Attention deficit hyperactivity disorder pharmacogenetics: the dopamine transporter and D4 receptor

Translational bioinformatics and data science for biomarker discovery in mental health: an analytical review

Translational bioinformatics and data science play a crucial role in biomarker discovery as it enables translational research and helps to bridge the gap between the bench research and the bedside clinical applications. Thanks to newer and faster molecular profiling technologies and reducing costs, there are many opportunities for researchers to explore the molecular and physiological mechanisms of diseases. Biomarker discovery enables researchers to better characterize patients, enables early detection and intervention/prevention and predicts treatment responses. Due to increasing prevalence and rising treatment costs, mental health (MH) disorders have become an important venue for biomarker discovery with the goal of improved patient diagnostics, treatment and care. Exploration of underlying biological mechanisms is the key to the understanding of pathogenesis and pathophysiology of MH disorders. In an effort to better understand the underlying mechanisms of MH disorders, we reviewed the major accomplishments in the MH space from a bioinformatics and data science perspective, summarized existing knowledge derived from molecular and cellular data and described challenges and areas of opportunities in this space.

DRD4 genotyping may differentiate symptoms of attention-deficit/hyperactivity disorder and sluggish cognitive tempo

OBJECTIVE

Studies to reduce the heterogeneity of attention-deficit/hyperactivity disorder (ADHD) have increased interest in the concept of sluggish cognitive tempo (SCT). The aim of this study was to investigate if the prevalence of two variable-number tandem repeats (VNTRs) located within the 3'-untranslated region of the DAT1 gene and in exon 3 of the dopamine D4 receptor (DRD4) gene differ among four groups (31 subjects with SCT but no ADHD, 146 individuals with ADHD but no SCT, 67 subjects with SCT + ADHD, and 92 healthy controls).

METHODS

We compared the sociodemographic profiles, neurocognitive domains, and prevalence of two VNTRs in SCT and ADHD subjects versus typically developing (TD) controls.

RESULTS

The SCT without ADHD group had a higher proportion of females and lower parental educational attainment. Subjects in this group performed worse on neuropsychological tests, except for psychomotor speed and commission errors, compared to controls. However, the ADHD without SCT group performed significantly worse on all neuropsychological domains than controls. We found that 4R homozygosity for the DRD4 gene was most prevalent in the ADHD without SCT group. The SCT without ADHD group had the highest 7R allele frequency, differing significantly from the ADHD without SCT group.

CONCLUSION

The 7R allele of DRD4 gene was found to be significantly more prevalent in SCT cases than in ADHD cases. No substantial neuropsychological differences were found between SCT and ADHD subjects.

Hemispheric brain asymmetry differences in youths with attention-deficit/hyperactivity disorder

Introduction

Attention-deficit hyperactive disorder (ADHD) is the most common neurodevelopmental disorder in children. Diagnosis is currently based on behavioral criteria, but magnetic resonance imaging (MRI) of the brain is increasingly used in ADHD research. To date however, MRI studies have provided mixed results in ADHD patients, particularly with respect to the laterality of findings.

Methods

We studied 849 children and adolescents (ages 6-21 y.o.) diagnosed with ADHD (n = 341) and age-matched typically developing (TD) controls with structural brain MRI. We calculated volumetric measures from 34 cortical and 14 non-cortical brain regions per hemisphere, and detailed shape morphometry of subcortical nuclei. Diffusion tensor imaging (DTI) data were collected for a subset of 104 subjects; from these, we calculated mean diffusivity and fractional anisotropy of white matter tracts. Group comparisons were made for within-hemisphere (right/left) and between hemisphere asymmetry indices (AI) for each measure.

Results

DTI mean diffusivity AI group differences were significant in cingulum, inferior and superior longitudinal fasciculus, and cortico-spinal tracts (p < 0.001) with the effect of stimulant treatment tending to reduce these patterns of asymmetry differences. Gray matter volumes were more asymmetric in medication free ADHD individuals compared to TD in twelve cortical regions and two non-cortical volumes studied (p < 0.05). Morphometric analyses revealed that caudate, hippocampus, thalamus, and amygdala were more asymmetric (p < 0.0001) in ADHD individuals compared to TD, and that asymmetry differences were more significant than lateralized comparisons.

Conclusions

Brain asymmetry measures allow each individual to serve as their own control, diminishing variability between individuals and when pooling data across sites. Asymmetry group differences were more significant than lateralized comparisons between ADHD and TD subjects across morphometric, volumetric, and DTI comparisons.

ADHD & Pharmacotherapy: Past, Present and Future: A Review of the Changing Landscape of Drug Therapy for Attention Deficit Hyperactivity Disorder

Attention deficit hyperactivity disorder (ADHD) is the most common neurobiological disorder in children, with a prevalence of ~6-7%^1,2 that has remained stable for decades². The social and economic burden associated with patients³, families, and broader systems (healthcare/educational) is substantial, with the annual economic impact of ADHD exceed $30 billion in the US alone⁴. Efficacy of pharmacotherapy in treating ADHD symptoms has generally been considerable with at least ¾ of individuals benefitting from pharmacotherapy, typically in the form of stimulants⁵. In this review, we begin by briefly reviewing the history of pharmacotherapy in relation to ADHD, before focusing (primarily) on the state-of-the-field on themes such as biophysiology, pharmacokinetics, and pharmacogenomics. We conclude with a summary of emerging clinical and research studies, particularly the potential role for precision therapy in matching ADHD patients and drug types.

Biomarkers in the diagnosis of ADHD--promising directions

The etiology and pathogenesis of attention-deficit/hyperactivity disorder (ADHD) are unclear and a more valid diagnosis would certainly be welcomed. Starting from the literature, we built an hypothetical pyramid representing a putative set of biomarkers where, at the top, variants in DAT1 and DRD4 genes are the best candidates for their associations to neuropsychological tasks, activation in specific brain areas, methylphenidate response and gene expression levels. Interesting data come from the noradrenergic system (norepinephrine transporter, norepinephrine, 3-methoxy-4-hydroxyphenylglycol, monoamine oxidase, neuropeptide Y) for their altered peripheral levels, their association with neuropsychological tasks, symptomatology, drugs effect and brain function. Other minor putative genetic biomarkers could be dopamine beta hydroxylase and catechol-O-methyltransferase. In the bottom, we placed endophenotype biomarkers. A more deep integration of "omics" sciences along with more accurate clinical profiles and new high-throughput computational methods will allow us to identify a better list of biomarkers useful for diagnosis and therapies.

Identification of a new selective dopamine D4 receptor ligand

The dopamine D4 receptor has been shown to play key roles in certain CNS pathologies including addiction to cigarette smoking. Thus, selective D4 ligands may be useful in treating some of these conditions. Previous studies in our laboratory have indicated that the piperazine analog of haloperidol exhibits selective and increased affinity to the DAD4 receptor subtype, in comparison to its piperidine analog. This led to further exploration of the piperazine moiety to identify new agents that are selective at the D4 receptor. Compound 27 (KiD4=0.84 nM) was the most potent of the compounds tested. However, it only had moderate selectivity for the D4 receptor. Compound 28 (KiD4=3.9 nM) while not as potent, was more discriminatory for the D4 receptor subtype. In fact, compound 28 has little or no binding affinity to any of the other four DA receptor subtypes. In addition, of the 23 CNS receptors evaluated, only two, 5HT1AR and 5HT2BR, have binding affinity constants better than 100 nM (Ki <100 nM). Compound 28 is a potentially useful D4-selective ligand for probing disease treatments involving the D4 receptor, such as assisting smoking cessation, reversing cognitive deficits in schizophrenia and treating erectile dysfunction. Thus, further optimization, functional characterization and evaluation in animal models may be warranted.

Non-negative matrix factorization of multimodal MRI, fMRI and phenotypic data reveals differential changes in default mode subnetworks in ADHD

In the multimodal neuroimaging framework, data on a single subject are collected from inherently different sources such as functional MRI, structural MRI, behavioral and/or phenotypic information. The information each source provides is not independent; a subset of features from each modality maps to one or more common latent dimensions, which can be interpreted using generative models. These latent dimensions, or "topics," provide a sparse summary of the generative process behind the features for each individual. Topic modeling, an unsupervised generative model, has been used to map seemingly disparate features to a common domain. We use Non-Negative Matrix Factorization (NMF) to infer the latent structure of multimodal ADHD data containing fMRI, MRI, phenotypic and behavioral measurements. We compare four different NMF algorithms and find that the sparsest decomposition is also the most differentiating between ADHD and healthy patients. We identify dimensions that map to interpretable, recognizable dimensions such as motion, default mode network activity, and other such features of the input data. For example, structural and functional graph theory features related to default mode subnetworks clustered with the ADHD-Inattentive diagnosis. Structural measurements of the default mode network (DMN) regions such as the posterior cingulate, precuneus, and parahippocampal regions were all related to the ADHD-Inattentive diagnosis. Ventral DMN subnetworks may have more functional connections in ADHD-I, while dorsal DMN may have less. ADHD topics are dependent upon diagnostic site, suggesting diagnostic differences across geographic locations. We assess our findings in light of the ADHD-200 classification competition, and contrast our unsupervised, nominated topics with previously published supervised learning methods. Finally, we demonstrate the validity of these latent variables as biomarkers by using them for classification of ADHD in 730 patients. Cumulatively, this manuscript addresses how multimodal data in ADHD can be interpreted by latent dimensions.

Individual treatment response in attention-deficit/hyperactivity disorder: broadening perspectives and improving assessments

Attention-deficit/hyperactivity disorder (ADHD) is a highly complex disorder with multiple treatment options. Impairments associated with ADHD, rather than symptoms defining the disorder, are the primary reason for referral of individuals to clinical services; consequently, they should also be key targets for intervention. Impairments are moderated by factors such as comorbidities, family environment and intelligence quotient, and particular challenges may vary between patients. The understanding of patient and family treatment preferences, as well as identification of treatment needs and goals, should drive future clinical practice. This review addresses the assessment of ADHD treatment goals and outcomes in clinical practice, and discusses changes in future clinical research studies necessary to progress the utilization of an individualized medicine approach in ADHD.

Discovery of highly potent and selective D4 ligands by interactive SAR study

A series of thienylmethylphenylpiperazins was synthesized and tested for affinity towards the five subtypes of dopaminergic receptors. Compound 5f showed more than 1000 folds selectivity to D4 receptors; analogue 5e showed the highest affinity to D4 receptors with Ki 3.9 nM. An interactive SAR approach was adopted and lead to compound 14a with Ki (D4) as low as 0.03 nM. Molecular docking studies showed a potential, first to report arene cation interaction between the D4 unique residue Arg-186 and the ligands' arene moiety, explaining the importance of having a strong negative electrostatic potential at this area of the compound structure.

Enhancement of gamma activity after selective activation of dopamine D4 receptors in freely moving rats and in a neurodevelopmental model of schizophrenia

Dopamine D4 receptor (D4R) mechanisms have been implicated in several psychiatric diseases, including schizophrenia, attention-deficit hyperactivity disorder (ADHD), and autism, which are characterized by cognitive deficits. The cellular mechanisms are poorly understood but impaired neuronal synchronization within cortical networks in the gamma frequency band has been proposed to contribute to these deficits. A D4R polymorphism was recently linked to variations in gamma power in both normal and ADHD subjects, and D4R activation was shown to enhance kainate-induced gamma oscillations in brain slices in vitro. The goal of this study was to investigate the effect of D4R activation on gamma oscillations in freely moving rats during natural behavior. Field potentials were recorded in the frontal, prefrontal, parietal, and occipital cortex and hippocampus. Gamma power was assessed before and after subcutaneous injection of a D4R agonist, A-412997, in several doses between 0.3 and 10.0 mg/kg. The experiments were also repeated in a neurodevelopmental model of schizophrenia, in which rats are prenatally treated with methylazoxymethanol (MAM). We found that the D4R agonist increased gamma power in all regions at short latency and lasted for ~2 h, both in normal and MAM-treated rats. The effect was dose dependent indicated by the significant difference between the effects after 3 and 10 mg/kg in pair-wise comparison, whereas 0.3 and 1.0 mg/kg injections were ineffective. This study demonstrates the involvement of D4R in cortical gamma oscillations in vivo and identifies this receptor as potential target for pharmacological treatment of cognitive deficits.

Insights into multimodal imaging classification of ADHD

Attention deficit hyperactivity disorder (ADHD) currently is diagnosed in children by clinicians via subjective ADHD-specific behavioral instruments and by reports from the parents and teachers. Considering its high prevalence and large economic and societal costs, a quantitative tool that aids in diagnosis by characterizing underlying neurobiology would be extremely valuable. This provided motivation for the ADHD-200 machine learning (ML) competition, a multisite collaborative effort to investigate imaging classifiers for ADHD. Here we present our ML approach, which used structural and functional magnetic resonance imaging data, combined with demographic information, to predict diagnostic status of individuals with ADHD from typically developing (TD) children across eight different research sites. Structural features included quantitative metrics from 113 cortical and non-cortical regions. Functional features included Pearson correlation functional connectivity matrices, nodal and global graph theoretical measures, nodal power spectra, voxelwise global connectivity, and voxelwise regional homogeneity. We performed feature ranking for each site and modality using the multiple support vector machine recursive feature elimination (SVM-RFE) algorithm, and feature subset selection by optimizing the expected generalization performance of a radial basis function kernel SVM (RBF-SVM) trained across a range of the top features. Site-specific RBF-SVMs using these optimal feature sets from each imaging modality were used to predict the class labels of an independent hold-out test set. A voting approach was used to combine these multiple predictions and assign final class labels. With this methodology we were able to predict diagnosis of ADHD with 55% accuracy (versus a 39% chance level in this sample), 33% sensitivity, and 80% specificity. This approach also allowed us to evaluate predictive structural and functional features giving insight into abnormal brain circuitry in ADHD.