已入深夜,您辛苦了!由于当前在线用户较少,发布求助请尽量完整地填写文献信息,科研通机器人24小时在线,伴您度过漫漫科研夜!祝你早点完成任务,早点休息,好梦!

Application of New Approach Methodologies to Improve Oral Biopharmaceutic Assessments

生物制药分类系统 广告 生物制药 计算机科学 风险分析(工程) 监管科学 生化工程 药物发现 制药工业 数据科学 食品药品监督管理局 基于生理学的药代动力学模型 管理科学 生物制药 监管事务 信任 工具箱 药物开发 系统药理学 药理学 监管机构 质量(理念) 生物仿制药 肠道通透性 标准化 模拟生物系统 现状 代理(哲学)
作者
Mauricio A. García,Miguel Ángel Cabrera-Pérez,Pablo M. González,Alexis Aceituno,Daniel Hachim
出处
期刊:Pharmaceutics [Multidisciplinary Digital Publishing Institute]
卷期号:18 (5): 552-552
标识
DOI:10.3390/pharmaceutics18050552
摘要

Background/Objectives: The rapid expansion of New Approach Methodologies (NAMs) is transforming oral biopharmaceutics by offering mechanistically rich, human-relevant tools that can reduce reliance on animal testing while improving translational confidence. Regulatory agencies, including the Food and Drug Administration (FDA) and the European Medicines Agency (EMA), are increasingly open to NAM-generated evidence, provided that methods are fit-for-purpose and scientifically justified. This review synthesizes current advances and evaluates how NAMs can be integrated across drug-development stages to enhance the prediction of oral absorption, formulation performance, and regulatory decision-making. Methods: A comprehensive literature review was conducted across classical and emerging methodologies, including in vitro permeability and solubility models, organoids, organ-on-a-chip (OoC) systems, machine learning frameworks, and mechanistic approaches such as the physiologically based pharmacokinetic (PBPK) and biopharmaceutics (PBBM) models. Emphasis was placed on physiological relevance, predictive performance, validation status, and regulatory applicability. Results: Classical tools remain essential for the Biopharmaceutics Classification System (BCS)-based biowaivers and risk-based assessments, yet they often lack physiological fidelity. NAMs provide enhanced representation of intestinal architecture, hydrodynamics, transporter activity, and metabolism. Organoids and microphysiological systems generate high-quality permeability and metabolic data, while computational NAMs enable scalable prediction of ADME properties and formulation behavior. When integrated into PBPK/PBBM models, these methods have great potential in predicting in vivo performance in humans. Evidence demonstrates that NAMs can refine, reduce, and, in specific contexts, replace animal studies without compromising scientific rigor. Conclusions: NAMs complement, rather than displace, classical biopharmaceutic tools, enabling a more mechanistic, human-centered, and ethically responsible framework for drug development. Their effective implementation will depend on continued validation, standardization, and regulatory harmonization as the field transitions toward fully NAM-supported biopharmaceutical assessment.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
goldNAN发布了新的文献求助10
1秒前
麻薯发布了新的文献求助10
1秒前
宁燕发布了新的文献求助10
1秒前
2秒前
Jerry完成签到 ,获得积分10
3秒前
5秒前
7秒前
zz发布了新的文献求助20
7秒前
JamesPei应助席月清辉采纳,获得10
7秒前
王洁完成签到,获得积分10
7秒前
SciGPT应助含羞草采纳,获得10
8秒前
乐研发布了新的文献求助10
9秒前
9秒前
10秒前
科研通AI6.3应助半个橙子采纳,获得10
10秒前
科研通AI6.4应助tiki采纳,获得10
11秒前
11秒前
luchen发布了新的文献求助10
12秒前
卡洛驳回了yjh123应助
12秒前
科研通AI6.3应助香蕉妙菱采纳,获得10
13秒前
MH完成签到,获得积分10
13秒前
albus完成签到,获得积分10
14秒前
15秒前
16秒前
英俊的铭应助yiiy采纳,获得10
16秒前
SciGPT应助felix采纳,获得10
17秒前
18秒前
18秒前
Flora发布了新的文献求助10
18秒前
19秒前
芝麻完成签到 ,获得积分10
20秒前
20秒前
李爱国应助zz采纳,获得10
21秒前
ABC完成签到,获得积分10
21秒前
热心十三发布了新的文献求助10
22秒前
含羞草发布了新的文献求助10
24秒前
徐yy完成签到 ,获得积分10
26秒前
27秒前
HL完成签到,获得积分10
28秒前
transition完成签到,获得积分10
28秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Evidence Summary. Injection (subcutaneous):op- timal administration 1000
悉尼大学博士学位论文,题目:Modelling and testing of one-sided stitched laminated composites. 作者:Kristopher P. Plain 700
Matrix Methods in Data Mining and Pattern Recognition Second Edition 610
Curating Socialism: A Handbook of International Art Exhibitions 1947-1989 530
文献求助-中国李庄学术史 500
Attractive Quality and Must-Be Quality 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 内科学 物理 复合材料 催化作用 细胞生物学 无机化学 光电子学 物理化学 电极 基因
热门帖子
关注 科研通微信公众号,转发送积分 7472601
求助须知:如何正确求助?哪些是违规求助? 9067620
关于积分的说明 19334085
捐赠科研通 7092491
什么是DOI,文献DOI怎么找? 3246053
关于科研通互助平台的介绍 2414821
邀请新用户注册赠送积分活动 2231097