Demand for Pharmacogenomics and Personalized Medicine in the United Arab Emirates

The application of personalized medicine (PM) is rapidly evolving [...]

Prioritizing Research in an Era of Personalized Medicine: The Potential Value of Unexplained Heterogeneity

Background Clinical care is moving from a “one size fits all” approach to a setting in which treatment decisions are based on individual treatment response, needs, preferences, and risk. Research into personalized treatment strategies aims to discover currently unknown markers that identify individuals who would benefit from treatments that are nonoptimal at the population level. Before investing in research to identify these markers, it is important to assess whether such research has the potential to generate value. Thus, this article aims to develop a framework to prioritize research into the development of new personalized treatment strategies by creating a set of measures that assess the value of personalizing care based on a set of unknown patient characteristics. Methods Generalizing ideas from the value of heterogeneity framework, we demonstrate 3 measures that assess the value of developing personalized treatment strategies. The first measure identifies the potential value of personalizing medicine within a given disease area. The next 2 measures highlight specific research priorities and subgroup structures that would lead to improved patient outcomes from the personalization of treatment decisions. Results We graphically present the 3 measures to perform sensitivity analyses around the key drivers of value, in particular, the correlation between the individual treatment benefits across the available treatment options. We illustrate these 3 measures using a previously published decision model and discuss how they can direct research in personalized medicine. Conclusion We discuss 3 measures that form the basis of a novel framework to prioritize research into novel personalized treatment strategies. Our novel framework ensures that research targets personalized treatment strategies that have high potential to improve patient outcomes and health system efficiency. Highlights It is important to undertake research prioritization before conducting any research that aims to discover novel methods (e.g., biomarkers) for personalizing treatment. The value of unexplained heterogeneity can highlight disease areas in which personalizing treatment can be valuable and determine key priorities within that area. These priorities can be determined under assumptions of the magnitude of the individual-level treatment effect, which we explore in sensitivity analyses.

Insights Into the Changing Landscape of Coronavirus Disease 2019

Coronavirus disease 2019 (COVID-19) is a highly contagious, infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which emerged in late 2019 in Wuhan China. A year after the World Health Organization declared COVID-19 a global pandemic, over 215 million confirmed cases and approximately 5 million deaths have been reported worldwide. In this multidisciplinary review, we summarize important insights for COVID-19, ranging from its origin, pathology, epidemiology, to clinical manifestations and treatment. More importantly, we also highlight the foundational connection between genetics and the development of personalized medicine and how these aspects have an impact on disease treatment and management in the dynamic landscape of this pandemic.

Biobank as a key component of supporting research in phthisiology and infectious diseases

Conducting fundamental and clinical research in the field of tuberculosis is an important step towards reducing related morbidity and mortality, but access to a sufficient number of high-quality samples required for research is an unsolved problem in Russia. This review is devoted to biobanking as a key component of modern research in personalized medicine, as well as to the status and prospects for developing this area in phthisiology and infectious diseases combined with tuberculosis.

Averting the next credibility crisis in psychological science: Within-person methods for personalized diagnostics and intervention.

Personalizing assessments, predictions, and treatments of individuals is currently a defining trend in psychological research and applied fields, including personalized learning, personalized medicine, and personalized advertisement. For instance, the recent pandemic has reminded parents and educators of how challenging yet crucial it is to get the right learning task to the right student at the right time. Increasingly, psychologists and social scientists are realizing that the between- person methods that we have long relied upon to describe, predict, and treat individuals may fail to live up to these tasks (e.g., Molenaar, 2004). Consequently, there is a risk of a credibility loss, possibly similar to the one seen during the replicability crisis (Ioannides, 2005), because we have only started to understand how many of the conclusions that we tend to draw based on between-person methods are based on a misunderstanding of what these methods can tell us and what they cannot. An imminent methodological revolution will likely lead to a change of even well-established psychological theories (Barbot et al., 2020). Fortunately, methodological solutions for personalized descriptions and predictions, such as many within-person analyses, are available and undergo rapid development, although they are not yet embraced in all areas of psychology, and some come with their own limitations. This article first discusses the extent of the theory-method gap, consisting of theories about within-person patterns being studied with between-person methods in psychology, and the potential loss of trust that might follow from this theory-method gap. Second, this article addresses advantages and limitations of available within- person methods. Third, this article discusses how within-person methods may help improving the individual descriptions and predictions that are needed in many applied fields that aim for tailored individual solutions, including personalized learning and personalized medicine.

The patients view on genetics and functional imaging for precision medicine: a willingness-to-pay analysis

Purpose: Willingness-to-pay (WTP) analyses can support allocation processes considering the patients preferences in personalized medicine. However, genetic testing especially might imply ethical concerns that have to be considered. Methods: A WTP questionnaire was designed to compare preferences for imaging and genetic testing in cancer patients and to evaluate potential ethical concerns. Results: Comparing the options of imaging and genetics showed comparable WTP values. Ethical concerns about genetic testing seemed to be minor. Treatment success was the top priority irrespective of the diagnostic modality. In general, the majority of patients considered personalized medicine to be beneficial. Conclusion: Most patients valued personalized approaches and rated the benefits of precision medicine of overriding importance irrespective of modality or ethical concerns.

Two Possible Strategies for Drug Modification of Gemcitabine and Future Contributions to Personalized Medicine

Drug repurposing is an emerging strategy, which uses already approved drugs for new medical indications. One such drug is gemcitabine, an anticancer drug that only works at high doses since a portion is deactivated in the serum, which causes toxicity. In this review, two methods were discussed that could improve the anticancer effect of gemcitabine. The first is a chemical modification by conjugation with cell-penetrating peptides, namely penetratin, pVEC, and different kinds of CPP6, which mostly all showed an increased anticancer effect. The other method is combining gemcitabine with repurposed drugs, namely itraconazole, which also showed great cancer cell inhibition growth. Besides these two strategies, physiologically based pharmacokinetic models (PBPK models) are also the key for predicting drug distribution based on physiological data, which is very important for personalized medicine, so that the correct drug and dosage regimen can be administered according to each patient’s physiology. Taking all of this into consideration, it is believed that gemcitabine can be repurposed to have better anticancer effects.