Dean G. Brown · 2025 · Journal of Medicinal Chemistry · ACS Publications · added 2026-04-20
An analysis of dose, dose frequency, human pharmacokinetics, and potential drug-drug interactions (DDI) was performed on small-molecule oral drugs approved by the FDA from 2020 to 2024 (n = 104 Show more
An analysis of dose, dose frequency, human pharmacokinetics, and potential drug-drug interactions (DDI) was performed on small-molecule oral drugs approved by the FDA from 2020 to 2024 (n = 104). Although most oral drugs are administered QD (67%), BID and TID regimens are also regularly approved (32%). First-in-class (FIC) drugs and drugs with Orphan Drug Designation (ODD) have a higher frequency of BID or TID administration compared to drugs without those designations (BID and TID = 50% for FIC drugs vs 19% for non-FIC; BID and TID = 41% for ODD vs 20% non-ODD). Most drugs are >95% plasma protein bound (58%), with a large fraction >99% bound (29%). Of these drugs, 22% have black box warnings and 42% list contraindications. An examination of DDI revealed the most frequent warning around CYP3A4 induction (60%). These findings will help medicinal chemists better understand and predict typical and nontypical profiles of oral drugs. Show less
Drug-drug interactions (DDIs) can produce unpredictable pharmacological effects and lead to adverse
events that have the potential to cause irreversible damage to the organism. Traditional methods to
Show more
Drug-drug interactions (DDIs) can produce unpredictable pharmacological effects and lead to adverse
events that have the potential to cause irreversible damage to the organism. Traditional methods to
detect DDIs through biological or pharmacological analysis are time-consuming and expensive, therefore,
there is an urgent need to develop computational methods to effectively predict drug-drug interactions.
Currently, deep learning and knowledge graph techniques which can effectively extract features of entities have been widely utilized to develop DDI prediction methods. In this research, we aim to systematically review DDI prediction researches applying deep learning and graph knowledge. The available
biomedical data and public databases related to drugs are firstly summarized in this review. Then, we
discuss the existing drug-drug interactions prediction methods which have utilized deep learning and
knowledge graph techniques and group them into three main classes: deep learning-based methods,
knowledge graph-based methods, and methods that combine deep learning with knowledge graph.
We comprehensively analyze the commonly used drug related data and various DDI prediction methods,
and compare these prediction methods on benchmark datasets. Finally, we briefly discuss the challenges
related to drug-drug interactions prediction, including asymmetric DDIs prediction and high-order DDI
prediction. Show less