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  • Erick A. Mosteller

Revolutionizing Cancer Therapy: From Molecular Jackhammers to AI-Driven Immunogenicity Prediction




The recent advancements in stimulating aminocyanine molecules with near-infrared light have opened a new frontier in cancer treatment. These aminocyanine molecules, known as "molecular jackhammers," are a class of fluorescent synthetic dyes traditionally used for medical imaging. The breakthrough in this method is the discovery of their ability to attach to the fatty outer lining of cells and create a mechanical action that can tear apart the membrane of cancer cells.


The process involves the utilization of the structural and chemical properties of the aminocyanine molecules. These molecules have the capability to oscillate in sync when exposed to near-infrared light, a phenomenon due to their molecular plasmons. A molecular plasmon in this context refers to the oscillation of the electrons in the aminocyanine molecule when stimulated by light. This results in a vibratory motion strong enough to disrupt the cell membrane, specifically targeting cancer cells.

A critical aspect of this method is that it is distinct from both photodynamic and photothermal therapies. The researchers have emphasized that this approach relies on mechanical action at the molecular scale, making it a novel form of cancer treatment. The method exploits the vibronic-driven action of the molecules, a term referring to the combined vibrational and electronic transitions in the molecules.


The research involved collaboration between Rice University and Texas A&M University, with significant contributions from Dr. Ciceron Ayala-Orozco and Professor Jorge Seminario. The latter performed time-dependent density functional theory analysis to understand the molecular features responsible for the "jackhammering" effect. Further, the effectiveness of this approach was demonstrated in cancer studies conducted on mice at the University of Texas MD Anderson Cancer Center.


This innovative approach opens up new avenues for cancer treatment, leveraging the mechanical forces at the molecular level to target and destroy cancer cells. It represents a significant leap forward in the field of oncology, offering a potential new therapy that is distinct from existing methods.

For more detailed information, you can refer to the studies and announcements from Rice University and the journal Nature Chemistry:


In addition to the advancements in cancer treatment through the stimulation of aminocyanine molecules with near-infrared light, there has been significant progress in the field of immunotherapy, particularly in the prediction and identification of immunogenic neoepitopes. One of the key challenges in developing personalized cancer vaccines is identifying neoepitopes that can elicit an adaptive immune response. To address this, a study from Johns Hopkins University presented computational methods for predicting Major Histocompatibility Complex class I (MHC-I) epitopes with high accuracy.


This research, titled "Deep Neural Networks Predict MHC-I Epitope Presentation and Transfer Learn Neoepitope Immunogenicity," utilized a method known as BigMHC. BigMHC is a deep neural network-based approach that significantly improves the prediction of epitope presentation on MHC molecules. It comprises an ensemble of seven pan-allelic deep neural networks trained on peptide-MHC eluted ligand data from mass spectrometry assays. This method was further enhanced through transfer learning on data from assays of antigen-specific immune response.


BigMHC showed a remarkable improvement over existing state-of-the-art classifiers in predicting presented epitopes. After transfer learning on immunogenicity data, it demonstrated high precision in identifying immunogenic neoepitopes. This makes BigMHC a potentially effective tool in clinical settings for the personalized treatment of cancer.


This approach represents a significant advancement in the field of immunotherapy, as it enhances the precision of neoepitope identification for cancer vaccines. Such developments in computational methods for predicting immunogenic neoepitopes are crucial for the ongoing progress in personalized medicine and cancer treatment strategies.


For more detailed information, you can refer to the study on bioRxiv: Deep Neural Networks Predict MHC-I Epitope Presentation and Transfer Learn Neoepitope Immunogenicity.

These two new cancer treatments represent significant advancements in cancer research, each focusing on different aspects of treatment and diagnosis.


Stimulation of Aminocyanine Molecules with Near-Infrared Light:


Strengths:

Innovative Approach: Utilizes molecular plasmons of aminocyanine molecules to mechanically disrupt cancer cell membranes.

Specificity: Targets the fatty outer lining of cells, potentially reducing the impact on healthy cells.

Novelty: Represents a new form of cancer treatment using mechanical forces at the molecular scale, distinct from photodynamic or photothermal therapies.



Weaknesses:

Early Stage: The research is still in its early stages and requires further validation and development.

Limited Scope: Current studies have been performed mainly in laboratory settings and in mice, necessitating further testing in human trials.


Ongoing Research and Timeframe:

Research is ongoing at institutions like Rice University and Texas A&M University. The timeframe for clinical application is uncertain and will depend on the results of future trials and regulatory approvals.


BigMHC Deep Neural Networks for Predicting MHC-I Epitope Presentation:


Strengths:

High Precision: Demonstrates improved accuracy in predicting epitope presentation and identifying immunogenic neoepitopes.


Potential for Personalized Medicine: Offers a tool for the development of personalized cancer vaccines.


Accessibility: All data and code are freely available, facilitating further research and collaboration.


Weaknesses:

Complexity: The deep neural network approach may require substantial computational resources and expertise.


Validation: While showing promise, the method needs further validation in clinical settings.


Ongoing Research and Timeframe:

The method was developed by researchers at Johns Hopkins University. As it is a relatively recent development, detailed timelines for clinical application are not specified. Further research and validation studies are required before it can be widely used in clinical settings.


Conclusion

Both research approaches demonstrate innovation excellence in cancer treatment and diagnosis, each with unique strengths and potential applications. The stimulation of aminocyanine molecules with near-infrared light offers a novel mechanical method to target cancer cells, while the BigMHC approach leverages advanced computational techniques for personalized vaccine development. However, both are in stages of research that precede widespread clinical application, and their timelines for becoming standard treatments will depend on the outcomes of ongoing and future studies.



Erick Mosteller is a 35-year entrepreneur and business development consultant who is passionate about elevating critical understanding through effective information. Mr. Mosteller has degrees in ethnography, business administration, and International Marketing. Mosteller believes development of the rational mind and thoughtful training of the reactive mind is the key to long lasting happiness and understanding. Stay tuned for greater insights.

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Megh Megh
Megh Megh
30 dic 2023
Obtuvo 5 de 5 estrellas.

awesome to see

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