Personalized Medicine Research Research Articles

Proteomic Blood Profiles Obtained by Totally Blind Biological Clustering in Stable and Exacerbated COPD Patients.

Although Chronic Obstructive Pulmonary Disease (COPD) is highly prevalent, it is often underdiagnosed. One of the main characteristics of this heterogeneous disease is the presence of periods of acute clinical impairment (exacerbations). Obtaining blood biomarkers for either COPD as a chronic entity or its exacerbations (AECOPD) will be particularly useful for the clinical management of patients. However, most of the earlier studies have been characterized by potential biases derived from pre-existing hypotheses in one or more of their analysis steps: some studies have only targeted molecules already suggested by pre-existing knowledge, and others had initially carried out a blind search but later compared the detected biomarkers among well-predefined clinical groups. We hypothesized that a clinically blind cluster analysis on the results of a non-hypothesis-driven wide proteomic search would determine an unbiased grouping of patients, potentially reflecting their endotypes and/or clinical characteristics. To check this hypothesis, we included the plasma samples from 24 clinically stable COPD patients, 10 additional patients with AECOPD, and 10 healthy controls. The samples were analyzed through label-free liquid chromatography/tandem mass spectrometry. Subsequently, the Scikit-learn machine learning module and K-means were used for clustering the individuals based solely on their proteomic profiles. The obtained clusters were confronted with clinical groups only at the end of the entire procedure. Although our clusters were unable to differentiate stable COPD patients from healthy individuals, they segregated those patients with AECOPD from the patients in stable conditions (sensitivity 80%, specificity 79%, and global accuracy, 79.4%). Moreover, the proteins involved in the blind grouping process to identify AECOPD were associated with five biological processes: inflammation, humoral immune response, blood coagulation, modulation of lipid metabolism, and complement system pathways. Even though the present results merit an external validation, our results suggest that the present blinded approach may be useful to segregate AECOPD from stability in both the clinical setting and trials, favoring more personalized medicine and clinical research.

Revisiting drug-protein interaction prediction: a novel global-local perspective.

Accurate inference of potential drug-protein interactions (DPIs) aids in understanding drug mechanisms and developing novel treatments. Existing deep learning models, however, struggle with accurate node representation in DPI prediction, limiting their performance. We propose a new computational framework that integrates global and local features of nodes in the drug-protein bipartite graph for efficient DPI inference. Initially, we employ pre-trained models to acquire fundamental knowledge of drugs and proteins and to determine their initial features. Subsequently, the MinHash and HyperLogLog algorithms are utilized to estimate the similarity and set cardinality between drug and protein subgraphs, serving as their local features. Then, an energy-constrained diffusion mechanism is integrated into the transformer architecture, capturing interdependencies between nodes in the drug-protein bipartite graph and extracting their global features. Finally, we fuse the local and global features of nodes and employ multilayer perceptrons to predict the likelihood of potential DPIs. A comprehensive and precise node representation guarantees efficient prediction of unknown DPIs by the model. Various experiments validate the accuracy and reliability of our model, with molecular docking results revealing its capability to identify potential DPIs not present in existing databases. This approach is expected to offer valuable insights for furthering drug repurposing and personalized medicine research. Our code and data are accessible at: https://github.com/ZZCrazy00/DPI.

Bibliometrics analysis based on the Web of Science: Current trends and perspective of gastric organoid during 2010-2023.

Three-dimensional organoid culture systems have been established as a robust tool for elucidating mechanisms and performing drug efficacy testing. The use of gastric organoid models holds significant promise for advancing personalized medicine research. However, a comprehensive bibliometric review of this bur-geoning field has not yet been published. To analyze and understand the development, impact, and direction of gastric organoid research using bibliometric methods using data from the Web of Science Core Collection (WoSCC) database. This analysis encompassed literature pertaining to gastric organoids published between 2010 and 2023, as indexed in the WoSCC. CiteSpace and VOSviewer were used to depict network maps illustrating collaborations among authors, institutions and keywords related to gastric organoid. Citation, co-citation, and burst analysis methodologies were applied to assess the impact and progress of research. A total of 656 relevant studies were evaluated. The majority of research was published in gastroenterology-focused journals. Globally, Yana Zavros, Hans Clevers, James M Wells, Sina Bartfeld, and Chen Zheng were the 5 most productive authors, while Hans Clevers, Huch Meritxell, Johan H van Es, Marc Van de Wetering, and Sato Toshiro were the foremost influential scientists in this area. Institutions from the University Medical Center Utrecht, Netherlands Institute for Developmental Biology (Utrecht), and University of Cincinnati (Cincinnati, OH, United States) made the most significant contributions. Currently, gastric organoids are used mainly in studies investigating gastric cancer (GC), Helicobacter pylori-infective gastritis, with a focus on the mechanisms of GC, and drug screening tests. Key focus areas of research using gastric organoids include unraveling disease mechanisms and enhancing drug screening techniques. Major contributions from renowned academic institutions highlight this field's dynamic growth.

Integrating China in the International Consortium for Personalised Medicine: a position paper on innovation and digitalization in Personalized Medicine

BackgroundThe advent of Personalized Medicine (PM) holds significant promise in revolutionizing healthcare by tailoring treatments to individual patients based on their data. However, its successful implementation requires the seamless integration of innovative technologies and presents formidable challenges in terms of sustainability. To tackle these challenges head-on, the International Consortium for Personalized Medicine (ICPerMed) was established, and the IC2PerMed project, as part of this consortium, seeks to foster collaboration between the European Union (EU) and China in the field of Personalized Medicine. Based on the results collected by the project, the objective of this study is to discern the key priorities for the implementation of Personalised Medicine concerning Information and Communication Technologies (ICT) and Big Data and digital solutions, with a particular emphasis on data management and protection.MethodsA Delphi survey was conducted to gather expert’s consensus on the main priorities for actions on Information and Communication Technologies (ICT) and Big Data and digital solutions in the field of Personalized Medicine.ResultsThe survey identified seven priorities in the area of Big Data and digital solutions, including data interoperability, standards, security measures, and international partnerships. Additionally, twelve priorities were identified for the innovation-to-market process, emphasizing cost-effectiveness, need assessment, and value definition in resource allocation.ConclusionsThe effective implementation of new technologies in Personalized Medicine research and practice is essential for the advancement of healthcare systems in both the European and Chinese contexts. The identified priorities play a pivotal role in promoting the sustainability of health systems and driving innovation in the implementation of Personalized Medicine. Addressing challenges related to data interoperability, standards, security, international collaboration, cost-effectiveness, and value assessment is of utmost importance in order to propel the progress of Personalized Medicine in healthcare systems.

MAC-ErrorReads: machine learning-assisted classifier for filtering erroneous NGS reads.

The rapid advancement of next-generation sequencing (NGS) machines in terms of speed and affordability has led to the generation of a massive amount of biological data at the expense of data quality as errors become more prevalent. This introduces the need to utilize different approaches to detect and filtrate errors, and data quality assurance is moved from the hardware space to the software preprocessing stages. We introduce MAC-ErrorReads, a novel Machine learning-Assisted Classifier designed for filtering Erroneous NGS Reads. MAC-ErrorReads transforms the erroneous NGS read filtration process into a robust binary classification task, employing five supervised machine learning algorithms. These models are trained on features extracted through the computation of Term Frequency-Inverse Document Frequency (TF_IDF) values from various datasets such as E. coli, GAGE S. aureus, H. Chr14, Arabidopsis thaliana Chr1 and Metriaclima zebra. Notably, Naive Bayes demonstrated robust performance across various datasets, displaying high accuracy, precision, recall, F1-score, MCC, and ROC values. The MAC-ErrorReads NB model accurately classified S. aureus reads, surpassing most error correction tools with a 38.69% alignment rate. For H. Chr14, tools like Lighter, Karect, CARE, Pollux, and MAC-ErrorReads showed rates above 99%. BFC and RECKONER exceeded 98%, while Fiona had 95.78%. For the Arabidopsis thaliana Chr1, Pollux, Karect, RECKONER, and MAC-ErrorReads demonstrated good alignment rates of 92.62%, 91.80%, 91.78%, and 90.87%, respectively. For the Metriaclima zebra, Pollux achieved a high alignment rate of 91.23%, despite having the lowest number of mapped reads. MAC-ErrorReads, Karect, and RECKONER demonstrated good alignment rates of 83.76%, 83.71%, and 83.67%, respectively, while also producing reasonable numbers of mapped reads to the reference genome. This study demonstrates that machine learning approaches for filtering NGS reads effectively identify and retain the most accurate reads, significantly enhancing assembly quality and genomic coverage. The integration of genomics and artificial intelligence through machine learning algorithms holds promise for enhancing NGS data quality, advancing downstream data analysis accuracy, and opening new opportunities in genetics, genomics, and personalized medicine research.

Consistency between Primary Uterine Corpus Malignancies and Their Corresponding Patient-Derived Xenograft Models.

Patient-derived xenograft (PDX) models retain the characteristics of tumors and are useful tools for personalized therapy and translational research. In this study, we aimed to establish PDX models for uterine corpus malignancies (UC-PDX) and analyze their similarities. Tissue fragments obtained from 92 patients with uterine corpus malignancies were transplanted subcutaneously into immunodeficient mice. Histological and immunohistochemical analyses were performed to compare tumors of patients with PDX tumors. DNA and RNA sequencing were performed to validate the genetic profile. Furthermore, the RNA in extracellular vesicles (EVs) extracted from primary and PDX tumors was analyzed. Among the 92 cases, 52 UC-PDX models were established, with a success rate of 56.5%. The success rate depended on tumor histology and staging. The pathological and immunohistochemical features of primary and PDX tumors were similar. DNA sequencing revealed similarities in gene mutations between the primary and PDX tumors. RNA sequencing showed similarities in gene expressions between primary and PDX tumors. Furthermore, the RNA profiles of the EVs obtained from primary and PDX tumors were similar. As UC-PDX retained the pathological and immunohistochemical features and gene profiles of primary tumors, they may provide a platform for developing personalized medicine and translational research.

Computational resources and chemoinformatics for translational health research.

The integration of computational resources and chemoinformatics has revolutionized translational health research. It has offered a powerful set of tools for accelerating drug discovery. This chapter overviews the computational resources and chemoinformatics methods used in translational health research. The resources and methods can be used to analyze large datasets, identify potential drug candidates, predict drug-target interactions, and optimize treatment regimens. These resources have the potential to transform the drug discovery process and foster personalized medicine research. We discuss insights into their various applications in translational health and emphasize the need for addressing challenges, promoting collaboration, and advancing the field to fully realize the potential of these tools in transforming healthcare.

A Bibliometric Analysis of 3D Printing in Personalized Medicine Research from 2012 to 2022.

In recent years, the 3D printing of personalized drug formulations has attracted the attention of medical practitioners and academics. However, there is a lack of data-based analyses on the hotspots and trends of research in this field. Therefore, in this study, we performed a bibliometric analysis to summarize the 3D printing research in the field of personalized drug formulation from 2012 to 2022. This study was based on the Web of Science Core Collection Database, and a total of 442 eligible publications were screened. Using VOSviewer and online websites for bibliometric analysis and scientific mapping, it was observed that annual publications have shown a significant growth trend over the last decade. The United Kingdom and the United States, which account for 45.5% of the total number of publications, are the main drivers of this field. The International Journal of Pharmaceutics and University College London are the most prolific and cited journals and institutions. The researchers with the most contributions are Basit, Abdul W. and Goyanes Alvaro. The keyword analysis concluded that the current research hotspots are "drug release" and "drug dosage forms". In conclusion, 3D printing has broad application prospects in the field of personalized drugs, which will bring the pharmaceutical industry into a new era of innovation.

The effect of Shengmai injection in patients with coronary heart disease in real world and its personalized medicine research using machine learning techniques.

Objective: Shengmai injection is a common treatment for coronary heart disease. The accurate dose regimen is important to maximize effectiveness and minimize adverse reactions. We aim to explore the effect of Shengmai injection in patients with coronary heart disease based on real-world data and establish a personalized medicine model using machine learning and deep learning techniques. Methods: 211 patients were enrolled. The length of hospital stay was used to explore the effect of Shengmai injection in a case-control study. We applied propensity score matching to reduce bias and Wilcoxon rank sum test to compare results between the experimental group and the control group. Important variables influencing the dose regimen of Shengmai injection were screened by XGBoost. A personalized medicine model of Shengmai injection was established by XGBoost selected from nine algorithm models. SHapley Additive exPlanations and confusion matrix were used to interpret the results clinically. Results: Patients using Shengmai injection had shorter length of hospital stay than those not using Shengmai injection (median 10.00days vs. 11.00days, p = 0.006). The personalized medicine model established via XGBoost shows accuracy = 0.81 and AUC = 0.87 in test cohort and accuracy = 0.84 and AUC = 0.84 in external verification. The important variables influencing the dose regimen of Shengmai injection include lipid-lowering drugs, platelet-lowering drugs, levels of GGT, hemoglobin, prealbumin, and cholesterol at admission. Finally, the personalized model shows precision = 75%, recall rate = 83% and F1-score = 79% for predicting 40mg of Shengmai injection; and precision = 86%, recall rate = 79% and F1-score = 83% for predicting 60mg of Shengmai injection. Conclusion: This study provides evidence supporting the clinical effectiveness of Shengmai injection, and established its personalized medicine model, which may help clinicians make better decisions.

The Versatility of Plectin in Cancer: A Pan-Cancer Analysis on Potential Diagnostic and Prognostic Impacts of Plectin Isoforms

Plectin, encoded by PLEC, is a cytoskeletal and scaffold protein with a number of unique isoforms that act on various cellular functions such as cell adhesion, signal transduction, cancer cell invasion, and migration. While plectin has been shown to display high expression and mislocalization in tumor cells, our knowledge of the biological significance of plectin and its isoforms in tumorigenesis remain limited. In this study, we first performed pathway enrichment analysis to identify cancer hallmark proteins associated with plectin. Then, a pan-cancer analysis was performed using RNA-seq data collected from the Cancer Genome Atlas (TCGA) to detect the mRNA expression levels of PLEC and its transcript isoforms, and the prognostic as well as diagnostic significance of the transcript isoforms was evaluated considering cancer stages. We show here that several tissue specific PLEC isoforms are dysregulated in different cancer types and stages but not the expression of PLEC. Among them, PLEC 1d and PLEC 1f are potential biomarker candidates and call for further translational and personalized medicine research. This study makes a contribution as a stride to unravel the molecular mechanisms underpinning plectin isoforms in cancer development and progression by revealing the potent plectin isoforms in different stages of cancer as potential early cancer detection biomarkers. Importantly, uncovering how plectin isoforms guide malignancy and particular cancer types by comprehensive functional studies might open new avenues toward novel cancer therapeutics.

Research and Innovation in Personalized Medicine: a Descriptive Synthesis of Actors in the EU and China

Aim: Research and innovation (R&I) actors are fundamental in shortening the translational gap of personalized medicine in health systems. In the context of the 'Integrating China in the International Consortium for Personalized Medicine' project, we aimed to map the current landscape of R&I actors in the field ofpersonalized medicine in the EU and China. Methods: A two-phase desk research study was conducted. Results: We identified 78 R&I actors. Research and technology organizations were the most frequent in both the EU and China. The identified R&I actors were active in a wide range of fields. The EU and China have many different R&I actors addressing personalized medicine-related issues, with few characteristics in common. Conclusion: More efforts are needed to ensure these R&I actors are encouraged to work together to bridge each other's gaps.

Antitumor pharmacological research in the era of personalized medicine.

Anticancer drug discovery has yielded unprecedented progress in recent decades, resulting in the approval of innovative treatment options for patients and the successful implementation of personalized medicine in clinical practice. This remarkable progress has also reshaped the research scope of pharmacological research. This article, as a tribute to cancer research at Shanghai Institute of Materia Medica in celebration of the institute's 90th birthday, provides an overview of the conceptual revolution occurring in anticancer therapy, and summarizes our recent progress in the development of molecularly targeted therapeutics and exploration of new strategies in personalized medicine. With this review, we hope to provide a glimpse into how antitumor pharmacological researchers have embraced the new era of personalized medicine research and to propose a future path for anticancer drug discovery and pharmacological research.

Lung Organoids—The Ultimate Tool to Dissect Pulmonary Diseases?

Organoids are complex multicellular three-dimensional (3D) in vitro models that are designed to allow accurate studies of the molecular processes and pathologies of human organs. Organoids can be derived from a variety of cell types, such as human primary progenitor cells, pluripotent stem cells, or tumor-derived cells and can be co-cultured with immune or microbial cells to further mimic the tissue niche. Here, we focus on the development of 3D lung organoids and their use as disease models and drug screening tools. We introduce the various experimental approaches used to model complex human diseases and analyze their advantages and disadvantages. We also discuss validation of the organoids and their physiological relevance to the study of lung diseases. Furthermore, we summarize the current use of lung organoids as models of host-pathogen interactions and human lung diseases such as cystic fibrosis, chronic obstructive pulmonary disease, or SARS-CoV-2 infection. Moreover, we discuss the use of lung organoids derived from tumor cells as lung cancer models and their application in personalized cancer medicine research. Finally, we outline the future of research in the field of human induced pluripotent stem cell-derived organoids.

Learning to Rank Articles for Molecular Queries

The cost of developing new drugs is estimated at billions of dollars per year. Identification of new molecules for drugs involves scanning existing bio-medical literature for relevant information. As the potential drug molecule is novel, retrieval of relevant information using a simple direct search is less likely to be productive. Identifying relevant papers is therefore a more complex and challenging task, which requires searching for information on molecules with similar characteristics to the novel drug. In this paper, we present the novel task of ranking documents based on novel molecule queries. Given a chemical molecular structure, we wish to rank medical papers that will contribute to a researcher's understanding of the novel molecule drug potential. We present a set of ranking algorithms and molecular embeddings to address the task. An extensive evaluation of the algorithms is performed over the molecular embeddings, studying their performance on a benchmark retrieval corpus, which we share with the community. Additionally, we introduce a heterogeneous edge-labeled graph embedding approach to address the molecule ranking task. Our evaluation shows that the proposed embedding model can significantly improve molecule ranking methods. The system is currently deployed in a targeted drug delivery and personalized medicine research laboratory.

Status and trends of personalized medicine research from 2000 to 2020: a bibliometric analysis

Background Personalized medicine (PM), as a rapidly growing research area, provides treatments, practices, and interventions being adapted to an individual patient based on his own risk of disease. This study aims to analyze the productivity of countries, institutions, and authors in this field, to determine the existing research trends worldwide, and to forecast future research activity for specific countries. Methods Documents published between 2000 and 2020 were retrieved from the Web of Science (WoS) database. Bibliometric analysis was performed to assess the outputs, correlation analysis was applied to analyze the relationship between Gross Domestic Product per capita (GDP-PP) and the number of publications, and an extrapolation method was used for predicting the future productivity trends for certain countries. Results A total of 7,772 documents were published globally on PM between 2000 and 2020. The most productive country, journal, and institution are the USA, Personalized Medicine, and Harvard Medical School, respectively. The USA is also first in line in terms of total citations. Netherlands, Denmark, and the USA are listed at the top in terms of the total number of papers and citations, after adjusting for GDP-PP and population size. Also, as predictions suggest, the USA will maintain its predominant role in the PM field in the next 5 years. Conclusions Owing to its both interdisciplinary and multidisciplinary nature, PM bestows researchers’ numerous sources to benefit and enables them a field that they can be productive of for the future. Therefore, this field is expected to progress and be the lead area in medicine in the upcoming years.

Identification of survival relevant genes with measurement error in gene expression incorporated

Modern gene expression technologies, such as microarray and the next generation RNA sequencing, enable simultaneous measurement of expressions of a large number of genes, and therefore represent important tools in the personalized medicine research for improving the patient survival prediction accuracy. However, survival analysis with gene expression data can be challenging due to the high dimensionality. Proper identification of survival relevant genes is thus imperative for building suitable prediction models. In spite of the fact that gene expressions are typically subject to measurement errors introduced from the complex experimental procedure, the issue of measurement error is often ignored in survival gene identifications. In this article, the effect of measurement error on the identification of survival relevant genes is explored under the accelerated failure time model setting. Survival relevant genes are identified by regularizing the weighted least square estimator with the adaptive LASSO penalty. The simulation-extrapolation method is incorporated to adjust for the impact of measurement error on gene identification. The performance of the proposed method is assessed by simulation studies and the utility of the proposed method is illustrated by a real data set collected from the diffuse large-B-cell lymphoma study. The results show that the proposed method yields better prediction models than traditional methods which ignore measurement error in gene expressions.

Block coordinate descent algorithm improves variable selection and estimation in error-in-variables regression.

Medical research increasingly includes high‐dimensional regression modeling with a need for error‐in‐variables methods. The Convex Conditioned Lasso (CoCoLasso) utilizes a reformulated Lasso objective function and an error‐corrected cross‐validation to enable error‐in‐variables regression, but requires heavy computations. Here, we develop a Block coordinate Descent Convex Conditioned Lasso (BDCoCoLasso) algorithm for modeling high‐dimensional data that are only partially corrupted by measurement error. This algorithm separately optimizes the estimation of the uncorrupted and corrupted features in an iterative manner to reduce computational cost, with a specially calibrated formulation of cross‐validation error. Through simulations, we show that the BDCoCoLasso algorithm successfully copes with much larger feature sets than CoCoLasso, and as expected, outperforms the naïve Lasso with enhanced estimation accuracy and consistency, as the intensity and complexity of measurement errors increase. Also, a new smoothly clipped absolute deviation penalization option is added that may be appropriate for some data sets. We apply the BDCoCoLasso algorithm to data selected from the UK Biobank. We develop and showcase the utility of covariate‐adjusted genetic risk scores for body mass index, bone mineral density, and lifespan. We demonstrate that by leveraging more information than the naïve Lasso in partially corrupted data, the BDCoCoLasso may achieve higher prediction accuracy. These innovations, together with an R package, BDCoCoLasso, make error‐in‐variables adjustments more accessible for high‐dimensional data sets. We posit the BDCoCoLasso algorithm has the potential to be widely applied in various fields, including genomics‐facilitated personalized medicine research.

Implementing Personalized Genetic Medicine

Implementing Personalized Genetic Medicine

My Data, My Choice? \u2013 German Patient Organizations\u2019 Attitudes towards Big Data-Driven Approaches in Personalized Medicine. An Empirical-Ethical Study

Personalized medicine (PM) operates with biological data to optimize therapy or prevention and to achieve cost reduction. Associated data may consist of large variations of informational subtypes e.g. genetic characteristics and their epigenetic modifications, biomarkers or even individual lifestyle factors. Present innovations in the field of information technology have already enabled the procession of increasingly large amounts of such data (‘volume’) from various sources (‘variety’) and varying quality in terms of data accuracy (‘veracity’) to facilitate the generation and analyzation of messy data sets within a short and highly efficient time period (‘velocity’) to provide insights into previously unknown connections and correlations between different items (‘value’). As such developments are characteristics of Big Data approaches, Big Data itself has become an important catchphrase that is closely linked to the emerging foundations and approaches of PM. However, as ethical concerns have been pointed out by experts in the debate already, moral concerns by stakeholders such as patient organizations (POs) need to be reflected in this context as well. We used an empirical-ethical approach including a website-analysis and 27 telephone-interviews for gaining in-depth insight into German POs’ perspectives on PM and Big Data. Our results show that not all POs are stakeholders in the same way. Comparing the perspectives and political engagement of the minority of POs that is currently actively involved in research around PM and Big Data-driven research led to four stakeholder sub-classifications: ‘mediators’ support research projects through facilitating researcher’s access to the patient community while simultaneously selecting projects they preferably support while ‘cooperators’ tend to contribute more directly to research projects by providing and implemeting patient perspectives. ‘Financers’ provide financial resources. ‘Independents’ keep control over their collected samples and associated patient-related information with a strong interest in making autonomous decisions about its scientific use. A more detailed terminology for the involvement of POs as stakeholders facilitates the adressing of their aims and goals. Based on our results, the ‘independents’ subgroup is a promising candidate for future collaborations in scientific research. Additionally, we identified gaps in PO’s knowledge about PM and Big Data. Based on these findings, approaches can be developed to increase data and statistical literacy. This way, the full potential of stakeholder involvement of POs can be made accessible in discourses around PM and Big Data.

Precision medicine path at personalized medicine research center/endocrinology and metabolism research institute: A systematic review

Precision medicine is an evolving predictive, early preventive, and treatment method that takes into account human gene heterogeneity for each individual. Personalized Medicine Research Center (PMRC) of Endocrinology and Metabolism Research Institute (EMRI) has been officially founded by the authority of the Ministry of Health and Medical Education on 18 December 2016 with the primary purpose of research, education, and dissemination of the personalized medicine approach throughout the country. In this review, we aimed to introduce the Personalized Medicine Research Center activities in the field of precision medicine since the establishment. We systematically reviewed PubMed, Scopus, and WoS from 2016 to December 2019 to identify our relevant topics with PMRC affiliation. After screening processes, a total of 18 studies met eligibility criteria to include in the systematic review. Personalized medicine that interchangeably called precision medicine is a new concept in medicine, the implementation of which needs to organize some infrastructures which are physical and the required education for healthcare professionals in different countries.