Challenges and prospects in bridging precision medicine and artificial intelligence in genomic psychiatric treatment

Table 1 Evidence based pharmacogenetic associations for psychiatric medications

Drug category	Drug name	Gene	Genotype group	Pharmgkb top FDA label testing level	Available clinical guidelines	Major clinically relevant drug-gene interactions
Anti-dementia drugs	Donepezil	CYP2D6	UM or PM	Actionable PGx	No data	May result in altered systemic concentrations
Anti-dementia drugs	Galantamine	CYP2D6	PM	Informative PGx	No data	Results in higher drug exposure compared to NMs
Antidepressants	Amitriptyline	CYP2C19	UM, IM or PM	No data	CPIC, DPWG	May result in altered conversion of tertiary amines to secondary amines
Antidepressants	Amitriptyline	CYP2D6	UM, IM or PM	Actionable PGx	CPIC, DPWG	May result in altered systemic concentrations
Antidepressants	Bupropion	CYP2D6		Testing required		Potential drug-drug interaction
Antidepressants	Citalopram	CYP2C19	PM	Actionable PGx	CPIC, DPWG	Results in higher drug exposure and higher risk of adverse reaction (QT prolongation) compared to NMs
Antidepressants	Clomipramine	CYP2C19	UM	Actionable PGx	CPIC, DPWG	Results in decreased drug exposure and increased risk of ineffectiveness compared to NMs
Antidepressants	Clomipramine	CYP2D6	PM	Actionable PGx	CPIC, DPWG	May result in altered systemic concentrations
Antidepressants	Desvenlafaxine	CYP2D6	PM	Informative PGx	CPIC	No difference in plasma concentration from NMs
Antidepressants	Doxepin	CYP2C19	IM or PM	Actionable PGx	CPIC, DPWG	Results in higher drug exposure compared to NMs
Antidepressants	Doxepin	CYP2D6	UM, IM or PM	Actionable PGx	CPIC, DPWG	May result in altered systemic concentrations
Antidepressants	Duloxetine	CYP2D6	PM	Actionable PGx	CPIC, DPWG	Potential drug-drug Interaction. May result in higher drug exposure
Antidepressants	Escitalopram	CYP2C19	UM, IM or PM	Actionable PGx	CPIC, DPWG	May result in altered systemic concentrations
Antidepressants	Fluvoxamine	CYP2D6	PM	Actionable PGx	CPIC, DPWG	Results in higher drug exposure compared to NMs
Antidepressants	Imipramine	CYP2C19	PM	No data	DPWG	Results in higher drug exposure and higher risk of adverse reaction compared to NMs. Avoid use in PMs
Antidepressants	Imipramine	CYP2D6	UM, IM or PM	Actionable PGx	CPIC, DPWG	May result in altered systemic concentrations
Antidepressants	Nortriptyline	CYP2D6	UM, IM or PM	Actionable PGx	CPIC, DPWG	May result in altered systemic concentrations
Antidepressants	Paroxetine	CYP2D6	UM, IM or PM	Criteria Not Met	CPIC, DPWG	May result in altered systemic concentrations
Antidepressants	Sertraline	CYP2C19	PM	No data	CPIC, DPWG	Results in higher drug exposure and higher risk of adverse reaction compared to NMs
Antidepressants	Venlafaxine	CYP2D6	PM	Actionable PGx	CPIC, DPWG	Results in altered parent drug and metabolite concentrations
Antidepressants	Vortioxetine	CYP2D6	PM	Actionable PGx	CPIC	Results in higher drug exposure compared to NMs
Antidepressants	Amoxapine	CYP2D6	UM, IM or PM	Actionable PGx	No data	May result in altered systemic concentrations
Antidepressants	Desipramine	CYP2D6	UM, IM or PM	Actionable PGx	CPIC	May result in altered systemic concentrations
Antidepressants	Trimipramine	CYP2D6	UM, IM or PM	Actionable PGx	CPIC	May result in altered systemic concentrations
Antiepileptics	Carbamazepine	HLA-A	*31: 01 positive	Actionable PGx	CPIC, DPWG, CPNDS	Results in higher risk of adverse reaction risk (severe skin reactions) compared to NMs
Antiepileptics	Carbamazepine	HLA-B	*15: 02 positive	Testing required	CPIC, DPWG, CPNDS	Results in higher risk of adverse reaction risk (severe skin reactions) compared to NMs. Consider alternative therapies, or use only if potential benefits outweigh risks
Antiepileptics	Lamotrigine	HLA-B	*15: 02 positive	No data	DPWG	Results in higher risk of adverse reaction risk (lamotrigine-induced SJS/TEN). Avoid use in patients with *15: 02 positive allele
Antiepileptics	Oxcarbazepine	HLA-B	*15: 02 positive	Testing required	CPIC, DPWG	Results in increased risk of adverse reaction (severe skin reactions)
Antiepileptics	Phenytoin	CYP2C9	IM or PM	Testing recommended	CPIC, DPWG	May result in higher drug exposure and higher risk of adverse reaction (CNS toxicity) compared to NMs
Antiepileptics	Phenytoin	HLA-B	*15: 02 positive	Testing recommended	CPIC, DPWG	May result in higher risk of adverse reaction (SJS/TEN) compared to NMs
Antiepileptics	Valproic acid	POLG	A467T and W748S mutations	Testing required	No data	Results in increased risk of adverse reaction (acute liver failure and resultant deaths). The use is contraindicated in patients with POLG mutations
Antimigraine preparations	Clonidine	CYP2D6	UM, IM or PM	No data	DPWG	No significant effect (No recommendation). Possible alternative for atomoxetine in variant CYP2D6 metabolisers
Antipsychotics	Aripiprazole	CYP2D6	PM	Actionable PGx	DPWG	Results in higher drug exposure compared to NMs and higher risk of adverse reaction
Antipsychotics	Clozapine	CYP2D6	PM	Actionable PGx	DPWG	Results in higher drug exposure compared to NMs
Antipsychotics	Haloperidol	CYP2D6	UM or PM	Actionable PGx	DPWG	Results in increased risk of adverse reaction In PMs and higher risk of reduced effectiveness In UMs
Antipsychotics	Olanzapine	CYP2D6	PM	Informative PGx	DPWG	No significant effect (No recommendation)
Antipsychotics	Paliperidone	CYP2D6	PM	Informative PGx	No data	No significant difference in exposure or clearance compared to NMs
Antipsychotics	Perphenazine	CYP2D6	PM	Actionable PGx	No data	Results in higher drug exposure and higher risk of adverse reaction compared to NMs
Antipsychotics	Pimozide	CYP2D6	PM	Testing required	DPWG	Results in higher drug exposure compared to NMs
Antipsychotics	Quetiapine	CYP3A4	PM	No data	DPWG	Results in decreased conversion of systemic parent drug (quetiapine) to the active metabolite. Use alternative therapy
Antipsychotics	Risperidone	CYP2D6	UM, IM or PM	Informative PGx	DPWG	Results in altered parent drug and metabolite concentrations
Anxiolytics	Clobazam	CYP2C19	IM or PM	Actionable PGx	No data	Results in increased active metabolite concentrations and increased risk of adverse reaction as compared to NMs
Anxiolytics	Diazepam	CYP2C19	PM	Actionable PGx	No data	May result in altered systemic concentrations
Psycholeptics and psychoanaleptics in combination	Fluoxetine	CYP2D6	PM	Actionable PGx	CPIC, DPWG	Results in higher drug exposure compared to NMs
Psycholeptics and psychoanaleptics in combination	Fluoxetine	FKBP5	PM	Actionable PGx	CPIC, DPWG	Results in higher drug exposure compared to NMs
Psychostimulants, agents used for adhd and nootropics	Atomoxetine	CYP2D6	PM	Actionable PGx	CPIC, DPWG	Results in higher drug exposure and higher risk of adverse reaction compared to NMs
Psychostimulants, agents used for adhd and nootropics	Modafinil	CYP2D6	PM	Actionable PGx	No data	May require dose modification when administered with medication metabolized by CYP2C19

NM: Normal metabolizer; UM: Ultra-rapid metabolizer; IM: Intermediate metabolizer; PM: Poor metabolizer; CPIC: Clinical pharmacogenetics implementation consortium; DPWG: Dutch pharmacogenetics working group; CPNDS: Canadian pharmacogenomics network for drug safety; SJS/TEN: Stevens-Johnson syndrome; TEN: Toxic epidermal necrolysis; PGx: Pharmacogenomic.

Table 2 Machine learning category, underlying principles, methods, and application examples

Machine learning category	Description	Machine learning techniques used	Algorithm	Uses	Ref.
Supervised learning	Supervised learning algorithm learns from labelled examples to train a model to predict future outcomes with high accuracy	Random forests, support vector machines, artificial neural networks	Classification, regression, sequence labelling	Predict treatment responses based on genomic profiles, aid in therapy selection	Nasteski et al[27]
Unsupervised learning	Unsupervised machine learning discerns patterns in unlabelled datasets to predict relationships and meaningful patterns	K-means clustering, principal component analysis	Clustering, dimensionality reduction	Identify patterns and relationships within patient data for treatment planning and prognostic analyses	Ghahramani[28]
Reinforcement learning	Reinforcement learning integrates user feedback to refine decision-making, enhancing the model's performance	Q-learning, Policy gradients	Sequential decision making	Optimize treatment selection by maximizing cumulative rewards over time	Sutton et al[29]

Table 3 Studies evaluating antidepressant drug response using machine learning predictive models

Psychiatric disorder	Machine learning method	Datatypes	Dataset features	Findings	Ref.
Bipolar disorder	Decision tree, random forest	Gene expression	RBPMS2, LILRA5 (male responders); ABRACL, FHL3, NBPF14 (female responders)	Predicted lithium responders in bipolar patients with AUC = 0.92	Eugene et al[36]
Major depressive disorder	ARPNet model-linear regression	SNPs, DNA methylation, demographic	Neuroimaging biomarkers, Genetic variants, DNA methylation, demographic information	Predicted the most effective antidepressant with 84% accuracy	Chang et al[37]
Major depressive disorder	Deep learning-MFNNs	SNPs, demographic, clinical	Genome-wide associations, marital status, age, sex, suicide attempt status, baseline hamilton rating scale for depression score, depressive episodes	Conducted GWAS to identify SNP associations with antidepressant treatment response and remission. MFNN models achieved high accuracy (AUC = 0.82 for response, AUC = 0.81 for remission).	Lin et al[39]
Major depressive disorder	Tree-based ensemble structure	Clinical, demographic	Clinical variables (patients with depression from STAR*D)	Predicted clinical antidepressant remission with 59% accuracy	Chekroud et al[40]
Major depressive disorder	Elastic net	Clinical, demographic	Clinical variables: Patients with major depressive disorder (GENDEN participants)	Forecasted antidepressant response with AUC = 0.72	Iniesta et al[41]
Treatment-resistant depression	Random forest	SNPs, clinical	SNP (rs6265 (BDNF gene), rs6313 (HTR2A gene), rs7430 (PPP3CC gene), Clinical variable - Melancholia	Predicted antidepressant treatment outcome with 25% accuracy	Kautzky et al[42]
Major depressive disorder	SVM, decision trees	SNPs	rs2036270 SNP (RARB gene), rs7037011 SNP (LOC105375971 gene)	Estimated antidepressant treatment response with 52% accuracy	Maciukiewicz et al[43]
Bipolar disorder	Random forest	Clinical	Clinical variables (patients with bipolar disorder treated primarily with lithium)	Predicted responders for lithium treatment outcome with AUC = 0.8	Nunes et al[44]
Late-life depression	Alternating decision tree	Clinical, demographic	Mini-mental status examination scores, age, structural imaging	Predicted antidepressant treatment response with 89% accuracy	Patel et al[45]
Major depressive disorder	Random forest	SNPs	SNPs (rs5743467, rs2741130, rs2702877, rs696692, rs17137566, rs10516436)	Predicted antidepressant therapy response with AUC > 0.7 and accuracy > 69%	Athreya et al[46]

AUC: Area under the receiver operating characteristic curve; SNP: Single nucleotide polymorphism.