Development of 3D organotypic cellular models utilizing animal cells is important for the validation of these systems by direct comparison to animal data. A few organizations have been successful in providing such assays for various organ systems. However, development of peripheral nerve assays is lagging. Commonly-used peripheral neural culture preparations are not predictive of clinical toxicity, partially since they typically utilize apoptosis or neurite elongation as measurable endpoints, whereas adult peripheral neurons are fully grown and known to resist apoptosis. Nerve conduction testing and histomorphometry of biopsies are the most clinically- relevant measures of neuropathy. Nevertheless, no culture models that provide such metrics currently exist. Various brain culture systems, such as 3D neural constructs, cerebral organoids or neurospheres to assess neurotoxicity were developed. However, none recapitulated the biomimetic complexity of the nervous system especially peripheral nervous system. 3D neural toxicity assays that seek to recapitulate the most relevant anatomic and physiological toxic pathology in a simple model require a stronger focus on system architecture. We have developed an innovative sensory-nerve-on-a-chip model by culturing dorsal root ganglia in micropatterned hydrogel constructs to constrain axon growth in a 3D arrangement analogous to peripheral nerve anatomy. Further, electrically-evoked population field potentials resulting from compound action potentials (CAPs) may be recorded reproducibly in these model systems. These early results demonstrate the feasibility of using microengineered neural tissues that are amenable to morphological and physiological measurements analogous to those of animal (and clinical) tests. From a single in vitro preparation, we can measure CAP amplitude and conduction velocity, and then subsequently section the tissue to measure histomorphological parameters such as axon diameter, axon density, and myelination. We hypothesize that this 3D organotypic system is capable of detecting neural toxicity parameters in ways that mimic clinical neuropathology. This versatile system could also further be used for performing ?omics? studies and thus will eventually be used for determining a large spectrum of toxicological parameters resulting in understanding mechanisms of action as well as improved understanding of biological processes. The objective of this project is to demonstrate that certain chemical toxins known for causing neurotoxicity in rats will induce toxicity in microengineered neural tissue that can be quantified using morphological and physiological measures analogous to clinical metrics. We will approach this objective by first enhancing the throughput of our system by engineering 3D microelectrodes for testing electrophysiological characteristics of the model system. Next, we will determine the baseline variability and characterize structure-function relationships using the 3D microelectrodes. We will then quantify changes induced by acute application of specific chemical toxins in order to demonstrate the technical merit of using the compound action potential (cAP) waveform as a preclinical assay of neurotoxicity.
While animal neural toxicity testing provides more specific anatomical and physiological parameters after chemical exposure, the cost and time of performing such experiments precludes their use for screening thousands of compounds. We have developed an innovative sensory-nerve-on-a-chip model of cultured rat dorsal root ganglia in a 3D arrangement analogous to peripheral nerve anatomy that can record compound action potential amplitude and conduction velocity as well as measure histomorphological parameters such as axon diameter, axon density, and myelination. If successful, the assay would be used as an animal neural toxicity screening assay, screening candidate compounds for neural toxicity, predicting compound in vivo toxicity profile. Thus, significantly obviating costly and cumbersome animal studies currently required for drug development.
