Evaluation of treatment response in depression studies using a Bayesian parametric cure rate model

A Simulation Study of the Effect of Clinical Characteristics and Treatment Choice on Reliever Medication Use, Symptom Control and Exacerbation Risk in Moderate-Severe Asthma

INTRODUCTION

The relationship between immediate symptom control, reliever medication use and exacerbation risk on treatment response and factors that modify it have not been assessed in an integrated manner. Here we apply simulation scenarios to evaluate the effect of individual baseline characteristics on treatment response in patients with moderate-severe asthma on regular maintenance dosing monotherapy with fluticasone propionate (FP) or combination therapy with fluticasone propionate/salmeterol (FP/SAL) or budesonide/formoterol (BUD/FOR).

METHODS

Reduction in reliever medication use (puffs/24 h), change in symptom control scores (ACQ-5), and annualised exacerbation rate over 12 months were simulated in a cohort of patients with different baseline characteristics (e.g. time since diagnosis, asthma control questionnaire (ACQ-5) symptom score, smoking status, body mass index (BMI) and sex) using drug-disease models derived from large phase III/IV clinical studies.

RESULTS

Simulation scenarios show that being a smoker, having higher baseline ACQ-5 and BMI, and long asthma history is associated with increased reliever medication use (p < 0.01). This increase correlates with a higher exacerbation risk and higher ACQ-5 scores over the course of treatment, irrespective of the underlying maintenance therapy. Switching non-responders to ICS monotherapy to combination therapy after 3 months resulted in immediate reduction in reliever medication use (i.e. 1.3 vs. 1.0 puffs/24 h for FP/SAL and BUD/FOR, respectively). In addition, switching patients with ACQ-5 > 1.5 at baseline to FP/SAL resulted in 34% less exacerbations than those receiving regular dosing BUD/FOR (p < 0.01).

CONCLUSIONS

We have identified baseline characteristics of patients with moderate to severe asthma that are associated with greater reliever medication use, poor symptom control and higher exacerbation risk. Moreover, the effects of different inhaled corticosteroid (ICS)/long-acting beta agonist (LABA) combinations vary significantly when considering long-term treatment performance. These factors should be considered in clinical practice as a basis for personalised management of patients with moderate-severe asthma symptoms.

Effect of Individual Patient Characteristics and Treatment Choices on Reliever Medication Use in Moderate-Severe Asthma: A Poisson Analysis of Randomised Clinical Trials

INTRODUCTION

Even though increased use of reliever medication, including short-acting beta agonists (SABA), provides an indirect measure of symptom worsening, there have been limited efforts to assess how different patterns of reliever use correlate with symptom control and future risk of exacerbations. Here, we evaluate the effect of individual baseline characteristics on reliever use in patients with moderate-severe asthma on regular maintenance therapy with fluticasone propionate (FP) or combination therapy with fluticasone propionate/salmeterol (FP/SAL) or budesonide/formoterol (BUD/FOR).

METHODS

A drug-disease model describing the number of 24-h puffs and overnight occasions was developed with data from five clinical studies (N = 6212). The model was implemented using a nonlinear mixed effects approach and a Poisson function, considering clinical and demographic baseline characteristics. Goodness of fit and model predictive performance were assessed. Heatmaps were created to summarise the effect of concurrent baseline factors on reliever utilisation.

RESULTS

The final model accurately described individual patterns of reliever use, which is significantly increased with time since diagnosis, smoking, higher Asthma Control Questionnaire (ACQ-5) score and higher body mass index (BMI) at baseline. Whilst the number of puffs decreases slowly after an initial drop relative to the start of treatment, exacerbating patients utilise significantly more reliever than those who do not exacerbate. The mean effect of FP/SAL (median dose: 250/50 μg BID) on reliever use was slightly higher than that of BUD/FOR (median dose: 160/4.5 μg BID), i.e. a 75.3% vs 69.3% reduction in reliever use, respectively.

CONCLUSIONS

The availability of individual-level patient data in conjunction with a parametric approach enabled the characterisation of interindividual differences in the patterns of reliever use in patients with moderate-severe asthma. Taken together, individual demographic and clinical characteristics, as well as exacerbation history, can be considered an indicator of the degree of asthma control. High SABA reliever use suggests suboptimal clinical management of patients on maintenance therapy.

In Situ Observation of Lysosomal Hypobromous Acid Fluctuations in the Brain of Mice with Depression Phenotypes by Two-Photon Fluorescence Imaging

Excessive oxidative stress is the main cause of neurotransmitter metabolism disorder in the brain with depression. Lysosomal hypobromic acid (HOBr) is an important reactive oxygen species produced in oxidative stress. Its abnormal content can lead to macromolecular damage and neurodegenerative diseases. However, due to the high reactivity and low concentration of HOBr and the lack of in situ imaging methods, the role of HOBr in depression is not clear. Herein, based on the HOBr-initiated aromatic substitution of a tertiary amine, we developed a novel two-photon (TP) fluorescence probe (NH-HOBr) for real-time visual monitoring of trace HOBr in living systems. NH-HOBr introduces N-(2-aminoethyl)-morpholine as a new recognition receptor for HOBr and a targeting group for lysosomes. It not only has excellent selectivity compared with other biomolecules (including hypochlorous acid), fast response (≤5 s) and high sensitivity (LOD = 15 nM) but also realizes sensitive detection of HOBr in cells, zebrafish, and mice tissues. It is worth noting that the in situ TP fluorescence imaging of mouse brain reveals the positive correlation between HOBr content and depression phenotype for the first time, providing strong direct evidence for the relationship between oxidative stress and depression. This work can provide reference to further study depression and the pathological mechanism of HOBr. In addition, HOBr-initiated aromatic substitution of a tertiary amine provides a new idea for the construction of specific and sensitive HOBr probes.

Apocynum venetum Leaf Extract Exerts Antidepressant-Like Effects and Inhibits Hippocampal and Cortical Apoptosis of Rats Exposed to Chronic Unpredictable Mild Stress

We investigated the effects of Apocynum venetum leaf extract (AVLE) on depressive behaviors and neuronal apoptosis in a chronic unpredictable mild stress (CUMS) rat model of depression. Rats were randomly divided into six groups: control, chronic unpredictable mild stress, fluoxetine, AVLE30, AVLE60, and AVLE120. Except for the control group, all rats were submitted to chronic unpredictable mild stress paradigms for four weeks to induce depressive behavior. Neuronal apoptosis was assessed by the terminal deoxynucleotidyl transferase- (TDT-) mediated dUTP-biotin nick end-labeling (TUNEL) method. The expression levels of apoptosis-related proteins, such as B-cell lymphoma 2 (Bcl-2), Bcl-2 Associated X Protein (Bax), cysteine-aspartic acid protease-3 and protease-9 (caspase-3 and caspase-9), cytochrome c (cyt-C), brain-derived neurotrophic factor (BDNF), and cAMP-response element binding (CREB) protein, were evaluated by western blot. Treatment with AVLE (60 or 120 mg/kg/day) significantly improved depressive behavior. Increased apoptosis of hippocampus and cortical neurons were observed in CUMS rats, while 120 mg/kg/day of AVLE significantly reversed these changes and achieved the best antidepressant-like effects among the doses tested. Moreover, AVLE (120 mg/kg) significantly increased Bcl-2, BDNF, and CREB protein expression and decreased Bax, cyt-C, and caspase family protein expression. Our results indicate that AVLE has potent antidepressant activity, likely due to its ability to suppress neuronal apoptosis.

Exploring factors related to metastasis free survival in breast cancer patients using Bayesian cure models

BACKGROUND

Breast cancer is a fatal disease and the most frequently diagnosed cancer in women with an increasing pattern worldwide. The burden is mostly attributed to metastatic cancers that occur in one-third of patients and the treatments are palliative. It is of great interest to determine factors affecting time from cancer diagnosis to secondary metastasis.

MATERIALS AND METHODS

Cure rate models assume a Poisson distribution for the number of unobservable metastatic-component cells that are completely deleted from the non-metastasis patient body but some may remain and result in metastasis. Time to metastasis is defined as a function of the number of these cells and the time for each cell to develop a detectable sign of metastasis. Covariates are introduced to the model via the rate of metastatic-component cells. We used non-mixture cure rate models with Weibull and log-logistic distributions in a Bayesian setting to assess the relationship between metastasis free survival and covariates.

RESULTS

The median of metastasis free survival was 76.9 months. Various models showed that from covariates in the study, lymph node involvement ratio and being progesterone receptor positive were significant, with an adverse and a beneficial effect on metastasis free survival, respectively. The estimated fraction of patients cured from metastasis was almost 48%. The Weibull model had a slightly better performance than log-logistic.

CONCLUSIONS

Cure rate models are popular in survival studies and outperform other models under certain conditions. We explored the prognostic factors of metastatic breast cancer from a different viewpoint. In this study, metastasis sites were analyzed all together. Conducting similar studies in a larger sample of cancer patients as well as evaluating the prognostic value of covariates in metastasis to each site separately are recommended.

Population modelling of patient responses in antidepressant studies: a stochastic approach

This paper addresses the problem of modelling longitudinal data describing patients' responses in clinical trials. In particular, a systematic approach relying on a system theoretic paradigm is proposed to deal with contexts where limited physiopathological knowledge is available on disease, drug response, and patients' characteristics. The model relies on the notion of patient's health state which summarizes the patient's condition. In order to cope with the limited number of clinical data usually available, the paper considers a very parsimonious realization where the two state variables are the clinical endpoint and its derivative. Within a population framework, the individual response is modelled as the sum of an individual shift and the average response of subjects belonging to the same study, both described as Markovian processes and identified by empirical Bayes techniques. The proposed approach is validated with experimental data from a Phase II, flexible-dose, depression trial. The dose changes due to the flexible-dose scheme are handled as perturbations on the state. The connection between inter-individual variability and model stability is evaluated showing that the introduction of stable poles helps to describe populations whose range of individual responses does not diverge with time. In this way, good individual fittings and visual predictive checks were obtained for the clinical data. The proposed analysis provides a systematic approach to semi-mechanistic modelling when a precise knowledge of the physiological mechanisms of the disease is incomplete or missing.

Continuous-time Markov modelling of flexible-dose depression trials

The aim of this paper is to provide a systematic methodology for modelling longitudinal data to be used in contexts of limited or even absent knowledge of the physiological mechanism underlying the disease time course. Adopting a system-theoretic paradigm, a population response model is developed where the clinical endpoint is described as a function of the patient's health state. In particular, a continuous-time stochastic approach is proposed where the clinical score and its time-derivative summarize the patient's health state affected by a random term accounting for exogenous unpredictable factors. The proposed approach is validated on experimental data from the placebo and drug arms of a Phase II depression trial. Since some subjects in the trial may undergo changes in their treatment dose due to the flexible dosing scheme, dose escalations are modelled as instantaneous perturbations on the state. In its simplest form--an integrated Wiener process--was able to correctly capture the individual responses in both treatment arms. However, a better description of inter-individual variability was obtained by means of a stable Markovian model. Parameter estimation has been carried out according to the empirical Bayes method.

A PCA approach to population analysis: with application to a Phase II depression trial

For psychiatric diseases, established mechanistic models are lacking and alternative empirical mathematical structures are usually explored by a trial-and-error procedure. To address this problem, one of the most promising approaches is an automated model-free technique that extracts the model structure directly from the statistical properties of the data. In this paper, a linear-in-parameter modelling approach is developed based on principal component analysis (PCA). The model complexity, i.e. the number of components entering the PCA-based model, is selected by either cross-validation or Mallows' Cp criterion. This new approach has been validated on both simulated and clinical data taken from a Phase II depression trial. Simulated datasets are generated through three parametric models: Weibull, Inverse Bateman and Weibull-and-Linear. In particular, concerning simulated datasets, it is found that the PCA approach compares very favourably with some of the popular parametric models used for analyzing data collected during psychiatric trials. Furthermore, the proposed method performs well on the experimental data. This approach can be useful whenever a mechanistic modelling procedure cannot be pursued. Moreover, it could support subsequent semi-mechanistic model building.

Joint modeling of efficacy, dropout, and tolerability in flexible-dose trials: a case study in depression

Many difficulties may arise during the modeling of the time course of Hamilton Rating Scale for Depression (HAM D)scores in clinical trials for the evaluation of antidepressant drugs: (i) flexible designs, used to increase the chance of selecting more efficacious doses, (ii) dropout events, and (iii) adverse effects related to the experimental compound.It is crucial to take into account all these factors when designing an appropriate model of the HAM D time course and to obtain a realistic description of the dropout process. In this work, we propose an integrated approach to the modeling of a double-blind, flexible-dose, placebo-controlled, phase II depression trial that comprises response,tolerability, and dropout. We investigate three different dropout mechanisms in terms of informativeness. Goodness of fit is quantitatively assessed with respect to response (HAM D score) and dropout data. We show that dropout is a complex phenomenon that may be influenced by HAM D evolution, dose changes, and occurrence of drug-related adverse effects.