Browse
Submit Data
Databases
API
Help

Dataset Information

21 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Active Clinical Trials for Personalized Medicine.

ABSTRACT: Individualized treatment rules (ITRs) tailor treatments according to individual patient characteristics. They can significantly improve patient care and are thus becoming increasingly popular. The data collected during randomized clinical trials are often used to estimate the optimal ITRs. However, these trials are generally expensive to run, and, moreover, they are not designed to efficiently estimate ITRs. In this article, we propose a cost-effective estimation method from an active learning perspective. In particular, our method recruits only the "most informative" patients (in terms of learning the optimal ITRs) from an ongoing clinical trial. Simulation studies and real-data examples show that our active clinical trial method significantly improves on competing methods. We derive risk bounds and show that they support these observed empirical advantages. Supplementary materials for this article are available online.

SUBMITTER: Minsker S

PROVIDER: S-EPMC5179145 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Active Clinical Trials for Personalized Medicine.

Minsker Stanislav S Zhao Ying-Qi YQ Cheng Guang G

Journal of the American Statistical Association 20160818 514

Individualized treatment rules (ITRs) tailor treatments according to individual patient characteristics. They can significantly improve patient care and are thus becoming increasingly popular. The data collected during randomized clinical trials are often used to estimate the optimal ITRs. However, these trials are generally expensive to run, and, moreover, they are not designed to efficiently estimate ITRs. In this article, we propose a cost-effective estimation method from an active learning p ...[more]

PMID: 28018014

Similar Datasets

Designing and analyzing clinical trials for personalized medicine via Bayesian models.

Project description:Patients with different characteristics (e.g., biomarkers, risk factors) may have different responses to the same medicine. Personalized medicine clinical studies that are designed to identify patient subgroup treatment efficacies can benefit patients and save medical resources. However, subgroup treatment effect identification complicates the study design in consideration of desired operating characteristics. We investigate three Bayesian adaptive models for subgroup treatment effect identification: pairwise independent, hierarchical, and cluster hierarchical achieved via Dirichlet Process (DP). The impact of interim analysis and longitudinal data modeling on the personalized medicine study design is also explored. Interim analysis is considered since they can accelerate personalized medicine studies in cases where early stopping rules for success or futility are met. We apply integrated two-component prediction method (ITP) for longitudinal data simulation, and simple linear regression for longitudinal data imputation to optimize the study design. The designs' performance in terms of power for the subgroup treatment effects and overall treatment effect, sample size, and study duration are investigated via simulation. We found the hierarchical model is an optimal approach to identifying subgroup treatment effects, and the cluster hierarchical model is an excellent alternative approach in cases where sufficient information is not available for specifying the priors. The interim analysis introduction to the study design lead to the trade-off between power and expected sample size via the adjustment of the early stopping criteria. The introduction of the longitudinal modeling slightly improves the power. These findings can be applied to future personalized medicine studies with discrete or time-to-event endpoints.

| S-EPMC8084911 | biostudies-literature

Prediction of treatment outcome in clinical trials under a personalized medicine perspective.

Project description:A central problem in most data-driven personalized medicine scenarios is the estimation of heterogeneous treatment effects to stratify individuals into subpopulations that differ in their susceptibility to a particular disease or response to a specific treatment. In this work, with an illustrative example on type 2 diabetes we showed how the increasing ability to access and analyzed open data from randomized clinical trials (RCTs) allows to build Machine Learning applications in a framework of personalized medicine. An ensemble machine learning predictive model is first developed and then applied to estimate the expected treatment response according to the medication that would be prescribed. Machine learning is quickly becoming indispensable to bridge science and clinical practice, but it is not sufficient on its own. A collaborative effort is requested to clinicians, statisticians, and computer scientists to strengthen tools built on machine learning to take advantage of this evidence flow.

| S-EPMC8904517 | biostudies-literature

Personalized medicine in metastatic non-small-cell lung cancer: promising targets and current clinical trials.

Project description:Non-small-cell lung cancer (nsclc) remains the leading cause of cancer-related death globally, with most patients presenting with non-curable disease. Platinum-based doublet chemotherapy has been the cornerstone of treatment for patients with advanced-stage disease and has resulted in a modest increase in overall survival (on the order of an incremental 2 months increased survival per decade) and quality of life. Improved knowledge of the molecular signalling pathways found in nsclc has led to the development of biomarkers with associated targeted therapeutics, thus changing the treatment paradigm for many nsclc patients. In this review, we present a summary of many of the currently investigated nsclc targets, discuss their current clinical trial status, and provide commentary as to the likelihood of their success making a positive impact for nsclc patients.

| S-EPMC3377759 | biostudies-literature

Personalized medicine in a phase I clinical trials program: the MD Anderson Cancer Center initiative.

Project description:PurposeWe initiated a personalized medicine program in the context of early clinical trials, using targeted agents matched with tumor molecular aberrations. Herein, we report our observations.Patient and methodsPatients with advanced cancer were treated in the Clinical Center for Targeted Therapy. Molecular analysis was conducted in the MD Anderson Clinical Laboratory Improvement Amendments (CLIA)-certified laboratory. Patients whose tumors had an aberration were treated with matched targeted therapy, when available. Treatment assignment was not randomized. The clinical outcomes of patients with molecular aberrations treated with matched targeted therapy were compared with those of consecutive patients who were not treated with matched targeted therapy.ResultsOf 1,144 patients analyzed, 460 (40.2%) had 1 or more aberration. In patients with 1 molecular aberration, matched therapy (n = 175) compared with treatment without matching (n = 116) was associated with a higher overall response rate (27% vs. 5%; P < 0.0001), longer time-to-treatment failure (TTF; median, 5.2 vs. 2.2 months; P < 0.0001), and longer survival (median, 13.4 vs. 9.0 months; P = 0.017). Matched targeted therapy was associated with longer TTF compared with their prior systemic therapy in patients with 1 mutation (5.2 vs. 3.1 months, respectively; P < 0.0001). In multivariate analysis in patients with 1 molecular aberration, matched therapy was an independent factor predicting response (P = 0.001) and TTF (P = 0.0001).ConclusionKeeping in mind that the study was not randomized and patients had diverse tumor types and a median of 5 prior therapies, our results suggest that identifying specific molecular abnormalities and choosing therapy based on these abnormalities is relevant in phase I clinical trials.

| S-EPMC4454458 | biostudies-literature

Personalized medicine and the clinical laboratory.

Project description:Personalized medicine is the use of biomarkers, most of them molecular markers, for detection of specific genetic traits to guide various approaches for preventing and treating different conditions. The identification of several genes related to heredity, oncology and infectious diseases lead to the detection of genetic polymorphisms that are involved not only in different clinical progression of these diseases but also in variations in treatment response. Currently, it is possible to detect these polymorphisms using several methodologies: detection of single nucleotide polymorphisms using polymerase chain reaction methods; nucleic acid microarray detection; and nucleic acid sequencing with automatized DNA sequencers using Sanger-derived methods and new generation sequencing. Personalized medicine assays are directed towards detecting genetic variations that alter interactions of drugs with targets or the metabolic pathways of drugs (upstream and downstream) and can be utilized for the selection of drug formulations and detect different immunogenicities of the drug. Personalized medicine applications have already been described in different areas of Medicine and allow specific treatment approaches to be applied to each patient and pathology according to the results of these assays. The application of such a protocol demands an increasing interaction between the clinical laboratory and the clinical staff. For its implementation, a coordinated team composed of basic researchers and physicians highly specialized in their areas supported by a highly specialized team of clinical analysts particularly trained in molecular biology assays is necessary.

| S-EPMC4872953 | biostudies-literature

An eMERGE Clinical Center at Partners Personalized Medicine.

Project description:The integration of electronic medical records (EMRs) and genomic research has become a major component of efforts to advance personalized and precision medicine. The Electronic Medical Records and Genomics (eMERGE) network, initiated in 2007, is an NIH-funded consortium devoted to genomic discovery and implementation research by leveraging biorepositories linked to EMRs. In its most recent phase, eMERGE III, the network is focused on facilitating implementation of genomic medicine by detecting and disclosing rare pathogenic variants in clinically relevant genes. Partners Personalized Medicine (PPM) is a center dedicated to translating personalized medicine into clinical practice within Partners HealthCare. One component of the PPM is the Partners Healthcare Biobank, a biorepository comprising broadly consented DNA samples linked to the Partners longitudinal EMR. In 2015, PPM joined the eMERGE Phase III network. Here we describe the elements of the eMERGE clinical center at PPM, including plans for genomic discovery using EMR phenotypes, evaluation of rare variant penetrance and pleiotropy, and a novel randomized trial of the impact of returning genetic results to patients and clinicians.

| S-EPMC4810384 | biostudies-literature

The challenge to bring personalized cancer medicine from clinical trials into routine clinical practice: the case of the Institut Gustave Roussy.

Project description:Research with high throughput technologies has propitiated the segmentation of different types of tumors into very small subgroups characterized by the presence of very rare molecular alterations. The identification of these subgroups and the apparition of new agents targeting these infrequent alterations are already affecting the way in which clinical trials are being conducted with an increased need to identify those patients harboring specific molecular alterations. In this review we describe some of the currently ongoing and future studies at the Institut Gustave Roussy that aim for the identification of potential therapeutic targets for cancer patients with the incorporation of high throughput technologies into daily practice including aCGH, next generation sequencing and the creation of a software that allows for target identification specific for each tumor. The initial intention is to enrich clinical trials with cancer patients carrying certain molecular alterations in order to increase the possibility of demonstrating benefit from a targeted agent. Mid and long term aims are to facilitate and speed up the process of drug development as well as to implement the concept of personalized medicine.

| S-EPMC5528360 | biostudies-literature

Clinical decision support for genetically guided personalized medicine: a systematic review.

Project description:OBJECTIVE:To review the literature on clinical decision support (CDS) for genetically guided personalized medicine (GPM). MATERIALS AND METHODS:MEDLINE and Embase were searched from 1990 to 2011. The manuscripts included were summarized, and notable themes and trends were identified. RESULTS:Following a screening of 3416 articles, 38 primary research articles were identified. Focal areas of research included family history-driven CDS, cancer management, and pharmacogenomics. Nine randomized controlled trials of CDS interventions for GPM were identified, seven of which reported positive results. The majority of manuscripts were published on or after 2007, with increased recent focus on genotype-driven CDS and the integration of CDS within primary clinical information systems. DISCUSSION:Substantial research has been conducted to date on the use of CDS to enable GPM. In a previous analysis of CDS intervention trials, the automatic provision of CDS as a part of routine clinical workflow had been identified as being critical for CDS effectiveness. There was some indication that CDS for GPM could potentially be effective without the CDS being provided automatically, but we did not find conclusive evidence to support this hypothesis. CONCLUSION:To maximize the clinical benefits arising from ongoing discoveries in genetics and genomics, additional research and development is recommended for identifying how best to leverage CDS to bridge the gap between the promise and realization of GPM.

| S-EPMC3638177 | biostudies-other

Proteasome Inhibitors against Glioblastoma-Overview of Molecular Mechanisms of Cytotoxicity, Progress in Clinical Trials, and Perspective for Use in Personalized Medicine.

Project description:Proteasome inhibitors are moieties targeting the proteolytic activity of a proteasome, with demonstrated efficacy in certain hematological malignancies and candidate drugs in other types of cancer, including glioblastoma (GBM). They disturb the levels of proteasome-regulated proteins and lead to the cell cycle inhibition and apoptosis of GBM cells. The accumulation of cell cycle inhibitors p21 and p27, and decreased levels of prosurvival molecules NFKB, survivin, and MGMT, underlie proteasome inhibitors' cytotoxicity when used alone or in combination with the anti-GBM cytostatic drug temozolomide (TMZ). The evidence gathered in preclinical studies substantiated the design of clinical trials that employed the two most promising proteasome inhibitors, bortezomib and marizomib. The drug safety profile, maximum tolerated dose, and interaction with other drugs were initially evaluated, mainly in recurrent GBM patients. A phase III study on newly diagnosed GBM patients who received marizomib as an adjuvant to the Stupp protocol was designed and completed in 2021, with the Stupp protocol receiving patients as a parallel control arm. The data from this phase III study indicate that marizomib does not improve the PFS and OS of GBM patients; however, further analysis of the genetic and epigenetic background of each patient tumor may shed some light on the sensitivity of individual patients to proteasome inhibition. The mutational and epigenetic makeup of GBM cells, like genetic alterations to TP53 and PTEN, or MGMT promoter methylation levels may actually determine the response to proteasome inhibition.

| S-EPMC10670062 | biostudies-literature

Jumping on the Train of Personalized Medicine: A Primer for Non- Geneticist Clinicians: Part 3. Clinical Applications in the Personalized Medicine Area.

Project description:The rapid decline of sequencing costs brings hope that personal genome sequencing will become a common feature of medical practice. This series of three reviews aim to help non-geneticist clinicians to jump into the fast-moving field of personalized genetic medicine. In the first two articles, we covered the fundamental concepts of molecular genetics and the methodologies used in genetic epidemiology. In this third article, we discuss the evolution of personalized medicine and illustrate the most recent success in the fields of Mendelian and complex human diseases. We also address the challenges that currently limit the use of personalized medicine to its full potential.

| S-EPMC4287884 | 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