Time-course and dose-effect of omalizumab in treating chronic idiopathic urticaria/chronic spontaneous urticaria

PURPOSE

Several studies have shown that subcutaneous injections of omalizumab can treat chronic idiopathic/spontaneous urticaria (CIU/CSU) patients by only assessing the efficacy on specific endpoints. This study aimed to quantitatively analyze different doses of omalizumab in CIU/CSU and compare it with ligelizumab.

METHODS

Literature searches were performed in PubMed, Embase, and Web of Science databases. A model-based meta-analysis (MBMA) was utilized to develop a model incorporating time since the initiation of treatment and dose for omalizumab, with the change from baseline in Urticaria Activity Score (CFB-UAS7) as the primary efficacy endpoint. The time-course and dose-effect relationship throughout the omalizumab treatment period was analyzed, and the findings were compared with those of the investigational ligelizumab.

RESULTS

The model equation for the CFB-UAS7 was established as E = -Emax × time/(ET50 + time) × (b0 + b1 × dose). The estimated values of the model parameters E max ,ET 50 , b 0 , and b 1 were -1.16, 1.26 weeks, -9.90, and -0.0361 mg-1, respectively. At week 12 after the first dose, the model-predicted CFB-UAS7 for 150 mg and 300 mg of omalizumab were -16.0 (95% CI, -17.2 to -14.8) and -21.7 (95% CI, -22.9 to -20.5), respectively. In the PEARL-1 trial, the CFB-UAS7 for 72 mg and 120 mg of ligelizumab were -19.4 (95% CI, -20.7 to -18.1) and -19.3 (95% CI, -20.6 to -18.0), respectively. In the PEARL-2 trial, these values were -19.2 (95% CI, -20.5 to -17.9) and -20.3 (95% CI, -21.6 to -19.0), respectively.

CONCLUSION

Omalizumab showed a significant dose-dependent effect in the treatment of CSU. Both 72 mg and 120 mg ligelizumab might have the potential to outperform 150 mg (but not 300 mg) omalizumab.

Model-based meta-analysis to quantify the effects of short interfering RNA therapeutics on hepatitis B surface antigen turnover in hepatitis B-infected mice

The objective of this study was to compare the efficacy of short interfering RNA therapeutics (siRNAs) in reducing hepatitis B surface antigen (HBsAg) levels in hepatitis B-infected (HBV) mice across multiple siRNA therapeutic classes using model-based meta-analysis (MBMA) techniques. Literature data from 10 studies in HBV-infected mice were pooled, including 13 siRNAs, formulated as liposomal nanoparticles (LNPs) or conjugated to either cholesterol (chol) or N-acetylgalactosamine (GalNAc). Time course of the baseline- and placebo-corrected mean HBsAg profiles were modeled using kinetics of drug effect (KPD) model coupled to an indirect response model (IRM) within a longitudinal non-linear mixed-effects MBMA framework. Single and multiple dose simulations were performed exploring the role of dosing regimens across evaluated siRNA classes. The HBsAg degradation rate (0.72 day-1) was consistent across siRNAs but exhibited a large between-study variability of 31.4% (CV%). The siRNA biophase half-life was dependent on the siRNA class and was highest for GalNAc-siRNAs (21.06 days) and lowest for chol-siRNAs (2.89 days). ID50 estimates were compound-specific and were lowest for chol-siRNAs and highest for GalNAc-siRNAs. Multiple dose simulations suggest GalNAc-siRNAs may require between 4 and 7 times less frequent dosing at higher absolute dose levels compared to LNP-siRNAs and chol-siRNAs, respectively, to reach equipotent HBsAg-lowering effects in HBV mice. In conclusion, non-clinical HBsAg concentration-time data after siRNA administration can be described using the presented KPD-IRM MBMA framework. This framework allows to quantitatively compare the effects of siRNAs on the HBsAg time course and inform dose and regimen selection across siRNA classes. These results may support siRNA development, optimize preclinical study designs, and inform data analysis methodology of future anti-HBV siRNAs; and ultimately, support siRNA model-informed drug development (MIDD) strategies.

A model-informed approach to accelerate the clinical development of cofrogliptin (HSK7653), a novel ultralong-acting dipeptidyl peptidase-4 inhibitor

AIM

To employ a model-informed drug development approach in facilitating decision making and expediting the clinical progress of cofrogliptin (HSK7653), a novel ultralong-acting dipeptidyl peptidase-4 (DPP-4) inhibitor, for the treatment of type 2 diabetes (T2D) via a biweekly dosing regimen.

METHODS

Firstly, a population pharmacokinetics and pharmacodynamics (PopPKPD) model was developed using PK and PD data from a single ascending dose study to simulate the PK and PD time profiles of HSK7653 after multiple doses. Secondly, model-based meta-analysis (MBMA) was performed on published clinical studies of Eastern Asian subjects for all DPP-4 inhibitors. We hypothesized a consistent relationship between PK and DPP-4 inhibition in both healthy individuals and in those with T2D, establishing a quantitative correlation between DPP-4 inhibition and HbA1c. Finally, the predicted PK/DPP-4 inhibition/HbA1c profiles were validated by T2D patients in late clinical trials.

RESULTS

The PK/DPP-4 inhibition/HbA1c profiles of T2D patients treated with HSK7653 matched the modelled data. Our PopPKPD and MBMA models predict multiple ascending dosing PK and PD characteristics from single ascending dosing data, as well as the long-term efficacy in T2D patients, based on healthy subjects.

CONCLUSIONS

Successful waiver approval for the phase 2b dose-finding study was achieved through model-informed recommendations, facilitating the clinical development of HSK7653 and other DPP-4 inhibitors.

Model-based meta-analysis using latent variable modeling to set benchmarks for new treatments of systemic lupus erythematosus

Several investigational agents are under evaluation in systemic lupus erythematosus (SLE) clinical trials but quantitative frameworks to enable comparison of their efficacy to reference benchmark treatments are lacking. To benchmark SLE treatment effects and identify clinically important covariates, we developed a model-based meta-analysis (MBMA) within a latent variable model framework for efficacy end points and SLE composite end point scores (BILAG-based Composite Lupus Assessment and Systemic Lupus Erythematosus Responder Index) using aggregate-level data on approved and investigational therapeutics. SLE trials were searched using PubMed and www.clinicaltrials.gov for treatment name, SLE and clinical trial as search criteria that resulted in four data structures: (1) study and investigational agent, (2) dose and regimen, (3) baseline descriptors, and (4) outcomes. The final dataset consisted of 25 studies and 81 treatment arms evaluating 16 different agents. A previously developed (K Goteti et al. 2022) SLE latent variable model of data from placebo arms (placebo + standard of care treatments) was used to describe aggregate SLE end points over time for the various SLE placebo and treatment arms in a Bayesian MBMA framework. Continuous dose-effect relationships using a maximum effect model were included for anifrolumab, belimumab, CC-220 (iberdomide), epratuzumab, lulizumab pegol, and sifalimumab, whereas the remaining treatments were modeled as discrete dose effects. The final MBMA model was then used to benchmark these compounds with respect to the maximal efficacy on the latent variable compared to the placebo. This MBMA illustrates the application of latent variable models in understanding the trajectories of composite end points in chronic diseases and should enable model-informed development of new investigational agents in SLE.

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.

Best Practices and Considerations for Clinical Pharmacology and Pharmacometric Aspects for Optimal Development of CAR-T and TCR-T Cell Therapies: An Industry Perspective

With the promise of a potentially "single dose curative" paradigm, CAR-T cell therapies have brought a paradigm shift in the treatment and management of hematological malignancies. Both CAR-T and TCR-T cell therapies have also made great progress toward the successful treatment of solid tumor indications. The field is rapidly evolving with recent advancements including the clinical development of "off-the-shelf" allogeneic CAR-T therapies that can overcome the long and difficult "vein-to-vein" wait time seen with autologous CAR-T therapies. There are unique clinical pharmacology, pharmacometric, bioanalytical, and immunogenicity considerations and challenges in the development of these CAR-T and TCR-T cell therapies. Hence, to help accelerate the development of these life-saving therapies for the patients with cancer, experts in this field came together under the umbrella of International Consortium for Innovation and Quality in Pharmaceutical Development (IQ) to form a joint working group between the Clinical Pharmacology Leadership Group (CPLG) and the Translational and ADME Sciences Leadership Group (TALG). In this white paper, we present the IQ consortium perspective on the best practices and considerations for clinical pharmacology and pharmacometric aspects toward the optimal development of CAR-T and TCR-T cell therapies.

Pharmacokinetic analysis across studies to drive knowledge-integration: A tutorial on individual patient data meta-analysis (IPDMA)

Answering challenging questions in drug development sometimes requires pharmacokinetic (PK) data analysis across different studies, for example, to characterize PKs across diverse regions or populations, or to increase statistical power for subpopulations by combining smaller size trials. Given the growing interest in data sharing and advanced computational methods, knowledge integration based on multiple data sources is increasingly applied in the context of model-informed drug discovery and development. A powerful analysis method is the individual patient data meta-analysis (IPDMA), leveraging systematic review of databases and literature, with the most detailed data type of the individual patient, and quantitative modeling of the PK processes, including capturing heterogeneity of variance between studies. The methodology that should be used in IPDMA in the context of population PK analysis is summarized in this tutorial, highlighting areas of special attention compared to standard PK modeling, including hierarchical nested variability terms for interstudy variability, and handling between-assay differences in limits of quantification within a single analysis. This tutorial is intended for any pharmacological modeler who is interested in performing an integrated analysis of PK data across different studies in a systematic and thorough manner, to answer questions that transcend individual primary studies.

Joint longitudinal model-based meta-analysis of FEV1 and exacerbation rate in randomized COPD trials

Model-based meta-analysis (MBMA) is an approach that integrates relevant summary level data from heterogeneously designed randomized controlled trials (RCTs). This study not only evaluated the predictability of a published MBMA for forced expiratory volume in one second (FEV₁) and its link to annual exacerbation rate in patients with chronic obstructive pulmonary disease (COPD) but also included data from new RCTs. A comparative effectiveness analysis across all drugs was also performed. Aggregated level data were collected from RCTs published between July 2013 and November 2020 (n = 132 references comprising 156 studies) and combined with data used in the legacy MBMA (published RCTs up to July 2013 - n = 142). The augmented data (n = 298) were used to evaluate the predictive performance of the published MBMA using goodness-of-fit plots for assessment. Furthermore, the model was extended including drugs that were not available before July 2013, estimating a new set of parameters. The legacy MBMA model predicted the post-2013 FEV₁ data well, and new estimated parameters were similar to those of drugs in the same class. However, the exacerbation model overpredicted the post-2013 mean annual exacerbation rate data. Inclusion of year when the study started on the pre-treatment placebo rate improved the model predictive performance perhaps explaining potential improvements in the disease management over time. The addition of new data to the legacy COPD MBMA enabled a more robust model with increased predictability performance for both endpoints FEV₁ and mean annual exacerbation rate.

Asia-Inclusive Clinical Research and Development Enabled by Translational Science and Quantitative Clinical Pharmacology: Toward a Culture That Challenges the Status Quo

Access lag to innovative therapies in Asian populations continues to present a challenge to global health. Recent progressive changes in the global regulatory landscape, including newer guidelines, are enabling simultaneous global drug development and near-simultaneous global drug registration. The International Conference on Harmonization (ICH) E17 guideline outlines general principles for the design and analysis of multiregional clinical trials (MRCTs). We posit that translational research and quantitative clinical pharmacology tools are core enablers for Asia-inclusive global drug development aligned with ICH E17 principles. Assessment of ethnic sensitivity should be initiated early in the development lifecycle to inform the need for, and extent of, Asian phase I ethno-bridging data. Relevant ethno-bridging data may be generated as standalone Asian phase I trials, as part of Western First-In-Human trials, or under accelerated development settings as a lead-in phase in an MRCT. Quantitative understanding of human clearance mechanisms and pharmacogenetic factors is vital to forecasting ethnic sensitivity in drug exposure using physiologically-based pharmacokinetic models. Stratification factors to control heterogeneity in MRCTs can be identified by reverse translational research incorporating pharmacometric disease models and model-based meta-analyses. Because epidemiological variations can extend to the molecular level, quantitative systems pharmacology models may be useful in forecasting how molecular variation in therapeutic targets or pathway proteins across populations might impact treatment outcomes. Through prospective evaluation of conservation in drug- and disease-related intrinsic and extrinsic factors, a pooled East Asian region can be implemented in Asia-inclusive MRCTs to maximize efficiency in substantiating evidence of benefit-risk for the region at-large with a Totality of Evidence approach.

Accelerating model-informed decisions for COVID-19 vaccine candidates using a model-based meta-analysis approach

Background

The COVID-19 pandemic has increased the need for innovative quantitative decision tools to support rapid development of safe and efficacious vaccines against SARS-CoV-2. To meet that need, we developed and applied a model-based meta-analysis (MBMA) approach integrating non-clinical and clinical immunogenicity and protection data.

Methods

A systematic literature review identified studies of vaccines against SARS-CoV-2 in rhesus macaques (RM) and humans. Summary-level data of 13 RM and 8 clinical trials were used in the analysis. A RM MBMA model was developed to quantify the relationship between serum neutralizing (SN) titres after vaccination and peak viral load (VL) post-challenge in RM. The translation of the RM MBMA model to a clinical protection model was then carried out to predict clinical efficacies based on RM data alone. Subsequently, clinical SN and efficacy data were integrated to develop three predictive models of efficacy – a calibrated RM MBMA, a joint (RM-Clinical) MBMA, and the clinical MBMA model. The three models were leveraged to predict efficacies of vaccine candidates not included in the model and efficacies against newer strains of SARS-CoV-2.

Findings

Clinical efficacies predicted based on RM data alone were in reasonable agreement with the reported data. The SN titre predicted to provide 50% efficacy was estimated to be about 21% of the mean human convalescent titre level, and that value was consistent across the three models. Clinical efficacies predicted from the MBMA models agreed with reported efficacies for two vaccine candidates (BBV152 and CoronaVac) not included in the modelling and for efficacies against delta variant.

Interpretation

The three MBMA models are predictive of protection against SARS-CoV-2 and provide a translational framework to enable early Go/No-Go and study design decisions using non-clinical and/or limited clinical immunogenicity data in the development of novel SARS-CoV-2 vaccines.

Funding

This study was funded by Merck Sharp & Dohme LLC, a subsidiary of Merck & Co., Inc., Rahway, NJ, USA.

Ulcerative Colitis and Acute Severe Ulcerative Colitis Patients Are Overlooked in Infliximab Population Pharmacokinetic Models: Results from a Comprehensive Review

Ulcerative colitis (UC) is part of the inflammatory bowels diseases, and moderate to severe UC patients can be treated with anti-tumour necrosis α monoclonal antibodies, including infliximab (IFX). Even though treatment of UC patients by IFX has been in place for over a decade, many gaps in modelling of IFX PK in this population remain. This is even more true for acute severe UC (ASUC) patients for which early prediction of IFX pharmacokinetic (PK) could highly improve treatment outcome. Thus, this review aims to compile and analyse published population PK models of IFX in UC and ASUC patients, and to assess the current knowledge on disease activity impact on IFX PK. For this, a semi-systematic literature search was conducted, from which 26 publications including a population PK model analysis of UC patients receiving IFX therapy were selected. Amongst those, only four developed a model specifically for UC patients, and only three populations included severe UC patients. Investigations of disease activity impact on PK were reported in only 4 of the 14 models selected. In addition, the lack of reported model codes and assessment of predictive performance make the use of published models in a clinical setting challenging. Thus, more comprehensive investigation of PK in UC and ASUC is needed as well as more adequate reports on developed models and their evaluation in order to apply them in a clinical setting.

The potential of a data centred approach & knowledge graph data representation in chemical safety and drug design

Big Data pervades nearly all areas of life sciences, yet the analysis of large integrated data sets remains a major challenge. Moreover, the field of life sciences is highly fragmented and, consequently, so is its data, knowledge, and standards. This, in turn, makes integrated data analysis and knowledge gathering across sub-fields a demanding task. At the same time, the integration of various research angles and data types is crucial for modelling the complexity of organisms and biological processes in a holistic manner. This is especially valid in the context of drug development and chemical safety assessment where computational methods can provide solutions for the urgent need of fast, effective, and sustainable approaches. At the same time, such computational methods require the development of methodologies suitable for an integrated and data centred Big Data view. Here we discuss Knowledge Graphs (KG) as a solution to a data centred analysis approach for drug and chemical development and safety assessment. KGs are knowledge bases, data analysis engines, and knowledge discovery systems all in one, allowing them to be used from simple data retrieval, over meta-analysis to complex predictive and knowledge discovery systems. Therefore, KGs have immense potential to advance the data centred approach, the re-usability, and informativity of data. Furthermore, they can improve the power of analysis, and the complexity of modelled processes, all while providing knowledge in a natively human understandable network data model.

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.

Methods for Extrapolating Survival Analyses for the Economic Evaluation of Advanced Therapy Medicinal Products

There are two significant challenges for analysts conducting economic evaluations of advanced therapy medicinal products (ATMPs): (i) estimating long-term treatment effects in the absence of mature clinical data, and (ii) capturing potentially complex hazard functions. This review identifies and critiques a variety of methods that can be used to overcome these challenges. The narrative review is informed by a rapid literature review of methods used for the extrapolation of survival analyses in the economic evaluation of ATMPs. There are several methods that are more suitable than traditional parametric survival modelling approaches for capturing complex hazard functions, including, cure-mixture models and restricted cubic spline models. In the absence of mature clinical data, analysts may augment clinical trial data with data from other sources to aid extrapolation, however, the relative merits of employing methods for including data from different sources is not well understood. Given the high and potentially irrecoverable costs of making incorrect decisions concerning the reimbursement or commissioning of ATMPs, it is important that economic evaluations are correctly specified, and that both parameter and structural uncertainty associated with survival extrapolations are considered. Value of information analyses allow for this uncertainty to be expressed explicitly, and in monetary terms.

Quantitative Comparison of the Clinical Efficacy of 6 Classes Drugs for IgA Nephropathy: A Model-Based Meta-Analysis of Drugs for Clinical Treatments

Introduction

There is a wide variety of drugs for the clinical treatment of immunoglobulin A (IgA) nephropathy; however, previous studies have failed to clarify the quantitative differences in the efficacy of various drugs. In this study, we aimed to quantitatively compare the clinical efficacy of 6 classes of drugs with different pharmacological mechanisms for the treatment of IgA nephropathy and to identify relevant influencing factors.

Methods

Clinical trials of drugs for the treatment of IgA nephropathy were obtained from public databases. The change in daily urinary protein excretion from baseline was used as the efficacy index, and the time–effect model was established using a model-based meta-analysis method. Based on the final model, the typical efficacy was simulated, and the differences in efficacy were compared.

Results

A total of 40 studies with 2288 subjects were included in this study. The results showed that the time–effect relationship of the placebo and 6 classes of drugs was consistent with the E_max model. The placebo reduced urinary protein excretion by up to 0.44 g/day, and it took more than 27 months to reach half of its maximum effect. The onset of the 6 classes of drugs were the same; they all reached half of their maximum effect after 5.59 months. More importantly, we found a significant influence of urinary protein baseline on drug efficacy, as indicated by an increase of 0.63 g/day in the theoretical maximum effect of drugs for every 1 g/day increase in urinary protein baseline. After correcting for the urinary protein baseline, the order of efficacy of the 6 classes of drugs was as follows: corticosteroids > immunosuppressants > other drugs > renin–angiotensin system blockers > antiplatelet agents > N-3 fatty acids.

Conclusion

This study provides the first comprehensive quantitative analysis of the differences in the efficacy of 6 classes of drugs with different pharmacological mechanisms for treating IgA nephropathy. The results of this study provide an important reference for the rational clinical use of drugs for IgA nephropathy, and also provide a reliable efficacy standard for the development of new drugs for IgA nephropathy.

Solving the Evidence Interpretability Crisis in Health Technology Assessment: A Role for Mechanistic Models?

Health technology assessment (HTA) aims to be a systematic, transparent, unbiased synthesis of clinical efficacy, safety, and value of medical products (MPs) to help policymakers, payers, clinicians, and industry to make informed decisions. The evidence available for HTA has gaps—impeding timely prediction of the individual long-term effect in real clinical practice. Also, appraisal of an MP needs cross-stakeholder communication and engagement. Both aspects may benefit from extended use of modeling and simulation. Modeling is used in HTA for data-synthesis and health-economic projections. In parallel, regulatory consideration of model informed drug development (MIDD) has brought attention to mechanistic modeling techniques that could in fact be relevant for HTA. The ability to extrapolate and generate personalized predictions renders the mechanistic MIDD approaches suitable to support translation between clinical trial data into real-world evidence. In this perspective, we therefore discuss concrete examples of how mechanistic models could address HTA-related questions. We shed light on different stakeholder's contributions and needs in the appraisal phase and suggest how mechanistic modeling strategies and reporting can contribute to this effort. There are still barriers dissecting the HTA space and the clinical development space with regard to modeling: lack of an adapted model validation framework for decision-making process, inconsistent and unclear support by stakeholders, limited generalizable use cases, and absence of appropriate incentives. To address this challenge, we suggest to intensify the collaboration between competent authorities, drug developers and modelers with the aim to implement mechanistic models central in the evidence generation, synthesis, and appraisal of HTA so that the totality of mechanistic and clinical evidence can be leveraged by all relevant stakeholders.

Quantitative Analysis of the Efficacy of PARP Inhibitors as Maintenance Therapy in Recurrent Ovarian Cancer

Objective: This study aimed to establish a pharmacodynamic model and to screen reasonable covariates to quantitatively describe the efficacy of poly (ADP-ribose) polymerase inhibitors (PARPis) as maintenance treatment for recurrent ovarian cancer (ROC).

Methods: The log normal hazard function model was established by using progression-free survival (PFS) data of 1,169 patients from published randomized trials on FDA-approved PARP inhibitors (olaparib, niraparib, and rucaparib). Monte Carlo simulation was used to compare PFS values in different scenarios, such as monotherapy (administered alone) and combination therapy (PARPis combined with chemo- or target-therapies), different biomarker statuses, and different PARP inhibitors. PFS was also estimated.

Results: The study showed that the median PFS was 8.5 months with monotherapy and 16.0 months with combination therapy. The median PFS of patients with the BRCA mutation, BRCA wild-type, and HRD-positivity were 11.0, 7.5, and 9.0 months in monotherapy, respectively, and 23.0, 14.0 and 17.5 months, in combination therapy, respectively. In addition, the median PFS of olaparib, niraparib, and rucaparib monotherapy were about 9.5, 10.5, and 12.0 months, respectively, and about 19.0, 20.0, and 25 months, respectively, in combination therapy. The median PFS values in combination with cediranib, bevacizumab, and chemotherapy were approximately 17.0, 12.5 and 19.5 months, respectively.

Conclusion: PARPi combination therapy is more effective as maintenance treatment for ROC than monotherapy, and the efficacy of PARPis in combination with chemotherapy is higher than that of the combination with antiangiogenic drugs. We found that the PFS of BRCA wild-type was similar to that of HRD-positive patients, and there was no significant difference in PFS between olaparib, niraparib, and rucaparib, which provides necessary quantitative information for the clinical practice of PARPis in the treatment of ROC.

Pharmacometrics meets statistics-A synergy for modern drug development

Modern drug development problems are very complex and require integration of various scientific fields. Traditionally, statistical methods have been the primary tool for design and analysis of clinical trials. Increasingly, pharmacometric approaches using physiology-based drug and disease models are applied in this context. In this paper, we show that statistics and pharmacometrics have more in common than what keeps them apart, and collectively, the synergy from these two quantitative disciplines can provide greater advances in clinical research and development, resulting in novel and more effective medicines to patients with medical need.

Linked Pharmacometric-Pharmacoeconomic Modeling and Simulation in Clinical Drug Development

Market access and pricing of pharmaceuticals are increasingly contingent on the ability to demonstrate comparative effectiveness and cost-effectiveness. As such, it is widely recognized that predictions of the economic potential of drug candidates in development could inform decisions across the product life cycle. This may be challenging when safety and efficacy profiles in terms of the relevant clinical outcomes are unknown or highly uncertain early in product development. Linking pharmacometrics and pharmacoeconomics, such that outputs from pharmacometric models serve as inputs to pharmacoeconomic models, may provide a framework for extrapolating from early-phase studies to predict economic outcomes and characterize decision uncertainty. This article reviews the published studies that have implemented this methodology and used simulation to inform drug development decisions and/or to optimize the use of drug treatments. Some of the key practical issues involved in linking pharmacometrics and pharmacoeconomics, including the choice of final outcome measures, methods of incorporating evidence on comparator treatments, approaches to handling multiple intermediate end points, approaches to quantifying uncertainty, and issues of model validation are also discussed. Finally, we have considered the potential barriers that may have limited the adoption of this methodology and suggest that closer alignment between the disciplines of clinical pharmacology, pharmacometrics, and pharmacoeconomics, may help to realize the potential benefits associated with linked pharmacometric-pharmacoeconomic modeling and simulation.

Incorporating Pharmacometrics into Pharmacoeconomic Models: Applications from Drug Development

Pharmacometrics is the science of quantifying the relationship between the pharmacokinetics and pharmacodynamics of drugs in combination with disease models and trial information to aid in drug development and dosing optimization for clinical practice. Considering the variability in the dose-concentration-effect relationship of drugs, an opportunity exists in linking pharmacokinetic and pharmacodynamic model-based estimates with pharmacoeconomic models. This link may provide early estimates of the cost effectiveness of drug therapies, thus informing late-stage drug development, pricing, and reimbursement decisions. Published case studies have demonstrated how integrated pharmacokinetic-pharmacodynamic-pharmacoeconomic models can complement traditional pharmacoeconomic analyses by identifying the impact of specific patient sub-groups, dose, dosing schedules, and adherence on the cost effectiveness of drugs, thus providing a mechanistic basis to predict the economic value of new drugs. Greater collaboration between the pharmacoeconomics and pharmacometrics community can enable methodological improvements in pharmacokinetic-pharmacodynamic-pharmacoeconomic models to support drug development.

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.

Can Population Pharmacokinetics of Antibiotics be Extrapolated? Implications of External Evaluations

BACKGROUND AND OBJECTIVE

External evaluation is an important issue in the population pharmacokinetic analysis of antibiotics. The purpose of this review was to summarize the current approaches and status of external evaluations and discuss the implications of external evaluation results for the future individualization of dosing regimens.

METHODS

We systematically searched the PubMed and EMBASE databases for external evaluation studies of population analysis and extracted the relevant information from these articles. A total of 32 studies were included in this review.

RESULTS

Vancomycin was investigated in 17 (53.1%) articles and was the most studied drug. Other studied drugs included gentamicin, tobramycin, amikacin, amoxicillin, ceftaroline, meropenem, fluconazole, voriconazole, and rifampicin. Nine (28.1%) studies were prospective, and the sample size varied widely between studies. Thirteen (40.6%) studies evaluated the population pharmacokinetic models by systematically searching for previous studies. Seven (21.9%) studies were multicenter studies, and 27 (84.4%) adopted the sparse sampling strategy. Almost all external evaluation studies of antibiotics (93.8%) used metrics for prediction-based diagnostics, while relatively fewer studies were based on simulations (46.9%) and Bayesian forecasting (25.0%).

CONCLUSION

The results of external evaluations in previous studies revealed the poor extrapolation performance of existing models of prediction- and simulation-based diagnostics, whereas the posterior Bayesian method could improve predictive performance. There is an urgent need for the development of standards and guidelines for external evaluation studies.

Quantitative prediction of bone mineral density by using bone turnover markers in response to antiresorptive agents in postmenopausal osteoporosis: A model-based meta-analysis

AIMS

This study aimed to predict time course of bone mineral density (BMD) by using corresponding response of bone turnover markers (BTMs) in women with postmenopausal osteoporosis under antiresorptive treatments.

METHODS

Data were extracted from literature searches in accessible public database. Time courses of percent change from baseline in serum C-telopeptide of type 1 collagen (sCTX) and N-telopeptide of type 1 collagen were described by complex exponential onset models. The relationship between BTM changes and BMD changes at lumbar spine and total hip was described using a multiscale indirect response model.

RESULTS

The dataset included 41 eligible published trials of 5 US-approved antiresorptive agents (alendronate, ibandronate, risedronate, zoledronic acid and denosumab), containing over 28 800 women with postmenopausal osteoporosis. The time courses of BTM changes for different drugs were differentiated by maximal effect and onset rate in developed model, while sCTX responses to zoledronic acid and denosumab were captured by another model formation. Furthermore, asynchronous relationship between BTMs and BMD was described by a bone remodelling-based semimechanistic model, including zero-order production and first-order elimination induced by N-telopeptide of type 1 collagen and sCTX, separately. After external and informative validations, the developed models were able to predict BMD increase using 1-year data.

CONCLUSION

This exploratory analysis built a quantitative framework linking BTMs and BMD among antiresorptive agents, as well as a modelling approach to enhance comprehension of dynamic relationship between early and later endpoints among agents in a certain mechanism of action. Moreover, the developed models can offer predictions of BMD from BTMs supporting early drug development.

Population Pharmacokinetics, Efficacy Exposure-response Analysis, and Model-based Meta-analysis of Fenebrutinib in Subjects with Rheumatoid Arthritis [corrected]

Purpose

Fenebrutinib (GDC-0853), a Bruton’s tyrosine kinase (BTK) inhibitor was investigated in a Phase 2 clinical trial in patients with rheumatoid arthritis (RA). Our aim was to apply a model-informed drug development (MIDD) approach to examine the totality of available clinical efficacy data.

Methods

Population pharmacokinetics (popPK) modeling, exposure-response (E-R) analysis, and model-based meta-analysis (MBMA) of fenebrutinib were performed based on the Phase 2 data.

Results

PopPK of fenebrutinib after oral administration was described using a 3-compartment model with linear elimination and a flexible absorption transit compartment model. Healthy subjects had a 52% higher apparent clearance than patients. E-R analyses based on longitudinal ACR20, ACR50, and ACR70 and DAS28 (CRP) data modeled fenebrutinib effect with an E_max function, and an efficacy plateau was achieved within the exposure range obtained in the Phase 2 clinical trial. Based on literature data, a summary-level clinical efficacy database was constructed, and MBMA determined ACR20, ACR50, and ACR70 responder rates in the placebo and adalimumab arms of the Phase 2 clinical trial were found to be consistent with historical data for these treatments.

Conclusions

Our multi-pronged approach applied MIDD to maximize knowledge extraction of efficacy data and enabled robust interpretation from a Phase 2 clinical trial.

Electronic supplementary material

The online version of this article (10.1007/s11095-019-2752-y) contains supplementary material, which is available to authorized users.