Activity-Limiting Role of Molecular Size: Size-Dependency of Maximum Activity for P450 Inhibition as Revealed by qHTS Data

Buchwald, Péter

doi:10.1124/dmd.114.059717

Cited by 6 publications

(4 citation statements)

References 37 publications

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“…To rigorously describe this biphasic behavior and quantitatively assess the time of the rate‐change, we fitted the data with our bilinear model (LinBiExp). This model was specifically developed to fit data that have two different phases of linear behavior separated by a rate‐change point . Fitting showed that a first phase of relatively rapid decline (with an average slope of α 1 = −1.80 ± 0.40 μg min/L/week) comes to essentially a stop ( α 2 = −0.12 ± 0.58 μg min/L/week) at a rate‐change point located at 27.8 ± 4.2 weeks.…”

Section: Resultsmentioning

confidence: 99%

“…Data analyses including plotting of the Kaplan‐Meier survival plots and statistical comparisons (one‐way ANOVA with Tukey's multiple comparison test) were done using GraphPad Prism 6.0 (GraphPad, La Jolla, CA). Fittings of the bilinear model (LinBiExp;

y = f (|, t) = η ln ([]|, e^{α_{1} (|, t - θ_{c}) / η} + e^{α_{2} (|, t - θ_{c}) / η}) + χ

) in Prism and model comparisons using the Akaike information criteria (AIC) as model selection criteria were done as described previously .…”

Section: Immunofluorescencementioning

confidence: 99%

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Biphasic decline of β‐cell function with age in euglycemic nonobese diabetic mice parallels diabetes onset

et al. 2015

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A gradual decline in insulin response is known to precede the onset of type 1 diabetes (T1D). To track age-related changes in the β-cell function of non-obese diabetic (NOD) mice, the most commonly used animal model for T1D, and to establish differences between those who do and do not become hyperglycemic, we performed a long-term longitudinal oral glucose tolerance test (OGTT) study (10–42 weeks) in combination with immunofluorescence imaging of islet morphology and cell proliferation. We observed a clear biphasic decline in insulin secretion (AUC0–30min) even in euglycemic animals. A first phase (10–28 weeks) consisted of a relatively rapid decline and paralleled diabetes development in the same cohort of animals. This was followed by a second phase (29–42 weeks) during which insulin secretion declined much slower while no additional animals became diabetic. Blood glucose profiles showed a corresponding, but less pronounced change: the area under the concentration curve (AUC0–150min) increased with age, and fit with a bilinear model indicated a rate-change in the trendline around 28 weeks. In control NOD scids, no such changes were observed. Islet morphology also changed with age as islets become surrounded by mononuclear infiltrates, and, in all mice, islets with immune cell infiltration around them showed increased β-cell proliferation. In conclusion, insulin secretion declines in a biphasic manner in all NOD mice. This trend, as well as increased β-cell proliferation, is present even in the NODs that never become diabetic, whereas, it is absent in control NOD scid mice.

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Section: Resultsmentioning

confidence: 99%

y = f (|, t) = η ln ([]|, e^{α_{1} (|, t - θ_{c}) / η} + e^{α_{2} (|, t - θ_{c}) / η}) + χ

) in Prism and model comparisons using the Akaike information criteria (AIC) as model selection criteria were done as described previously .…”

Section: Immunofluorescencementioning

confidence: 99%

Biphasic decline of β‐cell function with age in euglycemic nonobese diabetic mice parallels diabetes onset

et al. 2015

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“…Upon acquiring experimental input from the resource, scientists may use it in various ways to achieve their research objectives. Several review articles have been published in this regard [ 7 , 40 , 41 ], summarizing a wide range of studies that were conducted on the basis of PubChem BioAssay data [ 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 ]. In this section, we only place our focus on the research featuring benchmarking data collections that were constructed by the cheminformatics community from PubChem’s experimental results as a means of validating in silico screening protocols.…”

Section: What We Can Do With Pubchem Bioassay Data: From the Data mentioning

confidence: 99%

Benchmarking Data Sets from PubChem BioAssay Data: Current Scenario and Room for Improvement

Tran-Nguyen

Rognan

2020

IJMS

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Developing realistic data sets for evaluating virtual screening methods is a task that has been tackled by the cheminformatics community for many years. Numerous artificially constructed data collections were developed, such as DUD, DUD-E, or DEKOIS. However, they all suffer from multiple drawbacks, one of which is the absence of experimental results confirming the impotence of presumably inactive molecules, leading to possible false negatives in the ligand sets. In light of this problem, the PubChem BioAssay database, an open-access repository providing the bioactivity information of compounds that were already tested on a biological target, is now a recommended source for data set construction. Nevertheless, there exist several issues with the use of such data that need to be properly addressed. In this article, an overview of benchmarking data collections built upon experimental PubChem BioAssay input is provided, along with a thorough discussion of noteworthy issues that one must consider during the design of new ligand sets from this database. The points raised in this review are expected to guide future developments in this regard, in hopes of offering better evaluation tools for novel in silico screening procedures.

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“…While some of them were extracted from scientific articles, others were determined through HTS. Using these bioactivity data, several groups 28-32 have developed computational prediction models for CYP inhibition of small molecules. For example, Cheng et al .…”

Section: Computational Toxicity Prediction Models From Pubchem Biomentioning

confidence: 99%

Getting the most out of PubChem for virtual screening

Kim

2016

Expert Opinion on Drug Discovery

124

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Introduction With the emergence of the “big data” era, the biomedical research community has great interest in exploiting publicly available chemical information for drug discovery. PubChem is an example of public databases that provide a large amount of chemical information free of charge. Areas covered This article provides an overview of how PubChem’s data, tools, and services can be used for virtual screening and reviews recent publications that discuss important aspects of exploiting PubChem for drug discovery. Expert opinion PubChem offers comprehensive chemical information useful for drug discovery. It also provides multiple programmatic access routes, which are essential to build automated virtual screening pipelines that exploit PubChem data. In addition, PubChemRDF allows users to download PubChem data and load them into a local computing facility, facilitating data integration between PubChem and other resources. PubChem resources have been used in many studies for developing bioactivity and toxicity prediction models, discovering polypharmacologic (multi-target) ligands, and identifying new macromolecule targets of compounds (for drug-repurposing or off-target side effect prediction). These studies demonstrate the usefulness of PubChem as a key resource for computer-aided drug discovery and related area.

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Activity-Limiting Role of Molecular Size: Size-Dependency of Maximum Activity for P450 Inhibition as Revealed by qHTS Data

Cited by 6 publications

References 37 publications

Biphasic decline of β‐cell function with age in euglycemic nonobese diabetic mice parallels diabetes onset

Biphasic decline of β‐cell function with age in euglycemic nonobese diabetic mice parallels diabetes onset

Benchmarking Data Sets from PubChem BioAssay Data: Current Scenario and Room for Improvement

Getting the most out of PubChem for virtual screening

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