PMID- 35817846
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20220716
IS  - 2398-6352 (Electronic)
IS  - 2398-6352 (Linking)
VI  - 5
IP  - 1
DP  - 2022 Jul 11
TI  - Machine learning-based quantitative prediction of drug exposure in drug-drug 
      interactions using drug label information.
PG  - 88
LID - 10.1038/s41746-022-00639-0 [doi]
LID - 88
AB  - Many machine learning techniques provide a simple prediction for drug-drug 
      interactions (DDIs). However, a systematically constructed database with 
      pharmacokinetic (PK) DDI information does not exist, nor is there a machine 
      learning model that numerically predicts PK fold change (FC) with it. Therefore, 
      we propose a PK DDI prediction (PK-DDIP) model for quantitative DDI prediction 
      with high accuracy, while constructing a highly reliable PK-DDI database. 
      Reliable information of 3,627 PK DDIs was constructed from 3,587 drugs using 
      38,711 Food and Drug Administration (FDA) drug labels. This PK-DDIP model 
      predicted the FC of the area under the time-concentration curve (AUC) within +/- 
      0.5959. The prediction proportions within 0.8-1.25-fold, 0.67-1.5-fold, and 
      0.5-2-fold of the AUC were 75.77, 86.68, and 94.76%, respectively. Two external 
      validations confirmed good prediction performance for newly updated FDA labels 
      and FC from patients'. This model enables potential DDI evaluation before 
      clinical trials, which will save time and cost.
CI  - (c) 2022. The Author(s).
FAU - Jang, Ha Young
AU  - Jang HY
AUID- ORCID: 0000-0002-4752-1300
AD  - College of Pharmacy and Research Institute of Pharmaceutical Sciences, Seoul 
      National University, Seoul, Republic of Korea.
FAU - Song, Jihyeon
AU  - Song J
AD  - Department of Computer Science and Engineering, Seoul National University, Seoul, 
      Republic of Korea.
FAU - Kim, Jae Hyun
AU  - Kim JH
AUID- ORCID: 0000-0002-8609-7135
AD  - School of Pharmacy, Jeonbuk National University, Jeonju, Republic of Korea.
FAU - Lee, Howard
AU  - Lee H
AD  - Department of Clinical Pharmacology and Therapeutics, Seoul National University 
      College of Medicine and Hospital, Seoul, Korea.
FAU - Kim, In-Wha
AU  - Kim IW
AD  - College of Pharmacy and Research Institute of Pharmaceutical Sciences, Seoul 
      National University, Seoul, Republic of Korea.
FAU - Moon, Bongki
AU  - Moon B
AUID- ORCID: 0000-0001-9382-8306
AD  - Department of Computer Science and Engineering, Seoul National University, Seoul, 
      Republic of Korea. bkmoon@snu.ac.kr.
FAU - Oh, Jung Mi
AU  - Oh JM
AUID- ORCID: 0000-0002-1836-1707
AD  - College of Pharmacy and Research Institute of Pharmaceutical Sciences, Seoul 
      National University, Seoul, Republic of Korea. jmoh@snu.ac.kr.
LA  - eng
GR  - 19182MFDS407/Ministry of Food and Drug Safety (MFDS)/
GR  - 19182MFDS407/Ministry of Food and Drug Safety (MFDS)/
GR  - 19182MFDS407/Ministry of Food and Drug Safety (MFDS)/
GR  - 19182MFDS407/Ministry of Food and Drug Safety (MFDS)/
GR  - 19182MFDS407/Ministry of Food and Drug Safety (MFDS)/
GR  - 19182MFDS407/Ministry of Food and Drug Safety (MFDS)/
GR  - 19182MFDS407/Ministry of Food and Drug Safety (MFDS)/
PT  - Journal Article
DEP - 20220711
PL  - England
TA  - NPJ Digit Med
JT  - NPJ digital medicine
JID - 101731738
PMC - PMC9273620
COIS- The authors declare no competing interests.
EDAT- 2022/07/12 06:00
MHDA- 2022/07/12 06:01
PMCR- 2022/07/11
CRDT- 2022/07/11 23:21
PHST- 2022/02/13 00:00 [received]
PHST- 2022/06/16 00:00 [accepted]
PHST- 2022/07/11 23:21 [entrez]
PHST- 2022/07/12 06:00 [pubmed]
PHST- 2022/07/12 06:01 [medline]
PHST- 2022/07/11 00:00 [pmc-release]
AID - 10.1038/s41746-022-00639-0 [pii]
AID - 639 [pii]
AID - 10.1038/s41746-022-00639-0 [doi]
PST - epublish
SO  - NPJ Digit Med. 2022 Jul 11;5(1):88. doi: 10.1038/s41746-022-00639-0.