A targeted simulation‐extrapolation method for evaluating biomarkers based on new technologies in precision medicine

2021 ◽  
Author(s):  
Dong Wang ◽  
Sue‐Jane Wang ◽  
Joshua Xu ◽  
Samir Lababidi
Keyword(s):  
Precision Medicine ◽  
New Technologies ◽  
Extrapolation Method ◽  
Simulation Extrapolation

scholarly journals Contrasting Medical and Legal Standards of Evidence: A Precision Medicine Case Study

2016 ◽  
Vol 44 (1) ◽  
pp. 194-204 ◽  
Author(s):  
Gary E. Marchant ◽  
Kathryn Scheckel ◽  
Doug Campos-Outcalt
Keyword(s):  
Precision Medicine ◽  
New Technologies ◽  
Clinical Care ◽  
Large Body ◽  
Standard Of Care ◽  
Genetic Profile ◽  
Legal Standards ◽  
Standards Of Evidence ◽  
The Individual

As the health care system transitions to a precision medicine approach that tailors clinical care to the genetic profile of the individual patient, there is a potential tension between the clinical uptake of new technologies by providers and the legal system's expectation of the standard of care in applying such technologies. We examine this tension by comparing the type of evidence that physicians and courts are likely to rely on in determining a duty to recommend pharmacogenetic testing of patients prescribed the oral anti-coagulant drug warfarin. There is a large body of inconsistent evidence and factors for and against such testing, but physicians and courts are likely to weigh this evidence differently. The potential implications for medical malpractice risk are evaluated and discussed.

scholarly journals The Canadian Path from Discovery to Implementation of Personalized Medicine Approaches

2017 ◽  
Vol 2 (Suppl. 1) ◽  
pp. 1-3
Author(s):  
Étienne Richer ◽  
Rachel Syme ◽  
Stephen M. Robbins ◽  
Paul Lasko
Keyword(s):  
Personalized Medicine ◽  
Precision Medicine ◽  
Health Research ◽  
New Technologies ◽  
Healthcare Delivery ◽  
Data Sources ◽  
Future Developments ◽  
Success Stories ◽  
Canadian Perspective

Personalized (or precision) medicine approaches are currently being introduced in healthcare delivery following the development of new technologies and of novel ways to integrate and analyze various data sources. This editorial describes the efforts invested since 2012 by the Canadian Institutes of Health Research (CIHR) to foster the development and implementation of personalized medicine in Canada. Success stories from past investments as well as future developments are presented from a Canadian perspective.

Parents’, Health Care Professionals’, and Scientists’ Experiences of a Precision Medicine Pilot Trial for Patients With High-Risk Childhood Cancer: A Qualitative Study

2019 ◽  
pp. 1-11 ◽  
Author(s):  
Janine Vetsch ◽  
Claire E. Wakefield ◽  
Emily Duve ◽  
Brittany C. McGill ◽  
Meera Warby ◽  
...  
Keyword(s):  
Health Care ◽  
High Risk ◽  
Childhood Cancer ◽  
Precision Medicine ◽  
Health Care Professionals ◽  
New Technologies ◽  
Pilot Trial ◽  
Treatment Recommendations ◽  
Laboratory Research ◽  
Drug Access

PURPOSE Children with high-risk cancers have low survival rates because current treatment options are limited. Precision medicine trials are designed to offer patients individualized treatment recommendations, potentially improving their clinical outcomes. However, parents’ understanding is often limited, and expectations of benefit to their own child can be high. Health care professionals (HCPs) are often not familiar with precision medicine and might find managing families’ expectations challenging. Scientists find themselves working with high expectations among different stakeholders to rapidly translate their identification of actionable targets in real time. Therefore, we wanted to gain an in-depth understanding of the experiences of all stakeholders involved in a new precision medicine pilot trial called TARGET, including parents, their child’s HCPs, and the scientists who conducted the laboratory research and generated the data used to make treatment recommendations. METHODS We conducted semistructured interviews with all participants and analyzed the interviews thematically. RESULTS We interviewed 15 parents (9 mothers; 66.7% bereaved), 17 HCPs, and 16 scientists. We identified the following themes in parents’ interviews: minimal understanding and need for more information, hope as a driver of participation, challenges around biopsies, timing, and drug access, and few regrets. HCP and scientist interviews revealed themes such as embracing new technologies and collaborations and challenges managing families’ expectations, timing of testing and test results, and drug access. CONCLUSION Educating families, HCPs, and scientists to better understand the benefits and limitations of precision medicine trials may improve the transparency of the translation of discovery genomics to novel therapies, increase satisfaction with the child’s care, and ameliorate the additional long-term psychosocial burden for families already affected by high-risk childhood cancer.

scholarly journals Genome sequencing in the clinic: the past, present, and future of genomic medicine

2018 ◽  
Vol 50 (8) ◽  
pp. 563-579 ◽  
Author(s):  
Jeremy W. Prokop ◽  
Thomas May ◽  
Kim Strong ◽  
Stephanie M. Bilinovich ◽  
Caleb Bupp ◽  
...  
Keyword(s):  
Precision Medicine ◽  
Genome Sequencing ◽  
Clinical Setting ◽  
Structural Changes ◽  
New Technologies ◽  
Cancer Genomics ◽  
Genomic Medicine ◽  
Driver Gene ◽  
The Past ◽  
The Future

Genomic sequencing has undergone massive expansion in the past 10 yr, from a rarely used research tool into an approach that has broad applications in a clinical setting. From rare disease to cancer, genomics is transforming our knowledge of biology. The transition from targeted gene sequencing, to whole exome sequencing, to whole genome sequencing has only been made possible due to rapid advancements in technologies and informatics that have plummeted the cost per base of DNA sequencing and analysis. The tools of genomics have resolved the etiology of disease for previously undiagnosable conditions, identified cancer driver gene variants, and have impacted the understanding of pathophysiology for many diseases. However, this expansion of use has also highlighted research’s current voids in knowledge. The lack of precise animal models for gene-to-function association, lack of tools for analysis of genomic structural changes, skew in populations used for genetic studies, publication biases, and the “Unknown Proteome” all contribute to voids needing filled for genomics to work in a fast-paced clinical setting. The future will hold the tools to fill in these voids, with new data sets and the continual development of new technologies allowing for expansion of genomic medicine, ushering in the days to come for precision medicine. In this review we highlight these and other points in hopes of advancing and guiding precision medicine into the future for optimal success.

Simulation–extrapolation method to address errors in atomic bomb survivor dosimetry on solid cancer and leukaemia mortality risk estimates, 1950–2003

2015 ◽  
Vol 54 (3) ◽  
pp. 273-283 ◽  
Author(s):  
Rodrigue S. Allodji ◽  
Boris Schwartz ◽  
Ibrahima Diallo ◽  
Césaire Agbovon ◽  
Dominique Laurier ◽  
...  
Keyword(s):  
Mortality Risk ◽  
Atomic Bomb ◽  
Extrapolation Method ◽  
Solid Cancer ◽  
Atomic Bomb Survivor ◽  
Risk Estimates ◽  
Simulation Extrapolation

scholarly journals Patient attrition in Molecular Tumour Boards: A Review

2021 ◽  
Author(s):  
Hannah Frost ◽  
Donna M. Graham ◽  
Louise Carter ◽  
Paul O’Regan ◽  
Donal Landers ◽  
...  
Keyword(s):  
Decision Making ◽  
Literature Review ◽  
Precision Medicine ◽  
Clinical Decision Making ◽  
New Technologies ◽  
Clinical Decision ◽  
Clinical Deterioration ◽  
Treatment Data ◽  
Patient Attrition ◽  
Receive Treatment

AbstractMolecular Tumour Boards (MTBs) were created with the purpose of supporting clinical decision making within precision medicine. Though these meetings are in use globally reporting often focuses on the small percentages of patients that receive treatment via this process and are less likely to report on, and assess, patients who do not receive treatment. A literature review was performed to understand patient attrition within MTBs and barriers to patients receiving treatment. A total of 56 papers were reviewed spanning a 6 year period from 11 different countries. 20% of patients received treatment through the MTB process. Of those that did not receive treatment the main reasons were no mutations identified (26%), no actionable mutations (22%) and clinical deterioration (15%). However, the data was often incomplete due to inconsistent reporting of MTBs with only 54% reporting on patients having no mutations, 48% reporting on presence of actionable mutations and 57% reporting on clinical deterioration. Patient attrition in MTBs is an issue which is very rarely alluded to in reporting, more transparent reporting is needed to understand barriers to treatment and integration of new technologies is required to process increasing omic and treatment data.

scholarly journals Delivering Precision Medicine to Patients with Spinal Cord Disorders; Insights into Applications of Bioinformatics and Machine Learning from Studies of Degenerative Cervical Myelopathy

2021 ◽  
Author(s):  
Kalum J. Ost ◽  
David W. Anderson ◽  
David W. Cadotte
Keyword(s):  
Machine Learning ◽  
Precision Medicine ◽  
New Technologies ◽  
Machine Learning Techniques ◽  
Massive Datasets ◽  
Learning Framework ◽  
Learning Techniques ◽  
Machine Learning Approach ◽  
Spinal Cord Disorders ◽  
Degenerative Cervical Myelopathy

With the common adoption of electronic health records and new technologies capable of producing an unprecedented scale of data, a shift must occur in how we practice medicine in order to utilize these resources. We are entering an era in which the capacity of even the most clever human doctor simply is insufficient. As such, realizing “personalized” or “precision” medicine requires new methods that can leverage the massive amounts of data now available. Machine learning techniques provide one important toolkit in this venture, as they are fundamentally designed to deal with (and, in fact, benefit from) massive datasets. The clinical applications for such machine learning systems are still in their infancy, however, and the field of medicine presents a unique set of design considerations. In this chapter, we will walk through how we selected and adjusted the “Progressive Learning framework” to account for these considerations in the case of Degenerative Cervical Myeolopathy. We additionally compare a model designed with these techniques to similar static models run in “perfect world” scenarios (free of the clinical issues address), and we use simulated clinical data acquisition scenarios to demonstrate the advantages of our machine learning approach in providing personalized diagnoses.

scholarly journals A Preclinical Pipeline for Translational Precision Medicine—Experiences from a Transdisciplinary Brain Tumor Stem Cell Project

2021 ◽  
Vol 11 (9) ◽  
pp. 892
Author(s):  
Andres Vargas-Toscano ◽  
Christoph Janiak ◽  
Michael Sabel ◽  
Ulf Dietrich Kahlert
Keyword(s):  
Brain Tumor ◽  
Precision Medicine ◽  
Clinical Application ◽  
New Technologies ◽  
Basic Research ◽  
Therapy Resistance ◽  
Primary Brain Tumor ◽  
Point Of View ◽  
Tumor Stem Cell ◽  
Tumoral Cell

Efficient transdisciplinary cooperation promotes the rapid discovery and clinical application of new technologies, especially in the competitive sector of oncology. In this review, written from a clinical-scientist point of view, we used glioblastoma—the most common and most aggressive primary brain tumor as a model disease with a largely unmet clinical need, despite decades of intensive research—to promote transdisciplinary medicine. Glioblastoma stem-like cells (GSCs), a special tumoral cell population analogue to healthy stem cells, are considered largely responsible for the progression of the disease and the mediation of therapy resistance. The presented work followed the concept of translational science, which generates the theoretical backbones of translational research projects, and aimed to close the preclinical gap between basic research and clinical application. Thus, this generated an integrated translational precision medicine pipeline model based on recent theoretical and experimental publications, which supports the accelerated discovery and development of new paths in the treatment of GSCs. The work may be of interest to the general field of precision medicine beyond the field of neuro-oncology such as in Cancer Neuroscience.

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