scholarly journals Personalized medicine: consequences for drug research and therapy

2016 ◽  
Vol 1 (1) ◽  
Author(s):  
Thorsten Ruppert ◽  
Sabine Sydow ◽  
Günter Stock
Keyword(s):  
Health Care ◽  
Personalized Medicine ◽  
Diagnostic Tests ◽  
Molecular Mechanisms ◽  
Drug Research ◽  
Treatment Success ◽  
Molecular Medicine ◽  
Patient Specific ◽  
New Paradigm ◽  
Specific Patient

In drug research, a serious transformation has taken place. With increasing knowledge gained from molecular medicine, it became possible to refine and develop new therapies based on the molecular mechanisms of diseases. Medicine and drug development have seen a paradigm shift which can be characterized with the catchword “personalized medicine”, also called “stratified medicine” or “precision medicine”. Personalized medicine is based on defined tandems of therapeutic agents and diagnostic tests. With this addition to the regular medical examination of the patient, specific patient characteristics are determined. The results of such diagnostic tests are then decisive for the choice of therapy or control of the effectiveness of the chosen treatment. The benefit of personalized medicine for the patient is the higher probability of treatment success as well as improved effectiveness and reduced / avoided side effects. Health insurance systems and the public may have the advantage that the health funds can be used more efficiently on this basis. This new paradigm requires also a new debate on the remuneration in health care. In order to bring personalized therapies to patients as quickly as possible, all players in health care should work together to address the challenges associated with personalized medicine.

scholarly journals Implementing Personalized Medicine in COVID-19 in Andalusia: An Opportunity to Transform the Healthcare System

2021 ◽  
Vol 11 (6) ◽  
pp. 475
Author(s):  
Joaquín Dopazo ◽  
Douglas Maya-Miles ◽  
Federico García ◽  
Nicola Lorusso ◽  
Miguel Ángel Calleja ◽  
...  
Keyword(s):  
Public Health ◽  
Health Care ◽  
Clinical Practice ◽  
Personalized Medicine ◽  
Local Level ◽  
Individual Variability ◽  
Response To Therapy ◽  
Control Measures ◽  
Response To Treatment ◽  
Specific Patient

The COVID-19 pandemic represents an unprecedented opportunity to exploit the advantages of personalized medicine for the prevention, diagnosis, treatment, surveillance and management of a new challenge in public health. COVID-19 infection is highly variable, ranging from asymptomatic infections to severe, life-threatening manifestations. Personalized medicine can play a key role in elucidating individual susceptibility to the infection as well as inter-individual variability in clinical course, prognosis and response to treatment. Integrating personalized medicine into clinical practice can also transform health care by enabling the design of preventive and therapeutic strategies tailored to individual profiles, improving the detection of outbreaks or defining transmission patterns at an increasingly local level. SARS-CoV2 genome sequencing, together with the assessment of specific patient genetic variants, will support clinical decision-makers and ultimately better ways to fight this disease. Additionally, it would facilitate a better stratification and selection of patients for clinical trials, thus increasing the likelihood of obtaining positive results. Lastly, defining a national strategy to implement in clinical practice all available tools of personalized medicine in COVID-19 could be challenging but linked to a positive transformation of the health care system. In this review, we provide an update of the achievements, promises, and challenges of personalized medicine in the fight against COVID-19 from susceptibility to natural history and response to therapy, as well as from surveillance to control measures and vaccination. We also discuss strategies to facilitate the adoption of this new paradigm for medical and public health measures during and after the pandemic in health care systems.

scholarly journals Personalized Medicine: The Future of Health Care

2016 ◽  
Vol 8 (3) ◽  
pp. 127
Author(s):  
Anna Meiliana ◽  
Nurrani Mustika Dewi ◽  
Andi Wijaya
Keyword(s):  
Health Care ◽  
Personalized Medicine ◽  
Precision Medicine ◽  
Molecular Mechanisms ◽  
Care Needs ◽  
Medical Treatments ◽  
Treatment And Prevention ◽  
Human Illness ◽  
Make Health Care ◽  
Innovation Approach

BACKGROUND: Most medical treatments have been designed for the “average patients”. As a result of this “one-size-fits-all-approach”, treatments can be very successful for some patients but not for others. The issue is shifting by the new innovation approach in diseases treatment and prevention, precision medicine, which takes into account individual differences in people’s genes, environments, and lifestyles. This review was aimed to describe a new approach of healthcare performance strategy based on individual genetic variants.CONTENT: Researchers have discovered hundreds of genes that harbor variations contributing to human illness, identified genetic variability in patients’ responses to different of treatments, and from there begun to target the genes as molecular causes of diseases. In addition, scientists are developing and using diagnostic tests based on genetics or other molecular mechanisms to better predict patients’ responses to targeted therapy.SUMMARY: Personalized medicine seeks to use advances in knowledge about genetic factors and biological mechanisms of disease coupled with unique considerations of an individual’s patient care needs to make health care more safe and effective. As a result of these contributions to improvement in the quality of care, personalized medicine represents a key strategy of healthcare reform.KEYWORDS: precision medicine, genomic, proteomic, metabolomic

Artificial Intelligence-Driven Designer Drug Combinations: From Drug Development to Personalized Medicine

2018 ◽  
Vol 24 (1) ◽  
pp. 124-125
Author(s):  
Masturah Bte Mohd Abdul Rashid ◽  
Edward Kai-Hua Chow
Keyword(s):  
Artificial Intelligence ◽  
Personalized Medicine ◽  
Translational Medicine ◽  
Molecular Mechanisms ◽  
Disease Model ◽  
Drug Combinations ◽  
Designer Drug ◽  
Great Promise ◽  
Specific Patient ◽  
And Personalized Medicine

Artificial intelligence holds great promise in transforming how drugs are designed and patients are treated. In a study recently published in Science Translational Medicine, a unique artificial intelligence platform makes efficient use of small experimental datasets to design new drug combinations as well as identify the best drug combinations for specific patient samples. This quadratic phenotypic optimization platform (QPOP) does not rely on previous assumptions of molecular mechanisms of disease, but rather uses system-specific experimental data to determine the best drug combinations for a specific disease model or a patient sample. In this commentary, we explore how QPOP was applied toward multiple myeloma in the study. We also discuss how this study demonstrates the potential for applications of QPOP toward improving therapeutic regimen design and personalized medicine.

scholarly journals Reversibility of cellular aging by reprogramming through an embryonic-like state : a new paradigm for human cell rejuvenation

2014 ◽  
Vol 2 ◽  
Author(s):  
Jean-Marc Lemaitre
Keyword(s):  
Oxidative Stress ◽  
Stem Cells ◽  
Cellular Senescence ◽  
Human Cell ◽  
Molecular Mechanisms ◽  
Expression Profiles ◽  
Embryonic Stem ◽  
Cellular Aging ◽  
Patient Specific ◽  
New Paradigm

Direct reprogramming of somatic cells into induced pluripotent stem cells (iPSCs) provides a unique opportunity to derive patient-specific stem cells with potential application in autologous tissue replacement therapies and without the ethical concerns of Embryonic Stem Cells (hESC). However, this strategy still suffers from several hurdles that need to be overcome before clinical applications. Among them, cellular senescence, which contributes to aging and restricted longevity, has been described as a barrier to the derivation of iPSCs. This suggests that aging might be an important limitation for therapeutic purposes for elderly individuals. Senescence is characterized by an irreversible cell cycle arrest in response to various forms of stress, including activation of oncogenes, shortened telomeres, DNA damage, oxidative stress, and mitochondrial dysfunction. To overcome this barrier, we developed an optimized 6-factor-based reprogramming protocol that is able to cause efficient reversing of cellular senescence and reprogramming into iPSCs. We demonstrated that iPSCs derived from senescent and centenarian fibroblasts have reset telomere size, gene expression profiles, oxidative stress, and mitochondrial metabolism, and are indistinguishable from hESC. Finally, we demonstrate that re-differentiation led to rejuvenated cells with a reset cellular physiology, defining a new paradigm for human cell rejuvenation. We discuss the molecular mechanisms involved in cell reprogramming of senescent cells. 

The present state of aspirin and clopidogrel resistance

2009 ◽  
Vol 29 (03) ◽  
pp. 285-290 ◽  
Author(s):  
K. E. Guyer
Keyword(s):  
Diagnostic Tests ◽  
Arterial Thrombosis ◽  
Cardiovascular Events ◽  
Treatment Success ◽  
Antiplatelet Agents ◽  
Antiplatelet Drug ◽  
Coronary Syndrome ◽  
Testing Method ◽  
Pharmacological Response ◽  
Platelet Physiology

SummaryAntiplatelet therapy has demonstrated significant clinical benefit in the treatment of acute coronary syndrome. However, as with any treatment strategy it has been unable to prevent all cardiovascular events. This is far from surprising when considering the complexity of arterial thrombosis and more specifically platelet physiology. This lack of treatment success has provoked the introduction of various diagnostic tests and testing platforms with the intent of guiding and optimizing clinical treatment. Such tests have resulted in the generation of clinical data that suggest suboptimal response to antiplatelet agents such as aspirin and clopidogrel.In the case of both aspirin and clopidogrel, this suboptimal response has been termed resistance. Drug resistance would imply a lack of pharmacological response that has not been specifically investigated in many of the clinical studies performed to date. Rather, the term resistance has been used to describe various facets of platelet activation and aggregation relative to the testing method. Many of these measured parameters are not addressed in the therapeutic intent of the antiplatelet drug in question.

Artificial Intelligence for epigenetics: towards personalized medicine

2020 ◽  
Vol 27 ◽  
Author(s):  
Giulia De Riso ◽  
Sergio Cocozza
Keyword(s):  
Biological Sciences ◽  
Artificial Intelligence ◽  
Personalized Medicine ◽  
Molecular Mechanisms ◽  
Genomic Sequence ◽  
Health Care Interventions ◽  
Genome Wide ◽  
Genetic And Environmental Factors ◽  
Opening Up ◽  
Omic Data

: Epigenetics is a field of biological sciences focused on the study of reversible, heritable changes in gene function not due to modifications of the genomic sequence. These changes are the result of a complex cross-talk between several molecular mechanisms, that is in turn orchestrated by genetic and environmental factors. The epigenetic profile captures the unique regulatory landscape and the exposure to environmental stimuli of an individual. It thus constitutes a valuable reservoir of information for personalized medicine, which is aimed at customizing health-care interventions based on the unique characteristics of each individual. Nowadays, the complex milieu of epigenomic marks can be studied at the genome-wide level thanks to massive, highthroughput technologies. This new experimental approach is opening up new and interesting knowledge perspectives. However, the analysis of these complex omic data requires to face important analytic issues. Artificial Intelligence, and in particular Machine Learning, are emerging as powerful resources to decipher epigenomic data. In this review, we will first describe the most used ML approaches in epigenomics. We then will recapitulate some of the recent applications of ML to epigenomic analysis. Finally, we will provide some examples of how the ML approach to epigenetic data can be useful for personalized medicine.

HEALTH TECHNOLOGY ASSESSMENT AND PERSONALIZED MEDICINE: ARE ECONOMIC EVALUATION GUIDELINES SUFFICIENT TO SUPPORT DECISION MAKING?

2014 ◽  
Vol 30 (2) ◽  
pp. 179-187 ◽  
Author(s):  
Don Husereau ◽  
Deborah A. Marshall ◽  
Adrian R. Levy ◽  
Stuart Peacock ◽  
Jeffrey S. Hoch
Keyword(s):  
Decision Making ◽  
Economic Evaluation ◽  
Personalized Medicine ◽  
Health Technology Assessment ◽  
Technology Assessment ◽  
Health Technology ◽  
Population Based ◽  
New Paradigm ◽  
Current Guidance ◽  
Support Decision Making

Background: Many jurisdictions delivering health care, including Canada, have developed guidance for conducting economic evaluation, often in the service of larger health technology assessment (HTA) and reimbursement processes. Like any health intervention, personalized medical (PM) interventions have costs and consequences that must be considered by reimbursement authorities with limited resources. However, current approaches to economic evaluation to support decision making have been largely developed from population-based approaches to therapy—that is, evaluating the costs and consequences of single interventions across single populations. This raises the issue as to whether these methods, as they are or more refined, are adequate to address more targeted approaches to therapy, or whether a new paradigm for assessing value in PM is required.Objectives: We describe specific issues relevant to the economic evaluation of diagnostics-based PM and assess whether current guidance for economic evaluation is sufficient to support decision making for PM interventions.Methods: Issues were identified through literature review and informal interviews with national and international experts (n = 10) in these analyses. This article elaborates on findings and discussion at a workshop held in Ottawa, Canada, in January 2012.Results: Specific issues related to better guiding economic evaluation of personalized medicine interventions include: how study questions are developed, populations are characterized, comparators are defined, effectiveness is evaluated, outcomes are valued and how resources are measured. Diagnostics-based PM also highlights the need for analyses outside of economic evaluation to support decision making.Conclusions: The consensus of this group of experts is that the economic evaluation of diagnostics-based PM may not require a new paradigm. However, greater complexity means that existing approaches and tools may require improvement to undertake these more analyses.

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