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Related Concept Videos

Mouse Models of Cancer Study02:43

Mouse Models of Cancer Study

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Mice have long served as models for studying human biology and pathology because of their phylogenetic and physiological similarity with humans. They are also easy to maintain and breed in the laboratory, and hence, many inbred strains are now available for research. Studies on mice have contributed immeasurably to our understanding of cancer biology.
The development of transgenic, knockout, and knock-in mice has led to an exponential increase in their use as model organisms in research,...
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Embryonic and induced pluripotent stem cells are excellent models for disease research because of their ability to self-renew and differentiate into most cell types. Somatic cells from a patient are isolated and reprogrammed into induced pluripotent stem cells or iPSCs. These iPSCs are later differentiated into the desired cell type, which mirrors the diseased cell of the patient. In this way, disease models have been created for investigating diseases such as Down syndrome, type I diabetes,...
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  3. Research Domains
  5. Biomedical And Clinical Sciences
  7. Oncology And Carcinogenesis
  9. Predictive And Prognostic Markers
  11. The Roles Of Patient-derived Xenograft Models And Artificial Intelligence Toward Precision Medicine

The roles of patient-derived xenograft models and artificial intelligence toward precision medicine

Venkatachalababu Janitri1, Kandasamy Nagarajan ArulJothi2, Vijay Murali Ravi Mythili2

  • 1Department of Biomedical Engineering Rochester Institute of Technology Rochester New York USA.

Medcomm
|September 27, 2024

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Creating Matched In vivo/In vitro Patient-Derived Model Pairs of PDX and PDX-Derived Organoids for Cancer Pharmacology Research
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View abstract on PubMed

Summary
This summary is machine-generated.

Patient-derived xenografts (PDX) offer high-fidelity cancer models superior to traditional methods. This review details PDX generation, applications, and the integration of AI/ML for accelerated therapeutic evaluation in oncology research.

Area of Science:

  • Oncology
  • Translational Cancer Research
  • Biomedical Engineering

Background:

  • Patient-derived xenografts (PDX) are advanced cancer models that retain original tumor characteristics.
  • PDX models offer higher fidelity than cell-line xenografts and genetically engineered models.
  • Understanding factors influencing PDX reliability is crucial for their effective use.

Purpose of the Study:

  • To review PDX model generation, applications, and advantages in cancer research.
  • To explore the integration of artificial intelligence (AI) and machine learning (ML) in PDX studies.
  • To address challenges such as cost, time, and variability in PDX models.

Main Methods:

  • Detailed review of methodologies for generating PDX models.
  • Analysis of PDX model applications in preclinical and coclinical drug testing.
Keywords:
PDX modelartificial intelligencecancer biologynanodrug delivery

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Creating Matched In vivo/In vitro Patient-Derived Model Pairs of PDX and PDX-Derived Organoids for Cancer Pharmacology Research
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  • Exploration of AI and ML techniques applied to PDX data for therapeutic evaluation.

    • Main Results:

    • PDX models preserve patient tumor molecular and biologic features across generations.
    • PDX models demonstrate superior predictive power for therapeutic efficacy compared to other models.
    • AI and ML integration can accelerate the analysis of PDX studies and therapeutic assessments.

    Conclusions:

    • PDX models are valuable tools for advancing cancer research, biomarker discovery, and personalized medicine.
    • Addressing current limitations in PDX models will enhance their utility and predictive power.
    • Continued development and integration of advanced computational methods will further optimize PDX applications in oncology.
    patient‐derived xenografts
    personalized medicine
    tumor genetics
    tumor modeling