immunising mouse and rabbits respectively with peptide-carrier conjugates of C3f and V65. Affinity purified antibodies were used for immunoassays development and assays validated using serum from OA patients and controls.ResultsThe ELISAs developed showed spiked recovery of up to 96% for C3f and V65 peptides depending on serum dilutions with a coefficient of variation (CV) <10%. The intra- and inter-assay CVs for C3f and V65 were 1.3-10.8% and 4.2-10.3% respectively. Both assays were insensitive for measurements of the peptides in patients and the use of different signal amplification systems did not increase assay sensitivity.ConclusionWe have developed two immunoassays for measurements of C3f and V65 peptides biomarkers discovered by our earlier proteomic study. These assays could detect the endogenous peptides in serum samples from patients and controls but lacked sensitivity for accurate measurements of the peptides in patients. Our study highlights the difficulties and challenges of validating biomarker from proteomic studies and demonstrates how to overcome some of the technical challenges associated with developing immunoassays for small peptides.

| S-EPMC5513499 | biostudies-literature

Biomarker validation and testing.

Project description:A tumor biomarker is a molecular or process-based change that reflects the status of an underlying malignancy. A tumor biomarker may be identified and measured by one or more assays, or tests, for the biomarker. Increasingly, tumor biomarker tests are being used to drive patient management, either by identifying patients who do not require any, or any further, treatment, or by identifying patients whose tumors are so unlikely to respond to a given type of treatment that it will cause more harm than good. A tumor biomarker assay should only be used to guide management if it has analytical validity, meaning that it is accurate, reproducible, and reliable, and if it has been shown to have clinical utility. The latter implies that high levels of evidence are available that demonstrate that application of the tumor biomarker test for a given use context results in better outcomes, or similar outcomes with less cost, than if the assay were not applied. Use contexts include risk categorization, screening, differential diagnosis, prognosis, prediction of therapeutic activity or monitoring disease course. Very few tumor biomarker tests have passed these high bars for routine clinical application. However, if tumor biomarker tests are going to be used to drive patient care, than an understanding, and careful assessment, of these concepts are essential, since "A Bad Tumor Biomarker Test Is as Bad as a Bad Drug."

| S-EPMC5528748 | biostudies-other

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