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The Napiergram: A tool for visualising efficacy and safety data
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Abstracts
with a minimal number of targets, hit rate and relevance of a target as a liability indicator. Mining of the existing data from the outsourced panel of assays showed that several targets have had no hits or there has been redundancy in the chemical space that is covered by different targets. Here a panel of targets in functional assay format is presented that has a good balance across five target classes and relevance for use as liability indicators during the drug discovery process.
doi:10.1016/j.vascn.2010.11.038
Poster Number: 35 Board Number: 35 The Napiergram: A tool for visualising efficacy and safety data Carolyn Napier, Robert Wallis Pfizer Global R&D, Sandwich, United Kingdom During pre-clinical drug discovery and development a range of in vitro and in vivo studies are conducted to characterise the primary pharmacology, pharmacokinetics, safety pharmacology and toxicology of a new chemical entity. The integrated knowledge gained from these studies enables an understanding of the relationship between efficacy and safety. From this knowledge a therapeutic index (i.e. the ratio of drug exposure that produces an undesirable effect to the ratio that causes the desired effect) can be derived and a prediction of the safe doses for clinical trials can be made. As this process usually involves combining endpoints from multiple datasets from different biological models we have developed a simple tool for graphical representation of exposure-linked efficacy and safety-related data. The ‘Napiergram’ provides a simple means of visualising multiple datasets by combining efficacy and safety data in single graph. As most pharmacological effects are mediated through the free fraction (Trainor, 2007) our comparisons of efficacy and safety data are based on the free plasma drug concentration. Exposures that correspond to the NOAEL (no observed adverse effect level), LOAEL (lowest adverse effect level), MTD (maximum tolerated dose) and the predicted human efficacy exposure are easily viewable. Visualising data across studies can also provide an insight into potential mechanisms of adverse effects e.g. by relating in vitro and in vivo data. Overall, we find the Napiergram to be extremely useful tool for visualising and communicating multiple pre-clinical datasets in the run up to first in human studies. References Trainor, G. L. (2007). The Importance of Plasma Protein Binding In Drug Discovery. Expert Opinion on Drug Discovery, 2, 51−64. doi:10.1016/j.vascn.2010.11.039
Poster Number: 36 Board Number: 36 Achieving Full GLP/CFR Part 11 compliance with Microsoft Excel® Andrea Greiter Wilke, Roland Jenni, David Waiz, Henry Holzgrefe F. Hoffmann-La Roche, Ltd., Basel, Switzerland Introduction: Microsoft Excel® is a powerful tool for the analysis of large datasets such as provided by telemetry studies. To date, full
GLP/CFR Part 11-compliance with Excel has not been feasible due to the lack of audit trail functionality. The alternative, to perform a 100% data integrity check, is not possible due to large spreadsheet sizes. We employ Excel with custom macros to filter noise, correct the QT interval, and calculate consecutive 5 min mean values per parameter. eInfotree® (Cimcon Software) is marketed as an Excel-GLP solution, but has not been previously validated with large datasets (>106 entries). Methods: A GLP computer system validation plan was developed which evaluated the ability of eInfotree to accurately audit trail telemetry dataset processing (> 106 entries) involving complex macro instructions and PivotTable generation. Results: eInfotree provides administrator rights and permissions for designated users, and implements electronic signatures. Password-protected login is required and Part 11 compliance is achieved. Changes in every cell were recorded in a protected audit trail containing time, original cell value, and user name. Testing during GLP validation confirmed eInfotree reliability, however system performance was increased from 2 min to 6 h by addition of the audit trail. eInfotree validation was accepted by Roche QA and passed a recent Swiss Medic GLP inspection. Conclusion: eInfotree provides the ability to use Excel functions under GLP conditions, greatly facilitating the automated analysis of large datasets.
doi:10.1016/j.vascn.2010.11.040
Poster Number: 37 Board Number: 37 Evaluation of a prototype software for automated analysis of ECG data from guinea pig & rat in vitro and in vivo models David S. Ramireza, Jonathan R. Heyena, Jingjing Yea, Florence Koeppelb, Aileen D. McHarga a
Pfizer Global Research and Development, La Jolla, CA, United States Notocord Inc., Croissy Sur Sein, France
b
A major goal of the studies presented here was to evaluate the effectiveness of a new prototype software module, ECG50a (Notocord Inc.), to perform automated analysis of electrocardiogram (ECG) data from both in vitro and in vivo models. The ECG effects of several compounds (dofetilide, lidocaine, propafenone, nitrendipine, and milrinone) have been tested in the guinea pig isolated heart model. The ECG data (PR interval, QRS duration and QT interval) for these compounds has been analyzed both manually and using the ECG50a software. The resulting data are qualitatively and quantitatively similar regardless of the method of analysis employed. This suggests that ECG50a is an appropriate tool to facilitate ECG analysis of in vitro guinea pig ECG data. Furthermore, the ECG50a module has been successfully used to analyze ECG data from vehicle-treated rat isolated heart studies and vehicle-treated rats in an anesthetized rat model. Data comparing manual vs. automated ECG analysis from these studies gave similar results suggesting that the ECG50a module may be an effective tool to automatically analyze rat ECG data collected from both in vitro and in vivo models. Further experiments are ongoing to evaluate standard compounds in the rat isolated heart and anesthetized rat model to further test the capability of the ECG50a module and the results will be presented in the poster. doi:10.1016/j.vascn.2010.11.041
Abstracts
with a minimal number of targets, hit rate and relevance of a target as a liability indicator. Mining of the existing data from the outsourced panel of assays showed that several targets have had no hits or there has been redundancy in the chemical space that is covered by different targets. Here a panel of targets in functional assay format is presented that has a good balance across five target classes and relevance for use as liability indicators during the drug discovery process.
doi:10.1016/j.vascn.2010.11.038
Poster Number: 35 Board Number: 35 The Napiergram: A tool for visualising efficacy and safety data Carolyn Napier, Robert Wallis Pfizer Global R&D, Sandwich, United Kingdom During pre-clinical drug discovery and development a range of in vitro and in vivo studies are conducted to characterise the primary pharmacology, pharmacokinetics, safety pharmacology and toxicology of a new chemical entity. The integrated knowledge gained from these studies enables an understanding of the relationship between efficacy and safety. From this knowledge a therapeutic index (i.e. the ratio of drug exposure that produces an undesirable effect to the ratio that causes the desired effect) can be derived and a prediction of the safe doses for clinical trials can be made. As this process usually involves combining endpoints from multiple datasets from different biological models we have developed a simple tool for graphical representation of exposure-linked efficacy and safety-related data. The ‘Napiergram’ provides a simple means of visualising multiple datasets by combining efficacy and safety data in single graph. As most pharmacological effects are mediated through the free fraction (Trainor, 2007) our comparisons of efficacy and safety data are based on the free plasma drug concentration. Exposures that correspond to the NOAEL (no observed adverse effect level), LOAEL (lowest adverse effect level), MTD (maximum tolerated dose) and the predicted human efficacy exposure are easily viewable. Visualising data across studies can also provide an insight into potential mechanisms of adverse effects e.g. by relating in vitro and in vivo data. Overall, we find the Napiergram to be extremely useful tool for visualising and communicating multiple pre-clinical datasets in the run up to first in human studies. References Trainor, G. L. (2007). The Importance of Plasma Protein Binding In Drug Discovery. Expert Opinion on Drug Discovery, 2, 51−64. doi:10.1016/j.vascn.2010.11.039
Poster Number: 36 Board Number: 36 Achieving Full GLP/CFR Part 11 compliance with Microsoft Excel® Andrea Greiter Wilke, Roland Jenni, David Waiz, Henry Holzgrefe F. Hoffmann-La Roche, Ltd., Basel, Switzerland Introduction: Microsoft Excel® is a powerful tool for the analysis of large datasets such as provided by telemetry studies. To date, full
GLP/CFR Part 11-compliance with Excel has not been feasible due to the lack of audit trail functionality. The alternative, to perform a 100% data integrity check, is not possible due to large spreadsheet sizes. We employ Excel with custom macros to filter noise, correct the QT interval, and calculate consecutive 5 min mean values per parameter. eInfotree® (Cimcon Software) is marketed as an Excel-GLP solution, but has not been previously validated with large datasets (>106 entries). Methods: A GLP computer system validation plan was developed which evaluated the ability of eInfotree to accurately audit trail telemetry dataset processing (> 106 entries) involving complex macro instructions and PivotTable generation. Results: eInfotree provides administrator rights and permissions for designated users, and implements electronic signatures. Password-protected login is required and Part 11 compliance is achieved. Changes in every cell were recorded in a protected audit trail containing time, original cell value, and user name. Testing during GLP validation confirmed eInfotree reliability, however system performance was increased from 2 min to 6 h by addition of the audit trail. eInfotree validation was accepted by Roche QA and passed a recent Swiss Medic GLP inspection. Conclusion: eInfotree provides the ability to use Excel functions under GLP conditions, greatly facilitating the automated analysis of large datasets.
doi:10.1016/j.vascn.2010.11.040
Poster Number: 37 Board Number: 37 Evaluation of a prototype software for automated analysis of ECG data from guinea pig & rat in vitro and in vivo models David S. Ramireza, Jonathan R. Heyena, Jingjing Yea, Florence Koeppelb, Aileen D. McHarga a
Pfizer Global Research and Development, La Jolla, CA, United States Notocord Inc., Croissy Sur Sein, France
b
A major goal of the studies presented here was to evaluate the effectiveness of a new prototype software module, ECG50a (Notocord Inc.), to perform automated analysis of electrocardiogram (ECG) data from both in vitro and in vivo models. The ECG effects of several compounds (dofetilide, lidocaine, propafenone, nitrendipine, and milrinone) have been tested in the guinea pig isolated heart model. The ECG data (PR interval, QRS duration and QT interval) for these compounds has been analyzed both manually and using the ECG50a software. The resulting data are qualitatively and quantitatively similar regardless of the method of analysis employed. This suggests that ECG50a is an appropriate tool to facilitate ECG analysis of in vitro guinea pig ECG data. Furthermore, the ECG50a module has been successfully used to analyze ECG data from vehicle-treated rat isolated heart studies and vehicle-treated rats in an anesthetized rat model. Data comparing manual vs. automated ECG analysis from these studies gave similar results suggesting that the ECG50a module may be an effective tool to automatically analyze rat ECG data collected from both in vitro and in vivo models. Further experiments are ongoing to evaluate standard compounds in the rat isolated heart and anesthetized rat model to further test the capability of the ECG50a module and the results will be presented in the poster. doi:10.1016/j.vascn.2010.11.041