3. Background
• Current lack of knowledge in pathophysiology
• In a lifetime -multiple diagnosis according to
symptoms
• Symptoms based diagnosis but symptoms are in
continuum
• Analogy of Appendicitis-
4. Precision Medicine
• Treatments targeted to the needs of individual patients on
the basis of genetic, biomarker, phenotypic, or psychosocial
characteristics that distinguish a given patient from other
patients with similar clinical presentations.
• Aims to improve the diagnosis, prevention, and treatment of
disease by accounting for individual differences in genes,
environment and lifestyle
5. TECHNOLOGICAL ADVANCES AS DRIVERS IN
PRECISION MEDICINE
• Genetics, informatics, and imaging, cell sorting, epigenetics,
proteomics, and metabolomics, is rapidly expanding the scope of
precision medicine
• Electronic health records rich database of clinical information.
• And to propose pharmacogenetic guidelines to assist with drug
selection and administration.
6.
7. Need for precision psychiatry
• Need in Psychiatry- Burden of mental illness
1. Challenges in achieving remission
2. Non response to existing medications
3. Trial and error method of prescription
4. Poorly understood neurobiological mechanism of
mental illness
5. Frequent drug doses changes, drug changes
6. Side effects due to drugs and non adherence
8. Only clinical features not sufficing for clinical
decisions
• No Guideline mentions about specific drugs according to
clinical feature
• However, Recent literature speak of clinical and socio-
demografic characteristics to chose AD- past h/o response,
family h/o antidepressant response, possible drug
interaction
• 42% of variance in response to AD is explained by common
genetic polymorphism
9.
10. Organization
• Oxford Precision psychiatry lab- MARTHA, GRIZALDA (Andrea Cipriani)
• Brain Initiative by white house to develop neuro-technologies to
demystify brain disordres including depression
• First launched by Barack Obama in 2014
12. Genomics
• Development of neuropsychiatric disorders- Genetic heritability >
environmental influences> de novo mutations pl
• Fragile X, di-george syndrome – genetic basis for psychiatric
disorders
• Various methods used
-GWAS
-Linkage analysis
-Association studies
• Genotype to phenotype
• research into genetic vulnerability will bring us closer to
pathophysiology of disease
13. Genomics
• SNP, CNV, gene polymorphism
• Assessing genetic variants - Complete clinical phenotyping of
individuals in relations common variants with small effects
with rare variations with high effects variants(de-novo
mutation, insertion, deletion)
• Affected allele are also seen in healthy unaffected carriers-
similar cognitive performance as probands but not clinically
significant - role of gene –environment interaction
• Others forms of genetic variations- polygenic risk, pleiotropy
14. Genomics
• Polygenic Risk scores:
• Individual genetic variants( SNP) with small effect size-
cumulative effect in disease liability
• Most strongly reproducible biological disease predictor
• Can be quantified using quantitative genetic reasoning,
Ld regression etc.
• PRS- also predicts patient subgroup( PRS of BPAD will
predict affective symptoms in schizophrenia)
15. Genomics
• System genetics:
• epigenetic, molecular, cellular, pathway level profiling
across lifespan
• Links between common genetic variants and system
genetics shed light into actual neurobiological processes
• Can be helpful to understand why certain psychiatric
disorders manifest at certain age
16. Genomics
Gene regulation
• Genes coding for promoter and enhancer sequences
• Within 100kb
• Cell/tissue specific
• Expression quantitative trait loci (e-QTL): Regions in the
genome which alter gene expression
• E-QTL databases are recent and limited however , great
overlap was found in SNPs of GWAS and e-QTL
• Future studies targeting intersection of e-QTL and
genetic variant – good insight into disease process
18. Genomics: Limitations
• causal relationship between genetic variants ≉ causality
• (only 7% of variations attributed common genetic variants) –
• (applicable to population where GWAS is done) ( European countries)
• Lack of complete genomic annotations
• Including non coding region
• not included WES
• not published in WGS)
De-novo loss of function variants – high effect size .
• Ex: SHANK 3 allelic variants( loss of function mutation) associated with two
different phenotype- schizophrenia and autism
19. Pharmacogenomics:
• Definition: genetic profile’s influence on medication response
and adverse drug reactions
• It helps to choose
• drug for responders
• avoid in non responders
• Predicting vulnerability who will develop toxic side effects
21. Pharmacogenomics
• Major genetic variability in AD response –SNP
• Others - deletions, insertions and copy number variations-
unlikely role in AD response (O’Dushlaine et al. 2014)
• Polygenic Risk scores are helpful
e.g., Cumulative risk score for treatment resistance
• However some clinical relevance in PRSs in extremes
22. Pharmacogenomics
• Candidate genes coding cytochrome P450 superfamily
enzymes play role in pharmacokinetics and pharmacodynamics
of AD
• M/c Cytochrome enzyme – CYP2D6, CYP2C19
• Genes for these enzymes are polymorphic
• 2 allele- extensive metabolizers
• 1 allele-Intermediate metabolizers
• No allele- poor metabolizers
• Gene duplication- Ultrarapid metabolizers
• PM and UM functional groups are the most relevant
23. Pharmacogenomics
• CPIC and the Dutch Pharmacogenetic Working
Group(DPWG)
• Recommend the genotyping of functional variants of
CYP2C19 and CYP2D6 for guiding AD choice and dosing.
• Association between PM or UM status and clinical outcomes
established for seven TCAs(Change of drug or dosage)
(CPIC)
• Less evidence to change drug or adjust the dose in PM or UM
for enzymes involved in SSRI metabolism
• Non-linear relationship between plasmatic drug levels and
clinical outcomes for SSRI
24. Translation into clinical practice:
Pharmacogenomics testing
• In market many pharmacogenomic testing including which mentioned
in guidelines as well as variants with small or poorly established effect
size are available
• An Ideal test should
1. Be established and included in guidelines
2. Tell about clinical implications in each variant
3. Clinical benefits evaluated ion published clinical trials
25. Translation into clinical practice:
Pharmacogenomics testing
• Some test including CYP450 – significant improvement in remission
probability in RCT with relative risk of remission to standard care
being 1.7 (Bousman et al. 2019)
• Patients not responding or tolerating one previous treatment may
benefit from testing
• Less cost effective
27. 1. Complexity of finding gene variations that affect drug response
2. Confidentiality, privacy and the use and storage of genetic
information
3.Educating health care providers and patients
• Complicates the process of prescribing and dispensing drugs
• Physicians must execute an extra diagnostic step to determine which
drug is best suited to each patient
Barriers of Pharmacogenomics
29. Transcriptomics
• The transcriptome refers to all of the RNA transcripts in a cell or
tissue
• Human genome undergo alternative splicing - main reasons for the
study of the transcriptome
• Alternative splicing is a particular type of splicing in which two or
more rearrangements (removal of introns and reconnection of exons)
can occur in one mRNA in different cell or different stages of cell
growth.
30. Transcriptomics
• It is postulated that effects of multiple risk variants converge onto
‘key’ downstream molecular pathways- hence the importance of
transcriptomics
• Exome sequencing studies found enrichment for genes harboring
ASD-associated rare, de novo, protein-disrupting variants (RDNVs) in
synaptic, chromatin, and gene regulation pathways
• Similar pathway implicated in SCZ.
• Common-variants across SCZ, MDD, and BD, in aggregate, show
similar enrichments across synaptic and gene regulatory pathways
31. Metabolomics
• Metabolomics studies measure our metabolic state, determined not
only by genomic factors but also modified by diet, environmental
factors, and host factors such as the childhood experiences and gut
microbiome.
• Brain-specific alterations in glucoregulatory processes were intrinsic
to Schizophrenia (Holmes E et al.2006)
32. Metabolomics
• Lipidomic and metabolomic analysis -lipids associated with
medication-associated weight gain and metabolic predictors of
future weight gain (Suvitaival T et al.2016)
• Metabolomic analysis of plasma from older adults - lower levels of
several neurotransmitters and medium chain fatty acids in
depression. (Paige et al. 2007)
33. Challenges of “Omic” profiling
• Much work is needed to develop psychiatric multi-omics biomarkers(
Biosignature) that would not only predict risk, but could also offer an
individual-specific course of disorder and responses to therapeutics.
• Almost all studies used biospecimens taken from blood or urine and
not from the organ of disease origin, the brain.
34. Neuroimaging
• Recent trend - high temporal resolution imaging, particularly
Magnetoencephalography(MEG)
• spatiotemporal dynamics of cognitive phenomena such as reward,
avoidance, learning, memory, and planning.
• Different mental states identified and stored in specific MEG
databases
• Cognition or behavior can be assessed coding different mental states
and their neural correlates
35. Neuroimaging
Psychosis
• Predicting treatment response to conventional antipsychotic
medications using [18F]DOPA PET
• Monitoring the role of microglia and response to neuroimmune
therapies in psychosis using emerging radiotracers with PET
(Coughlin et al. 2019)
Bipolar
disorder
• Some neural changes including increased Right Inferior frontal
gyrus volume may be associated with the resilience to BD
(Cattarinussi et al. 2018)
36. Neuromodulation in Precision psychiatry
• Significant conceptual progress has been made in terms of NIBS
targets, i.e. from single brain regions to neural circuits and to
functional connectivity( using NIBS and functional neuroimaging)
• Precision interventions on three levels:
1) the NIBS intervention
2) the constitutional factors of a single patient
3) the phenotypes and pathophysiology of illness
38. Stem cells
• Patient-specific disease models and pathophysiology
• Disease condition in vitro - reprogramming the patient's somatic cells
into iPSCs - then re-differentiating the patient-specific iPSCs into
disease-specific cells.
• barrier in creating accurate in-vitro models of neuropsychiatric
disorders - Immature iPSC derived neural cells
39. Machine learning
• Using AI , Computation
• When number of potential predictors/variable is large and/or their
effects are non-linear.
40. Summary
• Genetics, clinical characteristics, environmental factors, neuroimaging
help us determine disease related outcome with more accuracy
• This data constitute a Biosignature unique to each patient closely
related to pathophysiological process
• Using this Biosignature with the help of computational technique we
can make tailor-made decisions for each patient
41. References
1. Precision Medicine — Personalized, Problematic, and Promising J. Larry Jameson, M.D., Ph.D., and Dan L. Longo, M.D.n engl j
med 372;23 nejm.org june 4, 2015
2. The Present and Future of Precision Medicine in Psychiatry: Focus on Clinical Psychopharmacology of Antidepressants
Alessandro Serretti Department of Biomedical and NeuroMotor Sciences, University of Bologna, Bologna, Italy
https://doi.org/10.9758/cpn.2018.16.1.1 pISSN 1738-1088/eISSN 2093-4327 Clinical Psychopharmacology and Neuroscience
2018;16(1):1-6
3. The road to precision psychiatry: translating genetics into disease mechanisms Michael J Gandal1–4, Virpi Leppa2–4, Hyejung
Won2–4, Neelroop N Parikshak2–4 & Daniel H Geschwind2–4
4. Pharmacogenomics in the treatment of mood disorders: Strategies and Opportunities for personalized psychiatry
5. Back to the future :on the road towards Precision Psychiatry; Frontiers in Psychiatry
6. The Limitations of Genetic Testing in Psychiatry S teven L. Dubovsky D epartment of Psychiatry, State University of New York at
Buffalo, B uffalo, N.Y., and Departments of Psychiatry and Medicine, University of Colorado, D enver, Colo., USA
7. Machine learning for precision Psychiatry:opportunities and challenges. Prof.Danilo Bzdok,MD,Phd Dept of Psychiatry,
psychotherapy and psychosomatics,RWTH Aachen university, Germany .
8. O’Dushlaine C, Ripke S, Ruderfer DM, Hamilton SP, Fava M, Iosifescu DV, Kohane IS, Churchill SE, Castro VM, Clements CC, et al.
2014. Rare copy number variation in treatment-resistant major depressive disorder. Biol Psychiatry. 76:536–541.
Gwas- Surveying the genomes of many people, looking for genomic variants that occur more frequently in those with a specific disease or trait compared to those without the disease or trait.
Linkage analysis uses DNA sequences with high variability (i.e.,polymorphisms) in order to increase the power to identify markers that are associated with a disease within families
Clinical guidelines for SSRI and TCA- Hicks etal
electrode arrays implanted intracranially in predetermined regions relevant for current symptoms
symptom monitoring and quantification while data from electrodes were continuously recorded
machine-learning algorithms to correlate electrophysiological data with ongoing symptoms.
Stimulation would then be delivered; if symptoms improved, it would be inferred that a casual neural circuit was identified
Therapeutic brain stimulation could then be delivered in a specific circuit-driven way.
