In Vitro Modeling of Drug-resistant Psychiatric Disorders Using Induced Pluripotent Cells

Overview

Major depressive disorder (MDD), is a major medical and economic burden for today's society. About 30% of MDD patients develop treatment-resistant depression - TRD with the related sequelae in terms of worse prognosis. If several risk factors can be assessed readily on presentation, it can guide treatment planning and ultimately improve clinical outcomes. Currently, unlike other areas of medicine, poly-risk tools to facilitate this stratification in practice among patients with MDD are lacking but demanded in the era of personalised/precision medicine - a challenge that the project takes up. Ketamine - a glutamate N-methyl-d-aspartate (NMDA) receptor antagonist, is the first exemplary agent with rapid (within hours) antidepressant effects, even in TRD patients.Its mechanisms of actions (MoA) are still unclear but greatly demanded. So far, insights about ketamine's MoA come from preclinical animal studies but it's known that animal models have limited ability/effectiveness in mimicking the clinical complexity and were not subjected to sequential application of different treatments - a key requisite in humans to be defined as TRD. This ambitious inter/multidisciplinary project, has 3 goals: 1. To develop a clinical risk stratification tool for predicting TRD development. 2. To unravel ketamine's fast-acting antidepressant mechanisms of action (MoA) on mature neurons obtained from human induced pluripotent stem cells (iPSCs) obtained from (ketamine-responsive \& non-responsive) patients with TRD. 3. To give maximum visibility to the project and spreading its contents \& findings to and in a way understood by all target groups variously implicated/interested in project research \& innovation.

Major depressive disorder (MDD), affecting roughly 300 million people, is a major medical and economic burden for today's society. Unsatisfactory response to current antidepressants contributes to the enormous public health burden of MDD. Critically, about 30% of MDD patients develop treatment-resistant depression - TRD (i.e. do not respond adequately to at least 2 successive antidepressant treatments under a proper therapeutic regimen) with the related sequelae in terms of worse prognosis. When an outpatient first presents for treatment of MDD, what is the likelihood that s/he will not reach symptomatic remission despite multiple treatment trials? If several risk factors can be assessed readily on presentation, it can guide treatment planning and ultimately improve clinical outcomes. Human genome-wide association studies have failed to identify common variants associated with TRD. Large-scale studies have pointed toward strong effects of clinical variables on treatment outcome yet, considered separately, clinical variables usually showed odds ratios around 1.5 and thus were not applicable for stratifying MDD patients and detecting those at high risk. On the other hand, compelling evidence supports the consideration of measuring peripheral biomarkers that could result in improved stratification and TRD prediction. Currently, unlike other areas of medicine, poly-risk tools to facilitate this stratification in practice among patients with MDD are lacking but demanded in the era of personalised/precision medicine - a challenge that the project takes up. Treatments that exert fast-acting antidepressant action are an unmet clinical need, as the effects of currently available medications often take several weeks to promote clinical response, if MDD patients show a response. Ketamine - a glutamate N-methyl-d-aspartate (NMDA) receptor antagonist, is the first exemplary agent with rapid (within hours) antidepressant effects, even in TRD patients. Multiple controlled trials demonstrate that 50-70% of TRD patients show clinical response after a single dose of ketamine delivered intravenously for 40 minutes. Although ketamine seems poised to transform the treatment of depression, its mechanisms of actions (MoA) are still unclear but greatly demanded, as the derived knowledge can provide a model for understanding the mechanisms behind rapidly acting antidepressants, which may lead to the discovery of novel compounds to treat depression. So far, insights about ketamine's MoA come from preclinical animal studies, which underscored complex pathways involvement and possible changes in synaptic structural plasticity. However, animal models have limited ability/effectiveness in mimicking the clinical complexity and were not subjected to sequential application of different treatments - a key requisite in humans to be defined as TRD. According to the American Psychiatric Association Council of Research Task Force on Novel Biomarkers and Treatments, although ketamine's antidepressant efficacy is promising for future glutamate-modulating strategies, the fact that other NMDA antagonists do not have antidepressant proprieties suggests that any forthcoming advances will depend on improving our understanding of ketamine's MoA, hopefully in humans - a challenge that the project takes up. This ambitious inter/multidisciplinary project, going beyond the state-of-the-art, embracing responsible research innovation principles and involving training and professional opportunities for young researchers, has 3 goals: 1. To develop a clinical risk stratification tool for predicting TRD development. Specifically, we'll implement machine learning (a form of artificial intelligence) to evaluate the prognostic value of 55 socio-demographic \& clinical variables (including comorbid anxiety disorders) and peripheral biomarkers to establish a predictive clinical risk stratification tool for TRD. 2. To unravel ketamine's fast-acting antidepressant mechanisms of action (MoA) on mature neurons obtained from human induced pluripotent stem cells (iPSCs) obtained from (ketamine-responsive \& non-responsive) patients with TRD. Here, we'll capitalize on iPSC technology and the distinctive paradigm of rapid mood normalization following ketamine infusion to disclose mechanisms of action accounting for fast-acting antidepressant effect on patient-derived differentiated neurons. We'll explore whether the changes and complex pathways observed in animal models can be validated in patients and potentially identity new ones. 3. To give maximum visibility to the project and spreading its contents \& findings to and in a way understood by all target groups variously implicated/interested in project research \& innovation. A series of public engagement initiatives will also foster communication and interaction between researchers \& civil society, making real research and its process more concrete, simple, accessible and closer to society and contributing to citizen science.