(640a) Intelligent High-Throughput Intervention Testing Platform in Daphnia | AIChE

(640a) Intelligent High-Throughput Intervention Testing Platform in Daphnia


Cho, Y. - Presenter, Georgia Institute of Technology
Kirschner, M., Harvard Medical School
Peshkin, L., Harvard Medical School
Developing pharmacological interventions that slow down the aging process and consequently postpone the onset and progression of age-associated diseases is highly sought after. However, since the mechanisms driving the aging process are not well understood, there currently exist few druggable targets for anti-aging treatments and therefore the evaluation of drug effects on the aging process requires the development of new high-throughput screening platforms. A key component of new high-throughput screening platforms will be quantitative biomarkers of aging. Since individuals may not age at the same rate, quantitative biomarkers of aging are valuable tools to measure physiological age, assess the extent of healthy aging, and potentially predict not only health and lifespan but also age-related outcomes for individuals within a population, even at the early age. Molecular biomarkers (often based on gene expression) are robust quantitative metrics and can reflect some of the molecular mechanisms underlying the aging process, but often require sacrifice of the subject, laborious sample processing, and constitute a single data endpoint. Phenotypic biomarkers can be harder to quantify but are fairly easy to obtain, non-invasive, and therefore are possible to repeatedly assay over the entire life of the subject and in future generations.

In this study, we present a novel platform for testing the effect of interventions on life- and health-span of a short-lived semi-transparent freshwater organism, sensitive to drugs with complex behavior and physiology – the planktonic crustacean Daphnia magna. Within this platform, dozens of complex behavioral features of both routine motion and response to stimuli are continuously accurately quantified for large homogeneous cohorts via an automated phenotyping pipeline. We build predictive machine learning models calibrated using chronological age and extrapolate onto phenotypic age. We further apply the model to estimate the phenotypic age under pharmacological perturbations including metformin and rapamycin. Our platform provides a scalable framework for drug screening and characterization in both life-long and instant assays as illustrated using a long-term dose-response profile of metformin and a short-term assay of well-studied substances such as caffeine and alcohol.