Abandoning personalization to get to precision in the pharmacotherapy of depression

Effectiveness studies and analyses of naturalistic cohorts demonstrate that many patients with major depressive disorder do not experience symptomatic remission with antidepressant treatments. In an effort to better match patients with effective treatments, numerous investigations of predictors or moderators of treatment response have been reported over the past five decades, including clinical features as well as biological measures. However, none of these have entered routine clinical practice; instead, clinicians typically personalize treatment on the basis of patient preferences as well as their own. Here, we review the reasons why it has been challenging to identify and deploy treatment‐specific predictors of response, and suggest strategies that may be required to achieve true precision in the pharmacotherapy of depression. We emphasize the need for changes in how depression care is delivered, measured, and used to inform future practice.     

Efficient and robust approaches for analysis of sequential multiple assignment randomized trials: Illustration using the ADAPT-R trial

Personalized intervention strategies, in particular those that modify treatment based on a participant's own response, are a core component of precision medicine approaches. Sequential multiple assignment randomized trials (SMARTs) are growing in popularity and are specifically designed to facilitate the evaluation of sequential adaptive strategies, in particular those embedded within the SMART. Advances in efficient estimation approaches that are able to incorporate machine learning while retaining valid inference can allow for more precise estimates of the effectiveness of these embedded regimes. However, to the best of our knowledge, such approaches have not yet been applied as the primary analysis in SMART trials. In this paper, we present a robust and efficient approach using targeted maximum likelihood estimation (TMLE) for estimating and contrasting expected outcomes under the dynamic regimes embedded in a SMART, together with generating simultaneous confidence intervals for the resulting estimates. We contrast this method with two alternatives (G-computation and inverse probability weighting estimators). The precision gains and robust inference achievable through the use of TMLE to evaluate the effects of embedded regimes are illustrated using both outcome-blind simulations and a real-data analysis from the Adaptive Strategies for Preventing and Treating Lapses of Retention in Human Immunodeficiency Virus (HIV) Care (ADAPT-R) trial (NCT02338739), a SMART with a primary aim of identifying strategies to improve retention in HIV care among people living with HIV in sub-Saharan Africa.     

A statistical framework for quantile equivalence clinical trials with application to pharmacokinetic studies that bridge from HIV-infected adults to children

SUMMARY: Bridging clinical trials are sometimes designed to evaluate whether a proposed dose for use in one population, for example, children, gives similar pharmacokinetic (PK) levels, or has similar effects on a surrogate marker as an established effective dose used in another population, for example, adults. For HIV bridging trials, because of the increased risk of viral resistance to drugs at low PK levels, the goal is often to determine whether the doses used in different populations result in similar percentages of patients with low PK levels. For example, it may be desired to evaluate that a proposed pediatric dose gives approximately 10% of children with PK levels below the 10th percentile of PK levels for the established adult dose. However, the 10th percentile for the adult dose is often imprecisely estimated in studies of relatively small size. Little attention has been given to the statistical framework for such bridging studies. In this article, a formal framework for the design and analysis of quantile-based bridging studies is proposed. The methodology is then developed for normally distributed outcome measures from both frequentist and Bayesian directions. Sample size and other design considerations are discussed.     

Inference in response-adaptive clinical trials when the enrolled population varies over time

A common assumption of data analysis in clinical trials is that the patient population, as well as treatment effects, do not vary during the course of the study. However, when trials enroll patients over several years, this hypothesis may be violated. Ignoring variations of the outcome distributions over time, under the control and experimental treatments, can lead to biased treatment effect estimates and poor control of false positive results. We propose and compare two procedures that account for possible variations of the outcome distributions over time, to correct treatment effect estimates, and to control type-I error rates. The first procedure models trends of patient outcomes with splines. The second leverages conditional inference principles, which have been introduced to analyze randomized trials when patient prognostic profiles are unbalanced across arms. These two procedures are applicable in response-adaptive clinical trials. We illustrate the consequences of trends in the outcome distributions in response-adaptive designs and in platform trials, and investigate the proposed methods in the analysis of a glioblastoma study.     

iPS cells: a game changer for future medicine

The induced pluripotent stem cell (iPSC) technology is instrumental in advancing the fields of disease modeling and cell transplantation. We herein discuss the various issues regarding disease modeling and cell transplantation presented in previous reports, and also describe new iPSC‐based medicine including iPSC clinical trials. In such trials, iPSCs from patients can be used to predict drug responders/non‐responders by analyzing the efficacy of the drug on iPSC‐derived cells. They could also be used to stratify patients after actual clinical trials, including those with sporadic diseases, based on the drug responsiveness of each patient in the clinical trials. iPSC‐derived cells can be used for the identification of response markers, leading to increased success rates in such trials. Since iPSCs can be used in micromedicine for drug discovery, and in macromedicine for actual clinical trials, their use would tightly connect both micro‐ and macromedicine. The use of iPSCs in disease modeling, cell transplantation, and clinical trials could therefore lead to significant changes in the future of medicine.     

Adaptive enrichment designs with a continuous biomarker

A popular design for clinical trials assessing targeted therapies is the two-stage adaptive enrichment design with recruitment in stage 2 limited to a biomarker-defined subgroup chosen based on data from stage 1. The data-dependent selection leads to statistical challenges if data from both stages are used to draw inference on treatment effects in the selected subgroup. If subgroups considered are nested, as when defined by a continuous biomarker, treatment effect estimates in different subgroups follow the same distribution as estimates in a group-sequential trial. This result is used to obtain tests controlling the familywise type I error rate (FWER) for six simple subgroup selection rules, one of which also controls the FWER for any selection rule. Two approaches are proposed: one based on multivariate normal distributions suitable if the number of possible subgroups, k, is small, and one based on Brownian motion approximations suitable for large k. The methods, applicable in the wide range of settings with asymptotically normal test statistics, are illustrated using survival data from a breast cancer trial.     

Triangulating variation in the population to define mechanisms for precision management of genetic disease

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No effect of a traditional Chinese medicine, Hochu-ekki-to, on antibody titer after influenza vaccination in man: A randomized, placebo-controlled, double-blind trial

BACKGROUND: It was shown that a traditional Chinese medicine, Hochu-ekki-to (HET), had adjuvant effects in influenza vaccination in an animal experiment. This, however, could not be assessed in a clinical study. METHODS: Thirty-two healthy subjects were randomly assigned to two groups (control and HET groups) in a double-blind manner. HET subjects (n=17) took 7.5 g of HET/day for two weeks; control subjects took the same amount of indistinguishable placebo. Then subjects were vaccinated against influenza (H1N1, H3N2 and B/Shandong). Hemagglutinin titers and natural killer (NK) activity were measured at weeks 0, 1, 2, 4, and 12. RESULTS: Antiinfluenza titers against the three viruses were increased continuously for the first two weeks and leveled off. However, there were no significant differences in any titers between the two groups. NK activity peaked at week 2 without any inter-group differences. CONCLUSION: We could not find any adjuvant effects of HET in this experimental condition.     

Electrical stimulation as an adjunct to spinal fusion: a meta-analysis of controlled clinical trials

This study was a meta-analysis to examine whether electrical stimulation has a specific effect on spinal fusion. Little evidence exists on the efficacy of electrical stimulation for improving fusion rate of spinal fusion surgery. Using MEDLINE (1966-2000) and EMBASE (1985-1999), a search for articles was carried out using the Medical Subject Headings: (1) electric stimulation or electromagnetic fields, (2) spinal fusion, (3) controlled or clinical trial, and (4) human. Data were extracted from all the hit articles and additionally collected from appropriate journal lists. A total of five randomized controlled trials (RCT) on bones assessing healing of spinal fusion were identified and scored on methodological quality. All the identified studies reported positive findings, but the quality score of each trial showed wide flaws. Because of relatively homogenous subjects who had spine fusion and radiographic assessment from these studies, pooling of the data was able to be performed. Excluding one trial with the lowest score, the combined results of four trials, whose major endpoints were the success rate of the fusion, revealed a statistically significant effect of electrical stimulation with various techniques, but the selected trials still showed wide variation in view of stimulation modalities and treatment protocol. The pooled result of the studies in this review revealed the efficacy of electrical stimulation based on proved methodological quality. As problems on therapeutic modality and protocol remain, there is a further need for improvement in design to constitute acceptable proof and to establish treatment programs that better demonstrate electrical stimulation effects on spinal fusion.     

Predation has a greater impact in less productive environments: variation in roe deer, Capreolus capreolus, population density across Europe

Aim We aimed to describe the large‐scale patterns in population density of roe deer Caprelous capreolus in Europe and to determine the factors shaping variation in their abundance. Location Europe. Methods We collated data on roe deer population density from 72 localities spanning 25° latitude and 48° longitude and analysed them in relation to a range of environmental factors: vegetation productivity (approximated by the fraction of photosynthetically active radiation) and forest cover as proxies for food supply, winter severity, summer drought and presence or absence of large predators (wolf, Canis lupus, and Eurasian lynx, Lynx lynx), hunter harvest and a competitor (red deer, Cervus elaphus). Results Roe deer abundance increased with the overall productivity of vegetation cover and with lower forest cover (sparser forest cover means that a higher proportion of overall plant productivity is allocated to ground vegetation and thus is available to roe deer). The effect of large predators was relatively weak in highly productive environments and in regions with mild climate, but increased markedly in regions with low vegetation productivity and harsh winters. Other potentially limiting factors (hunting, summer drought and competition with red deer) had no significant impact on roe deer abundance. Main conclusions The analyses revealed the combined effect of bottom‐up and top‐down control on roe deer: on a biogeographical scale, population abundance of roe deer has been shaped by food‐related factors and large predators, with additive effects of the two species of predators. The results have implications for management of roe deer populations in Europe. First, an increase in roe deer abundance can be expected as environmental productivity increases due to climate change. Secondly, recovery plans for large carnivores should take environmental productivity and winter severity into account when predicting their impact on prey.     

Economic impact of clinical mastitis in a dairy herd assessed by stochastic simulation using different methods to model yield losses

The main aim of the present study was to examine the economic consequences of a reduction in the incidence of clinical mastitis (CM) at herd level under current Swedish farming conditions. A second objective was to ask whether the estimated cost of CM alters depending upon whether the model reflects the fact that in different stages of lactation, CM gives rise to different yield-loss patterns or postulates just one type of yield-loss pattern irrespective of when, during lactation, CM occurs. A dynamic and stochastic simulation model, SimHerd, was used to study the effects of CM in a herd with 150 cows (9000 kg of energy-corrected milk per cow-year). Four herd types, defined by production level and reproductive performance, were modelled to investigate possible interactions between herd type and response to a reduction in the risk of CM. Technical and economic results, given the initial incidence of CM (25.6 per 100 cow-years), were studied together with the consequences of reducing the initial risk of CM by 50% and 90% throughout lactation and the consequences of reducing the initial risk by 50% and 90% before peak yield. A conventional way of modelling yield losses - i.e. one employing a single yield-loss pattern irrespective of when, during the lactation period, the cow develops CM - was compared with a new modelling strategy in which CM was assumed to affect production differently depending on its lactational timing. The effect of the choice of reference level when estimating yield losses was investigated by comparing the results obtained using the potential yield of mastitic cows, had they not developed CM, with those obtained using the yield of non-mastitic cows. The yearly maximum avoidable cost of CM at herd level was estimated at €14 504, corresponding to 6.9% of the net return given the initial incidence of CM. Expressed per cow-year, the maximum avoidable cost was €97. The cost per case of CM was estimated at €428. Herd types all responded in a similar manner to the reduced relative risk of CM. There were no major differences in the results obtained using the new and the conventional modelling strategy, with the exception of the cost per case of CM. Similarities between the results obtained using the two methods were particularly evident when the mastitic cows' own yield level, had they not developed CM, was used as the reference for production in healthy cows when yield losses were estimated. It was concluded that the conventional way of modelling yield losses is adequate and should, for the foreseeable future, be used in decision support systems.     

The future of psychopharmacology: a critical appraisal of ongoing phase 2/3 trials,and of some current trends aiming to de-risk trial programmes of novel agents

Despite considerable progress in pharmacotherapy over the past seven decades, many mental disorders remain insufficiently treated. This situation is in part due to the limited knowledge of the pathophysiology of these disorders and the lack of biological markers to stratify and individualize patient selection, but also to a still restricted number of mechanisms of action being targeted in monotherapy or combination/augmentation treatment, as well as to a variety of challenges threatening the successful development and testing of new drugs. In this paper, we first provide an overview of the most promising drugs with innovative mechanisms of action that are undergoing phase 2 or 3 testing for schizophrenia, bipolar disorder, major depressive disorder, anxiety and trauma-related disorders, substance use disorders, and dementia. Promising repurposing of established medications for new psychiatric indications, as well as variations in the modulation of dopamine, noradrenaline and serotonin receptor functioning, are also considered. We then critically discuss the clinical trial parameters that need to be considered in depth when developing and testing new pharmacological agents for the treatment of mental disorders. Hurdles and perils threatening success of new drug development and testing include inadequacy and imprecision of inclusion/exclusion criteria and ratings, sub-optimally suited clinical trial participants, multiple factors contributing to a large/increasing placebo effect, and problems with statistical analyses. This information should be considered in order to de-risk trial programmes of novel agents or known agents for novel psychiatric indications, increasing their chances of success.