Sean Ekins

Last updated

Sean Ekins
SeanEkins-headshot.jpg
Sean Ekins in 2020
Born
Cleethorpes, England
NationalityBritish, American
Alma mater The University of Aberdeen
Nottingham Trent University
Known for ADME/Tox models
Pharmacophores
New technologies for pharmaceutical R&D
Scientific career
Fields Pharmacology
Cheminformatics
Scientific collaborations
Alternatives to animal testing
Institutions
Doctoral advisors Gabrielle M. Hawksworth and M. Danny Burke

Sean Ekins is a British pharmacologist and expert in the fields of ADME/Tox, computational toxicology and cheminformatics at Collaborations in Chemistry, a division of corporate communications firm Collaborations in Communications. He is also the editor of four books and a book series for John Wiley & Sons.

Contents

Biography

Sean Ekins is a scientific leader with over twenty-three years of broad experience in drug discovery. He 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 MSc 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 PhD 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.

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] [11] [12] PXR [13] and enzymes. [14] [15]

In December 2001 he started work for a start-up company, Concurrent Pharmaceuticals (now Vitae Pharmaceuticals) [16] 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 MetaDrug product (patent pending). [17] [18] [19] [20]

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".

From 2006-2016 Ekins consulted for several companies including for Collaborative Drug Discovery.

In 2011 Ekins Co-Founded Phoenix Nest working on treatments for Sanfilippo Syndrome.

In 2015, Ekins founded Collaborations Pharmaceuticals, a privately owned company that performs research and development on innovative therapeutics for multiple rare and infectious diseases. Collaborations Pharmaceuticals partners with academics and companies to identify and translate early preclinical to clinical stage assets.

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 of seminal papers around data sharing and making pharmaceutical data more open publishing papers:

1. on the long overdue need for making preclinical ADME/Tox data precompetitive [21]

2. how crowdsourcing could be used in the pharmaceutical industry [22]

3. how computational models for pharmacoeconomics could be shared by the scientific community [23]

4. what tools are still needed in cheminformatics and how methods for model sharing will be important [24]

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 [25]

This work is important because it was the first prominent advocacy for making a broad array of approaches to make preclinical and postmarketing data and models available as well as the demonstration of the feasibility of such approaches. Ekins 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 GlaxoSmithKline 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. [26] These data were compared to other malaria and tuberculosis data. [27]

In addition he provided analyses of very large libraries of tuberculosis data which highlight important physicochemical properties,. [28] [29]

Ekins has highlighted gaps in TB research, specifically in how cheminformatics and other computational tools could be integrated to improve efficiency [30] and provided examples of how computational methods can be used to assist in screening for compounds active against TB [31]

In February 2011 Ekins began participating in the MM4TB project as part of Collaborative Drug Discovery. [32] led by Professor Stewart Cole. [33]

Science Mobile Applications

Ekins co-developed a Wiki with Antony John Williams called Science Mobile Applications [34] launched 21 June 2011. [35] Initially this grew out of a desire to track chemistry Apps [36] (for a paper submitted) and then Apps for science in the chemistry classroom. [37]

Database quality

Using their respective blogs, Ekins and Antony Williams alerted the scientific community within days of the release of the NCGC NPC browser. [38] that there were significant errors in molecule structures. These observations were later published as an editorial in Drug Discovery Today. [39]

Collaborations Pharmaceuticals

In 2015, Sean founded Collaborations Pharmaceuticals to build upon collaborations and projects that came out of applying using machine learning approaches. The projects involved neglected diseases such as Tuberculosis, Chagas disease and rare diseases such as Batten Disease, Pitt Hopkins Syndrome and others. To date they have obtained 8 orphan drug designations across 5 rare or neglected diseases, and have widely published their results in peer reviewed journals.[SE1]  The company has obtained over $7.6M of funding from NIH and DOD grants to date.

Ebola Research

Since 2014 Sean has worked on Ebola drug discovery publishing 19 articles. One of these was the first use of a machine learning model to identify compounds active against Ebola (here). [SE2] This identified three active compounds (tilorone, quinacrine and pyronaridine) in vitro which have been subsequently tested in vivo and found to be active in mouse (Articles 1,2,3) (Most recently pyronaridine was shown to have some in vivo activity against Ebola in a Guinea pig. These molecules have also shown activity against Marburg, and bind the Ebola glycoprotein.

Chagas Disease Research

In 2015 Sean developed a machine learning model to predict molecules with activity against T.Cruzi, the parasite which cause Chagas Disease. Pyronaridine was one of several molecules identified with in vitro and in vivo activity.

SARS-CoV-2 Research

In 2020 the three molecules identified with activity against Ebola were tested against SARS-CoV-2 and were of potential interest as tilorone was shown to inhibit MERS and is well known to inhibit other viruses.

Software Products

Collaborations Pharmaceuticals Inc. has developed several software products including Assay Central®, MegaTox®, MegaTrans® and MegaPredict® which leverage data curation and machine learning to curate models relevant for drug discovery and computational ADME/Tox.

Editorships

Ekins has edited or co-edited 4 books for Wiley including: Computer Applications in Pharmaceutical Research and Development (2006), Computational Toxicology: Risk Assessment For Pharmaceutical and Environmental Chemicals(1007), Drug Efficacy, Safety, and Biologics Discovery(2009) and Collaborative Computational Technologies for Biomedical Research (2011) All the books have an underlying connection with computational technologies and their application for pharmaceutical R&D.

His most recent edited book is Computational Toxicology: Risk Assessment for Chemicals which follows on from the earlier book Computational Toxicology: Risk Assessment for Pharmaceutical and Environmental Chemicals.

Patents

Ekins is inventor on four issued US patents, [40] and [41]

Related Research Articles

Cheminformatics refers to the use of physical chemistry theory with computer and information science techniques—so called "in silico" techniques—in application to a range of descriptive and prescriptive problems in the field of chemistry, including in its applications to biology and related molecular fields. Such in silico techniques are used, for example, by pharmaceutical companies and in academic settings to aid and inform the process of drug discovery, for instance in the design of well-defined combinatorial libraries of synthetic compounds, or to assist in structure-based drug design. The methods can also be used in chemical and allied industries, and such fields as environmental science and pharmacology, where chemical processes are involved or studied.

<span class="mw-page-title-main">Drug design</span> Inventive process of finding new medications based on the knowledge of a biological target

Drug design, often referred to as rational drug design or simply rational design, is the inventive process of finding new medications based on the knowledge of a biological target. The drug is most commonly an organic small molecule that activates or inhibits the function of a biomolecule such as a protein, which in turn results in a therapeutic benefit to the patient. In the most basic sense, drug design involves the design of molecules that are complementary in shape and charge to the biomolecular target with which they interact and therefore will bind to it. Drug design frequently but not necessarily relies on computer modeling techniques. This type of modeling is sometimes referred to as computer-aided drug design. Finally, drug design that relies on the knowledge of the three-dimensional structure of the biomolecular target is known as structure-based drug design. In addition to small molecules, biopharmaceuticals including peptides and especially therapeutic antibodies are an increasingly important class of drugs and computational methods for improving the affinity, selectivity, and stability of these protein-based therapeutics have also been developed.

Quantitative structure–activity relationship models are regression or classification models used in the chemical and biological sciences and engineering. Like other regression models, QSAR regression models relate a set of "predictor" variables (X) to the potency of the response variable (Y), while classification QSAR models relate the predictor variables to a categorical value of the response variable.

<span class="mw-page-title-main">ADME</span> Acronym for process of disposition of pharmaceutical compounds

ADME is an abbreviation in pharmacokinetics and pharmacology for "absorption, distribution, metabolism, and excretion", and describes the disposition of a pharmaceutical compound within an organism. The four criteria all influence the drug levels and kinetics of drug exposure to the tissues and hence influence the performance and pharmacological activity of the compound as a drug. Sometimes, liberation and/or toxicity are also considered, yielding LADME, ADMET, or LADMET.

<span class="mw-page-title-main">Medicinal chemistry</span> Scientific branch of chemistry

Medicinal or pharmaceutical chemistry is a scientific discipline at the intersection of chemistry and pharmacy involved with designing and developing pharmaceutical drugs. Medicinal chemistry involves the identification, synthesis and development of new chemical entities suitable for therapeutic use. It also includes the study of existing drugs, their biological properties, and their quantitative structure-activity relationships (QSAR).

<span class="mw-page-title-main">Pharmacophore</span> Abstract description of molecular features

In medicinal chemistry and molecular biology, a pharmacophore is an abstract description of molecular features that are necessary for molecular recognition of a ligand by a biological macromolecule. IUPAC defines a pharmacophore to be "an ensemble of steric and electronic features that is necessary to ensure the optimal supramolecular interactions with a specific biological target and to trigger its biological response". A pharmacophore model explains how structurally diverse ligands can bind to a common receptor site. Furthermore, pharmacophore models can be used to identify through de novo design or virtual screening novel ligands that will bind to the same receptor.

In pharmacology, biological activity or pharmacological activity describes the beneficial or adverse effects of a drug on living matter. When a drug is a complex chemical mixture, this activity is exerted by the substance's active ingredient or pharmacophore but can be modified by the other constituents. Among the various properties of chemical compounds, pharmacological/biological activity plays a crucial role since it suggests uses of the compounds in the medical applications. However, chemical compounds may show some adverse and toxic effects which may prevent their use in medical practice.

This page describes mining for molecules. Since molecules may be represented by molecular graphs this is strongly related to graph mining and structured data mining. The main problem is how to represent molecules while discriminating the data instances. One way to do this is chemical similarity metrics, which has a long tradition in the field of cheminformatics.

<span class="mw-page-title-main">Chemical Computing Group</span> Software company in Canada

Chemical Computing Group is a software company specializing in research software for computational chemistry, bioinformatics, cheminformatics, docking, pharmacophore searching and molecular simulation. The company's main customer base consists of pharmaceutical and biotechnology companies, as well as academic research groups. It is a private company that was founded in 1994; it is based in Montreal, Quebec, Canada. Its main product, Molecular Operating Environment (MOE), is written in a self-contained programming system, the Scientific Vector Language (SVL).

<span class="mw-page-title-main">UGT1A10</span> Protein-coding gene in the species Homo sapiens

UDP-glucuronosyltransferase 1-10 is an enzyme that in humans is encoded by the UGT1A10 gene.

<span class="mw-page-title-main">Flavin containing monooxygenase 1</span> Protein-coding gene in the species Homo sapiens

Dimethylaniline monooxygenase [N-oxide-forming] 1 is an enzyme that in humans is encoded by the FMO1 gene.

Simcyp Limited is a research-based company which provides modelling and simulation software to the pharmaceutical industry for use during drug development. Simcyp is based in Sheffield, UK.

Molecular Discovery Ltd is a software company working in the area of drug discovery.

In pharmacology, an antitarget is a receptor, enzyme, or other biological target that, when affected by a drug, causes undesirable side-effects. During drug design and development, it is important for pharmaceutical companies to ensure that new drugs do not show significant activity at any of a range of antitargets, most of which are discovered largely by chance.

<span class="mw-page-title-main">Collaborative Drug Discovery</span>

Collaborative Drug Discovery (CDD) is a software company founded in 2004 as a spin-out of Eli Lilly by Barry Bunin, PhD. CDD utilizes a web-based database solution for managing drug discovery data, primarily through the CDD Vault product which is focused around small molecules and associated bio-assay data. In 2021, CDD launched its first commercial data offering, PharmaKB, formerly BioHarmony, as The Pharma KnowledgeBase, which is centered around pharma company, drug, and disease information for research, business intelligence, and investors.

<span class="mw-page-title-main">Antony John Williams</span> British chemist

Antony John Williams is a British chemist and expert in the fields of both nuclear magnetic resonance (NMR) spectroscopy and cheminformatics at the United States Environmental Protection Agency. He is the founder of the ChemSpider website that was purchased by the Royal Society of Chemistry in May 2009. He is a science blogger and an author.

eTOX Toxicology data consortium

eTOX is a temporary consortium established in 2010 to share and use toxicology data. It is a pre-competitive collaboration which main goal is to create and distribute tools to predict drug side-effects based on pre-clinical experiments. Aims are a better in silico predictability of potential adverse events and a decrease of the use of animals in toxicological research. eTOX is funded by the Innovative Medicines Initiative (IMI).

Pharmaceutical bioinformatics is a research field related to bioinformatics but with the focus on studying biological and chemical processes in the pharmaceutical area; to understand how xenobiotics interact with the human body and the drug discovery process.

Yvonne Connolly Martin is an American cheminformatics and computer-aided drug design expert who rose to the rank of Senior Volwiler Research Fellow at Abbott Laboratories. Trained in chemistry at Northwestern University, she became a leader in collaborative science aimed at discovering and developing bioactive molecules as therapeutic agents, with her contributions proceeding from application of methods to understand how descriptors of molecular shapes and physicochemical properties relate to their biological activity. She is the author of a seminal volume in cheminformatics, Quantitative Drug Design, and has been the recipient of numerous awards in her field, including being named as a fellow of the American Association for the Advancement of Science (1985) and of the International Union of Pure and Applied Chemistry (2000), and receiving the Herman Skolnik Award (2009) and the Award for Computers in Chemical and Pharmaceutical Research (2017) from the American Chemical Society.

Molecular Operating Environment (MOE) is a drug discovery software platform that integrates visualization, modeling and simulations, as well as methodology development, in one package. MOE scientific applications are used by biologists, medicinal chemists and computational chemists in pharmaceutical, biotechnology and academic research. MOE runs on Windows, Linux, Unix, and macOS. Main application areas in MOE include structure-based design, fragment-based design, ligand-based design, pharmacophore discovery, medicinal chemistry applications, biologics applications, structural biology and bioinformatics, protein and antibody modeling, molecular modeling and simulations, virtual screening, cheminformatics & QSAR. The Scientific Vector Language (SVL) is the built-in command, scripting and application development language of MOE.

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 (2): 165P–166P. doi:10.1111/j.1365-2125.1993.tb04214.x. PMC   1364582 . PMID   8398587.
  2. ↑ Ekins, J.H.; Bravi, S.; Binkley, G.; Gillespie, S.; Ring, J.S.; Wikel, B.J.; Wrighton, S.A. (1999). "Three and four dimensional-quantitative structure activity relationship analyses of CYP3A4 inhibitors". J. Pharmacol. 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 (4): 477–489. PMID   10780267.
  4. ↑ Ekins, S.; et al. (1999). "Three dimensional-quantitative structure activity relationship analyses of substrates for CYP2B6". Pharm Exp Ther. 288 (1): 21–29. PMID   9862748.
  5. ↑ Ekins, G. Wikel J.H.; Bravi, S.; Wrighton, SA (1999). "Three dimensional quantitative structure activity relationship (3D-QSAR) analysis of CYP3A4 substrates". J Pharmacol Exp Ther. 291: 424–433.
  6. ↑ Ekins, S.; Wrighton, S.A. (1999). "The role of CYP2B6 in human xenobiotic metabolism". Drug Metab Rev. 31 (3): 719–754. doi:10.1081/dmr-100101942. PMID   10461547.
  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. doi:10.1016/s1056-8719(00)00109-x. PMID   11274894.
  9. ↑ Ekins, S.; et al. (2002). "Application of three dimensional quantitative structure-activity relationships of P-glycoprotein inhibitors and substrates". Mol Pharmacol. 61 (5): 974–981. doi:10.1124/mol.61.5.974. PMID   11961114. S2CID   7619918.
  10. ↑ Ekins, S.; et al. (2002). "Three dimensional quantitative structure-activity relationships of inhibitors of P-glycoprotein". Mol Pharmacol. 61 (5): 964–973. doi:10.1124/mol.61.5.964. PMID   11961113. S2CID   8018230.
  11. ↑ hERG
  12. ↑ 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 Ther 301, 427-434
  13. ↑ Ekins, S.; Erickson, J.A. (2002). "A pharmacophore for human pregnane-X-receptor ligands". Drug Metab Dispos. 30 (1): 96–99. doi:10.1124/dmd.30.1.96. PMID   11744617. S2CID   20521103.
  14. ↑ Ekins, S. et al. (2002) Pharmacophore insights into the active sites of the CYP3A enzymes. The Pharmacologist 44 Supplement, 114.
  15. ↑ Ethell, B.T.; et al. (2002). "Quantitative structure activity relationships for the glucuronidation of simple phenols by expressed human UGT1A6 and UGT1A9". Drug Metab. Dispos. 30 (6): 734–738. doi:10.1124/dmd.30.6.734. PMID   12019203. S2CID   18689723.
  16. ↑ "Concurrent Pharmaceuticals Changes Name To Vitae Pharmaceuticals And Secures $34 Million in Financing". biospace.com. 19 October 2005. Retrieved 23 May 2014.
  17. ↑ Ekins, S.; et al. (2005). "Computational Prediction of Human Drug Metabolism". Expert Opin Drug Metab Toxicol. 1 (2): 303–324. doi:10.1517/17425255.1.2.303. PMID   16922645. S2CID   32612412.
  18. ↑ Ekins, S. et al. (2005) Systems biology: applications in drug discovery. In Drug discovery handbook (Gad, S., ed.), pp. 123-183, Wiley
  19. ↑ Ekins, S.; et al. (2005). "A Novel Method for Visualizing Nuclear Hormone Receptor Networks Relevant to Drug Metabolism". Drug Metab Dispos. 33 (3): 474–481. doi:10.1124/dmd.104.002717. PMID   15608136. S2CID   25141855.
  20. ↑ Ekins, S.; et al. (2005). "Techniques: Application of Systems Biology to Absorption, Distribution, Metabolism, Excretion, and Toxicity". Trends Pharmacol Sci. 26 (4): 202–209. doi:10.1016/j.tips.2005.02.006. PMID   15808345.
  21. ↑ Ekins, S; Williams, AJ (2010). "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 (1): 13–22. doi:10.1039/b917760b. PMID   20024044.
  22. ↑ Ekins S. and Williams AJ, Reaching out to collaborators: crowdsourcing for pharmaceutical research, Pharm Res, 27: 393-395, 2010.
  23. ↑ Arnold RJG and Ekins S, Time for cooperation in health economics among the modeling community, PharmacoEconomics, 28(8):609-613, 2010
  24. ↑ Ekins S, Gupta R, Gifford E, Bunin BA, Waller CL, Chemical Space: missing pieces in cheminformatics, Pharm Res, 27: 2035-2039, 2010
  25. ↑ Gupta, Rishi R.; Gifford, EM; Liston, T; Waller, CL; Hohman, M; Bunin, BA; Ekins, S (2010). "Using open source computational tools for predicting human metabolic stability and additional ADME/Tox properties". Drug Metab Dispos. 38 (11): 2083–2090. doi:10.1124/dmd.110.034918. PMID   20693417. S2CID   8095030.
  26. ↑ Ekins, Sean; John Williams, Antony (2010). "When Pharmaceutical Companies Publish Large Datasets: An Abundance of riches or fool's gold?". Drug Discov Today. 15 (19–20): 812–815. doi:10.1016/j.drudis.2010.08.010. PMID   20732447.
  27. ↑ Ekins S and Williams AJ, 2010 Meta-analysis of molecular property patterns and filtering of public datasets of antimalarial "hits" and drugs, MedChemComm 1: 325-330,
  28. ↑ Ekins, S; Bradford, J; Dole, K; Spektor, A; Gregory, K; Blondeau, D; Hohman, M; Bunin, BA (2010). "A Collaborative Database and Computational Models for Tuberculosis Drug Discovery". Mol BioSyst. 6 (5): 840–851. doi:10.1039/b917766c. PMID   20567770.
  29. ↑ Ekins, S; Kaneko, T; Lipinski, CA; Bradford, J; Dole, K; Spektor, A; Gregory, K; Blondeau, D; Ernst, S; Yang, J; Goncharoff, N; Hohman, M; Bunin, BA (2010). "Analysis and hit filtering of a very large library of compounds screened against Mycobacterium tuberculosis". Mol Biosyst. 6 (11): 2316–2324. doi:10.1039/c0mb00104j. PMID   20835433.
  30. ↑ Ekins, S; Freundlich, JS; Choi, I; Sarker, M; Talcott, C (2011). "Computational Databases, Pathway and Cheminformatics Tools for Tuberculosis Drug Discovery". Trends in Microbiology. 19 (2): 65–74. doi:10.1016/j.tim.2010.10.005. PMC   3034835 . PMID   21129975.
  31. ↑ Lamichhane, Gyanu; Freundlich, Joel S.; Ekins, Sean; Wickramaratne, Niluka; Nolan, Scott T.; Bishai, William R. (1 February 2011). "Essential metabolites of Mycobacterium tuberculosis and their mimics". mBio. 2 (1): e00301–00310. doi:10.1128/mBio.00301-10. ISSN   2150-7511. PMC   3031304 . PMID   21285434.
  32. ↑ "MM4TB project". Archived from the original on 27 July 2011. Retrieved 3 March 2011.
  33. ↑ "UPCOL – Cole Lab – Chair of Microbial Pathogenesis" . Retrieved 5 August 2023.
  34. ↑ The SciMobileApps Wiki
  35. ↑ Announcement of the SciMobileApps Wiki
  36. ↑ "Comment" . Retrieved 5 August 2023.
  37. ↑ Williams, Antony J. (2011). "Smart Phones, a Powerful Tool in the Chemistry Classroom". Journal of Chemical Education. 88 (6): 683–686. Bibcode:2011JChEd..88..683W. doi:10.1021/ed200029p.
  38. ↑ "Tripod Development".
  39. ↑ Williams, AJ; Ekins, S (September 2011). "A quality alert and call for improved curation of public chemistry databases". Drug Discov. Today. 16 (17–18): 747–50. doi:10.1016/j.drudis.2011.07.007. PMID   21871970.
  40. ↑ 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."
  41. ↑ U.S. Patent no 6489094: Ekins S, Kelly KG, Johnson DL, "Method and device for drug-drug interaction testing sample preparation" .
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