ADMET Profiling in Drug Discovery and Development: Perspectives of In Silico, In Vitro and Integrated Approaches

Daoud, Nour El-Huda; Borah, Pobitra; Deb, Pran Kishore; Venugopala, Katharigatta N.; Hourani, Wafa; Alzweiri, Muhammed; Bardaweel, Sanaa K.; Tiwari, Vinod

doi:10.2174/1389200222666210705122913

Cited by 57 publications

(31 citation statements)

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“…Prediction of ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties (Amin et al, 2021;Daoud et al, 2021;Dwivedi et al, 2021) for all the target compounds was the same as that for compounds 1e, 1f, and 1g, which indicated that modified compounds would be expected to have similar pharmaceutical properties to the original drugs. All the compounds except f-1 and 7-e to 10-e met the requirements of Lipinski's rule of five (their molecular weight ≤500 Da, lipophilicity, and the number of HBD ≤5, and the number of HBA and rotatable bonds ≤10).…”

Section: Admet Predictionmentioning

confidence: 79%

Synthesis and antibacterial activity evaluation of N (7) position-modified balofloxacins

Hong

Mao

et al. 2022

Front. Chem.

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A series of small-molecule fluoroquinolones were synthesized, characterized by HRMS and NMR spectroscopy, and screened for their antibacterial activity against MRSA, P. aeruginosa, and E. coli as model G+/G− pathogens. Compounds 2-e, 3-e, and 4-e were more potent than the reference drug balofloxacin against MRSA and P. aeruginosa (MIC values of 0.0195 and 0.039 μg/ml for 2-e, 0.039 and 0.078 μg/ml for each of 3-e and 4-e, respectively). Analysis of the time-dependent antibacterial effect of compound 2-e toward MRSA showed that in the early logarithmic growth phase, bactericidal effects occurred, while in the late logarithmic growth phase, bacterial inhibition occurred because of concentration effects and possibly the development of drug resistance. Compound 2-e exhibited low toxicity toward normal mammalian cell lines 3T3 and L-02 and tumor cell lines A549, H520, BEL-7402, and MCF-7. The compound was not hemolytic. Atomic force microscopy (AFM) revealed that compound 2-e could effectively destroy the membrane and wall of MRSA cells, resulting in the outflow of the cellular contents. Docking studies indicated the good binding profile of these compounds toward DNA gyrase and topoisomerase IV. ADMET’s prediction showed that most of the synthesized compounds followed Lipinski’s “rule of five” and possessed good drug-like properties. Our data suggested that compound 2-e exhibited potent anti-MRSA activity and is worthy of further investigation.

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Section: Admet Predictionmentioning

confidence: 79%

Synthesis and antibacterial activity evaluation of N (7) position-modified balofloxacins

Hong

Mao

et al. 2022

Front. Chem.

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“…These products can be chemically reactive and lead to DNA adducts modifications, mutations, and disruption of enzyme reactions. Many current QSAR models, molecular docking tools and ADMET properties prediction tool in silico are available to weigh the toxicity of chemicals (Daoud et al 2021 ). Table 1 shows the comprehensive list of ADMET profiling tools, the parameters one can predict with their link adopted with permission from references (Shin et al 2017 ).…”

Section: Essential Role Of Pbpk Models and Admet Profilers In Health ...mentioning

confidence: 99%

Artificial intelligence and machine learning disciplines with the potential to improve the nanotoxicology and nanomedicine fields: a comprehensive review

et al. 2023

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The use of nanomaterials in medicine depends largely on nanotoxicological evaluation in order to ensure safe application on living organisms. Artificial intelligence (AI) and machine learning (MI) can be used to analyze and interpret large amounts of data in the field of toxicology, such as data from toxicological databases and high-content image-based screening data. Physiologically based pharmacokinetic (PBPK) models and nano-quantitative structure–activity relationship (QSAR) models can be used to predict the behavior and toxic effects of nanomaterials, respectively. PBPK and Nano-QSAR are prominent ML tool for harmful event analysis that is used to understand the mechanisms by which chemical compounds can cause toxic effects, while toxicogenomics is the study of the genetic basis of toxic responses in living organisms. Despite the potential of these methods, there are still many challenges and uncertainties that need to be addressed in the field. In this review, we provide an overview of artificial intelligence (AI) and machine learning (ML) techniques in nanomedicine and nanotoxicology to better understand the potential toxic effects of these materials at the nanoscale.

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“…Lipophilicity is a valuable parameter of the drug which affects its activity in the human body. The Log P value of the compound indicates the permeability of the drugs to reach the target tissue in the body [39] LogS…”

Section: Logpmentioning

confidence: 99%

“…Our estimated logS value is a unit stripped logarithm (base 10) of the solubility measured in mol/liter. [39] CYP inhibitors…”

Section: Logpmentioning

confidence: 99%

A Comprehensive Review and Application of Interpretable Deep Learning Model for ADR Prediction

Dubey¹,

Pandit²

2022

IJACSA

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Drug safety is a pressing need in today's healthcare. Minimizing drug toxicity and improving the individual's health and society is the key objective of the healthcare domain. Drugs are clinically tested in laboratories before marketing as medicines. However, the unintended and harmful effects of drugs are called Adverse Drug Reactions (ADRs). The impact of ADRs can range from mild discomfort to more severe health hazards leading to hospitalization and in some cases death. Therefore, the objective of this research paper is to design a framework based on which research papers are collected from both ADR detection and prediction domain. Around 172 research articles are collected from the sites like ResearchGate, PubMed, etc. After applying the elimination criteria the author categorized them into ADR detection and prediction themes. Further, common data sources and algorithms as well as the evaluation metrics were analyzed and their contribution to their respective domains is stated in terms of percentages. A deep learning framework is also designed and implemented based on the research gaps identified in the existing ADR detection and prediction models. The performance of the deep learning model with two hidden layers was found to be optimum for ADR prediction and further, the noninterpretability part of the model is addressed using a global surrogate model. The proposed architecture has successfully addressed multiple limitations of existing models and also highlights the importance of early detection & prediction of adverse drug reactions in the healthcare industry.

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ADMET Profiling in Drug Discovery and Development: Perspectives of In Silico, In Vitro and Integrated Approaches

Cited by 57 publications

References 0 publications

Synthesis and antibacterial activity evaluation of N (7) position-modified balofloxacins

Synthesis and antibacterial activity evaluation of N (7) position-modified balofloxacins

Artificial intelligence and machine learning disciplines with the potential to improve the nanotoxicology and nanomedicine fields: a comprehensive review

A Comprehensive Review and Application of Interpretable Deep Learning Model for ADR Prediction

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