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Pharmacogenomics: Identification of New Drug Targets01:29

Pharmacogenomics: Identification of New Drug Targets

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Advances in genomics have profoundly influenced drug discovery by increasing both the speed and accuracy of pharmaceutical development. Pharmacogenomics, which examines how genetic variation influences drug response, facilitates the identification of novel therapeutic targets and enables patient stratification for personalized treatment. These strategies contribute to improved drug efficacy, minimized adverse effects, and more efficient clinical trial design.Mapping genetic differences...
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Impact of Pharmacokinetic–Pharmacodynamic Models: Regulatory Decisions01:15

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PK–PD modeling has significantly influenced FDA regulatory decisions, particularly drug approval, dosage optimization, and labeling. These models integrate pharmacokinetics (PK) and pharmacodynamics (PD) to predict drug behavior and effects, aiding in optimizing dosing regimens and enhancing the probability of clinical trial success.One notable example is Nesiritide (Natrecor®), a recombinant human brain natriuretic peptide for treating acute decompensated congestive heart failure...
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Pharmacogenetics of Drug Targets: β₂-Adrenergic Receptors, Apo E, Thymidylate Synthase01:11

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Genetic polymorphisms in drug targets have emerged as critical determinants of interindividual variability in drug response and toxicity. Pharmacogenomic investigations increasingly focus on identifying these variations to personalize and optimize therapeutic interventions. A drug target may be a receptor, enzyme, or signaling protein involved in pharmacologic responses or disease-related pathways. While early pharmacogenetic studies focused primarily on drug metabolism, current research...
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The empirical approach to drug therapy optimization relies on correlating pharmacological response with administered dosage. Such an approach can be costly, time-consuming, and often yields poor correlation due to variables like formulation factors and drug elimination characteristics. A more precise approach correlates response with plasma drug concentration or the amount of drug in the body, rather than dosage. This is achieved through pharmacokinetic-pharmacodynamic (PK/PD) modeling, which...
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Genetic variations significantly influence drug response through pharmacokinetics, receptor interactions, and biologic milieu modifications. Pharmacokinetic alterations impact drug metabolism and clearance, affecting efficacy and toxicity. Variants in drug-metabolizing enzymes, such as CYP2C9 and CYP2C19, alter drug activation and elimination. For example, CYP2C9 loss-of-function variants require lower warfarin doses to prevent excessive bleeding, while CYP2C19 variants reduce clopidogrel...
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Genetic polymorphism in drug metabolism is crucial to the inter-individual variability observed in drug responses. Drug metabolism primarily involves the chemical modification of drugs and other xenobiotics to enhance their elimination by increasing their polarity. Two main classes of enzymes mediate this biotransformation process: Phase I enzymes, primarily cytochrome P450s, catalyze oxidation and reduction reactions, while other enzymes, such as esterases, mediate hydrolysis, and Phase II...
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一个基于机器学习的药理动力学预测器 (EGFR-PROPK) 用于针对EGFR的PROTACs.

Ran Zhang1, Fenglei Li1,2, Yao Liu3

  • 1Shanghai Institute for Advanced Immunochemical Studies, ShanghaiTech University, Shanghai, China.

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蛋白质溶解向金马 (PROTACs) 显示出治疗前景,但优化它们的药理动力学 (PK) 是一个挑战. 这项研究开发了一种特定于PROTAC的模型,EGFR-PROPK,提高了对半衰期和清除等关键PK属性的预测准确度.

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科学领域:

  • 药物发现 药物发现 药物发现
  • 药理学 药理学是指药理学的学科.
  • 生物技术是生物技术.

背景情况:

  • 蛋白质溶解向金马 (PROTACs) 为向蛋白质降解提供了一种新的策略,以解决以前无法治疗的向.
  • 优化药理动力学 (PK) 特性,包括ADMET,仍然是PROTAC开发的一个重大障碍.
  • 传统的机器学习模型通常与PROTACs与小分子相比具有独特特征而扎.

研究的目的:

  • 开发和验证PROTAC特定的药理动力学性质预测模型.
  • 评估传统机器学习模型与PROTAC定制模型的预测性能.
  • 为了更好地了解针对EGFR向的PROTACs的PK参数 (CL,T1/2,Vss).

主要方法:

  • 结合了传统的机器学习与多个分子指纹.
  • 开发了EGFR-PROPK模型用于PROTAC药理动力学性质预测.
  • 对100个向EGFR的PROTAC分子进行了in-vivo实验,以评估CL,T1/2和Vss.

主要成果:

  • 在小分子上训练的传统模型在应用于PROTACs时表现不佳.
  • 基于PROTAC特定数据的培训模型显著提高了预测准确性.
  • 在预测值和观察值之间,T1/2的相关系数为0.78,CL为0.75,Vss为0.52.

结论:

  • 对于PROTAC的药理动力学评估,需要采用与小分子药物开发不同的量身定制方法.
  • EGFR-PROPK模型证明了PROTAC特定数据在改善PK预测方面的有效性.
  • 这些发现对于推进基于PROTAC的治疗方法的合理设计和开发至关重要.