Prediction Analysis of Pharmacokinetic Parameters of Several Oral Systemic Drugs Using Insilico Method

Iswandana, Raditya; Aisyah, Permata; Syahdi, Rezi Riadhi

doi:10.22159/ijap.2020.v12s1.ff057

Cited by 3 publications

(2 citation statements)

References 20 publications

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“…This is because modeling PK data means looking back at the data and extracting PK parameters. In contrast, simulation helps to predict what might happen in similar cases of the drug in question, looking at future circumstances [18]. There are several modeling strategies [2,3,9].…”

Section: Fig 1: Major Divisions Of Pkmentioning

confidence: 99%

Importance of Sufficient Time Points for Efficient Pharmacokinetic (Pk) Compartmental Modeling

Nandi

2023

Int J App Pharm

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Objective: Modeling and simulation are the two widely used terms, usually simultaneously mentioned in most PK discussions. There are several modeling strategies to model pharmacokinetic (PK) profiles. Compartmental modeling divides the body into different compartments based on the observed C-t profile and model comparison functions. Most C-t profiles are efficiently modeled using at max three compartments model (one, two, or three compartments). While there are many important applications of classical compartmental models, it emphasizes the importance of selecting the best model to explain the observed data. Therefore, initial data generation is very important. In many instances, insufficient data collection might not lead to the best model, which can be proved later costly by underpredicting or overpredicting PK parameters. This paper illustrates that adequate data collection can lead to correct model selection. Methods: Data was generated using the three-compartmental model's explicit equation for twenty-five simulated patients with 15% random variability. Generated data were fitted to different compartmental models using sufficient time points (case a) and without enough time points (case b). Results: In the case of a, generated data from three compartmental models was explained best by three compartmental models. In the case of b, the same data was presented better by two compartmental models. Finally, in the case of b, with sufficient time points, data generated from three compartmental models could be explained better by three compartmental models. Conclusion: With sufficient time points, the compartmental PK model can converge to an accurate one. Although almost all pharmacometricians know the importance of time points, there is no paper with a mathematical explanation of this incident. This paper will help the current and future pharmacometricians to help design efficient in vivo works.

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Section: Fig 1: Major Divisions Of Pkmentioning

confidence: 99%

Importance of Sufficient Time Points for Efficient Pharmacokinetic (Pk) Compartmental Modeling

Nandi

2023

Int J App Pharm

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“…Swandana et al [23] showed that the pharmacokinetic parameters could be predicted using the in silico method.…”

Section: Introductionmentioning

confidence: 99%

Computer-Aided Classification of New Psychoactive Substances

Bărbulescu

Barbeș

Dumitriu³

2021

Journal of Chemistry

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The appearance on the free market of synthetic cannabinoids raised the researchers’ interest in establishing their molecular similarity by QSAR analysis. A rigorous criterion for classifying drugs is their chemical structure. Therefore, this article presents the structural similarity of two groups of drugs: benzoylindoles and phenylacetylindoles. Statistical analysis and clustering of the molecules are performed based on their numerical characteristics extracted using Cheminformatics methods. Their similarities/dissimilarities are emphasized using the dendrograms and heat map. The highest discrepancies are found in the phenylacetylindoles group.

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Computer-Aided Methods for Molecular Classification

Bărbulescu

Barbeș

Dumitriu³

2022

Mathematics

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The study aims to analyze the degree of similarity of some molecules belonging to two subgroups of Aminoalkylindoles. After extracting the molecules’ characteristics using Cheminformatics methods, and the computation of the Tanimoto coefficients, dendrograms and heatmaps were built to reveal the degree of similarity of the analyzed drugs. Some atom-pair similarities between the molecules in the same group were detected. The clusters determined by the k-means method divided the Benzoylindoles into two subgroups but kept all the Phenylacetylindoles together in the same set. The activity spectrum of the elements in each group was also analyzed, and similarities have been emphasized. The clustering has been validated using the Kruskal–Wallis test on the series of computed probabilities of the main effects.

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Prediction Analysis of Pharmacokinetic Parameters of Several Oral Systemic Drugs Using Insilico Method

Cited by 3 publications

References 20 publications

Importance of Sufficient Time Points for Efficient Pharmacokinetic (Pk) Compartmental Modeling

Importance of Sufficient Time Points for Efficient Pharmacokinetic (Pk) Compartmental Modeling

Computer-Aided Classification of New Psychoactive Substances

Computer-Aided Methods for Molecular Classification

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