Cargando…
Predicting Total Drug Clearance and Volumes of Distribution Using the Machine Learning-Mediated Multimodal Method through the Imputation of Various Nonclinical Data
[Image: see text] Pharmacokinetic research plays an important role in the development of new drugs. Accurate predictions of human pharmacokinetic parameters are essential for the success of clinical trials. Clearance (CL) and volume of distribution (Vd) are important factors for evaluating pharmacok...
Autores principales: | Iwata, Hiroaki, Matsuo, Tatsuru, Mamada, Hideaki, Motomura, Takahisa, Matsushita, Mayumi, Fujiwara, Takeshi, Maeda, Kazuya, Handa, Koichi |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Chemical Society
2022
|
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9472274/ https://www.ncbi.nlm.nih.gov/pubmed/35993595 http://dx.doi.org/10.1021/acs.jcim.2c00318 |
Ejemplares similares
-
Novel QSAR Approach for a Regression Model of Clearance
That Combines DeepSnap-Deep Learning and Conventional Machine Learning
por: Mamada, Hideaki, et al.
Publicado: (2022) -
Prediction Model of Clearance by a Novel Quantitative
Structure–Activity Relationship Approach, Combination DeepSnap-Deep
Learning and Conventional Machine Learning
por: Mamada, Hideaki, et al.
Publicado: (2021) -
Responses to the Standard for Exchange of Nonclinical Data (SEND) in non-US countries
por: Anzai, Takayuki, et al.
Publicado: (2015) -
Specific pathologist responses for Standard for Exchange of Nonclinical Data (SEND)
por: Watanabe, Atsushi, et al.
Publicado: (2017) -
Nonclinical statistics for pharmaceutical and biotechnology industries
por: Zhang, Lanju
Publicado: (2016)