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Tu G, Fu T, Zheng G, Xu B, Gou R, Luo D, Wang P, Xue W. Computational Chemistry in Structure-Based Solute Carrier Transporter Drug Design: Recent Advances and Future Perspectives. J Chem Inf Model 2024; 64:1433-1455. [PMID: 38294194 DOI: 10.1021/acs.jcim.3c01736] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2024]
Abstract
Solute carrier transporters (SLCs) are a class of important transmembrane proteins that are involved in the transportation of diverse solute ions and small molecules into cells. There are approximately 450 SLCs within the human body, and more than a quarter of them are emerging as attractive therapeutic targets for multiple complex diseases, e.g., depression, cancer, and diabetes. However, only 44 unique transporters (∼9.8% of the SLC superfamily) with 3D structures and specific binding sites have been reported. To design innovative and effective drugs targeting diverse SLCs, there are a number of obstacles that need to be overcome. However, computational chemistry, including physics-based molecular modeling and machine learning- and deep learning-based artificial intelligence (AI), provides an alternative and complementary way to the classical drug discovery approach. Here, we present a comprehensive overview on recent advances and existing challenges of the computational techniques in structure-based drug design of SLCs from three main aspects: (i) characterizing multiple conformations of the proteins during the functional process of transportation, (ii) identifying druggability sites especially the cryptic allosteric ones on the transporters for substrates and drugs binding, and (iii) discovering diverse small molecules or synthetic protein binders targeting the binding sites. This work is expected to provide guidelines for a deep understanding of the structure and function of the SLC superfamily to facilitate rational design of novel modulators of the transporters with the aid of state-of-the-art computational chemistry technologies including artificial intelligence.
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Affiliation(s)
- Gao Tu
- Chongqing Key Laboratory of Natural Product Synthesis and Drug Research, School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Tingting Fu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | | | - Binbin Xu
- Chengdu Sintanovo Biotechnology Co., Ltd., Chengdu 610200, China
| | - Rongpei Gou
- Chongqing Key Laboratory of Natural Product Synthesis and Drug Research, School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Ding Luo
- Chongqing Key Laboratory of Natural Product Synthesis and Drug Research, School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Panpan Wang
- College of Chemistry and Pharmaceutical Engineering, Huanghuai University, Zhumadian 463000, China
| | - Weiwei Xue
- Chongqing Key Laboratory of Natural Product Synthesis and Drug Research, School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
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Sakai N, Kamimura K, Terai S. Repurposable Drugs for Immunotherapy and Strategies to Find Candidate Drugs. Pharmaceutics 2023; 15:2190. [PMID: 37765160 PMCID: PMC10536625 DOI: 10.3390/pharmaceutics15092190] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Revised: 08/20/2023] [Accepted: 08/22/2023] [Indexed: 09/29/2023] Open
Abstract
Conventional drug discovery involves significant steps, time, and expenses; therefore, novel methods for drug discovery remain unmet, particularly for patients with intractable diseases. For this purpose, the drug repurposing method has been recently used to search for new therapeutic agents. Repurposed drugs are mostly previously approved drugs, which were carefully tested for their efficacy for other diseases and had their safety for the human body confirmed following careful pre-clinical trials, clinical trials, and post-marketing surveillance. Therefore, using these approved drugs for other diseases that cannot be treated using conventional therapeutic methods could save time and economic costs for testing their clinical applicability. In this review, we have summarized the methods for identifying repurposable drugs focusing on immunotherapy.
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Affiliation(s)
- Norihiro Sakai
- Division of Gastroenterology and Hepatology, Graduate School of Medical and Dental Sciences, Niigata University, 1-757, Aasahimachi-Dori, Chuo-Ku, Niigata 951-8510, Japan; (N.S.); (S.T.)
| | - Kenya Kamimura
- Division of Gastroenterology and Hepatology, Graduate School of Medical and Dental Sciences, Niigata University, 1-757, Aasahimachi-Dori, Chuo-Ku, Niigata 951-8510, Japan; (N.S.); (S.T.)
- Department of General Medicine, Niigata University School of Medicine, 1-757, Aasahimachi-Dori, Chuo-Ku, Niigata 951-8510, Japan
| | - Shuji Terai
- Division of Gastroenterology and Hepatology, Graduate School of Medical and Dental Sciences, Niigata University, 1-757, Aasahimachi-Dori, Chuo-Ku, Niigata 951-8510, Japan; (N.S.); (S.T.)
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3
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Miao H, Chen X, Luan Y. Small Molecular Gemcitabine Prodrugs for Cancer Therapy. Curr Med Chem 2020; 27:5562-5582. [PMID: 31419928 DOI: 10.2174/0929867326666190816230650] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2019] [Revised: 05/22/2019] [Accepted: 07/09/2019] [Indexed: 02/04/2023]
Abstract
Gemcitabine as a pyrimidine nucleoside analog anticancer drug has high efficacy for a broad spectrum of solid tumors. Gemcitabine is activated within tumor cells by sequential phosphorylation carried out by deoxycytidine kinase to mono-, di-, and triphosphate nucleotides with the last one as the active form. But the instability, drug resistance and toxicity severely limited its utilization in clinics. In the field of medicinal chemistry, prodrugs have proven to be a very effective means for elevating drug stability and decrease undesirable side effects including the nucleoside anticancer drug such as gemcitabine. Many works have been accomplished in design and synthesis of gemcitabine prodrugs, majority of which were summarized in this review.
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Affiliation(s)
- He Miao
- Department of Medicinal Chemistry, School of Pharmacy, Qingdao University, Shandong Province, Qingdao, China
| | - Xuehong Chen
- Department of Pharmacology, College of Basic Medicine, Qingdao University, Shandong Province, Qingdao, China
| | - Yepeng Luan
- Department of Medicinal Chemistry, School of Pharmacy, Qingdao University, Shandong Province, Qingdao, China
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Baidya ATK, Ghosh K, Amin SA, Adhikari N, Nirmal J, Jha T, Gayen S. In silico modelling, identification of crucial molecular fingerprints, and prediction of new possible substrates of human organic cationic transporters 1 and 2. NEW J CHEM 2020. [DOI: 10.1039/c9nj05825g] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
The cation membrane transporters are crucial to regulate movement of foreign molecules within the body. The present study found out structural fingerprints within molecules to be recognized as substrate/non-substrate against these transporters.
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Affiliation(s)
- Anurag T. K. Baidya
- Laboratory of Drug Design and Discovery
- Department of Pharmaceutical Sciences
- Dr H. S. Gour University
- Sagar
- India
| | - Kalyan Ghosh
- Laboratory of Drug Design and Discovery
- Department of Pharmaceutical Sciences
- Dr H. S. Gour University
- Sagar
- India
| | - Sk. Abdul Amin
- Natural Science Laboratory
- Division of Medicinal and Pharmaceutical Chemistry
- Department of Pharmaceutical Technology
- Jadavpur University
- Kolkata
| | - Nilanjan Adhikari
- Natural Science Laboratory
- Division of Medicinal and Pharmaceutical Chemistry
- Department of Pharmaceutical Technology
- Jadavpur University
- Kolkata
| | - Jayabalan Nirmal
- Translational Pharmaceutics Laboratory
- Department of Pharmacy
- BITS-Pilani
- Hyderabad Campus
- Hyderabad 500078
| | - Tarun Jha
- Natural Science Laboratory
- Division of Medicinal and Pharmaceutical Chemistry
- Department of Pharmaceutical Technology
- Jadavpur University
- Kolkata
| | - Shovanlal Gayen
- Laboratory of Drug Design and Discovery
- Department of Pharmaceutical Sciences
- Dr H. S. Gour University
- Sagar
- India
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5
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Ekins S, Gerlach J, Zorn KM, Antonio BM, Lin Z, Gerlach A. Repurposing Approved Drugs as Inhibitors of K v7.1 and Na v1.8 to Treat Pitt Hopkins Syndrome. Pharm Res 2019; 36:137. [PMID: 31332533 DOI: 10.1007/s11095-019-2671-y] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2018] [Accepted: 07/10/2019] [Indexed: 12/18/2022]
Abstract
PURPOSE Pitt Hopkins Syndrome (PTHS) is a rare genetic disorder caused by mutations of a specific gene, transcription factor 4 (TCF4), located on chromosome 18. PTHS results in individuals that have moderate to severe intellectual disability, with most exhibiting psychomotor delay. PTHS also exhibits features of autistic spectrum disorders, which are characterized by the impaired ability to communicate and socialize. PTHS is comorbid with a higher prevalence of epileptic seizures which can be present from birth or which commonly develop in childhood. Attenuated or absent TCF4 expression results in increased translation of peripheral ion channels Kv7.1 and Nav1.8 which triggers an increase in after-hyperpolarization and altered firing properties. METHODS We now describe a high throughput screen (HTS) of 1280 approved drugs and machine learning models developed from this data. The ion channels were expressed in either CHO (KV7.1) or HEK293 (Nav1.8) cells and the HTS used either 86Rb+ efflux (KV7.1) or a FLIPR assay (Nav1.8). RESULTS The HTS delivered 55 inhibitors of Kv7.1 (4.2% hit rate) and 93 inhibitors of Nav1.8 (7.2% hit rate) at a screening concentration of 10 μM. These datasets also enabled us to generate and validate Bayesian machine learning models for these ion channels. We also describe a structure activity relationship for several dihydropyridine compounds as inhibitors of Nav1.8. CONCLUSIONS This work could lead to the potential repurposing of nicardipine or other dihydropyridine calcium channel antagonists as potential treatments for PTHS acting via Nav1.8, as there are currently no approved treatments for this rare disorder.
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Affiliation(s)
- Sean Ekins
- Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina, 27606, USA.
| | - Jacob Gerlach
- Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina, 27606, USA
| | - Kimberley M Zorn
- Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina, 27606, USA
| | - Brett M Antonio
- Icagen, Inc., 4222 Emperor Blvd, Durham, North Carolina, 27703, USA
| | - Zhixin Lin
- Icagen, Inc., 4222 Emperor Blvd, Durham, North Carolina, 27703, USA
| | - Aaron Gerlach
- Icagen, Inc., 4222 Emperor Blvd, Durham, North Carolina, 27703, USA
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6
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Türková A, Zdrazil B. Current Advances in Studying Clinically Relevant Transporters of the Solute Carrier (SLC) Family by Connecting Computational Modeling and Data Science. Comput Struct Biotechnol J 2019; 17:390-405. [PMID: 30976382 PMCID: PMC6438991 DOI: 10.1016/j.csbj.2019.03.002] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2018] [Revised: 02/28/2019] [Accepted: 03/01/2019] [Indexed: 01/18/2023] Open
Abstract
Organic anion and cation transporting proteins (OATs, OATPs, and OCTs), as well as the Multidrug and Toxin Extrusion (MATE) transporters of the Solute Carrier (SLC) family are playing a pivotal role in the discovery and development of new drugs due to their involvement in drug disposition, drug-drug interactions, adverse drug effects and related toxicity. Computational methods to understand and predict clinically relevant transporter interactions can provide useful guidance at early stages in drug discovery and design, especially if they include contemporary data science approaches. In this review, we summarize the current state-of-the-art of computational approaches for exploring ligand interactions and selectivity for these drug (uptake) transporters. The computational methods discussed here by highlighting interesting examples from the current literature are ranging from semiautomatic data mining and integration, to ligand-based methods (such as quantitative structure-activity relationships, and combinatorial pharmacophore modeling), and finally structure-based methods (such as comparative modeling, molecular docking, and molecular dynamics simulations). We are focusing on promising computational techniques such as fold-recognition methods, proteochemometric modeling or techniques for enhanced sampling of protein conformations used in the context of these ADMET-relevant SLC transporters with a special focus on methods useful for studying ligand selectivity.
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Affiliation(s)
- Alžběta Türková
- Department of Pharmaceutical Chemistry, Divison of Drug Design and Medicinal Chemistry, University of Vienna, Althanstraße 14, A-1090 Vienna, Austria
| | - Barbara Zdrazil
- Department of Pharmaceutical Chemistry, Divison of Drug Design and Medicinal Chemistry, University of Vienna, Althanstraße 14, A-1090 Vienna, Austria
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7
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Korotcov A, Tkachenko V, Russo DP, Ekins S. Comparison of Deep Learning With Multiple Machine Learning Methods and Metrics Using Diverse Drug Discovery Data Sets. Mol Pharm 2017; 14:4462-4475. [PMID: 29096442 PMCID: PMC5741413 DOI: 10.1021/acs.molpharmaceut.7b00578] [Citation(s) in RCA: 184] [Impact Index Per Article: 26.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Machine learning methods have been applied to many data sets in pharmaceutical research for several decades. The relative ease and availability of fingerprint type molecular descriptors paired with Bayesian methods resulted in the widespread use of this approach for a diverse array of end points relevant to drug discovery. Deep learning is the latest machine learning algorithm attracting attention for many of pharmaceutical applications from docking to virtual screening. Deep learning is based on an artificial neural network with multiple hidden layers and has found considerable traction for many artificial intelligence applications. We have previously suggested the need for a comparison of different machine learning methods with deep learning across an array of varying data sets that is applicable to pharmaceutical research. End points relevant to pharmaceutical research include absorption, distribution, metabolism, excretion, and toxicity (ADME/Tox) properties, as well as activity against pathogens and drug discovery data sets. In this study, we have used data sets for solubility, probe-likeness, hERG, KCNQ1, bubonic plague, Chagas, tuberculosis, and malaria to compare different machine learning methods using FCFP6 fingerprints. These data sets represent whole cell screens, individual proteins, physicochemical properties as well as a data set with a complex end point. Our aim was to assess whether deep learning offered any improvement in testing when assessed using an array of metrics including AUC, F1 score, Cohen's kappa, Matthews correlation coefficient and others. Based on ranked normalized scores for the metrics or data sets Deep Neural Networks (DNN) ranked higher than SVM, which in turn was ranked higher than all the other machine learning methods. Visualizing these properties for training and test sets using radar type plots indicates when models are inferior or perhaps over trained. These results also suggest the need for assessing deep learning further using multiple metrics with much larger scale comparisons, prospective testing as well as assessment of different fingerprints and DNN architectures beyond those used.
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Affiliation(s)
- Alexandru Korotcov
- Science Data Software, LLC, 14914 Bradwill Court, Rockville, MD 20850, USA
| | - Valery Tkachenko
- Science Data Software, LLC, 14914 Bradwill Court, Rockville, MD 20850, USA
| | - Daniel P Russo
- Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, NC 27606, USA
- The Rutgers Center for Computational and Integrative Biology, Camden, NJ, 08102, USA
| | - Sean Ekins
- Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, NC 27606, USA
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8
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Visentin M, Gai Z, Torozi A, Hiller C, Kullak-Ublick GA. Colistin is Substrate of the Carnitine/Organic Cation Transporter 2 (OCTN2, SLC22A5). Drug Metab Dispos 2017; 45:1240-1244. [DOI: 10.1124/dmd.117.077248] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2017] [Accepted: 10/03/2017] [Indexed: 11/22/2022] Open
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9
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Wang G, Chen H, Zhao D, Ding D, Sun M, Kou L, Luo C, Zhang D, Yi X, Dong J, Wang J, Liu X, He Z, Sun J. Combination of l-Carnitine with Lipophilic Linkage-Donating Gemcitabine Derivatives as Intestinal Novel Organic Cation Transporter 2-Targeting Oral Prodrugs. J Med Chem 2017; 60:2552-2561. [DOI: 10.1021/acs.jmedchem.7b00049] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Gang Wang
- School
of Pharmacy, Shenyang Pharmaceutical University, Wenhua Road, Shenyang 110016, China
| | - Hongxiang Chen
- School
of Pharmacy, Shenyang Pharmaceutical University, Wenhua Road, Shenyang 110016, China
| | - Dongyang Zhao
- School
of Pharmacy, Shenyang Pharmaceutical University, Wenhua Road, Shenyang 110016, China
| | - Dawei Ding
- School
of Pharmacy, Shenyang Pharmaceutical University, Wenhua Road, Shenyang 110016, China
| | - Mengchi Sun
- School
of Pharmacy, Shenyang Pharmaceutical University, Wenhua Road, Shenyang 110016, China
| | - Longfa Kou
- School
of Pharmacy, Shenyang Pharmaceutical University, Wenhua Road, Shenyang 110016, China
| | - Cong Luo
- School
of Pharmacy, Shenyang Pharmaceutical University, Wenhua Road, Shenyang 110016, China
| | - Dong Zhang
- School
of Pharmacy, Shenyang Pharmaceutical University, Wenhua Road, Shenyang 110016, China
| | - Xiulin Yi
- State
Key Laboratory of Drug Delivery Technology and Pharmacokinetics, Tianjin Institute of Pharmaceutical Research, Tianjin, China
| | - Jinhua Dong
- School
of Pharmacy, Shenyang Pharmaceutical University, Wenhua Road, Shenyang 110016, China
| | - Jian Wang
- Key
Laboratory of Structure-Based Drug Design and Discovery, Shenyang Pharmaceutical University, Ministry of Education, Shenyang, China
| | - Xiaohong Liu
- School
of Pharmacy, Shenyang Pharmaceutical University, Wenhua Road, Shenyang 110016, China
| | - Zhonggui He
- School
of Pharmacy, Shenyang Pharmaceutical University, Wenhua Road, Shenyang 110016, China
| | - Jin Sun
- School
of Pharmacy, Shenyang Pharmaceutical University, Wenhua Road, Shenyang 110016, China
- Municipal
Key Laboratory of Biopharmaceutics, School of Pharmacy, Shenyang Pharmaceutical University, Shenyang, China
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10
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Ekins S. The Next Era: Deep Learning in Pharmaceutical Research. Pharm Res 2016; 33:2594-603. [PMID: 27599991 DOI: 10.1007/s11095-016-2029-7] [Citation(s) in RCA: 99] [Impact Index Per Article: 12.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2016] [Accepted: 08/23/2016] [Indexed: 01/22/2023]
Abstract
Over the past decade we have witnessed the increasing sophistication of machine learning algorithms applied in daily use from internet searches, voice recognition, social network software to machine vision software in cameras, phones, robots and self-driving cars. Pharmaceutical research has also seen its fair share of machine learning developments. For example, applying such methods to mine the growing datasets that are created in drug discovery not only enables us to learn from the past but to predict a molecule's properties and behavior in future. The latest machine learning algorithm garnering significant attention is deep learning, which is an artificial neural network with multiple hidden layers. Publications over the last 3 years suggest that this algorithm may have advantages over previous machine learning methods and offer a slight but discernable edge in predictive performance. The time has come for a balanced review of this technique but also to apply machine learning methods such as deep learning across a wider array of endpoints relevant to pharmaceutical research for which the datasets are growing such as physicochemical property prediction, formulation prediction, absorption, distribution, metabolism, excretion and toxicity (ADME/Tox), target prediction and skin permeation, etc. We also show that there are many potential applications of deep learning beyond cheminformatics. It will be important to perform prospective testing (which has been carried out rarely to date) in order to convince skeptics that there will be benefits from investing in this technique.
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Affiliation(s)
- Sean Ekins
- Collaborations Pharmaceuticals, Inc, 5616 Hilltop Needmore Road, Fuquay-Varina, North Carolina, 27526, USA. .,Collaborative Drug Discovery, 1633 Bayshore Highway, Suite 342, Burlingame, California, 94010, USA.
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Clark AM, Dole K, Ekins S. Open Source Bayesian Models. 3. Composite Models for Prediction of Binned Responses. J Chem Inf Model 2016; 56:275-85. [PMID: 26750305 PMCID: PMC4764945 DOI: 10.1021/acs.jcim.5b00555] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
![]()
Bayesian models constructed from
structure-derived fingerprints
have been a popular and useful method for drug discovery research
when applied to bioactivity measurements that can be effectively classified
as active or inactive. The results can be used to rank candidate structures
according to their probability of activity, and this ranking benefits
from the high degree of interpretability when structure-based fingerprints
are used, making the results chemically intuitive. Besides selecting
an activity threshold, building a Bayesian model is fast and requires
few or no parameters or user intervention. The method also does not
suffer from such acute overtraining problems as quantitative structure–activity
relationships or quantitative structure–property relationships
(QSAR/QSPR). This makes it an approach highly suitable for automated
workflows that are independent of user expertise or prior knowledge
of the training data. We now describe a new method for creating a
composite group of Bayesian models to extend the method to work with
multiple states, rather than just binary. Incoming activities are
divided into bins, each covering a mutually exclusive range of activities.
For each of these bins, a Bayesian model is created to model whether
or not the compound belongs in the bin. Analyzing putative molecules
using the composite model involves making a prediction for each bin
and examining the relative likelihood for each assignment, for example,
highest value wins. The method has been evaluated on a collection
of hundreds of data sets extracted from ChEMBL v20 and validated data
sets for ADME/Tox and bioactivity.
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Affiliation(s)
- Alex M Clark
- Molecular Materials Informatics, Inc. , 1900 St. Jacques #302, Montreal H3J 2S1, Quebec, Canada
| | - Krishna Dole
- Collaborative Drug Discovery, Inc. , 1633 Bayshore Highway, Suite 342, Burlingame, California 94010, United States
| | - Sean Ekins
- Collaborative Drug Discovery, Inc. , 1633 Bayshore Highway, Suite 342, Burlingame, California 94010, United States.,Collaborations in Chemistry , 5616 Hilltop Needmore Road, Fuquay-Varina, North Carolina 27526, United States
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12
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Parks A, Charest-Morin X, Boivin-Welch M, Bouthillier J, Marceau F. Autophagic flux inhibition and lysosomogenesis ensuing cellular capture and retention of the cationic drug quinacrine in murine models. PeerJ 2015; 3:e1314. [PMID: 26500823 PMCID: PMC4614855 DOI: 10.7717/peerj.1314] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2015] [Accepted: 09/21/2015] [Indexed: 11/23/2022] Open
Abstract
The proton pump vacuolar (V)-ATPase is the driving force that mediates the concentration of cationic drugs (weak bases) in the late endosome-lysosome continuum; secondary cell reactions include the protracted transformation of enlarged vacuoles into autophagosomes. We used the inherently fluorescent tertiary amine quinacrine in murine models to further assess the accumulation and signaling associated with cation trapping. Primary fibroblasts concentrate quinacrine ∼5,000-fold from their culture medium (KM 9.8 µM; transport studies). The drug is present in perinuclear granules that are mostly positive for Rab7 and LAMP1 (microscopy). Both drug uptake and retention are extensively inhibited by treatments with the V-ATPase inhibitor bafilomycin A1. The H+ ionophore monensin also prevented quinacrine concentration by fibroblasts. However, inhibition of plasma membrane transporters or of the autophagic process with spautin-1 did not alter quinacrine transport parameters. Ancillary experiments did not support that low micromolar concentrations of quinacrine are substrates for organic cation transporters-1 to -3 or P-glycoprotein. The secondary autophagy induced by quinacrine in cells may derive from the accumulation of incompetent autophagolysosomes, as judged from the accumulation of p62/SQSTM1 and LC3 II (immunoblots). Accordingly, protracted lysosomogenesis is evidenced by increased expression of LAMP1 and LAMP2 in quinacrine-treated fibroblasts (48 h, immunoblots), a response that follows the nuclear translocation of the lysosomal genesis transcription factor TFEB and upregulation of LAMP1 and −2 mRNAs (24 h). Quinacrine administration to live mice evidenced variable distribution to various organs and heterogeneous accumulation within the lung (stereo-microscopy, extraction). Dose-dependent in vivo autophagic and lysosomal accumulation was observed in the lung (immunoblots). No evidence has been found for transport or extrusion mechanisms modulating the cellular uptake of micromolar quinacrine at the plasma membrane level. As shown in vitro and in vivo, V-ATPase-mediated cation sequestration is associated, above a certain threshold, to autophagic flux inhibition and feed-back lysosomogenesis.
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Affiliation(s)
- Alexandre Parks
- Research Center CHU de Québec, Université Laval , Quebec City QC , Canada
| | | | | | | | - Francois Marceau
- Research Center CHU de Québec, Université Laval , Quebec City QC , Canada
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13
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Ekins S, Clark AM, Wright SH. Making Transporter Models for Drug-Drug Interaction Prediction Mobile. Drug Metab Dispos 2015. [PMID: 26199424 DOI: 10.1124/dmd.115.064956] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
The past decade has seen increased numbers of studies publishing ligand-based computational models for drug transporters. Although they generally use small experimental data sets, these models can provide insights into structure-activity relationships for the transporter. In addition, such models have helped to identify new compounds as substrates or inhibitors of transporters of interest. We recently proposed that many transporters are promiscuous and may require profiling of new chemical entities against multiple substrates for a specific transporter. Furthermore, it should be noted that virtually all of the published ligand-based transporter models are only accessible to those involved in creating them and, consequently, are rarely shared effectively. One way to surmount this is to make models shareable or more accessible. The development of mobile apps that can access such models is highlighted here. These apps can be used to predict ligand interactions with transporters using Bayesian algorithms. We used recently published transporter data sets (MATE1, MATE2K, OCT2, OCTN2, ASBT, and NTCP) to build preliminary models in a commercial tool and in open software that can deliver the model in a mobile app. In addition, several transporter data sets extracted from the ChEMBL database were used to illustrate how such public data and models can be shared. Predicting drug-drug interactions for various transporters using computational models is potentially within reach of anyone with an iPhone or iPad. Such tools could help prioritize which substrates should be used for in vivo drug-drug interaction testing and enable open sharing of models.
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Affiliation(s)
- Sean Ekins
- Collaborations Pharmaceuticals, Inc., and Collaborations in Chemistry, Fuquay-Varina, North Carolina (S.E.); Collaborative Drug Discovery, Burlingame, California (S.E.); Molecular Materials Informatics, Inc., Montreal, Quebec, Canada (A.M.C.); and Department of Physiology, University of Arizona, Tucson, Arizona (S.H.W.)
| | - Alex M Clark
- Collaborations Pharmaceuticals, Inc., and Collaborations in Chemistry, Fuquay-Varina, North Carolina (S.E.); Collaborative Drug Discovery, Burlingame, California (S.E.); Molecular Materials Informatics, Inc., Montreal, Quebec, Canada (A.M.C.); and Department of Physiology, University of Arizona, Tucson, Arizona (S.H.W.)
| | - Stephen H Wright
- Collaborations Pharmaceuticals, Inc., and Collaborations in Chemistry, Fuquay-Varina, North Carolina (S.E.); Collaborative Drug Discovery, Burlingame, California (S.E.); Molecular Materials Informatics, Inc., Montreal, Quebec, Canada (A.M.C.); and Department of Physiology, University of Arizona, Tucson, Arizona (S.H.W.)
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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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15
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Clark AM, Ekins S. Open Source Bayesian Models. 2. Mining a "Big Dataset" To Create and Validate Models with ChEMBL. J Chem Inf Model 2015; 55:1246-60. [PMID: 25995041 DOI: 10.1021/acs.jcim.5b00144] [Citation(s) in RCA: 67] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
In an associated paper, we have described a reference implementation of Laplacian-corrected naïve Bayesian model building using extended connectivity (ECFP)- and molecular function class fingerprints of maximum diameter 6 (FCFP)-type fingerprints. As a follow-up, we have now undertaken a large-scale validation study in order to ensure that the technique generalizes to a broad variety of drug discovery datasets. To achieve this, we have used the ChEMBL (version 20) database and split it into more than 2000 separate datasets, each of which consists of compounds and measurements with the same target and activity measurement. In order to test these datasets with the two-state Bayesian classification, we developed an automated algorithm for detecting a suitable threshold for active/inactive designation, which we applied to all collections. With these datasets, we were able to establish that our Bayesian model implementation is effective for the large majority of cases, and we were able to quantify the impact of fingerprint folding on the receiver operator curve cross-validation metrics. We were also able to study the impact that the choice of training/testing set partitioning has on the resulting recall rates. The datasets have been made publicly available to be downloaded, along with the corresponding model data files, which can be used in conjunction with the CDK and several mobile apps. We have also explored some novel visualization methods which leverage the structural origins of the ECFP/FCFP fingerprints to attribute regions of a molecule responsible for positive and negative contributions to activity. The ability to score molecules across thousands of relevant datasets across organisms also may help to access desirable and undesirable off-target effects as well as suggest potential targets for compounds derived from phenotypic screens.
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Affiliation(s)
- Alex M Clark
- †Molecular Materials Informatics, Inc., 1900 St. Jacques No. 302, Montreal H3J 2S1, Quebec, Canada
| | - Sean Ekins
- ‡Collaborations Pharmaceuticals, Inc., 5616 Hilltop Needmore Road, Fuquay-Varina, North Carolina 27526, United States.,§Collaborations in Chemistry, 5616 Hilltop Needmore Road, Fuquay-Varina, North Carolina 27526, United States.,∥Collaborative Drug Discovery, 1633 Bayshore Highway, Suite 342, Burlingame, California 94010, United States
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16
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Ekins S, Freundlich JS, Coffee M. A common feature pharmacophore for FDA-approved drugs inhibiting the Ebola virus. F1000Res 2014; 3:277. [PMID: 25653841 PMCID: PMC4304229 DOI: 10.12688/f1000research.5741.2] [Citation(s) in RCA: 38] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 12/12/2014] [Indexed: 01/01/2023] Open
Abstract
We are currently faced with a global infectious disease crisis which has been anticipated for decades. While many promising biotherapeutics are being tested, the search for a small molecule has yet to deliver an approved drug or therapeutic for the Ebola or similar filoviruses that cause haemorrhagic fever. Two recent high throughput screens published in 2013 did however identify several hits that progressed to animal studies that are FDA approved drugs used for other indications. The current computational analysis uses these molecules from two different structural classes to construct a common features pharmacophore. This ligand-based pharmacophore implicates a possible common target or mechanism that could be further explored. A recent structure based design project yielded nine co-crystal structures of pyrrolidinone inhibitors bound to the viral protein 35 (VP35). When receptor-ligand pharmacophores based on the analogs of these molecules and the protein structures were constructed, the molecular features partially overlapped with the common features of solely ligand-based pharmacophore models based on FDA approved drugs. These previously identified FDA approved drugs with activity against Ebola were therefore docked into this protein. The antimalarials chloroquine and amodiaquine docked favorably in VP35. We propose that these drugs identified to date as inhibitors of the Ebola virus may be targeting VP35. These computational models may provide preliminary insights into the molecular features that are responsible for their activity against Ebola virus
in vitro and
in vivo and we propose that this hypothesis could be readily tested.
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Affiliation(s)
- Sean Ekins
- Collaborations in Chemistry, Fuquay-Varina, NC, 27526, USA ; Collaborative Drug Discovery, Burlingame, CA, 94010, USA
| | - Joel S Freundlich
- Departments of Pharmacology & Physiology and Medicine, Center for Emerging and Reemerging Pathogens, UMDNJ - New Jersey Medical School, NJ, 07103, USA
| | - Megan Coffee
- Center for Infectious Diseases and Emerging Readiness, University of California, Berkeley, CA, 94720, USA
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17
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Ekins S, Freundlich JS, Coffee M. A common feature pharmacophore for FDA-approved drugs inhibiting the Ebola virus. F1000Res 2014; 3:277. [PMID: 25653841 DOI: 10.12688/f1000research.5741.1] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 11/14/2014] [Indexed: 01/05/2023] Open
Abstract
We are currently faced with a global infectious disease crisis which has been anticipated for decades. While many promising biotherapeutics are being tested, the search for a small molecule has yet to deliver an approved drug or therapeutic for the Ebola or similar filoviruses that cause haemorrhagic fever. Two recent high throughput screens published in 2013 did however identify several hits that progressed to animal studies that are FDA approved drugs used for other indications. The current computational analysis uses these molecules from two different structural classes to construct a common features pharmacophore. This ligand-based pharmacophore implicates a possible common target or mechanism that could be further explored. A recent structure based design project yielded nine co-crystal structures of pyrrolidinone inhibitors bound to the viral protein 35 (VP35). When receptor-ligand pharmacophores based on the analogs of these molecules and the protein structures were constructed, the molecular features partially overlapped with the common features of solely ligand-based pharmacophore models based on FDA approved drugs. These previously identified FDA approved drugs with activity against Ebola were therefore docked into this protein. The antimalarials chloroquine and amodiaquine docked favorably in VP35. We propose that these drugs identified to date as inhibitors of the Ebola virus may be targeting VP35. These computational models may provide preliminary insights into the molecular features that are responsible for their activity against Ebola virus in vitro and in vivo and we propose that this hypothesis could be readily tested.
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Affiliation(s)
- Sean Ekins
- Collaborations in Chemistry, Fuquay-Varina, NC, 27526, USA ; Collaborative Drug Discovery, Burlingame, CA, 94010, USA
| | - Joel S Freundlich
- Departments of Pharmacology & Physiology and Medicine, Center for Emerging and Reemerging Pathogens, UMDNJ - New Jersey Medical School, NJ, 07103, USA
| | - Megan Coffee
- Center for Infectious Diseases and Emerging Readiness, University of California, Berkeley, CA, 94720, USA
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18
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Pochini L, Scalise M, Galluccio M, Indiveri C. OCTN cation transporters in health and disease: role as drug targets and assay development. ACTA ACUST UNITED AC 2013; 18:851-67. [PMID: 23771822 DOI: 10.1177/1087057113493006] [Citation(s) in RCA: 71] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
The three members of the organic cation transporter novel subfamily are known to be involved in interactions with xenobiotic compounds. These proteins are characterized by 12 transmembrane segments connected by nine short loops and two large hydrophilic loops. It has been recently pointed out that acetylcholine is a physiological substrate of OCTN1. Its transport could be involved in nonneuronal cholinergic functions. OCTN2 maintains the carnitine homeostasis, resulting from intestinal absorption, distribution to tissues, and renal excretion/reabsorption. OCTN3, identified only in mouse, mediates also carnitine transport. OCTN1 and OCTN2 are associated with several pathologies, such as inflammatory bowel disease, primary carnitine deficiency, diabetes, neurological disorders, and cancer, thus representing useful pharmacological targets. The function and interaction with drugs of OCTNs have been studied in intact cell systems and in proteoliposomes. The latter experimental model enables reduced interference from other transporters or enzyme pathways. Using proteoliposomes, the molecular bases of toxicity of some drugs have recently been revealed. Therefore, proteoliposomes represent a promising experimental tool suitable for large-scale molecular screening of interactions of OCTNs with chemicals regarding human health.
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Affiliation(s)
- Lorena Pochini
- Laboratory of Biochemistry and Molecular Biotechnology, Department BEST (Biologia, Ecologia, Scienze della Terra), University of Calabria, Italy
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19
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Petrie M, Lynch KL, Ekins S, Chang JS, Goetz RJ, Wu AHB, Krasowski MD. Cross-reactivity studies and predictive modeling of "Bath Salts" and other amphetamine-type stimulants with amphetamine screening immunoassays. Clin Toxicol (Phila) 2013; 51:83-91. [PMID: 23387345 DOI: 10.3109/15563650.2013.768344] [Citation(s) in RCA: 41] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
INTRODUCTION The increasing abuse of amphetamine-like compounds presents a challenge for clinicians and clinical laboratories. Although these compounds may be identified by mass spectrometry-based assays, most clinical laboratories use amphetamine immunoassays that have unknown cross-reactivity with novel amphetamine-like drugs. To date, there has been a little systematic study of amphetamine immunoassay cross-reactivity with structurally diverse amphetamine-like drugs or of computational tools to predict cross-reactivity. METHODS Cross-reactivities of 42 amphetamines and amphetamine-like drugs with three amphetamines screening immunoassays (AxSYM(®) Amphetamine/Methamphetamine II, CEDIA(®) amphetamine/Ecstasy, and EMIT(®) II Plus Amphetamines) were determined. Two- and three-dimensional molecular similarity and modeling approaches were evaluated for the ability to predict cross-reactivity using receiver-operator characteristic curve analysis. RESULTS Overall, 34%-46% of the drugs tested positive on the immunoassay screens using a concentration of 20,000 ng/mL. The three immunoassays showed differential detection of the various classes of amphetamine-like drugs. Only the CEDIA assay detected piperazines well, while only the EMIT assay cross-reacted with the 2C class. All three immunoassays detected 4-substituted amphetamines. For the AxSYM and EMIT assays, two-dimensional molecular similarity methods that combined similarity to amphetamine/methamphetamine and 3,4-methylenedioxymethampetamine most accurately predicted cross-reactivity. For the CEDIA assay, three-dimensional pharmacophore methods performed best in predicting cross-reactivity. Using the best performing models, cross-reactivities of an additional 261 amphetamine-like compounds were predicted. CONCLUSIONS Existing amphetamines immunoassays unevenly detect amphetamine-like drugs, particularly in the 2C, piperazine, and β-keto classes. Computational similarity methods perform well in predicting cross-reactivity and can help prioritize testing of additional compounds in the future.
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Affiliation(s)
- M Petrie
- Department of Laboratory Medicine, University of California San Francisco, San Francisco, CA, USA
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20
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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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21
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Ekins S, Polli JE, Swaan PW, Wright SH. Computational modeling to accelerate the identification of substrates and inhibitors for transporters that affect drug disposition. Clin Pharmacol Ther 2012; 92:661-5. [PMID: 23010651 DOI: 10.1038/clpt.2012.164] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Affiliation(s)
- S Ekins
- Collaborations in Chemistry, Fuquay Varina, North Carolina, USA.
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22
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Astorga B, Ekins S, Morales M, Wright SH. Molecular determinants of ligand selectivity for the human multidrug and toxin extruder proteins MATE1 and MATE2-K. J Pharmacol Exp Ther 2012; 341:743-55. [PMID: 22419765 DOI: 10.1124/jpet.112.191577] [Citation(s) in RCA: 62] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023] Open
Abstract
The present study compared the selectivity of two homologous transport proteins, multidrug and toxin extruders 1 and 2-K (MATE1 and MATE2-K), and developed three-dimensional pharmacophores for inhibitory ligand interaction with human MATE1 (hMATE1). The human orthologs of MATE1 and MATE2-K were stably expressed in Chinese hamster ovary cells, and transport function was determined by measuring uptake of the prototypic organic cation (OC) substrate 1-methyl-4-phenylpyridinium (MPP). Both MATEs had similar apparent affinities for MPP, with K(tapp) values of 4.4 and 3.7 μM for MATE1 and MATE2-K, respectively. Selectivity was assessed for both transporters from IC(50) values for 59 structurally diverse compounds. Whereas the two transporters discriminated markedly between a few of the test compounds, the IC(50) values for MATE1 and MATE2-K were within a factor of 3 for most of them. For hMATE1 there was little or no correlation between IC(50) values and the individual molecular descriptors LogP, total polar surface area, or pK(a). The IC(50) values were used to generate a common-features pharmacophore, quantitative pharmacophores for hMATE1, and a bayesian model suggesting molecular features favoring and not favoring the interaction of ligands with hMATE1. The models identified hydrophobic regions, hydrogen bond donor and hydrogen bond acceptor sites, and an ionizable (cationic) feature as key determinants for ligand binding to MATE1. In summary, using a combined in vitro and computational approach, MATE1 and MATE2-K were found to have markedly overlapping selectivities for a broad range of cationic compounds, including representatives from seven novel drug classes of Food and Drug Administration-approved drugs.
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Affiliation(s)
- Bethzaida Astorga
- Department of Physiology, University of Arizona, Tucson, AZ 85724, USA
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23
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Ekins S, Diao L, Polli JE. A substrate pharmacophore for the human organic cation/carnitine transporter identifies compounds associated with rhabdomyolysis. Mol Pharm 2012; 9:905-13. [PMID: 22339151 DOI: 10.1021/mp200438v] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Abstract
The human organic cation/carnitine transporter (hOCTN2) is a high affinity cation/carnitine transporter expressed widely in human tissues and is physiologically important for the homeostasis of L-carnitine. The objective of this study was to elucidate the substrate requirements of this transporter via computational modeling based on published in vitro data. Nine published substrates of hOCTN2 were used to create a common feature pharmacophore that was validated by mapping other known OCTN2 substrates. The pharmacophore was used to search a drug database and retrieved molecules that were then used as search queries in PubMed for instances of a side effect (rhabdomyolysis) associated with interference with L-carnitine transport. The substrate pharmacophore was composed of two hydrogen bond acceptors, a positive ionizable feature and ten excluded volumes. The substrate pharmacophore also mapped 6 out of 7 known substrate molecules used as a test set. After searching a database of ~800 known drugs, thirty drugs were predicted to map to the substrate pharmacophore with L-carnitine shape restriction. At least 16 of these molecules had case reports documenting an association with rhabdomyolysis and represent a set for prioritizing for future testing as OCTN2 substrates or inhibitors. This computational OCTN2 substrate pharmacophore derived from published data partially overlaps a previous OCTN2 inhibitor pharmacophore and is also able to select compounds that demonstrate rhabdomyolysis, further confirming the possible linkage between this side effect and hOCTN2.
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Affiliation(s)
- Sean Ekins
- Department of Pharmaceutical Sciences, School of Pharmacy, University of Maryland , 20 Penn Street, Baltimore, Maryland 21201, USA.
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24
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Sacan A, Ekins S, Kortagere S. Applications and limitations of in silico models in drug discovery. Methods Mol Biol 2012; 910:87-124. [PMID: 22821594 DOI: 10.1007/978-1-61779-965-5_6] [Citation(s) in RCA: 31] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Drug discovery in the late twentieth and early twenty-first century has witnessed a myriad of changes that were adopted to predict whether a compound is likely to be successful, or conversely enable identification of molecules with liabilities as early as possible. These changes include integration of in silico strategies for lead design and optimization that perform complementary roles to that of the traditional in vitro and in vivo approaches. The in silico models are facilitated by the availability of large datasets associated with high-throughput screening, bioinformatics algorithms to mine and annotate the data from a target perspective, and chemoinformatics methods to integrate chemistry methods into lead design process. This chapter highlights the applications of some of these methods and their limitations. We hope this serves as an introduction to in silico drug discovery.
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Affiliation(s)
- Ahmet Sacan
- School of Biomedical Engineering, Drexel University, Philadelphia, PA, USA
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25
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Kortagere S, Lill M, Kerrigan J. Role of computational methods in pharmaceutical sciences. Methods Mol Biol 2012; 929:21-48. [PMID: 23007425 DOI: 10.1007/978-1-62703-050-2_3] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/08/2022]
Abstract
Over the past two decades computational methods have eased up the financial and experimental burden of early drug discovery process. The in silico methods have provided support in terms of databases, data mining of large genomes, network analysis, systems biology on the bioinformatics front and structure-activity relationship, similarity analysis, docking, and pharmacophore methods for lead design and optimization. This review highlights some of the applications of bioinformatics and chemoinformatics methods that have enriched the field of drug discovery. In addition, the review also provided insights into the use of free energy perturbation methods for efficiently computing binding energy. These in silico methods are complementary and can be easily integrated into the traditional in vitro and in vivo methods to test pharmacological hypothesis.
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Affiliation(s)
- Sandhya Kortagere
- Department of Microbiology and Immunology, Drexel University College of Medicine, Philadelphia, PA, USA.
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26
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Ekins S, Williams AJ, Krasowski MD, Freundlich JS. In silico repositioning of approved drugs for rare and neglected diseases. Drug Discov Today 2011; 16:298-310. [PMID: 21376136 DOI: 10.1016/j.drudis.2011.02.016] [Citation(s) in RCA: 191] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2010] [Revised: 02/09/2011] [Accepted: 02/22/2011] [Indexed: 02/08/2023]
Abstract
One approach to speed up drug discovery is to examine new uses for existing approved drugs, so-called 'drug repositioning' or 'drug repurposing', which has become increasingly popular in recent years. Analysis of the literature reveals many examples of US Food and Drug Administration-approved drugs that are active against multiple targets (also termed promiscuity) that can also be used to therapeutic advantage for repositioning for other neglected and rare diseases. Using proof-of-principle examples, we suggest here that with current in silico technologies and databases of the structures and biological activities of chemical compounds (drugs) and related data, as well as close integration with in vitro screening data, improved opportunities for drug repurposing will emerge for neglected or rare/orphan diseases.
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Affiliation(s)
- Sean Ekins
- Collaborations in Chemistry, 601 Runnymede Avenue, Jenkintown, PA 19046, USA.
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