Construction of Metabolism Prediction Models for CYP450 3A4, 2D6, and 2C9 Based on Microsomal Metabolic Reaction System

He, Shuaibing; Li, Manman; Zhang, Baixia; Ye, Xiaotong; Du, Ran-Feng; Wang, Yun; Qiao, Yanjiang

doi:10.3390/ijms17101686

Cited by 8 publications

(3 citation statements)

References 52 publications

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“…As a rapidly used chemical editor, Marvin provided a series of molecular descriptors which have been proven to be useful and effective in various QSAR research [52,53,54,55]. In this work, a total of 85 molecular descriptors offered by Marvin were calculated which can be divided into two categories, physicochemical properties (30) and topological geometry properties (55).…”

Section: Methodsmentioning

confidence: 99%

An In Silico Model for Predicting Drug-Induced Hepatotoxicity

Wang

et al. 2019

IJMS

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As one of the leading causes of drug failure in clinical trials, drug-induced liver injury (DILI) seriously impeded the development of new drugs. Assessing the DILI risk of drug candidates in advance has been considered as an effective strategy to decrease the rate of attrition in drug discovery. Recently, there have been continuous attempts in the prediction of DILI. However, it indeed remains a huge challenge to predict DILI successfully. There is an urgent need to develop a quantitative structure–activity relationship (QSAR) model for predicting DILI with satisfactory performance. In this work, we reported a high-quality QSAR model for predicting the DILI risk of xenobiotics by incorporating the use of eight effective classifiers and molecular descriptors provided by Marvin. In model development, a large-scale and diverse dataset consisting of 1254 compounds for DILI was built through a comprehensive literature retrieval. The optimal model was attained by an ensemble method, averaging the probabilities from eight classifiers, with accuracy (ACC) of 0.783, sensitivity (SE) of 0.818, specificity (SP) of 0.748, and area under the receiver operating characteristic curve (AUC) of 0.859. For further validation, three external test sets and a large negative dataset were utilized. Consequently, both the internal and external validation indicated that our model outperformed prior studies significantly. Data provided by the current study will also be a valuable source for modeling/data mining in the future.

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Section: Methodsmentioning

confidence: 99%

An In Silico Model for Predicting Drug-Induced Hepatotoxicity

Wang

et al. 2019

IJMS

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“…Cytochrome P450 (CYP) enzymes are membrane-bound haemoproteins and are known to be the heme-thiolate proteins family [1,2]. CYP450 enzymes mainly reside in the liver and circulate throughout the body.…”

Section: Introductionmentioning

confidence: 99%

The Construction of CRISPR-Cas9 System Targeting Vector for CYP3A4 Gene in Hepatic Cell Lines

Fazleen Haslinda,

Fazlia Natasha Abdul Aziz,

Shafiq-ur-Rehman

et al. 2024

ARASET

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Drug metabolism in the human liver initiated by Cytochrome P450 (CYP450) has been widely acknowledged. They oxidise drugs, harmful compounds, and endogenous molecules like steroids. In our research study, we mainly focused on CYP3A4 of the CYP450 subfamily that is widely available in the liver around 15% - 20% of hepatic content and plays a significant function in metabolising up to 50% of drugs in the market today. However, the CYP3A4 gene encodes CYP3A4 enzymes that are highly polymorphic, which could affect enzymatic activity and cause variable responses in drug metabolism. Clustered Regularly Interspaced Short Palindromic Repeats Associated Protein 9 also known as the CRISPR-Cas9 system is a gene editing method that allows scientists to edit parts of the genome by insertions and deletions. Hence, this method would be useful for genetic variants such as the CYP3A4 gene. The objectives of this study are to design a guide RNA (gRNA), to insert the designed gRNA into pGuide-it-Zs-Green1 vector, to quantify the purified CYP3A4-KO plasmid vector by using NanoDrop® and to transfect the constructed vector in a hepatic cell line. The methodology involved the selection of the gRNA by using the online gene editing tool, Synthego (https://design.synthego.com), annealing oligos of the gRNA for CYP3A4, cloning gRNA into a plasmid vector, isolation, and purification of the CYP3A4-KO plasmid vector. The construction of the CRISPR-Cas9 targeting vector in this study was successfully achieved and promising since the selected gRNA for CYP3A4 gene which is 5’-ATAAATCCCACTGGACCAAA-3’ and located in exon 5 was correctly ligated after the confirmed with sequencing reaction and cloned it into a plasmid vector. The yield of pCYP3A4-KO plasmid DNA was a good candidate for transfection.

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“…Another recent contribution to the world of ligand-based approaches is a method published by He et al In this case, several models were created for each CYP-mediated reaction. The method relies on 56 quantum chemical descriptors, 29 physicochemical properties and 27 topological descriptors.…”

Section: Introductionmentioning

confidence: 99%

FAME 2: Simple and Effective Machine Learning Model of Cytochrome P450 Regioselectivity

Šícho

Kops

Stork

et al. 2017

J. Chem. Inf. Model.

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2013, 53, 2896-2907), a collection of random forest models for the prediction of sites of metabolism (SoMs) of xenobiotics. A broad set of descriptors was explored, from simple 2D descriptors such as those used in FAME, to quantum chemical descriptors employed in some of the most accurate models for SoM prediction currently available. In line with the original FAME approach, our objective was to keep things simple and to come up with accurate and robust models that are based on a small number of 2D descriptors. We found that circular descriptions of atoms and their environments with such descriptors in combination with an extremely randomized trees algorithm can yield models that perform equally well compared to more complex approaches. Thorough evaluation experiments on an independent test set showed that the best of these models obtained a Matthews correlation coefficient, area under the receiver operating characteristic curve, and Top-2 accuracy of 0.57, 0.91 and 94.1%, respectively. Models for the prediction of isoform-specific regioselectivity of CYP 3A4, 2D6, and 2C9 were also developed and showed competitive performance. The best models have been integrated into a newly developed software package (FAME 2), which is available free of charge from the authors.

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Construction of Metabolism Prediction Models for CYP450 3A4, 2D6, and 2C9 Based on Microsomal Metabolic Reaction System

Cited by 8 publications

References 52 publications

An In Silico Model for Predicting Drug-Induced Hepatotoxicity

An In Silico Model for Predicting Drug-Induced Hepatotoxicity

The Construction of CRISPR-Cas9 System Targeting Vector for CYP3A4 Gene in Hepatic Cell Lines

FAME 2: Simple and Effective Machine Learning Model of Cytochrome P450 Regioselectivity

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