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Статті в журналах з теми "Personalized predictive medicine"

Автор: Grafiati

Опубліковано: 2 листопада 2024

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1

Nassimbwa, Kabanda D. "The Role of Biomarkers in Personalized Cancer Treatment." RESEARCH INVENTION JOURNAL OF PUBLIC HEALTH AND PHARMACY 3, no. 2 (September 1, 2024): 3–33. http://dx.doi.org/10.59298/rijpp/2024/323033.

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Biomarkers are vital instruments in modern medicine, providing information about biological processes and disease progression. Biomarkers in oncology have had a substantial impact on the development of personalised cancer treatments by predicting therapy responses and outcomes. The study investigates the different types of biomarkers, such as diagnostic, prognostic, and predictive biomarkers, and their function in precision medicine. Personalised cancer treatment, which is guided by biomarker testing, improves patient outcomes by adapting medicines to individual genetic profiles. However, obstacles such as biomarker validation, resistance to targeted medicines, and regulatory barriers persist. Overcoming these obstacles will propel future advances in biomarker-driven oncology. Keywords: Biomarkers, personalized medicine, cancer, precision oncology, diagnostic biomarkers.

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2

Giglia, Giuseppe, Giuditta Gambino, and Pierangelo Sardo. "Through Predictive Personalized Medicine." Brain Sciences 10, no. 9 (August 28, 2020): 594. http://dx.doi.org/10.3390/brainsci10090594.

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Neuroblastoma (NBM) is a deadly form of solid tumor mostly observed in the pediatric age. Although survival rates largely differ depending on host factors and tumor-related features, treatment for clinically aggressive forms of NBM remains challenging. Scientific advances are paving the way to improved and safer therapeutic protocols, and immunotherapy is quickly rising as a promising treatment that is potentially safer and complementary to traditionally adopted surgical procedures, chemotherapy and radiotherapy. Improving therapeutic outcomes requires new approaches to be explored and validated. In-silico predictive models based on analysis of a plethora of data have been proposed by Lombardo et al. as an innovative tool for more efficacious immunotherapy against NBM. In particular, knowledge gained on intracellular signaling pathways linked to the development of NBM was used to predict how the different phenotypes could be modulated to respond to anti-programmed cell death-ligand-1 (PD-L1)/programmed cell death-1 (PD-1) immunotherapy. Prediction or forecasting are important targets of artificial intelligence and machine learning. Hopefully, similar systems could provide a reliable opportunity for a more targeted approach in the near future.

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3

Reena Dhan, Archana, and Binod Kumar. "Machine Learning for Healthcare: Predictive Analytics and Personalized Medicine." International Journal of Science and Research (IJSR) 13, no. 6 (June 5, 2024): 1307–13. http://dx.doi.org/10.21275/mr24608013906.

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4

Farooq, Faisal, Balaji Krishnapuram, Romer Rosales, Shipeng Yu, Jude Shavlik, and Raju Kucherlapati. "Predictive Models in Personalized Medicine." ACM SIGHIT Record 1, no. 1 (March 2011): 23–25. http://dx.doi.org/10.1145/1971706.1971714.

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5

DOSAY-AKBULUT, Mine. "A Review on Determination and Future of the Predictive and Personalized Medicine." International Journal of Biology 8, no. 1 (November 11, 2015): 32. http://dx.doi.org/10.5539/ijb.v8n1p32.

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<p class="1Body">Medicine contents’ have extended to predictive, personalized, preventive and participatory medicine (P4). ‘Personalized medicine' focuses on the prediction of potential benefits or risks for individuals as possible as in detailed. Biomarker discovery, biocomputing and nanotechnology have opened a new horizon on ‘personalized medicine’ (including disease detection, diagnosis and therapy by using individual's molecular profile) and ‘predictive medicine’ (to predict disease development, progression and clinical outcome, by using the genetic and molecular information).</p><p class="1Body">Personalized medicine can be applied to a lot of different areas. P4 medicine, based on use of marker-assisted diagnosis and targeted therapies, comes from an individual's molecular profile, will form a new way on drugs development and medicine administration. Genetic screening aimed to identify carrier and affected individuals in a particular population. Molecular diagnostic test, including genome-derived tests are getting more attention within the medicine with genotyping, RNA expression analyses, metabolic profiling, and other biomarkers. Genomics research has getting more attention on the biomedical research, translational science, and personalized medicine; divided into 3 main parts: 1) genomics to biology, 2) genomics to health, and 3) genomics to society.</p><p class="1Body">We conducted a literature search via PubMed databases with using “personalized medicine”, and “application areas of P4” keywords, and summarized some of new studies.</p><p class="1Body">Personalized medicine is described as an individualized treatment based on the individual's genetic variants. In other words, “for predicting health, preventing and preempting disease, and personalizing treatment depending on the each person’ unique biology", has a speedy improvement.</p>

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6

Nie, Shuming. "Nanotechnology for personalized and predictive medicine." Nanomedicine: Nanotechnology, Biology and Medicine 2, no. 4 (December 2006): 305. http://dx.doi.org/10.1016/j.nano.2006.10.115.

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7

Workman, Paul, Paul A. Clarke, and Bissan Al-Lazikani. "Personalized Medicine: Patient-Predictive Panel Power." Cancer Cell 21, no. 4 (April 2012): 455–58. http://dx.doi.org/10.1016/j.ccr.2012.03.030.

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8

Rizvi, S. Mohd Shiraz, Farzana Mahdi, Abbas Ali Mahdi, Tabrez Jafar, and Saliha Rizvi. "PERSONALIZED MEDICINE: ROLE OF ASYMMETRIC DIMETHYLARGININE AS A PREDICTIVE MARKER OF CAD." Era's Journal of Medical Research 7, no. 1 (June 2020): 86–91. http://dx.doi.org/10.24041/ejmr2020.15.

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9

Mathieu, Thierry, Laurent Bermont, Jean-Christophe Boyer, Céline Versuyft, Alexandre Evrard, Isabelle Cuvelier, Remy Couderc, and Katell Peoc’h. "Lexical fields of predictive and personalized medicine." Annales de biologie clinique 70, no. 6 (November 2012): 651–58. http://dx.doi.org/10.1684/abc.2012.0767.

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10

Martin, Greg, and Dean Jones. "The Road to Personalized and Predictive Medicine." American Journal of Respiratory and Critical Care Medicine 188, no. 2 (July 15, 2013): 257. http://dx.doi.org/10.1164/rccm.201212-2248le.

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11

Hood, Leroy, and Stephen H. Friend. "Predictive, personalized, preventive, participatory (P4) cancer medicine." Nature Reviews Clinical Oncology 8, no. 3 (March 2011): 184–87. http://dx.doi.org/10.1038/nrclinonc.2010.227.

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12

Antoñanzas, F., C. A. Juárez-Castelló, and R. Rodríguez-Ibeas. "Some economics on personalized and predictive medicine." European Journal of Health Economics 16, no. 9 (November 8, 2014): 985–94. http://dx.doi.org/10.1007/s10198-014-0647-8.

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13

Opeyemi Olaoluawa Ojo and Blessing Kiobel. "Integrating predictive analytics in clinical trials: A paradigm shift in personalized medicine." World Journal of Biology Pharmacy and Health Sciences 19, no. 3 (September 30, 2024): 308–20. http://dx.doi.org/10.30574/wjbphs.2024.19.3.0630.

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The integration of predictive analytics in clinical trials represents a transformative advancement in personalized medicine, reshaping traditional paradigms of drug development and patient care. This study explores the pivotal role predictive analytics plays in optimizing clinical trials by leveraging artificial intelligence (AI) and machine learning models to process vast datasets, including genetic information, patient demographics, and biomarkers. The purpose of this research is to analyze how predictive models enhance patient selection, streamline trial designs, and ultimately improve clinical outcomes. A comprehensive review of current methodologies reveals that predictive analytics offers significant advantages in enhancing precision and reducing trial timelines through adaptive designs. By predicting patient responses and adverse events, these models not only improve the efficiency of clinical trials but also mitigate risks, ensuring higher safety and efficacy. Despite these benefits, the study identifies challenges such as data bias, privacy concerns, and the need for robust regulatory frameworks, which remain critical hurdles to widespread adoption. Key findings highlight the importance of addressing these ethical and operational challenges to fully realize the potential of predictive analytics. The study concludes with recommendations for ongoing research into explainable AI, federated learning, and real-time analytics to expand the applicability of predictive models. As healthcare moves towards increasingly data-driven approaches, predictive analytics is set to play a central role in delivering personalized, equitable, and effective care, driving forward the future of clinical trials and personalized medicine.

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14

Grosu, Oxana. "Predictive, preventive, personalized and participatory medicine (4P): narrative review." Bulletin of the Academy of Sciences of Moldova. Medical Sciences 74, no. 3 (February 2023): 22–28. http://dx.doi.org/10.52692/1857-0011.2022.3-74.03.

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4 P medicine is a concept used in healthcare and biomedical research that is based on the principles of prediction, personalization, prevention, and participation. It is increasingly used which has enabled the rapid development of medicine: several forms/types of pathologies have been discovered, for which specific treatments and appropriate prevention strategies have been developed, it has become possible to predict the evolution of health and disease, all these would be useless without the participation of the patient, family, and decision makers.The purpose of this article is to provide an overview of the 4 P medicine and its preventive, predictive, personalized, and participatory principles through the lens of clinical practice.Materials and Methods: The database was searched for the keywords “4P medicine”, “personalized medicine”, “participatory medicine”, “systems medicine”. For the term “systems medicine” we obtained 5,257,904 results, in the last 10 years - there were 2,396,921 publications, journal articles - 2.4 million citations, book chapters - 11 thousand, related to medicine - 1.6 million of the references. For the term “precision medicine” 158,239 references appeared, for “personalized medicine” - 91,883 citations and for “participatory medicine” - 17,579 references. Most references were published in English. For the search for “personalized medicine oncology” - 18,209 references appeared, for “personalized medicine neurology” 4,751 references were discovered, in the last 10 years 4,336 of them were published, being journal articles4,300 and 16 book chapters. Most publications talk about the use of the principles of personalized medicine (4P) in demyelinating pathologies, epilepsy, dementia, neuro-oncology, stroke, migraine, neuroimaging, and neurorehabilitation. Results and discussions: 4P medicine is at the intersection of three major trends: the growing ability of systems biology and medicine to decipher the biological complexity of pathologies, the digital revolution that has increased theability to collect, integrate, store, analyze and communicate data and information, and consumer access to information.Conclusion: 4P medicine is a concept used in biomedical research and human health care that was taken from systems biology and systems medicine. It is a form of approach that uses the principles of prevention, prediction, personalization, and participation to transform healthcare from a reactive to a proactive one. The use of the principles of 4P medicine in clinical practice allows the deciphering of forms of the disease, the stratification according to pathophysiological mechanisms, the application of personalized treatment to the patient, respectively more efficiently and with fewer adverse reactions.

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15

Rodrigues-Ferreira, Sylvie, and Clara Nahmias. "Predictive biomarkers for personalized medicine in breast cancer." Cancer Letters 545 (October 2022): 215828. http://dx.doi.org/10.1016/j.canlet.2022.215828.

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16

Baranov, Vladislav S. "Gene polymorphism, ecogenetic diseases and predictive personalized medicine." Ecological genetics 9, no. 3 (September 15, 2011): 3–14. http://dx.doi.org/10.17816/ecogen933-14.

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The problems concerned with identification of genes involved in the origin of complex diseases, analysis of their epistatic (gene to gene) interactions and adequate interpretation of genetic testing results in Predictive Personalized Medicine (PPM) are reviewed. The practical meaning of already available PPM data, the options and volume of their feasible clinical implications are discussed.

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17

Jiang, Xiaoqian, Melanie Osl, Jihoon Kim, and Lucila Ohno-Machado. "Calibrating predictive model estimates to support personalized medicine." Journal of the American Medical Informatics Association 19, no. 2 (March 2012): 263–74. http://dx.doi.org/10.1136/amiajnl-2011-000291.

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18

Gusev, I. V., D. V. Gavrilov, R. E. Novitsky, T. Yu Kuznetsova, and S. A. Boytsov. "Improvement of cardiovascular risk assessment using machine learning methods." Russian Journal of Cardiology 26, no. 12 (October 25, 2021): 4618. http://dx.doi.org/10.15829/1560-4071-2021-4618.

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The increase in the prevalence of cardiovascular diseases (CVDs) specifies the importance of their prediction, the need for accurate risk stratification, preventive and treatment interventions. Large medical databases and technologies for their processing in the form of machine learning algorithms that have appeared in recent years have the potential to improve predictive accuracy and personalize treatment approaches to CVDs. The review examines the application of machine learning in predicting and identifying cardiovascular events. The role of this technology both in the calculation of total cardiovascular risk and in the prediction of individual diseases and events is discussed. We compared the predictive accuracy of current risk scores and various machine learning algorithms. The conditions for using machine learning and developing personalized tactics for managing patients with CVDs are analyzed.

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19

Umulisa, Kanyana J. "Statistics in Personalized Medicine: Challenges and Innovations." NEWPORT INTERNATIONAL JOURNAL OF BIOLOGICAL AND APPLIED SCIENCES 5, no. 2 (August 20, 2024): 39–43. http://dx.doi.org/10.59298/nijbas/2024/5.2.39431.

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Personalized medicine represents a paradigm shift in healthcare, aiming to tailor medical treatments to individual patients based on their unique genetic profiles, lifestyle factors, and environmental influences. At the forefront of this transformation lies statistics, which plays a pivotal role in integrating diverse data sources, identifying biomarkers, and developing predictive models that guide personalized treatment decisions. However, statistics in personalized medicine face challenges such as data integration complexities, small sample sizes, and ethical considerations. Despite these challenges, innovative statistical approaches including machine learning, Bayesian inference, and multi-omics integration are driving advancements. The future of statistics in personalized medicine lies in integrating multi-omics data, adopting artificial intelligence for predictive modeling, enhancing quantitative pharmacology, leveraging real-world evidence, and addressing ethical and regulatory frameworks. By advancing these fronts, statistics holds the promise to optimize treatment outcomes, improve patient care, and redefine the landscape of healthcare delivery in the 21st century. Keywords: Personalized medicine, Statistics, Data integration, Machine learning, Ethical considerations

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20

Belousov, D. Yu. "Future developments in precision and personalized medicine." Patient-Oriented Medicine and Pharmacy 1, no. 4 (December 30, 2023): 8–13. http://dx.doi.org/10.37489/2949-1924-0027.

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Precision and personalized medicine tailors’ therapy, disease prevention, and health maintenance to each individual. Precision and personalized medicine aims to optimize care for individual patients using predictive biomarkers to improve outcomes and prevent side effects. Precision and personalized medicine combined with pharmacogenomics.This article examines the current path of personalized medicine from a broader perspective with the goal of finding a better direction for its future development. Based on the analysis and demonstration of the research methods and problems found in precision medicine research, as well as its scientific limitations, this review points out that although precision medicine belongs to the model of personalized medicine, it cannot yet achieve the ideal personalized medicine on its current path development.

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21

Elemento, Olivier. "The future of precision medicine: towards a more predictive personalized medicine." Emerging Topics in Life Sciences 4, no. 2 (August 28, 2020): 175–77. http://dx.doi.org/10.1042/etls20190197.

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Precision medicine can be defined as personalized medicine enhanced by technology. In the past, medicine has, in some cases, been personalized. For example, some drugs are dosed on an individualized basis based on age, body-mass index, comorbidities and other clinical parameters. However, overall, medicine has largely followed the ‘one-size-fits-all' paradigm as exemplified in the treatment of essential hypertension or type 2 diabetes mellitus. What has changed in the past few years is that technologies such as high throughput sequencing, mass spectrometry, microfluidics, and imaging can help conduct a multitude of complex measurements on clinical samples. Aided by analytics, these technologies have been providing an increasingly detailed picture of molecular and cellular alterations underlying numerous diseases and have revealed tremendous variability between individuals and patients at the molecular and cellular level. These findings have motivated a more personalized or ‘precision' approach to medicine, in which molecular and cellular markers help tailor patient management to each individual. Here we provide an overview of the key factors driving adoption of precision medicine and highlight current research that may soon make precision medicine more predictive.

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Prins, Bram Peter, Liis Leitsalu, Katri Pärna, Krista Fischer, Andres Metspalu, Toomas Haller, and Harold Snieder. "Advances in Genomic Discovery and Implications for Personalized Prevention and Medicine: Estonia as Example." Journal of Personalized Medicine 11, no. 5 (April 29, 2021): 358. http://dx.doi.org/10.3390/jpm11050358.

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The current paradigm of personalized medicine envisages the use of genomic data to provide predictive information on the health course of an individual with the aim of prevention and individualized care. However, substantial efforts are required to realize the concept: enhanced genetic discoveries, translation into intervention strategies, and a systematic implementation in healthcare. Here we review how further genetic discoveries are improving personalized prediction and advance functional insights into the link between genetics and disease. In the second part we give our perspective on the way these advances in genomic research will transform the future of personalized prevention and medicine using Estonia as a primer.

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Gorbachenko, Vladimir. "Digital Model for Diagnosis of Postoperative Complications in Medicine Using Bioinformatics." International Journal of Applied Research in Bioinformatics 9, no. 2 (July 2019): 1–23. http://dx.doi.org/10.4018/ijarb.2019070101.

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Digital models are needed in medicine using bioinformatics for diagnosis and prediction. Such models are especially needed in personalized medicine using bioinformatics. In this area, it is necessary to evaluate and predict the patient's condition from a priori knowledge obtained from other patients. Therefore, a new direction appeared - predictive medicine using bioinformatics. Predictive medicine, or “in silico medicine” is the use of computer modelling and intelligent technologies in the diagnosis, treatment and prevention of diseases. Using predictive medicine, the doctor can determine the likelihood of the development of certain diseases and choose the optimal treatment using bioinformatics. Predictive medicine begins to be applied in surgery. The prognosis in surgery consists in the preoperative evaluation of various surgical interventions and in the evaluation of possible outcomes of surgical interventions.

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24

Chavda, Vivek P. "Personalized Medicine: Futuristic Predictive Nanomedicines for Diagnosis and Therapeutics." Genes Review 2, no. 1 (2016): 1–11. http://dx.doi.org/10.18488/journal.103/2016.2.1/103.1.1.11.

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25

Jenkins, Sherry L., and Avi Ma’ayan. "Systems pharmacology meets predictive, preventive, personalized and participatory medicine." Pharmacogenomics 14, no. 2 (January 2013): 119–22. http://dx.doi.org/10.2217/pgs.12.186.

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Baranov, V. S. "Genome Paths A Way to Personalized and Predictive Medicine." Acta Naturae 1, no. 3 (December 15, 2009): 70–80. http://dx.doi.org/10.32607/20758251-2009-1-3-70-80.

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Baranov, V. S. "Genome Paths A Way to Personalized and Predictive Medicine." Acta Naturae 1, no. 3 (December 15, 2009): 70–80. http://dx.doi.org/10.32607/actanaturae.10773.

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28

La Thangue, Nicholas B., and David J. Kerr. "Predictive biomarkers: a paradigm shift towards personalized cancer medicine." Nature Reviews Clinical Oncology 8, no. 10 (August 23, 2011): 587–96. http://dx.doi.org/10.1038/nrclinonc.2011.121.

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29

Joskowicz, Leo. "Computer-aided surgery meets predictive, preventive, and personalized medicine." EPMA Journal 8, no. 1 (March 2017): 1–4. http://dx.doi.org/10.1007/s13167-017-0084-8.

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Yang, Hui, Yutao Liu, and Hua Liang. "Focused information criterion on predictive models in personalized medicine." Biometrical Journal 57, no. 3 (February 4, 2015): 422–40. http://dx.doi.org/10.1002/bimj.201400106.

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31

Al-Quraishi, Tahsien, Naseer Al-Quraishi, Hussein AlNabulsi, Hussein AL-Qarishey, and Ahmed Hussein Ali. "Big Data Predictive Analytics for Personalized Medicine: Perspectives and Challenges." Applied Data Science and Analysis 2024 (April 11, 2024): 32–38. http://dx.doi.org/10.58496/adsa/2024/004.

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The integration of predictive analytics into personalized medicine has become a promising approach for improving patient outcomes and treatment efficacy. This paper provides a review of the field, examining the tools, methodologies, and challenges associated with this advanced statistical methodology. Predictive analytics leverages machine learning algorithms to analyze vast datasets, including Electronic Health Records (EHRs), genomic data, medical imaging, and real-time data from wearable devices. The review explores key tools such as the Hadoop Distributed File System (HDFS), Apache Spark, and Apache Hive, which facilitate scalable storage, efficient data processing, and comprehensive data analysis. Key challenges identified include managing the immense volume of healthcare data, ensuring data quality and integration, and addressing privacy and security concerns. The paper also highlights the difficulties in achieving real-time data processing and integrating predictive insights into clinical practice. Effective data governance and ethical considerations are critical to maintaining trust and transparency. The strategic use of big data tools, combined with investment in skill development and interdisciplinary collaboration, is essential for harnessing the full potential of predictive analytics in personalized medicine. By overcoming these challenges, healthcare providers can enhance patient care, optimize resource management, and drive medical discoveries, ultimately revolutionizing healthcare delivery on a global scale.

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Pandey, Akash, and Surya Prakash Gupta. "Personalized Medicine: A Comprehensive Review." Oriental Journal Of Chemistry 40, no. 4 (August 30, 2024): 933–44. http://dx.doi.org/10.13005/ojc/400403.

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Personalized medicine, also known as precision medicine, represents a revolutionary approach to healthcare, tailoring medical interventions to individuals based on their unique characteristics such as genetics, environment, and lifestyle. This shift from a one-size-fits-all model to a targeted approach holds great promise for enhancing patient outcomes, improving treatment effectiveness, and reducing adverse effects. Advancements in genomics, proteomics, and data analysis have facilitated the identification of biomarkers and treatment targets, leading to the development of personalized diagnostics and therapies across various medical fields. However, the widespread adoption of personalized medicine is hindered by challenges like data privacy, regulatory obstacles, and ensuring equal access to innovative technologies. This summary outlines the principles, technological progress, clinical applications, obstacles, and future prospects of personalized medicine, underscoring its potential to transform healthcare delivery and introduce a new era of precision medicine. Personalized medicine represents a healthcare model incorporating periodic, individualized, participatory, and predictive measures. It aims to improve treatment outcomes by pinpointing the genetic factors underlying an individual's illness. Personalized medicine holds promise for decreasing both financial and time costs while enhancing patients' quality of life and potentially extending their life spans. It represents an approach to improve treatment outcomes by identifying the genomic makeup responsible for causing diseases in individuals. Personalized medicine encompasses a wide range of applications and can be utilized for diagnosing various illnesses.

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Sharipov, K. O., A. M. Almuratov, and A. A. Batyrbaeva. "TRACE ELEMENTS STATUS AS AN EFFECTIVE BIOMARKER IN PREDICTIVE AND PERSONALIZED MEDICINE." Biological Markers in Fundamental and Clinical Medicine (collection of abstracts) 2, no. 1 (March 24, 2018): 17–18. http://dx.doi.org/10.29256/v.02.01.2018.escbm06.

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Ajdari, Ali, Yunhe Xie, Christian Richter, Maximilian Niyazi, Dan G. Duda, Theodore S. Hong, and Thomas Bortfeld. "Toward Personalized Radiation Therapy of Liver Metastasis: Importance of Serial Blood Biomarkers." JCO Clinical Cancer Informatics, no. 5 (March 2021): 315–25. http://dx.doi.org/10.1200/cci.20.00118.

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PURPOSE To assess the added value of serial blood biomarkers in liver metastasis stereotactic body radiation therapy (SBRT). MATERIALS AND METHODS Eighty-nine patients were retrospectively included. Pre- and midtreatment blood samples were analyzed for potential biomarkers of the treatment response. Three biomarker classes were studied: gene mutation status, complete blood count, and inflammatory cytokine concentration in plasma. One-year local failure (LF) and 2-year overall survival (OS) were chosen as study end points. Multivariate logistic regression was used for response prediction. Added predictive benefit was assessed by quantifying the difference between the predictive performance of a baseline model (clinicopathologic and dosimetric predictors) and that of the biomarker-enhanced model, using three metrics: (1) likelihood ratio, (2) predictive variance, and (3) area under the receiver operating characteristic curve (AUC). RESULTS The most important predictors of LF were mutation in KRAS gene (hazard ratio [HR] = 2.92, 95% CI, [1.17 to 7.28], P = .02) and baseline and midtreatment concentration of plasma interleukin-6 (HR = 1.15 [1.04 to 1.26] and 1.06 [1.01 to 1.13], P = .01). Absolute lymphocyte count and platelet-to-lymphocyte ratio at baseline as well as neutrophil-to-lymphocyte ratio at baseline and before fraction 3 (HR = 1.33 [1.16 to 1.51] and 1.19 [1.09 to 1.30]) had the most significant association with OS ( P = .0003). Addition of baseline GEN and inflammatory plasma cytokine biomarkers in predicting LF, respectively, increased AUC by 0.06 (from 0.73 to 0.79) and 0.07 (from 0.77 to 0.84). In predicting OS, inclusion of midtreatment complete blood count biomarkers increased AUC from 0.72 to 0.80, along with significant boosts in likelihood ratio and predictive variance. CONCLUSION Inclusion of serial blood biomarkers leads to significant gain in predicting response to liver metastasis stereotactic body radiation therapy and can guide treatment personalization.

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Serrano, Dolores R., Francis C. Luciano, Brayan J. Anaya, Baris Ongoren, Aytug Kara, Gracia Molina, Bianca I. Ramirez, et al. "Artificial Intelligence (AI) Applications in Drug Discovery and Drug Delivery: Revolutionizing Personalized Medicine." Pharmaceutics 16, no. 10 (October 14, 2024): 1328. http://dx.doi.org/10.3390/pharmaceutics16101328.

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Анотація:

Artificial intelligence (AI) encompasses a broad spectrum of techniques that have been utilized by pharmaceutical companies for decades, including machine learning, deep learning, and other advanced computational methods. These innovations have unlocked unprecedented opportunities for the acceleration of drug discovery and delivery, the optimization of treatment regimens, and the improvement of patient outcomes. AI is swiftly transforming the pharmaceutical industry, revolutionizing everything from drug development and discovery to personalized medicine, including target identification and validation, selection of excipients, prediction of the synthetic route, supply chain optimization, monitoring during continuous manufacturing processes, or predictive maintenance, among others. While the integration of AI promises to enhance efficiency, reduce costs, and improve both medicines and patient health, it also raises important questions from a regulatory point of view. In this review article, we will present a comprehensive overview of AI’s applications in the pharmaceutical industry, covering areas such as drug discovery, target optimization, personalized medicine, drug safety, and more. By analyzing current research trends and case studies, we aim to shed light on AI’s transformative impact on the pharmaceutical industry and its broader implications for healthcare.

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SVOBODA, M., R. LOHAJOVA BEHULOVA, T. SLAMKA, L. SEBEST, and V. REPISKA. "Comprehensive Genomic Profiling in Predictive Testing of Cancer." Physiological Research 72, S3 (October 23, 2023): S267—S275. http://dx.doi.org/10.33549/physiolres.935154.

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Despite the rapid progress in the field of personalized medicine and the efforts to apply specific treatment strategies to patients based on the presence of pathogenic variants in one, two, or three genes, patient response to the treatment in terms of positive benefit and overall survival remains heterogeneous. However, advances in sequencing and bioinformatics technologies have facilitated the simultaneous examination of somatic variants in tens to thousands of genes in tumor tissue, enabling the determination of personalized management based on the patient's comprehensive genomic profile (CGP). CGP has the potential to enhance clinical decision-making and personalize innovative treatments for individual patients, by providing oncologists with a more comprehensive molecular characterization of tumors. This study aimed to highlight the utility of CGP in routine clinical practice. Here we present three patient cases with various advanced cancer indicated for CGP analysis using a combination of SOPHiA Solid Tumor Solution (STS, 42 genes) for DNA and SOPHiA RNAtarget Oncology Solution (ROS, 45 genes and 17 gene fusions with any random partners) for RNA. We were able to identify actionable genomic alterations in all three cases, thereby presenting valuable information for future management of these patients. This approach has the potential to transform clinical practice and greatly improve patient outcomes in the field of oncology.

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37

Noor, Hania. "A Short Review on 4P Domain in Oncology (Personalized, Predictive, Preventive and Participative)." South Asian Research Journal of Medical Sciences 5, no. 02 (March 21, 2023): 30–32. http://dx.doi.org/10.36346/sarjms.2023.v05i02.004.

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Since the 1970s, our society has been undergoing rapid digitalization. This has been paralleled by rapid advancements in biology, in particular with the sequencing of the human genome and the development of proteomics and metabolomics. This digital world presents an unmatched opportunity to revolutionize health. Today, genetic medicines deal with patients who have inherited diseases. Tomorrow, we will be able to deal with a much larger scope, including the multigenic susceptibility to disease. This is the promise of precision medicine. Cancer is a major focus of precision medicine. Initiative and developments in precise and effective treatment could benefit many other chronic diseases. Precision oncology focuses on matching the most accurate and effective treatment to this individual cancer patient based on the genetic profile of cancer and the individual,(Precision Medicine the Foundation of Future Cancer Therapeutics - Google Search, n.d.). Because every single cancer patient exhibits a different genetic profile. It can help make more accurate diagnosis and improve treatment that can help people to make decisions regarding their own health and they can deal with areas of uncertainty that might have lowered their risk of cancer.

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38

Auffray, Charles, Dominique Charron, and Leroy Hood. "Predictive, preventive, personalized and participatory medicine: back to the future." Genome Medicine 2, no. 8 (2010): 57. http://dx.doi.org/10.1186/gm178.

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39

Gonzalez de Castro, D., P. A. Clarke, B. Al-Lazikani, and P. Workman. "Personalized Cancer Medicine: Molecular Diagnostics, Predictive biomarkers, and Drug Resistance." Clinical Pharmacology & Therapeutics 93, no. 3 (December 7, 2012): 252–59. http://dx.doi.org/10.1038/clpt.2012.237.

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Cheng, Tingting, and Xianquan Zhan. "Pattern recognition for predictive, preventive, and personalized medicine in cancer." EPMA Journal 8, no. 1 (March 2017): 51–60. http://dx.doi.org/10.1007/s13167-017-0083-9.

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41

Fosse, Vibeke, Emanuela Oldoni, Chiara Gerardi, Rita Banzi, Maddalena Fratelli, Florence Bietrix, Anton Ussi, Antonio L. Andreu, and Emmet McCormack. "Evaluating Translational Methods for Personalized Medicine—A Scoping Review." Journal of Personalized Medicine 12, no. 7 (July 19, 2022): 1177. http://dx.doi.org/10.3390/jpm12071177.

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The introduction of personalized medicine, through the increasing multi-omics characterization of disease, brings new challenges to disease modeling. The scope of this review was a broad evaluation of the relevance, validity, and predictive value of the current preclinical methodologies applied in stratified medicine approaches. Two case models were chosen: oncology and brain disorders. We conducted a scoping review, following the Joanna Briggs Institute guidelines, and searched PubMed, EMBASE, and relevant databases for reports describing preclinical models applied in personalized medicine approaches. A total of 1292 and 1516 records were identified from the oncology and brain disorders search, respectively. Quantitative and qualitative synthesis was performed on a final total of 63 oncology and 94 brain disorder studies. The complexity of personalized approaches highlights the need for more sophisticated biological systems to assess the integrated mechanisms of response. Despite the progress in developing innovative and complex preclinical model systems, the currently available methods need to be further developed and validated before their potential in personalized medicine endeavors can be realized. More importantly, we identified underlying gaps in preclinical research relating to the relevance of experimental models, quality assessment practices, reporting, regulation, and a gap between preclinical and clinical research. To achieve a broad implementation of predictive translational models in personalized medicine, these fundamental deficits must be addressed.

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42

Yadav, Shivani. "Heart Disease Prediction Using Machine Learning." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 07 (July 27, 2024): 1–14. http://dx.doi.org/10.55041/ijsrem36858.

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Heart disease remains a leading cause of mortality worldwide, necessitating improved methods for early detection and risk assessment. This paper reviews and analyzes the application of machine learning techniques in heart disease prediction, focusing on five primary algorithms: Naïve Bayes, k-Nearest Neighbor (KNN), Decision Tree, Artificial Neural Network (ANN), and Random Forest. By examining existing studies and datasets, we evaluate the effectiveness of these algorithms in predicting heart disease risk. Our analysis demonstrates that machine learning models can significantly enhance the accuracy and efficiency of heart disease prediction compared to traditional diagnostic methods. The Random Forest algorithm exhibited the highest overall performance, with studies reporting accuracy rates up to 95% in identifying potential heart disease cases. This review highlights the potential of machine learning in revolutionizing cardiovascular healthcare by enabling more personalized risk assessments and facilitating early intervention strategies. The integration of these advanced predictive models into clinical practice could substantially improve patient outcomes and reduce the global burden of heart disease. Keywords: Cardiovascular Risk Prediction, Machine Learning Algorithms, Electronic Health Records, Random Forest, Artificial Neural Networks, Feature Importance, Clinical Decision Support, Personalized Medicine, Predictive Analytics in Healthcare, Early Disease Detection.

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Patel, Jai, Mei Fong, and Megan Jagosky. "Colorectal Cancer Biomarkers in the Era of Personalized Medicine." Journal of Personalized Medicine 9, no. 1 (January 14, 2019): 3. http://dx.doi.org/10.3390/jpm9010003.

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The 5-year survival probability for patients with metastatic colorectal cancer has not drastically changed over the last several years, nor has the backbone chemotherapy in first-line disease. Nevertheless, newer targeted therapies and immunotherapies have been approved primarily in the refractory setting, which appears to benefit a small proportion of patients. Until recently, rat sarcoma (RAS) mutations remained the only genomic biomarker to assist with therapy selection in metastatic colorectal cancer. Next generation sequencing has unveiled many more potentially powerful predictive genomic markers of therapy response. Importantly, there are also clinical and physiologic predictive or prognostic biomarkers, such as tumor sidedness. Variations in germline pharmacogenomic biomarkers have demonstrated usefulness in determining response or risk of toxicity, which can be critical in defining dose intensity. This review outlines such biomarkers and summarizes their clinical implications on the treatment of colorectal cancer. It is critical that clinicians understand which biomarkers are clinically validated for use in practice and how to act on such test results.

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Shieh, Grace S. "Harnessing Synthetic Lethal Interactions for Personalized Medicine." Journal of Personalized Medicine 12, no. 1 (January 12, 2022): 98. http://dx.doi.org/10.3390/jpm12010098.

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Two genes are said to have synthetic lethal (SL) interactions if the simultaneous mutations in a cell lead to lethality, but each individual mutation does not. Targeting SL partners of mutated cancer genes can kill cancer cells but leave normal cells intact. The applicability of translating this concept into clinics has been demonstrated by three drugs that have been approved by the FDA to target PARP for tumors bearing mutations in BRCA1/2. This article reviews applications of the SL concept to translational cancer medicine over the past five years. Topics are (1) exploiting the SL concept for drug combinations to circumvent tumor resistance, (2) using synthetic lethality to identify prognostic and predictive biomarkers, (3) applying SL interactions to stratify patients for targeted and immunotherapy, and (4) discussions on challenges and future directions.

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Rizzo, Stanislao, Alfonso Savastano, Jacopo Lenkowicz, Maria Cristina Savastano, Luca Boldrini, Daniela Bacherini, Benedetto Falsini, and Vincenzo Valentini. "Artificial Intelligence and OCT Angiography in Full Thickness Macular Hole. New Developments for Personalized Medicine." Diagnostics 11, no. 12 (December 9, 2021): 2319. http://dx.doi.org/10.3390/diagnostics11122319.

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Purpose: To evaluate the 1-year visual acuity predictive performance of an artificial intelligence (AI) based model applied to optical coherence tomography angiography (OCT-A) vascular layers scans from eyes with a full-thickness macular hole (FTMH). Methods: In this observational cross-sectional, single-center study, 35 eyes of 35 patients with FTMH were analyzed by OCT-A before and 1-year after surgery. Superficial vascular plexus (SVP) and deep vascular plexus (DVP) images were collected for the analysis. AI approach based on convolutional neural networks (CNN) was used to generate a continuous predictive variable based on both SVP and DPV. Different pre-trained CNN networks were used for feature extraction and compared for predictive accuracy. Results: Among the different tested models, the inception V3 network, applied on the combination of deep and superficial OCT-A images, showed the most significant differences between the two obtained image clusters defined in C1 and C2 (best-corrected visual acuity [BCVA] C1 = 49.10 [±18.60 SD] and BCVA C2 = 66.67 [±16.00 SD, p = 0.005]). Conclusions: The AI-based analysis of preoperative OCT-A images of eyes affected by FTMH may be a useful support system in setting up visual acuity recovery prediction. The combination of preoperative SVP and DVP images showed a significant morphological predictive performance for visual acuity recovery.

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Sreenivasarao Amirineni. "The role of artificial intelligence in revolutionizing personalized medicine: A comprehensive review of techniques and applications." World Journal of Advanced Research and Reviews 23, no. 2 (August 30, 2024): 2026–30. http://dx.doi.org/10.30574/wjarr.2024.23.2.2559.

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This comprehensive review explores the transformative role of Artificial Intelligence (AI) in revolutionizing personalized medicine, focusing on techniques, applications, and future directions. Personalized medicine, which tailors medical interventions to individual characteristics, has significantly advanced through the integration of AI technologies. AI's capabilities in data integration, genomic and molecular data analysis, predictive modeling, and personalized treatment development are highlighted. The review also addresses the challenges and limitations of AI in personalized medicine, such as data privacy, bias, and the need for seamless clinical integration. Furthermore, it discusses future trends, including advances in AI technologies, the integration of multi-omics data, and the importance of ethical and regulatory considerations. The historical evolution of personalized medicine is traced, emphasizing key milestones that have led to current innovations. This review underscores AI's pivotal role in enhancing the precision and effectiveness of personalized healthcare, offering insights into how AI-driven approaches can shape the future of medicine.

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Tian, Q., N. D. Price, and L. Hood. "Systems cancer medicine: towards realization of predictive, preventive, personalized and participatory (P4) medicine." Journal of Internal Medicine 271, no. 2 (January 17, 2012): 111–21. http://dx.doi.org/10.1111/j.1365-2796.2011.02498.x.

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48

Koulis, Christine, Raymond Yap, Rebekah Engel, Thierry Jardé, Simon Wilkins, Gemma Solon, Jeremy D. Shapiro, Helen Abud, and Paul McMurrick. "Personalized Medicine—Current and Emerging Predictive and Prognostic Biomarkers in Colorectal Cancer." Cancers 12, no. 4 (March 28, 2020): 812. http://dx.doi.org/10.3390/cancers12040812.

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Colorectal cancer (CRC) is the third most common cancer diagnosed worldwide and is heterogeneous both morphologically and molecularly. In an era of personalized medicine, the greatest challenge is to predict individual response to therapy and distinguish patients likely to be cured with surgical resection of tumors and systemic therapy from those resistant or non-responsive to treatment. Patients would avoid futile treatments, including clinical trial regimes and ultimately this would prevent under- and over-treatment and reduce unnecessary adverse side effects. In this review, the potential of specific biomarkers will be explored to address two key questions—1) Can the prognosis of patients that will fare well or poorly be determined beyond currently recognized prognostic indicators? and 2) Can an individual patient’s response to therapy be predicted and those who will most likely benefit from treatment/s be identified? Identifying and validating key prognostic and predictive biomarkers and an understanding of the underlying mechanisms of drug resistance and toxicity in CRC are important steps in order to personalize treatment. This review addresses recent data on biological prognostic and predictive biomarkers in CRC. In addition, patient cohorts most likely to benefit from currently available systemic treatments and/or targeted therapies are discussed in this review.

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Giordano, Thomas J. "Molecular Profiling and Personalized Predictive Pathology." American Journal of Surgical Pathology 30, no. 3 (March 2006): 402–4. http://dx.doi.org/10.1097/01.pas.0000194941.17372.60.

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Even Chorev, Nadav. "Personalized Medicine in Practice: Postgenomics from Multiplicity to Immutability." Body & Society 26, no. 1 (November 19, 2019): 26–54. http://dx.doi.org/10.1177/1357034x19886925.

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This article explores the ways in which predictive information technologies are used in the field of personalized medicine and the relations between this use and how patients and disease are perceived. This is examined in a qualitative case study of a personalized cancer clinical trial, where oncologists made clinical decisions for each patient based on drug matchings and efficacy predictions produced by bioinformatic technologies and algorithms. I focus on personalized practice itself, as a postgenomic phenomenon, rather than on epistemic, ethical and institutional critiques. Personalized medicine aims to process molecular, clinical, environmental and social data into individually tailored decisions. In this case, however, the engagement of clinicians with data and digital artefacts that processed multiple information sources resulted in treatment choices that were paradoxically both immutable and uncertain. In contrast to the situatedness of the body in postgenomics, this practice subverted the personalized medical approach while decontextualizing both cancer and patients.

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