Many Flavors of Model-Based Meta-Analysis: Part II - Modeling Summary Level Longitudinal Responses

Quantitative evaluation of the efficacy and safety of first-line systemic therapies for advanced hepatocellular carcinoma

OBJECTIVES

This study aimed to quantitatively evaluate the efficacy and safety of first-line systemic therapies for treating advanced hepatocellular carcinoma (aHCC).

METHODS

The study included clinical trials of first-line systemic therapies for aHCC since the approval of sorafenib in 2007. Hazard function models were used to describe changes in overall survival (OS) and progression-free survival (PFS) over time. Monte Carlo simulation was used to compare OS and PFS for different treatments, including sorafenib, antiangiogenic therapies (AATs) (except sorafenib), immune checkpoint inhibitor (ICI) monotherapy, AAT + targeted therapy, AAT + chemotherapy, AAT + ICIs, and ICIs + ICIs. Furthermore, the objective response rate (ORR) and incidence of grade ≥ 3 adverse events were analyzed.

RESULTS

Fifty studies comprising 12,918 participants were included. AAT + ICIs demonstrated a significant benefit in median OS (mOS), median PFS (mPFS), and ORR (20.5 [95% CI 17.5-24] months, 7.5 [95% CI 6.5-8.8] months, and 24% [95% CI 17%-30%], respectively). ICIs + ICIs and ICI monotherapy ranked second and third, respectively with an mOS of 20 (95% CI 18.5-21.5) months and 14.5 (95% CI 13.5-16) months, respectively. The OS, PFS, and ORR of patients treated with AAT, AAT + targeted therapy, and AAT + chemotherapy were similar to those of patients treated with sorafenib. A higher proportion of patients with Barcelona Clinic Liver Cancer (BCLC) stage C had a shorter OS. OS was associated with publication year, and PFS was associated with the proportion of patients with BCLC stage C. The incidence of grade ≥ 3 adverse events in the ICIs and ICIs + ICIs treatment groups was low.

CONCLUSIONS

The study results provide valuable information from which to base rational clinical drug use and serves as a reliable external control for evaluating new treatments for aHCC.

Model-informed drug discovery and development approaches to inform clinical trial design and regulatory decisions: A primer for the MENA region

Model-Informed Drug Discovery and Development (MID3) represents a transformative approach in pharmaceutical research, integrating quantitative models to inform and optimize decision-making throughout the drug development process. This review explores the current applications, challenges, and future prospects of MID3 within the Middle East and North Africa (MENA) region. By leveraging local data and advanced computational techniques, MID3 has the potential to significantly enhance the efficiency and success rates of drug development tailored to regional health priorities. We discussed successful case studies of applying MID3 at different phases of drug development and clinical trials. Furthermore, we emphasized the critical need for MENA countries to embrace MID3 by investing in workforce training, aligning regulatory frameworks, and fostering collaborative research initiatives. This call to action underscores the importance of a robust MID3 ecosystem, urging policymakers, academic institutions, and industry stakeholders to prioritize and support its integration into the MENA region's healthcare.

New Horizons of Model Informed Drug Development in Rare Diseases Drug Development

Model-informed approaches provide a quantitative framework to integrate all available nonclinical and clinical data, thus furnishing a totality of evidence approach to drug development and regulatory evaluation. Maximizing the use of all available data and information about the drug enables a more robust characterization of the risk-benefit profile and reduces uncertainty in both technical and regulatory success. This offers the potential to transform rare diseases drug development, where conducting large well-controlled clinical trials is impractical and/or unethical due to a small patient population, a significant portion of which could be children. Additionally, the totality of evidence generated by model-informed approaches can provide confirmatory evidence for regulatory approval without the need for additional clinical data. In the article, applications of novel quantitative approaches such as quantitative systems pharmacology, disease progression modeling, artificial intelligence, machine learning, modeling of real-world data using model-based meta-analysis and strategies such as external control and patient-reported outcomes as well as clinical trial simulations to optimize trials and sample collection are discussed. Specific case studies of these modeling approaches in rare diseases are provided to showcase applications in drug development and regulatory review. Finally, perspectives are shared on the future state of these modeling approaches in rare diseases drug development along with challenges and opportunities for incorporating such tools in the rational development of drug products.

Time-Efficacy Relationship of Semaglutide in the Treatment of Type 2 Diabetes Mellitus: A Model-Based Meta-Analysis

OBJECTIVE

Our objective was to quantify the efficacy of subcutaneous once-weekly semaglutide in treating type 2 diabetes mellitus (T2DM) over time.

METHODS

Based on a literature search of the PubMed, Embase, Cochrane, and Web of Science databases, a modified maximum effect (Emax) model including rebound effects was built using model-based meta-analysis with change from baseline in glycated hemoglobin as the efficacy endpoint. This was combined with the covariate model to form a final model, and then theoretical values of Emax and time to reach 50% of Emax (ET50) were obtained for each dose. Model fit and prediction were assessed using goodness-of-fit plots and visual prediction checking.

RESULTS

Emax and ET50 were influenced by the proportion of males and the baseline values, respectively. There was no evidence of a placebo effect with semaglutide. The efficacy of other doses became more significant over time, and a rebound effect was observed after maximum efficacy, at a rate of 0.018. Simulation of the typical efficacy at the different doses yielded a maximum efficacy of -1.58% with 0.5 mg and a maximum efficacy of -1.87% with 1 mg. In addition, the six simulated doses (0, 0.1, 0.2, 0.5, 1, and 2 mg) showed a dose-dependent relationship between dose and efficacy except for 0.4 mg and 0.8 mg. A higher dose would result in greater efficacy and a faster onset of action.

CONCLUSION

The efficacy of semaglutide in glucose control was investigated using the model-based meta-analysis method, which yields new insights into the treatment of T2DM with semaglutide.

SGLT2 Inhibitors in Patients with Heart Failure: A Model-Based Meta-Analysis

AIMS

This study aimed to quantify the effects of sodium-glucose co-transporter 2 (SGLT2) inhibitors on N-terminal pro-B-type natriuretic peptide (NT-proBNP) as a therapeutic approach for heart failure.

METHODS

A systematic literature review was conducted to collect pharmacokinetics (PK) and pharmacodynamics (PD) data on empagliflozin, dapagliflozin, and canagliflozin. Population pharmacokinetic models were developed separately for each drug, along with PK/PD turnover models for SGLT2 inhibitors, to describe the time course of NT-proBNP and simulate its changes over 52 weeks.

RESULTS

A total of 42 publications were included in this study. The results showed that baseline NT-proBNP levels, estimated glomerular filtration rate levels, and body weight significantly influenced the therapeutic effects of SGLT2 inhibitors. Among the studied drugs, canagliflozin demonstrated a greater reduction in NT-proBNP at comparable baseline levels.

CONCLUSIONS

Baseline NT-proBNP concentration, renal function, and body weight were covariates affecting the efficacy of SGLT2 inhibitors in reducing NT-proBNP. Canagliflozin showed the most favorable treatment outcomes at similar baseline levels. This model-based meta-analysis approach may support further drug development for SGLT2 inhibitors.

Quantitative risk-benefit profiles of oral contraceptives, insulin sensitizers and antiandrogens for women with polycystic ovary syndrome: A model-based meta-analysis

Oral contraceptives (OCs), insulin sensitizers, and antiandrogens (AAs), alone or in combination, are commonly used for treating non-fertility indications in polycystic ovary syndrome (PCOS). However, unclear risk-benefit profiles jeopardize their appropriate clinical applications. This study aimed to quantitatively evaluate the effects of the aforementioned medications and to compare their risk-benefit profiles. Randomized controlled trials published until 14th March 2022 were searched in PubMed and Embase. A model-based meta-analysis was developed to examine the time-effect profiles of each medication. The maximal percentage change of the effect (Emax) and time to achieve half of Emax (T50) were estimated. Primary outcomes included menstruation, hirsutism score, free androgen index (FAI), body mass index (BMI), insulin sensitivity, and lipid profiles. Overall, 200 studies (9,685 patients and 385 arms) were identified for modeling. OCs performed exceptionally well in improving menstruation (Emax: 149%; T50: 7.44 weeks), hirsutism score (Emax: 66.2%; T50: 26.2 weeks), and FAI (Emax: 75.7%; T50: 0.51 weeks). However, OCs elevated the triglyceride (TG) level (Emax: 12.6%; T50:1.19 weeks). After 12-week OC treatment, the TG level of approximately 30% of patients, whose baselines were normal, exceeded the reference limit. This suggested that OC-induced dyslipidemia should be routinely monitored. The maximal BMI-lowering effect of metformin was similar to that of placebo (Emax: 3.80%); however, metformin had a shorter T50 (6.67 weeks versus 12.9 weeks). Further, active lifestyle intervention plus placebo significantly decreased BMI (Emax: 8.78%). Adding metformin to active lifestyle intervention accelerated the BMI-lowering effect within 24 weeks, whereas with the extension of this addition beyond 24 weeks, BMI did not reduce further, which indicated that benefits were limited from this prolonged addition. AAs were less potent in reducing hirsutism score (Emax: 40.2% versus 66.2%) and FAI (Emax: 34.5% versus 75.7%) compared to OCs. OC plus metformin combined OC-derived androgen-suppressing effects and metformin-derived insulin-sensitizing effects, and partially relieved the OC-induced TG increase (Emax: 9.76%). Baseline dependency was found in most clinical responses, implying that pharmacotherapies tailored based on baselines achieved more clinical improvements. This study presents new quantitative evidence on pharmacotherapies for PCOS. Currently, long-term risk-benefit profiles and emerging therapies are inadequately reported and require more further research.

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.

Optimization of secukinumab dose regimens in patients with moderate-to-severe plaque psoriasis via exposure-response modeling

BACKGROUND

Further dose optimization is required for patients with moderate-to-severe plaque psoriasis who do not benefit from the approved secukinumab dose regimen. This study aimed to develop an exposure-response model for secukinumab to recommend dose regimens for patients of different body weights.

METHODS

We searched the PubMed and Cochrane Library databases for randomized controlled trials using PASI 75 and PASI 90 response rates as primary outcomes. A model-based meta-analysis was developed to quantitatively analyze the distribution of six secukinumab dose regimens in patients weighing 50-120 kg.

RESULTS

Sixteen trials involving 6,197 subjects were included in the analysis. The established model accurately described the time-course characteristics of PASI 75 and PASI 90 response rates over 52 weeks. Simulations indicated that maintenance doses could be reduced to 150 mg every 4 weeks and to 150 mg every 3 weeks for patients weighing 50 and 60 kg, respectively. In contrast, maintenance doses of 300 mg every 3 weeks should be selected for patients weighing 120 kg. Patients weighing 70-110 kg remained on approved maintenance doses of 300 mg every 4 weeks.

CONCLUSIONS

Based on patient body weights, the exposure-response model recommends efficacious and economical dose regimens for patients with moderate-to-severe plaque psoriasis.

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.

Pharmacokinetic Characteristics of Siponimod in Healthy Volunteers and Patients With Multiple Sclerosis: Analyses of Published Clinical Trials

Objectives: This study aimed to investigate the pharmacokinetic characteristics of siponimod in healthy volunteers and patients with MS based on aggregated data from published clinical trials, and to explore the factors influencing siponimod exposure.

Methods: A total of 476 siponimod plasma concentrations aggregated from 28 dosage groups (corresponding to 294 healthy volunteers and 207 patients with MS) were collected from published clinical trials. Population pharmacokinetic (PPK) analysis was performed using a nonlinear, mixed-effect modeling approach. The pharmacokinetic properties of siponimod in healthy volunteers and patients with MS were compared, and the influence of covariates on siponimod exposure was evaluated using both PPK analysis and noncompartmental analysis (NCA).

Results: A one-compartment model with first-order absorption and elimination adequately described siponimod pharmacokinetics. The typical population parameter estimates of clearance (CL/F), apparent volume of distribution (V/F), and absorption rate constant (ka) were 3.17 L/h, 112.70 L, and 0.38 h⁻¹, respectively. An 11.85% lower siponimod clearance was estimated for patients with MS relative to healthy volunteers. Subgroup analyses using NCA assessments revealed that siponimod presented an accumulation index of approximately 2 after multiple administration. Compared with nonobese participants, obese participants had a relatively lower dose-corrected area under the concentration-time curve (AUC_0-∞/D) (0.31 vs. 0.42 h/L) and V/F (120.95 vs. 133.75 L), and a relatively higher CL/F (3.25 vs. 3.21 L/h). Participants with CYP2C9*2/*3, *1/*3, and *3/*3 genotypes experienced an increased (1.3- and 3.4-fold, respectively) AUC_0-∞/D and a decreased (0.7- and 0.3-fold, respectively) CL/F compared with those in participants with the CYP2C9*1/*1, *1*2, and *2*2 genotypes. Fluconazole combination led to a decrease in CL/F (approximately 0.5 times) and an increase in AUC_0-∞/D (approximately 1.3 times).

Conclusion: Siponimod pharmacokinetic properties in healthy volunteers and patients with MS were explored using complementary model-based meta-analysis (MBMA) and NCA approaches. A slightly lower siponimod clearance was observed in patients with MS than in healthy volunteers. The dosage regimen, body mass index, CYP2C9 genetic polymorphism and fluconazole combination may had influences on siponimod pharmacokinetics. Such model paves the road to more population-based analyses in different patient populations with MS to quantify the effect of any influencing factors on siponimod pharmacokinetics.

Model‐based meta‐analysis of salbutamol pharmacokinetics and practical implications for doping control

Salbutamol was included in the prohibited list of the World Anti‐Doping Agency (WADA) in 2004. Although systemic intake is banned, inhalation for asthma is permitted but with dosage restrictions. The WADA established a urinary concentration threshold to distinguish accordingly prohibited systemic self‐administration from therapeutic prescription by inhalation. This study aimed at evaluating the ability of the WADA threshold to differentiate salbutamol therapeutic use from violation of antidoping rules. Concentration‐time profile of salbutamol in plasma and its excretion in urine was characterized through a model‐based meta‐analysis of individual and aggregate data collected after administration of a large range of doses following different modes of administration and under a variety of conditions. The developed model adequately fitted salbutamol plasma and urine concentration‐time profiles of the 13 selected studies. Model‐based simulations confirmed that a wide range of salbutamol urine concentrations might be measured after drug intake. Although violation of the WADA Code can be strongly suspected in individuals showing very high salbutamol urine concentrations, uncertainty remains for values close to the WADA threshold as they can be compatible with both permitted therapeutic use and violation. Although not entirely discriminant, the current WADA rule is globally supported by our appraisal. It could be further improved by a slight and reasonable adjustment of inhaled daily dosages allowed for therapeutic use. Our model might help antidoping experts in the evaluation of suspected doping cases through confronting the athlete's urine measurements with their allegations about salbutamol treatment.

Development and Application of the Placebo Response Model in Clinical Trials for Primary Sjögren's Syndrome

This study aimed to develop a placebo response model for pharmaceutical clinical trials of primary Sjogren's syndrome,and to quantitatively analyze the distribution and related factors influencing the placebo response to further optimize the design of clinical trials and evaluate the results of single-arm clinical trials. Public databases, including PubMed, Embase, and Cochrane Library were searched for reports on randomized placebo-controlled trials for Sjögren's syndrome which used the change from baseline in ESSDAI score as the primary outcome. The model-based meta-analysis method was used to evaluate the time course and the related influencing factors of the placebo response for ESSDAI in such clinical trials. A virtual placebo control group was constructed based on the final placebo response model to determine the treatment efficacy of belimumab and cyclosporine A for primary Sjögren's syndrome in a single-arm study. A total of 12 studies involving 450 subjects were included in the analysis. The established model described the time-course characteristics of the changes in ESSDAI score from the baseline in the 48 weeks placebo group. We found that the onset time of placebo response was approximately 12 weeks, and its efficacy plateaued at 48 weeks. The baseline ESSDAI score had a significant effect on the maximum value of the placebo response; the maximum value of the placebo response decreased by 0.552 for every 1 score rise in the baseline ESSDAI score. The efficacy of belimumab and cyclosporine A in the single-arm trial was comparable to that of the placebo response at the same baseline; no significant therapeutic advantage was observed. The placebo response model established in this study could provide a basis for designing clinical trials for primary Sjogren's syndrome in the future. It may also provide a reliable external efficacy control standard for single-arm clinical trials.

A Systematic Review and Meta-Analysis on the Longitudinal Effects of Unilateral Knee Extension Exercise on Muscle Strength

The aim of the study was to investigate the time-dependent increase in the knee extensors' isometric strength as a response to voluntary, unilateral, isometric knee extension exercise (UIKEE). To do so, a systematic review was carried out to obtain data for a Bayesian longitudinal model-based meta-analysis (BLMBMA). For the systematic review, PubMed, Web of Science, SCOPUS, Chochrane Library were used as databases. The systematic review included only studies that reported on healthy, young individuals performing UIKEE. Studies utilizing a bilateral training protocol were excluded as the focus of this review lied on unilateral training. Out of the 3,870 studies, which were reviewed, 20 studies fulfilled the selected inclusion criteria. These 20 studies were included in the BLMBMA to investigate the time-dependent effects of UIKEE. If compared to the baseline strength of the trained limb, these data reveal that UKIEE can increase the isometric strength by up to 46%. A meta-analysis based on the last time-point of each available study was employed to support further investigations into UIKEE-induced strength increase. A sensitivity analysis showed that intensity of training (%MVC), fraction of male subjects and the average age of the subject had no significant influence on the strength gain. Convergence of BLMBMA revealed that the peak strength increase is reached after ~4 weeks of UIKEE training.

Population Pharmacokinetics of Sertraline in Healthy Subjects: a Model-Based Meta-analysis

Sertraline pharmacokinetics is poorly understood and highly variable due to large between-subject variability with inconsistent reports for oral bioavailability. The study objective was to characterize sertraline pharmacokinetics by developing and validating a sertraline population pharmacokinetic (PK) model in healthy subjects using published clinical PK data. We carried a systematic literature search in PubMed in October 2015 and identified 27 pharmacokinetic studies of sertraline conducted in healthy adult subjects and reported in the English language. Sixty mean plasma concentration-time profiles made of 748 plasma concentrations following IV, single, and multiple oral doses ranging from 5 to 400 mg were extracted and analyzed for dose proportionality by a log-linear model and fitted to a 2-compartment pharmacokinetic model in NONMEM using a model-based meta-analysis (MBMA) approach. After a single oral dose, sertraline C_max and AUC_∞ increased with dose proportionally between 50 and 200 mg, and bioavailability increased nonlinearly with dose from 5 to 50 mg and plateaued afterwards while T_max and t_1/2 did not change with dose. Following multiple oral doses, C_max and AUC_∞ increased proportionally with dose across the entire dose range (5-200 mg) while bioavailability, T_max, and t_1/2 remained constant with dose. Sertraline absorption was time-dependent and best described by a sigmoidal E_max function of time after dose. Study findings indicate that sertraline PK is linear in healthy adult subjects at doses ≥ 50 mg, and exposures were nonlinear only after single oral doses < 50 mg likely due to reduced bioavailability.

Predicting the dose of vancomycin in ICU patients receiving different types of RRT therapy: a model-based meta-analytic approach

AIM

Previous pharmacokinetic (PK) studies have proposed various dosing regimens for vancomycin in intensive care unit (ICU) patients undergoing renal replacement therapy (RRT), but all are restricted to specific RRT modalities. To be useful in practice, a population PK model would need to predict vancomycin clearance during any RRT modality. Development of such a model is feasible using meta-analysis of published summarized estimates of vancomycin PK parameters. Our aims were: (i) to develop and validate a population PK model for vancomycin that takes into account any RRT modalities, and (ii) to predict vancomycin dosing for RRT patients in ICU.

METHODS

Vancomycin pharmacokinetics were assumed to be two-compartmental, total body clearance being the sum of non-RRT clearance and RRT-induced clearance. Drug disposition and non-RRT clearance parameters were estimated by systematic review and meta-analysis of previously published parameter estimates. The relationship between RRT-induced clearance and RRT flowrate settings was assessed using a model-based meta-analysis. Prediction performances of the PK model were assessed using external data.

RESULTS

The meta-analyses of disposition parameters, non-RRT clearance and RRT-induced clearance included 11, 6 and 38 studies (84 RRT clearance measurements) respectively. The model performed well in predicting external individual PK data. Individual vancomycin concentrations during RRT were accurately predicted using Bayesian estimation based solely on pre-RRT measurements.

CONCLUSIONS

The PK model allowed accurate prediction of the vancomycin pharmacokinetics during RRT in ICU patients. Based on the model of RRT-induced clearance, an appropriate adjustment of the vancomycin dosing regimen could be proposed for any kind of flowrate settings.

Model-Based Meta-Analysis in Ankylosing Spondylitis: A Quantitative Comparison of Biologics and Small Targeted Molecules

Information on the comparative efficacy is important for drug development as well as drug therapy. Up to now, the relative efficacy of approved biologics and many agents under investigation in ankylosing spondylitis (AS) are still unclear. The objective of this study was to quantify the relative efficacy and time course of various treatments measured by the Ankylosing Spondylitis Assessment Study group response criteria 20 scores (ASAS20), change from baseline in Bath Ankylosing Spondylitis Disease Activity Index (BASDAI), and Bath Ankylosing Spondylitis Functional Index (BASFI). There were 34 double-blinded trials of 10 biologics and small molecules encompassing 5,339 patients with AS were included in this analysis. Three mathematical models with nonparametric placebo estimations were used to describe the longitudinal profile for the above three efficacy measures. The results detected significant differences among included treatments, and infliximab and golimumab were found to have the highest efficacy in given dosage regimens across all measures.