Quantifying placebo responses in clinical evaluation of neuropsychiatric symptoms in Alzheimer's disease

Longitudinal Meta-Analysis of Historical Parkinson's Disease Trials to Inform Future Trial Design

BACKGROUND

The outcome of clinical trials in neurodegeneration can be highly uncertain due to the presence of a strong placebo effect.

OBJECTIVES

To develop a longitudinal model that can enhance the success of future Parkinson's disease trials by quantifying trial-to-trial variations in placebo and active treatment response.

METHODS

A longitudinal model-based meta-analysis was conducted on the total score of Unified Parkinson's Disease Rating Scale (UPDRS) Parts 1, 2, and 3. The analysis included aggregate data from 66 arms (observational [4], placebo [28], or investigational-drug-treated [34]) from 4 observational studies and 17 interventional trials. Inter-study variabilities in key parameters were estimated. Residual variability was weighted by the size of study arms.

RESULTS

The baseline total UPDRS was estimated to average at 24.5 points. Disease score was estimated to worsen by 3.90 points/year for the duration of the treatments; whilst notably, arms with a lower baseline progressed faster. The model captured the transient nature of the placebo response and sustained symptomatic drug effect. Both placebo and drug effects peaked within 2 months; although, 1 year was needed to observe the full treatment difference. Across these studies, the progression rate varied by 59.4%, the half-life for offset of placebo response varied by 79.4%, and the amplitude for drug effect varied by 105.3%.

CONCLUSION

The longitudinal model-based meta-analysis describes UPDRS progression rate, captures the dynamics of the placebo response, quantifies the effect size of the available therapies, and sets the expectation of uncertainty for future trials. The findings provide informative priors to enhance the rigor and success of future trials of promising agents, including potential disease modifiers. © 2023 GSK. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Applications of Model-Based Meta-Analysis in Drug Development

Model-based meta-analysis (MBMA) is a quantitative approach that leverages published summary data along with internal data and can be applied to inform key drug development decisions, including the benefit-risk assessment of a treatment under investigation. These risk-benefit assessments may involve determining an optimal dose compared against historic external comparators of a particular disease indication. MBMA can provide a flexible framework for interpreting aggregated data from historic reference studies and therefore should be a standard tool for the model-informed drug development (MIDD) framework.In addition to pairwise and network meta-analyses, MBMA provides further contributions in the quantitative approaches with its ability to incorporate longitudinal data and the pharmacologic concept of dose-response relationship, as well as to combine individual- and summary-level data and routinely incorporate covariates in the analysis.A common application of MBMA is the selection of optimal dose and dosing regimen of the internal investigational molecule to evaluate external benchmarking and to support comparator selection. Two case studies provided examples in applications of MBMA in biologics (durvalumab + tremelimumab for safety) and small molecule (fenebrutinib for efficacy) to support drug development decision-making in two different but well-studied disease areas, i.e., oncology and rheumatoid arthritis, respectively.Important to the future directions of MBMA include additional recognition and engagement from drug development stakeholders for the MBMA approach, stronger collaboration between pharmacometrics and statistics, expanded data access, and the use of machine learning for database building. Timely, cost-effective, and successful application of MBMA should be part of providing an integrated view of MIDD.

Extensive frontal focused ultrasound mediated blood-brain barrier opening for the treatment of Alzheimer's disease: a proof-of-concept study

BACKGROUND

Focused ultrasound (FUS)-mediated blood-brain barrier (BBB) opening has shown efficacy in removal of amyloid plaque and improvement of cognitive functions in preclinical studies, but this is rarely reported in clinical studies. This study was conducted to evaluate the safety, feasibility and potential benefits of repeated extensive BBB opening.

METHODS

In this open-label, prospective study, six patients with Alzheimer's disease (AD) were enrolled at Severance Hospital in Korea between August 2020 and September 2020. Five of them completed the study. FUS-mediated BBB opening, targeting the bilateral frontal lobe regions over 20 cm3, was performed twice at three-month intervals. Magnetic resonance imaging, 18F-Florbetaben (FBB) positron emission tomography, Caregiver-Administered Neuropsychiatric Inventory (CGA-NPI) and comprehensive neuropsychological tests were performed before and after the procedures.

RESULTS

FUS targeted a mean volume of 21.1 ± 2.7 cm3 and BBB opening was confirmed at 95.7% ± 9.4% of the targeted volume. The frontal-to-other cortical region FBB standardized uptake value ratio at 3 months after the procedure showed a slight decrease, which was statistically significant, compared to the pre-procedure value (- 1.6%, 0.986 vs1.002, P = 0.043). The CGA-NPI score at 2 weeks after the second procedure significantly decreased compared to baseline (2.2 ± 3.0 vs 8.6 ± 6.0, P = 0.042), but recovered after 3 months (5.2 ± 5.8 vs 8.6 ± 6.0, P = 0.89). No adverse effects were observed.

CONCLUSIONS

The repeated and extensive BBB opening in the frontal lobe is safe and feasible for patients with AD. In addition, the BBB opening is potentially beneficial for amyloid removal in AD patients.

Exposure-Response Modeling to Support Dosing Selection for Phase IIb Development of Kukoamine B in Sepsis Patients

Aim: Kukoamine B, a small molecule compound, is being developed for the treatment of sepsis in a Phase II clinical trial. The objective of this study was to optimize dosing selection for a Phase IIb clinical trial using an exposure-response model.

Methods: Data of 34 sepsis patients from a Phase IIa clinical trial were used in the model: 10 sepsis patients from the placebo group and a total of 24 sepsis patients from the 0.06 mg/kg, 0.12 mg/kg, and 0.24 mg/kg drug groups. Exposure-response relationship was constructed to model the impact of the standard care therapy and area under curve (AUC) of kukoamine B to the disease biomarker (SOFA score). The model was evaluated by goodness of fit and visual predictive check. The simulation was performed 1,000 times based on the built model.

Results: The data of the placebo and the drug groups were pooled and modeled by a nonlinear mixed-effect modeling approach in sepsis. A latent-variable approach in conjunction with an inhibitory indirect response model was used to link the standard care therapy effect and drug exposure to SOFA score. The maximum fraction of the standard care therapy was estimated to 0.792. The eliminate rate constant of the SOFA score was 0.263/day for the standard care therapy. The production rate of SOFA score (K_in) was estimated at 0.0569/day and the AUC at half the maximal drug effect (EAUC₅₀) was estimated at 1,320 h*ng/mL. Model evaluation showed that the built model could well describe the observed SOFA score. Model-based simulations showed that the SOFA score on day 7 decreased to a plateau when AUC increased to 1,500 h*ng/mL.

Conclusion: We built an exposure-response model characterizing the pharmacological effect of kukoamine B from the standard care therapy in sepsis patients. A dose regimen of 0.24 mg/kg was finally recommended for the Phase IIb clinical trial of kukoamine B based on modeling and simulation results.

Strategies to minimize placebo effects in research investigations

Placebo-controlled trials are the research standard to evaluate new interventions for which there is no standard of care. While lessening performance and detection bias, such design provides a direct mode of comparison against the probed intervention. Still, using placebo arms may pose new challenges to the design, conduct and analysis of clinical trials. This is particularly relevant in circumstances of non-additivity between the therapeutic and the placebo effects, if the outcome of interest has floor or ceiling effects, or when the predicted effect size of the intervention is large and leads to small sample sizes. There are several possible strategies to mitigate the confounding effects of the placebo, each relevant to specific clinical trial designs. This chapter puts into context the new challenges created by the placebo effect, discusses possible ways around them, and explores the future of the field.

Testing whether the progression of Alzheimer's disease changes with the year of publication, additional design, and geographical area: a modeling analysis of literature aggregate data

BACKGROUND

Our objectives were to develop a disease progression model for cognitive decline in Alzheimer's disease (AD) and to determine whether disease progression of AD is related to the year of publication, add-on trial design, and geographical regions.

METHODS

Placebo-controlled randomized AD clinical trials were systemically searched in public databases. Longitudinal placebo response (mean change from baseline in the cognitive subscale of the Alzheimer's Disease Assessment Scale [ADAS-cog]) and the corresponding demographic information were extracted to establish a disease progression model. Covariate screening and subgroup analyses were performed to identify potential factors affecting the disease progression rate.

RESULTS

A total of 134 publications (140 trials) were included in this model-based meta-analysis. The typical disease progression rate was 5.82 points per year. The baseline ADAS-cog score was included in the final model using an inverse U-type function. Age was found to be negatively correlated with disease progression rate. After correcting the baseline ADAS-cog score and the age effect, no significant difference in the disease progression rate was found between trials published before and after 2008 and between trials using an add-on design and those that did not use an add-on design. However, a significant difference was found among different trial regions. Trials in East Asian countries showed the slowest decline rate and the largest placebo effect.

CONCLUSIONS

Our model successfully quantified AD disease progression by integrating baseline ADAS-cog score and age as important predictors. These factors and geographic location should be considered when optimizing future trial designs and conducting indirect comparisons of clinical outcomes.