Recent Studies of Artificial Intelligence on In Silico Drug Distribution Prediction

Tran, Thi Tuyet Van; Tayara, Hilal; Chong, Kil To

doi:10.3390/ijms24031815

Cited by 18 publications

(10 citation statements)

References 113 publications

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“…Tong et al [ 59 ] showed the utility of uncertainty estimations for improving the predictability of deep learning BBB permeability models. An excellent review of classification models using different datasets and different ML and DL algorithms is published by Saxena et al [ 60 ] and Tran et al [ 61 ]. These qualitative classification models are helpful for quick screening of large compound databases at early-stage drug discovery.…”

Section: Bbb Penetration Scoring Schemes For Predicting Brain Penetra...mentioning

confidence: 99%

Experimental and Computational Methods to Assess Central Nervous System Penetration of Small Molecules

Gupta,

Feng,

Bhisetti

2024

Molecules

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In CNS drug discovery, the estimation of brain exposure to lead compounds is critical for their optimization. Compounds need to cross the blood–brain barrier (BBB) to reach the pharmacological targets in the CNS. The BBB is a complex system involving passive and active mechanisms of transport and efflux transporters such as P-glycoproteins (P-gp) and breast cancer resistance protein (BCRP), which play an essential role in CNS penetration of small molecules. Several in vivo, in vitro, and in silico methods are available to estimate human brain penetration. Preclinical species are used as in vivo models to understand unbound brain exposure by deriving the Kp,uu parameter and the brain/plasma ratio of exposure corrected with the plasma and brain free fraction. The MDCK-mdr1 (Madin Darby canine kidney cells transfected with the MDR1 gene encoding for the human P-gp) assay is the commonly used in vitro assay to estimate compound permeability and human efflux. The in silico methods to predict brain exposure, such as CNS MPO, CNS BBB scores, and various machine learning models, help save costs and speed up compound discovery and optimization at all stages. These methods enable the screening of virtual compounds, building of a CNS penetrable compounds library, and optimization of lead molecules for CNS penetration. Therefore, it is crucial to understand the reliability and ability of these methods to predict CNS penetration. We review the in silico, in vitro, and in vivo data and their correlation with each other, as well as assess published experimental and computational approaches to predict the BBB penetrability of compounds.

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Section: Bbb Penetration Scoring Schemes For Predicting Brain Penetra...mentioning

confidence: 99%

Experimental and Computational Methods to Assess Central Nervous System Penetration of Small Molecules

Gupta,

Feng,

Bhisetti

2024

Molecules

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“…AI-powered decision support systems unite the information on patients' demographics, clinical histories and treatment outcomes and make diagnosticians insightsbased decisions, which consequently lead to better treatment outcomes in the management of tinnitus. [17] And they (Van Tran et al, 2023) go on a pathway of the most recent research of AI in in silico drug distribution prediction. Such researchers emphasize the potential utilization of AI-based predictive modeling techniques for improve drug distribution procedures.…”

Section: Iirelated Workmentioning

confidence: 99%

Data-Driven Decision Support Systems for Drug Discovery and Development: A Case Study in Pharmaceutical Management

Rohit R Dixit

2024

jes

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This paper covers the use and influence of data-driven decision support systems (DSS) on drug management, particularly in the areas of drug exploration and production. The study, which employed a mixed-methods approach, involving literature review, qualitative interviews, quantitative assessments, and case study analysis, reveals the AI, machine learning and big data analytics ability to drive drug discovery and development processes revolution in the pharmaceutical industry. The study shows that handling of DSS allows sound decision making, consequently resulting in improved efficiency, decreased expenses and better innovation throughout the drug development process. Areas like data integration, algorithm robustness, and regulation are identified as the major issues, which offers an insight into the importance of these areas of concern in the effective application of a data-driven approach in pharmaceutical management. Such contrast with the like works underlines the role of our findings as an essential link in the AI-driven healthcare innovations chain. The research provides a foundation for further improving the state of knowledge and understanding of DSS in drug management, guiding future research aiming at expanding conceptual frameworks and designing practical implementations.

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“…However, we only focus on highlighting 11 properties that have been of interest to many AI-based ADMET researchers, including the logarithm of the octanol–water partition coefficient (log P), the logarithm of pH-dependent distribution coefficient (log D), the logarithm of the aqueous solubility (log S), p K a , human oral bioavailability (HOB), human intestinal absorption (HIA), Caco-2 cell permeability, P-glycoprotein (P-gp) inhibitor and substrate, parallel artificial membrane permeability assay (PAMPA), and Madin-Darby Canine Kidney Cells (MDCK) permeability. Interested readers can refer to other useful ADMET property reviews. − …”

Section: Progress On Ai-based Drug Absorption Predictionmentioning

confidence: 99%

Recent Studies of Artificial Intelligence on In Silico Drug Absorption

Tran,

Tayara,

Chong

2023

J. Chem. Inf. Model.

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Absorption is an important area of research in pharmacochemistry and drug development, because the drug has to be absorbed before any drug effects can occur. Furthermore, the ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) profile of drugs can be directly and considerably altered by modulating factors affecting absorption. Many drugs in development fail because of poor absorption. The research and continuous efforts of researchers in recent years have brought many successes and promises in drug absorption property prediction, especially in silico, which helps to reduce the time and cost significantly for screening undesirable drug candidates. In this report, we explicitly provide an overview of recent in silico studies on predicting absorption properties, especially from 2019 to the present, using artificial intelligence. Additionally, we have collected and investigated public databases that support absorption prediction research. On those grounds, we also proposed the challenges and development directions of absorption prediction in the future. We hope this review can provide researchers with valuable guidelines on absorption prediction to facilitate the development of newer approaches in drug discovery.

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Recent Studies of Artificial Intelligence on In Silico Drug Distribution Prediction

Cited by 18 publications

References 113 publications

Experimental and Computational Methods to Assess Central Nervous System Penetration of Small Molecules

Experimental and Computational Methods to Assess Central Nervous System Penetration of Small Molecules

Data-Driven Decision Support Systems for Drug Discovery and Development: A Case Study in Pharmaceutical Management

Recent Studies of Artificial Intelligence on In Silico Drug Absorption

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