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Combined Effects of Drugs: Synergism01:27

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Synergism is a useful mechanism where combining two or more drugs is more effective than each constituent used alone. Such combinations are also called supra-additive interactions. The drugs collectively enhance the final therapeutic effect by acting on different targets. Another advantage is that the low dose of each constituent drug is sufficient to achieve the desired effect. This helps reduce the duration of therapy and lower the adverse effects of these drugs.
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The receptor occupancy theory connects a drug's response to the number of occupied receptors. With higher drug concentrations, more receptors are occupied, leading to increased responses. The formation of drug-receptor complexes involves association and dissociation rates, which reach equilibrium when the forward and backward reactions are equal. The equilibrium association constant (Ka) and its inverse, the equilibrium dissociation constant (Kd), indicate drug affinity. Higher Ka and lower...
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Drug interactions occur when the pharmacological effect of one drug is altered by another substance, either enhancing or diminishing its activity. The drug whose activity is altered is known as the object drug, and the substance causing the alteration is called the agent drug or the precipitant. The net effects of these interactions are mostly undesirable, leading to decreased effectiveness or increased adverse effects. In rare cases, interactions can be beneficial, such as the enhanced...
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Pharmaceutical substances known as xenobiotics are predominantly lipophilic and nonionized. This enables them to permeate lipid bilayers, such as cell membranes, and interact with intracellular target receptors. Lipophilic drugs have an advantage in crossing biological barriers and reaching their intended sites of action. However, lipophilic drugs often have a restricted capacity for renal expulsion or elimination from the body. When these drugs enter the kidneys and undergo glomerular...
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Biotransformation, also known as drug metabolism, is a vital physiological process that chemically alters drugs, facilitating their elimination from the body and terminating their action. This process involves two main phases: phase I and phase II reactions. Phase I reactions, including oxidation, reduction, and hydrolysis, introduce or unmask polar functional groups on the drug molecule, thereby increasing its water solubility. By enhancing water solubility, the drug becomes more hydrophilic...
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Un marco de hipergrafo multicapa para la predicción de interacciones fármaco-fármaco basado en transformador y

Lu Shen1, Feng Hu1, Libing Bai1

  • 1Computer College of Qinghai Normal University, Xining, Qinghai 810008, China; The State Key Laboratory of Tibetan Intelligence, Xining, Qinghai 810008, China.

Computational biology and chemistry
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Resumen
Este resumen es generado por máquina.

La predicción de interacciones fármaco-fármaco (IFF) es vital para la medicación segura. Un nuevo marco de hipergrafo multicapa que utiliza Transformer y convolución de hipergrafo (MLHTHC) mejora la precisión de la predicción de IFF al capturar relaciones complejas.

Palabras clave:
Interacción fármaco-fármacoConvolución de hipergrafoHipergrafo multicapaTransformer

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Área de la Ciencia:

  • Farmacología y Quimioinformática
  • Inteligencia Artificial en el Descubrimiento de Fármacos

Sus antecedentes:

  • Las interacciones fármaco-fármaco (IFF) plantean importantes desafíos en la investigación de fármacos y la práctica clínica.
  • Los modelos de red existentes tienen dificultades para capturar interacciones sinérgicas complejas y multielemento entre fármacos.
  • La predicción precisa de IFF es crucial para mejorar la seguridad del tratamiento y optimizar los regímenes de medicación.

Objetivo del estudio:

  • Desarrollar un marco avanzado para predecir interacciones fármaco-fármaco (IFF) que supere las limitaciones de los modelos convencionales.
  • Representar y analizar eficazmente las interacciones sinérgicas multielemento entre fármacos.
  • Mejorar la precisión y fiabilidad de la predicción de IFF.

Principales métodos:

  • Se propuso un marco de hipergrafo multicapa para la predicción de interacciones farmacológicas utilizando Transformer y convolución de hipergrafo (MLHTHC).
  • Se construyó un hipergrafo de similitud de fármacos multicapa utilizando la estructura química, el código ATC, la categoría del fármaco y la información del objetivo.
  • Se empleó la similitud espectral de Hamming para la determinación del peso de la capa, redes convolucionales de hipergrafo para la incrustación de nodos, Transformer para la fusión de características y MLP para la predicción de IFF.

Principales resultados:

  • El modelo MLHTHC demostró un rendimiento superior en comparación con métodos existentes como DPSP y DANN.
  • La integración de Transformer y convolución de hipergrafo mejoró significativamente la precisión de la predicción de interacciones fármaco-fármaco.
  • El marco captura eficazmente relaciones sinérgicas complejas y multielemento entre fármacos.

Conclusiones:

  • El marco propuesto MLHTHC ofrece una herramienta potente y eficaz para predecir interacciones fármaco-fármaco.
  • Este enfoque avanza el campo de la farmacología computacional al mejorar las capacidades de predicción de IFF.
  • La predicción precisa de IFF utilizando MLHTHC puede conducir a una farmacoterapia más segura y eficaz.