MetStabOn—Online Platform for Metabolic Stability Predictions

Podlewska, Sabina; Kafel, Rafał

doi:10.3390/ijms19041040

Cited by 31 publications

(22 citation statements)

References 60 publications

(61 reference statements)

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“…The true class for each molecule is determined based on its half-lifetime expressed in hours. We follow the cut-offs from Podlewska et al [ 39 ]: ≤ 0.6 — low stability, (0.6 − 2.32 > — medium stability, > 2.32 — high stability. …”

Section: Methodsmentioning

confidence: 99%

How can SHAP values help to shape metabolic stability of chemical compounds?

2021

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Background Computational methods support nowadays each stage of drug design campaigns. They assist not only in the process of identification of new active compounds towards particular biological target, but also help in the evaluation and optimization of their physicochemical and pharmacokinetic properties. Such features are not less important in terms of the possible turn of a compound into a future drug than its desired affinity profile towards considered proteins. In the study, we focus on metabolic stability, which determines the time that the compound can act in the organism and play its role as a drug. Due to great complexity of xenobiotic transformation pathways in the living organisms, evaluation and optimization of metabolic stability remains a big challenge. Results Here, we present a novel methodology for the evaluation and analysis of structural features influencing metabolic stability. To this end, we use a well-established explainability method called SHAP. We built several predictive models and analyse their predictions with the SHAP values to reveal how particular compound substructures influence the model’s prediction. The method can be widely applied by users thanks to the web service, which accompanies the article. It allows a detailed analysis of SHAP values obtained for compounds from the ChEMBL database, as well as their determination and analysis for any compound submitted by a user. Moreover, the service enables manual analysis of the possible structural modifications via the provision of analogous analysis for the most similar compound from the ChEMBL dataset. Conclusions To our knowledge, this is the first attempt to employ SHAP to reveal which substructural features are utilized by machine learning models when evaluating compound metabolic stability. The accompanying web service for metabolic stability evaluation can be of great help for medicinal chemists. Its significant usefulness is related not only to the possibility of assessing compound stability, but also to the provision of information about substructures influencing this parameter. It can assist in the design of new ligands with improved metabolic stability, helping in the detection of privileged and unfavourable chemical moieties during stability optimization. The tool is available at https://metstab-shap.matinf.uj.edu.pl/.

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Section: Methodsmentioning

confidence: 99%

How can SHAP values help to shape metabolic stability of chemical compounds?

2021

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“…To evaluate this hypothesis further, we used the open source program MetStabOn 23 to predict human metabolic stability. Of the 700 zebrafish-active compounds, 0 were predicted to exhibit high metabolic stability, and 691 were predicted to have medium metabolic stability; low metabolic stability was predicted for only nine compounds.…”

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confidence: 99%

Do Zebrafish Obey Lipinski Rules?

Long

Kostman

Fernandez

et al. 2019

ACS Med. Chem. Lett.

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The use of zebrafish in whole organism phenotypic assays has become a valuable strategy throughout the drug discovery process. Zebrafish assays can be used not only to screen libraries of compounds at the earliest stages but also to evaluate advanced leads for their effects on specific biological pathways or for toxicity. However, when confronted with inactivity of a compound in a zebrafish assay, there are little data that can be used to judge if the compound is truly biologically inert or inactive due to a lack of permeability into the model organism. While medicinal chemistry principles suggest parameters that are predictive of human oral bioavailability, cellular permeability, and even bacterial permeability, there have been no such parameters developed for zebrafish absorption. To address this question, we compiled a set of 700 compounds reported in the literature to be active in zebrafish assays, evaluated their properties, and compared them to properties derived from a set of historical drugs and a set of recently approved oral drugs. While some properties overlap, the averages and 10th and 90th percentiles of molecular weight, octanol−water partition coefficient (logP), H-bond counts, and polar surface area for zebrafish-active compounds are statistically different from those of known drugs. This analysis should be useful to scientists interpreting structure−activity relationships based on data from zebrafish assays and help to inform the translation from fish to mammals.

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“…That said, there are models under development to provide high-throughput Cytochrome P450 metabolizing enzyme reaction estimations and metabolic stability predictions, which could help drive an overall high-throughput metabolite disposition model. ( 56 – 58 )…”

Section: Discussionmentioning

confidence: 99%

Development and evaluation of a high throughput inhalation model for organic chemicals

Linakis

Sayre

Pearce

et al. 2020

J Expo Sci Environ Epidemiol

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Currently it is difficult to prospectively estimate human toxicokinetics (particularly for novel chemicals) in a high-throughput manner. The R software package httk has been developed, in part, to address this deficiency, and the aim of this investigation was to develop a generalized inhalation model for httk . The structure of the inhalation model was developed from two previously published physiologically-based models from Jongeneelen et al. (2011) and Clewell et al. (2001) while calculated physicochemical data was obtained from EPA’s CompTox Chemicals Dashboard. In total, 142 exposure scenarios across 41 volatile organic chemicals were modeled and compared to published data. The slope of the regression line of best fit between log-transformed simulated and observed combined measured plasma and blood concentrations was 0.46 with an r 2 = 0.45 and a Root Mean Square Error (RMSE) of direct comparison between the log-transformed simulated and observed values of 1.11.. Approximately 5.1% (n = 108) of the data points analyzed were > 2 orders of magnitude different than expected. The volatile organic chemicals examined in this investigation represent small, generally lipophilic molecules. Ultimately this paper details a generalized inhalation component that integrates with the httk physiologically-based toxicokinetic model to provide high-throughput estimates of inhalation chemical exposures.

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MetStabOn—Online Platform for Metabolic Stability Predictions

Cited by 31 publications

References 60 publications

How can SHAP values help to shape metabolic stability of chemical compounds?

How can SHAP values help to shape metabolic stability of chemical compounds?

Do Zebrafish Obey Lipinski Rules?

Development and evaluation of a high throughput inhalation model for organic chemicals

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