Comparison of Deconvolution-Based and Absorption Modeling IVIVC for Extended Release Formulations of a BCS III Drug Development Candidate

IVIVC公司 基于生理学的药代动力学模型 反褶积 吸收(声学) 化学 药代动力学 计算机科学 溶解试验 材料科学 药理学 算法 医学 生物制药分类系统 复合材料
作者
Filippos Kesisoglou,Binfeng Xia,Nancy Agrawal
出处
期刊:Aaps Journal [Springer Science+Business Media]
卷期号:17 (6): 1492-1500 被引量:45
标识
DOI:10.1208/s12248-015-9816-7
摘要

In vitro–in vivo correlations (IVIVC) are predictive mathematical models describing the relationship between dissolution and plasma concentration for a given drug compound. The traditional deconvolution/convolution-based approach is the most common methodology to establish a level A IVIVC that provides point to point relationship between the in vitro dissolution and the in vivo input rate. The increasing application of absorption physiologically based pharmacokinetic model (PBPK) has provided an alternative IVIVC approach. The current work established and compared two IVIVC models, via the traditional deconvolution/convolution method and via absorption PBPK modeling, for two types of modified release (MR) formulations (matrix and multi-particulate tablets) of MK-0941, a BCS III drug development candidate. Three batches with distinct release rates were studied for each formulation technology. A two-stage linear regression model was used for the deconvolution/convolution approach while optimization of the absorption scaling factors (a model parameter that relates permeability and input rate) in GastroplusTM Advanced Compartmental Absorption and Transit model was used for the absorption PBPK approach. For both types of IVIVC models established, and for either the matrix or the multiparticulate formulations, the average absolute prediction errors for AUC and C max were below 10% and 15%, respectively. Both the traditional deconvolution/convolution-based and the absorption/PBPK-based level A IVIVC model adequately described the compound pharmacokinetics to guide future formulation development. This case study highlights the potential utility of absorption PBPK model to complement the traditional IVIVC approaches for MR products.
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
复杂的海完成签到,获得积分10
1秒前
llopcop完成签到,获得积分10
7秒前
9秒前
健壮映波完成签到,获得积分10
10秒前
11秒前
Bg发布了新的文献求助10
13秒前
lifeng完成签到 ,获得积分10
13秒前
华仔应助xingxing采纳,获得10
14秒前
YEEze发布了新的文献求助10
14秒前
小米的稻田完成签到 ,获得积分10
15秒前
ElviraHuang完成签到 ,获得积分10
15秒前
CodeCraft应助KEYANKANG采纳,获得10
15秒前
hhh发布了新的文献求助10
16秒前
yy完成签到,获得积分20
20秒前
大个应助鹏笑采纳,获得10
21秒前
bkagyin应助qi采纳,获得10
23秒前
pudding完成签到,获得积分10
23秒前
23秒前
23秒前
称心的语梦完成签到,获得积分10
24秒前
贪玩飞珍完成签到,获得积分10
24秒前
MingzhenZhou完成签到,获得积分10
25秒前
英俊的铭应助不二子采纳,获得10
25秒前
26秒前
可靠听双完成签到,获得积分10
26秒前
Owen应助一个迷途小书童采纳,获得10
27秒前
欢喜若灵发布了新的文献求助10
27秒前
29秒前
hyishu完成签到,获得积分10
29秒前
Nolan完成签到,获得积分10
29秒前
实验一定成功完成签到,获得积分10
30秒前
KEYANKANG发布了新的文献求助10
30秒前
d甩甩发布了新的文献求助10
30秒前
可靠听双发布了新的文献求助10
31秒前
31秒前
32秒前
蜡笔完成签到,获得积分10
32秒前
我学个P完成签到,获得积分10
35秒前
36秒前
36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
PowerCascade: A Synthetic Dataset for Cascading Failure Analysis in Power Systems 2000
Picture this! Including first nations fiction picture books in school library collections 1500
Signals, Systems, and Signal Processing 610
Unlocking Chemical Thinking: Reimagining Chemistry Teaching and Learning 555
Photodetectors: From Ultraviolet to Infrared 500
Cancer Targets: Novel Therapies and Emerging Research Directions (Part 1) 400
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 物理 内科学 复合材料 催化作用 物理化学 光电子学 电极 细胞生物学 基因 无机化学
热门帖子
关注 科研通微信公众号,转发送积分 6359701
求助须知:如何正确求助?哪些是违规求助? 8173732
关于积分的说明 17215390
捐赠科研通 5414697
什么是DOI,文献DOI怎么找? 2865615
邀请新用户注册赠送积分活动 1842916
关于科研通互助平台的介绍 1691124