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

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 I Kr have the greatest prolonging effect of the QT interval, and that blocking the L-type calcium current I CaL and late sodium current I NaL 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 I Kr 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.

Scalable Manufacturing of Solderable and Stretchable Physiologic Sensing Systems.

Methods for microfabrication of solderable and stretchable sensing systems (S4s) and a scaled production of adhesive-integrated active S4s for health monitoring are presented. S4s' excellent solderability is achieved by the sputter-deposited nickel-vanadium and gold pad metal layers and copper interconnection. The donor substrate, which is modified with "PI islands" to become selectively adhesive for the S4s, allows the heterogeneous devices to be integrated with large-area adhesives for packaging. The feasibility for S4-based health monitoring is demonstrated by developing an S4 integrated with a strain gauge and an onboard optical indication circuit. Owing to S4s' compatibility with the standard printed circuit board assembly processes, a variety of commercially available surface mount chip components, such as the wafer level chip scale packages, chip resistors, and light-emitting diodes, can be reflow-soldered onto S4s without modifications, demonstrating the versatile and modular nature of S4s. Tegaderm-integrated S4 respiration sensors are tested for robustness for cyclic deformation, maximum stretchability, durability, and biocompatibility for multiday wear time. The results of the tests and demonstration of the respiration sensing indicate that the adhesive-integrated S4s can provide end users a way for unobtrusive health monitoring.