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Clinical development focuses on how the drug will interact with the human body and encompasses four key phases of clinical trials, each serving a specific purpose in assessing the safety and effectiveness of new drugs. These phases overlap and build upon one another. Phase I involves a small group of healthy volunteers (typically 20-80 individuals) or, in cases where significant toxicity is expected, patients with the targeted disease, such as cancer or AIDS. The volunteers are tested for...
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In vitro dissolution and drug release tests assess how quickly and how much of a drug is released from its dosage form into an aqueous medium under standardized laboratory conditions. These tests are essential tools in pharmaceutical development and quality assurance, offering insight into the drug's performance before clinical use.During formulation development, dissolution testing identifies incomplete or inconsistent drug release issues. It also supports decisions on selecting the optimal...
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Desarrollo de Fármacos

Qing Zeng1,2, Edward Zamrini3, Ali Ahmed4

  • 1George Washington University, Washington, DC, USA.

Alzheimer's & dementia : the journal of the Alzheimer's Association
|December 26, 2025
PubMed
Resumen
Este resumen es generado por máquina.

Este estudio utilizó un estudio de asociación de medicamentos (MWAS) e IA para encontrar fármacos para la prevención de la enfermedad de Alzheimer y trastornos relacionados (ADRD). La atorvastatina mostró potencial para la reducción del riesgo de ADRD, especialmente cuando se combina con escitalopram.

Palabras clave:
reposición de fármacosenfermedad de Alzheimeratorvastatinaescitalopraminteligencia artificialdatos del mundo realestudio de asociación de medicamentosprevención

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Área de la Ciencia:

  • Farmacogenómica y Biología Computacional; Neurociencia e Investigación sobre el Envejecimiento; Inteligencia Artificial en la Atención Sanitaria

Sus antecedentes:

  • Estudio piloto de asociación de medicamentos (MWAS) para identificar fármacos para la prevención de la enfermedad de Alzheimer y trastornos relacionados (ADRD).
  • Se utilizó el conjunto de datos Million Veteran Project (MVP), que abarca genotipos, exposición a fármacos e incidencia de ADRD.
  • Exploración agnóstica y sin hipótesis de datos del mundo real.

Objetivo del estudio:

  • Identificar fármacos candidatos para la reposición de ADRD utilizando un conjunto de datos del mundo real a gran escala.
  • Explorar el impacto de los medicamentos, los datos genómicos y los determinantes sociales de la salud (SDOH) en el riesgo de ADRD.
  • Aprovechar la IA explicable para el descubrimiento de interacciones novedosas entre fármacos y factores de riesgo.

Principales métodos:

  • Se entrenó un modelo de Gradient Boosting basado en histogramas (HGB) en una cohorte emparejada (n=263,256) del MVP.
  • Se incorporaron el estado de PTSD, los SDOH (puntuaciones ADI) y el estado de APOE ε4 y las puntuaciones de riesgo poligénico (PRS) para ADRD.
  • Se aplicaron métodos novedosos de IA explicable para calcular las puntuaciones de impacto e interacción de medicamentos como la atorvastatina.

Principales resultados:

  • Se confirmaron hallazgos existentes y se identificaron asociaciones novedosas entre medicamentos y el riesgo de ADRD. La atorvastatina demostró una puntuación de impacto negativa, lo que sugiere una reducción del riesgo de ADRD. Se observó una interacción novedosa y negativa entre la atorvastatina y el escitalopram, que podría reducir el riesgo de ADRD.

Conclusiones:

  • El enfoque MWAS, que integra datos genómicos y de SDOH, es robusto para identificar estrategias de prevención de ADRD.
  • La IA explicable revela eficazmente interacciones complejas entre fármacos y entre fármacos y factores de riesgo.
  • Destaca el potencial de los datos del mundo real y la IA en el descubrimiento de nuevos candidatos terapéuticos para la prevención de ADRD.