Placebo effect model in asthma clinical studies: longitudinal meta-analysis of forced expiratory volume in 1 second

Current status and challenges of model-informed drug discovery and development in China

In the past decade, biopharmaceutical research and development in China has been notably boosted by government policies, regulatory initiatives and increasing investments in life sciences. With regulatory agency acting as a strong driver, model-informed drug development (MIDD) is transitioning rapidly from an academic pursuit to a critical component of innovative drug discovery and development within the country. In this article, we provided a cross-sectional summary on the current status of MIDD implementations across early and late-stage drug development in China, illustrated by case examples. We also shared insights into regulatory policy development and decision-making. Various modeling and simulation approaches were presented across a range of applications. Furthermore, the challenges and opportunities of MIDD in China were discussed and compared with other regions where these practices have a more established history. Through this analysis, we highlighted the potential of MIDD to enhance drug development efficiency and effectiveness in China's evolving pharmaceutical landscape.

Longitudinal Model-Based Meta-Analysis of Lung Function Response to Support Phase III Study Design in Chinese Patients With Asthma

Asthma is a chronic disease of the lungs characterized by airway inflammation, bronchoconstriction, and increased airway responsiveness. Forced expiratory volume in the first second (FEV1) is used as a measure of lung function and to help diagnose and monitor lung diseases, including asthma. An exponential longitudinal model has been previously developed to adequately describe the FEV1 response in asthma patients with placebo. This model was the basis of a longitudinal model-based meta-analysis which was undertaken to describe the trough FEV1 responses ranging up to 1 year from nine clinical studies in a population with asthma (N = 3,896), following placebo, dual combination (fluticasone furoate/vilanterol), and triple combination (fluticasone furoate/umeclidinium/vilanterol) given via inhalation. Numerical, graphical and simulation-based diagnostics showed that a Weibull model adequately characterized the longitudinal trough FEV1 response with time. Automatic covariate selection supported by statistically based regression models identified a range of patient characteristics influencing the model parameters. Race was a significant covariate on baseline but not on the parameters that impact the FEV1 trajectory. Based on the trough FEV1, all active treatments were found to be significantly different when compared with placebo and showed clinically meaningful improvement in FEV1. The model was able to predict the longitudinal FEV1 response in Chinese patients with inadequately controlled asthma and was used to provide additional support with respect to the design for a shorter-duration phase III study to the China National Medical Products Administration (NMPA).

Longitudinal model-based meta-analysis for survival probabilities in patients with castration-resistant prostate cancer

PURPOSE

The aims of this longitudinal model-based meta-analysis (MBMA) were to indirectly compare the time courses of survival probabilities and to identify corresponding potential significant covariates across approved drugs in patients with castration-resistant prostate cancer (CRPC).

METHODS

A systematic literature review for monotherapy studies in patients with CRPC was conducted up to August 8, 2018. The time courses of progression-free survival (PFS) and overall survival (OS) were fitted with parametric survival models. Covariate analyses were performed to determine the impact of treatment drugs, dosing regimens, and patient characteristics on the survival probabilities. Simulations were carried out to quantify the magnitude of covariate effects.

RESULTS

A total of 146 studies including clinical trials and real-world data on longitudinal survival probabilities in 20,712 patients with CRPC were included in our meta-database. The time courses of PFS and OS probabilities were best described by the log-logistic model. There was no significant difference in median OS and PFS between docetaxel, cabazitaxel, abiraterone acetate, and enzalutamide. There was no significant dose-response relationship in PFS or OS for docetaxel at 50 to 120 mg/m² every 3 weeks (Q3W) and cabazitaxel at 20 to 25 mg/m² Q3W. Model-based simulations indicated that PFS probability was associated with chemotherapy, Gleason score, and baseline prostate-specific antigen (BLPSA), while OS probability was associated with chemotherapy, Gleason score, visceral metastasis, Eastern Cooperative Oncology Group performance status, and BLPSA.

CONCLUSION

Our modeling and simulation framework can be applied to support indirect comparison, dose selection, and go/no-go decision-making for new agents targeting CRPC.

Model selection and averaging of nonlinear mixed-effect models for robust phase III dose selection

Population model-based (pharmacometric) approaches are widely used for the analyses of phase IIb clinical trial data to increase the accuracy of the dose selection for phase III clinical trials. On the other hand, if the analysis is based on one selected model, model selection bias can potentially spoil the accuracy of the dose selection process. In this paper, four methods that assume a number of pre-defined model structure candidates, for example a set of dose-response shape functions, and then combine or select those candidate models are introduced. The key hypothesis is that by combining both model structure uncertainty and model parameter uncertainty using these methodologies, we can make a more robust model based dose selection decision at the end of a phase IIb clinical trial. These methods are investigated using realistic simulation studies based on the study protocol of an actual phase IIb trial for an oral asthma drug candidate (AZD1981). Based on the simulation study, it is demonstrated that a bootstrap model selection method properly avoids model selection bias and in most cases increases the accuracy of the end of phase IIb decision. Thus, we recommend using this bootstrap model selection method when conducting population model-based decision-making at the end of phase IIb clinical trials.

Pharmacokinetic-pharmacodynamic modeling of the antitumor effect of TM208 and EGFR-TKI resistance in human breast cancer xenograft mice

AIM

The novel anticancer compound TM208 is an EGFR tyrosine kinase inhibitor (EGFR-TKI). Since the development of resistance to EGFR-TKIs is a major challenge in their clinical usage, we investigated the profiles of resistance following continuous treatment with TM208 in human breast cancer xenograft mice, and identified the relationship between the tumor pEGFR levels and tumor growth inhibition.

METHODS

Female BALB/c nude mice were implanted with human breast cancer MCF-7 cells, and the xenograft mice received TM208 (50 or 150 mg·kg(-1)·d(-1), ig) or vehicle for 18 d. The pharmacokinetics (PK) and pharmacodynamics (PD) of TM208 were evaluated.

RESULTS

The PK properties of TM208 were described by a one-compartment model with first-order absorption kinetics. Our study showed the inhibitory effects of TM208 on tumor pEGFR levels gradually reached a maximum effect, after which it became weaker over time, which was characterized by a combined tolerance/indirect response PD model with an estimated EC50 (55.9 μg/L), as well as three parameters ('a' of 27.2%, 'b' of 2730%, 'c' of 0.58 h(-1)) denoting the maximum, extent and rate of resistance, respectively. The relationship between the tumor pEGFR levels and tumor growth inhibition was characterized by a combined logistic tumor growth/transit compartment model with estimated parameters associated with tumor growth characteristics kng (0.282 day(-1)), drug potency kTM208 (0.0499 cm(3)/day) and the kinetics of tumor cell death k1 (0.141 day(-1)), which provided insight into drug mechanisms and behaviors.

CONCLUSION

The proposed PK/PD model provides a better understanding of the pharmacological properties of TM208 in the treatment of breast cancer. Furthermore, simulation based on a tolerance model allows prediction of the occurrence of resistance.

The placebo response of injectable GLP-1 receptor agonists vs. oral DPP-4 inhibitors and SGLT-2 inhibitors: a systematic review and meta-analysis

AIMS

The size of the placebo response in type 2 diabetes (T2DM) treatment and its relation to the route of drug administration have not been systematically reviewed. We aimed to determine weight loss, change in HbA1c and incidence of adverse events after treatment with injectable placebo GLP-1 receptor agonist (GLP-1ra), compared with oral placebo DPP-4 inhibitor (DPP-4i) and placebo SGLT-2 inhibitor (SGLT-2i).

METHODS

PubMed, EMBASE and Central were searched up to September 2014 for randomized placebo controlled trials investigating GLP-1ra, DPP-4i or SGLT2-i. Data on placebo groups were extracted and pooled using a generic inverse variance random effects model.

RESULTS

Sixty-seven trials were included, involving 2522, 5290 and 2028 patients randomized to placebo GLP-1ra, placebo DPP-4i and placebo SGLT-2i, respectively. Body weight decreased by -0.67 kg (95% CI -1.03, -0.31) after treatment with placebo GLP-1ra (-0.76 kg [95% CI -1.10, -0.43] with placebo short acting GLP-1ra and -0.32 kg [95% CI -1.75, 1.10] with placebo long acting GLP-1ra) and by -0.31 kg (95% CI -0.64, 0.01) with placebo DPP-4i (P = 0.06 for difference with placebo short acting GLP-1ra). Placebo SGLT-2i resulted in an intermediate -0.48 kg (95% CI -0.81, -0.15) weight loss. Weight loss with placebo showed a strong correlation with the active comparator drug (r(2)  = 0.40-0.78). HbA1c changed little with placebo treatment (-0.23%, 0.10% and -0.13% for placebo GLP-1ra, DPP-4i and SGLT-2i). Adverse events occurred frequently with placebo, were often similar to the active comparator drug and led to drop-out in 2.0-2.7% of cases.

CONCLUSIONS

The response to placebo treatment was related to its active comparator, with injectable placebo GLP-1ra showing a relevant response on weight, whereas oral placebo DPP4i showed no significant response. These findings may suggest that subjective expectations influence T2DM treatment efficacy, which can possibly be employed therapeutically.

Neuro-Bio-Behavioral Mechanisms of Placebo and Nocebo Responses: Implications for Clinical Trials and Clinical Practice

The placebo effect has often been considered a nuisance in basic and particularly clinical research. This view has gradually changed in recent years due to deeper insight into the neuro-bio-behavioral mechanisms steering both the placebo and nocebo responses, the evil twin of placebo. For the neuroscientist, placebo and nocebo responses have evolved as indispensable tools to understand brain mechanisms that link cognitive and emotional factors with symptom perception as well as peripheral physiologic systems and end organ functioning. For the clinical investigator, better understanding of the mechanisms driving placebo and nocebo responses allow the control of these responses and thereby help to more precisely define the efficacy of a specific pharmacological intervention. Finally, in the clinical context, the systematic exploitation of these mechanisms will help to maximize placebo responses and minimize nocebo responses for the patient's benefit. In this review, we summarize and critically examine the neuro-bio-behavioral mechanisms underlying placebo and nocebo responses that are currently known in terms of different diseases and physiologic systems. We subsequently elaborate on the consequences of this knowledge for pharmacological treatments of patients and the implications for pharmacological research, the training of healthcare professionals, and for the health care system and future research strategies on placebo and nocebo responses.

Age and sex as moderators of the placebo response – an evaluation of systematic reviews and meta-analyses across medicine

Predictors of the placebo response (PR) in randomized controlled trials (RCT) have been searched for ever since RCT have become the standard for testing novel therapies and age and gender are routinely documented data in all trials irrespective of the drug tested, its indication, and the primary and secondary end points chosen. To evaluate whether age and gender have been found to be reliable predictors of the PR across medical subspecialties, we extracted 75 systematic reviews, meta-analyses, and meta-regressions performed in major medical areas (neurology, psychiatry, internal medicine) known for high PR rates. The literature database used contains approximately 2,500 papers on various aspects of the genuine PR. These ‘meta-analyses’ were screened for statistical predictors of the PR across multiple RCT, including age and gender, but also other patient-based and design-based predictors of higher PR rates. Retrieved papers were sorted for areas and disease categories. Only 15 of the 75 analyses noted an effect of younger age to be associated with higher PR, and this was predominantly in psychiatric conditions but not in depression, and internal medicine but not in gastroenterology. Female gender was associated with higher PR in only 3 analyses. Among the patient-based predictors, the most frequently noted factor was lower symptom severity at baseline, and among the design- based factors, it was a randomization ratio that selected more patients to drugs than to placebo, more frequent study visits, and more recent trials that were associated with higher PR rates. While younger age may contribute to the PR in some conditions, sex does not. There is currently no evidence that the PR is different in the elderly. PR are, however, markedly influenced by the symptom severity at baseline, and by the likelihood of receiving active treatment in placebo- controlled trials.

The effects of the multispecies probiotic mixture Ecologic®Barrier on migraine: results of an open-label pilot study

Migraine prevalence is associated with gastrointestinal disorders. Possible underlying mechanisms could be increased gut permeability and inflammation. Probiotics may decrease intestinal permeability as well as inflammation, and therefore may reduce the frequency and/or intensity of migraine attacks. Therefore we assessed feasibility, possible clinical efficacy, and adverse reactions of probiotic treatment in migraine patients. 29 migraine patients took 2 g/d of a probiotic food supplement (Ecologic(®)Barrier, 2.5×10(9) cfu/g) during 12 weeks. Participants recorded frequency and intensity of migraine in a headache diary and completed the Migraine Disability Assessment Scale (MIDAS) and Henry Ford Hospital Headache Disability Inventory (HDI) at baseline and after 12 weeks of treatment. Compliance was measured every 4 weeks by counting the remaining sachets with probiotics. The study was completed by 27/29 (93%) patients who took 95% of the supplements. Obstipation was reported by 4 patients during the first 2 weeks of treatment only. The mean±standard deviation (SD) number of migraine days/month decreased significantly from 6.7±2.4 at baseline to 5.1±2.2 (P=0.008) in week 5-8 and 5.2±2.4 in week 9-12 (P=0.001). The mean±SD intensity of migraine decreased significantly from 6.3±1.5 at baseline to 5.5±1.9 after treatment (P=0.005). The MIDAS score improved from 24.8±25.5 to 16.6±13.5 (P=0.031). However, the mean HDI did not change significantly. In conclusion, probiotics may decrease migraine supporting a possible role for the intestine in migraine management. Feasibility and lack of adverse reactions justify further placebo-controlled studies.

Population model of longitudinal FEV1 data in asthmatics: meta-analysis and predictability of placebo response

Asthma is an obstructive lung disease where the mechanism of disease progression is not fully understood hence motivating the use of empirical models to describe the evolution of the patient's health state. With reference to placebo response, measured in terms of FEV1 (Forced Expiratory Volume in 1 s), a range of empirical models taken from the literature were compared at a single trial level. In particular, eleven GSK trials lasting 12 weeks in mild-to-moderate asthma were used for the modelling of longitudinal placebo responses. Then, the chosen exponential model was used to carry out an individual participant data meta-analysis on eleven trials. A covariate analysis was also performed to find relevant covariates in asthma to be accounted for in the meta-analysis model. Age, gender, and height were found statistically significant (e.g. the taller the patients the higher the FEV1, the older the patients the lower the FEV1, and females have lower FEV1). By truncating each trial at week 4, the predictive properties of the meta-analysis model were also investigated, showing its ability to predict long-term FEV1 response from truncated trials. Summarizing, the study suggests that: (i) the exponential model effectively describes the placebo response; (ii) the meta-analysis approach may prove helpful to simulate new trials as well as to reduce trial duration in view of its predictive properties; (iii) the inclusion of available covariates within the meta-analysis model provides a reduction of the inter-individual variability.

Does short-term placebo response predict the long-term observation? Meta-analysis on forced expiratory volume in 1 second from asthma trials

The objectives of this work were: (1) to characterize the placebo FEV1 response in asthma; (2) to identify the potential factors with strong influence on FEV1; (3) to determine the predictability of the early (week 2) placebo FEV1 response to the longer term (week 12) FEV1 response. Placebo FEV1 data of about 800 subjects from 11 randomized 12-week clinical trials in mild-to-moderate asthmatics were collected. Stepwise logistic regression methods using SAS were used to model the week 12 trough FEV1 change from baseline greater than a clinically relevant value (150 mL) and to select the predictive covariates. The study effect was assessed using hierarchical logistic regression models implemented in WinBUGS. The results indicated that the early (week 2) placebo response was significantly predictive of the FEV1 response at week 12. Age, baseline predicted FEV1, and season showed statistical significance in the model. The final model showed satisfactory predictability with the area under the receiver operating characteristic curve (ROC) of 80%. The late (week 12) FEV1 response with placebo was positively related to the early (week 2) FEV1 change. The use of the predictive modeling approach proposed in this article presents a valuable method to increase the efficiency of clinical trial design in asthma population.

Population-based efficacy modeling of omalizumab in patients with severe allergic asthma inadequately controlled with standard therapy

Omalizumab, a recombinant humanized monoclonal antibody, is the first approved anti-immunoglobulin E (IgE) agent for the treatment of subjects with moderate to severe persistent allergic asthma that are inadequately controlled by the standard of care. The objective of this study was to quantitatively characterize relationships between serum free IgE and pulmonary function (as measured by forced expiratory volume in 1 s [FEV1]) as well as serum free IgE and airway inflammation (as measured by fractional exhaled nitric oxide [FeNO]) using population-based efficacy models. Data were collected from patients in the EXTRA trial who received omalizumab or placebo 150 to 375 mg subcutaneously every 2 or 4 weeks from week 0 to 48 with constant standard of care as background therapy. None of the covariates evaluated, including demographics, disease status, and baseline pharmacodynamic biomarkers, were significant in explaining the variability in the FEV1 or FeNO response to omalizumab. Results from the efficacy models further confirmed the current omalizumab dosing rationale based on the mean target free IgE level of 25 ng/ml and quantified the variability for the target. In addition, the resulting population models could be used to predict population FEV1 or FeNO response for omalizumab and/or other anti-IgE therapeutics for which PK-IgE models are constructed.