Machine learning in drug development: Characterizing the effect of 30 drugs on the QT interval using Gaussian process regression, sensitivity analysis, and uncertainty quantification

By F. Sahli Costabal, K. Matsuno, Jiang Yao, Paris Perdikaris, Ellen Kuhl. 2019

92 citations recorded by the source corpus.

Prolonged QT intervals are a major risk factor for ventricular arrhythmias and a leading cause of sudden cardiac death. Various drugs are known to trigger QT interval prolongation and increase the proarrhythmic potential. Yet, how precisely the action of drugs on the cellular level translates into QT interval prolongation on the whole organ level remains insufficiently understood. Here we use machine learning techniques to systematically characterize the effect of 30 common drugs on the QT interval. We combine information from high fidelity three-dimensional human heart simulations with low fidelity one-dimensional cable simulations to build a surrogate model for the QT interval using multi-fidelity Gaussian process regression. Once trained and cross-validated, we apply our surrogate model to perform sensitivity analysis and uncertainty quantification. Our sensitivity analysis suggests that compounds that block the rapid delayed rectifier potassium current IKr have the greatest prolonging effect of the QT interval, and that blocking the L-type calcium current ICaL and late sodium current INaL shortens the QT interval. Our uncertainty quantification allows us to propagate the experimental variability from individual block-concentration measurements into the QT interval and reveals that QT interval uncertainty is mainly driven by the variability in IKr block. In a final validation study, we demonstrate an excellent agreement between our predicted QT interval changes and the changes observed in a randomized clinical trial for the drugs dofetilide, quinidine, ranolazine, and verapamil. We anticipate that both the machine learning methods and the results of this study will have great potential in the efficient development of safer drugs.

Subfields: Cardiology and Cardiovascular Medicine, Control and Systems Engineering

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cardiology and Cardiovascular Medicine

1. Paris Perdikaris
2. F. Sahli Costabal
3. Daniel E. Hurtado
4. Ellen Kuhl
5. Yibo Yang
6. G. Em Karniadakis

In Control and Systems Engineering

1. G. Em Karniadakis
2. Paris Perdikaris
3. Yibo Yang
4. Ellen Kuhl
5. Maizar Raissi

Citations in the corpus

• Physics-Informed Neural Networks for Cardiac Activation Mapping (2020, methodology): “We compare our method against three other methodologies: a neural network with the same architecture and parameters except without including the physics, linear interpolation [2], and Gaussian process regression [3, 29].”
• Integrating machine learning and multiscale modeling—perspectives, challenges, and opportunities in the biological, biomedical, and behavioral sciences (2019, methodology): “…Classifying the effects of individual single nucleotide polymorphisms on depression [Athreya et al., 2019]; of ion channel blockage on arrhythmogenic risk in drug development [Sahli Costabal et al., 2019c]; and of chemotherapeutic agents in personalized cancer medicine [Deist et al., 2019].”
• Multiscale modeling meets machine learning: What can we learn? (2019, methodology): “Examples in biomedicine include predicting an individual’s life expectancy, identifying a tolerable dose of chemotherapy, or exploring the interplay between drug concentration and arrhythmogenic risk [102], among many others.”