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Navigating Clinical Neuroscience: From Causal Inference to Personalized Cures

Written by Emil Koch



What if some wiring in the brain goes wrong? What if your genetic setup predisposes you to neurodegeneration? These questions and how to tackle them investigate clinical neuroscience. The field focuses on conceptualizing the diagnosis of disorders and the development of treatments by primarily investigating their biological causes from an internal perspective within the body. This is where neurologists meet neuropsychiatrists, where neurobiology and social psychology factors converge in the study of mind and neurological disorders.


To begin with, how can you perform research in clinical neuroscience? Treatments can involve brain stimulation, targeted stimuli, and more, demanding a transparent approach for causal inference. That is what Wang et al. (2023) criticized and demanded: causal inference in clinical research, that is, how causal claims are made, must be advanced. One way to solve this is by, “We require that the [potential outcome] observation on one unit should be unaffected by the particular assignment of treatments to the other units (Cox 1958, §2.4)."In other words, the Stable Unit Treatment Value Assumption (SUTVA), with randomized controlled trials (RCTs) as the gold standard for estimating causal effects. However, the authors recognize limits of RCT data availability due to ethical concerns, for example, involving the need for patients' consent and increased risks of randomization. While there seems to be a consensus for streamlining regulatory procedures when risks for patients are low, Goldstein et al. (2018) reported that most literature discussing this question endorses a need for consent to treat humans not as a means but as an end. Wang et al. (2023) proposed quasi-experimental techniques for the sake of data interpretability.


Usually, clinical studies progress through four phases:

Phase 1: Involves a small group (around 20-80 people) to assess the safety, side effects, and dosage of an experimental drug or device.

Phase 2: Enroll a larger group (around 100-300 people) to determine effectiveness in people with a specific disease or condition. Safety and short-term side effects are further examined. Phase 3: Gather data from several hundred to a few thousand individuals, evaluating safety and effectiveness across diverse populations, various dosages, and comparing with other treatments. FDA approval may follow if results support intervention use.

Phase 4: Occurs after FDA approval, monitoring the treatment's safety and effectiveness in large, diverse populations over an extended period. This phase can uncover side effects not apparent in earlier phases.


Let us look at an example of a clinical study. Recently, Hideyuki Okano, MD, Ph.D., the senior study author of the phase 1/2a trial in amyotrophic lateral sclerosis (ALS) patients, announced that ropinirole would be safe to use in ALS patients and has a potential, yet not confirmed therapeutic effect. The study employed cutting-edge induced pluripotent stem cells (iPSCs) in the context of drug discovery and effectiveness evaluation. The results indicated that the cultivation and assessment of motor neurons derived from patient-specific iPSCs might hold clinical promise for predicting drug efficacy on a case-by-case basis (Morimoto et al., 2023). Given that the sample size of only 20 patients was small, replication and further data gathering will be required in full phase 2 and subsequent stages. Following the conclusion of the treatment, a 4-week follow-up study collected data on patients' self-reported physical activity and their ability to independently eat and drink. This information was obtained through the use of wearable devices and physician assessments. Such follow-up reports are crucial to ensure medication and treatment actually achieve the desired effect Here, pointing toward higher survival rates and mobility in the treated ASL than in the control group. In addition, Morimoto et al. (2023) created iPSCs from patients’ blood cells and determined that neurites were more healthy in ropinirole-treated individuals as compared to the placebo group. Moreover, 29 genes for cholesterol synthesis were upregulated in untreated ALS patients but more normalized with ropinirole. Employing iPSCs for causal inference could be an interesting future technique to ensure the conditions of SUTVA.

One crucial issue in clinical research is small sample sizes and high involved costs. For example, MRI facilities can cost between $1 million to as high as $3 million for a single state-of-the-art machine. However, the costs don’t end there. Clinical trials, on median, require $41,117 per patient and $3,562 per patient visit. One possible way to cut costs and facilitate trials is open-source data banks with previous trials’ data, such as the Pooled Resource Open-Access ALS Clinical Trials (PRO-ACT) database. In fact, a recent trial on ALS applied post-hoc analysis and compared CENTAUR patients with those not given the therapy. The control group was part of the publicly available open data bank, and results indicate an extended median survival of 10.4 months or reduced risk of death by 52% compared to the external controls. Now, the tested drug Relyvrio was approved by FED based on the CENTAUR double-blind Phase 2 trial, meaning 137 patients were randomly assigned to Relyvrio or a placebo for six months and, after main trial completion, chose to leave or enter the open-label extension (OLE) study that lasted for approximately 2.5 years. Double-blind describes the case where neither the participant of the study nor the researcher knows what they get, aimed at unbiased conditions to better test for treatment efficacy. This outlined example illustrates how randomized trials can be achieved with lower means, thereby allowing for causal inference.


Lastly, let’s examine the scenario of animal models. First and foremost, animal models can replicate certain aspects of biological processes observed in humans. The selection of model species is based on their resemblance to human anatomy, physiology, or their response to pathogens. Frequently, mice or zebrafish are preferred due to their short generation times and similarities in biological processes. Nonetheless, a lingering question persists regarding the adequacy of the similarity between pathophysiological mechanisms in animals and humans when it comes to applying therapeutic approaches in a translational context. In this regard, Hall et al. (2009) assert that in contrast to psychiatric diseases such as schizophrenia and autism that involve a wide spectrum and difficulty in interpreting behavior and emotional states in animals, neuropathology of CNS diseases have a well-established pathophysiology (yet not pathogenesis!). Thus, the rationale for animal model usage is still strong. Aartsma‐Rus et al. (2019) add that genetically humanized animal models open doors to personalized medicine by testing for a multitude of mutations, which can simultaneously involve great endeavors. Despite all the debates revolving around the suitability of animal models in clinical research, De Lorenzo et al. (2023) used three mouse models, expressing the wild type and human mutations TDP43-M337V as well as SOD1-G93A and discovered the neurotrophic factor CDNF, mostly found in the endoplasmic reticulum (ER) within the cell, that prolongs the lifespan of and alleviates disease symptoms of ALS. Consequently, animal models offer doors for causal inference regarding the pathogenesis and potential therapeutic interventions of human diseases that would be later replicated in clinical studies.

In conclusion, the field of clinical research, and particularly clinical neuroscience, is undergoing significant advancements driven by innovative techniques such as iPSCs, open data repositories, and animal models. These approaches are not only cost-efficient but also enhance the robustness of scientific investigations and hold the promise to contribute to personalized cures of neuropsychiatric disorders and degenerative diseases. Clinical neuroscience remains a dynamic and enriching domain where the convergence of biology, psychology, and medical science holds the potential to transform the future of healthcare.


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References:

Aartsma‐Rus, A., & Van Putten, M. (2019). The use of genetically humanized animal models for personalized medicine approaches. Disease Models & Mechanisms, 13(2), dmm041673. https://doi.org/10.1242/dmm.041673.

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Goldstein, C. E., Weijer, C., Brehaut, J., Fergusson, D., Grimshaw, J., Horn, A. R., & Taljaard, M. (2018). Ethical issues in pragmatic randomized controlled trials: a review of the recent literature identifies gaps in ethical argumentation. BMC Medical Ethics, 19(1). https://doi.org/10.1186/s12910-018-0253-x.

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