Pharmacogenomic testing and personalized treatment of depression

Explainable multimodal prediction of treatment-resistance in patients with depression leveraging brain morphometry and natural language processing

Although 20 % of patients with depression receiving treatment do not achieve remission, predicting treatment-resistant depression (TRD) remains challenging. In this study, we aimed to develop an explainable multimodal prediction model for TRD using structured electronic medical record data, brain morphometry, and natural language processing. In total, 247 patients with a new depressive episode were included. TRD-predictive models were developed based on the combination of following parameters: selected tabular dataset features, independent components-map weightings from brain T1-weighted magnetic resonance imaging (MRI), and topic probabilities from clinical notes. All models applied the extreme gradient boosting (XGBoost) algorithm via five-fold cross-validation. The model using all data sources showed the highest area under the receiver operating characteristic of 0.794, followed by models that used combined brain MRI and structured data, brain MRI and clinical notes, clinical notes and structured data, brain MRI only, structured data only, and clinical notes only (0.770, 0.762, 0.728, 0.703, 0.684, and 0.569, respectively). Classifications of TRD were driven by several predictors, such as previous exposure to antidepressants and antihypertensive medications, sensorimotor network, default mode network, and somatic symptoms. Our findings suggest that a combination of clinical data with neuroimaging and natural language processing variables improves the prediction of TRD.

Beyond the Asylum Walls: Tracing the Tapestry of Mental Health Interventions Across Eras and Cultures

This article offers an extensive review of the changing field of mental health therapies, charting a transformational path from traditional methods to modern breakthroughs and speculating on potential future developments. The story develops by investigating historical viewpoints while reflecting on the present and highlighting the lessons learned and their impact on contemporary practices. We have advanced from the stigmatized constraints of asylums to a paradigm that puts human rights, dignity, and individualized, culturally sensitive treatment first. Modern methods are much more varied and evidence-based, from cutting-edge technical advancements to evidence-based psychotherapies. The ethical considerations arising from the delicate balance of pharmacological therapies underline the responsibility of administering drugs that significantly affect mental health. Cultural factors become a pillar, highlighting how crucial cultural sensitivity is to promoting tolerance. By acknowledging how many facets of the human experience are interrelated, holistic methods help close the gap between the mind and body. Integrative medicine and alternative therapies represent a shift away from reductionist approaches and toward a holistic viewpoint. The delivery of mental health treatment is being reimagined by technological advancements, with virtual and digital environments opening up new access and support channels. These developments cut beyond regional boundaries, reinventing conventional therapy dynamics and paving the way for individualized therapies. Cultural concerns highlight the significance of cultural competency in navigating the complex mental health treatment system and adapting interventions to fit the particular requirements of various cultural contexts. With telepsychiatry, virtual reality, and artificial intelligence among the new technologies that promise to further revolutionize mental health therapies, the essay looks to the future. This review concludes by imagining a day when mental health is prioritized, therapies are available, and the diversity of human experience is valued. The path to a society that values, nurtures, and celebrates mental health continues.

Symptom clustering of major depression in a national telehealth sample

BACKGROUND

Major depressive disorder (MDD) is a heterogeneous disorder whose possible symptom combinations have not been well delineated. The aim of this study was to explore the heterogeneity of symptoms experienced by those with MDD to characterize phenotypic presentations.

METHODS

Cross-sectional data (N = 10,158) from a large telemental health platform were used to identify subtypes of MDD. Symptom data, gathered from both clinically-validated surveys and intake questions, were analyzed via polychoric correlations, principal component analysis, and cluster analysis.

RESULTS

Principal components analysis (PCA) of baseline symptom data revealed 5 components, including anxious distress, core emotional, agitation/irritability, insomnia, and anergic/apathy components. PCA-based cluster analysis resulted in four MDD phenotypes, the largest of which was characterized by a prominent elevation on the anergic/apathy component, but also core emotional. The four clusters differed on demographic and clinical characteristics.

LIMITATIONS

The primary limitation of this study is that the phenotypes uncovered are limited by the questions asked. These phenotypes will need to be cross validated with other samples, potentially expanded to include biological/genetic variables, and followed longitudinally.

CONCLUSIONS

The heterogeneity in MDD, as illustrated by the phenotypes in this sample, may explain the heterogeneity of treatment response in large-scale treatment trials. These phenotypes can be used to study varying rates of recovery following treatment and to develop clinical decision support tools and artificial intelligence algorithms. Strengths of this study include its size, breadth of included symptoms, and novel use of a telehealth platform.

Effect of pharmacogenomics testing guiding on clinical outcomes in major depressive disorder: a systematic review and meta-analysis of RCT

BACKGROUND

Pharmacogenomic testing guided treatment have been developed to guide drug selection or conversion in major depressive disorder patients. Whether patients benefit from pharmacogenetic testing remains unclear. We aim to evaluates the effect of pharmacogenomic testing guiding on clinical outcomes of major depressive disorder.

METHODS

Pubmed, Embase, and Cochrane Library of Clinical Trials were searched from inception until August 2022. Key terms included pharmacogenomic and antidepressive. Odds ratios (RR) with 95% confidence intervals (95%CIs) were calculated using fixed-effects model for low or moderate heterogeneity or random-effects model for high heterogeneity.

RESULTS

Eleven studies (5347 patients) were included. Compared with usual group, pharmacogenomic testing guided group was associated with an increased response rate at week 8 (OR 1.32, 95%CI 1.15-1.53, 8 studies, 4328 participants) and week 12 (OR 1.36, 95%CI 1.15-1.62, 4 studies, 2814 participants). Similarly, guided group was associated with an increased rate of remission at week 8 (OR 1.58, 95%CI 1.31-1.92, 8 studies, 3971 participants) and week 12 (OR 2.23, 95%CI 1.23-4.04, 5 studies, 2664 participants). However, no significant differences were found between the two groups in response rate at week 4 (OR 1.12, 95%CI 0.89-1.41, 2 studies, 2261 participants) and week 24 (OR 1.16, 95%CI 0.96-1.41, 2 studies, 2252 participants), and remission rate at week 4 (OR 1.26, 95%CI 0.93-1.72, 2 studies, 2261 participants) and week 24 (OR 1.06, 95%CI 0.83-1.34, 2 studies, 2252 participants). Medication congruence in 30 days was significantly reduced in the pharmacogenomic guided group compared with the usual care group (OR 2.07, 95%CI 1.69-2.54, 3 studies, 2862 participants). We found significant differences between subgroups of target population in response and remission rate.

CONCLUSION

Patients with major depressive disorder may benefit from pharmacogenomic testing guided treatment by achieving target response and remission rates more quickly.

Conceptualizing eating disorder recovery research: Current perspectives and future research directions

BACKGROUND

How we research eating disorder (ED) recovery impacts what we know (perceive as fact) about it. Traditionally, research has focused more on the "what" of recovery (e.g., establishing criteria for recovery, reaching consensus definitions) than the "how" of recovery research (e.g., type of methodologies, triangulation of perspectives). In this paper we aim to provide an overview of the ED field's current perspectives on recovery, discuss how our methodologies shape what is known about recovery, and suggest a broadening of our methodological "toolkits" in order to form a more complete picture of recovery. BODY: This paper examines commonly used methodologies in research, and explores how incorporating different perspectives can add to our understanding of the recovery process. To do this, we (1) provide an overview of commonly used methodologies (quantitative, qualitative), (2) consider their benefits and limitations, (3) explore newer approaches, including mixed-methods, creative methods (e.g., Photovoice, digital storytelling), and multi-methods (e.g., quantitative, qualitative, creative methods, psycho/physiological, behavioral, laboratory, online observations), and (4) suggest that broadening our methodological "toolkits" could spur more nuanced and specific insights about ED recoveries. We propose a potential future research model that would ideally have a multi-methods design, incorporate different perspectives (e.g., expanding recruitment of diverse participants, including supportive others, in study co-creation), and a longitudinal course (e.g., capturing cognitive and emotional recovery, which often comes after physical). In this way, we hope to move the field towards different, more comprehensive, perspectives on ED recovery.

CONCLUSION

Our current perspectives on studying ED recovery leave critical gaps in our knowledge about the process. The traditional research methodologies impact our conceptualization of recovery definitions, and in turn limit our understanding of the phenomenon. We suggest that we expand our range of methodologies, perspectives, and timeframes in research, in order to form a more complete picture of what is possible in recovery; the multiple aspects of an individual's life that can improve, the greater number of people who can recover than previously believed, and the reaffirmation of hope that, even after decades, individuals can begin, and successfully continue, their ED recovery process.

Optimizing prediction of response to antidepressant medications using machine learning and integrated genetic, clinical, and demographic data

Major depressive disorder (MDD) is complex and multifactorial, posing a major challenge of tailoring the optimal medication for each patient. Current practice for MDD treatment mainly relies on trial and error, with an estimated 42-53% response rates for antidepressant use. Here, we sought to generate an accurate predictor of response to a panel of antidepressants and optimize treatment selection using a data-driven approach analyzing combinations of genetic, clinical, and demographic factors. We analyzed the response patterns of patients to three antidepressant medications in the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) study, and employed state-of-the-art machine learning (ML) tools to generate a predictive algorithm. To validate our results, we assessed the algorithm's capacity to predict individualized antidepressant responses on a separate set of 530 patients in STAR*D, consisting of 271 patients in a validation set and 259 patients in the final test set. This assessment yielded an average balanced accuracy rate of 72.3% (SD 8.1) and 70.1% (SD 6.8) across the different medications in the validation and test set, respectively (p < 0.01 for all models). To further validate our design scheme, we obtained data from the Pharmacogenomic Research Network Antidepressant Medication Pharmacogenomic Study (PGRN-AMPS) of patients treated with citalopram, and applied the algorithm's citalopram model. This external validation yielded highly similar results for STAR*D and PGRN-AMPS test sets, with a balanced accuracy of 60.5% and 61.3%, respectively (both p's < 0.01). These findings support the feasibility of using ML algorithms applied to large datasets with genetic, clinical, and demographic features to improve accuracy in antidepressant prescription.

ZNF804A Gene Variants Have a Cross-diagnostic Influence on Psychosis and Treatment Improvement in Mood Disorders

Objective

Genetic variations in the gene encoding zinc finger protein 804A gene (ZNF804A) have been associated with major depression and bipolar disorder. In this work we focused on the potential influence of ZNF804A variations on the risk of developing specific sub-phenotypes as well as the individual response to available treatments.

Methods

We used two samples of different ethnic origin: a Korean sample, composed by 242 patients diagnosed with major depression and 132 patients diagnosed with bipolar disorder and 326 healthy controls; an Italian sample composed 151 major depression subjects, 189 bipolar disorder subjects and 38 outpatients diagnosed for a primary anxiety disorder.

Results

Our analyses reported an association of rs1344706 with psychotic phenotype in the cross-diagnostic pooled sample (geno p = 4.15 × 10⁻⁴, allelic p = 1.06 × 10⁻⁴). In the cross-diagnosis Italian sample but not in the Korean one, rs7597593 was involved with depressive symptoms improvement after treatment (geno p = 0.025, allelic p = 0.007).

Conclusion

The present study evidenced the role of ZNF804A alterations in symptoms improvement after treatment. Both manic and depressive symptoms seem to be modulated by ZNF804A, though the latter was observed in the bipolar pooled sample only. The role of this factor is likely related to synaptic development and maintenance; however, further analyses will be needed to better understand the molecular mechanics involved with ZNF804A.

Effects of Pharmacogenetic Screening for CYP2D6 Among Elderly Starting Therapy With Nortriptyline or Venlafaxine: A Pragmatic Randomized Controlled Trial (CYSCE Trial)

PURPOSE/BACKGROUND

The duration of untreated depression is a predictor for poor future prognosis, making rapid dose finding essential. Genetic variation of the CYP2D6 isoenzyme can influence the optimal dosage needed for individual patients. The aim of this study was to determine the effectiveness of CYP2D6 pharmacogenetic screening to accelerate drug dosing in older patients with depression initiating nortriptyline or venlafaxine.

METHODS/PROCEDURES

In this randomized controlled trial, patients were randomly allocated to one of the study arms. In the intervention arm (DG-I), the specific genotype accompanied by a standardized dosing recommendation based on the patients' genotype and the prescribed drug was directly communicated to the physician of the participant. In both the deviating genotype control arm (DG-C) and the nonrandomized control arm, the physician of the participants was not informed about the genotype and the associated dosing advise. The primary outcome was the time needed to reach adequate drug levels: (1) blood levels within the therapeutic range and (2) no dose adjustments within the previous 3 weeks.

FINDINGS/RESULTS

No significant difference was observed in mean time to reach adequate dose or time to adequate dose between DG-I and DG-C. Compared with the nonrandomized control arm group, adequate drug levels were reached significantly faster in the DG-I group (log-rank test; P = 0.004), and there was a similar nonsignificant trend for the DG-C group (log-rank test; P = 0.087).

IMPLICATIONS/CONCLUSIONS

The results of this study do not support pharmacogenetic CYP2D6 screening to accelerate dose adjustment for nortriptyline and venlafaxine in older patients with depression.

Clinician Liability in Prescribing Antidepressants

Malpractice claims frequently focus on the clinician's prescription of medications. Claims may arise in many environments: inpatient units, outpatient offices, prisons, journal articles, pharmaceutical talks, and clinical trials of new medications. The basis of the claim may be product liability, informed consent, deliberate indifference, violation of the Federal Food, Drug, and Cosmetic Act, or academic malpractice. All malpractice claims include a duty, a breach of duty, causation, and damages. The duty and breach of duty may be obvious, but causation can vary considerably in malpractice claims. Perhaps the damages are most apparent when the patient has suffered side effects. This article explores clinician liability for the use of antidepressants from the clinical trial to the removal from the market.

Predictors of response and remission in a naturalistic inpatient sample undergoing multimodal treatment for depression

BACKGROUND

Many depressed patients do not achieve response or remission despite adequate treatment. Identifying predictors of outcome can contribute to developing therapeutic algorithms for difficult-to-treat depression. Therefore, we examined clinical predictors of response and remission in a naturalistic inpatient sample undergoing multimodal treatment for depression.

METHODS

Three hundred and fifty-one consecutive inpatients admitted to a tertiary care university hospital (specialized psychiatry unit for treatment of unipolar and bipolar depression) between January 2014 and December 2016 were characterized by a set of sociodemographic and clinical variables. The predictive value of these variables for response (≥ 50% decrease from baseline Montgomery-Åsberg Depression Rating Scale (MADRS) score) and remission (MADRS score at discharge < 10) were explored using bivariate analysis and logistic regression.

RESULTS

Greater symptom severity and fewer psychotropic medications at the time of admission predicted response. Remission rates were higher for patients with non-chronic depression, higher number of previous depressive episodes, fewer psychotropic medications and less severe depression at admission.

LIMITATIONS

This was a retrospective study without a control group. The sample was drawn from a single inpatient ward specialized for difficult-to-treat depression.

CONCLUSIONS

Greater baseline depression severity might be a proxy for a less chronic course of depression thereby explaining its association with greater response rates. Fewer episodes in the past and polypharmacy could indicate treatment-resistance and chronicity, contributing to lower remission rates. Therefore, preventing chronicity should be a central aim of depression treatment.

Clinical factors predicting treatment resistant depression: affirmative results from the European multicenter study

OBJECTIVES

Clinical variables were investigated in the 'treatment resistant depression (TRD)- III' sample to replicate earlier findings by the European research consortium 'Group for the Study of Resistant Depression' (GSRD) and enable cross-sample prediction of treatment outcome in TRD.

EXPERIMENTAL PROCEDURES

TRD was defined by a Montgomery and Åsberg Depression Rating Scale (MADRS) score ≥22 after at least two antidepressive trials. Response was defined by a decline in MADRS score by ≥50% and below a threshold of 22. Logistic regression was applied to replicate predictors for TRD among 16 clinical variables in 916 patients. Elastic net regression was applied for prediction of treatment outcome.

RESULTS

Symptom severity (odds ratio (OR) = 3.31), psychotic symptoms (OR = 2.52), suicidal risk (OR = 1.74), generalized anxiety disorder (OR = 1.68), inpatient status (OR = 1.65), higher number of antidepressants administered previously (OR = 1.23), and lifetime depressive episodes (OR = 1.15) as well as longer duration of the current episode (OR = 1.022) increased the risk of TRD. Prediction of TRD reached an accuracy of 0.86 in the independent validation set, TRD-I.

CONCLUSION

Symptom severity, suicidal risk, higher number of lifetime depressive episodes, and comorbid anxiety disorder were replicated as the most prominent risk factors for TRD. Significant predictors in TRD-III enabled robust prediction of treatment outcome in TRD-I.

The Impact of Pharmacogenomics in Personalized Medicine

Recent advances in Pharmacogenomics have made it possible to understand the reasons behind the different response of a drug. Discovery of genetic variants and its association with the varying response of drug provide the basis for recommending a drug and its dose to an individual patient. Genetic makeup-based prescription, design, and implementation of therapy not only improve the outcome of treatments but also reduce the risk of toxicity and other adverse effects. A better understanding of individual variations and their effect on drug response, metabolism excretion, and toxicity will replace the trial-and-error approach of treatment. Evidence of the clinical utility of pharmacogenetics testing is only available for a few medications, and FDA labels only require pharmacogenetics testing for a small number of drugs. Although there is a great promise, there are not many examples where Pharmacogenomics impacts clinical utility. Some genetic variants related to different diseases have been reported, and many have not been studied yet. The information related to the outcome of treatment with a particular drug and a genetic variant can be used to release a warning/label for the use of that drug. There are many limitations in the way of implementing the goal of personalized medicine. Future advances in the field of genomics, diagnosis approaches, data analysis, clinical decision-making, and sustainable business model for personalization of therapy can speed up the individualization of therapy based on genetic makeup.

The effect of pharmacogenomic testing on response and remission rates in the acute treatment of major depressive disorder: A meta-analysis

BACKGROUND

Pharmacogenomic testing has recently become scalable and available to guide the treatment of major depressive disorder (MDD). The objective of the current meta-analysis was to determine if guidance from pharmacogenomic testing results in relatively higher rates of remission and response compared to treatment as usual (i.e., 'unguided' trial-and-error method) in adults with MDD.

METHODS

Article databases were systematically searched from inception to December 2, 2017 for human studies assessing the clinical utility of pharmacogenomics in the acute treatment of MDD. Treatment outcomes in MDD may be defined continuously or categorically (i.e., response/remission). Herein, we delimit our focus on categorical outcomes. Using a random-effects model, data was pooled to determine the risk ratio (RR) of response and remission, respectively, in the pharmacogenomic-guided treatment group compared to the unguided group.

RESULTS

Four randomized controlled trials (RCTs) and two open-label, controlled cohort studies were included. The pooled RR for treatment response comparing guided versus unguided treatment was 1.36 (95% confidence interval [CI] = 1.14 to 1.62; p = 0.0006; n = 799) in favour of guided treatment. The pooled RR for remission was 1.74 (95%CI = 1.09 to 2.77; p = 0.02, n = 735) also in favour of guided treatment. Heterogeneity in study results suggest that different genetic tests may variably impact response and remission rates.

LIMITATIONS

The available evidence is limited, with significant methodological deficiencies.

CONCLUSION

The current analysis provides preliminary support for improved response and remission rates in MDD when treatment is guided by pharmacogenomics.

Pharmacogenetic tests to guide drug treatment in depression: Comparison of the available testing kits and clinical trials

The empirical approach to drug choice and dosing in depression often results into inadequate response and side effects. Pharmacogenetic (PGx) testing appears a promising way to implement personalized treatments. A systematic review was performed to identify available PGx tests, compare the genes they include with clinical guidelines and drug labels, and assess the quality of published clinical studies. ~40 commercial PGx tests are available and potential benefits were estimated for nine of them by clinical studies. The most part of studies are observational (9/21) or non-randomized case-control trials that compared standard care with PGx-guided treatment (6/21), six randomized controlled trials (RCTs) are available. The only genes included in all the available PGx tests and with recommendations in current clinical guidelines and drug labels are CYP2D6 and CYP2C19. There is heterogeneity among outcome measures across studies (response, remission, improvement, health care utilization, medication tolerability), as well as in trial design. Relatively weak clinical benefits were reported by RCTs and higher clinical benefits by non-RCTs, but the last group showed greater risk of bias. Lack of patient and rater's blindness, retrospective design and possible confounders (concomitant medications and medical diseases, lack of wash out prior to inclusion, no assessment of compliance etc.) were the main issues. Estimations of cost savings provided heterogeneous findings. Variants in CYP2D6 and CYP2C19 have already adequate support for clinical application. The development of future PGx tests should include best practices for clinical evidence development and for health economic assessment.

Progress in Elucidating Biomarkers of Antidepressant Pharmacological Treatment Response: A Systematic Review and Meta-analysis of the Last 15 Years

BACKGROUND

Antidepressant drugs are widely prescribed, but response rates after 3 months are only around one-third, explaining the importance of the search of objectively measurable markers predicting positive treatment response. These markers are being developed in different fields, with different techniques, sample sizes, costs, and efficiency. It is therefore difficult to know which ones are the most promising.

OBJECTIVE

Our purpose was to compute comparable (i.e., standardized) effect sizes, at study level but also at marker level, in order to conclude on the efficacy of each technique used and all analyzed markers.

METHODS

We conducted a systematic search on the PubMed database to gather all articles published since 2000 using objectively measurable markers to predict antidepressant response from five domains, namely cognition, electrophysiology, imaging, genetics, and transcriptomics/proteomics/epigenetics. A manual screening of the abstracts and the reference lists of these articles completed the search process.

RESULTS

Executive functioning, theta activity in the rostral Anterior Cingular Cortex (rACC), and polysomnographic sleep measures could be considered as belonging to the best objectively measured markers, with a combined d around 1 and at least four positive studies. For inter-category comparisons, the approaches that showed the highest effect sizes are, in descending order, imaging (combined d between 0.703 and 1.353), electrophysiology (0.294-1.138), cognition (0.929-1.022), proteins/nucleotides (0.520-1.18), and genetics (0.021-0.515).

CONCLUSION

Markers of antidepressant treatment outcome are numerous, but with a discrepant level of accuracy. Many biomarkers and cognitions have sufficient predictive value (d ≥ 1) to be potentially useful for clinicians to predict outcome and personalize antidepressant treatment.

NMR-based pharmacometabonomics: A new paradigm for personalised or precision medicine

Metabolic profiling by NMR spectroscopy or hyphenated mass spectrometry, known as metabonomics or metabolomics, is an important tool for systems-based approaches in biology and medicine. The experiments are typically done in a diagnostic fashion where changes in metabolite profiles are interpreted as a consequence of an intervention or event; be that a change in diet, the administration of a drug, physical exertion or the onset of a disease. By contrast, pharmacometabonomics takes a prognostic approach to metabolic profiling, in order to predict the effects of drug dosing before it occurs. Differences in pre-dose metabolite profiles between groups of subjects are used to predict post-dose differences in response to drug administration. Thus the paradigm is inverted and pharmacometabonomics is the metabolic equivalent of pharmacogenomics. Although the field is still in its infancy, it is expected that pharmacometabonomics, alongside pharmacogenomics, will assist with the delivery of personalised or precision medicine to patients, which is a critical goal of 21st century healthcare.

Pharmacogenomic testing: aiding in the management of psychotropic therapy for adolescents with autism spectrum disorders

Adolescents with autism have higher rates of anxiety than the general adolescent population. They often struggle to express psychological symptoms verbally where their symptoms may manifest as withdrawal and agitation. Adolescent patients with autism have higher rates of polypharmacy and high-risk psychiatric medication use (eg, atypical antipsychotics) than other patients with psychiatric illness. Primary care pediatricians are at the front lines of psychiatric management for patients with autism. Yet, they have inadequate access to pediatric psychiatry for complex medication management. Pharmacogenomic testing can provide personalized drug metabolism profiles for a majority of psychotropic medications. Primary care based pharmacogenomic testing for adolescents with autism on one or more psychiatric medications may help individualize and optimize complex medication regimens, while promoting drug safety.

Efficacy of prospective pharmacogenetic testing in the treatment of major depressive disorder: results of a randomized, double-blind clinical trial

BACKGROUND

A 12-week, double-blind, parallel, multi-center randomized controlled trial in 316 adult patients with major depressive disorder (MDD) was conducted to evaluate the effectiveness of pharmacogenetic (PGx) testing for drug therapy guidance.

METHODS

Patients with a CGI-S ≥ 4 and requiring antidepressant medication de novo or changes in their medication regime were recruited at 18 Spanish public hospitals, genotyped with a commercial PGx panel (Neuropharmagen®), and randomized to PGx-guided treatment (n = 155) or treatment as usual (TAU, control group, n = 161), using a computer-generated random list that locked or unlocked psychiatrist access to the results of the PGx panel depending on group allocation. The primary endpoint was the proportion of patients achieving a sustained response (Patient Global Impression of Improvement, PGI-I ≤ 2) within the 12-week follow-up. Patients and interviewers collecting the PGI-I ratings were blinded to group allocation. Between-group differences were evaluated using χ2-test or t-test, as per data type.

RESULTS

Two hundred eighty patients were available for analysis at the end of the 12-week follow-up (PGx n = 136, TAU n = 144). A difference in sustained response within the study period (primary outcome) was not observed (38.5% vs 34.4%, p = 0.4735; OR = 1.19 [95%CI 0.74-1.92]), but the PGx-guided treatment group had a higher responder rate compared to TAU at 12 weeks (47.8% vs 36.1%, p = 0.0476; OR = 1.62 [95%CI 1.00-2.61]), and this difference increased after removing subjects in the PGx-guided group when clinicians explicitly reported not to follow the test recommendations (51.3% vs 36.1%, p = 0.0135; OR = 1.86 [95%CI 1.13-3.05]). Effects were more consistent in patients with 1-3 failed drug trials. In subjects reporting side effects burden at baseline, odds of achieving a better tolerability (Frequency, Intensity and Burden of Side Effects Rating Burden subscore ≤2) were higher in the PGx-guided group than in controls at 6 weeks and maintained at 12 weeks (68.5% vs 51.4%, p = 0.0260; OR = 2.06 [95%CI 1.09-3.89]).

CONCLUSIONS

PGx-guided treatment resulted in significant improvement of MDD patient's response at 12 weeks, dependent on the number of previously failed medication trials, but not on sustained response during the study period. Burden of side effects was also significantly reduced.

TRIAL REGISTRATION

European Clinical Trials Database 2013-002228-18 , registration date September 16, 2013; ClinicalTrials.gov NCT02529462 , retrospectively registered: August 19, 2015.

Using patient self-reports to study heterogeneity of treatment effects in major depressive disorder

BACKGROUNDS

Clinicians need guidance to address the heterogeneity of treatment responses of patients with major depressive disorder (MDD). While prediction schemes based on symptom clustering and biomarkers have so far not yielded results of sufficient strength to inform clinical decision-making, prediction schemes based on big data predictive analytic models might be more practically useful.

METHOD

We review evidence suggesting that prediction equations based on symptoms and other easily-assessed clinical features found in previous research to predict MDD treatment outcomes might provide a foundation for developing predictive analytic clinical decision support models that could help clinicians select optimal (personalised) MDD treatments. These methods could also be useful in targeting patient subsamples for more expensive biomarker assessments.

RESULTS

Approximately two dozen baseline variables obtained from medical records or patient reports have been found repeatedly in MDD treatment trials to predict overall treatment outcomes (i.e., intervention v. control) or differential treatment outcomes (i.e., intervention A v. intervention B). Similar evidence has been found in observational studies of MDD persistence-severity. However, no treatment studies have yet attempted to develop treatment outcome equations using the full set of these predictors. Promising preliminary empirical results coupled with recent developments in statistical methodology suggest that models could be developed to provide useful clinical decision support in personalised treatment selection. These tools could also provide a strong foundation to increase statistical power in focused studies of biomarkers and MDD heterogeneity of treatment response in subsequent controlled trials.

CONCLUSIONS

Coordinated efforts are needed to develop a protocol for systematically collecting information about established predictors of heterogeneity of MDD treatment response in large observational treatment studies, applying and refining these models in subsequent pragmatic trials, carrying out pooled secondary analyses to extract the maximum amount of information from these coordinated studies, and using this information to focus future discovery efforts in the segment of the patient population in which continued uncertainty about treatment response exists.

From Metabonomics to Pharmacometabonomics: The Role of Metabolic Profiling in Personalized Medicine

Variable patient responses to drugs are a key issue for medicine and for drug discovery and development. Personalized medicine, that is the selection of medicines for subgroups of patients so as to maximize drug efficacy and minimize toxicity, is a key goal of twenty-first century healthcare. Currently, most personalized medicine paradigms rely on clinical judgment based on the patient's history, and on the analysis of the patients' genome to predict drug effects i.e., pharmacogenomics. However, variability in patient responses to drugs is dependent upon many environmental factors to which human genomics is essentially blind. A new paradigm for predicting drug responses based on individual pre-dose metabolite profiles has emerged in the past decade: pharmacometabonomics, which is defined as “the prediction of the outcome (for example, efficacy or toxicity) of a drug or xenobiotic intervention in an individual based on a mathematical model of pre-intervention metabolite signatures.” The new pharmacometabonomics paradigm is complementary to pharmacogenomics but has the advantage of being sensitive to environmental as well as genomic factors. This review will chart the discovery and development of pharmacometabonomics, and provide examples of its current utility and possible future developments.

Pharmacokinetic Pharmacogenetic Prescribing Guidelines for Antidepressants: A Template for Psychiatric Precision Medicine

Antidepressants are commonly prescribed medications in the United States, and there is increasing interest in individualizing treatment selection for more than 20 US Food and Drug Administration-approved treatments for major depressive disorder. Providing greater precision to pharmacotherapeutic recommendations for individual patients beyond the large-scale clinical trials evidence base can potentially reduce adverse effect toxicity profiles and increase response rates and overall effectiveness. It is increasingly recognized that genetic variation may contribute to this differential risk to benefit ratio and thus provides a unique opportunity to develop pharmacogenetic guidelines for psychiatry. Key studies and concepts that review the rationale for cytochrome P450 2D6 (CYP2D6) and cytochrome P450 2C19 (CYP2C19) genetic testing can be delineated by serum levels, adverse events, and clinical outcome measures (eg, antidepressant response). In this article, we report the evidence that contributed to the implementation of pharmacokinetic pharmacogenetic guidelines for antidepressants primarily metabolized by CYP2D6 and CYP2C19.

Pharmacometabonomics in humans: a new tool for personalized medicine

Pharmacogenomics is now over 50 years old and has had some impact in clinical practice, through its use to select patient subgroups who will enjoy efficacy without side effects when treated with certain drugs. However, pharmacogenomics, has had less impact than initially predicted. One reason for this is that many diseases, and the way in which the patients respond to drug treatments, have both genetic and environmental elements. Pure genomics is almost blind to the environmental elements. A new methodology has emerged, termed pharmacometabonomics that is concerned with the prediction of drug effects through the analysis of predose, biofluid metabolite profiles, which reflect both genetic and environmental influences on human physiology. In this review we will cover what pharmacometabonomics is, how it works, what applications exist and what the future might hold in this exciting new area.

The usefulness of genotyping cytochrome P450 enzymes in the treatment of depression

INTRODUCTION

Pharmacogenomics, which is derived from genome-wide association studies (GWAS), and pharmacogenetics, which involves candidate gene association studies (CGASs), are proving increasingly useful in personalized cancer care. Research in psychiatric applications has primarily involved genetic polymorphisms of P450 CYP enzymes, which mediate oxidative metabolism, particularly CYP2D6, which is involved in the metabolism of at least 30 psychotropic medications. This work has been supplemented by genotyping of proteins for the drug efflux pump P-glycoprotein (P-gp), serotonin receptors, and the serotonin reuptake pump.

AREAS COVERED

This review covers principles of pharmacogenetics and pharmacogenomics, previous analyses of pharmacokinetic and pharmacodynamics studies, newer studies of the predictive value of genetic testing in the treatment of depression, obstacles to implementation of genetic testing in predicting treatment response and side effects, and suggestions for future research.

EXPERT OPINION

Studies of multiple genes have produced some positive results in groups of patients, but genetic testing does not yet seem to be applicable to choosing medications for a specific patient.

Genetic determinants of depression: recent findings and future directions

LEARNING OBJECTIVES

After participating in this activity, learners should be better able to: 1. Evaluate current evidence regarding the genetic determinants of depression 2. Assess findings from studies of gene-environment interaction 3. Identify challenges to gene discovery in depression Depression is one of the most prevalent, disabling, and costly mental health conditions in the United States and also worldwide. One promising avenue for preventing depression and informing its clinical treatment lies in uncovering the genetic and environmental determinants of the disorder as well as their interaction (G × E). The overarching goal of this review article is to translate recent findings from studies of genetic association and G × E related to depression, particularly for readers without in-depth knowledge of genetics or genetic methods. The review is organized into three major sections. In the first, we summarize what is currently known about the genetic determinants of depression, focusing on findings from genome-wide association studies (GWAS). In the second section, we review findings from studies of G × E, which seek to simultaneously examine the role of genes and exposure to specific environments or experiences in the etiology of depression. In the third section, we describe the challenges to genetic discovery in depression and promising strategies for future progress.

Harnessing pharmacological knowledge for personalized medicine and pharmacotyping: Challenges and lessons learned

The contribution of the genetic make-up to an individual’s capacity has long been recognized in modern pharmacology as a crucial factor leading to therapy inefficiency and toxicity, negatively impacting the economic burden of healthcare and restricting the monitoring of diseases. In practical terms, and in order for drug prescription to be improved toward meeting the personalized medicine concept in drug delivery, the maximum clinical outcome for most, if not all, patients must be achieved, i.e., pharmacotyping. Such a direction although promising and of high expectation from the society, it is however hardly to be afforded for healthcare worldwide. To overcome any existed hurdles, this means that practical clinical utility of personalized medicine decisions have to be documented and validated in the clinical setting. The latter implies for drug delivery the efficient implementation of previously gained in vivo pharmacology experience with pharmacogenomics knowledge. As an approach to work faster and in a more productive way, the elaboration of advanced physiologically based pharmacokinetics models is discussed. And in better clarifying this topic, the example of tamoxifen is thoroughly presented. Overall, pharmacotyping represents a major challenge in modern therapeutics for which pharmacologists need to work in successfully fulfilling this task.

The neuroscience of depression: implications for assessment and intervention

Major Depressive Disorder (MDD) is among the most prevalent of all psychiatric disorders and is the single most burdensome disease worldwide. In attempting to understand the profound deficits that characterize MDD across multiple domains of functioning, researchers have identified aberrations in brain structure and function in individuals diagnosed with this disorder. In this review we synthesize recent data from human neuroimaging studies in presenting an integrated neural network framework for understanding the impairments experienced by individuals with MDD. We discuss the implications of these findings for assessment of and intervention for MDD. We conclude by offering directions for future research that we believe will advance our understanding of neural factors that contribute to the etiology and course of depression, and to recovery from this debilitating disorder.

Does poor health predict moving, move quality, and desire to move?: A study examining neighborhood selection in US adolescents and adults

To date, research has rarely considered the role of health in shaping characteristics of the neighborhood, including mobility patterns. We explored whether individual health status shapes and constrains where individuals live. Using the National Longitudinal Study of Adolescent Health data, we examined whether 16 health indicators predicted moving, move quality, and desire to move. 3.8% of adolescents (n=490) reported a move in the past year. In the unadjusted models, 10 health indicators were associated with moving; the magnitude of association for these health indicators was similar to socio-demographic characteristics. 7 of these health-moving associations persisted after adjusting for covariates. Health was also associated with moving quality, with a greater number of past year health problems in the child being associated with moving to a lower income neighborhood and parent disability or poor health being associated with moving to a higher income neighborhood. Almost every poor health status indicator was associated with a greater desire to move. Findings suggest that health status influences moving, and a reciprocal framework is more appropriate for examining health-neighborhood linkages.