Recent advances in precision medicine for individualized immunosuppression

Fu, Shengyi; Zarrinpar, Ali

doi:10.1097/mot.0000000000000771

Cited by 9 publications

(10 citation statements)

References 35 publications

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“…6 Several reviews have discussed the potential future role of ML in immunocompromised patients. [7][8][9][10][11][12] For example, ML-CDSSs have already been developed to predict patients at a risk of developing severe noninfectious posttransplant complications 13 such as assessing rejection in kidney transplant biopsies. 14 However, literature on antimicrobial stewardship based on ML in solid organ recipients is scarce.…”

Section: Introductionmentioning

confidence: 99%

Machine learning, antimicrobial stewardship, and solid organ transplantation: Is this the future?

Kherabi

Messika

Peiffer-Smadja

2022

Transplant Infectious Dis

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Background: The use of machine learning (ML) in infectious diseases is expanding.Objectives: This review aims to provide an overview of the literature on ML for clinical decision support in antimicrobial stewardship in the particular context of solid organ transplantation (SOT).Methods: References for this review were identified through searches of MEDLINE/ PubMed and Google Scholar databases up to July 2022.Results: ML may improve the prediction of infectious complications and the diagnosis and treatment of infectious diseases in SOT recipients. One of the most studied applications for antimicrobial stewardship is the individual prediction of antimicrobial resistance that could guide the empiric use of anti-infective treatments. ML may also guide the choice of antimicrobial dose taking into account the interactions with immunosuppressive drugs. The main challenge to the development of ML clinical decision support systems (CDSSs) in SOT is the development of large clinical databases, accessible to all, with good quality, comprehensive, and diversified data. ML-driven CDSSs are still at an experimental stage, and the education of clinicians about the benefits and limits of ML is essential. Conclusion:ML could improve antimicrobial stewardship for SOT, but literature on that specific topic is scarce. Future studies are needed to design ML-CDSS in the particular population of solid organ recipients and report clinical outcomes following use in routine practice.

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Section: Introductionmentioning

confidence: 99%

Machine learning, antimicrobial stewardship, and solid organ transplantation: Is this the future?

Kherabi

Messika

Peiffer-Smadja

2022

Transplant Infectious Dis

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“…[24,25] Recently, precision medicine that proposed the customization of diagnose and treatments has been considered as a strategy to overcome the issue of low efficacy, which could accomplish by decreasing the immunosuppression and resistance of cancers. [26] A large number of research results indicated that genetic heterogeneity and metabolic disorder of tumor are help for establishing complex immunosuppressive tumor microenvironment, which is the main cause of drug resistance in tumor. [27] These factors directly lead to the low accuracy and efficacy of traditional therapeutic strategies for anticancer treatment.…”

Section: Introductionmentioning

confidence: 99%

Cell/Bacteria‐Based Bioactive Materials for Cancer Immune Modulation and Precision Therapy

Pan

Zheng

et al. 2021

Advanced Materials

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limitations, such as serious side effects, and are ineffective against subclinical metastasis and recurrence. [1] Recently, precision medicine approaches, such as novel targeting therapy, genomic and proteomic analysis, have become the mainstream in cancer clinical trials, but only 11-23% of the patients had an objective responses. [2,3] Current treatment strategies have generated good therapeutic effects and conquered some unwanted adverse impacts via improved biodistribution and accumulation of antitumor drugs to some degree, but these systems are still limited by their unsatisfactory target delivery efficacy. [4] Moreover, the treatment effect and patient objective response to monotherapy are also seriously limited by complex biological barriers and immunosuppression. [5] Encouragingly, these current challenges in cancer care, such as serious adverse events, poor tumor penetration, and intense drug resistance, have strongly prompted the continuous development of novel alternative approaches.Nanomedicine is an important application of synthetic material engineering in the medical field to improve tumor diagnostic imaging, facilitate targeted drug delivery, and enhance antitumor capabilities. Conventional synthetic nanotherapeutics, including protein/polymer-drug conjugates, liposomal preparations, dendrimers, and inorganic nanoparticles (NPs), have been investigated in lab or clinical research. [6] Synthetic materials constructed by lipid bilayers with active targeting peptide or cell-targeting antibodies have been proved to exhibit efficient delivery and cytotoxicity for tumor cell. [7,8] The advantages of synthetic materials, such as suitable size, targeting delivery, and appropriate surface chemistry, have the potential to enable therapeutic agents to better reach tumor site via tumor active targeting or enhanced permeability and retention (EPR) effect. However, only a few of the proposed engineered materials are currently in clinical use for patients or in preclinical trials, and most material agents are still subjected to unsatisfied targeting delivery and complex tumor microenvironment barriers. [9] The efficiency and accuracy of targeted delivery of synthetic materials are still not satisfactory because they depend on only the active or passive targeting of nanomedicine. Particularly, the majority of NPs circulating in the blood are typically removed by the in vivo mononuclear phagocyte system, which forms a rigorous biological barrier for nanomedicine transport. [10] In addition, tumor microenvironment (TME) Numerous clinical trials for cancer precision medicine research are limited due to the drug resistance, side effects, and low efficacy. Unsatisfactory outcomes are often caused by complex physiologic barriers and abnormal immune events in tumors, such as tumor target alterations and immunosuppression. Cell/bacteria-derived materials with unique bioactive properties have emerged as attractive tools for personalized therapy in cancer. Naturally derived bioactive materials, such as cell and bacterial t...

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“…Several excellent reviews have been published on machine learning in transplant medicine (3)(4)(5)(6). This review builds on prior knowledge by incorporating additional applications of machine learning in predicting acute post-surgical and longterm outcomes, caring for critically ill patients, classifying biopsy and radiographic data, augmenting pharmacologic decision making, and elucidating the complexity of host immune response.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Applications in Solid Organ Transplantation and Related Complications

et al. 2021

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The complexity of transplant medicine pushes the boundaries of innate, human reasoning. From networks of immune modulators to dynamic pharmacokinetics to variable postoperative graft survival to equitable allocation of scarce organs, machine learning promises to inform clinical decision making by deciphering prodigious amounts of available data. This paper reviews current research describing how algorithms have the potential to augment clinical practice in solid organ transplantation. We provide a general introduction to different machine learning techniques, describing their strengths, limitations, and barriers to clinical implementation. We summarize emerging evidence that recent advances that allow machine learning algorithms to predict acute post-surgical and long-term outcomes, classify biopsy and radiographic data, augment pharmacologic decision making, and accurately represent the complexity of host immune response. Yet, many of these applications exist in pre-clinical form only, supported primarily by evidence of single-center, retrospective studies. Prospective investigation of these technologies has the potential to unlock the potential of machine learning to augment solid organ transplantation clinical care and health care delivery systems.

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Recent advances in precision medicine for individualized immunosuppression

Cited by 9 publications

References 35 publications

Machine learning, antimicrobial stewardship, and solid organ transplantation: Is this the future?

Machine learning, antimicrobial stewardship, and solid organ transplantation: Is this the future?

Cell/Bacteria‐Based Bioactive Materials for Cancer Immune Modulation and Precision Therapy

Machine Learning Applications in Solid Organ Transplantation and Related Complications

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