Computational Tools And Resources For Metabolism-Related Property Predictions. 2. Application To Prediction Of Half-Life Time In Human Liver Microsomes

Zakharov, Alexey; Peach, Megan L.; Sitzmann, Markus; Filippov, Igor V.; McCartney, Heather; Smith, Layton H.; Pugliese, Angelo; Nicklaus, Marc C.

doi:10.4155/fmc.12.152

Cited by 32 publications

(27 citation statements)

References 25 publications

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“…Machine Learning Method: RF : For the development of QSAR models, the RF implemented in the KNIME analytic platform was used, which is a modern and predictive machine learning approach. RF is an ensemble of decision trees and more trees reduce the variance.…”

Section: Methodsmentioning

confidence: 99%

Programmable Nucleic Acid Based Polygons with Controlled Neuroimmunomodulatory Properties for Predictive QSAR Modeling

Johnson

Halman

Satterwhite

et al. 2017

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In the past few years, the study of therapeutic RNA nanotechnology has expanded tremendously to encompass a large group of interdisciplinary sciences. It is now evident that rationally designed programmable RNA nanostructures offer unique advantages in addressing contemporary therapeutic challenges such as distinguishing target cell types and ameliorating disease. However, to maximize the therapeutic benefit of these nanostructures, it is essential that we understand the immunostimulatory aptitude of such tools and identify potential complications. We present a set of 16 nanoparticle platforms that are highly configurable. These novel nucleic acid-based polygonal platforms are programmed for controllable self-assembly from RNA and/or DNA strands via canonical Watson-Crick interactions. We demonstrate that the immunostimulatory properties of these particular designs can be tuned to elicit the desired immune response or lack thereof. To advance our current understanding of the nanoparticle properties that contribute to the observed immunomodulatory activity and establish corresponding designing principles, we conducted QSAR (quantitative structure-activity relationships) modeling. The results demonstrate that molecular weight, together with melting temperature and half-life, strongly predict the observed immunomodulatory activity. This framework provides the fundamental guidelines necessary for the development of a new library of nanoparticles with predictable immunomodulatory activity.

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Section: Methodsmentioning

confidence: 99%

Programmable Nucleic Acid Based Polygons with Controlled Neuroimmunomodulatory Properties for Predictive QSAR Modeling

Johnson

Halman

Satterwhite

et al. 2017

Small

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“…Previous studies on the development of in silico models for metabolic stability shared common features including: (1) closed datasets (typically derived from a company's in‐house data), (2) the use of commercial software to calculate the descriptors of compounds, (3) the target property being either intrinsic clearance ( CL int ) or half‐life (T 1/2 ) and (4) the resulting models being mostly binary classifiers (stable or unstable) ,…”

Section: Introductionmentioning

confidence: 99%

Data Curation can Improve the Prediction Accuracy of Metabolic Intrinsic Clearance

Esaki

Watanabe

Kawashima

et al. 2018

Molecular Informatics

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A key consideration at the screening stages of drug discovery is in vitro metabolic stability, often measured in human liver microsomes. Computational prediction models can be built using a large quantity of experimental data available from public databases, but these databases typically contain data measured using various protocols in different laboratories, raising the issue of data quality. In this study, we retrieved the intrinsic clearance ( CL int ) measurements from an open database and performed extensive manual curation. Then, chemical descriptors were calculated using freely available software, and prediction models were built using machine learning algorithms. The models trained on the curated data showed better performance than those trained on the non‐curated data and achieved performance comparable to previously published models, showing the importance of manual curation in data preparation. The curated data were made available, to make our models fully reproducible.

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“…BayesNet node, made available in KNIME by WEKA, facilitates learning using different Bayes Network learning algorithms. The method was previously employed by Zakharov et al [25] in predicting metabolic stability of compounds in human liver microsomes and was reported to be among those methods that provided higher sensitivity in predicting unstable compounds. Three sets of molecular descriptors were independently used as features: two-dimensional (2D) descriptors available from the Molecular Operating Environment (MOE; Chemical Computing Group ULC.)…”

Section: Cytosolic Stability Prediction Modelmentioning

confidence: 99%

Predicting liver cytosol stability of small molecules

et al. 2020

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Over the last few decades, chemists have become skilled at designing compounds that avoid cytochrome P (CYP) 450 mediated metabolism. Typical screening assays are performed in liver microsomal fractions and it is possible to overlook the contribution of cytosolic enzymes until much later in the drug discovery process. Few data exist on cytosolic enzyme-mediated metabolism and no reliable tools are available to chemists to help design away from such liabilities. In this study, we screened 1450 compounds for liver cytosol-mediated metabolic stability and extracted transformation rules that might help medicinal chemists in optimizing compounds with these liabilities. In vitro half-life data were collected by performing in-house experiments in mouse (CD-1 male) and human (mixed gender) cytosol fractions. Matched molecular pairs analysis was performed in conjunction with qualitative-structure activity relationship modeling to identify chemical structure transformations affecting cytosolic stability. The transformation rules were prospectively validated on the test set. In addition, selected rules were validated on a diverse chemical library and the resulting pairs were experimentally tested to confirm whether the identified transformations could be generalized. The validation results, comprising nearly 250 library compounds and corresponding half-life data, are made publicly available. The datasets were also used to generate in silico classification models, based on different molecular descriptors and machine learning methods, to predict cytosol-mediated liabilities. To the best of our knowledge, this is the first systematic in silico effort to address cytosolic enzyme-mediated liabilities.

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Computational Tools And Resources For Metabolism-Related Property Predictions. 2. Application To Prediction Of Half-Life Time In Human Liver Microsomes

Cited by 32 publications

References 25 publications

Programmable Nucleic Acid Based Polygons with Controlled Neuroimmunomodulatory Properties for Predictive QSAR Modeling

Programmable Nucleic Acid Based Polygons with Controlled Neuroimmunomodulatory Properties for Predictive QSAR Modeling

Data Curation can Improve the Prediction Accuracy of Metabolic Intrinsic Clearance

Predicting liver cytosol stability of small molecules

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