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相关概念视频

Model Approaches for Pharmacokinetic Data: Physiological Models01:15

Model Approaches for Pharmacokinetic Data: Physiological Models

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Physiological models in pharmacokinetics are instrumental in understanding the distribution and elimination of drugs within the body. These models describe the drug concentration within target organs, influenced by factors such as drug uptake, tissue volume, and blood flow. Drug uptake is governed by the partition coefficient, which signifies the drug concentration ratio in tissue to that in the blood. The blood flow rate to a specific tissue is expressed as Qt, and the rate of change in tissue...
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Drug Discovery: Overview01:26

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Drug discovery is a multifaceted process involving extensive screening, testing, and optimization of lead compounds to identify potential new drugs for therapeutic use. It combines several approaches, including screening large numbers of natural products, chemical modification of known active molecules, identification of new drug targets, and rational design based on biological mechanisms and drug-receptor structure. These approaches are carried out in both academic research laboratories and...
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Structure-Activity Relationships and Drug Design01:28

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Drug design is a dynamic field that involves discovering and developing new medications based on specific biological targets. This process heavily relies on structure-activity relationships (SAR) and quantitative structure-activity relationships (QSAR) to guide the design and optimization of efficient drugs.
SAR studies the intricate relationship between a drug's chemical structure and biological activity. It focuses on understanding how modifications to a drug's structure can influence...
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Targets for Drug Action: Overview01:26

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Drugs target macromolecules to modify ongoing cellular processes. Primary drug targets include receptors, ion channels, transporters, and enzymes.
Receptors are either membrane-spanning or intracellular proteins, which upon binding a ligand, get activated and transmit the signal downstream to elicit a response. Drugs bind receptors, either mimicking the action of endogenous ligands or blocking the receptor activity to bring about a modified response. Nearly 35% of approved drugs target the G...
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相关实验视频

Updated: Jan 15, 2026

Author Spotlight: Developing a Simple and Robust Hepatic Model for Pharmacological and Toxicological Applications
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人工智能驱动的可编程虚拟人类,以人类生理学为基础的药物发现.

You Wu1, Philip E Bourne2, Lei Xie3

  • 1School of Pharmacy and Pharmaceutical Sciences & Center for Drug Discovery, Northeastern University, Boston, MA, USA; Ph.D. Program in Computer Science, The Graduate Center, The City University of New York, New York, NY, USA.

Drug discovery today
|October 10, 2025
PubMed
概括

人工智能 (AI) 能够为药物发现提供虚拟的人类模型,预测化合物的有效性和安全性. 这种新的范式超越了数字化实验,以测试新型药物在中进行早期优化.

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科学领域:

  • 药理学 药理学是指药理学的学科.
  • 计算生物学 计算生物学
  • 药物发现 药物发现 药物发现

背景情况:

  • 目前在药物发现中的人工智能 (AI) 可以将实验数字化,但无法预测临床结果.
  • 药理学数字双胞胎仅限于晚期发育阶段,不能弥合早期的翻译差距.
  • 人工智能的真正潜力涉及虚拟实验,用于在人类模型中测试新型化合物.

研究的目的:

  • 用人工智能驱动的虚拟人类在基于生理学的药物发现中引入新的范式.
  • 为了能够在早期评估和优化化合物的疗效和安全性.
  • 为了利用人工智能和omics在in silico药物测试中的进步.

主要方法:

  • 开发代表可编程虚拟人类的动态,多尺度模型.
  • 整合人工智能 (AI),高吞吐量测试,单细胞和空间奥米克.
  • 创建基于生理学的模型,用于in silico化合物评估.

主要成果:

  • 建立一个新的范式,以生理学为基础的药物发现.
  • 使虚拟实验能够在人类模型中测试新型化合物.
  • 促进早期评估化合物的疗效和安全性.

结论:

  • 人工智能驱动的虚拟人类代表了药物发现的变革性方法.
  • 这种方法可以进行in silico测试,克服当前实验数字化的局限性.
  • 这种方法有望在药物开发管道的早期优化化合物的疗效和安全性.