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Artificial Intelligence application for diagnosis biomarkers, medicine designing and Chemotherapy for Breast Cancer (BC) using PEG linkage
Aplicación de la Inteligencia Artificial para Biomarcadores Diagnósticos, Desarrollo de Fármacos y Quimioterapia para el Cáncer de Mama (CM) Mediante la Unión a PEG
Aplicação de Inteligência Artificial para biomarcadores de diagnóstico, desenvolvimento de medicamentos e quimioterapia para câncer de mama (CM) utilizando ligação PEG
DOI:
https://doi.org/10.15446/rcciquifa.v55n3.129588Palabras clave:
Breast Cancer, Artificial Intelligence (AI), cyclophosphamide, 5-fluorouracil, methotrexate, Machine Learning (ML), Deep learning (DL), Molecular Mechanic and docking simulation, QM/MM methods (en)Cáncer de mama, Inteligencia Artificial (IA), ciclofosfamida, 5-fluorouracilo, metotrexato, Aprendizaje Automático (AA), Aprendizaje Profundo (AP), Mecánica Molecular y simulación de acoplamiento molecular (es)
Câncer de mama, Inteligência Artificial (IA), ciclofosfamida, 5-fluorouracilo, metotrexato, Aprendizado de Máquina (AM), Aprendizado Profundo (AP), Mecânica Molecular e simulação de acoplamento molecular, Métodos QM/MM (pt)
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Background: Chemotherapy is a fundamental and rapidly acting modality for cancer treatment that employs specific anticancer agents such as cyclophosphamide, 5-fluorouracil, and methotrexate. It is administered either with curative intent, typically through defined drug combinations, or with palliative intent, aiming to prolong survival and alleviate symptoms. Aim: This work highlights the transformative synergy between artificial intelligence (AI) and quantum biology in advancing the detection and treatment of breast cancer. Recent clinical research in chemotherapy underscores the expanding role of AI-driven deep learning (DL) frameworks and machine learning (ML) applications in OMIC-based precision diagnostics, rational drug design, and personalized therapeutic strategies enabled through biomarker discovery. The integration of intelligent medical engineering with quantum biological principles and deep learning methodologies offers significant advancements in targeted drug delivery, minimizes the adverse effects and toxicity associated with chemotherapeutic agents, and enables robust real-time monitoring of treatment responses. Collectively, these innovations demonstrate substantial potential to enhance therapeutic efficacy and improve clinical outcomes in breast cancer (BC) management. Methodology: We simulated a series of anti-breast-cancer agents including cyclophosphamide, 5-fluorouracil, and methotrexate combined via a polyethylene glycol (PEG) linker using QM/MM methods. This approach aims to improve therapeutic effectiveness while reducing side effects, guided by deep learning and AI-based optimization strategies. Finally, this study provides an overview of current trends in AI-based healthcare nanotechnologies and concludes that the integration of AI into healthcare offers significant benefits, paving the way toward a more advanced and effective future in medicine. Results: PEG conjugation in CP–[PEG(n)]–5-FU conjugates represents a strategy to extend circulation time and reduce immunogenicity by increasing hydrodynamic size and shielding epitopes from immune surveillance .The amino acids from the ligand {CP–[PEG(n)]–5-FU (n = 2–18)} that interact with 1JNX are Cys1697, Arg1699, and Asp1739 in the H–M interaction, Asp1692 and Cys1692 in the V–M interaction, Glu1698 and Val1740 in the V–S interaction, and finally only one residue (Glu1698) in the H–S interaction. These residues are also completely located within the active site of 1JNX, which represents the crystal structure of the BRCT region associated with BC. Conclusions: Up to now, biotechnology in medical Artificial intelligence (AI) has immense potential for various applications in the pharmaceutical industry. AI enables the design of novel drugs for delivery to tissue and cell targets based on the discovery of specific chemical structures. A subfield of AI known as machine learning is commonly used in disease analysis and detection. Obviously, the results of predicting experimental help improve the precision of the testing output, significantly. Based on machine learning analysis, the systems {CP–[PEG(n)]–5-FU (n = 30–35)} were identified as the most favorable candidates for these simulations.
Contexto: La quimioterapia es una modalidad fundamental y de acción rápida para el tratamiento del cáncer, que emplea agentes anticancerígenos específicos como la ciclofosfamida, el 5-fluorouracilo y el metotrexato. Se administra con intención curativa, generalmente mediante combinaciones de fármacos definidas, o con intención paliativa, con el objetivo de prolongar la supervivencia y aliviar los síntomas. Objetivo: Este trabajo destaca la sinergia transformadora entre la inteligencia artificial (IA) y la biología cuántica para el avance en la detección y el tratamiento del cáncer de mama. La investigación clínica reciente en quimioterapia subraya el creciente papel de los marcos de aprendizaje profundo (DL) y las aplicaciones de aprendizaje automático (ML) basadas en IA en el diagnóstico de precisión basado en ómicas, el desarrollo racional de fármacos y las estrategias terapéuticas personalizadas posibilitadas por el descubrimiento de biomarcadores. La integración de la ingeniería médica inteligente con los principios de la biología cuántica y las metodologías de aprendizaje profundo ofrece avances significativos en la administración dirigida de fármacos, minimiza los efectos adversos y la toxicidad asociados a los agentes quimioterapéuticos, y permite una monitorización robusta y en tiempo real de las respuestas al tratamiento. En conjunto, estas innovaciones demuestran un potencial sustancial para aumentar la eficacia terapéutica y mejorar los resultados clínicos en el tratamiento del cáncer de mama. Metodología: Simulamos una serie de agentes anticancerígenos para el cáncer de mama, incluyendo ciclofosfamida, 5-fluorouracilo y metotrexato, combinados mediante un enlace de polietilenglicol (PEG) utilizando métodos QM/MM. Este enfoque tiene como objetivo mejorar la eficacia terapéutica reduciendo los efectos secundarios, guiado por estrategias de optimización basadas en aprendizaje profundo e IA. Finalmente, este estudio proporciona una visión general de las tendencias actuales en nanotecnologías sanitarias basadas en IA y concluye que la integración de la IA en la atención médica ofrece beneficios significativos, allanando el camino hacia un futuro más avanzado y eficaz en medicina. Resultados: La conjugación de PEG en conjugados CP–[PEG(n)]–5-FU representa una estrategia para prolongar el tiempo de circulación y reducir la inmunogenicidad al aumentar el tamaño hidrodinámico y proteger los epítopos de la vigilancia inmunitaria. Los aminoácidos del ligando {CP–[PEG(n)]–5-FU (n = 2–18)} que interactúan con 1JNX son Cys1697, Arg1699 y Asp1739 en la interacción H–M, Asp1692 y Cys1692 en la interacción V–M, Glu1698 y Val1740 en la interacción V–S, y finalmente, solo un residuo (Glu1698) en la interacción H–S. Estos residuos también están completamente localizados dentro del sitio activo de 1JNX, que representa la estructura cristalina de la región BRCT asociada con el cáncer de mama. Conclusiones: Hasta la fecha, la biotecnología en el campo médico y la inteligencia artificial (IA) tienen un inmenso potencial para diversas aplicaciones en la industria farmacéutica. La Inteligencia Artificial (IA) permite el desarrollo de nuevos fármacos dirigidos a tejidos y células, basándose en el descubrimiento de estructuras químicas específicas. Un subcampo de la IA, conocido como aprendizaje automático, se utiliza comúnmente en el análisis y la detección de enfermedades. Evidentemente, los resultados de la predicción experimental contribuyen significativamente a mejorar la precisión de los resultados de las pruebas. Basándose en el análisis de aprendizaje automático, los sistemas {CP–[PEG(n)]–5-FU (n = 30–35)} se identificaron como los candidatos más prometedores para estas simulaciones.
Contexto: A quimioterapia é uma modalidade fundamental e de ação rápida para o tratamento do câncer, que emprega agentes anticancerígenos específicos, como ciclofosfamida, 5-fluorouracilo e metotrexato. Ela é administrada com intenção curativa, tipicamente por meio de combinações definidas de medicamentos, ou com intenção paliativa, visando prolongar a sobrevida e aliviar os sintomas. Objetivo: Este trabalho destaca a sinergia transformadora entre inteligência artificial (IA) e biologia quântica no avanço da detecção e do tratamento do câncer de mama. Pesquisas clínicas recentes em quimioterapia ressaltam o papel crescente de estruturas de aprendizado profundo (AP) e aplicações de aprendizado de máquina (AM) baseadas em IA em diagnósticos de precisão baseados em ômicas, desenvolvimento racional de medicamentos e estratégias terapêuticas personalizadas viabilizadas pela descoberta de biomarcadores. A integração da engenharia médica inteligente com princípios da biologia quântica e metodologias de aprendizado profundo oferece avanços significativos na administração direcionada de medicamentos, minimiza os efeitos adversos e a toxicidade associados a agentes quimioterápicos e permite o monitoramento robusto e em tempo real das respostas ao tratamento. Coletivamente, essas inovações demonstram um potencial substancial para aumentar a eficácia terapêutica e melhorar os resultados clínicos no tratamento do câncer de mama. Metodologia: Simulamos uma série de agentes anticancerígenos para o câncer de mama, incluindo ciclofosfamida, 5-fluorouracilo e metotrexato, combinados por meio de um ligante de polietilenoglicol (PEG) usando métodos QM/MM. Essa abordagem visa melhorar a eficácia terapêutica, reduzindo os efeitos colaterais, guiada por aprendizado profundo e estratégias de otimização baseadas em IA. Finalmente, este estudo fornece uma visão geral das tendências atuais em nanotecnologias de saúde baseadas em IA e conclui que a integração da IA na área da saúde oferece benefícios significativos, abrindo caminho para um futuro mais avançado e eficaz na medicina. Resultados: A conjugação de PEG em conjugados CP–[PEG(n)]–5-FU representa uma estratégia para prolongar o tempo de circulação e reduzir a imunogenicidade, aumentando o tamanho hidrodinâmico e protegendo os epítopos da vigilância imunológica. Os aminoácidos do ligante {CP–[PEG(n)]–5-FU (n = 2–18)} que interagem com 1JNX são Cys1697, Arg1699 e Asp1739 na interação H–M, Asp1692 e Cys1692 na interação V–M, Glu1698 e Val1740 na interação V–S e, finalmente, apenas um resíduo (Glu1698) na interação H–S. Esses resíduos também estão completamente localizados dentro do sítio ativo de 1JNX, o que representa a estrutura cristalina da região BRCT associada ao câncer de mama. Conclusões: Até o momento, a biotecnologia na área médica e a inteligência artificial (IA) têm um imenso potencial para diversas aplicações na indústria farmacêutica. A Inteligência Artificial (IA) permite o desenvolvimento de novos fármacos para direcionamento a tecidos e células-alvo, com base na descoberta de estruturas químicas específicas. Uma subárea da IA, conhecida como aprendizado de máquina, é comumente utilizada na análise e detecção de doenças. Obviamente, os resultados da predição experimental contribuem significativamente para a melhoria da precisão dos resultados dos testes. Com base na análise de aprendizado de máquina, os sistemas {CP–[PEG(n)]–5-FU (n = 30–35)} foram identificados como os candidatos mais promissores para essas simulações.
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