Utilization of model-based meta-analysis to delineate the net efficacy of taspoglutide from the response of placebo in clinical trials

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.

Analysis of time course and dose effect of tacrolimus on proteinuria in lupus nephritis patients

WHAT IS KNOWN AND OBJECTIVES

Tacrolimus is used to treat patients with lupus nephritis; however, its time course and dose effect on proteinuria in lupus nephritis patients remain unknown. The purpose of this study was to determine the time course and dose effect of tacrolimus on proteinuria in lupus nephritis patients via model-based meta-analysis (MBMA).

METHODS

PubMed, Web of Science, Cochrane Library and ClinicalTrials.gov databases were systematically searched for information on the efficacy of tacrolimus against proteinuria in lupus nephritis patients. Useful data were extracted to build a model for the population studied using a non-linear mixed-effect model (NONMEM). This model was applied to simulate time course of tacrolimus on proteinuria using Monte Carlo simulations.

RESULTS

Ten clinical studies that recruited 222 patients with lupus nephritis were included. Based on various diagnostic plots, we found that the established model described the observed data reasonably well. In addition, the typical E_max and ET₅₀ of tacrolimus for 24-hour proteinuria in lupus nephritis patients were -5.88 g and 0.37 months, respectively. The baseline value of 24-hour proteinuria affected E_max . No significant dose-response relationship was observed in the range of tacrolimus concentration used in the present study (3-10 ng/mL), indicating that the effect of tacrolimus on proteinuria depends on effective concentration range and not the dose. However, the time course relationship was obvious; the efficacy of tacrolimus increased over time, reaching a plateau (80% E_max ) at approximately 1.48 months from the beginning of treatment.

WHAT IS NEW AND CONCLUSION

When the concentration range of tacrolimus is maintained at 3-10 ng/mL, at least 1.48 months of treatment is required to achieve a better outcome with regard to proteinuria in lupus nephritis patients.

The response-time relationship and covariate effects of acupuncture for chronic pain: A systematic review and model-based longitudinal meta-analysis

BACKGROUND AND OBJECTIVE

Critical clinical questions regarding how soon and how long the analgesic effect will be achieved by acupuncture, as well as who will be responsive to acupuncture, need further address. The aim of the study was to investigate the response-time relationship and covariate effects of acupuncture.

DATABASES AND DATA TREATMENT

PubMed and EMBASE were searched up to December 2018 for randomized controlled trials that involved sham acupuncture, true acupuncture and conventional therapy. We used a model-based longitudinal meta-analysis to characterize the response-time profile of these treatments.

RESULTS

Seventy-seven randomized clinical trials involved chronic shoulder, neck, knee and low back pain were included. The response-time analysis suggested that the treatment duration of acupuncture will be 5 weeks or more to achieve 80% of maximum analgesic effect. Moreover a lower baseline pain intensity and the location of low back pain resulted in a lower pain relief of acupuncture intervention. The absolute maximum analgesic effects of sham acupuncture and conventional therapy were 22.6 and 15.8 points at a 0-100 NRS scale. The absolute effect of true acupuncture was 26.1 points for low back pain (relative effect of 3.5 and 9.4 points to sham and conventional therapy), 34.9 points for other pain body locations (relative effect of 12.3 and 19.1 points to sham and conventional therapy), in patients with a baseline pain intensity of 60 points.

CONCLUSION

The treatment duration of acupuncture will not be less than 5 weeks to achieve 80% maximum analgesic effect. Higher analgesic effect was related to higher baseline pain intensity and pain location of neck, shoulder and knee.

SIGNIFICANCE

Our systematic review and meta-analysis provides the clear evidence for the treatment duration and significant related covariates of acupuncture intervention for chronic pain. These results provide useful suggestion for acupuncture intervention in clinical pain management.