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ABSTRACT Pharmacogenetics is a promising strategy to facilitate individualized care for patients with Major Depressive Disorder (MDD). Research is ongoing to identify the optimal genetic markers for predicting outcomes to antidepressant therapies. The primary aim of this systematic review was to summarize antidepressant pharmacogenetic studies to enhance understanding of the genes, variants, datatypes/methodologies, and outcomes investigated in the context of MDD. The secondary aim was to identify clinical genetic panels indicated for antidepressant prescribing and summarize their genes and variants. Screening ofN = 5793 articles yieldedN = 390 for inclusion, largely comprising adult (≥ 18 years) populations. Top‐studied variants identified in the search were discussed and compared with those represented on theN = 34 clinical genetic panels that were identified. Summarization of articles revealed sources of heterogeneity across studies and low rates of replicability of pharmacogenetic associations. Heterogeneity was present in outcome definitions, treatment regimens, and differential inclusion of mediating variables in analyses. Efficacy outcomes (i.e., response, remission) were studied at greater frequency than adverse‐event outcomes. Studies that used advanced analytical approaches, such as machine learning, to integrate variants with complimentary biological datatypes were fewer in number but achieved higher rates of significant associations with treatment outcomes than candidate variant approaches. As large biological datasets become more prevalent, machine learning will be an increasingly valuable tool for parsing the complexity of antidepressant response. This review provides valuable context and considerations surrounding pharmacogenetic associations in MDD which will help inform future research and translation efforts for guiding antidepressant care. 

Abstract The ability to model physiological systems through 3D neural in-vitro systems may enable new treatments for various diseases while lowering the need for challenging animal and human testing. Creating such an environment, and even more impactful, one that mimics human brain tissue under mechanical stimulation, would be extremely useful to study a range of human-specific biological processes and conditions related to brain trauma. One approach is to use human cerebral organoids (hCOs) in-vitro models. hCOs recreate key cytoarchitectural features of the human brain, distinguishing themselves from more traditional 2D cultures and organ-on-a-chip models, as well as in-vivo animal models. Here, we propose a novel approach to emulate mild and moderate traumatic brain injury (TBI) using hCOs that undergo strain rates indicative of TBI. We subjected the hCOs to mild (2 s⁻¹) and moderate (14 s⁻¹) loading conditions, examined the mechanotransduction response, and investigated downstream genomic effects and regulatory pathways. The revealed pathways of note were cell death and metabolic and biosynthetic pathways implicating genes such as CARD9, ENO1, and FOXP3, respectively. Additionally, we show a steeper ascent in calcium signaling as we imposed higher loading conditions on the organoids. The elucidation of neural response to mechanical stimulation in reliable human cerebral organoid models gives insights into a better understanding of TBI in humans. 

Abstract The evolution of tissue on a chip systems holds promise for mimicking the response of biological functionality of physiological systems. One important direction for tissue on a chip approaches are neuron‐based systems that could mimic neurological responses and lessen the need for in vivo experimentation. For neural research, more attention has been devoted recently to understanding mechanics due to issues in areas such as traumatic brain injury (TBI) and pain, among others. To begin to address these areas, a 3D Nerve Integrated Tissue on a Chip (NITC) approach combined with a Mechanical Excitation Testbed (MET) System is developed to impose external mechanical stimulation toward more realistic physiological environments. PC12 cells differentiated with nerve growth factor, which were cultured in a controlled 3D scaffolds, are used. The cells are labeled in a 3D NITC system with Fluo‐4‐AM to examine their calcium response under mechanical stimulation synchronized with image capture. Understanding the neural responses to mechanical stimulation beyond 2D systems is very important for neurological studies and future personalized strategies. This work will have implications in a diversity of areas including tissue‐on‐a‐chip systems, biomaterials, and neuromechanics.