Enhanced Detection of Drug-induced Cardiotoxicity in Human Stem Cell-Derived Cardiomyocytes Using Optical Flow, Biomimetic Substrates, and Machine Learning
- Author(s): Lee, Eugene
- Advisor(s): Khine, Michelle
- et al.
Current preclinical screening methods are ineffective at detecting cardiotoxicity: 30% of drug attritions are attributed to drug-induced cardiotoxicity. With recent advancements in stem cell technologies, human pluripotent stem cells-derived cardiomyocytes (hPSC-CM) can now provide a physiologically relevant in vitro model of the myocardium. Availability of such cells has led to the emergence of various platforms that utilize them. However, current platforms still encounter challenges that deter their adoption for commercial use. One issue is hPSC-CMs exhibit fetal-like phenotypes; screening with them leads to unpredictable results that don’t accurately represent cardiotoxicity in adults. Another persistent challenge is the need to develop a simple and reliable method to measure key electrophysiological and contractile parameters. In addition, analytical approaches need to be created for accurate and automated detection of cardiotoxicity from platform readouts. In this thesis, strategies that collectively form a platform are described to address these issues.
Alignment of hPSC-CMs has been shown to regulate sarcomere orientation, produce stronger contractile forces, and cause anisotropic action potential propagation. We demonstrate that biomimetic substrates with topographical alignment cues (uniaxial and multi-scale ‘wrinkles’) can be fabricated by using pre-stressed thermoplastic shrink film. These wrinkles recapitulate the anisotropic nature of the ECM of the native myocardium. When aligned on these substrates, hPSC-CMs exhibited a more sensitive response to cardioactive compounds than their unaligned counterparts.
Using brightfield microscopy and optical flow, contractility of hPSC-CMs exposed to compounds can be monitored in a non-invasive and inexpensive manner. Furthermore this brightfield technique was readily applied to cardiac constructs of various tissue geometries, ranging from 2D monolayers to 3D cardiac organoids. For improved and automated analysis, we mated the brightfield technique with supervised machine learning. The machine learning provides a singular quantitative index that summarizes the impact of multiple parameters, and thus simplifies the assessment of drug effects on hPSC-CMs. Through the evaluation of several cardioactive drugs with dissimilar effects, this paired method was comparable – and even superior to – a fluorescence-based detection scheme common in commercially available systems. The machine learning was further leveraged for the analysis of a cardiac tissue strip platform. A model of drug classes was created and successfully predicted the mechanistic action of an unknown cardioactive compound.