Implementing electronic laboratory notebooks to improve the efficiency of pre-clinical drug discovery
4
SHARES
Posted: 13 December 2011 |
The pre-clinical phase of drug discovery spans a period in the region of five years and requires contributions from multi-disciplinary teams often working at different sites. These teams can generate significant amounts of data which are processed using standard as well as specialist software. The recording of a substantial amount of project related experimental work has historically been performed using paper-based laboratory notebooks completed manually with all files usually being stored locally.
This scenario poses a variety of issues such as delayed access to important information to the project team members which could ultimately reduce its efficiency and thus increase the time taken to complete the project. These paper-based notebooks are now being replaced by an electronic laboratory notebook (eLNB) within research laboratories in industry and academia. Such software allows the documentation of experimental data and its sharing within the multi-disciplinary research team and would be expected to improve data integrity, reduce the time to complete the project and improve communication. This article discusses some of the advantages that would be expected to be achieved upon implementing an eLNB in pre-clinical drug discovery.
The pre-clinical phase of drug discovery spans a period in the region of five years and requires contributions from multi-disciplinary teams often working at different sites. These teams can generate significant amounts of data which are processed using standard as well as specialist software. The recording of a substantial amount of project related experimental work has historically been performed using paper-based laboratory notebooks completed manually with all files usually being stored locally.
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This scenario poses a variety of issues such as delayed access to important information to the project team members which could ultimately reduce its efficiency and thus increase the time taken to complete the project. These paper-based notebooks are now being replaced by an electronic laboratory notebook (eLNB) within research laboratories in industry and academia. Such software allows the documentation of experimental data and its sharing within the multi-disciplinary research team and would be expected to improve data integrity, reduce the time to complete the project and improve communication. This article discusses some of the advantages that would be expected to be achieved upon implementing an eLNB in pre-clinical drug discovery.
The stages of small molecule pre-clinical drug discovery and associated data that is generated
Within the well defined stages of pre-clinical drug discovery, there are an ever increasing number of reagents, methods and technologies available to improve their productivity and efficiency. The gene-to-target stage involves the identification of a target implicated in a disease process and the subsequent generation of biological reagent/s that contain the target protein and in some cases also its protein substrate1,2. These activities will generate data in a variety of forms such as gene sequence data in text files, agarose gel and SDS-PAGE gel images, Western blot images and protein purification elution traces, all of which are analysed using suitable software to provide a quantitative output. The biological reagent/s can sub – sequently be utilised to develop assays to monitor target activity and these are usually compatible with microtitre plates of various densities3. These assays often make use of the target protein of interest in isolation (e.g. a biochemical assay) or in a more complex setting (e.g. cell based assay)4,5. Having developed an appropriate assay, it is subsequently utilised in a high throughput screening (HTS) campaign against libraries of small molecules in order to identify those that modulate the activity of the target in the desired manner. Upon completion of the HTS campaign, those compounds that give suitable activity are first re-tested in a confirmation assay. Depending upon the numbers of compounds that are evaluated, these activities could generate a vast amount of data in terms of the numbers of text files containing the raw data from a microtitre platereader. These files are subsequently processed using suitable software to calculate top level microtitre plate level statistics (Z’ and signal/background), determine the presence of microtitre plate edge effects (which are particularly prominent in cell based assays), normalise the data (using the high and low controls) and calculate hit rates6. The activity of the hits would be determined in a confirmation assay as well as a suitable counter assay to identify assay format specific false positives together with secondary assays (typically cell based assays). Confirmed hits would then be assessed in dose-response experiments in the primary target specific assay in order to allow their potencies to be determined. Although these activities would be performed upon a smaller number of compounds than the HTS campaign itself, each compound would be tested at a number of concentrations to provide preliminary structure-activity-relationships. All the raw data files would be archived and the processed data available to the project team. In order to select compounds for further study, they would be annotated with additional selectivity and physicochemical data after which an informed decision can be made to select appropriate compounds for progression. From the initial list of validated hit molecules, a relatively small number are usually considered for further exploration during the Hit-to-Lead (H2L) phase and subsequently the Lead-to- Candidate phase. During both these phases, synthesis of new compounds would be undertaken with a view to (i) optimising their potency at the target of interest, (ii) improving their selectivity and liability profile, (iii) increasing the synthetic yield, (iv) search databases for the activities of the compounds against other targets, (v) determine patentability and competitor activity for compounds and target and (vi) ensuring the compounds have an acceptable physicochemical and in vivo profile (e.g. solubility, stability in aqueous solution and human plasma, suitable in vivo pharmacokinetics, Absorption, Distribution, Metabolism and Excretion (ADME) properties). The time to take a project from the cloning of a target protein to the successful development of a clinical candidate molecule can take five years to complete and will involve individuals within an organisation as well as external organisations to whom work may be contracted7-9. The data generated during the various studies are likely to be acquired and processed using specialised software and the results collated using more general software for circulation to the project team and discussed at regular project meetings. It is critical that the experimental protocols, raw and processed data are archived in a clear manner so that it can be fully searched and specific information retrieved even when individuals may no longer be employed within the same department or organisation.
The role of an electronic notebook in pre-clinical drug discovery
The recording of pre-clinical drug discovery related information has for a considerable time been documented by scientists within organisations in paper-based notebooks which are completed manually10,11. Subsequent to completion of the paper-based notebook, it would be scanned and stored in a database. However, text recognition would not be possible upon scanning and therefore these would have limited use when searching for specific information. The use of an electronic laboratory notebook (eLNB) is becoming commonplace in drug discovery, especially within the large pharmaceutical organisations, biotechs and academia12. These are available from a number of suppliers and there are many types available for purchase13-17. An alternative is to design and develop a custom eLNB and this is exemplified by OSIRIS (developed at Actelion Ltd) which covers biology and chemistry related aspects of pre-clinical drug discovery. Additional cheminformatics functionalities included within software are physicochemical property prediction, comparisons of 3Dpharma cophores, docking of ligands into proteins and data visualisation18. However, this option is in most cases not possible due to the lack of expertise and the significant amount of time that would be required to build such software. Therefore, in most cases the purchase of an eLNB from a vendor would be optimal. The use of an eLNB would be expected to maximise the use of the data generated as described above and allow its management in a consistent manner thus saving the valuable time of scientists19.
Pre-clinical drug discovery is a lengthy process and requires the input of multidisciplinary teams which are often based on multiple sites. The documentation of experimental details, raw data and results are now being performed using eLNBs. These allow for improved data integrity, enable immediate archiving and dissemination of the experimental protocols and results and thus improve the overall productivity of an organisation. Additional benefits of implementing an eLNB include the potential to track projects, make searches for specific information and generate reports without the need to contact individual team members, increase productivity, ensure regulatory compliance and protect intellectual property. The eLNB may be web based and therefore be accessed remotely making it fully portable. Although initial training would be necessary to become familiar with the eLNB, the return on investment would be rapid.
About the Author
Sheraz Gul is Vice President and Head of Biology at European ScreeningPort, Hamburg, Germany. He is responsible for the management and development of Medium and High Throughput Screening activities for academic partners across Europe. He has 12 years research and development experience in both academia (University of London) and industry (GlaxoSmithKline Pharmaceuticals). This has ranged from the detailed study of catalysis by biological catalysts (enzymes and catalytic antibodies) to the design and development of assays for High Throughput Screening for the major biological target classes. He is the co-author of numerous papers, chapters and the Enzyme Assays: Essential Data handbook.
References
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4. Ma H, Deacon S. Horiuchi K. The challenge of selecting protein kinase assays for lead discovery optimization. Expert Opin Drug Discov. 2008, 3, 607-621.
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8. Swinney DC, Anthony J. How were new medicines discovered? Nat Rev Drug Discov. 2011, 10, 507-519
9. Bass AS, Cartwright ME, Mahon C, Morrison R, Snyder R, McNamara P, Bradley P, Zhou YY, Hunter J. Exploratory drug safety: a discovery strategy to reduce attrition in development. Journal of Pharmacological and Toxicological Methods. 2009, 60, 69-78
10. Beato B, Pisek A, White J, Grever T, Engel B, Pugh M, Schneider M, Carel B, Branstrator L, Shoup R. Going paperless: implementing an electronic laboratory notebook in a bioanalytical laboratory. Bioanalysis. 2011, 3, 1457-1470
11. Wright JM. Make it better but don’t change anything. Autom Exp. 2009, 1, 1-3.
12. Scoffin R. The new wave in electronic laboratory notebook systems. Chem Biol Drug Des. 2006, 67, 184-185
13. Rubacha M, Rattan AK, Hosselet SC. A review of electronic laboratory notebooks available in the market today. J Lab Autom. 2011, 16, 90-98
14. Taylor KT. The status of electronic laboratory notebooks for chemistry and biology. Curr Opin Drug Discov Devel. 2006, 9, 348-353
15. Goddard NH, Macneil R, Ritchie J. eCAT: Online electronic lab notebook for scientific research. Autom Exp. 2009, 1, 1-7
16. Khan AM, Hahn JD, Cheng WC, Watts AG, Burns GA. NeuroScholar’s electronic laboratory notebook and its application to neuroendocrinology. Neuroinformatics. 2006, 4, 139-162
17. Sakai H, Aoyama T, Yamaji K, Usui S. Concierge: personal database software for managing digital research resources. Front Neuroinform. 2007, 1, 1-6
18. Sander T, Freyss J, von Korff M, Reich JR, Rufener C. OSIRIS, an entirely in-house developed drug discovery informatics system. J Chem Inf Model. 2009, 49, 232-246
19. Zeng J, Hillman M, Arnold M. Impact of the implementation of a well-designed electronic laboratory notebook on bioanalytical laboratory function. Bioanalysis. 2011, 3, 1501-1511
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