Bipolar disorder (BD) is a heritable mental illness with complex etiology. We performed a genome-wide association study (GWAS) of 41,917 BD cases and 371,549 controls of European ancestry, which identified 64 associated genomic loci. BD risk alleles were enriched in genes in synaptic signaling pathways and brain-expressed genes, particularly those with high specificity of expression in neurons of the prefrontal cortex and hippocampus. Significant signal enrichment was found in genes encoding targets of antipsychotics, calcium channel blockers, antiepileptics, and anesthetics. Integrating eQTL data implicated 15 genes robustly linked to BD via gene expression, encoding druggable targets such as HTR6, MCHR1, DCLK3 and FURIN. Analyses of BD subtypes indicated high but imperfect genetic correlation between BD type I and II and identified additional associated loci. Together, these results advance our understanding of the biological etiology of BD, identify novel therapeutic leads, and prioritize genes for functional follow-up studies.

Bipolar disorder (BD) is a heritable mental illness with complex etiology. We performed a genome-wide association study (GWAS) of 41,917 BD cases and 371,549 controls, which identified 64 associated genomic loci. BD risk alleles were enriched in genes in synaptic and calcium signaling pathways and brain-expressed genes, particularly those with high specificity of expression in neurons of the prefrontal cortex and hippocampus. Significant signal enrichment was found in genes encoding targets of antipsychotics, calcium channel blockers and antiepileptics. Integrating eQTL data implicated 15 genes robustly linked to BD via gene expression, including druggable genes such as HTR6, MCHR1, DCLK3 and FURIN. This GWAS provides the best-powered BD polygenic scores to date, when applied in both European and diverse ancestry samples. Together, these results advance our understanding of the biological etiology of BD, identify novel therapeutic leads and prioritize genes for functional follow-up studies.

Pharmaceutical industry and the art and science of drug development are sorely in need of novel transformative technologies in the current age of digital health and artificial intelligence (AI). Often described as gamechanging technologies, AI and machine learning algorithms have slowly but surely begun to revolutionize pharmaceutical industry and drug development over the past 5 years. In this expert review, we describe the most frequently used machine learning algorithms in drug development pipelines and the -omics databases well poised to support machine learning and drug discovery. Subsequently, we analyze the emerging new computational approaches to drug discovery and the in silico pipelines for drug repositioning and the synergies among -omics system sciences, AI and machine learning. As with system sciences, AI and machine learning embody a system scale and Big Data driven vision for drug discovery and development. We conclude with a future outlook on the ways in which machine learning approaches can be implemented to buttress and expedite drug discovery and precision medicine. As AI and machine learning are rapidly entering pharmaceutical industry and the art and science of drug development, we need to critically examine the attendant prospects and challenges to benefit patients and public health.

Nowadays, many relevant drug-gene associations have been discovered, but pharmacogenomics (PGx)-guided treatment needs to be cost-effective as well as clinically beneficial to be incorporated into standard health care. To address current challenges, this systematic review provides an update regarding previously published studies, which assessed the cost-effectiveness of PGx testing for the prescription of antidepressants and antipsychotics. From a total of 1159 studies initially identified by literature database querying, and after manual assessment and curation of all of them, a mere 18 studies met our inclusion criteria. Of the 18 studies evaluations, 16 studies (88.89%) drew conclusions in favor of PGx testing, of which 9 (50%) genome-guided interventions were costeffective and 7 (38.9%) were less costly compared to standard treatment based on cost analysis. More precisely, supportive evidence exists for CYP2D6 and CYP2C19 drug-gene associations and for combinatorial PGx panels, but evidence is limited for many other drug-gene combinations. Amongst the limitations of the field are the unclear explanation of perspective and cost inputs, as well as the underreporting of study design elements, which can influence though the economic evaluation. Overall, the findings of this article demonstrate that although there is growing evidence on the cost-effectiveness of genome-guided interventions in psychiatric diseases, there is still a need for performing additional research on economic evaluations of PGx implementation with an emphasis on psychiatric disorders.