Estimation of ADME Properties in Drug Discovery: Predicting Caco-2 Cell Permeability Using Atom-Based Stochastic and Non-Stochastic Linear Indices

Castillo-Garit, Juan A.; Marrero-Ponce, Yovani; Torrens, Francisco; García-Domènech, Ramón

doi:10.3390/ecsoc-11-01367

Cited by 8 publications

(8 citation statements)

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“…The linear discriminant analysis (LDA) has become an important tool for the prediction of chemical properties. Because of the simplicity of this method, many useful discriminant models have been developed and presented by different authors in the literature (21,23,32,42–44). It was the technique used in the generation of a discriminant function in the present work.…”

Section: Resultsmentioning

confidence: 99%

“…The approach [known as tomocomd acronym of TOpological MOlecular COMputer Design ] ( a , 17–19) allows us to perform rational in silico molecular design (selection/identification) and quantitative structure–activity/property relationship (QSAR/QSPR) studies. In fact, this scheme has been successfully applied to the prediction of several physical, physicochemical, chemical, pharmacokinetical, toxicological as well as biological properties (20–25). Furthermore, these molecular descriptors (MDs) have been extended to consider three‐dimensional (3D) features of small‐/medium‐sized molecules based on the trigonometric‐3D‐chirality‐correction factor approach (26–31).…”

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confidence: 99%

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Identification In Silico and In Vitro of Novel Trypanosomicidal Drug‐Like Compounds

et al. 2012

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Atom-based bilinear indices and linear discriminant analysis are used to discover novel trypanosomicidal compounds. The obtained linear discriminant analysis-based quantitative structure-activity relationship models, using non-stochastic and stochastic indices, provide accuracies of 89.02% (85.11%) and 89.60% (88.30%) of the chemicals in the training (test) sets, respectively. Later, both models were applied to the virtual screening of 18 in-house synthesized compounds to find new pro-lead antitrypanosomal agents. The in vitro antitrypanosomal activity of this set against epimastigote forms of Trypanosoma cruzi is assayed. Predictions agree with experimental results to a great extent (16 ⁄ 18) of the chemicals. Sixteen compounds show more than 70% of epimastigote inhibition at a concentration 100 lg ⁄ mL. In addition, three compounds (CRIS 112, CRIS 140 and CRIS 147) present more than 70% of epimastigote inhibition at a concentration of 10 lg ⁄ mL (79.95%, 73.97% and 78.13%, respectively) with low values of cytotoxicity (19.7%, 7.44% and 20.63%, correspondingly).Taking into account all these results, we could say that these three compounds could be optimized in forthcoming works. Even though none of them resulted more active than nifurtimox, the current results constitute a step forward in the search for efficient ways to discover new lead antitrypanosomals.

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Section: Resultsmentioning

confidence: 99%

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Identification In Silico and In Vitro of Novel Trypanosomicidal Drug‐Like Compounds

et al. 2012

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“…Chemicalize is the most accurate software in predicting HBA, a Absorption data were taken from Reference [13], b molecular weight data were taken from reference [13], c logP data were taken from Reference [13], d hydrogen bond acceptor (HBA) data were taken from reference [13], e hydrogen bond donor (HBD) data were taken from reference [13], f Lipinski's rule of five (RO5) data were taken from Reference [13]. Checkmark () means the compound fulfilled the rule, g polar surface area (PSA) data were taken from reference [13], h pKa data were taken from reference [14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33], i Caco2 permeability data were taken from reference [34,35], -No data, ABS: Absorption PSA, and pKa. Furthermore, admetSAR is the most accurate software in predicting Caco2 permeability.…”

Section: Optimization Of Predicted Pharmacokinetic Parametersmentioning

confidence: 99%

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

2020

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Objective: This research aims to observe the pharmacokinetic parameters that can be predicted using a software, discover the best software to predictpharmacokinetic properties, and analyze the correlation between pharmacokinetic parameters used as descriptors with absorption percentage(%ABS) from references.Methods: This research was conducted using Molinspiration, QikProp, admetSAR, SwissADME, Chemicalize, and pkCSM software. This researchanalyzed 34 oral systemic drug compounds for absorption rate and six descriptors comprising molecular weight (MW), logP, hydrogen bond acceptor(HBA), hydrogen bond donor (HBD), polar surface area (PSA), and pKa.Results: SwissADME showed the most accurate prediction of MW, logP, and HBD. Chemicalize showed the most accurate prediction of HBA, PSA, andpKa. Further, admetSAR showed the most accurate prediction of Caco-2 permeability. The highest R value was obtained from the correlation between%ABS with Caco-2 permeability on 34 drug compounds (R=0.8211).Conclusion: The highest R value was obtained from the correlation between %ABS with Caco2 permeability on 34 drug compounds (R=0.8211),which showed a significant relationship (*p<0.001). This indicates that oral systemic drugs are affected by Caco-2 permeability. Moreover, the result of this research can be considered for the development of oral systemic drugs.

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“…ToMoCoMD demonstrated the capacity to offer solution for large spectra of problems. Some examples for small molecule are the prediction of tyrosinase inhibitors using the atom linear indices [56], prediction of aquatic toxicity [57], and predicting Caco-2 cell permeability [58]. In the case of macromolecules, the tool has been used to predict the protein stability effects of a complete set of alanine substitutions in the Are repressor [59,60], and to build the nucleic acid QSAR models [61].…”

Section: Molecular Descriptor Toolsmentioning

confidence: 99%

Graph-Based Processing of Macromolecular Information

Munteanu¹,

Aguiar‐Pulido²,

Freire³

et al. 2015

CBIO

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The complex information encoded into the element connectivity of a system gives rise to the possibility of graphical processing of divisible systems by using the Graph theory. An application in this sense is the quantitative characterization of molecule topologies of drugs, proteins and nucleic acids, in order to build mathematical models as Quantitative Structure-Activity Relationships between the molecules and a specific biological activity. These types of models can predict new drugs, molecular targets and molecular properties of new molecular structures with an important impact on the Drug Discovery, Medicinal Chemistry, Molecular Diagnosis, and Treatment. The current review is focused on the mathematical methods to encode the connectivity information in three types of graphs such as star graphs, spiral graphs and contact networks and three in-house scientific applications dedicated to the calculation of molecular graph topological indices such as S2SNet, CULSPIN and MInD-Prot. In addition, some examples are presented, such as results of this methodology on drugs, proteins and nucleic acids, including the Web implementation of the best molecular prediction models based on graphs.

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Estimation of ADME Properties in Drug Discovery: Predicting Caco-2 Cell Permeability Using Atom-Based Stochastic and Non-Stochastic Linear Indices

Cited by 8 publications

References 0 publications

Identification In Silico and In Vitro of Novel Trypanosomicidal Drug‐Like Compounds

Identification In Silico and In Vitro of Novel Trypanosomicidal Drug‐Like Compounds

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

Graph-Based Processing of Macromolecular Information

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