WO2024025536A1 - Médicament de précision pour troubles de l'anxiété : évaluation objective, prédiction de risque, pharmacogénomique et médicaments réaffectés - Google Patents

Médicament de précision pour troubles de l'anxiété : évaluation objective, prédiction de risque, pharmacogénomique et médicaments réaffectés Download PDF

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WO2024025536A1
WO2024025536A1 PCT/US2022/038673 US2022038673W WO2024025536A1 WO 2024025536 A1 WO2024025536 A1 WO 2024025536A1 US 2022038673 W US2022038673 W US 2022038673W WO 2024025536 A1 WO2024025536 A1 WO 2024025536A1
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anxiety
biomarkers
subject
score
biomarker
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PCT/US2022/038673
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Alexander Bogdan Niculescu
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Indiana University Research And Technology Corporation
The United States Government As Represented By The Department Of Veterans Affairs
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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61K—PREPARATIONS FOR MEDICAL, DENTAL OR TOILETRY PURPOSES
    • A61K31/00—Medicinal preparations containing organic active ingredients
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61P—SPECIFIC THERAPEUTIC ACTIVITY OF CHEMICAL COMPOUNDS OR MEDICINAL PREPARATIONS
    • A61P25/00—Drugs for disorders of the nervous system
    • A61P25/22—Anxiolytics
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
    • G16H20/10—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
    • G16H20/70—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to mental therapies, e.g. psychological therapy or autogenous training
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment

Definitions

  • Anxiety disorders are highly prevalent and insidious in their effects on one’s ability to do things and quality of life.
  • Psychiatric patients may have increased anxiety, as well as increased reasons for anxiety, due to their often, adverse life trajectory. Improvements are needed to adequately diagnose and treat all individuals suffering with anxiety disorders.
  • biomarkers for anxiety provide a means of assessing state severity, short-term risk, and long-term risk.
  • the biomarkers can also be used for drug repurposing.
  • Treating involves administered at least one course of treatment, treatment may include psychiatric counseling, administering certain physical intervention, and/or prescribing and/or administering at least one therapeutic compound. Treating may include at least one of the following outcomes, curing, mitigating, managing or otherwise recuing the severity and/or the number of frequency anxious behaviors, especially behaviors that negatively impact a individual’s quality of life.
  • a first embodiment is a therapeutic compound selected from one or more compounds in Tables 4 and Table 6, for use in a method of mitigating and anxiety disorder in a subject in need thereof for example wherein the therapy is a drug, a natural compound, and combinations thereof.
  • a second embodiment is the therapeutic compound for use according to the first embodiment, wherein the drug is selected from the group consisting of: estradiol, loperamide, omage-3 fatty acids, lithium, fluoxetine, norfluoxetine, imipramine, citalopram, carbamazepine,
  • a third embodiment is the therapeutic compound for use according to the first embodiment, wherein the subject has an anxiety disorder.
  • a fourth embodiment is a therapeutic compound for use according to the first embodiment, wherein the subject is a male subject or a female subject.
  • a fifth embodiment is a computer-implemented method for assessing the level of anxiety in a subject, and for assessing risk of developing anxiety disorder in the future in a subject, the method comprising: computing a score based on RNA level, protein level, DNA methylation, a single nucleotide polymorphism, for a panel of biomarkers chosen from Table 2, Table 5, and Figure 2, in a sample obtained from a subject; computing a score based on a reference expression level of the panel of biomarkers; and identifying a difference between the score in the sample obtained from the subject and the score in the reference sample, wherein the difference in the score in the sample obtained from the subject and the score in the reference sample indicates an anxiety disorder, and a risk of future anxiety disorders.
  • a sixth embodiment is the method according to the fifth embodiment, wherein the subject is: a male or a female and the biomarker is at least one biomarker selected from the group consisting of: ERCC6L2, SLC6A2, FZD10, and RPL14; or a female and the biomarker is at least one biomarker selected from the group consisting of: PHF21A, SLC6A2, 240253_at, and TRIM9; or a male and the biomarker is at least one biomarker selected from the group consisting of: ERCC6L2, ANKRD28, ACPI, and RP
  • a seventh embodiment is the method according to the fifth or the sixth embodiments, further including the steps of: identifying a difference between the score in the sample obtained from the subject and the score in the reference sample, administering treatment to the subject, wherein the treatment reduces the difference between the score in the sample from the subject and the score in the reference sample, and mitigates anxiety in the subject, or the risk for developing anxiety disorder in the subject.
  • An eighth embodiment is the method according to the seventh embodiment, further including, the step of: measuring a change in the score after treatment.
  • a ninth embodiment is the method according to the eighth embodiment, wherein the treatment is at least one treatment selected from the group consisting of: lifestyle modification,
  • SUBSTITUTE SHEET (RULE 26) electro-magnetic intervention, and administering a therapeutic compound.
  • a tenth embodiment is the method according to the ninth embodiment, wherein the therapy is selected by a computer- implemented method selected from the group consisting of one or more psychiatric compounds from Table 6, and wherein each therapy selection is based on a panel of one or more individual biomarkers.
  • An eleventh embodiment is the method according to the tenth embodiment, wherein the therapy is selected based on a panel of individual biomarkers changed in a subject, by a computer-implemented method for therapy selection, and comprises, of one or more compounds in Table 4.
  • An twelfth embodiment is a method for assessing and mitigating anxiety disorder, comprising determining an expression level of a panel of biomarkers listed in Table 2, Table 5 and Figure 2, in a sample for example wherein the sample comprises a peripheral tissue, blood, saliva, cerebrospinal fluid (CSF), serum, urine, or stool; wherein the expression level of the biomarkers in the sample is different relative to a reference expression level; and includes the steps of: identifying the subject currently having or at risk of having an anxiety disorder based on a biomarker panel score relative to a biomarker panel score of a reference; selecting a therapy based on the score from the biomarkers, wherein the therapy includes; and administering one or more compounds selected from the group consisting of compounds selected from the groups consisting of compounds selected from Tables 4 and Table 6.
  • CSF cerebrospinal fluid
  • a thirteenth embodiment is a composition comprising one or more compounds selected from Tables 4 and Table 6, for use in a method for treating anxiety disorder.
  • FIG. 1 A Discovery cohort, within subject changes in anxiety state (N of 58).
  • FIG. 1 B Discovery cohort 141 male and 17 female psychiatric participants have at least one switch between a low anxiety state visit and a high anxiety state visit.
  • FIG. 1 C Convergent functional genomics. Multiple independent lines of evidence for identification and prioritization of anxiety biomarkers.
  • FIG. 1 E Graphic representation of Discovery, Prioritization and Validation of biomarkers.
  • FIG. 2A Graph of predictions for state- high anxiety state (SAS4>60). See also TABLE 6.
  • FIG. 2B Graph of predictions for state- high STAI (STA>55). Light colored bars, cross sectional; dark colored bars, longitudinal.
  • FIG. 2C Graph of anxiety trait predictions first year. Light colored bars, cross sectional; dark colored bars, longitudinal.
  • FIG. 2D Graph of anxiety trait predictions-all future year. Light colored bars, cross sectional; dark colored bars, longitudinal.
  • FIG. 5 SASA4 scaler for measuring anxiety State (Niculescu, et al., 006, 2015). The score is an average of 4 items, Ranges from 0-100.
  • FIG. 6 Graphical depiction of the correlation between SAS4 and STAI State.
  • Our SAS4 scale shows a moderate to strong correlation with a well-validated clinical measure of anxiety, the STAI State, while being more temporally related to that particular moment in time, quantitative, and simple.
  • SUBSTITUTE SHEET (RULE 26) worsening (hospitalizations with anxiety as the primary cause), in another independent cohort of subjects.
  • the present disclosure is generally directed at methods for assessing anxiety disorders and early identification of risk for future episodes of acute or chronic anxiety, as well as methods for matching patients and drugs for prevention and mitigation of anxiety and for monitoring response to treatment.
  • the methods may further include the generation of a report providing a risk score and/or personalized treatment options.
  • the present disclosure generally is directed to drugs for mitigating anxiety in subjects.
  • Particular drugs have been found that can mitigate anxiety in subjects universally; that is, drugs that can be used for mitigating anxiety across genders an in the presence of confounding factors such as other psychiatric disorders.
  • Some drugs, however, have been found that can be used more effectively for mitigating anxiety dependent on gender, confounding factors, and combinations thereof.
  • the present disclosure is directed to blood gene expression biomarkers that are more universal in nature; that is, blood biomarkers that can be used for predicting anxiety disorders in one of more genders. Accordingly, a longitudinal within- participant design and large cohorts were used. Additionally, subtypes of anxiety were identified based on mental state at the time that anxiety was manifest.
  • FIG. 1 Steps 1-3: Discovery, Prioritization and Validation of Biomarkers for Anxiety (A.) Cohorts used in study, depicting flow of discovery, prioritization, validation of biomarkers from each step and independent testing cohorts. (B.) Discovery cohort longitudinal within- subject analysis. Phchp### is study ID for each subject. V# denotes visit number.
  • C. Convergent Functional Genomics lines of evidence from human to non-human.
  • D In the validation step biomarkers are assessed for stepwise change from the validation group with severe Anxiety, to the discovery groups of subjects with high Anxiety, low Anxiety, to the validation group with severe Anxiety, using ANOVA.
  • N number of testing visits.
  • the histograms depict a top increase and a top decrease
  • Subjects were recruited from the patient population at the Indianapolis VA Medical Center. All subjects understood and signed informed consent forms detailing the research goals, procedure, caveats and safeguards, per IRB approved protocol. Subjects completed diagnostic assessments and extensive structured neuropsychological testing at each testing visit, 3-6 months apart or whenever a new psychiatric
  • SUBSTITUTE SHEET (RULE 26) hospitalization occurred. At each testing visit, they received a series of rating scales, including a self-report visual analog scale (1-100) for quantitatively assessing state anxiety at that particular moment in time (Simplified Anxiety Scale- SAS-4). This 4- item scale looks at anxiety overall, as well as fear, anger, and uncertainty. Each of the items are a VAS of 0 to 100, related to that moment in time. As such, it generates temporal, quantitative, and targeted data.
  • the prioritization step that occurs after discovery is based on a field-wide convergence with literature that includes genetic data and animal model data, that are unrelated to medication effects.
  • the discovery, validation and replication by testing in independent cohorts of the biomarkers, with our design occurs despite the subjects having different genders, diagnoses, being on various different medications, and other lifestyle variables.
  • RNA extraction Whole blood (2.5 ml) was collected into each PaxGene tube by routine venipuncture. PaxGene tubes contain proprietary reagents for the stabilization of RNA. Total RNA was extracted and processed as previously described.
  • Microarrays Microarray work was carried out using previously described methodology 1 .
  • probesets that have a 1.2-fold change are then assigned either a 1 (increased in high anxiety) or a -1 (decreased in high anxiety) in each comparison. Fold changes between 1.1 and 1.2 are given 0.5, and fold changes less than 1.1 are given 0. These values were then summed for each probeset across all the comparisons and subjects, yielding a range of raw scores.
  • the probesets above the 33.3% of raw scores were carried forward in analyses (Figure 1), and received an internal score of 2 points; those above 50% 4 points, and those above 80% 6 points 2 3 4 . We have developed in our labs R scripts to automate and conduct all these large dataset analyses in bulk, checked against human manual scoring 4 .
  • Step 2 Prioritization using Convergent Functional Genomics (CFG) Databases.
  • the AP derived and DE derived lists of genes were combined, and the gene expression data corresponding to them was used for the validation analysis.
  • the 3 groups (low anxiety, high anxiety, clinical severe anxiety) were assembled out of Affymetrix. cell data that was RMA normalized by gender and diagnosis. We transferred the log transformed expression data to an Excel sheet, and non-log transformed the data by taking 2 to the power of the
  • IPA Ingenuity Pathway Analysis, version 24390178, Qiagen
  • David Functional Annotation Bioinformatics Microarray Analysis National Institute of Allergy and Infectious Diseases
  • KEGG Kyoto Encyclopedia of Genes and Genomes
  • CFG beyond Anxiety evidence for involvement in other psychiatric and related disorders.
  • SUBSTITUTE SHEET (RULE 26) or STAI), and predict future risk of anxiety (trait -future hospitalizations with anxiety).
  • biomarkers were combined by simple summation of the increased risk biomarkers minus the decreased risk biomarkers. Predictions were performed using R-studio. For cross-sectional analyses, we used biomarker expression levels, z-scored by gender and diagnosis. For longitudinal analyses, we combined four measures: biomarker expression levels, slope (defined as ratio of levels at current testing visit vs. previous visit, divided by time between visits), maximum levels (at any of the current or past visits), and maximum slope (between any adjacent current or past visits). For decreased biomarkers, we used the minimum rather than the maximum for level calculations. All four measures were Z-scored, then combined in an additive fashion into a single measure. The longitudinal analysis was carried out in a sub-cohort of the testing cohort consisting of subjects that had at least two visits (timepoints).
  • Receiver-operating characteristic (ROC) analyses between marker levels and anxiety state were performed by assigning subjects visits with a anxiety SAS-4 score of >60 into the high anxiety category, and subjects with STAI scores >55 in the high anxiety category.
  • ROC Receiver-operating characteristic
  • SUBSTITUTE SHEET (RULE 26) (2 D) are at least nominally significant. Some gender and diagnosis group are missing from the graph as they did not have any significant biomarkers or that the cohort was too small with limited data for the z-scoring by gender-dx.
  • Cross-sectional is based on levels at one visit. Longitudinal is based on levels at multiple visits (integrates levels at most recent visit, maximum levels, slope into most recent visit, and maximum slope). Dividing lines represent the cutoffs for a test performing at chance levels (white), and at the same level as the best biomarkers for all subjects in cross-sectional (gray) and longitudinal (black) based predictions. Biomarkers perform better than chance. Biomarkers performed better when personalized by gender and diagnosis. * survived Bonferroni correction for the number of candidate biomarkers tested. Predicting Trait- Future Psychiatric Hospitalization with Anxiety as a Symptom/Reason for Admission.
  • a Cox regression was performed using the time in days from the testing visit date to first hospitalization date in the case of patients who had been hospitalized, or 365 days for those who did not.
  • the odds ratio was calculated such that a value greater than 1 always indicates increased risk for hospitalization, regardless if the biomarker is increased or decreased in expression.
  • SUBSTITUTE SHEET (RULE 26) geographically and/or be lost to follow-up.
  • the Cox regression was performed using the time in days from visit date to first hospitalization date in the case of patients who had hospitalizations with anxiety, or from visit date to last note date in the electronic medical records for those who did not.
  • the first average should be higher than the second average in increased biomarkers, and the reverse is true for decreased biomarkers. 15 out of 19 biomarkers were thus concordant.
  • the digitized biomarkers were also used for matching with existing psychiatric medications and alternative treatments (nutraceuticals and others). We used our large datasets and literature databases to match biomarkers to medications that had effects on gene expression opposite to their expression in high anxiety. Each medication matched to a biomarker got the biomarker score of 1, 0.5 or 0. The scores for the medications were added, normalized for the number of biomarkers that were 1 or 0.5 in that patient, resulting in a percentile match. Thus, psychiatric medications matched to the patient and ranked in order of impact on the panel.
  • Step 1 Discovery we identified candidate blood gene expression biomarkers that: 1. change in expression in blood between self-reported low anxiety and high anxiety states, 2. track the anxiety state across visits in a subject, and 3. track the anxiety state in multiple subjects.
  • SAS-4 visual analog measure for anxiety state
  • the SAS-4 quantitates anxiety state at a particular moment in time, and normalizes anxiety
  • Step 2 Prioritization we used a Convergent Functional Genomics (CFG) approach to prioritize the candidate biomarkers identified in the discovery step (33% cutoff, internal score of >2pt.) by using published literature evidence (genetic, gene expression and proteomic), from human and animal model studies, for involvement in anxiety disorders (Figure 1 and Table 2). For Axniety there were 284 probesets (corresponding to 238 unique genes) that had a total score (combined discovery score and prioritization CFG score) of 6 and above. These were carried forward to the validation step. This represents approximately a 10-fold enrichment of the probesets on the Affymetrix array.
  • CFG Convergent Functional Genomics
  • HTR2A is at the overlap of a network containing GABBR1, GAD1 and SLC6A4, and one centered on CDH1 that also contains PIK3R1 and IGFR1.
  • a third network includes DLGAP1, PTPRD and DYNLL2. These networks may have biological significance and could be targeted therapeutically.
  • the gene expression data in the test cohorts was normalized (Z-scored) across genders and various psychiatric diagnoses, before those different demographic groups were combined.
  • CFE4 convergent functional evidence
  • SUBSTITUTE SHEET (RULE 26) first year hospitalization with anxiety, trait all future hospitalizations with anxiety- up to 3 points each if it significantly predicts in all subjects, 2 points if in gender, 1 points if in gender/diagnosis).
  • the total score can be up to 36 points: 24 from our empirical data, and 12 from literature data used for CFG. We weigh our new empirical data more than the literature data, as it is functionally related to mood in 3 independent cohorts (discovery, validation, testing).
  • the goal is to highlight, based on the totality of our data and of the evidence in the field to date, biomarkers that have all around evidence: track anxiety, have convergent evidence for involvement in anxiety disorders, and predict anxiety state and future clinical events (Table 2). Such biomarkers merit priority evaluation in future clinical trials.
  • Bioinformatic analyses using the gene expression signature of the panel of top biomarkers for anxiety identified new potential therapeutics for anxiety, such as estrogen.
  • SUBSTITUTE SHEET (RULE 26) Table 1. Aggregate Demographics, BP-bipolar, MDD- depression, MOOD- mood nos, SZ- schizophrenia, SZA- schizoaffective, PSYCH- psychosis nos, PTSD-post-traumatic stress disorder.
  • CMAP from genes ADRA2A, ATP1B2, CCKBR, FZD1O,GAD1,GPX7,GRK4 ,NRG1,NTRK3,SLC6A2,TFRC). Drugs that have opposite gene expression effects to the gene expression signature.
  • SUBSTITUTE SHEET (RULE 26) Table 6. Pharmacogenomics. Top biomarkers (from Table 2) that are targets of existing drugs and are changed in expression in opposite direction to high anxiety. (I)- increased in expression, (D)- decreased in expression. DE- differential expression, AP-Absent/Present.
  • Biomarkers may be useful for matching patients to medications and measuring response to treatment (pharmacogenomics) ( Figure 4, Tables 2 and 6), as well as new drug discovery and repurposing (Table 4).
  • SUBSTITUTE SHEET (RULE 26) anxiety disorders episode in their lifetime, that anxiety disorders can severely affect quality of life, sometimes leading to suicides, and that not all patients respond to current treatments or the treatments are addictive, the need for and importance of efforts such as ours cannot be overstated.

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Abstract

Nous avons utilisé une conception longitudinale puissante intra-sujet chez des individus atteints de troubles psychiatriques pour découvrir une modification d'expression de gène sanguin entre des états autorapportés de faible anxiété et d'anxiété élevée. Nous avons priorisé la liste de biomarqueurs candidats avec une approche génomique fonctionnelle convergente à l'aide de preuves supplémentaires dans le domaine et validé nos biomarqueurs supérieurs à partir de la découverte et de la priorisation dans une cohorte indépendante de sujets psychiatriques présentant une anxiété cliniquement grave. Nous avons ensuite testé si ces biomarqueurs candidats sont en mesure de prédire l'état de gravité de l'anxiété et de future aggravation clinique (hospitalisations ayant l'anxiété en tant que cause de contribution), dans une autre cohorte indépendante de sujets psychiatriques. Nous avons montré une précision accrue de biomarqueurs individuels avec une approche personnalisée, par genre et diagnostic, en particulier chez les femmes. Enfin, nous avons identifié lesquels de nos biomarqueurs sont des cibles de médicaments existants et peuvent ainsi être utilisés pour la stratification de la population pharmacogénomique et la mesure d'une réponse au traitement, ainsi que des signatures d'expression de gène de biomarqueur utilisées pour identifier des médicaments qui peuvent être réaffectés pour le traitement de l'anxiété.
PCT/US2022/038673 2022-07-28 2022-07-28 Médicament de précision pour troubles de l'anxiété : évaluation objective, prédiction de risque, pharmacogénomique et médicaments réaffectés WO2024025536A1 (fr)

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US20140274764A1 (en) * 2013-03-15 2014-09-18 Pathway Genomics Corporation Method and system to predict response to treatments for mental disorders
WO2018209341A1 (fr) * 2017-05-12 2018-11-15 Indiana University Research And Technology Corporation Médicament de précision pour le traitement et la prévention du risque suicidaire

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