Covariate-adjusted value-guided subgroup identification via boosting.

It is widely recognized that treatment effects could differ across subgroups of patients. Subgroup analysis, which assesses such heterogeneity, provides valuable information in developing personalized therapies. There has been extensive research developing novel statistical methods for subgroup identification. The recent contribution is a value-guided subgroup identification method that directly maximizes treatment benefit at the subgroup level for survival outcome, rather than relying on individual treatment effect estimation. In this paper, we first completed this framework by illustrating its application to continuous and binary outcomes. More importantly, we extended the original framework to account for the prognostic effects and named this new method Covariate-Adjusted Value-guided subgroup identification via boosting (CAVboost). The original method directly used the outcome to formulate the value function for subgroup identification. Since the outcome can further be decomposed as prognostic effects and treatment effects, specifying the prognostic effects as the covariates of a model for the outcome can single out the treatment effects and improve the power to detect them across subgroups. Our proposed CAVboost was based on this key idea. It used a covariate-adjusted treatment effect estimator, instead of the outcome itself, to formulate the value function for subgroup identification. CAVboost estimates the treatment effect by using covariates to account for the prognostic effects, which mimics the idea of using covariates in an ANCOVA estimator. We showed that CAVboost could effectively improve the subgroup identification capability for both continuous and binary outcomes.

A nonparametric method for value function guided subgroup identification via gradient tree boosting for censored survival data.

In randomized clinical trials with survival outcome, there has been an increasing interest in subgroup identification based on baseline genomic, proteomic markers, or clinical characteristics. Some of the existing methods identify subgroups that benefit substantially from the experimental treatment by directly modeling outcomes or treatment effect. When the goal is to find an optimal treatment for a given patient rather than finding the right patient for a given treatment, methods under the individualized treatment regime framework estimate an individualized treatment rule that would lead to the best expected clinical outcome as measured by a value function. Connecting the concept of value function to subgroup identification, we propose a nonparametric method that searches for subgroup membership scores by maximizing a value function that directly reflects the subgroup-treatment interaction effect based on restricted mean survival time. A gradient tree boosting algorithm is proposed to search for the individual subgroup membership scores. We conduct simulation studies to evaluate the performance of the proposed method and an application to an AIDS clinical trial is performed for illustration.

Deep Dive into Machine Learning Models for Protein Engineering.

Protein redesign and engineering has become an important task in pharmaceutical research and development. Recent advances in technology have enabled efficient protein redesign by mimicking natural evolutionary mutation, selection, and amplification steps in the laboratory environment. For any given protein, the number of possible mutations is astronomical. It is impractical to synthesize all sequences or even to investigate all functionally interesting variants. Recently, there has been an increased interest in using machine learning to assist protein redesign, since prediction models can be used to virtually screen a large number of novel sequences. However, many state-of-the-art machine learning models, especially deep learning models, have not been extensively explored. Moreover, only a small selection of protein sequence descriptors has been considered. In this work, the performance of prediction models built using an array of machine learning methods and protein descriptor types, including two novel, single amino acid descriptors and one structure-based three-dimensional descriptor, is benchmarked. The predictions were evaluated on a diverse collection of public and proprietary data sets, using a variety of evaluation metrics. The results of this comparison suggest that Convolution Neural Network models built with amino acid property descriptors are the most widely applicable to the types of protein redesign problems faced in the pharmaceutical industry.

Pembrolizumab in patients with advanced hepatocellular carcinoma previously treated with sorafenib (KEYNOTE-224): a non-randomised, open-label phase 2 trial.

BACKGROUND: Immune checkpoint blockade therapy has shown promising results in patients with advanced hepatocellular carcinoma. We aimed to assess the efficacy and safety of pembrolizumab in this patient population. METHODS: KEYNOTE-224 is a non-randomised, multicentre, open-label, phase 2 trial that is set in 47 medical centres and hospitals across ten countries. Eligible patients had pathologically confirmed hepatocellular carcinoma; had previously been treated with sorafenib and were either intolerant to this treatment or showed radiographic progression of their disease after treatment; an Eastern Cooperative Oncology Group performance status of 0-1; adequate organ function, and were Child-Pugh class A. Participants received 200 mg pembrolizumab intravenously every 3 weeks for about 2 years or until disease progression, unacceptable toxicity, patient withdrawal, or investigator decision. The primary endpoint was objective response, defined as the proportion of patients with complete or partial response in all patients who received at least one dose of pembrolizumab, which was radiologically confirmed by use of the Response Evaluation Criteria in Solid Tumors version 1.1 by central review. Safety was also assessed in all treated patients. This trial is ongoing but closed to enrolment and is registered with ClinicalTrials.gov number NCT02702414. FINDINGS: Between June 7, 2016, and Feb 9, 2017, we screened 169 patients with advanced hepatocellular carcinoma, of whom 104 eligible patients were enrolled and treated. As of data cutoff on Feb 13, 2018, 17 (16%) patients were still receiving pembrolizumab. We recorded an objective response in 18 (17%; 95% CI 11-26) of 104 patients. The best overall responses were one (1%) complete and 17 (16%) partial responses; meanwhile, 46 (44%) patients had stable disease, 34 (33%) had progressive disease, and six (6%) patients who did not have a post-baseline assessment on the cutoff date were considered not to be assessable. Treatment-related adverse events occurred in 76 (73%) of 104 patients, which were serious in 16 (15%) patients. Grade 3 treatment-related events were reported in 25 (24%) of the 104 patients; the most common were increased aspartate aminotransferase concentration in seven (7%) patients, increased alanine aminotransferase concentration in four (4%) patients, and fatigue in four (4%) patients. One (1%) grade 4 treatment-related event of hyperbilirubinaemia occurred. One death associated with ulcerative oesophagitis was attributed to treatment. Immune-mediated hepatitis occurred in three (3%) patients, but there were no reported cases of viral flares. INTERPRETATION: Pembrolizumab was effective and tolerable in patients with advanced hepatocellular carcinoma who had previously been treated with sorafenib. These results indicate that pembrolizumab might be a treatment option for these patients. This drug is undergoing further assessment in two phase 3, randomised trials as a second-line treatment in patients with hepatocellular carcinoma. FUNDING: Merck & Co, Inc.

EEG spectral analysis of NREM sleep in a large sample of patients with insomnia and good sleepers: effects of age, sex and part of the night.

Previous studies of the differences between patients with insomnia and good sleepers with regard to quantitative electroencephalographic measures have mostly utilized small samples and consequently had limited ability to account for potentially important confounding factors of age, sex and part of the night. We conducted a power spectral analysis using a large database of sleep electroencephalographic recordings to evaluate differences between patients with insomnia (N = 803) and good sleepers (N = 811), while simultaneously accounting for these factors and their interaction. Comparisons of power as a function of age and part of the night were made between cohorts (patients with insomnia versus good sleepers) by sex. Absolute power in the delta, theta and sigma bands declined with age for both females and males. Females had significantly greater power than males at all ages, and for each band, cohort and part of the night. These sex differences were much greater than differences between patients with insomnia and good sleepers. Compared with good sleepers, patients with insomnia under age 40-45 years had reduced delta band power during Part 1 of the night. Females with insomnia over age 45 years had increased delta and theta band power in Parts 2 and 3 of the night, and males with insomnia under age 40 years had reduced theta power in Part 1. Females with insomnia had increased beta2 power in all parts of the night, and males with insomnia had reduced alpha power during all parts of the night. Relative power (the proportion that an individual frequency band contributes to the total power) decreased in the delta band and increased in all other bands with age for both cohorts, sexes and all parts of the night. This analysis provides a unique resource for quantitative information on the differences in power spectra between patients with insomnia and good sleepers accounting for age, sex and part of the night.

Demystifying Multitask Deep Neural Networks for Quantitative Structure-Activity Relationships.

Deep neural networks (DNNs) are complex computational models that have found great success in many artificial intelligence applications, such as computer vision1,2 and natural language processing.3,4 In the past four years, DNNs have also generated promising results for quantitative structure-activity relationship (QSAR) tasks.5,6 Previous work showed that DNNs can routinely make better predictions than traditional methods, such as random forests, on a diverse collection of QSAR data sets. It was also found that multitask DNN models-those trained on and predicting multiple QSAR properties simultaneously-outperform DNNs trained separately on the individual data sets in many, but not all, tasks. To date there has been no satisfactory explanation of why the QSAR of one task embedded in a multitask DNN can borrow information from other unrelated QSAR tasks. Thus, using multitask DNNs in a way that consistently provides a predictive advantage becomes a challenge. In this work, we explored why multitask DNNs make a difference in predictive performance. Our results show that during prediction a multitask DNN does borrow "signal" from molecules with similar structures in the training sets of the other tasks. However, whether this borrowing leads to better or worse predictive performance depends on whether the activities are correlated. On the basis of this, we have developed a strategy to use multitask DNNs that incorporate prior domain knowledge to select training sets with correlated activities, and we demonstrate its effectiveness on several examples.

Extreme Gradient Boosting as a Method for Quantitative Structure-Activity Relationships.

In the pharmaceutical industry it is common to generate many QSAR models from training sets containing a large number of molecules and a large number of descriptors. The best QSAR methods are those that can generate the most accurate predictions but that are not overly expensive computationally. In this paper we compare eXtreme Gradient Boosting (XGBoost) to random forest and single-task deep neural nets on 30 in-house data sets. While XGBoost has many adjustable parameters, we can define a set of standard parameters at which XGBoost makes predictions, on the average, better than those of random forest and almost as good as those of deep neural nets. The biggest strength of XGBoost is its speed. Whereas efficient use of random forest requires generating each tree in parallel on a cluster, and deep neural nets are usually run on GPUs, XGBoost can be run on a single CPU in less than a third of the wall-clock time of either of the other methods.

Efavirenz modulation of sleep spindles and sleep spectral profile.

Non-nucleoside reverse transcriptase inhibitors are important antiretroviral agents for the treatment of human immunodeficiency virus. Some non-nucleoside reverse transcriptase inhibitors, in particular efavirenz, have prominent effects on sleep, cognition and psychiatric variables that limit their tolerability. To avoid confounds due to drug-drug and drug-disease interactions, we assessed the effects of efavirenz in healthy volunteers on sleep, cognition and psychological endpoints during the first week of treatment. Forty healthy male subjects were randomized to receive placebo or efavirenz 600 mg nightly for 7 days after completion of a 3-day placebo run-in period. Treatment with efavirenz was associated with reduced time to sleep onset in the Maintenance of Wakefulness Test, an increase in non-rapid eye movement sleep, a large exposure-related decrease in sigma band spectral density and sleep spindle density during non-rapid eye movement sleep, and reduced performance on an attention switching task. Because efavirenz has been shown to have serotonin 2A receptor partial-agonist properties, we reasoned that antagonism of serotonin 2A receptor signalling in the thalamic reticular nucleus, which generates sleep spindles and promotes attention, may be responsible. Consistent with predictions, treatment of healthy volunteers with a single dose of a serotonin 2A receptor antagonist was found to significantly suppress sigma band spectral density in an exposure-related manner and modulated the overall spectral profile in a manner highly similar to that observed with efavirenz, consistent with the notion that efavirenz exhibits serotonin 2A receptor partial-agonist pharmacology in humans.

Deep neural nets as a method for quantitative structure-activity relationships.

Neural networks were widely used for quantitative structure-activity relationships (QSAR) in the 1990s. Because of various practical issues (e.g., slow on large problems, difficult to train, prone to overfitting, etc.), they were superseded by more robust methods like support vector machine (SVM) and random forest (RF), which arose in the early 2000s. The last 10 years has witnessed a revival of neural networks in the machine learning community thanks to new methods for preventing overfitting, more efficient training algorithms, and advancements in computer hardware. In particular, deep neural nets (DNNs), i.e. neural nets with more than one hidden layer, have found great successes in many applications, such as computer vision and natural language processing. Here we show that DNNs can routinely make better prospective predictions than RF on a set of large diverse QSAR data sets that are taken from Merck's drug discovery effort. The number of adjustable parameters needed for DNNs is fairly large, but our results show that it is not necessary to optimize them for individual data sets, and a single set of recommended parameters can achieve better performance than RF for most of the data sets we studied. The usefulness of the parameters is demonstrated on additional data sets not used in the calibration. Although training DNNs is still computationally intensive, using graphical processing units (GPUs) can make this issue manageable.

Qualification of a 21-valent pneumococcal urine antigen detection assay and development of clinical positivity cutoffs.

Aim: Serotype-specific assays detecting pneumococcal polysaccharides in bodily fluids are needed to understand the pneumococcal serotype distribution in non-bacteremic pneumonia.Methods: We developed a urine antigen detection assay and using urine samples from adult outpatients without pneumonia developed positivity cutoffs for both a previously published 15-valent and the new 21-valent assay. Clinical sensitivity was confirmed with samples from patients with invasive pneumococcal disease.Results: Total assay precision ranged from 7.6 to 17.8% coefficient of variation while accuracy ranged between 80 and 150% recovery, except for three serotypes where recoveries ranged from 32 to 60%. Clinical sensitivity was 86.4% and specificity was 96.5% across all 30 serotypes.Conclusion: The assay could potentially assess serotype-distribution in non-infected and infected participants with pneumococcal disease.

[Box: see text].

Contribution of tumour and immune cells to PD-L1 expression as a predictive biomarker in metastatic triple-negative breast cancer: exploratory analysis from KEYNOTE-119.

The efficacy of pembrolizumab monotherapy versus chemotherapy increased with increasing programmed death ligand 1 (PD-L1) expression, as quantified by combined positive score (CPS; PD-L1 expression on both tumour cells and immune cells) in patients with previously treated metastatic triple-negative breast cancer (mTNBC) in the phase 3 KEYNOTE-119 study. This exploratory analysis was conducted to determine whether the expression of PD-L1 on tumour cells contributes to the predictive value of PD-L1 CPS in mTNBC. PD-L1 expression in tumour samples was assessed using PD-L1 IHC 22C3 pharmDx and quantified using both CPS and tumour proportion score (TPS; PD-L1 expression on tumour cells alone). Calculated immune cell density (CID) was defined as CPS minus TPS. The ability of each scoring method (CPS, TPS, and CID) to predict clinical outcomes with pembrolizumab was evaluated. With pembrolizumab, the area under the receiver operating characteristic curve was 0.69 (95% CI = 0.58-0.80) for CPS, 0.55 (95% CI = 0.46-0.64) for TPS, and 0.67 (95% CI = 0.56-0.77) for CID. After correction for cutoff prevalence, CPS performed as well as, if not better than, CID with respect to predicting objective response rate, progression-free survival, and overall survival. Data from this exploratory analysis suggest that, although PD-L1 expression on immune cells alone is predictive of response to programmed death 1 blockade in mTNBC, adding tumour PD-L1 expression assessment (i.e. CPS, which combines immune cell and tumour cell PD-L1 expression) may improve prediction. PD-L1 CPS thus remains an effective and broadly applicable uniform scoring system for enriching response to programmed death 1 blockade with pembrolizumab in mTNBC as well as other tumour types.

Guidelines for the recording and evaluation of pharmaco-sleep studies in man: the International Pharmaco-EEG Society (IPEG).

The International Pharmaco-EEG Society (IPEG) presents guidelines summarising the requirements for the recording and computerised evaluation of pharmaco-sleep data in man. Over the past years, technical and data-processing methods have advanced steadily, thus enhancing data quality and expanding the palette of sleep assessment tools that can be used to investigate the activity of drugs on the central nervous system (CNS), determine the time course of effects and pharmacodynamic properties of novel therapeutics, hence enabling the study of the pharmacokinetic/pharmacodynamic relationship, and evaluate the CNS penetration or toxicity of compounds. However, despite the presence of robust guidelines on the scoring of polysomnography -recordings, a review of the literature reveals inconsistent -aspects in the operating procedures from one study to another. While this fact does not invalidate results, the lack of standardisation constitutes a regrettable shortcoming, especially in the context of drug development programmes. The present guidelines are intended to assist investigators, who are using pharmaco-sleep measures in clinical research, in an effort to provide clear and concise recommendations and thereby to standardise methodology and facilitate comparability of data across laboratories.

Putative Biomarkers of Clinical Benefit With Pembrolizumab in Advanced Urothelial Cancer: Results from the KEYNOTE-045 and KEYNOTE-052 Landmark Trials.

PURPOSE: In an exploratory analysis, we investigated the association between programmed death ligand 1 (PD-L1), tumor mutational burden (TMB), T-cell-inflamed gene expression profile (TcellinfGEP), and stromal signature with outcomes of pembrolizumab in urothelial carcinoma (UC). PATIENTS AND METHODS: Patients with advanced UC received first-line pembrolizumab 200 mg every 3 weeks in the single-arm phase II KEYNOTE-052 trial (NCT02335424) and salvage pembrolizumab 200 mg every 3 weeks or chemotherapy (paclitaxel/docetaxel/vinflunine) in the randomized phase III KEYNOTE-045 trial (NCT02256436). The association of each biomarker (continuous variable) with objective response rate (ORR), progression-free survival (PFS), and overall survival (OS) was evaluated using logistic regression (ORR) and Cox PH (PFS, OS), adjusted for ECOG PS; nominal P values were calculated without multiplicity adjustment (one-sided, pembrolizumab; two-sided, chemotherapy). Significance was prespecified at α = 0.05. RESULTS: In KEYNOTE-052, PD-L1, TMB, and TcellinfGEP were significantly associated with improved outcomes; stromal signature was significantly associated with worse outcomes. In KEYNOTE-045, although findings for TMB and TcellinfGEP with pembrolizumab were consistent with those of KEYNOTE-052, PD-L1 was not significantly associated with improved outcomes, nor was stromal signature associated with worse outcomes with pembrolizumab; chemotherapy was not associated with outcomes in a consistent manner for any of the biomarkers. Hazard ratio (HR) estimates at prespecified cutoffs showed an advantage for pembrolizumab versus chemotherapy regardless of PD-L1 or TMB, with a trend toward lower HRs in the combined positive score ≥10 and the TMB ≥175 mutation/exome subgroup. For TcellinfGEP, PFS and OS HRs were lower in the TcellinfGEP-nonlow subgroup regardless of treatment. CONCLUSIONS: Multiple biomarkers characterizing the tumor microenvironment may help predict response to pembrolizumab monotherapy in UC, and potential clinical utility of these biomarkers may be context-dependent.

Alterations in cyclic alternating pattern associated with phase advanced sleep are differentially modulated by gaboxadol and zolpidem.

OBJECTIVE: to evaluate cyclic alternating pattern (CAP) in a phase advance model of transient insomnia and the effects of gaboxadol and zolpidem. DESIGN: a randomized, double-blind, cross-over study in which habitual sleep time was advanced by 4 h. SETTING: 6 sleep research laboratories in US PARTICIPANTS: 55 healthy subjects (18-57 y) INTERVENTIONS: Gaboxadol 15 mg (GBX), zolpidem 10 mg (ZOL), and placebo (PBO). MEASUREMENTS: routine polysomnographic (PSG) measures, CAP, spectral power density, and self-reported sleep measures RESULTS: The phase advance model of transient insomnia produced significant changes in CAP parameters. Both GBX and ZOL significantly and differentially modified CAP parameters in the direction of more stable sleep. GBX brought the CAP rate in stage 1 sleep and slow wave sleep (SWS) closer to baseline levels but did not significantly change the CAP rate in stage 2. ZOL reduced the CAP rate in stage 2 to near baseline levels, whereas the CAP rate in stage 1 and SWS was reduced substantially below baseline levels. The CAP parameter A1 index (associated with SWS and sleep continuity) showed the highest correlation with self-reported sleep quality, higher than any traditional PSG, spectral, or other self-reported measures. CONCLUSION: disruptions in CAP produced by phase advanced sleep were significantly and differentially modulated by gaboxadol and zolpidem. The relative independence of CAP parameters from other electrophysiological measures of sleep, their high sensitivity to sleep disruption, and their strong association with subjective sleep quality suggest that CAP variables may serve as valuable endpoints in future insomnia research.

Using artificial intelligence tools in answering important clinical questions: The KEYNOTE-183 multiple myeloma experience.

The phase III, randomized, active-controlled, multicenter, open-label KEYNOTE-183 study (NCT02576977) evaluating pomalidomide and low dose dexamethasone (standard-of-care [SOC]) with or without pembrolizumab in patients with refractory or relapsed and refractory multiple myeloma (rrMM) was placed on full clinical hold by the US FDA on July 03, 2017 due to an imbalance in the number of deaths between arms. Clinically-led subgroup analyses are typically used to shed light on clinical findings. However, this approach is not always successful. We propose a systematic approach using the artificial intelligence tools to identifying risk factors and subgroups contributing to the overall death (prognostic) or to the excess death observed in the pembrolizumab plus SOC arm (predictive) of the KEYNOTE-183 study. In KEYNOTE-183, with a data cutoff date of June 02, 2017, we identified plasmacytoma as a prognostic factor, and ECOG performance status as a predictive factor of death. In addition, a qualitative interaction was observed between ECOG performance status and the treatment arm. The subsequent subgroup analysis based on ECOG performance status confirmed that more deaths were associated with pembrolizumab plus SOC versus SOC alone in patients with ECOG performance status 1.

Evaluation of automated and semi-automated scoring of polysomnographic recordings from a clinical trial using zolpidem in the treatment of insomnia.

OBJECTIVE: To evaluate the performance of 2 automated systems, Morpheus and Somnolyzer24X7, with various levels of human review/editing, in scoring polysomnographic (PSG) recordings from a clinical trial using zolpidem in a model of transient insomnia. METHODS: 164 all-night PSG recordings from 82 subjects collected during 2 nights of sleep, one under placebo and one under zolpidem (10 mg) treatment were used. For each recording, 6 different methods were used to provide sleep stage scores based on Rechtschaffen & Kales criteria: 1) full manual scoring, 2) automated scoring by Morpheus 3) automated scoring by Somnolyzer24X7, 4) automated scoring by Morpheus with full manual review, 5) automated scoring by Morpheus with partial manual review, 6) automated scoring by Somnolyzer24X7 with partial manual review. Ten traditional clinical efficacy measures of sleep initiation, maintenance, and architecture were calculated. RESULTS: Pair-wise epoch-by-epoch agreements between fully automated and manual scores were in the range of intersite manual scoring agreements reported in the literature (70%-72%). Pair-wise epoch-by-epoch agreements between automated scores manually reviewed were higher (73%-76%). The direction and statistical significance of treatment effect sizes using traditional efficacy endpoints were essentially the same whichever method was used. As the degree of manual review increased, the magnitude of the effect size approached those estimated with fully manual scoring. CONCLUSION: Automated or semi-automated sleep PSG scoring offers valuable alternatives to costly, time consuming, and intrasite and intersite variable manual scoring, especially in large multicenter clinical trials. Reduction in scoring variability may also reduce the sample size of a clinical trial.

Pharmacodynamic effects of suvorexant and zolpidem on EEG during sleep in healthy subjects.

The objective of this study was to evaluate sleep electrophysiology in healthy subjects after bedtime administration of therapeutic doses of two insomnia treatments - the orexin receptor antagonist suvorexant or the GABAergic agonist zolpidem. Eighteen healthy men received single bedtime doses of suvorexant 20mg, zolpidem 10mg, or placebo in a double-blinded, randomized, balanced 3-period crossover study. EEG power spectral densities during non-rapid eye movement (NREM) and rapid eye movement (REM) sleep were recorded in a polysomnography (PSG) laboratory using a 19-lead EEG recording array. Spectral density was analyzed for each lead for frequencies between 1-32Hz. During NREM and REM sleep, zolpidem treatment reduced spectral density across theta and alpha frequency bands in all leads. In contrast, suvorexant had no significant effects on spectral density in any frequency band during NREM sleep, and modestly increased spectral density in the theta frequency band during REM sleep. Although the study was not designed to detect effects on PSG sleep endpoints in healthy subjects, both suvorexant and zolpidem increased mean total sleep time and sleep efficiency. Zolpidem reduced latency to persistent sleep whereas suvorexant did not. Suvorexant decreased wake after sleep onset, whereas zolpidem did not. These findings suggest that EEG power spectral density profile after administration of suvorexant in healthy subjects more closely approximates placebo sleep physiology than after zolpidem treatment.

Effects of suvorexant on sleep architecture and power spectral profile in patients with insomnia: analysis of pooled phase 3 data.

BACKGROUND: The orexin receptor antagonist, suvorexant, is approved for treating insomnia at a maximum dose of 20 mg. We evaluated its effects on sleep architecture. METHODS: The analyses included pooled polysomnography data from two similar randomized, double-blind, placebo-controlled, 3-month trials evaluating two age-adjusted (non-elderly/elderly) dose regimes of 20/15 mg and 40/30 mg in 1482 patients with insomnia. Polysomnography was recorded at baseline and on three nights during the treatment: Night-1, Month-1, and Month-3. Effects on non-REM sleep stages 1 (N1), 2 (N2), 3 (N3)/slow wave sleep (SWS), and REM sleep were evaluated. A power spectral analysis of non-REM sleep was also performed. RESULTS: Suvorexant increased the time (in minutes) spent in all sleep stages compared with placebo. When suvorexant and placebo were compared in terms of changes in percentage of total sleep time spent in each stage, there were small decreases of ≤1%, ≤2.2%, and ≤0.8% for N1, N2, and N3/SWS on average, respectively, and an average increase of ≤3.9% in REM. The largest differences from placebo were observed at Night-1 and generally diminished over time. Suvorexant reduced REM latency (number of non-REM 30-s epochs from lights-off to the first REM epoch) compared with placebo; the reduction was greater at Night-1 (~40-50 non-REM epochs) in comparison to later time points (~12-25 non-REM epochs at Month-3). The spectral analysis of non-REM showed a small decrease in power of 3-6% in the gamma and beta bands, and a small increase of 4-8% in the delta band, at Night-1 for suvorexant relative to placebo; these effects were not apparent at the later Month-1 and Month-3 time points. CONCLUSION: Overall sleep architecture appears to be preserved in insomnia patients taking suvorexant. The power spectral profile of suvorexant is generally similar to placebo.

Electroencephalographic power spectral density profile of the orexin receptor antagonist suvorexant in patients with primary insomnia and healthy subjects.

STUDY OBJECTIVES: Suvorexant, an orexin receptor antagonist, improves sleep in healthy subjects (HS) and patients with insomnia. We compared the electroencephalographic (EEG) power spectral density (PSD) profile of suvorexant with placebo using data from a phase 2 trial in patients with insomnia. We also compared suvorexant's PSD profile with the profiles of other insomnia treatments using data from 3 HS studies. DESIGN: Phase 2 trial--randomized, double-blind, two-period (4 w per period) crossover. HS studies--randomized, double-blind, crossover. SETTING: Sleep laboratories. PARTICIPANTS: Insomnia patients (n = 229) or HS (n = 124). INTERVENTIONS: Phase 2 trial--suvorexant 10 mg, 20 mg, 40 mg, 80 mg, placebo; HS study 1--suvorexant 10 mg, 50 mg, placebo; HS study 2--gaboxadol 15 mg, zolpidem 10 mg, placebo; HS study 3--trazodone 150 mg, placebo. MEASUREMENTS AND RESULTS: The PSD of the EEG signal at 1-32 Hz of each PSG recording during nonrapid eye movement (NREM) and rapid eye movement (REM) sleep were calculated. The day 1 and day 28 PSD profiles of suvorexant at all four doses during NREM and REM sleep in patients with insomnia were generally flat and close to 1.0 (placebo) at all frequencies. The day 1 PSD profile of suvorexant in HS was similar to that in insomnia patients. In contrast, the other three drugs had distinct PSD profiles in HS that differed from each other. CONCLUSIONS: Suvorexant at clinically effective doses had limited effects on power spectral density compared with placebo in healthy subjects and in patients with insomnia, in contrast to the three comparison insomnia treatments. These findings suggest the possibility that antagonism of the orexin pathway might lead to improvements in sleep without major changes in the patient's neurophysiology as assessed by electroencephalographic.

Effects of suvorexant, an orexin receptor antagonist, on sleep parameters as measured by polysomnography in healthy men.

STUDY OBJECTIVES: Suvorexant (MK-4305) is an orexin receptor antagonist being developed for the treatment of insomnia. This report describes the effects of nighttime administration of suvorexant on polysomnography (PSG) sleep parameters in healthy young men. DESIGN: Randomized, double-blind, placebo-controlled, 4-period crossover PSG study, followed by an additional 5(th) period to assess pharmacokinetics. SETTING: Sleep laboratory. PARTICIPANTS: Healthy young men between 18 and 45 years of age (22 enrolled, 19 completed). INTERVENTIONS: Periods 1-4: suvorexant (10 mg, 50 mg, or 100 mg) or placebo 1 h before nighttime PSG recording. Period 5: suvorexant 10 mg, 50 mg, or 100 mg. MEASUREMENTS AND RESULTS: In Periods 1-4, overnight sleep parameters were recorded by PSG and next-morning residual effects were assessed by psychomotor performance tests and subjective assessments. Statistically significant sleep-promoting effects were observed with all doses of suvorexant compared to placebo. Suvorexant 50 mg and 100 mg significantly decreased latency to persistent sleep and wake after sleep onset time, and increased sleep efficiency. Suvorexant 10 mg significantly decreased wake after sleep onset time. There were no statistically significant effects of suvorexant on EEG frequency bands including delta (slow wave) activity based on power spectral analysis. Suvorexant was well tolerated. There was no evidence of next-day residual effects for suvorexant 10 mg. Suvorexant 50 mg statistically significantly reduced subjective alertness, and suvorexant 100 mg significantly increased reaction time and reduced subjective alertness. There were no statistically significant effects of any suvorexant dose on digit symbol substitution test performance. In Period 5, plasma samples of suvorexant were collected for pharmacokinetic evaluation. The median T(max) was 3 hours and apparent terminal t(½) was 9-13 hours. CONCLUSIONS: In healthy young men without sleep disorders, suvorexant promoted sleep with some evidence of residual effects at the highest doses.