Recent Advances in the Prediction of Pharmacokinetics Properties in Drug Design Studies: A Review

Pantaleão, Simone Queiroz; Fernandes, Philipe Oliveira; Gonçalves, José Eduardo; Maltarollo, Vinícius Gonçalves; Honório, Káthia Maria

doi:10.1002/cmdc.202100542

Cited by 80 publications

(40 citation statements)

References 98 publications

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“…In the last decade, about 90% of drug failures were due to poor pharmacokinetic profiles [ 1 , 2 ]: a lack of clinical efficacy (40–50%), unmanaged toxicity (30%), and inadequate drug-like properties (10–15%) [ 3 ], but the main problem arises in the late development failure [ 4 ]. Faced with this bad prospect, the efforts of the scientific community have focused on improving the drug discovery process by evaluating the ADMET properties in the early stages of drug development [ 5 , 6 ].…”

Section: Introductionmentioning

confidence: 99%

“…Recently, Pantaleão et al carried out an excellent revision of the free webpages available for the prediction of such ADMET properties, analyzing a total of 18 and giving some data about the validation of the predictions [ 2 ]. So, taking her job as the starting point and going one step further, we collected and presented a total of 34 available software packages and webservers for predicting ADMET properties.…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of Free Online ADMET Tools for Academic or Small Biotech Environments

et al. 2023

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For a new molecular entity (NME) to become a drug, it is not only essential to have the right biological activity also be safe and efficient, but it is also required to have a favorable pharmacokinetic profile including toxicity (ADMET). Consequently, there is a need to predict, during the early stages of development, the ADMET properties to increase the success rate of compounds reaching the lead optimization process. Since Lipinski’s rule of five, the prediction of pharmacokinetic parameters has evolved towards the current in silico tools based on empirical approaches or molecular modeling. The commercial specialized software for performing such predictions, which is usually costly, is, in many cases, not among the possibilities for research laboratories in academia or at small biotech companies. Nevertheless, in recent years, many free online tools have become available, allowing, more or less accurately, for the prediction of the most relevant pharmacokinetic parameters. This paper studies 18 free web servers capable of predicting ADMET properties and analyzed their advantages and disadvantages, their model-based calculations, and their degree of accuracy by considering the experimental data reported for a set of 24 FDA-approved tyrosine kinase inhibitors (TKIs) as a model of a research project.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Evaluation of Free Online ADMET Tools for Academic or Small Biotech Environments

et al. 2023

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“…Recently, computer-aided drug design using in silico models to predict the absorption, distribution, metabolism, excretion, and toxicity (ADMET) parameters has been widely implemented in the field of drug discovery and development. , This approach is effective for evaluating the physicochemical properties and in vivo pharmacokinetics during the early stages of drug discovery. , In addition, the use of in silico prediction techniques is expected to minimize the expenses and risks of subsequent withdrawals during clinical trials. To the best of our knowledge, there have been no previous reports on predicting in vivo fm from structural information alone.…”

Section: Introductionmentioning

confidence: 99%

Prediction of the Contribution Ratio of a Target Metabolic Enzyme to Clearance from Chemical Structure Information

et al. 2022

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The contribution ratio of metabolic enzymes such as cytochrome P450 to in vivo clearance (fraction metabolized: fm) is a pharmacokinetic index that is particularly important for the quantitative evaluation of drug–drug interactions. Since obtaining experimental in vivo fm values is challenging, those derived from in vitro experiments have often been used alternatively. This study aimed to explore the possibility of constructing machine learning models for predicting in vivo fm using chemical structure information alone. We collected in vivo fm values and chemical structures of 319 compounds from a public database with careful manual curation and constructed predictive models using several machine learning methods. The results showed that in vivo fm values can be obtained from structural information alone with a performance comparable to that based on in vitro experimental values and that the prediction accuracy for the compounds involved in CYP induction or inhibition is significantly higher than that by using in vitro values. Our new approach to predicting in vivo fm values in the early stages of drug discovery should help improve the efficiency of the drug optimization process.

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“…ADME is an important medicinal chemistry tool that provides information about in-silico ADME behavior ( Bocci et al, 2017 ; Pantaleão et al, 2022 ). The detailed pharmacokinetic ADME-Tox and drug-likeness results are given in Table 1 and Table 2 .…”

Section: Discussionmentioning

confidence: 99%

“…Molecular docking is a convenient in-silico method which can be used to evaluate the binding affinity of the ligand on the receptor and can predict the position of the these molecules ( Trott and Olson, 2010 ; Mert Ozupek and Cavas, 2017 ). ADME provides information about in-silico ADME behavior which is important for medicinal chemistry ( Bocci et al, 2017 ; Dege et al, 2022 ; Gokce et al, 2022 ; Pantaleão et al, 2022 ). Drug-likeness analysis using in-silico is of great importance for evaluating the pharmacokinetic features of fast and cheap ( Gokce et al, 2022 ; Hasan et al, 2022 ).…”

Section: Introductionmentioning

confidence: 99%

In-silico molecular interactions among the secondary metabolites of Caulerpa spp. and colorectal cancer targets

et al. 2022

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Caulerpa spp. secrete more than thirty different bioactive chemicals which have already been used in cancer treatment research since they play a pivotal role in cancer metabolism. Colorectal cancer is one of the most common cancer types, thus using novel and effective chemicals for colorectal cancer treatment is crucial. In the cheminformatics pipeline of this study, ADME-Tox and drug-likeness tests were performed for filtering the secondary metabolites of Caulerpa spp. The ligands which were selected from the ADME test were used for in silico molecular docking studies against the enzymes of the oxidative branch of the pentose phosphate pathway (glucose-6-phosphate dehydrogenase and 6-phosphoglutarate dehydrogenase), which is of great importance for colorectal cancer, by using AutoDock Vina. Pharmacophore modeling was carried out to align the molecules. Molecular dynamic simulations were performed for each target to validate the molecular docking studies and binding free energies were calculated. According to the ADME test results, 13 different secondary metabolites were selected as potential ligands. Molecular docking studies revealed that vina scores of caulerpin and monomethyl caulerpinate for G6PDH were found as −10.6 kcal mol-1, −10.5 kcal mol-1, respectively. Also, the vina score of caulersin for 6PGD was found as −10.7 kcal mol-1. The highest and the lowest binding free energies were calculated for monomethyl caulerpinate and caulersin, respectively. This in silico study showed that caulerpin, monomethyl caulerpinate, and caulersin could be evaluated as promising marine phytochemicals against pentose phosphate pathway enzymes and further studies are recommended to investigate the detailed activity of these secondary metabolites on these targets.

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Recent Advances in the Prediction of Pharmacokinetics Properties in Drug Design Studies: A Review

Cited by 80 publications

References 98 publications

Evaluation of Free Online ADMET Tools for Academic or Small Biotech Environments

Evaluation of Free Online ADMET Tools for Academic or Small Biotech Environments

Prediction of the Contribution Ratio of a Target Metabolic Enzyme to Clearance from Chemical Structure Information

In-silico molecular interactions among the secondary metabolites of Caulerpa spp. and colorectal cancer targets

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