Computational ADME/Tox Modeling: Aiding Understanding and Enhancing Decision Making in Drug Design

DeLisle, Robert Kirk; Lowrie, Jeffery F.; Hobbs, Doug W.; Diller, David J.

doi:10.2174/157340905774330309

Cited by 11 publications

(11 citation statements)

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“…For the chosen chemicals, 38 chemicals were signified to have high absorption in the intestine. Next, AlogP98 (log of the partition coefficient between n -octanol and water) was predicted for estimating the lipophilicity of a particular chemical which is a parameter of the distribution of a chemical . The prediction showed that 11 chemicals fell in the range of high distribution category, 28 chemicals showed moderate distribution, and 1 chemical had a value as 7.07, so it was kept out of both the mentioned categories.…”

Section: Resultsmentioning

confidence: 99%

Integrated QSAR and Adverse Outcome Pathway Analysis of Chemicals Released on 3D Printing Using Acrylonitrile Butadiene Styrene

Pandit

Singh

Sinha

et al. 2021

Chem. Res. Toxicol.

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Additive manufacturing commonly known as 3D printing has numerous applications in several domains including material and biomedical technologies and has emerged as a tool of capabilities by providing fast, highly customized, and cost-effective solutions. However, the impact of the printing materials and chemicals present in the printing fumes has raised concerns about their adverse potential affecting humans and the environment. Thus, it is necessary to understand the properties of the chemicals emitted during additive manufacturing for developing safe and biocompatible fibers having controlled emission of fumes including its sustainable usage. Therefore, in this study, we have developed a computational predictive risk-assessment framework on the comprehensive list of chemicals released during 3D printing using the acrylonitrile butadiene styrene (ABS) filament. Our results showed that the chemicals present in the fumes of the ABS-based fiber used in additive manufacturing have the potential to lead to various toxicity end points such as inhalation toxicity, oral toxicity, carcinogenicity, hepatotoxicity, and teratogenicity. Moreover, because of their absorption, distribution in the body, metabolism, and excretion properties, most of the chemicals exhibited a high absorption level in the intestine and the potential to cross the blood-brain barrier. Furthermore, pathway analysis revealed that signaling like alpha-adrenergic receptor signaling, heterotrimeric G-protein signaling, and Alzheimer's disease-amyloid secretase pathway are significantly overrepresented given the identified target proteins of these chemicals. These findings signify the adversities associated with 3D printing fumes and the necessity for the development of biodegradable and considerably safer fibers for 3D printing technology.

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Section: Resultsmentioning

confidence: 99%

Integrated QSAR and Adverse Outcome Pathway Analysis of Chemicals Released on 3D Printing Using Acrylonitrile Butadiene Styrene

Pandit

Singh

Sinha

et al. 2021

Chem. Res. Toxicol.

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“…For example, the data source and modelling procedure of a model should be expanded to help users understand the model and predicted property. Delisle et al (2005) suggested some common considerations when building models, including the intended use of the model, data quality, appropriate modelling strategy, and model validation. Regarding model validation, the European Chemicals Agency has proposed the Organization for Economic Co-operation and Development (OECD) principles, which state that a valid model should be associated with the following information: (1) a defined endpoint; (2) an unambiguous algorithm; (3) a defined domain of applicability; (4) appropriate measures of goodness-of-fit, robustness and productivity; and (5) a mechanistic interpretation, if possible (ECHA, 2008).…”

Section: Discussionmentioning

confidence: 99%

In silicoADME/T modelling for rational drug design

Wang

Xing

Yue

et al. 2015

Quart. Rev. Biophys.

265

155

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Abstract. In recent decades, in silico absorption, distribution, metabolism, excretion (ADME), and toxicity (T) modelling as a tool for rational drug design has received considerable attention from pharmaceutical scientists, and various ADME/T-related prediction models have been reported. The high-throughput and low-cost nature of these models permits a more streamlined drug development process in which the identification of hits or their structural optimization can be guided based on a parallel investigation of bioavailability and safety, along with activity. However, the effectiveness of these tools is highly dependent on their capacity to cope with needs at different stages, e.g. their use in candidate selection has been limited due to their lack of the required predictability. For some events or endpoints involving more complex mechanisms, the current in silico approaches still need further improvement. In this review, we will briefly introduce the development of in silico models for some physicochemical parameters, ADME properties and toxicity evaluation, with an emphasis on the modelling approaches thereof, their application in drug discovery, and the potential merits or deficiencies of these models. Finally, the outlook for future ADME/T modelling based on big data analysis and systems sciences will be discussed.

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“…Serum Protein Binding Data Set. The 260 compounds used for the serum protein binding data set were taken from the set used to train ADME Profiler's SPB model . The compounds were randomly divided into the training and test sets using Pipeline Pilot by assigning ∼75% of the compounds to the training set, resulting in 207 and 53 compounds in the training and test sets, respectively.…”

Section: Methodsmentioning

confidence: 99%

“…For MOE, the additional set of descriptors used consisted of the sum of the atomic van der Waals surface area (VSA) contributions for three descriptors implemented in scientific vector language: the octanol/water partition coefficient (SlogP-VSA), molar refractivity (SMR-VSA), and Gasteiger charges (PEOE-VSA). ADME Profiler's BBB-penetration model , uses the calculated values for FPSA and ALogP for each compound. The values for the native descriptors calculated by MOE and ADME Profiler were subsequently exported and used to train our implementation of the naïve Bayesian classifier, generating two new Bayesian models.…”

Section: Methodsmentioning

confidence: 99%

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Improved Naïve Bayesian Modeling of Numerical Data for Absorption, Distribution, Metabolism and Excretion (ADME) Property Prediction

Klon¹,

Lowrie²,

Diller³

2006

J. Chem. Inf. Model.

104

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We have implemented a naïve Bayesian classifier which models continuous numerical data using a Gaussian distribution. Several cases of interest in the area of absorption, distribution, metabolism, and excretion prediction are presented which demonstrate that this approach is superior to the implementation of naïve Bayesian classifiers in which continuous chemical descriptors are modeled as binary data. We demonstrate that this enhanced performance, upon comparison with other implementations, is independent of the descriptor sets chosen. We also compare the performance of three implementations of naïve Bayesian classifiers with other previously described models.

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Computational ADME/Tox Modeling: Aiding Understanding and Enhancing Decision Making in Drug Design

Cited by 11 publications

References 0 publications

Integrated QSAR and Adverse Outcome Pathway Analysis of Chemicals Released on 3D Printing Using Acrylonitrile Butadiene Styrene

Integrated QSAR and Adverse Outcome Pathway Analysis of Chemicals Released on 3D Printing Using Acrylonitrile Butadiene Styrene

In silicoADME/T modelling for rational drug design

Improved Naïve Bayesian Modeling of Numerical Data for Absorption, Distribution, Metabolism and Excretion (ADME) Property Prediction

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