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Hou TY, Weng CF, Leong MK. Insight Analysis of Promiscuous Estrogen Receptor α-Ligand Binding by a Novel Machine Learning Scheme. Chem Res Toxicol 2018; 31:799-813. [PMID: 30019586 DOI: 10.1021/acs.chemrestox.8b00130] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
Estrogen receptor α (ERα) plays a significant role in occurrence of breast cancer and may cause various adverse side-effects when ERα is an off-target protein. A theoretical model was derived to predict the binding affinity of ERα using the pharmacophore ensemble/support vector machine (PhE/SVM) scheme to consider the promiscuous characteristic of ERα. The estimations by PhE/SVM were discovered to be in good agreement with the observed values for those training molecules ( n = 31, r2 = 0.80, qCV2 = 0.77, RMSE = 0.57, s = 0.58), test molecules ( n = 179, q2 = 0.91-0.96, RMSE = 0.33, s = 0.26) and outliers ( n = 15, q2 = 0.80-0.86, RMSE = 0.56, s = 0.49). When subjected to various statistical validations, the PhE/SVM model consistently fulfilled the strictest criteria. A mock test also asserted its predictivity. When compared with crystal structures, the calculated results are consistent with the reported ERα-ligand co-complex structure, and the plasticity nature of ERα is also disclosed. Consequently, this precise, fast, and robust model can be adopted to predict ERα-ligand binding affinities and to design safer non-ERα-targeted pharmaceuticals in the process of drug discovery and development.
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Discovery of a selective, safe and novel anti-malarial compound with activity against chloroquine resistant strain of Plasmodium falciparum. Sci Rep 2015; 5:13838. [PMID: 26346444 PMCID: PMC4561909 DOI: 10.1038/srep13838] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2015] [Accepted: 07/27/2015] [Indexed: 12/19/2022] Open
Abstract
In recent years the DNA minor groove has attracted much attention for the development of anti-malarial agents. In view of this we have attempted to discover novel DNA minor groove binders through in-silico and in-vitro workflow. A rigorously validated pharmacophore model comprising of two positive ionizable (PI), one hydrophobic (HY) and one ring aromatic (RA) features was used to mine NCI chemical compound database. This led to retrieval of many hits which were screened on the basis of estimated activity, fit value and Lipinski's violation. Finally two compounds NSC639017 and NSC371488 were evaluated for their in-vitro anti-malarial activities against Plasmodium falciparum 3D7 (CQ sensitive) and K1 (CQ resistant) strains by SYBR green-I based fluorescence assay. The results revealed that out of two, NSC639017 posses excellent anti-malarial activity particularly against chloroquine resistant strain and moreover NSC639017 also appeared to be safe (CC50 126.04 μg/ml) and selective during cytotoxicity evaluation.
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Ai N, Fan X, Ekins S. In silico methods for predicting drug-drug interactions with cytochrome P-450s, transporters and beyond. Adv Drug Deliv Rev 2015; 86:46-60. [PMID: 25796619 DOI: 10.1016/j.addr.2015.03.006] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2014] [Revised: 01/05/2015] [Accepted: 03/11/2015] [Indexed: 12/13/2022]
Abstract
Drug-drug interactions (DDIs) are associated with severe adverse effects that may lead to the patient requiring alternative therapeutics and could ultimately lead to drug withdrawal from the market if they are severe. To prevent the occurrence of DDI in the clinic, experimental systems to evaluate drug interaction have been integrated into the various stages of the drug discovery and development process. A large body of knowledge about DDI has also accumulated through these studies and pharmacovigillence systems. Much of this work to date has focused on the drug metabolizing enzymes such as cytochrome P-450s as well as drug transporters, ion channels and occasionally other proteins. This combined knowledge provides a foundation for a hypothesis-driven in silico approach, using either cheminformatics or physiologically based pharmacokinetics (PK) modeling methods to assess DDI potential. Here we review recent advances in these approaches with emphasis on hypothesis-driven mechanistic models for important protein targets involved in PK-based DDI. Recent efforts with other informatics approaches to detect DDI are highlighted. Besides DDI, we also briefly introduce drug interactions with other substances, such as Traditional Chinese Medicines to illustrate how in silico modeling can be useful in this domain. We also summarize valuable data sources and web-based tools that are available for DDI prediction. We finally explore the challenges we see faced by in silico approaches for predicting DDI and propose future directions to make these computational models more reliable, accurate, and publically accessible.
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Affiliation(s)
- Ni Ai
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, 866 Yuhangtang Road, Hangzhou, Zhejiang 310058, PR China
| | - Xiaohui Fan
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, 866 Yuhangtang Road, Hangzhou, Zhejiang 310058, PR China.
| | - Sean Ekins
- Collaborations in Chemistry, 5616 Hilltop Needmore Road, Fuquay-Varina, NC 27526, USA.
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4
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Directed evolution of cytochrome P450 enzymes for biocatalysis: exploiting the catalytic versatility of enzymes with relaxed substrate specificity. Biochem J 2015; 467:1-15. [DOI: 10.1042/bj20141493] [Citation(s) in RCA: 58] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Cytochrome P450 enzymes are renowned for their ability to insert oxygen into an enormous variety of compounds with a high degree of chemo- and regio-selectivity under mild conditions. This property has been exploited in Nature for an enormous variety of physiological functions, and representatives of this ancient enzyme family have been identified in all kingdoms of life. The catalytic versatility of P450s makes them well suited for repurposing for the synthesis of fine chemicals such as drugs. Although these enzymes have not evolved in Nature to perform the reactions required for modern chemical industries, many P450s show relaxed substrate specificity and exhibit some degree of activity towards non-natural substrates of relevance to applications such as drug development. Directed evolution and other protein engineering methods can be used to improve upon this low level of activity and convert these promiscuous generalist enzymes into specialists capable of mediating reactions of interest with exquisite regio- and stereo-selectivity. Although there are some notable successes in exploiting P450s from natural sources in metabolic engineering, and P450s have been proven repeatedly to be excellent material for engineering, there are few examples to date of practical application of engineered P450s. The purpose of the present review is to illustrate the progress that has been made in altering properties of P450s such as substrate range, cofactor preference and stability, and outline some of the remaining challenges that must be overcome for industrial application of these powerful biocatalysts.
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Paliwal S, Mittal A, Sharma M, pandey A, Singh A, Paliwal S. Pharmacophore and molecular docking based identification of novel structurally diverse PDE-5 inhibitors. Med Chem Res 2014. [DOI: 10.1007/s00044-014-1144-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
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6
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Ding YL, Shih YH, Tsai FY, Leong MK. In silico prediction of inhibition of promiscuous breast cancer resistance protein (BCRP/ABCG2). PLoS One 2014; 9:e90689. [PMID: 24614353 PMCID: PMC3948701 DOI: 10.1371/journal.pone.0090689] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2013] [Accepted: 02/03/2014] [Indexed: 12/17/2022] Open
Abstract
BACKGROUND Breast cancer resistant protein has an essential role in active transport of endogenous substances and xenobiotics across extracellular and intracellular membranes along with P-glycoprotein. It also plays a major role in multiple drug resistance and permeation of blood-brain barrier. Therefore, it is of great importance to derive theoretical models to predict the inhibition of both transporters in the process of drug discovery and development. Hitherto, very limited BCRP inhibition predictive models have been proposed as compared with its P-gp counterpart. METHODOLOGY/PRINCIPAL FINDINGS An in silico BCRP inhibition model was developed in this study using the pharmacophore ensemble/support vector machine scheme to take into account the promiscuous nature of BCRP. The predictions by the PhE/SVM model were found to be in good agreement with the observed values for those molecules in the training set (n= 22, r2 =0.82, qCV2=0.73, RMSE= 0.40, s = 0.24), test set (n =97, q2=0.75-0.89, RMSE= 0.31, s= 0.21), and outlier set (n= 16, q2 =0.72-0.91, RMSE= 0.29, s=0.17). When subjected to a variety of statistical validations, the developed PhE/SVM model consistently met the most stringent criteria. A mock test by HIV protease inhibitors also asserted its predictivity. CONCLUSIONS/SIGNIFICANCE It was found that this accurate, fast, and robust PhE/SVM model can be employed to predict the BCRP inhibition of structurally diverse molecules that otherwise cannot be carried out by any other methods in a high-throughput fashion to design therapeutic agents with insignificant drug toxicity and unfavorable drug-drug interactions mediated by BCRP to enhance clinical efficacy and/or circumvent drug resistance.
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Affiliation(s)
- Yi-Lung Ding
- Department of Chemistry, National Dong Hwa University, Shoufeng, Hualien, Taiwan
| | - Yu-Hsuan Shih
- Department of Chemistry, National Dong Hwa University, Shoufeng, Hualien, Taiwan
| | - Fu-Yuan Tsai
- Center for General Education, Chang Gung University, Taoyuan, Taiwan
| | - Max K Leong
- Department of Chemistry, National Dong Hwa University, Shoufeng, Hualien, Taiwan; Department of Life Science and Institute of Biotechnology, National Dong Hwa University, Shoufeng, Hualien, Taiwan; Department of Medical Research and Teaching, Mennonite Christian Hospital, Hualien, Taiwan
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7
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Abstract
Frequent failure of drug candidates during development stages remains the major deterrent for an early introduction of new drug molecules. The drug toxicity is the major cause of expensive late-stage development failures. An early identification/optimization of the most favorable molecule will naturally save considerable cost, time, human efforts and minimize animal sacrifice. (Quantitative) Structure Activity Relationships [(Q)SARs] represent statistically derived predictive models correlating biological activity (including desirable therapeutic effect and undesirable side effects) of chemicals (drugs/toxicants/environmental pollutants) with molecular descriptors and/or properties. (Q)SAR models which categorize the available data into two or more groups/classes are known as classification models. Numerous techniques of diverse nature are being presently employed for development of classification models. Though there is an increasing use of classification models for prediction of either biological activity or toxicity, the future trend will naturally be towards the development of classification models capable of simultaneous prediction of biological activity, toxicity, and pharmacokinetic parameters so as to accelerate development of bioavailable safe drug molecules.
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The promiscuous binding of pharmaceutical drugs and their transporter-mediated uptake into cells: what we (need to) know and how we can do so. Drug Discov Today 2012. [PMID: 23207804 DOI: 10.1016/j.drudis.2012.11.008] [Citation(s) in RCA: 117] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
A recent paper in this journal sought to counter evidence for the role of transport proteins in effecting drug uptake into cells, and questions that transporters can recognize drug molecules in addition to their endogenous substrates. However, there is abundant evidence that both drugs and proteins are highly promiscuous. Most proteins bind to many drugs and most drugs bind to multiple proteins (on average more than six), including transporters (mutations in these can determine resistance); most drugs are known to recognise at least one transporter. In this response, we alert readers to the relevant evidence that exists or is required. This needs to be acquired in cells that contain the relevant proteins, and we highlight an experimental system for simultaneous genome-wide assessment of carrier-mediated uptake in a eukaryotic cell (yeast).
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Abstract
Structure-based drug design has become an essential tool for rapid lead discovery and optimization. As available structural information has increased, researchers have become increasingly aware of the importance of protein flexibility for accurate description of the native state. Typical protein-ligand docking efforts still rely on a single rigid receptor, which is an incomplete representation of potential binding conformations of the protein. These rigid docking efforts typically show the best performance rates between 50 and 75%, while fully flexible docking methods can enhance pose prediction up to 80-95%. This review examines the current toolbox for flexible protein-ligand docking and receptor surface mapping. Present limitations and possibilities for future development are discussed.
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Affiliation(s)
- Katrina W. Lexa
- Department of Medicinal Chemistry, University of Michigan, 428 Church Street, Ann Arbor, MI 48109-1065, USA
| | - Heather A. Carlson
- Department of Medicinal Chemistry, University of Michigan, 428 Church Street, Ann Arbor, MI 48109-1065, USA
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Leong MK, Chen HB, Shih YH. Prediction of promiscuous p-glycoprotein inhibition using a novel machine learning scheme. PLoS One 2012; 7:e33829. [PMID: 22439003 PMCID: PMC3306300 DOI: 10.1371/journal.pone.0033829] [Citation(s) in RCA: 23] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2011] [Accepted: 02/20/2012] [Indexed: 12/22/2022] Open
Abstract
BACKGROUND P-glycoprotein (P-gp) is an ATP-dependent membrane transporter that plays a pivotal role in eliminating xenobiotics by active extrusion of xenobiotics from the cell. Multidrug resistance (MDR) is highly associated with the over-expression of P-gp by cells, resulting in increased efflux of chemotherapeutical agents and reduction of intracellular drug accumulation. It is of clinical importance to develop a P-gp inhibition predictive model in the process of drug discovery and development. METHODOLOGY/PRINCIPAL FINDINGS An in silico model was derived to predict the inhibition of P-gp using the newly invented pharmacophore ensemble/support vector machine (PhE/SVM) scheme based on the data compiled from the literature. The predictions by the PhE/SVM model were found to be in good agreement with the observed values for those structurally diverse molecules in the training set (n = 31, r(2) = 0.89, q(2) = 0.86, RMSE = 0.40, s = 0.28), the test set (n = 88, r(2) = 0.87, RMSE = 0.39, s = 0.25) and the outlier set (n = 11, r(2) = 0.96, RMSE = 0.10, s = 0.05). The generated PhE/SVM model also showed high accuracy when subjected to those validation criteria generally adopted to gauge the predictivity of a theoretical model. CONCLUSIONS/SIGNIFICANCE This accurate, fast and robust PhE/SVM model that can take into account the promiscuous nature of P-gp can be applied to predict the P-gp inhibition of structurally diverse compounds that otherwise cannot be done by any other methods in a high-throughput fashion to facilitate drug discovery and development by designing drug candidates with better metabolism profile.
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Affiliation(s)
- Max K Leong
- Department of Chemistry, National Dong Hwa University, Shoufeng, Hualien, Taiwan.
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Kirchmair J, Williamson MJ, Tyzack JD, Tan L, Bond PJ, Bender A, Glen RC. Computational prediction of metabolism: sites, products, SAR, P450 enzyme dynamics, and mechanisms. J Chem Inf Model 2012; 52:617-48. [PMID: 22339582 PMCID: PMC3317594 DOI: 10.1021/ci200542m] [Citation(s) in RCA: 187] [Impact Index Per Article: 15.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
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Metabolism of xenobiotics remains a central challenge
for the discovery
and development of drugs, cosmetics, nutritional supplements, and
agrochemicals. Metabolic transformations are frequently related to
the incidence of toxic effects that may result from the emergence
of reactive species, the systemic accumulation of metabolites, or
by induction of metabolic pathways. Experimental investigation of
the metabolism of small organic molecules is particularly resource
demanding; hence, computational methods are of considerable interest
to complement experimental approaches. This review provides a broad
overview of structure- and ligand-based computational methods for
the prediction of xenobiotic metabolism. Current computational approaches
to address xenobiotic metabolism are discussed from three major perspectives:
(i) prediction of sites of metabolism (SOMs), (ii) elucidation of
potential metabolites and their chemical structures, and (iii) prediction
of direct and indirect effects of xenobiotics on metabolizing enzymes,
where the focus is on the cytochrome P450 (CYP) superfamily of enzymes,
the cardinal xenobiotics metabolizing enzymes. For each of these domains,
a variety of approaches and their applications are systematically
reviewed, including expert systems, data mining approaches, quantitative
structure–activity relationships (QSARs), and machine learning-based
methods, pharmacophore-based algorithms, shape-focused techniques,
molecular interaction fields (MIFs), reactivity-focused techniques,
protein–ligand docking, molecular dynamics (MD) simulations,
and combinations of methods. Predictive metabolism is a developing
area, and there is still enormous potential for improvement. However,
it is clear that the combination of rapidly increasing amounts of
available ligand- and structure-related experimental data (in particular,
quantitative data) with novel and diverse simulation and modeling
approaches is accelerating the development of effective tools for
prediction of in vivo metabolism, which is reflected by the diverse
and comprehensive data sources and methods for metabolism prediction
reviewed here. This review attempts to survey the range and scope
of computational methods applied to metabolism prediction and also
to compare and contrast their applicability and performance.
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Affiliation(s)
- Johannes Kirchmair
- Unilever Centre for Molecular Science Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, CB2 1EW, Cambridge, United Kingdom
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12
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Dong D, Wu B. Substrate selectivity of drug-metabolizing cytochrome P450s predicted from crystal structures andin silicomodeling. Drug Metab Rev 2012; 44:1-17. [DOI: 10.3109/03602532.2011.645581] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
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13
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Dong D, Wu B, Chow D, Hu M. Substrate selectivity of drug-metabolizing cytochrome P450s predicted from crystal structures and in silico modeling. Drug Metab Rev 2012; 44:192-208. [PMID: 22251142 DOI: 10.3109/03602532.2011.645580] [Citation(s) in RCA: 32] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
Enormous efforts toward predicting the metabolic fate of a drug have been driven by the high attrition rate in drug development. To accelerate such efforts, it is critical to elucidate the molecular mechanisms of drug recognition by drug-metabolizing enzymes. Therefore, it is not surprising that an increasing number of crystal structures have been determined (by X-ray crystallography) and numerous insightful in silico (computational) models have been established for the most important metabolic enzymes, cytochrome P450s (CYPs). In this review, we provide a detailed analysis of the available crystal structures for CYPs to reveal the structural features and protein flexibility determining substrate selectivity. The ligand-based in silico models (including pharmacophore and molecular field analysis models) are also discussed, with a focus on their ability to characterize the structural features of the substrates for various CYP isoforms.
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Affiliation(s)
- Dong Dong
- Department of Pharmacological and Pharmaceutical Sciences, College of Pharmacy, University of Houston, Houston, Texas 77030, USA
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Rahnasto MK, Raunio HA, Wittekindt C, Salminen KA, Leppänen J, Juvonen RO, Poso A, Lahtela-Kakkonen MK. Identification of novel CYP2A6 inhibitors by virtual screening. Bioorg Med Chem 2011; 19:7186-93. [PMID: 22019468 DOI: 10.1016/j.bmc.2011.09.054] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2011] [Revised: 09/01/2011] [Accepted: 09/28/2011] [Indexed: 11/19/2022]
Abstract
The human CYP2A6 enzyme metabolises several xenobiotics including nicotine, the addictive component in tobacco. Reduced activity of CYP2A6, either for genetic reasons or by administering inhibitors of CYP2A6, reduces tobacco smoking. The aim was to design novel inhibitors of CYP2A6 using 3D-QSAR analysis combined with virtual screening. A 3D-QSAR model was utilised to identify the most important features of the inhibitors, and this knowledge was used to design inhibitors for CYP2A6. Chemical database screening yielded several potent inhibitor candidates such as alkylamine derivatives (compound no. 5, IC(50)=0.1 μM) and 1-benzothiophene-3-carbaldehyde that can be used as lead compounds in the development of drugs for smoking reduction therapy.
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Affiliation(s)
- Minna K Rahnasto
- School of Pharmacy, University of Eastern Finland, POB 1627, 70211 Kuopio, Finland.
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15
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Predicting Activation of the Promiscuous Human Pregnane X Receptor by Pharmacophore Ensemble/Support Vector Machine Approach. Chem Res Toxicol 2011; 24:1765-78. [DOI: 10.1021/tx200310j] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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16
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Docking, molecular dynamics simulation studies, and structure-based QSAR model on cytochrome P450 2A6 inhibitors. Struct Chem 2011. [DOI: 10.1007/s11224-011-9874-0] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
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17
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Mishra NK. Computational modeling of P450s for toxicity prediction. Expert Opin Drug Metab Toxicol 2011; 7:1211-31. [DOI: 10.1517/17425255.2011.611501] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
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Kell DB, Dobson PD, Oliver SG. Pharmaceutical drug transport: the issues and the implications that it is essentially carrier-mediated only. Drug Discov Today 2011; 16:704-14. [PMID: 21624498 DOI: 10.1016/j.drudis.2011.05.010] [Citation(s) in RCA: 148] [Impact Index Per Article: 11.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2010] [Revised: 04/04/2011] [Accepted: 05/11/2011] [Indexed: 01/04/2023]
Abstract
All cells necessarily contain tens, if not hundreds, of carriers for nutrients and intermediary metabolites, and the human genome codes for more than 1000 carriers of various kinds. Here, we illustrate using a typical literature example the widespread but erroneous nature of the assumption that the 'background' or 'passive' permeability to drugs occurs in the absence of carriers. Comparison of the rate of drug transport in natural versus artificial membranes shows discrepancies in absolute magnitudes of 100-fold or more, with the carrier-containing cells showing the greater permeability. Expression profiling data show exactly which carriers are expressed in which tissues. The recognition that drugs necessarily require carriers for uptake into cells provides many opportunities for improving the effectiveness of the drug discovery process.
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Affiliation(s)
- Douglas B Kell
- School of Chemistry, The University of Manchester, 131 Princess St, Manchester, M1 7DN, UK.
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Klon AE. Machine learning algorithms for the prediction of hERG and CYP450 binding in drug development. Expert Opin Drug Metab Toxicol 2011; 6:821-33. [PMID: 20465523 DOI: 10.1517/17425255.2010.489550] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Abstract
IMPORTANCE OF THE FIELD The cost of developing new drugs is estimated at approximately $1 billion; the withdrawal of a marketed compound due to toxicity can result in serious financial loss for a pharmaceutical company. There has been a greater interest in the development of in silico tools that can identify compounds with metabolic liabilities before they are brought to market. AREAS COVERED IN THIS REVIEW The two largest classes of machine learning (ML) models, which will be discussed in this review, have been developed to predict binding to the human ether-a-go-go related gene (hERG) ion channel protein and the various CYP isoforms. Being able to identify potentially toxic compounds before they are made would greatly reduce the number of compound failures and the costs associated with drug development. WHAT THE READER WILL GAIN This review summarizes the state of modeling hERG and CYP binding towards this goal since 2003 using ML algorithms. TAKE HOME MESSAGE A wide variety of ML algorithms that are comparable in their overall performance are available. These ML methods may be applied regularly in discovery projects to flag compounds with potential metabolic liabilities.
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Affiliation(s)
- Anthony E Klon
- Ansaris, Computational Chemistry, Four Valley Square, 512 East Township Line Road, Blue Bell, PA 19422, USA.
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20
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Xie Z, Zhang T, Wang JF, Chou KC, Wei DQ. The computational model to predict accurately inhibitory activity for inhibitors towardsCYP3A4. Comput Biol Med 2010; 40:845-52. [DOI: 10.1016/j.compbiomed.2010.09.004] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2010] [Revised: 08/09/2010] [Accepted: 09/21/2010] [Indexed: 10/18/2022]
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21
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Hammann F, Gutmann H, Baumann U, Helma C, Drewe J. Classification of cytochrome p(450) activities using machine learning methods. Mol Pharm 2010; 6:1920-6. [PMID: 19813762 DOI: 10.1021/mp900217x] [Citation(s) in RCA: 34] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
The cytochrome P(450) (CYP) system plays an integral part in the metabolism of drugs and other xenobiotics. Knowledge of the structural features required for interaction with any of the different isoforms of the CYP system is therefore immensely valuable in early drug discovery. In this paper, we focus on three major isoforms (CYP 1A2, CYP 2D6, and CYP 3A4) and present a data set of 335 structurally diverse drug compounds classified for their interaction (as substrate, inhibitor, or any interaction) with these isoforms. We also present machine learning models using a variety of commonly used methods (k-nearest neighbors, decision tree induction using the CHAID and CRT algorithms, random forests, artificial neural networks, and support vector machines using the radial basis function (RBF) and homogeneous polynomials as kernel functions). We discuss the physicochemical features relevant for each end point and compare it to similar studies. Many of these models perform exceptionally well, even with 10-fold cross-validation, yielding corrected classification rates of 81.7 to 91.9% for CYP 1A2, 89.2 to 92.9% for CYP 2D6, and 87.4 to 89.9% for CYP3A4. Our models help in understanding the structural requirements for CYP interactions and can serve as sensitive tools in virtual screenings and lead optimization for toxicological profiles in drug discovery.
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Affiliation(s)
- Felix Hammann
- Department of Gastroenterology & Hepatology, University Hospital Basel, University of Basel, Basel, Switzerland
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Abstract
Cytochrome P450 (CYP450) enzymes are predominantly involved in the Phase I metabolism of xenobiotics. Metabolic inhibition and induction can give rise to clinically important drug-drug interactions. Metabolic stability is a prerequisite for sustaining the therapeutically relevant concentrations, and very often drug candidates are sacrificed due to poor metabolic profiles. Computational tools such as quantitative structure-activity relationships are widely used to study different metabolic end points successfully to accelerate the drug discovery process. There are a lot of computational studies on clinically important CYPs already reported in recent years. But other clinically significant families are to yet be explored computationally. Powerfulness of quantitative structure-activity relationship will drive computational chemists to develop new potent and selective inhibitors of different classes of CYPs for the treatment of different diseases with least drug-drug interactions. Furthermore, there is a need to enhance the accuracy, interpretability and confidence in the computational models in accelerating the drug discovery pathways.
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Affiliation(s)
- Kunal Roy
- Jadavpur University, Division of Medicinal and Pharmaceutical Chemistry, Department of Pharmaceutical Technology, Drug Theoretics and Cheminformatics Lab, Kolkata 700 032, India.
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