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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.
Such synergistic combinations...
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The combined effects of drugs can result in various interactions, of which an important type is antagonism. Antagonism is a mechanism where one drug inhibits or counteracts the effects of another drug. Antagonism can occur through various means, including receptor binding, allosteric modulation, functional interaction, chemical reactions, and pharmacokinetic processes.
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Combining two or more treatment methods increases the life span of cancer patients while reducing damage to vital organs or tissue from the overuse of a single treatment. Combination therapy also targets different cancer-inducing pathways, thus reducing the chances of developing resistance to treatment.
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Antipsychotic drugs primarily block dopamine and serotonin receptors and cholinergic, adrenergic, and histaminergic receptors, thereby reducing hallucinations and delusions in conditions like schizophrenia. However, they can trigger unwanted extrapyramidal effects such as dystonias, Parkinson-like symptoms, and tardive dyskinesia.
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The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
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PAIRWISE: Predicción de combinaciones efectivas de fármacos personalizados en cáncer basada en aprendizaje profundo

Olivier Elemento, Chengqi Xu, Ilkay Us

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    |February 6, 2026
    PubMed
    Resumen

    Este estudio presenta PAIRWISE, un modelo computacional novedoso para predecir combinaciones sinérgicas de fármacos en cáncer. PAIRWISE identifica con precisión terapias personalizadas y efectivas contra el cáncer, impulsando la oncología de precisión.

    Palabras clave:
    aprendizaje profundomedicina personalizadaoncología de precisiónterapia combinadacáncerfármacossinergiamodelado computacionalDLBCLinhibidores de BTK

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    Área de la Ciencia:

    • Biología computacional
    • Oncología
    • Farmacología

    Sus antecedentes:

    • Las terapias combinadas son cruciales para mejorar la eficacia del tratamiento del cáncer y prevenir la recurrencia.
    • Identificar combinaciones óptimas de fármacos es un desafío debido a las numerosas posibilidades y la heterogeneidad tumoral.
    • El cribado preclínico puede priorizar combinaciones sinérgicas de fármacos, pero se necesitan enfoques personalizados.

    Objetivo del estudio:

    • Desarrollar un modelo computacional, PAIRWISE, para predecir combinaciones sinérgicas de fármacos en cáncer.
    • Abordar el desafío de identificar combinaciones de fármacos personalizadas adaptadas a subtipos de cáncer y pacientes específicos.
    • Acelerar el desarrollo de la oncología de precisión mediante la nominación efectiva de combinaciones de fármacos.

    Principales métodos:

    • Se desarrolló PAIRWISE, un modelo diseñado explícitamente para predecir los efectos sinérgicos de las combinaciones de fármacos.
    • Se aplicó PAIRWISE a líneas celulares de cáncer y a un conjunto de datos independiente de Linfoma Difuso de Células B Grandes (DLBCL) tratado con inhibidores de la Tirosina Quinasa de Bruton (BTK).
    • Se validaron las predicciones utilizando cribado de alto rendimiento (HTS) de agentes aprobados o en investigación para DLBCL.

    Principales resultados:

    • PAIRWISE demostró un rendimiento superior en comparación con modelos competidores, logrando un AUROC de 0.847 en líneas celulares de cáncer.
    • El modelo predijo con precisión combinaciones sinérgicas en DLBCL con un AUROC de 0.720.
    • PAIRWISE mostró una fuerte concordancia con los resultados de cribado in vitro, validando su capacidad predictiva.

    Conclusiones:

    • PAIRWISE modela eficazmente los efectos sinérgicos de los fármacos y nomina combinaciones de fármacos personalizadas para el tratamiento del cáncer.
    • El modelo muestra un potencial significativo para acelerar el desarrollo de la oncología de precisión.
    • Este enfoque puede guiar la selección de terapias combinadas efectivas para pacientes individuales.