Key result
Safety pharmacologists' visual predictions of Torsades de Pointes risk correlated strongly with the complex CiPA model metric Qnet (ROC AUC 0.86-1.00 for 11 of 12 scientists).
Why the study?
Given evidence that the input-output relationship of complex biophysical models predicting TdeP risk is almost linear, this pilot study evaluated whether scientists could predict model outcomes.
Safety pharmacologists can accurately predict the TdeP risk of compounds based on simple linear input-output relationships of ion-channel blocking data, correlating strongly with complex biophysical models like CiPA.
May simplify early TdeP risk screening with linear models; leaves open prospective validation against complex biophysical approaches.
There has been a lot of interest and publicity regarding the use of a complex biophysical model within drug development for predicting the TdeP risk of new compounds. Throughout the development of the complex model numerous groups have shown that a simple linear mechanistic model explains the predictive behaviour of complex mechanistic models. That is the input-output relationship is almost linear even when complex kinetic assays are used. We hypothesized that given this linear relationship that scientist would be able to predict the outcome of the biophysical model. The objective of this pilot study was to assess the feasibility of such an analysis but also assess the initial degree of correlation. A set of 15 compounds with diverse ion-channel blocking against 4 ion-channel currents, IKr, ICaL, INa and INaL, was generated. Safety pharmacologists across numerous companies were approached and asked to categorize the TdeP risk of these compounds using only the % block depicted via a bar chart into one of 3 categories: Risk, No-risk or Unsure. 12 scientists participated in the study, of which 11 correlated strongly with the model (11 person ROC AUC range: 0.86-1, 7 scientists had a value >0.9). The combined prediction of all scientists also correlated strongly with the model. These results highlight that the linear input-output relationship can indeed be predicted by the scientist. A future study exploring the degree of correlation with a wider group of scientists and wider set of compounds would be required to get a more precise estimate of the correlation. We hope this initial exploratory study will encourage the community to pursue this idea.
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Mistry et al. (2020) studied Torsades de Pointes risk prediction (n=12). Visual prediction of TdeP risk using % block data vs. CiPA model (Qnet) predictions was evaluated on Correlation between scientist risk categorization and Qnet (ROC AUC 0.86-1.00, p=0.001). Safety pharmacologists' visual predictions of Torsades de Pointes risk correlated strongly with the complex CiPA model metric Qnet (ROC AUC 0.86-1.00 for 11 of 12 scientists).
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