| 1. Introduction |
> 1.1. Brief history of drug discovery for neuropsychiatric disorders
Humanity has used psychoactive substances for millennia both for recreation and to relieve suffering.
The first neuroleptic, chlorpromazine , was introduced in 1952 followed shortly after by the first tricyclic antidepressant imipramine. In 1949 the International Statistical Classification of Diseases (ICD) included a section on mental disorders for the first time while the DSM-1 (Diagnostic and Statistical Manual of Mental Disorders) emerged to accurately classify psychiatric disorders. These advances led to great improvements in the perception and treatment of mental disorders, but neither classification nor treatment was based on any understanding of biological cause.
It is important to remember that most mental health conditions are still classified, diagnosed and treated solely based on observed symptoms. It is now recognized that many neuropsychiatric diseases share symptoms, making it difficult to understand the pathophysiological mechanisms.
This lack of understanding of the fundamental pathophysiological causes of neuropsychiatric disorders is one of the reasons for the slowdown in the development of new medications to treat these conditions.
The emergence of new ways of measuring brain activity (for example, functional magnetic resonance imaging (fMRI), which records blood flow to functional areas of the brain, or electroencephalogram (EEG) to evaluate evoked potentials of the brain) is opening the door to the development of new hypotheses based on the understanding of the disturbance of brain systems and neural circuits in different mental health conditions.
This can be the basis of an improved nosology to describe, classify and diagnose disorders, guide prescribing, stimulate new drug discovery, identify and improve regulatory categories, and increase the effectiveness of clinical trials.
> 1.2. The RDOC initiative
In view of the above limitations, in 2009, the National Institute of Mental Health (NIMH) proposed a new research classification system: Criterion Domain of Research (CDoI). The spirit of CDoI is that complex problems cannot be solved with simple solutions, and that different levels of complexity need to be dissected from genes to behavior, subjective experiences or even paradigms.
This classification is based on observable behavioral dimensions and neurobiological measures to identify fundamental components that can span multiple disorders. The CDoI framework integrates many different levels of data in order to classify a mental disorder based on pathophysiology and link it more precisely to interventions for a given individual.
It is clear that a number of treatment modalities, including pharmaceutical and psychosocial or behavioral treatments, as well as medical devices, have been shown to be effective in a wide range of disorders (for example, selective serotonin reuptake inhibitors to improve mood). mood in many different categorical disorders or benzodiazepines for anxiety in a variety of DSM or ICD disorders).
The CDoI proposes to focus on a clinical problem by defining it as a domain or construct (e.g., social isolation) independently of the DSM/ICD diagnosis and enrolling patients in clinical trials based on ? in the deficits of that mechanism and not in the DSM/ICD diagnosis.
> 1.3. Technical advances
As there is growing philosophical concern about trying to “model” complex disorders such as schizophrenia and depression, the advent of technologies that enable high-resolution physiological monitoring offer a potential alternative approach. These approaches include electrophysiology, structure and function imaging, and neurochemistry.
Therefore, if such techniques and parameters are used, a quantitative description of the abnormalities of specific aspects of a particular disorder can be made. Some examples of the advances observed include a better understanding of the application of transgenic technologies, inducing neurodegenerative disease states using clinically identical protein, drug and optogenetic triggers.
The advances of the last decade in clinical research in mental health are numerous:
1. First, brain imaging techniques such as structural or functional MRI (sRMI or fRMI) and EEG are being widely applied. This has allowed us to examine brain structure and activity to better understand the role of the brain in conditions and interventions that alter mental health.
2. Second, the systems biology approach has become applicable to clinical mental health research. This allows a deeper exploration of gene expression patterns, epigenetics, metabolomics and proteomics and their interactions on a broader scale, and their relationship with mental health conditions.
3. Third, the application of e-Health and mHealth technology is another technical innovation with potential impact. It allows people to be followed both passively and actively over time through outpatient assessments of the impact of routines on daily life, and to track patterns of individual mental health symptoms, as well as alterations in social and physical activity.
The application of these new clinical methods can generate "big data" in the field of neuropsychiatry. The data-driven approach is an emerging field in computational neuroscience that seeks to identify specific characteristics of disorders among large multimodal dimensions of data.
Emerging techniques, such as those that estimate normative models for mappings between biology and behavior, may provide new ways to analyze the heterogeneity of underlying neuropsychiatric diseases.
| 2. From quantitative biology to neuropsychiatry |
> 2.1. Conceptual outline of the approach
The current classification scheme for neuropsychiatric disorders separates each disease into non-overlapping diagnostic categories, which, while providing a basis for clinical management, do not describe the underlying neurobiology that gives rise to individual symptoms.
The ability to link these symptoms with the underlying neurobiology would provide patients with a better understanding of the complexities of their disease, facilitating its management. The main difficulty in constructing biologically valid diagnoses is the lack of objective biomarkers.
This is based in part on the notion of etiological overlap between psychiatric and neurodegenerative disorders, and best described as cross-disorder domains rather than separable categories.
> 2.2. Implementation in the PRISM project
The PRISM (Psychiatric Ratings Using Intermediate Stratified Markers) project was founded to develop a quantitative biological approach to the understanding and classification of neuropsychiatric diseases to accelerate the discovery and development of better treatments.
In PRISM, patients with a variety of neuropsychiatric symptoms are evaluated using various analytical platforms to analyze current data for heterogeneous syndromes in homogeneous groups. Additionally, a deeper understanding of the quantitative biology of social withdrawal is developed using clinical data from patients with schizophrenia (ZZ), major depression (MD), and Alzheimer’s disease (AD).
> 23. Key areas for PRISM implementation
The challenge of the PRISM project was to identify a target dimension that satisfies a set of eligibility criteria. Although the spectrum of neuropsychiatric disorders is heterogeneous, they largely share the expression of negative symptoms, particularly social withdrawal. In fact, it is one of the first indicators of emerging psychiatric and neurological disorders.
Although social isolation is a complex behavior that can be modulated by several factors, growing evidence suggests that it could also be, at least in part, a behavioral trait with a specific underlying biological substrate.
A psychiatric syndrome characterized by pure social isolation (juvenile social withdrawal behavior or "Hikikomori") has recently been described as gaining increasing attention because it appears to be more common than previously thought and does not appear to be limited to specific cultures.
Social withdrawal is the end result of a large series of processes and is sensitive to the most basic domain deficits.
Therefore, after careful analysis, attention, working memory, and sensory processing were identified as potential confounders for the variability of social isolation. Interestingly, these are also shared cognitive deficits in patients with EZ, AD, and DM that contribute to interpersonal behavior.
Abnormalities in working memory, attention, and sensory processing are similar in all diagnostic groups and are an ideal complement to determine a causal model of social withdrawal. In fact, recent data suggest that interpersonal behavior could be predicted by processing speed, attention, and working memory, along with executive functions and depressive and negative symptoms.
Interestingly, the effects of attention, working memory, and processing speed appear to be mediated by their effects on social competence. Therefore, it can be hypothesized that these cognitive deficits induce deficiencies in the patient’s social competence that eventually result in high social withdrawal.
If biomarkers based on quantitative phenotypes that share a common neurobiological basis could be designed and developed, the development of improved preclinical assays and treatments will follow.
| 3. Future perspectives |
The prognostic, biological, and therapeutic validity of psychiatric diagnoses is moderate to poor depending on the specific diagnosis. They do not constitute separate entities as in other medical fields. This undermines research into biological determinants and individualization of treatments, which has driven the development of the CDoI initiative. This perspective has sparked interest in recent years with the goal of identifying cross-diagnostic clinical and biological constructs.
The classic classification based on indications of psychotropic drugs into antidepressants, antipsychotics, anxiolytics, hypnotics and mood stabilizers is no longer the most appropriate for current treatments.
Quetiapine exemplifies this limitation: it is a hypnotic at low doses (<100 mg), an antidepressant at intermediate doses (150-300 mg), a mood stabilizer at 300-600 mg, and an antipsychotic at higher doses. Neuroscience-based nomenclature follows this line by defining compounds by their mechanism of action and their approved efficacy profile for all diagnoses or conditions.
Unmet medical need in psychiatry remains high. Resources are being devoted to finding a modifying treatment for neuropsychiatric disorders. However, successful treatment should ideally prevent rather than reverse the effects of the disease, and if so, at least one, if not two, generations of neuropsychiatric patients will continue to require treatment for the relief of symptoms such as social isolation.
However, despite the unmet medical need, the discovery of drugs for the treatment of these disorders was largely unsuccessful in recent decades. Heterogeneous trial populations with respect to disease cause and etiologies dilute efficacy signals and disguise the positive outcome.
PRISM’s new approach calls for a "reclassification of diseases based on their root cause." This will help address “unmet therapeutic needs” which is a key objective of this project.
One of the expected outcomes is to identify the neurobiological underpinnings of symptoms shared between different diseases by determining the neurobiological basis of social isolation and cognitive deficits, providing an analysis of plausible cause-effect relationships in AD and schizophrenia.
It will eventually provide relevant targets for new treatments that push the boundaries of existing diseases in both psychiatry and neurology. In addition, it will provide biomarkers capable of bridging the gap between preclinical readings and final results, facilitating the translation of preclinical findings into clinical tests. PRISM will address these goals by combining new technology with the latest imaging techniques to provide links between real-world data, symptoms and neurobiology.
Finally, PRISM will provide a multicenter clinical trials network to support drug development with reliable and reproducible data.
In summary, the project introduces an unprecedented systematic approach to linking relevant symptoms across neurodegenerative and psychiatric conditions with quantifiable biological dimensions.
In this sense, it is truly integrative, covering all phases of the drug development cycle, from early drug discovery to registration and market access, i.e. regulatory acceptance of newly identified drugs.
In this way, the project paves the way towards a paradigm shift for drug discovery and development in neuropsychiatry and has the potential to serve as a model for CDoI-guided approaches, with rapid adoption and acceptance by the scientific community.
