Ekins, Sean

From Scientistsdb
Jump to: navigation, search
Sean Ekins

Born Cleethorpes, England
Residence United States
Nationality United Kingdom, United States
Fields Pharmacology
Clinical Pharmacology
Drug Discovery
Cheminformatics
Cheminformatics
Neglected Diseases
Scientific Collaborations
Alternatives to Animal Models
Computational toxicology
Systems biology
Alma mater The University of Aberdeen and Nottingham Trent University
Doctoral advisor Gabrielle M. Hawksworth and M. Danny Burke
Known for ADME/Tox models
Pharmacophores
New technologies for pharmaceutical R&D

Sean Ekins, is a British pharmacologist and expert in the fields of ADME/Tox, computational toxicology and cheminformatics at Collaborations in Chemistry. He is also the editor of 4 books and a book series for Wiley. Ekins is married with two children.

Contents

Contents

Sean Ekins was born in Cleethorpes, England, on 2 March 1970 to John Ekins and Elsie May Ekins. He grew up in Grimsby. Ekins attended Edward Street Primary and Middle School followed by Havelock School. Ekins then earned his HND Science (Applied Biology from Nottingham Trent University (formerly Polytechnic, 1988–1991), graduating in 1991, with a sandwich year (1989–1990) at the pharmaceutical company Servier in Fulmer, UK where his interest in drug discovery was established. Ekins then earned his M.Sc. in Clinical Pharmacology (1991–1992) at the University of Aberdeen with a dissertation entitled “Speculations on the relative roles of cytochrome P450 and flavin containing monooxygenase in the metabolism of S12363”[1] he then earned a Ph.D. in clinical pharmacology, at the University of Aberdeen in 1996, funded by Servier, and wrote a thesis entitled “Maintenance and cryopreservation of xenobiotic metabolism in precision-cut liver slices. Evaluation of an alternative in vitro model to isolated hepatocytes”. During his PhD he developed an interest in predicting drug-drug interactions computationally as an alternative to using animal models.

Career

From 1996-1998 Ekins continued his research as a Postdoc at Eli Lilly and Company laboratories characterizing the little known CYP2B6 and applied computational methods to this enzyme. He collected drug-drug interaction Ki data for other P450s and generated pharmacophores. He created test sets to test the models. that were ultimately published,[2][3][4][5],.[6] He published seminal ideas on how such models could be used to profile libraries of compounds for predicted drug-drug interactions.,[7][8]

In late 1998 Ekins joined Pfizer and continued his interest in predicting drug-drug interactions and ADME properties. In 1999 he moved to Lilly to build a predictive ADME/Tox group. Between 1999 and late 2001 he generated pharmacophores and statistical models for various proteins including P-glycoprotein,[9][10] hERG,[11] PXR [12] and enzymes.[13][14]

In December 2001 he started work for a start-up company, Concurrent Pharmaceuticals (now Vitae Pharmaceuticals) as the Associate Director, Computational Drug Discovery. He was responsible for developing computational models for ADME/Tox and targets of interest. During this time he developed an interest in the polypharmacology of ADME/Tox proteins. In 2004 he joined GeneGo (now owned by Thomson Reuters) as Vice President, Computational Biology and developed the MetaDrugTM product (patent pending),[15][16],,.[17][18]

In 2005 he earned his D.Sc. in Science from the University of Aberdeen with a thesis entitled “Computational and in vitro models for predicting drug interactions in humans”.

Since 2006 Ekins has consulted for several companies including on pharmacoeconomics and performing research on Tuberculosis Drug Discovery for Collaborative Drug Discovery.,[19][20],.[21]

Ekins has also carried out independent research and collaborative research on topics including pharmacophores for drug transporters, cheminformatics for predicting immunoassay cross reactivity, models for studying nuclear receptor-ligand co-evolution, computational models for PXR agonists and antagonists as well as analyses of large datasets and crowdsourcing data.

Making Pharmaceutical Data Open

In 2010 Sean Ekins was the co-author on seminal papers around data sharing and making pharmaceutical data more open that suggest:

1. the long overdue need for making preclinical ADME/Tox data precompetitive [22]

2. how crowdsourcing could be used in the pharmaceutical industry [23]

3. how computational models for pharmacoeconomics could be shared by the scientific community[24]

4. what tools are still needed in cheminformatics and how methods for model sharing will be important[25]

5. How pharmaceutical companies could use open source molecular descriptors and algorithms which would facilitate computational model sharing with the academic and neglected disease community[26]

This work is important because it is the first time that anyone has proposed a broad array of approaches to make preclinical and postmarketing data and models available as well as demonstrate the feasibility of such approaches. Ekins has served on the advisory group for ChemSpider and provided an array of pharmaceutical data sets to the database to make it available to the community.

Tuberculosis and Malaria Research

While working for Collaborative Drug Discovery, (funded by the Bill and Melinda Gates Foundation) he analyzed data provided to the public domain by the pharmaceutical industry. Specifically this was malaria screening data from GSK for over 13,000 compounds. As a result of this work an important caution was provided to the scientific community in accepting such data at face value.[27] These data were compared to other malaria and tuberculosis data.[28]

In addition he has provided analyses of very large libraries of tuberculosis data which highlight important physicochemical properties,.[29][30]

Ekins has highlighted gaps in TB research, specifically in how cheminformatics and other computational tools could be integrated to improve efficiency[31] and provided examples of how computational methods can be used to assist in screening for compounds active against TB[32]

In February 2011 Ekins began participating in the MM4TB project as part of Collaborative Drug Discovery [33] lead by Professor Stewart Cole.[34]

Science Mobile Applications

Ekins co-developed a Wiki with Antony John Williams called Science Mobile Applications[35] launched June 21 2011[36]. Initially this grew out of a desire to track chemistry Apps[37] (for a paper submitted) and then Apps for science in the chemistry classroom [38].

Database quality

Using their respective blogs, Ekins and Antony Williams alerted the scientific community within days of the release of the NCGC NPC browser [39]that there were significant errors in molecule structures. These observations were later published as an editorial in Drug Discovery Today [40].

Green Solvents

While attending a green chemistry conference in 2011 Ekins proposed the need for a mobile app to make the ACS Green Chemistry Institute's solvent selection guide data [41] more visible. Alex M. Clark responded by Twitter and then built this app for iOS in days.

Open Drug Discovery Teams

ODDT (Open Drug Discovery Teams) [42] was developed with Alex M Clark as part of a challenge organized by the Pistoia Alliance [43]. It is a free mobile app for iOS for rare and neglected diseases. We have created a user interface via the ODDT app, for iOS-based devices (iPhone, iPod and iPad) that is "Flipboard or magazine-like". The user initially selects from a list of topics, and from there can flip through recently posted content. The app was launched April 12 2012 and is free for anyone to use, and provides content-consumption features as its primary purpose. We are capturing content on a server and make use of Twitter as the primary source (as a proof of concept), which is regularly polled and assimilated into the data collection. The service provides an API for accessing ODDT topics and content. As the project evolves, the server will be gradually augmented to recognize particular data sources and information streams, and provide value added functionality. Currently it is able to recognize chemical data such as molecular structures, reactions and datasheets. The project is open to participation from anyone and provides the ability for users to make annotations and assertions, thereby contributing to the collective value of the data to the engaged community.

TB Mobile

TB Mobile [44] was developed with Alex M Clark and Malabika Sarker. It contains over 740 molecules and their known targets in Mtb. Additional information and links to other databases are provided. The mobile application can be used to infer potential targets based on molecule similarity for a query structure. TB Mobile was funded as part of a STTR funded by NIAID and is owned by Collaborative Drug Discovery, Inc.

Book editing

Ekins has edited or co-edited 4 books for Wiley including: Computer Applications in Pharmaceutical Research and Development, Computational Toxicology: Risk Assessment For Pharmaceutical and Environmental Chemicals, Drug Efficacy, Safety, and Biologics Discovery: Emerging Technologies and Tools and Collaborative Computational Technologies for Biomedical Research. All the books have an underlying connection with computational technologies and their application for pharmaceutical R& D.

Journal editing

Ekins is the Editor, Expert Reviews for the journal Pharmaceutical Research[45] [46], a Springer Journal. He solicits reviews, commentaries and perspectives on important topics for the field of pharmaceutical research. He was an editorial board member for Drug Metabolism and Disposition 2003-2011, Journal of Pharmacological and Toxicological Methods [47], Mutation research - reviews [48], and Drug Discovery Today [49]

Patents

Ekins is inventor on two issued US patents,[50][51]

References

  1. Ekins, S., et al. (1993) The role of cytochrome P4503A in the metabolism of the vinca alkaloid a-aminophosphonate derivative S12363 by human liver microsomes. Br J Clin. Pharmacol 36, 165-166P
  2. http://jpet.aspetjournals.org/content/291/1/424.full.pdf+html?sid=99bffa04-48cb-4cbf-bac6-d94e568b5b31 Ekins, S. Bravi, G. Binkley, S. Gillespie, J.S. Ring, B.J. Wikel, J.H. and Wrighton S.A. (1999) Three and four dimensional-quantitative structure activity relationship analyses of CYP3A4 inhibitors. J Pharm Exp Ther 290, 429-438
  3. Ekins, S. et al. (1999) Three and four dimensional-quantitative structure activity relationship (3D / 4D-QSAR) analyses of CYP2D6 inhibitors. Pharmacogenetics 9, 477-489
  4. Ekins, S. et al. (1999) Three dimensional-quantitative structure activity relationship analyses of substrates for CYP2B6. Pharm Exp Ther 288, 21-29
  5. http://jpet.aspetjournals.org/content/291/1/424.full.pdf+html?sid=99bffa04-48cb-4cbf-bac6-d94e568b5b31 Ekins, S. Bravi, G. Wikel J.H. and Wrighton SA (1999) Three dimensional quantitative structure activty relationship (3D-QSAR) analysis of CYP3A4 substrates. J Pharmacol Exp Thera 291, 424-433
  6. Ekins, S. and Wrighton, S.A. (1999) The role of CYP2B6 in human xenobiotic metabolism. Drug Metab Rev 31, 719-754
  7. Ekins, S. et al. (2000) Predicting drug-drug interactions in silico using pharmacophores: a paradigm for the next millennium. In Pharmacophore perception, development, and use in drug design (Guner, O.F., ed.), pp. 269-299, IUL
  8. Ekins, S. et al. (2000) Progress in predicting human ADME parameters in silico. J Pharmacol Toxicol Methods 44 (1), 251-272
  9. Ekins, S. et al. (2002) Application of three dimensional quantitative structure-activity relationships of P-glycoprotein inhibitors and substrates. Mol Pharmacol 61, 974-981
  10. Ekins, S. et al. (2002) Three dimensional quantitative structure-activity relationships of inhibitors of P-glycoprotein. Mol Pharmacol 61, 964-973
  11. http://jpet.aspetjournals.org/content/301/2/427.full.pdf+html?sid=99bffa04-48cb-4cbf-bac6-d94e568b5b31 Ekins, S. Crumb, W.J. Sarazan, R.D. Wikel, J.H. and Wrigton, S.A. (2002) Three dimensional quantitative structure activity relationship for the inhibition of the hERG (human ether-a-gogo related gene) potassium channel. J Pharmacol Exp Thera 301, 427-434
  12. Ekins, S. and Erickson, J.A. (2002) A pharmacophore for human pregnane-X-receptor ligands. Drug Metab Dispos 30, 96-99
  13. Ekins, S. et al. (2002) Pharmacophore insights into the active sites of the CYP3A enzymes. The Pharmacologist 44 Supplement, 114.
  14. Ethell, B.T. et al. (2002) Quantitative structure activity relationships for the glucuronidation of simple phenols by expressed human UGT1A6 and UGT1A9. Drug Metab. Dispo.s 30, 734-738
  15. Ekins, S. et al. (2005) Computational Prediction of Human Drug Metabolism. Exp Opin Drug Metab Toxicol 1, 303-324
  16. Ekins, S. et al. (2005) Systems biology: applications in drug discovery. In Drug discovery handbook (Gad, S., ed.), pp. 123-183, Wiley
  17. Ekins, S. et al. (2005) A Novel Method for Visualizing Nuclear Hormone Receptor Networks Relevant to Drug Metabolism. Drug Metab Dispos 33, 474-481
  18. Ekins, S. et al. (2005) Techniques: Application of Systems Biology to Absorption, Distribution, Metabolism, Excretion, and Toxicity. Trends Pharmacol Sci 26, 202-209
  19. Ekins, S. et al. (2010) A Collaborative Database And Computational Models For Tuberculosis Drug Discovery. Mol BioSystems 6, 840-851
  20. Ekins, S. et al. (2010) Analysis and hit filtering of a very large library of compounds screened against Mycobacterium tuberculosis Mol BioSyst 6, 2316-2324
  21. Lamichhane, G. et al. (2011) Essential Metabolites of M. tuberculosis and their Mimics. Mbio 2, e00301-00310
  22. Ekins S and Williams AJ, Precompetitive Preclinical ADME/Tox Data: Set It Free on The Web to Facilitate Computational Model Building to Assist Drug Development. Lab On A Chip, 10: 13-22, 2010.
  23. Ekins S. and Williams AJ, Reaching out to collaborators: crowdsourcing for pharmaceutical research, Pharm Res, 27: 393-395, 2010.
  24. Arnold RJG and Ekins S, Time for cooperation in health economics among the modeling community, PharmacoEconomics, 28(8):609-613, 2010
  25. Ekins S, Gupta R, Gifford E, Bunin BA, Waller CL, Chemical Space: missing pieces in cheminformatics, Pharm Res, 27: 2035-2039, 2010
  26. Rishi R. Gupta, Gifford, EM, Liston T, Waller CL, Hohman M, Bunin BA and Ekins S, Using open source computational tools for predicting human metabolic stability and additional ADME/Tox properties, Drug Metab Dispos, 38: 2083-2090, 2010
  27. Sean Ekins and Antony John Williams, When Pharmaceutical Companies Publish Large Datasets: An Abundance of riches or fool’s gold? Drug Disc Today, 15; 812-815, 2010 http://www.ncbi.nlm.nih.gov/pubmed/20732447
  28. Ekins S and Williams AJ, Meta-analysis of molecular property patterns and filtering of public datasets of antimalarial “hits” and drugs, MedChemComm, 1: 325-330, 2010
  29. Ekins S, Bradford J, Dole K, Spektor A, Gregory K, Blondeau D, Hohman M and Bunin BA, A Collaborative Database and Computational Models for Tuberculosis Drug Discovery, Mol BioSyst, 6: 840-851, 2010 http://www.ncbi.nlm.nih.gov/pubmed/20567770
  30. Ekins S, Kaneko T, Lipinski CA, Bradford J, Dole K, Spektor A, Gregory K, Blondeau D, Ernst S, Yang J, Goncharoff N, Hohman M and Bunin BA, Analysis and hit filtering of a very large library of compounds screened against Mycobacterium tuberculosis, Mol Biosyst, 6: 2316-2324, 2010 http://www.ncbi.nlm.nih.gov/pubmed/20835433
  31. Ekins S, Freundlich JS, Choi I, Sarker M and Talcott C, Computational Databases, Pathway and Cheminformatics Tools for Tuberculosis Drug Discovery, Trends In Microbiology, 19: 65-74, 2011 http://www.ncbi.nlm.nih.gov/pubmed/21129975
  32. Lamichhane G, Freundlich JS, Ekins S , Wickramaratne N, Nolan, S and Bishai WR, Essential Metabolites of M. tuberculosis and their small molecule mimics, Mbio, 2: e00301-10, 2011 http://www.ncbi.nlm.nih.gov/pubmed/21285434
  33. http://mm4tb.org/
  34. http://cole-lab.epfl.ch/
  35. The SciMobileApps Wiki
  36. [http://www.chemconnector.com/2011/06/21/announcing-the-scimobileapps-wiki-for-community-based-listing-of-science-apps/ Announcement of the SciMobileApps Wiki
  37. http://www.rsc.org/chemistryworld/Issues/2010/May/MobileChemistryChemistryHandsFace.asp
  38. http://pubs.acs.org/doi/abs/10.1021/ed200029p Smart Phones, a Powerful Tool in the Chemistry Classroom
  39. http://tripod.nih.gov/npc/
  40. 1. Williams AJ and Ekins S. A Quality Alert and Call for Improved Curation of Public Chemistry Databases, Drug Disc Today, 16: 747-750, 2011 http://www.ncbi.nlm.nih.gov/pubmed/21871970.
  41. http://portal.acs.org/portal/acs/corg/content?_nfpb=true&_pageLabel=PP_TRANSITIONMAIN&node_id=1422&use_sec=false&sec_url_var=region1&__uuid=900e9cd2-4da9-4368-9c2d-e58be45ea050
  42. http://www.scimobileapps.com/index.php?title=Open_Drug_Discovery_Teams
  43. http://pistoiaalliance.org/blog/2012/06/when-dragons-speak
  44. http://www.scimobileapps.com/index.php?title=TB_Mobile
  45. http://www.springer.com/biomed/pharmaceutical+science/journal/11095
  46. http://www.springer.com/biomed/pharmaceutical+science/journal/11095?detailsPage=editorialBoard
  47. http://www.elsevier.com/wps/find/journaleditorialboard.cws_home/505776/editorialboard
  48. http://www.elsevier.com/wps/find/journaleditorialboard.cws_home/506097/editorialboard
  49. http://www.elsevier.com/wps/find/journaleditorialboard.cws_home/30921/editorialboard
  50. U.S. Patent no 6564152: Ekins S and Smith BJ, Pharmacophore models for, methods of screening for, and identification of the cytochrome P-450 inhibitory potency of neurokinin-1 receptor antagonists.http://patft.uspto.gov/netacgi/nph-Parser?Sect1=PTO2&Sect2=HITOFF&p=1&u=%2Fnetahtml%2FPTO%2Fsearch-bool.html&r=1&f=G&l=50&co1=AND&d=PTXT&s1=ekins&s2=neurokinin-1&OS=ekins+AND+neurokinin-1&RS=ekins+AND+neurokinin-1
  51. U.S. Patent no 6489094: Ekins S, Kelly KG, Johnson DL, Method and device for drug-drug interaction testing sample preparation http://patft.uspto.gov/netacgi/nph-Parser?Sect1=PTO2&Sect2=HITOFF&p=1&u=%2Fnetahtml%2FPTO%2Fsearch-bool.html&r=22&f=G&l=50&co1=AND&d=PTXT&s1=Ekins&s2=CYP2D6&OS=Ekins+AND+CYP2D6&RS=Ekins+AND+CYP2D6.

Publications

Papers


1. Ekins S, Casey A.C, Roberts D, Parish T. and Bunin BA, Bayesian Models for Screening and TB Mobile for Target Inference with Mycobacterium tuberculosis, Submitted 2013.

2. Ekins S, Freundlich JS and Reynolds RC, Fusing dual-event datasets for Mycobacterium Tuberculosis machine learning models and their evaluation, Submitted 2013.

3. Ekins S, Freundlich JS, Hobrath JV, White EL, Reynolds RC, Combining computational methods for hit to lead optimization in Mycobacterium tuberculosis drug discovery, Pharm Res, In Press 2013.

4. Ponder EL, Freundlich JS, Sarker M, Ekins S, Computational models for neglected diseases: gaps and opportunities, Pharm Res, In Press 2013.

5. Fukuda Y, Takenaka K, Sparreboom A, Cheepala SB, Wu C-P, Ekins S, Ambudkar SV and Schuetz JD, HIV protease inhibitors interact with ABCC4/MRP4: a basis for unanticipated enhanced efficacy and toxicity, Mol Pharmacol, In press 2013.

6. Wood J, Sames L, Moore A, Ekins S, The multifaceted roles of rare disease parent / patient advocates in drug discovery, Drug Disc Today, In press 2013

7. Ekins S, Goldsmith M-R, Simon A, Zsoldos Z, Ravitz O and Williams AJ, LASSO-ing potential nuclear receptor agonists and antagonists: A New Computational Method For Database Screening, Journal of Computational Medicine, In press, 2013.

8. Li H, Redinbo MR, Venkatesh M, Ekins S, Chaudhary A, Bloch N, Negassa A, Mukherjee P, Kalpana G, Mani S, Novel Yeast-Based Strategy Unveils Antagonist Binding Regions on the Nuclear Xenobiotic Receptor PXR, J Biol Chem, 288:13655-68, 2013

9. Ekins S and Freundlich JS, Computational models for tuberculosis drug discovery, In In silico models in drug discovery, Methods Mol Biol, 993:245-62, 2013.

10. Ekins S and Bunin BA, The collaborative drug discovery (CDD) database, Methods Mol Biol. 993:139-54, 2013

11. Ekins S, Clark AM, Sarker M, TB Mobile: A Mobile App for Anti-tuberculosis Molecules with Known Targets, J Cheminform, 5:13, 2013.

12. Clark AM, Williams AJ and Ekins S, Cheminformatics workflows using mobile apps, Chem-Bio Informatics Journal, 13: 1-18 2013.

13. Anderson JW, Sarantakis D, Terpinski J, Kumar TRS, Tsai H-C, Kuo M, Ager, AL, Jacobs, Jr WR., Schiehser GA, Ekins S, Sacchettini JC, Jacobus DP, Fidock DA, Freundlich JS, Novel diaryl ureas with efficacy in a mouse model of malaria, Bioorg Med Chem Lett, 23: 1022, 2013. PMID: 23313245

14. Ekins S, Clark AM and Williams AJ, Incorporating Green Chemistry Concepts into Mobile Applications and their potential uses, ACS Sustain Chem Eng, 1. 8-13, 2013.

15. Southan C, Williams AJ and Ekins S, Challenges and Recommendations for obtaining chemical structures of industry provided repurposing candidates, Drug Disc Today, 18: 58-70, 2013.

16. Ekins S, Waller CL, Bradley MP, Clark AM and Williams AJ, Four disruptive strategies for removing drug discovery bottlenecks, Drug Discovery Today, 18: 265-271, 2013.

17. Ekins S, Reynolds RC, Franzblau SG, Wan B, Freundlich JS and Bunin BA. Enhancing hit identification in Mycobacterium tuberculosis drug discovery using validated dual-event Bayesian models, PLOS ONE, 8(5): e63240, 2013.

18. Ekins S, Reynolds RC, Kim H, Koo M-S, Ekonomidis M, Talaue M, Paget SD, Woolhiser LK Lenaerts AJ, Bunin BA, Connell N and Freundlich JS. Bayesian models leveraging bioactivity and cytotoxicity information for drug discovery, Chem Biol, 20: 370-378, 2013.

19. Ekins S, Olechno J and Williams AJ, Dispensing processes impact apparent biological activity as determined by computational and statistical analyses, PLOSONE, 8: e62325, 2013.

20. Petrie M, Lynch KL, Ekins S, 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, 51(2):83-91, 2013.

21. Dong Z, Ekins S and Polli JE, Structure activity relationship for FDA approved drugs as inhibitors of the human sodium taurocholate co-transporting polypeptide (NTCP), Mol Pharmaceutics, 10:1008-19, 2013.

22. Yasuda K, Cline C, Vogel P, Onciu M, Fatima S, Sorrentino BP, Thirumaran RK, Ekins S, Urade Y, Fujimori K and Schuetz EG, Drug transporters on arachnoid barrier cells contribute to the blood-cerobrospinal fluid barrier, Drug Metab Dispos, 41(4):923-31, 2013.

23. Zhang L, Fourches D, Sedykh A, Zhu H, Golbraikh A, Ekins S, Clark J, Connelly MC, Sigal M, Hodges D, Guiguemde A, Guy RK, and Tropsha A, The Discovery of Novel Antimalarial Compounds enabled by QSAR-based Virtual Screening, J Chem Inf Model, 25: 475-92. 2013.

24. Ekins S, Polli JE, Swaan PW, Wright SH, Computational Modeling to accelerate the identification of transporter substrates and inhibitors that affect drug disposition, Clin Pharm Thera, 92: 661-665, 2012.

25. Williams AJ, Wilbanks J and Ekins S, Why Open Drug Discovery Needs Four Simple Rules for Licensing Data and Models, PLoS Comput Biol, 8(9):e1002706, 2012.

26. Ekins S, Clark AM and Williams AJ, Open Drug Discovery Teams: A Chemistry Mobile App for Collaboration, Mol Informatics, 31: 585-597, 2012.

27. Williams AJ, Ekins S, Spjuth O and Willighagen EL, Accessing, using and creating chemical property databases for computational toxicology modeling, Methods in Molecular Biology, 929: 221-241, 2012

28. Kortagere S, Ekins S and Krasowski MD, Ligand and Structure-based Pregnane X receptor models, Methods in Molecular Biology, 929: 359-375, 2012.

29. Beaulieu C, Samuels ME, Ekins S, McMaster CR, Edwards AM, Krainer AR, Hicks GG, Frey BJ, Boycott KM and MacKenzie, AE., A generalizable pre-clinical research approach for orphan disease therapy. Orphanet, 7(1):39, 2012.

30. Sacan A, Kortagere S and Ekins S, Applications and limitations of in silico methods in drug discovery, Methods Mol Biol, 910:87-124, 2012.

31. Astorga B, Ekins S, Morales M and Wright SH, Molecular Determinants of Ligand Selectivity for the Human Multidrug And Toxin Extrusion Proteins, MATE1 and MATE-2K, J Pharmacol Exp Ther, 341: 743-755, 2012.

32. Ekins S, Waller CL, Bradley MP and Williams AJ, Disruptive strategies for removing drug discovery bottlenecks, Nature Preceedings, doi:10.1038/npre.2012.6961.1 2012.

33. Ekins S, Shigeta R and Bunin BA, Bottlenecks caused by software gaps in miRNA and RNAi research, Pharm Res, 29: 1717-1721, 2012.

34. Clark, AM, Ekins S and Williams AJ, Redefining cheminformatics with intuitive collaborative mobile apps, Mol Informatics, 31: 569-584, 2012.

35. Williams AJ, Ekins S and Tkachenko V, Towards a Gold Standard: Regarding Quality in Public Domain Chemistry Databases and Approaches to Improving the Situation, Drug Discovery Today,17 (13-14):685-701, 2012.

36. Sarker M, Talcott C, Madrid P, Chopra S, Bunin BA, Lamichhane G, Freundlich JS and Ekins S, Combining Cheminformatics methods and pathway analysis to identify molecules with whole cell activity against Mycobacterium tuberculosis, Pharm Res, 29: 2115-2127, 2012.

37. Ekins S, Diao L and Polli JE, A substrate pharmacophore for the human organic cation/cationic transporter identifies compounds associated with rhabdomyolysis, Mol Pharmaceutics, 9:905-913, 2012.

38. Ekins S and Williams AJ, The future of computational models for predicting human toxicities, ALTEX Proceedings,1/12, Proceedings of WC8, 2012. http://www.altex.ch/resources/549554_EkinsandWilliams2.pdf

39. Fidler AE, Holland PT, Reschly EJ, Ekins S and Krasowski MD, Activation of a tunicate (Ciona intestinalis) xenobiotic receptor orthologue by both natural toxins and synthetic toxicants, Toxicon, 59:365-72, 2012.

40. Williams, AJ, Ekins S, Clark AM, Jack JJ and Apodaca RL, Mobile apps for chemistry in the world of drug discovery, Drug Disc Today, 16:928-939, 2011.

41. Ekins S and Williams AJ, Finding promiscuous old drugs for new uses, Pharm Res, 28: 1785-1791, 2011.

42. Ekins S, Williams AJ, Krasowski MD and Freundlich JS, In silico repositioning of approved drugs for rare and neglected diseases, Drug Disc Today, 16: 298-310, 2011.

43. Ekins S and Freundlich JS, Validating new tuberculosis computational models with public whole cell screening aerobic activity datasets, Pharm Res, 28, 1859-1869, 2011.

44. Lamichhane G, Freundlich JS, Ekins S , Wickramaratne N, Nolan, S and Bishai WR, Essential Metabolites of M. tuberculosis and their small molecule mimics, Mbio, 2: e00301-10, 2011.

45. Krasowski MD, Ai N, Hagey LR, Kolliz EM, Kullman SW, Reschly EJ and Ekins S, The evolution of farnesoid X, vitamin D, and pregnane X receptors: insights from the green-spotted pufferfish (Tetraodon nigriviridis) and other non-mammalian species, BMC Biochem 12: 5, 2011

46. Pan Y, Li L, Kim G, Ekins S, Wang H and Swaan PW, Identification of novel hPXR activators amongst prescribed drugs by integrated structure- and ligand-based virtual screening, Drug Metab Dispos, 39:337-344, 2011.

47. Poulin P, Ekins S and Theil F-P, A hybrid approach to advancing quantitative prediction of tissue distribution of basic drugs in human, Toxicol Appl Pharmacol, 250: 194-212, 2011.

48. Ekins S, Freundlich JS, Choi I, Sarker M and Talcott C, Computational Databases, Pathway and Cheminformatics Tools for Tuberculosis Drug Discovery, Trends In Microbiology, 19: 65-74, 2011.

49. Ekins S and Williams AJ, Meta-analysis of molecular property patterns and filtering of public datasets of antimalarial “hits” and drugs, MedChemComm, 1: 325-330, 2010.

50. Ekins S, Kaneko T, Lipinski CA, Bradford J, Dole K, Spektor A, Gregory K, Blondeau D, Ernst S, Yang J, Goncharoff N, Hohman M and Bunin BA, Analysis and hit filtering of a very large library of compounds screened against Mycobacterium tuberculosis, Mol Biosyst, 6: 2316-2324, 2010.

51. Ekins S, Xu JJ and Williams AJ, A predictive ligand-based Bayesian models for human drug induced liver injury, Drug Metab Dispos, 38: 2302-2308, 2010.

52. Gupta RR, Gifford, EM, Liston T, Waller CL, Hohman M, Bunin BA and Ekins S, Using open source computational tools for predicting human metabolic stability and additional ADME/Tox properties, Drug Metab Dispos, 38: 2083-2090, 2010.

53. Ekins S and Williams AJ, When Pharmaceutical Companies Publish Large Datasets: An Abundance of riches or fool’s gold? Drug Disc Today, 15; 812-815, 2010.

54. Ekins S, Gupta R, Gifford E, Bunin BA, Waller CL, Chemical Space: missing pieces in cheminformatics, Pharm Res, 27: 2035-2039, 2010.

55. Zheng X, Diao L, Ekins S and Polli JE, Why we should be vigilant: drug cytotoxicity observed with in vitro transporter inhibition studies, Biochem Pharmacol, 80: 1087-1092, 2010.

56. Diao, L, Ekins S and Polli JE, Quantitative structure activity relationship for inhibition of human organic cation/carnitine transporter (OCTN2), Mol Pharm, 7: 2120-2131, 2010.

57. Kortagere S, Krasowski MD, Reschly EJ, Venkatesh M, Mani S and Ekins S, Evaluation of Computational Docking to Identify PXR Agonists in the ToxCastTM Database, Env Health Perspect, 118: 1412-1417, 2010.

58. Krasowski MD, Ni A, Hagey LR and Ekins S, Evolution of promiscuous nuclear hormone receptors LXR, FXR, VDR, PXR and CAR, Mol Cell Endocrinol 334: 39-48, 2010.

59. Ekins S, Honeycutt JD and Metz JT, Evolving molecules using multi-objective optimization: applying to ADME/Tox, Drug Disc Today, 15: 451-460, 2010.

60. Arnold RJG and Ekins S, Time for cooperation in health economics among the modeling community, PharmacoEconomics, 28(8):609-613, 2010.

61. Howarth DL, Hagey LR, Law SHW, Ai N, Krasowski MD, Ekins S, Moore JT, Kollitz EM, Hinton DE and Kullman SW. Two farnesoid X receptor α isoforms in Japanese medaka (Oryzias latipes) are differentially activated in vitro. Aquat Toxicol 2010; 98: 245-255.

62. Ekins S. and Williams AJ, Reaching out to collaborators: crowdsourcing for pharmaceutical research, Pharm Res, 27: 393-395, 2010.

63. Kortagere S and Ekins S, Troubleshooting computational methods in drug discovery, J Pharm Tox Methods, 61: 67-75, 2010.

64. Ekins S and Williams AJ, Precompetitive Preclinical ADME/Tox Data: Set It Free on The Web to Facilitate Computational Model Building to Assist Drug Development. Lab On A Chip, 10: 13-22, 2010.

65. Zientek M, Stoner C, Ayscue R, Klug-McLeod J, Jiang Y, West M, Collins C and Ekins S, Integrated In Silico- In vitro strategy for addressing Cytochrome P450 3A4 time dependent inhibition, Chem Res Toxicol, 23: 664-676, 2010.

66. Ekins S, Bradford J, Dole K, Spektor A, Gregory K, Blondeau D, Hohman M and Bunin BA, A Collaborative Database and Computational Models for Tuberculosis Drug Discovery, Mol BioSyst, 6: 840-851, 2010.

67. Berg, EL, Yang J, Melrose J, Nguyen D, Privat S, Rosler E, Kunkel EJ and Ekins S., Chemical target and pathway toxicity mechanisms defined in primary human cell systems BioMAP systems for analysis of toxicity pathways and mechanisms. J Pharmacol Toxicol Methods, 61:3-15, 2010.

68. Williams AJ, Tkachenko V, Lipinski C, Tropsha A and Ekins S, Free online resources enabling crowdsourced drug discovery, Drug Discovery World, Winter 2009/10, 33-39.

69. Ekins S, Kortagere S, Iyer M, Reschly EJ, Lill MA, Redinbo MR and Krasowski MD, Challenges Predicting Ligand-Receptor Interactions of Promiscuous Proteins: The Nuclear Receptor PXR, PLoS Comput Biol, 5(12): e1000594, 2009.

70. Louise-May S, Bunin B and Ekins S, Towards integrated web-based tools in drug discovery, Touch Briefings - Drug Discovery, 6: 17-21, 2009.

71. Zheng X, Ekins S, Raufman J-P, and Polli J.E., Computational models for drug inhibition of the Human Apical Sodium-dependent Bile Acid Transporter, Mol Pharm, 6: 1591-1603, 2009.

72. Chekmarev D, Kholodovych V, Kortagere S, Welsh WJ and Ekins S, Predicting inhibitors of acetylcholinesterase by regression and classification machine learning approaches with combinations of molecular descriptors, Pharm Res, 26: 2216-2224, 2009.

73. Ivanenkov, YA, Savchuk NP, Ekins S and Balakin KV, Computational mapping tools for drug discovery, Drug Disc Today, 14: 767-775, 2009.

74. Diao L, Ekins S and Polli JE, Novel Inhibitors of Human Organic Cation/Carnitine Transporter (hOCTN2) via Computational Modeling and In Vitro Testing, Pharm Res, 26:1890-1900, 2009.

75. Biswas A, Mani S, Redinbo MR, Krasowski MD, Li H and Ekins S, Elucidating the ‘Jekyll and Hyde’ nature of PXR: The case for discovering antagonists, Pharm Res, 26:1807-15, 2009.

76. Krasowski MD, Siam MG, Iyer M, Pizon AG, Giannoutsos S and Ekins S, Predicting interference in immunoassays for drug of abuse/toxicology screening using chemoinformatics methods, Clinical Chemistry, 55:1203-13, 2009.

77. Lin YS, Yasuda K, Assem M, Cline C, Barber J, Li C-W, Kholodovych V, Ai N, Chen JD, Welsh WJ, Ekins S and Schuetz EG, The major human PXR splice variant, PXR.2, exhibits significantly diminished ligand-activated transcriptional regulation, Drug Metab Dispos, 37:1295-304, 2009.

78. Krasowski MD, Siam MG, Iyer M and Ekins S, Molecular similarity methods for predicting cross reactivity with therapeutic drug monitoring immunoassays, Thera Drug Monitoring, 31:337-44, 2009.

79. Ai N, Krasowski MD, Welsh WJ and Ekins S, Understanding nuclear receptors using computational methods, Drug Disc Today, 14: 486-494, 2009.

80. Krasowski MD, Pizon AF, Siam MG, Giannoutsos S, Iyer M and Ekins S, Using molecular similarity to highlight the challenges of routine immunoassay-based drug of abuse/toxicology screening in emergency medicine. BMC Emergency Medicine, 9:5, 2009.

81. Ekins S and Tropsha A, A turning point for blood-brain barrier modeling, Pharm Res, 26: 1283-1284, 2009.

82. Kortagere S, Chekmarev D, Welsh WJ, Ekins S, Hybrid scoring and classification approaches to predict human pregnane X receptor activators, Pharm Res, 26:1001-11, 2009.

83. Kortagere S, Krasowski MD and Ekins S, The importance of discerning shape in molecular pharmacology, Trends Pharmacol Sci, 30:138-47, 2009.

84. Hohman M, Gregory K, Chibale K, Smith PJ, Ekins S and Bunin B, Novel web-based tools combining chemistry informatics, biology and social networks for drug discovery, Drug Disc Today, 14: 261-270, 2009.

85. Ekins S, Predicting endogenous or exogenous molecules that interact with P-glycoprotein using pharmacophores, The ChemSpider Journal of Chemistry, http://www.chemmantis.com/Article.aspx?id=846, 2009

86. Marcus P, Arnold RJG, Ekins S, Sacco P, Massanari M, Young SS, Gold H, Kim-Kuan R, Donohue J, Bukstein D, A retrospective randomized analysis of treatment patterns and asthma control in the US: results of the CHARIOT Study, Current Medical Research and Opinion, 24: 3443-3452, 2008.

87. Ekins S, Kholodovych V, Ai N, Sinz M, Gal J, Gera L, Welsh WJ, Bachmann K and Mani S, Computational discovery of novel low micromolar human pregnane X receptor antagonists, Mol Pharmacol, 74: 662-672, 2008.

88. Krasowski MD, Reschly EJ and Ekins S, Intrinsic disorder in nuclear hormone receptors, J Proteome Res, 7:4359-4372, 2008.

89. Yasuda K, Ranade A, Venkataramanan R, Strom S, Chupka J, Ekins S, Schuetz E and Bachmann K, A comprehensive in vitro and in silico analysis of antibiotics that activate and induce CYP3A4 in liver and intestine, Drug Metab Dispos, 36:1689-97, 2008.

90. Khandelwal A, Krasowski MD, Reschly EJ, Sinz M, Peter W Swaan and Ekins S, Machine learning methods and docking for human Pregnane X Receptor activation, Chem Res Toxicol, 21:1457-67, 2008.

91. Swaan PW, Bensman T, Bahadduri PM, Hall MW, Sarker A, Ekins S and Knoell DL, Bacterial dipeptide recognition and immune activation is facilitated by the human peptide transporter PEPT2, Am J Resp Cell Mol Biol, 39: 536-542, 2008.

92. Kortagere S, Ekins S and Welsh WJ, Halogenated ligands and their interactions with amino acids: implications for structure-activity and structure-toxicity relationships, J Mol Graph Model. 27:170-7, 2008.

93. Kortagere S, Chekmarev DS, Welsh WJ and Ekins S, New predictive models for Blood-Brain Barrier permeability of drug-like molecules, Pharm Res, 25:1836-45, 2008.

94. Reschly EJ, Ai N, Ekins S, Welsh WJ, Hagey LR, Hoffman AF, Krasowski MD, Evolution of the bile salt nuclear receptor FXR in vertebrates, J Lipid Res, 49:1577-1587, 2008.

95. Chekmarev DS, Kholodovych V, Balakin KV, Ivanenkov Y, Ekins S and Welsh WJ, Shape Signatures: new descriptors for predicting cardiotoxicity, Chem Res Toxicol, 21:1304-14 2008.

96. Ekins S, Reschly EJ, Hagey LR and Krasowski MD, Evolution of pharmacologic specificity in the Pregane X Receptor, BMC Evolutionary Biology, 8:103, 2008.

97. Reschly EJ, Ai N, Welsh WJ, Ekins S, Hagey LR, Krasowski MD, Ligand specificity and evolution of liver X receptors, J Steroid Biochem Mol Biol, 110:83-94, 2008.

98. Ekins S, Iyer M, Krasowski MD and Kharasch ED, Molecular Characterization of CYP2B6 substrates, Curr Drug Metab, 9:363-373, 2008.

99. Zhou H, Wu S, Zhai S, Liu A, Sun Y, Li R, Zhang Y, Ekins S, Swaan PW, Fang B, Zhang B, Yan B, Design, Synthesis, Cytoselective Toxicity, Structure-Activity Relationships and Pharmacophores Of Thiazolidinone Derivatives Targeting Drug-Resistant Lung Cancer Cells, J Med Chem, 51: 1242–1251, 2008

100. Stranz DD, Miao S, Campbell S, Maydwell G and Ekins S, Combined computational metabolite prediction and automated structure based analysis of mass spectrometric data, Toxicology Mechanisms and Methods, 18: 243-250, 2008.

101. Ekins S, Ecker GF, Chiba P and Swaan PW, Future directions for drug transporter modeling, Xenobiotica, 37:1152-1170, 2007.

102. Khandelwal A, Bahadduri PM, Chang C, Polli JE, Swaan PW and Ekins S, Computational models to assign biopharmaceutics drug disposition classification from molecular structure, Pharm Res, 24: 2249-2262, 2007.

103. Hupcey MAZ and Ekins S, Improving the drug selection process for combination medical devices, Drug Disc Today, 12; 844-852, 2007.

104. Ekins S, Chang C, Mani S, Krasowski MD, Reschly EJ, Iyer M, Kholodovych V, Ai N, Welsh WJ, Sinz M, Swaan PW, Patel R and Bachmann K, Human pregnane X receptor antagonists and agonists define molecular requirements for different binding sites, Mol Pharmacol, 72: 592-603, 2007.

105. Ekins S, Mestres J and Testa B, In silico pharmacology for drug discovery: methods for virtual ligand screening and profiling. Br J Pharmacol, 152: 9-20, 2007.

106. Ekins S, Mestres J and Testa B, In silico pharmacology for drug discovery: applications to targets and beyond. Br J Pharmacol, 152: 21-37, 2007.

107. Jones DR, Ekins S, Li L and Hall SD, Computational approaches that predict metabolic intermediate complex formation with CYP3A4 (+b5). Drug Metab Dispos, 35: 1466-1475, 2007.

108. Embrechts MJ and Ekins S, Classification of metabolites with kernel partial least squares. Drug Metab Dispos, 35: 325-327, 2007.

109. Ekins S, Mankowski DC, Hoover DJ, Lawton MP, Treadway JL and Harwood HJ Jr, Three dimensional-quantitative structure activity relationship (3D-QSAR) analysis of human CYP51 inhibitors, Drug Metab Dispos, 35: 493-500, 2007.

110. Jolivette, LJ and Ekins S, Methods for predicting human drug metabolism, Adv Clin Chem, 43:131-76, 2007.

111. Chang C, Bahadduri P, Swaan, PW and Ekins, S, Application of integrated computational pharmacophore models and in vitro approaches to rapidly identify novel P-glycoprotein ligands. Drug Metab Dispos, 34: 1976-1984, 2006.

112. Chang C, Ekins S, Bahadduri P, Swaan PW, Pharmacophore-based discovery of ligands for drug transporters. Adv Drug Del Rev, 58:1431-50, 2006.

113. Ekins S, Shimada J and Chang C, Application of data-mining approaches to drug delivery. Adv Drug Del Rev, 58: 1409-1430, 2006.

114. Ekins S, Bugrim A, Brovold L, Kirillov E, Nikolsky Y, Rakhmatulin E, Sorokina S, Ryabov, A, Serebryiskaya T, Melnikov A, Metz J and Nikolskaya T. Algorithms for Network Analysis in Systems-ADME/Tox using the MetaCore and MetaDrug platforms. Xenobiotica, 36:877-901, 2006.

115. Ekins S, Balakin K, Savchuk N and Ivanenkov Y. Assessment of Human Ether-a-Go-Go-Related Gene potassium channel inhibition Using Recursive Partitioning and Kohonen and Sammon mapping Techniques. J Med Chem, 49: 5059-5071, 2006.

116. Crumb WJ, Ekins S, Sarazan RD, Wikel JH, Wrighton SA, Carlson C and Beasley CM jr. Effects of Antipsychotic Drugs on Ito, INa, Isus, IKi and hERG: QT Prolongation, Structure Activity Relationship and Network Analysis. Pharm Res, 23: 1133-1143, 2006.

117. Ekins S, Andreyev S, Ryabov A, Kirillov E, Rakhmatulin E, Sorokina S, Bugrim A and Nikolskaya T. A combined approach to drug metabolism and toxicity assessment. Drug Metab Dispos, 34:495-503, 2006.

118. Ekins, S. Systems ADME/Tox: resources and approaches. J Pharm Tox Methods. 53: 38-66, 2006.

119. Swaan PW and Ekins S. Reengineering the pharmaceutical industry by crash-testing molecules. Drug Disc Today, 10: 1191-1200, 2005.

120. Chang C, Pang KS, Swaan PW and Ekins S. Pharmacophore Modeling of Organic Anion Transporting Polypeptides: A Meta-analysis of Rat Oatp1a1 and Human OATP1B1. J Pharmacol Exp Ther 314:533-541 2005.

121. Nikolsky Y, Ekins S, Nikolskaya T and Bugrim A. A Novel Method for Generation of Signature Networks as Biomarkers from Complex High Throughput Data. Tox Lett 158: 20-29, 2005.

122. Ekins S, Andreyev S, Ryabov A, Kirillov E, Rakhmatulin EA, Bugrim A and Nikolskaya T. Computational prediction of human drug metabolism. Expert Opin Drug Metab Toxicol, 1: 303-324, 2005.

123. Balakin KV, Ivanenkov YA, Savchuk NP, Ivashchenko AA and Ekins S, Comprehensive computational assessment of ADME/Tox properties using maps. Curr Drug Disc Tech, 2: 99-113, 2005.

124. Ekins S, Johnston JS, Bahadduri P, D’Souza VM, Ray A, Chang C and Swaan PW, In vitro and pharmacophore based discovery of novel hPepT1 inhibitors. Pharm Res, 22: 512-517, 2005.

125. Ekins S, Nikolsky Y and Nikolskaya T, Techniques: Application of Systems Biology to Absorption, Distribution, Metabolism, Excretion, and Toxicity, Trends Pharmacol Sci, 26: 202-209, 2005.

126. Suhre WM, Ekins S, Chang C, Swaan PW and Wright SH, Molecular determinants of substrate/inhibitor binding to the human and rabbit renal organic cation transporters, hOCT2 and rbOCT2, Mol Pharmacol 67:1067-1077, 2005.

127. Ekins S, Kirillov E, Rakhmatulin EA and Nikolskaya T, A Novel Method For Visualizing Nuclear Hormone Receptor networks Relevant To Drug Metabolism, Drug Metab Dispos 33: 474-481, 2005.

128. Balakin KV, Ekins S, Bugrim A, Ivanenkov YA, Korolev D, Nikolsky YV, Ivashchenko AA, Savchuk NP and Nikolskaya T, Quantitative structure-metabolism relationship of the metabolic N-dealkylation reaction rates, Drug Metab Dispos, 32: 1111-1120, 2004.

129. Balakin KV, Ekins S, Bugrim A, Ivanenkov YA, Korolev D, Nikolsky YV, Skorenko AV, Ivashchenko AA, Savchuk NP and Nikolskaya T, Kohonen maps for prediction of binding to human cytochrome P450 3A4, Drug Metab Dispos, 32: 1183-1189, 2004.

130. Ekins S and Swaan PW, Computational models for enzymes, transporters, channels and receptors relevant to ADME/TOX. Rev Comp Chem. 20: 333-415, 2004.

131. Bachmann K, Patel H, Batayneh Z, Slama J, White D, Posey J, Ekins S, Gold D and Sambucetti L, PXR and the regulation of apoA1 and HDL-cholesterol in rodents. Pharmacol Res. 50: 237-246, 2004.

132. Ekins S, Predicting undesirable drug interactions with promiscuous proteins in silico. Drug Disc Today. 9: 276-285, 2004.

133. Mankowski DC and Ekins S. Prediction of human drug metabolizing enzyme induction. Curr Drug Metab, 4: 381-391, 2003.

134. Ekins S, Berbaum J and Harrison, RK, Generation and validation of rapid computational filters for CYP2D6 and CYP3A4. Drug Metab Dispos, 31: 1077-1080 2003.

135. Ekins S, In silico approaches to predicting metabolism, toxicology and beyond, Biochem Soc Trans, 31(Pt 3):611-4, 2003.

136. Ekins S, Stresser DM and Williams JA, In vitro and pharmacophore insights for CYP3A enzymes, Trends Pharmacol Sci 24: 161-166, 2003.

137. Bednarczyk D, Ekins S, Wikel JH and Wright SH, Influence of molecular structure of substrate binding to the human organic cation transporter, hOCT1. Mol Pharmacol 63: 489-498, 2003.

138. Ekins S, Mirny L and Scheutz EG. A ligand-based approach to understanding selectivity of nuclear receptors PXR, CAR, FXR, LXR and LXR Pharm Res 19: 1788-1800, 2002.

139. Shimada J, Ekins S, Elkin C, Shaknovich EI and Wery J-P. Integrating computer-based de novo drug design and multidimensional filtering for desirable drugs. Targets 1: 196-205, 2002.

140. Young SS, Ekins S and Lambert G, So many targets, so many compounds, but so few resources. Curr Drug Disc, December: 17-22, 2002.

141. Ekins S, Boulanger B, Swaan PW and Hupcey MAZ. Towards a new age of virtual ADME/TOX and multidimensional drug discovery. J Comp Aided Mol Design, 16: 381-401, 2002.

142. Zhang EY, Knipp GT, Ekins S and Swaan PW, Structural biology and function of solute transporters: implications for identifying and designing substrates, Drug Metab Rev, 34: 709-750, 2002

143. Ekins S and McGowan R, Postgraduate education and the changing interaction with the pharmaceutical industry: a cross-cultural perspective, Foundations of Science, 7: 413-424, 2002.

144. Snyder R, Sangar R, Wang J and Ekins S. Three-dimensional quantitative structure activity relationship for CYP2D6 substrates. Quant Struct Act Relat 21: 357- 368, 2002.

145. Ethell BT, Ekins S, Wang J and Burchell B. Quantitative structure activity relationships for the glucuronidation of simple phenols by expressed human UGT1A6 and UGT1A9. Drug Metab Dispos, 30: 734-738, 2002.

146. Ekins S, Kim RB, Leake BF, Dantzig AH, Schuetz E, Lan LB, Yasuda K, Shepard RL, Winter MA, Wikel JH and Wrighton SA. Application of three dimensional quantitative structure activity relationships of P-glycoprotein inhibitors and substrates. Mol Pharmacol, 61: 974-981, 2002.

147. Ekins S, Kim RB, Leake BF, Dantzig AH, Schuetz E, Lan LB, Yasuda K, Shepard RL, Winter MA, Wikel JH and Wrighton SA. Three dimensional quantitative structure activity relationships of inhibitors of P-glycoprotein. Mol Pharmacol, 61: 964-973 2002.

148. Zhang EY, Phelps MA, Chang C, Ekins S and Swaan PW, Modeling of active transport processes, Adv Drug Del Rev., 54 :329-354 2002.

149. Ekins S, Crumb WJ, Sarazan RD, Wikel JH and Wrighton SA. Three dimensional quantitative structure activity relationship for the inhibition of the hERG (human ether-a-gogo related gene) potassium channel. J Pharmacol Exp Thera, 301: 427-434 2002.

150. Ekins, S and Schuetz EG. The PXR crystal structure: the end of the beginning. Trends Pharmacol Sci, 23: 49-50, 2002.

151. Ekins, S and Erickson, JA. A preliminary pharmacophore for human pregnane-X-receptor ligands. Drug Metab Dispos, 30: 96-99, 2002.

152. Gao F, Johnson DL, Ekins S, Janiszewski J, Kelly KG, Meyer RD and West M, Optimizing higher throughput methods to assess drug-drug interactions for CYP1A2, CYP2C9, CYP2C19, CYP2D6, rCYP2D6 and CYP3A4 in vitro using a single point IC50, J Biomol Screen, 7: 373-382, 2002.

153. de Groot M and Ekins S, Pharmacophore modeling of human cytochrome P450s. Adv Drug Del Rev, 54: 367-383, 2002.

154. Ekins S and Rose JP, In Silico ADME/TOX: The state of the art at the 220th ACS meeting, J Mol Graph, 20: 305-309, 2002.

155. Ekins S, de Groot M and Jones JP, Pharmacophore and three dimensional quantitative Structure activity relationship methods for modeling cytochrome P450 active sites. Drug Metab Dispos, 29: 936-944, 2001.

156. Ekins S and Wrighton SA, Application of in silico approaches to predicting drug-drug interactions: a commentary. J Pharm Tox Methods, 45: 65-69, 2001.

157. Ekins S, Durst GL, Stratford RE, Thorner DA, Lewis R, Loncharich RJ and Wikel JH Three-dimensional quantitative structure activity relationship analysis of caco-2 permeability for a series of inhibitors of rhinovirus replication. J Chem Inf Comput Sci, 41: 1578-1586, 2001.

158. Ekins S and McGowan R, The limits of reductionism: The shifting genomic paradigm’s impact on industry and academia, Philosophy in Science, 9: 179-202, 2001.

159. Ekins S, Ring BJ, Grace J, McRobie-Belle DJ and Wrighton SA, Present and future in vitro approaches for drug metabolism. J Pharm Tox Methods, 44: 313-324, 2000.

160. Ekins S, Waller CL, Swaan PW, Cruciani G, Wrighton SA and Wikel JH, Progress in predicting human ADME parameters in silico. J Pharm Tox Methods, 44; 251-272, 2000.

161. Ekins S and Obach RS, Three dimensional-quantitative structure activity relationship computational approaches for prediction of human in vitro intrinsic clearance. J Pharmacol Exp Ther, 295: 463-473, 2000.

162. Margolis JM, O'Donnell JP, Mankowski DC, Ekins S, Obach RS, R-, S-, and racemic fluoxetine are metabolized by multiple human cytochrome P450 enzymes in vitro. Drug Metab Dispos, 28: 1187-1191, 2000.

163. Ekins S, Bravi G, Binkley S, Gillespie JS, Ring BJ, Wikel JH and Wrighton SA, Three and four dimensional-quantitative structure activity relationship (3D / 4D-QSAR) analyses of CYP2C9 inhibitors. Drug Metab Dispos 28: 994-1002, 2000.

164. Mankowski DC, Lawton MP and Ekins S, Characterisation of transgenic mouse strains using six human hepatic cytochrome P450 probe substrates. Xenobiotica, 30; 745-754, 2000.

165. Mankowski DC, Laddison KJ, Christopherson PA, Ekins S, Tweedie DJ and Lawton MP, Molecular cloning, expression, and characterization of CYP2D17 from Cynomolgus monkey liver. Arch Biochem Biophys 372: 189-196, 1999.

166. Ekins S, Bravi G, Ring BJ, Gillespie TA, Gillespie JS, VandenBranden M, Wrighton SA, Wikel JH, Three dimensional-quantitative structure activity relationship (3D-QSAR) analyses of substrates for CYP2B6. J Pharmacol Exp Ther, 288:21-29, 1999.

167. Ekins S, Bravi G, Binkley S, Gillespie JS, Ring BJ, Wikel JH, Wrighton SA. Three and four dimensional-quantitative structure activity relationship (3D / 4D-QSAR) analyses of CYP3A4 inhibitors. J Pharmacol Exp Ther, 290:429-438, 1999.

168. Ekins S, Bravi G, Binkley S, Gillespie JS, Ring BJ, Wikel JH, Wrighton SA. Three and four dimensional-quantitative structure activity relationship (3D / 4D-QSAR) analyses of CYP2D6 inhibitors. Pharmacogenetics, 9: 477-489, 1999.

169. Ekins S, Wrighton SA, The role of CYP2B6 in human xenobiotic metabolism. Drug Metab Rev, 31: 719-754, 1999.

170. Ekins S, Bravi G, Wikel JH, Wrighton SA. Three dimensional-quantitative structure activity relationship (3D-QSAR) analysis of CYP3A4 substrates. J Pharmacol Exp Ther, 291: 424-433, 1999.

171. Ekins S, Ring BJ, Binkley SN, Hall SD, Wrighton SA, Autoactivation and activation of the cytochrome P450s. Int J Clin Pharmacol Ther 36: 642-651, 1998.

172. Ekins S, VandenBranden M, Ring BJ, Gillespie JS, Yang TJ, Gelboin HV, Wrighton SA, Characterization of the expression in liver and catalytic activity of human CYP2B6. J Pharmacol Exp Ther 286: 1253-1259, 1998.

173. VandenBranden M, Wrighton SA, Ekins S, Gillespie JS, Binkley SN, Ring BJ, Gadberry MG, Mullins DC, Strom SC, Jensen CB, Alterations of the catalytic activities of the drug metabolizing enzymes in cultures of human liver slices. Drug Metab Dispos, 26: 1063-1068, 1998.

174. Ekins S, VandenBranden M, Ring BJ and Wrighton SA, Examination of purported probes of human CYP2B6. Pharmacogenetics 7: 165-179, 1997.

175. Ekins S, Williams JA, Murray GI, Burke MD, Marchant NC, Engeset J and Hawksworth GM, Xenobiotic metabolism in rat, dog, and human precision-cut liver slices, freshly isolated hepatocytes, and vitrified precision-cut liver slices. Drug Metab Dispos 24: 990-995, 1996.

176. Ekins S, Murray GI and Hawksworth GM, Ultrastructural and metabolic effects after vitrification of precision-cut rat liver slices with antifreeze proteins. Cryo-lett 17: 157-164, 1996.

177. Ekins S, Vitrification of precision-cut rat liver slices. Cryo-lett 17: 7-14, 1996.

178. Ekins S, Short term maintenance of phase I and II metabolism in precision-cut liver slices in dynamic organ culture. Drug Metab Dispos 24: 364-366, 1996.

179. Ekins S, Past, present and future applications of precision-cut liver slices for in vitro xenobiotic metabolism Drug Metab Rev 28: 591-623, 1996.

180. Ekins S, Murray GI, Burke MD, Williams JA, Marchant NC and Hawksworth GM, Quantitative differences in phase I and II metabolism between rat precision-cut liver slices and isolated hepatocytes. Drug Metab Dispos 23: 1274-1279, 1995.


Edited Books 1. Ekins S, Hupcey MAZ and Williams AJ, Collaborative Computational Technologies for Biomedical Research, Series Editor Ekins S. John Wiley and Sons, July 2011. ISBN 978-0470638033.

2. Ekins S and Xu JJ, Drug Efficacy, Safety, and Biologics Discovery: Emerging Technologies and Tools, Series Editor Ekins S, John Wiley and Sons. Hoboken, NJ. January 2009. ISBN 978-0-470-22555-4.

3. Ekins S, Computational Toxicology: Risk Assessment For Pharmaceutical and Environmental Chemicals, Series Editor Ekins S. John Wiley and Sons, June 2007. ISBN 978-0-470-04962-4.

4. Ekins S, Computer Applications in Pharmaceutical Research and Development, Series Editor Wang B, John Wiley and Sons, June 2006. ISBN 0-471-73779-8. Edited Book Series

1. Sasic S, Pharmaceutical Applications of Raman Spectroscopy, Series Editor Ekins S. John Wiley and Sons, November 2007.

2. Yuryev A, Pathway analysis for drug discovery: computational infrastructure and applications, Series Editor Ekins S. John Wiley and Sons, September 2008

3. Robson B and Baek OK, The Engines of Hippocrates: From the dawn of medicines to medical and pharmaceutical informatics, Series Editor Ekins S. John Wiley and Sons, 2009.

4. Balakin K, Pharmaceutical Data Mining, approaches and applications for drug discovery, Series Editor Ekins S. John Wiley and Sons, December, 2009.

5. Rosenberg MJ, The Agile Approach to Adaptive Research, Series Editor Ekins S. John Wiley and Sons, February, 2010.

6. Babler S, Pharmaceutical and Biomedical Project Management in a Changing Global Environment, Series Editor Ekins S. John Wiley and Sons, September, 2010.

7. Young DL and Michelson S, Systems Biology in Drug Discovery and Development, Series Editor Ekins S. John Wiley and Sons, July, 2011.


Book Chapters

1. C.M.Grulke, A.M.Clark, S. Ekins, A.J. Williams, C. Morris, M.R. Goldsmith, Mobile Modeling in the Molecular Sciences. McGraw-Hill Yearbook of Science & Technology 2013 (and accessscience.com) , in press

2. Ekins S, Progress in Computational Toxicology. In Davis A.M. and Livingstone D.J., Medicinal Chemistry, Royal Society of Chemistry, Submitted 2013.

3. Ekins S and Bunin BA, Computational approaches and collaborative drug discovery for trypanosomal diseases.In Trypanosomatid diseases: Molecular routes to Drug discovery, Eds Timo Jäger, Oliver Koch and Leopold Flohė,, Wiley-VCH, 2013. pp81-102.

4. Bunin BA and Ekins S, Academic, Commercial and Biodefense Case Studies for Collaborative Drug Discovery : Potential for Disrupting Drug Discovery, in Collaborative Drug Discovery: Strategies for Academic, Industry and Government Partnerships, ed Rathnam Chaturguru, Wiley, In Press 2013.

5. Ekins S, Kortagere S, Krasowski MD, Williams AJ, Xu JJ and Zientek M, Ligand-based modeling of toxicity, In Davis A.M. and Livingstone D.J., Drug Design Strategies: Quantitative Approaches, Royal Society of Chemistry, Nov 2011, pp310-342.

6. Ekins S, Williams AJ and Pikas CK, Collaborations in Chemistry, In Ekins S, Hupcey MAZ and Williams AJ, Collaborative Computational Technologies for Biomedical Research, Series Editor Ekins S. John Wiley and Sons, July 2011. pp85-98.

7. Ekins S, Williams AJ and Hupcey MAZ, Standards for Collaborative Computational Technologies For Biomedical Research in Chemistry, In Ekins S, Hupcey MAZ and Williams AJ, Collaborative Computational Technologies for Biomedical Research, Series Editor Ekins S. John Wiley and Sons, July 2011. pp201-208.

8. Ekins S, Hohman M and Bunin BA, Pioneering Use of the Cloud for the Development of the Collaborative Drug Discovery (CDD) database., In Ekins S, Hupcey MAZ and Williams AJ, Collaborative Computational Technologies for Biomedical Research, Series Editor Ekins S. John Wiley and Sons, July 2011. pp335-361.

9. Williams AJ, Arnold RJG, Neylon C, Spencer R, Schürer S and Ekins S, Current and Future Challenges for Collaborative Computational Technologies for the Life Sciences., In Ekins S, Hupcey MAZ and Williams AJ, Collaborative Computational Technologies for Biomedical Research, Series Editor Ekins S. John Wiley and Sons, July 2011. pp491-517.

10. Ekins S and Kortagere S, Computational pharmacophores for ADME/Tox Proteins, In Scheiber J and Jenkins J.L., In-silico Methods for Adverse Effect Prediction in Preclinical Drug Discovery, John Wiley and Sons. Hoboken, NJ. submitted, 2009

11. Ekins S, Honeycutt JD and Metz JT, Multiobjective optimization for drug discovery, In Abraham, D.J. and Rotella, D.P., Burger’s Medicinal Chemistry, Drug Discovery and Development, 7th Edition, John Wiley & Sons, Inc. 2010, P259-277.

12. Bahadduri PM, Polli JM, Swaan PW and Ekins S, Targeting drug transporters - combining in silico and in vitro approaches to predict in vivo, In Yan Q, Membrane Transporters in Drug Discovery and Development: Methods and Protocols, The Humana Press/ Springer Press, Totowa, NJ, 2010. Methods Mol Biol. 2010;637:65-103.

13. Bachmann K and Ekins S, The potential of In Silico and In Vitro approaches to predict In Vivo, In Christ D. In Vitro-In Vivo correlations in drug discovery and development. concepts and applications, Series Editor Ekins S, John Wiley and Sons. Hoboken, NJ. Submitted.

14. Ekins S and Arnold RJG, Computer aided decision making from drug discovery to pharmacoeconomics, In, Arnold RJG. Pharmacoeconomics: from theory to practice CRC Press, P209—226, 2009.

15. Krasowski, MD, Siam MG and Ekins S, Immunoassays for tricyclic antidepressants: Unsuitable for therapeutic drug monitoring?, In. Dasgupta A. Recent Advances in Chromatographic Techniques for Therapeutic Drug Monitoring; Edited by Amitava Dasgupta Recent Advances in Chromatographic Techniques for Therapeutic Drug Monitoring, CRC Press, P179-190, 2009.

16. Ekins S and Scheiber J, Toxicity Pathways and Models: Mining for Potential Side Effects, in Ekins S and Xu JJ, Drug Efficacy, Safety, and Biologics Discovery: Emerging Technologies and Tools, Series Editor Ekins S, John Wiley and Sons. Hoboken, NJ. P135-153, 2009.

17. Xu JJ, Ekins S, McGlashen M and Lauffenburger D, Future Perspectives of Biological Engineering in Pharmaceutical Research: The Paradigm of Modeling, Mining, Manipulation and Measurements in Ekins S and Xu JJ, Drug Efficacy, Safety, and Biologics Discovery: Emerging Technologies and Tools, Series Editor Ekins S, Wiley and Sons, P351-379, 2009.

18. Ekins S, Drug transporter pharmacophores, in Ecker G and Chiba P, Transporters as drug carriers, Vol 33 of series Methods and Principles in Medicinal Chemistry John Wiley and Sons. Hoboken, NJ. P215-227, 2009.

19. Ekins S and Abramovitz DL, A systems-biology view of drug transporters, in Ecker G and Chiba P, Transporters as drug carriers, Vol 33 of series Methods and Principles in Medicinal Chemistry, John Wiley and Sons. Hoboken, NJ. P365-385, 2009.

20. Ekins S and Giroux C, Mammalian proteome and toxicant network analysis, in Yuryev A, Pathway analysis for drug discovery: computational infrastructure and applications, Series Editor Ekins S, John Wiley and Sons. Hoboken, NJ. P165-194, 2008.

21. Ekins S, Application of QSAR to enzymes involved in toxicology, in Ekins S, Computational toxicology: Risk assessment for pharmaceutical and environmental chemicals, Series Editor Ekins S, John Wiley and Sons, Hoboken, NJ. p277-294, 2007.

22. Aronov AM, Balakin KV, Kiselyov A, Varma-O’Brien S and Ekins S, Applications of computational methods to ion channels, in Ekins S, Computational toxicology: Risk assessment for pharmaceutical and environmental chemicals, Series Editor Ekins S, John Wiley and Sons, Hoboken, NJ. p353-390, 2007.

23. Ekins S, Embrechts MJ, Breneman CM, Jim K and Wery J-P. Novel applications of kernel-partial least squares to modeling a comprehensive array of properties for drug discovery, in Ekins S, Computational toxicology: Risk assessment for pharmaceutical and environmental chemicals, Series Editor Ekins S, John Wiley and Sons, Hoboken, NJ. p403-432, 2007.

24. Ekins S and Giroux C, Computers and systems biology for Pharmaceutical Research and Development, in Ekins S, Computer Applications in Pharmaceutical Research and Development, Series Editor Wang B, John Wiley and Sons, Hoboken, NJ. p139-165, 2006.

25. Ekins S Computer methods for predicting drug metabolism, in Ekins S, Computer Applications in Pharmaceutical Research and Development, Series Editor Wang B, John Wiley and Sons, Hoboken, NJ. p445-468, 2006.

26. Ekins S, Nikolsky Y, Bugrim A, Kirillov E and Nikolskaya T, Pathway Mapping Tools For Analysis of High Content Data, in Taylor DL, Haskins JA and Giuliano KA, High Content Screening: A Powerful Approach to Systems Cell Biology and Drug Discovery, (Methods Mol Biol:356), The Humana Press, Totowa, NJ p319-350, 2006.

27. Chang C and Ekins S. Pharmacophores for human ADME/Tox related proteins, in Hoffman RD and Langer T, Pharmacophores and Pharmacophore Searches. Wiley-VCH, p299-324, 2006.

28. Ekins S, Bugrim A, Nikolsky Y and Nikolskaya T, Systems biology: applications in drug discovery. In: Gad, S.C., Drug Discovery Handbook, John Wiley & Sons, Hoboken, NJ. p123-183, 2005.

29. Ekins S, Berbaum J, Harrison RK, Zecher M, Yuan J, Ischchenko AV, Berezin K, Chubukov, V, Lawson D and Hupcey MAZ. Applying Computational and In Vitro Approaches To Lead Selection. In: Borchardt RT, Kerns EH, Lipinski CA, Thakker DR, Wang B, Pharmaceutical Profiling in Drug Discovery for Lead Selection. AAPS Press p361-389, 2004.

30. Ekins S, Ring BJ, Bravi G, Wikel JH and Wrighton SA, Predicting drug-drug interactions in silico using pharmacophores: a paradigm for the next millennium. In: Guner OF, Pharmacophore perception, development, and use in drug design. IUL, San Diego, pp 269-299, 2000.

31. Ekins S, Mäenpää J and Wrighton SA, In vitro metabolism: Subcellular fractions. In: Woolf TF, Handbook of drug metabolism. Marcel Dekker Inc, New York, pp 363-399, 1999.


Editorials 1. Ekins S. Disrupting Drug Discovery with alternative business models, Drug Discovery Today, Editors Choice February 2013. http://www.drugdiscoverytoday.com/view/30121/disrupting-drug-discovery-with-alternative-business-models/

2. Ekins, S. In: Annual Review 2011, Royal Society of Chemistry, p22 http://www.rsc.org/images/AnnualReview2011_tcm18-219895.pdf

3. Williams AJ and Ekins S, The long term cost of inferior database quality. Drug Discovery Today, Editors Choice January 2012. http://www.drugdiscoverytoday.com/view/22579/the-long-term-cost-of-inferior-database-quality/

4. Williams AJ and Ekins S. A Quality Alert and Call for Improved Curation of Public Chemistry Databases, Drug Disc Today, 16: 747-750, 2011.

5. Bunin BA and Ekins S, Alternative business models for drug discovery, Drug Disc Today, 16: 643-645, 2011.

6. Williams AJ and Ekins S, Invited editorial for Drug Discovery Today, Editors Choice – Drug Metabolism, Jan 2010 http://csemails.elsevier.com/DDT/jan2010/

7. Bingham A and Ekins S, Competitive Collaboration in the Pharmaceutical and Biotechnology Industry, Drug Disc Today, 14, 1079-1081, 2009.

8. Ekins S, ChemSpider, Chemistry World, October 70, 2009. 9. Ekins S, J Pharm Tox Methods, 53:30, 2006. 10. Ekins S, Invited editorial for Drug Discovery Today, Editors Choice – Drug Metabolism, July 2006. http://www.drugdiscoverytoday.com/echoice/jul2006/


Abstracts

1. Ekins S, Reynolds RC, Wan B, Franzblau SG, Freundlich JS and Bunin BA, Enhancing High Throughput Screening For Mycobacterium tuberculosis Drug Discovery Using Bayesian Models, ACS – New Orleans 2013

2. Ekins S, Clark AM, Sarker M, Talcott C and Bunin BA,TB Mobile: Appifying Data on Anti-tuberculosis Molecule Targets, ACS – New Orleans 2013

3. Ekins S, Reynolds RC, Kim H, Koo M-S, Ekonomidis M, Talaue M, Paget SD, Woolhiser LK, Lenaerts AJ, Bunin BA, Connell N and Freundlich JS, Dual-event Machine Learning Models to Accelerate Drug Discovery, ACS – New Orleans 2013

4. Southan C, Williams AJ and Ekins S, Challenges and Recommendations for Obtaining Chemical Structures of Industry-Provided Repurposing Candidates, ACS – New Orleans 2013

5. Ekins S, Olechno J, Williams AJ, Dispensing Processes Profoundly Impact Biological Assays and Computational and Statistical Analyses, ACS – New Orleans 2013

6. Ekins S and Williams AJ, Collaborative Computational Technologies for Biomedical Research: An Enabler Of More Open Drug Discovery, ACS –[107] San Diego, 2012.

7. Ekins S and Williams AJ, Finding Promiscuous Old Drugs for new uses, ACS –[23] San Diego, 2012.

8. Williams AJ, Ekins S, Clark AM, Mobile Apps for drug discovery, ACS –[70] San Diego, 2012.

9. Williams AJ and Ekins S, Towards a Gold standard: Improving the quality of public domain chemistry databases, ACS –[129] San Diego, 2012.

10. Sarker M, Talcott C, Madrid P, Chopra S, Bunin BA, Lamichhane G, Freundlich JS and Ekins S, Combining Cheminformatics Methods and Pathway Analysis To Identify Molecules With Whole-Cell Activity Against Mycobacterium tuberculosis ACS –[524] San Diego, 2012

11. Ekins S, Dole K, Spektor A, Gregory K, Blondeau D, Ernst S, Hohman MM, and Bunin BA, Collaborative Drug Discovery: A platform for transforming Tuberculosis R&D and beyond. Keystone Symposia Tuberculosis: Immunology, cell biology and novel vaccination strategies. Jan 15-20, Fairmont Hotel Vancouver, Vancouver, British Columbia, Canada. 2011.

12. Lamichhane G, Freundlich, JS, Ekins S, Wickramaratne N, Nolan S, Bishai WR, Essential Metabolites of M. tuberculosis and their Molecular Mimics. Keystone Symposia Tuberculosis: Immunology, cell biology and novel vaccination strategies. Jan 15-20, Fairmont Hotel Vancouver, Vancouver, British Columbia, Canada. 2011.

13. Dehal, SS, Gangl E, Perloff ES, Mason AK, Blanchard AP, Ekins S and Stresser DM. The alcohol withdrawal agent clomethiazole is a time-dependent inhibitor of CYP2E1, ISSX 2010

14. Pan Y, Li L, Ekins S, Wang H and Swaan P, Rational Identification of hPXR Activators by Integrated Structure and ligand Based Strategies, AAPS 2010.

15. Krasowski MD, Ekins S, Kortagere S. Prediction of Immunoassay Cross-Reactivity Using Chemoinformatic Methods. Academy of Clinical Laboratory Physicians and Scientists Annual Meeting, Nashville, TN, June 2010. Published in Am J Clin Pathol 2010;134:502.

16. Gupta RR, Gifford EM, Liston T, Waller C, Bunin BA and Ekins S, Using open source descriptors and algorithms for modeling ADME properties, ACS Boston 2010.

17. Ekins, S, Bradford J, Dole, K, Spektor A, Gregory K, Blondeau D, Ernst S, Hohman M and Bunin BA, Collaborative Drug Discovery: A platform for transforming neglected disease R&D and beyond, ACS Boston 2010.

18. Ekins, S, Bradford J, Dole, K, Spektor A, Gregory K, Blondeau D, Hohman M and Bunin BA, Collaborative database and computational models for tuberculosis drug discovery decision making, ACS Boston 2010.

19. Lamichane G, Freundlich JS, Ekins S, Wickramaratne N and Bishai WR, Essential metabolites of M. Tuberculosis and their molecular mimics as therapeutic agents, ACS Boston 2010.

20. Simon A, Zsoldos Z, Ravitz O, Williams AJ and Ekins S, LASSO-ing potential pregnane X receptor agonists, ACS Boston 2010.

21. Zheng X, Ekins S, Polli JE. In Vitro and Pharmacophore Based Discovery of FDA Approved Drugs Includes Calcium Channel Blockers and HMG CoA-Reductase Inhibitors that Inhibit human Apical Sodium-dependent Bile Acid Transporter (hASBT). AAPS Annual Meeting, Nov 2009, Los Angeles, CA.

22. Zheng X, Ekins S, and Polli JE. Discovery of FDA Approved Drugs that are hASBT Inhibitors. AAPS Workshop on Evolving Science and Technology in Physical Pharmacy and Biopharmaceutics. May, 2009. Baltimore, MD; AAPS Workshop on Drug Transporters in ADME: From the Bench to the Bedside. Apr, 2009. Baltimore, MD. 23. Ekins S, Reschly, Hagey LR and Krasowski MD, Evolution of pharmacologic specificity and species differences in the Pregnane X Receptor, Drug Metab Rev 41 Supplement 1: Abstract 30, 2009.

24. Ekins S, Hohman M, Ernst S, Gregory K and Bunin B, Global launch of the collaborative drug discovery TB researcher network, Keystone Symposia, Tuberculosis: biology, pathology and therapy, Abstract 149, 2009.

25. Ekins S, Hohman M and Bunin B, A new collaborative web-based database for the tuberculosis research community, Keystone Symposia, Tuberculosis: biology, pathology and therapy, Abstract 204, 2009.

26. Welsh WJ, Ekins S, Chekmarev D, Kortagere S and Kholodvych V, Shape Signatures: an integrated computational platform useful for defense applications, CBDPST 2008.

27. Diao L, Ekins S and Polli JE, Inhibition Requirements of Human Organic Cation/Carnitine Transporter (OCTN2). AAPS 2008.

28. Krasowski MD and Ekins S, Increasing Limitations of Drug Screens in an Era of Expanding Molecular Diversity. Academy of Clinical Laboratory Physicians and Scientists. 2008.

29. Marcus P, Arnold RJG, Ekins S, Sacco P, Massanari M, Donohue J and Bukstein D. Treatment patterns and asthma control among US allergy and pulmonary community practices: Results of the CHARIOT study. American Academy of Allergy Asthma and Immunology, 2008.

30. Balakin K, Ekins S, Savchuk NP and Ivanenkov YA, New insights for hERG inhibition using mapping techniques. CMPTI, Russia, 2007.

31. Olinga P, Ekins S, Elferink M.G.L., Bauerschmidt S, Polman J, Schoonen WGEJ, Draaisma AL, Merema MT and Groothuis G.M.M. Gene network analysis of acetaminophen and carbon tetrachloride treated rat liver slices identifies hepatotoxicity mechanisms observed in vivo. Drug Metab Rev: 39, S1, 1-388, 2007.

32. Bahadduri PM, Chang C, Ekins S and Swaan PW; Integrated Approach to Identify novel P-glycoprotein Substrates and Inhibitors. Experimental Biology Meeting, Washington, DC, 2007.

33. Bahadduri PM, Chang C, Swaan PW and Ekins S Application of integrated computational pharmacophore models and in vitro approaches to rapidly identify P-glycoprotein ligands. AAPS 2006.

34. Chang C, Bahadduri PM, Swaan PW and Ekins S; Application of Integrated Pharmacophore Models and In Vitro Approaches to Rapidly Identify Novel P-glycoprotein Ligands; 231st ACS Atlanta, GA, 2006.

35. Chang C, Bahadduri P, Polli JE, Swaan PW and Ekins S, Application of pharmacophore models to rapidly identify novel P-glycoprotein ligands. ISSX 2006.

36. Ekins S, Jones DR, Li L and Hall SD, Computational approaches that predict metabolic intermediate complex formation with CYP3A4. ACS San Francisco 2006.

37. Ekins S, Computational Prediction of Human Drug Metabolism. Drugs Fut 31 (suppl A) L65, 2006.

38. Ekins S, Andreyev S, Ryabov A, Kirillov E, Rakhmatulin EA, Sorokina S, Bugrim A, Nikolskaya T. A combined quantitative structure activity relationship and systems biology approach to drug metabolism and toxicity assessment. SOT 2006.

39. Bahadduri PM, Johnston J, D’Souza VM, Ray A, Swaan PW and Ekins S; Identification of novel hPEPT1 inhibitors by in vitro and pharmacophore based approaches. AAPS Workshop on Drug Transporters in ADME: From the Bench to the Bedside, Parsippany, NJ, USA. 2005

40. Bahadduri P, Chang C, Swaan P and Ekins S. Rapid identification of molecules with affinity for P-glycoprotein by pharmacophore modeling and in vitro screening. AAPS 2005.

41. Ekins S. In silico toxicity prediction, Drug Metab Reviews, 37 Suppl 1, 30. 2005.

42. Ekins S, Giroux C, Nikolsky Y, Bugrim A and Nikolsyaka T. A signature gene network approach to toxicity. SOT 2005.

43. Giroux C, Ekins S, Fan J, Abdullah I, Nikolsky Y, Bugrim A and Nikolskaya T. A genetic network approach to comparative toxicogenomics and risk assessment: the oxidative stress response. SOT 2005.

44. Ekins S, Progress in computational modeling of P-glycoprotein. ACS, San Diego 2005.

45. Chang C, Pang KS, Ekins S and Swaan P. Comparative pharmacophore modeling of organic anion transporting polypeptides: a meta-analysis of rat Oatp1a1, human OATP1A2 and OATP1B1. ACS, San Diego 2005.

46. Chang C, Ekins S and Swaan P. Application of P-gp Pharmacophore Models in Database Screening ACS, San Diego 2005.

47. Ekins S, Bugrim A, Nikolsky YV and Nikolskaya, T. “Network signatures” as predictors of cell response to chemical treatments. IBC 9th Annual World Congress, Drug Discovery technology 2004.

48. Balakin KV, Ekins S, Bugrim A, Ivanenkov YA, Korolev D, Nikolsky YV, Skorenko AV, Ivashchenko AA, Savchuk NP and Nikolskaya T. Kohonen self organizing maps and neural networks for predicting human CYP affinity and rate of metabolism Drug Metab Rev 36:67, 2004.

49. Jones DR, Ekins S and Hall SD. Computational approaches that predict metabolic intermediate complex formation with CYP3A4. Drug Metab Rev 36:284, 2004.

50. D’Souza VM, Johnston J, Bahadurri P, Ray A, Swaan PW and Ekins S. In vitro and pharmacophore based discovery of novel hpept1 inhibitors. Drug Metab Rev 36:258, 2004.

51. Ekins S, Williams JA and Stresser DM. Pharmacophore insights into the active sites of the CYP3A enzymes. The Pharmacologist 44: (suppl) 114.4, 2002.

52. Ekins S, Snyder R, Sanger R, Jones DR, Hall SD, Lewis RA, Wikel JH and Wrighton SA. QSAR and pharmacophore models for the prediction of cytochrome P450 substrate and inhibitor specificity. Drug Metab Revs 33: 22, 2001.

53. Ekins S, Kim RB, Dantzig AH, Schuetz EG, Leake BF, Lan L-B, Yasuda K, Shepard RL, Winter MA, Wikel JH and Wrighton SA. Multiple P-glycoprotein pharmacophore models for inhibition of binding and transport. Drug Metab Revs 33: 178, 2001.

54. Ekins S and McGowan RJ. Investigating the feminist standpoint in modern science. ILS 12th Interdisciplinary conference on Science and Culture, Kentucky State University, 2001.

55. Ekins S, Durst GL, Stratford RE, Thorner DA, Lewis R, Loncharich RJ and Wikel JH Multiple 3D-QSAR approaches for prediction of Caco-2 permeability for rhinovirus replication inhibitors. Drug Metab Revs 33: 301, 2000.

56. Ekins S, Molecular modeling – shifting the drug metabolism paradigm. ISSX training course, Indianapolis 2000.

57. Ekins S, Bravi G, Binkley S, Gillespie JS, Ring BJ, Wikel JH and Wrighton SA. Present and future applications of pharmacophores for prediction of CYP drug-drug interactions. Drug Metab Revs 33: 140, 2000.

58. Margolis JM, O'Donnell JP, Mankowski DC, Ekins S, Obach RS, R-, S-, and racemic fluoxetine are metabolized by multiple human cytochrome P450 enzymes in vitro. Drug Metab Revs 33: 250, 2000

59. Johnson DL, Kelly KG, Gao F, Meyer RD, Morris JC, Ekins S and West M, Single point screening for high throughput methods to predict drug-drug interaction potential. Drug Metab Revs 33: 185, 2000.

60. Ekins S and Obach RS, Computational approaches for prediction of human in vitro intrinsic clearance, ACS August 2000

61. Mankowski DC and Ekins S, Characterization of hepatic cytochrome P450 activities in transgenic mouse strains, ISSX Proceedings 15: 322, 1999.

62. Ekins, S and McGowan, R, Postgraduate education and the changing interaction with the pharmaceutical Industry: a cross-cultural perspective. ILS 10th Interdisciplinary conference on Science and Culture, Kentucky State University, 1999.

63. Ekins S, Bravi G, Wikel JH and Wrighton SA, Three dimensional quantitative structure activity relationship (3D-QSAR) analysis of CYP3A4 substrates. 28th Annual Gordon Research Conference on Drug Metabolism A40, 1998.

64. Ekins S, The limits of reductionism: The shifting genomic paradigm’s impact on industry and academia, ILS 9th Interdisciplinary conference on Science and Culture, Kentucky State University, 1998.

65. Wrighton SA, Maenpaa, J, Ekins S. The CYP3A family. Pharmacological and toxicological implications. Microsomes and Drug Oxidations, Montpellier, France, 1998.

66. Ekins S, Binkley S, Gillespie JS, VandenBranden M, Ring BJ, Wikel JH and Wrighton SA, Preliminary inhibitor pharmacophores for human CYP’s, ISSX Proceedings 12: 314, 1997.

67. Ekins S, VandenBranden M, Gillespie JS, Ring BJ, Wrighton SA and Wikel JH, A preliminary substrate pharmacophore for CYP2B6, ISSX Proceedings 12: 125, 1997.

68. Wrighton SA, Ekins S. The examination of in vitro probes reported to be selective for human CYP2B6, ISSX Proceedings 12; 22, 1997.

69. Jensen CB, Gadberry MG, Mullins DC, Gillespie JS, VandenBranden MR, Ekins S, Horn JW, Meador VP and Wrighton SW, Alterations in the catalytic activities of drug metabolizing enzymes in long-term cultures of precision-cut liver slices, ISSX Proceedings 12: 328, 1997.

70. VandenBranden MR, Wrighton SA, Gillespie JS, Binkley SN, Ekins S, Ring BJ, Gadberry MG, Mullins DC, Strom SC and Jensen CB, Alteration of the drug metabolizing enzymes in human liver slice culture over 96 hours, ISSX Proceedings 12; 331, 1997.

71. Ekins S, VandenBranden M, Ring BJ and Wrighton SA, Kinetic and inhibition characteristics of CYP2B6. ISSX Proceedings 10: 232,1996.

72. Ekins S, Murray GI, Watson RD, Merle O, Hawksworth GM and Burke MD, A new substrate for the fluorescence detection of cytochrome P450 activity in individual cells. Hum Exp Toxicol 15: 147, 1996.

73. Ekins S, Murray GI, Burke MD, Marchant NC and Hawksworth GM, Quantitative differences in xenobiotic metabolism by precision-cut liver slices and isolated hepatocytes. ISSX Proceedings 8: 419, 1995.

74. Ekins S, Murray GI, Burke MD, Marchant NC and Hawksworth GM, Epoxidation, diol formation and glutathione conjugation in rat hepatocytes and precision-cut liver slices. Br J Clin Pharmacol 40: 180P, 1995.

75. Ekins S, Murray GI, Marchant NC, Burke MD and Hawksworth GM, Qualitative and quantitative comparison of testosterone hydroxylation and 7-ethoxycoumarin O-deethylation in Sprague Dawley rat hepatocytes and precision-cut liver slices. Br J Clin Pharmacol 38: 164-165P, 1994.

76. Ekins S, Collins GC, Newlands AJ, Williams JA, Marchant NC, Lucas C and Hawksworth GM, The role of cytochrome P4503A in the metabolism of the vinca alkaloid -aminophosphonate derivative S12363 by human liver microsomes. Br J Clin Pharmacol 36: 165-166P, 1993.


External links

Personal tools
Namespaces

Variants
Actions
Navigation
Tools