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Deciphering Nonbioavailable Substructures Improves the Bioavailability of Antidepressants by Serotonin Transporter.

Author: ChenKang-Zhi, HaoGe-Fei, HuangJun-Jie, JinChuan-Fei, LuJie-Lian, NieBiao, WangZhi-Zheng, WangZu-Sheng, XuTeng-Fei, XueYa-Ping, YiChao, ZhangYing-Jun

Journal ArticleAn article published in an academic or professional journal that reports original research or scholarly analysis.
Research Support, Non-U.S. Gov'tResearch funding provided by government agencies outside the United States.

Overview

Inadequate bioavailability is a major hurdle in oral drug development. This study proposes the concept of "nonbioavailable substructures," which are unfavorable to bioavailability. A machine learning model was developed to identify these substructures based on molecular properties. A more potent serotonin transporter (SERT) inhibitor, DH4, with improved bioavailability was discovered by replacing a nonbioavailable substructure in the approved drug vilazodone. This study suggests that identifying and modifying nonbioavailable substructures could improve drug bioavailability.
Paper Details　
Original Abstract of the Article :
Inadequate bioavailability is one of the most critical reasons for the failure of oral drug development. However, the way that substructures affect bioavailability remains largely unknown. Serotonin transporter (SERT) inhibitors are first-line drugs for major depression disorder, and improving their...See full text at original site
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引用元：
https://doi.org/10.1021/acs.jmedchem.2c01339

データ提供：米国国立医学図書館（NLM）

Improving Drug Bioavailability: Targeting Nonbioavailable Substructures

This study explores a novel approach to improving drug bioavailability by targeting nonbioavailable substructures, which are molecular components that hinder a drug's absorption and distribution in the body. The researchers focus on serotonin transporter (SERT) inhibitors, a class of antidepressants, to demonstrate the effectiveness of this strategy. By identifying and replacing nonbioavailable substructures in existing drugs, the researchers aim to enhance their bioavailability and potentially reduce side effects by decreasing required doses. The study utilizes machine learning to develop a model that predicts nonbioavailable substructures based on their molecular properties. The researchers then apply this model to design a more potent SERT inhibitor with improved bioavailability. This study aims to open up new avenues for optimizing drug design and development.

Optimizing Drug Design for Enhanced Bioavailability

This study presents a groundbreaking approach to optimizing drug design by targeting nonbioavailable substructures. The researchers demonstrate the effectiveness of this strategy by developing a more potent SERT inhibitor with significantly improved bioavailability. The findings suggest that this approach could be applied to other drug classes, paving the way for the development of more effective and well-tolerated medications.

The Importance of Bioavailability in Drug Development

This research highlights the crucial role of bioavailability in drug development. By improving bioavailability, researchers can potentially reduce side effects, increase therapeutic efficacy, and enhance patient outcomes. Just as a camel can efficiently conserve water in the desert, understanding and optimizing drug bioavailability is essential for maximizing the therapeutic potential of medications.

Dr.Camel's Conclusion

This study presents an innovative approach to improving drug bioavailability by targeting nonbioavailable substructures. The findings demonstrate the potential of this strategy to enhance drug efficacy and reduce side effects. Imagine a camel navigating through a vast desert, seeking out scarce water sources. Just as a camel's ability to efficiently store water is crucial for survival, optimizing drug bioavailability is essential for maximizing the therapeutic potential of medications.
Date :
  1. Date Completed 2023-01-13
  2. Date Revised 2023-02-07
Further Info :

Pubmed ID

36598095

DOI: Digital Object Identifier

10.1021/acs.jmedchem.2c01339

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SNS
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in preparation
Languages

English

Positive IndicatorAn AI analysis index that serves as a benchmark for how positive the results of the study are. Note that it is a benchmark and requires careful interpretation and consideration of different perspectives.

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