Artificial Intelligence in Drug Metabolism and Excretion Prediction: Recent Advances, Challenges, and Future Perspectives

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

doi:10.3390/pharmaceutics15041260

Cited by 30 publications

(10 citation statements)

References 123 publications

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“…Thus, the predicted dose uncertainty in these regions will determine the confidence level in therapeutic outcomes or the bioequivalence compared to a counterpart device or medication [ 62 ]. Note that in recent years, machine learning has been actively explored in inhalation drug delivery that aimed to increase the prediction speed, identify causal relationships, and quantify dosimetry uncertainty [ 63 , 64 , 65 , 66 , 67 ].…”

Section: Discussionmentioning

confidence: 99%

Sensitivity Analysis and Uncertainty Quantification of Nanoparticle Deposition from Tongue Morphological Variations

Yang,

Si,

2024

Life

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The human tongue has highly variable morphology. Its role in regulating respiratory flows and deposition of inhaled aerosols remains unclear. The objective of this study was to quantify the uncertainty of nanoparticle deposition from the variability in tongue shapes and positions and to rank the importance of these morphological factors. Oropharyngeal models with different tongue postures were reconstructed by modifying an existent anatomically accurate upper airway geometry. An LRN k-ω model was applied to solve the multiregime flows, and the Lagrangian tracking approach with near-wall treatment was used to simulate the behavior and fate of inhaled aerosols. Once the database of deposition rates was completed, a surrogate model was trained using Gaussian process regression with polynomial kernels and was validated by comparing its predictions to new CFD simulations. Input sensitivity analysis and output updateability quantification were then performed using the surrogate model. Results show that particle size is the most significant parameter in determining nanoparticle deposition in the upper airway. Among the morphological factors, the shape variations in the central tongue had a higher impact on the total deposition than those in the back tongue and glottal aperture. When considering subregional deposition, mixed sensitivity levels were observed among morphological factors, with the back tongue being the major factor for throat deposition and the central tongue for oral deposition. Interaction effects between flow rate and morphological factors were much higher than the effects from individual parameters and were most significant in the throat (pharyngolaryngeal region). Given input normal variances, the nanoparticle deposition exhibits logarithmical normal distributions, with much lower uncertainty in 100-nm than 2-nm aerosols.

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Section: Discussionmentioning

confidence: 99%

Sensitivity Analysis and Uncertainty Quantification of Nanoparticle Deposition from Tongue Morphological Variations

Yang,

Si,

2024

Life

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“…By training on large datasets of known drug metabolism information, AI models can identify structural features associated with specific metabolic transformations. These models enable the prediction of potential metabolites and provide insights into the major enzymes involved in drug metabolism [ 198 ].…”

Section: Ai For Pharmacokinetics and Pharmacodynamicsmentioning

confidence: 99%

Artificial Intelligence in Pharmaceutical Technology and Drug Delivery Design

Vora,

Gholap,

Jetha

et al. 2023

Pharmaceutics

247

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Artificial intelligence (AI) has emerged as a powerful tool that harnesses anthropomorphic knowledge and provides expedited solutions to complex challenges. Remarkable advancements in AI technology and machine learning present a transformative opportunity in the drug discovery, formulation, and testing of pharmaceutical dosage forms. By utilizing AI algorithms that analyze extensive biological data, including genomics and proteomics, researchers can identify disease-associated targets and predict their interactions with potential drug candidates. This enables a more efficient and targeted approach to drug discovery, thereby increasing the likelihood of successful drug approvals. Furthermore, AI can contribute to reducing development costs by optimizing research and development processes. Machine learning algorithms assist in experimental design and can predict the pharmacokinetics and toxicity of drug candidates. This capability enables the prioritization and optimization of lead compounds, reducing the need for extensive and costly animal testing. Personalized medicine approaches can be facilitated through AI algorithms that analyze real-world patient data, leading to more effective treatment outcomes and improved patient adherence. This comprehensive review explores the wide-ranging applications of AI in drug discovery, drug delivery dosage form designs, process optimization, testing, and pharmacokinetics/pharmacodynamics (PK/PD) studies. This review provides an overview of various AI-based approaches utilized in pharmaceutical technology, highlighting their benefits and drawbacks. Nevertheless, the continued investment in and exploration of AI in the pharmaceutical industry offer exciting prospects for enhancing drug development processes and patient care.

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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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Artificial Intelligence in Drug Metabolism and Excretion Prediction: Recent Advances, Challenges, and Future Perspectives

Cited by 30 publications

References 123 publications

Sensitivity Analysis and Uncertainty Quantification of Nanoparticle Deposition from Tongue Morphological Variations

Sensitivity Analysis and Uncertainty Quantification of Nanoparticle Deposition from Tongue Morphological Variations

Artificial Intelligence in Pharmaceutical Technology and Drug Delivery Design

Recent Studies of Artificial Intelligence on In Silico Drug Absorption

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