Pharmacokinetic Tools and Applications

Madden, Judith C.; Thompson, Courtney V

doi:10.1007/978-1-0716-1960-5_3

Cited by 5 publications

(5 citation statements)

References 38 publications

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“…The Pred‐hERG app, used specifically for cardiotoxicity analysis, played a critical role given the significant importance of cardiac safety in drug development 34 . The pkCSM tool, which was used in the evaluation of a variety of properties such as water solubility (log mol/L), Caco 2 permeability (Log Papp in 10), absorption in human intestine (HIA) (% absorbed), provided indispensable insights into the solubility, permeability and potential hepatotoxicity of the ligands, which are critical for the evaluation of their drug and toxicity profiles 35–37 . The vNN‐ADMET tool was used for a comprehensive toxicity assessment that included human liver microsomal (HLM) stability, cardiotoxicity, cytotoxicity, MMP (mitochondrial toxicity), and Ames toxicity/mutagenesis, and contributed significantly to characterize the safety profile of the ligands 38,39 .…”

Section: Methodsmentioning

confidence: 99%

“… 34 The pkCSM tool, which was used in the evaluation of a variety of properties such as water solubility (log mol/L), Caco 2 permeability (Log Papp in 10), absorption in human intestine (HIA) (% absorbed), provided indispensable insights into the solubility, permeability and potential hepatotoxicity of the ligands, which are critical for the evaluation of their drug and toxicity profiles. 35 , 36 , 37 The vNN‐ADMET tool was used for a comprehensive toxicity assessment that included human liver microsomal (HLM) stability, cardiotoxicity, cytotoxicity, MMP (mitochondrial toxicity), and Ames toxicity/mutagenesis, and contributed significantly to characterize the safety profile of the ligands. 38 , 39 Finally, the PreADMET tool was used for its capabilities in MDCK permeability analysis and carcinogenicity assessment along with Salmonella strain mutagenicity assessment, which was essential for assessing the potential genotoxic and carcinogenic risks of the ligands.…”

Section: Methodsmentioning

confidence: 99%

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A robust computational quest: Discovering potential hits to improve the treatment of pyrazinamide‐resistant Mycobacterium tuberculosis

Shahab,

de Farias Morais,

Akash

et al. 2024

J Cellular Molecular Medi

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The rise of pyrazinamide (PZA)‐resistant strains of Mycobacterium tuberculosis (MTB) poses a major challenge to conventional tuberculosis (TB) treatments. PZA, a cornerstone of TB therapy, must be activated by the mycobacterial enzyme pyrazinamidase (PZase) to convert its active form, pyrazinoic acid, which targets the ribosomal protein S1. Resistance, often associated with mutations in the RpsA protein, complicates treatment and highlights a critical gap in the understanding of structural dynamics and mechanisms of resistance, particularly in the context of the G97D mutation. This study utilizes a novel integration of computational techniques, including multiscale biomolecular and molecular dynamics simulations, physicochemical and medicinal chemistry predictions, quantum computations and virtual screening from the ZINC and Chembridge databases, to elucidate the resistance mechanism and identify lead compounds that have the potential to improve treatment outcomes for PZA‐resistant MTB, namely ZINC15913786, ZINC20735155, Chem10269711, Chem10279789 and Chem10295790. These computational methods offer a cost‐effective, rapid alternative to traditional drug trials by bypassing the need for organic subjects while providing highly accurate insight into the binding sites and efficacy of new drug candidates. The need for rapid and appropriate drug development emphasizes the need for robust computational analysis to justify further validation through in vitro and in vivo experiments.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

A robust computational quest: Discovering potential hits to improve the treatment of pyrazinamide‐resistant Mycobacterium tuberculosis

Shahab,

de Farias Morais,

Akash

et al. 2024

J Cellular Molecular Medi

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“…The pharmacokinetic properties, including absorption, distribution, metabolism, and excretion, are critical for determining the optimal dose of compounds and the frequency of administration as well as for ensuring drug adherence, and all of these play an important role in clinical application [170]. After rat oral administration, tectorigenin can be directly absorbed from the intestinal tract by passive diffusion, and it then undergoes glucuronidation and/or sulfation metabolic pathways by UDP-glucuronosyltransferases (UGT) and sulfotransferases to form corresponding metabolites [171].…”

Section: Pharmacokinetics Of Tectorigeninmentioning

confidence: 99%

Tectorigenin: A Review of Its Sources, Pharmacology, Toxicity, and Pharmacokinetics

Rong,

Fu,

Han

et al. 2023

Molecules

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Tectorigenin is a well-known natural flavonoid aglycone and an active component that exists in numerous plants. Growing evidence suggests that tectorigenin has multiple pharmacological effects, such as anticancer, antidiabetic, hepatoprotective, anti-inflammatory, antioxidative, antimicrobial, cardioprotective, and neuroprotective. These pharmacological properties provide the basis for the treatment of many kinds of illnesses, including several types of cancer, diabetes, hepatic fibrosis, osteoarthritis, Alzheimer’s disease, etc. The purpose of this paper is to provide a comprehensive summary and review of the sources, extraction and synthesis, pharmacological effects, toxicity, pharmacokinetics, and delivery strategy aspects of tectorigenin. Tectorigenin may exert certain cytotoxicity, which is related to the administration time and concentration. Pharmacokinetic studies have demonstrated that the main metabolic pathways in rats for tectorigenin are glucuronidation, sulfation, demethylation and methoxylation, but that it exhibits poor bioavailability. From our perspective, further research on tectorigenin should cover: exploring the pharmacological targets and mechanisms of action; finding an appropriate concentration to balance pharmacological effects and toxicity; attempting diversified delivery strategies to improve the bioavailability; and structural modification to obtain tectorigenin derivatives with higher pharmacological activity.

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“…Additionally, QSAR models have only local validity, and the low structural similarity between the compounds in the validation and training sets can result in poor predictive performance. 10 Another drawback of the QSAR models is that, usually, they do not consider pharmacokinetic (PK) information, such as the absorption, distribution, metabolism, and excretion (ADME) properties of compounds, 11 and they might have difficulties in characterizing the actual chemical risk of a compound since the toxicity of a compound is linked to the exposure. 12 …”

Section: Introductionmentioning

confidence: 99%

Integrating Mechanistic and Toxicokinetic Information in Predictive Models of Cholestasis

Rodríguez-Belenguer,

Mangas-Sanjuan,

Soria-Olivas

et al. 2023

J. Chem. Inf. Model.

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Drug development involves the thorough assessment of the candidate's safety and efficacy. In silico toxicology (IST) methods can contribute to the assessment, complementing in vitro and in vivo experimental methods, since they have many advantages in terms of cost and time. Also, they are less demanding concerning the requirements of product and experimental animals. One of these methods, Quantitative Structure−Activity Relationships (QSAR), has been proven successful in predicting simple toxicity end points but has more difficulties in predicting end points involving more complex phenomena. We hypothesize that QSAR models can produce better predictions of these end points by combining multiple QSAR models describing simpler biological phenomena and incorporating pharmacokinetic (PK) information, using quantitative in vitro to in vivo extrapolation (QIVIVE) models. In this study, we applied our methodology to the prediction of cholestasis and compared it with direct QSAR models. Our results show a clear increase in sensitivity. The predictive quality of the models was further assessed to mimic realistic conditions where the query compounds show low similarity with the training series. Again, our methodology shows clear advantages over direct QSAR models in these situations. We conclude that the proposed methodology could improve existing methodologies and could be suitable for being applied to other toxicity end points.

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Pharmacokinetic Tools and Applications

Cited by 5 publications

References 38 publications

A robust computational quest: Discovering potential hits to improve the treatment of pyrazinamide‐resistant Mycobacterium tuberculosis

A robust computational quest: Discovering potential hits to improve the treatment of pyrazinamide‐resistant Mycobacterium tuberculosis

Tectorigenin: A Review of Its Sources, Pharmacology, Toxicity, and Pharmacokinetics

Integrating Mechanistic and Toxicokinetic Information in Predictive Models of Cholestasis

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