ABSTRACT
Population pharmacokinetic (PK) models are widely used to inform drug development by pharmaceutical companies and facilitate drug evaluation by regulatory agencies. Developing a population PK model is a multi-step, challenging, and time-consuming process involving iterative manual model fitting and evaluation. A tool for fully automatic model development (AMD) of common population PK models is presented here. The AMD tool is implemented in Pharmpy, a versatile open-source library for pharmacometrics. It consists of different modules responsible for developing the different components of population PK models, including the structural model, the inter-individual variability (IIV) model, the inter-occasional variability (IOV) model, the residual unexplained variability (RUV) model, the covariate model, and the allometry model. The AMD tool was evaluated using 10 real PK datasets involving the structural, IIV, and RUV modules in three sequences. The different sequences yielded generally consistent structural models; however, there were variations in the results of the IIV and RUV models. The final models of the AMD tool showed lower Bayesian Information Criterion (BIC) values and similar visual predictive check plots compared with the available published models, indicating reasonable quality, in addition to reasonable run time. A similar conclusion was also drawn in a simulation study. The developed AMD tool serves as a promising tool for fast and fully automatic population PK model building with the potential to facilitate the use of modeling and simulation in drug development.
ABSTRACT
Crovalimab is a novel C5 complement inhibitor that enables rapid and sustained C5 inhibition with subcutaneous, low-volume self-administration every 4 weeks. COMMODORE 2 (NCT04434092) is a global, randomized, open-label, multicenter, phase 3 trial evaluating the non-inferiority of crovalimab versus eculizumab in patients with paroxysmal nocturnal hemoglobinuria not previously treated with C5 inhibition. C5 inhibitor-naive patients with lactate dehydrogenase (LDH) ≥2 × upper limit of normal (ULN) were randomized 2:1 to crovalimab or eculizumab. Co-primary efficacy endpoints were proportion of patients with hemolysis control (centrally assessed LDH ≤1.5 × ULN) and proportion with transfusion avoidance. Secondary efficacy endpoints were proportions of patients with breakthrough hemolysis, stabilized hemoglobin, and change in FACIT-Fatigue score. The primary treatment period was 24 weeks. Two hundred and four patients were randomized (135 crovalimab; 69 eculizumab). Crovalimab was non-inferior to eculizumab in the co-primary endpoints of hemolysis control (79.3% vs. 79.0%; odds ratio, 1.0 [95% CI, 0.6, 1.8]) and transfusion avoidance (65.7% vs. 68.1%; weighted difference, -2.8 [-15.7, 11.1]), and in the secondary efficacy endpoints of breakthrough hemolysis (10.4% vs. 14.5%; weighted difference, -3.9 [-14.8, 5.3]) and hemoglobin stabilization (63.4% vs. 60.9%; weighted difference, 2.2 [-11.4, 16.3]). A clinically meaningful improvement in FACIT-Fatigue score occurred in both arms. Complete terminal complement activity inhibition was generally maintained with crovalimab. The safety profiles of crovalimab and eculizumab were similar with no meningococcal infections. Most patients who switched from eculizumab to crovalimab after the primary treatment period preferred crovalimab. These data demonstrate the positive benefit-risk profile of crovalimab.
Subject(s)
Antibodies, Monoclonal, Humanized , Complement Inactivating Agents , Hemoglobinuria, Paroxysmal , Humans , Hemoglobinuria, Paroxysmal/drug therapy , Antibodies, Monoclonal, Humanized/therapeutic use , Antibodies, Monoclonal, Humanized/adverse effects , Antibodies, Monoclonal, Humanized/administration & dosage , Male , Female , Middle Aged , Adult , Complement Inactivating Agents/therapeutic use , Complement Inactivating Agents/adverse effects , Hemolysis/drug effects , Complement C5/antagonists & inhibitors , AgedABSTRACT
Crovalimab, a novel C5 inhibitor, allows for low-volume, every-4- week, subcutaneous self-administration. COMMODORE 1 (NCT04432584) is a phase 3, global, randomized trial evaluating crovalimab versus eculizumab in C5 inhibitor-experienced patients with paroxysmal nocturnal hemoglobinuria (PNH). Adults with lactate dehydrogenase ≤1.5 × upper limit of normal and receiving approved eculizumab doses for ≥24 weeks were randomized 1:1 to receive crovalimab (weight-based tiered dosing) or continue eculizumab. The original primary study objective was efficacy; however, given the evolving treatment landscape, target recruitment was not met, and all efficacy endpoints became exploratory, with safety as the new primary objective. Exploratory efficacy endpoints included transfusion avoidance, hemolysis control, breakthrough hemolysis, hemoglobin stabilization, FACIT-Fatigue score, and patient preference (crovalimab vs. eculizumab). Eighty-nine patients were randomized (45 to crovalimab; 44 to eculizumab). During the 24-week primary treatment period, adverse events (AEs) occurred in 77% of patients receiving crovalimab and 67% receiving eculizumab. No AEs led to treatment withdrawal or death, and no meningococcal infections occurred. 16% of crovalimab-treated patients had transient immune complex reactions (also known as Type III hypersensitivity events), an expected risk when switching between C5 inhibitors that bind to different C5 epitopes; most were mild/moderate and all resolved without treatment modification. Crovalimab-treated patients had sustained terminal complement activity inhibition, maintained disease control, and 85% preferred crovalimab over eculizumab. Together with phase 3 COMMODORE 2 results in complement inhibitor-naive patients, these data support crovalimab's favorable benefit-risk profile. Crovalimab is a new C5 inhibitor for PNH that is potentially less burdensome than existing therapies for this lifelong disease.
Subject(s)
Antibodies, Monoclonal, Humanized , Complement Inactivating Agents , Hemoglobinuria, Paroxysmal , Humans , Hemoglobinuria, Paroxysmal/drug therapy , Antibodies, Monoclonal, Humanized/therapeutic use , Antibodies, Monoclonal, Humanized/adverse effects , Antibodies, Monoclonal, Humanized/administration & dosage , Female , Male , Middle Aged , Adult , Complement Inactivating Agents/therapeutic use , Complement Inactivating Agents/adverse effects , Complement Inactivating Agents/administration & dosage , Aged , Complement C5/antagonists & inhibitors , Treatment OutcomeABSTRACT
Covariate analysis in population pharmacokinetics is key for adjusting doses for patients. The main objective of this work was to compare the adequacy of various modeling approaches on covariate clinical relevance decision-making. The full model, stepwise covariate model (SCM) and SCM+ PsN algorithms were compared in a clinical trial simulation of a 383-patient population pharmacokinetic study mixing rich and sparse designs. A one-compartment model with first-order absorption was used. A base model including a body weight effect on CL/F and V/F and a covariate model including 4 additional covariates-parameters relationships were simulated. As for forest plots, ratios between covariates at a specific value and that of a typical individual were calculated with their 90% confidence interval (CI90) using standard errors. Covariates on CL, V and KA were considered relevant if their CI90 fell completely outside the reference area [0.8-1.2]. All approaches provided unbiased covariate ratio estimates. For covariates with a simulated effect, the 3 approaches correctly identify their clinical relevance. However, significant covariates were missed in up to 15% of cases with SCM/SCM+. For covariate with no simulated effects, the full model mainly identified them as non-relevant or with insufficient information while SCM/SCM+ mainly did not select them. SCM/SCM+ assume that non-selected covariates are non-relevant when it could be due to insufficient information, whereas the full model does not make this assumption and is faster. This study must be extended to other methods and completed by a more complex high-dimensional simulation framework.
ABSTRACT
Drug-target-drug complexes (DTDCs) are phenomena newly observed in patients who switch from the complement component 5 (C5) inhibitor eculizumab to crovalimab, a novel, anti-C5 antibody in development for paroxysmal nocturnal hemoglobinuria (PNH), because these agents bind to different C5 epitopes. In Part 3 of the four-part, phase I/II COMPOSER study, 19 patients with PNH switching from eculizumab received 1,000-mg crovalimab intravenously, then subcutaneous maintenance doses from Day 8 (680 mg every 4 weeks (q4w), 340 mg every 2 weeks, or 170 mg every week). Crovalimab exposure was transiently reduced, and size-exclusion chromatography and crovalimab-specific enzyme-linked immunosorbent assays revealed DTDCs in all 19 patients' sera. Additionally, self-limiting mild to moderate symptoms suggestive of type III hypersensitivity reactions occurred in two patients. Mathematical modeling simulations of DTDC kinetics and effects of dosing on DTDC size distribution using Part 3 data predicted that increased crovalimab concentrations could reduce the proportion of large, slow-clearing DTDCs in the blood. A simulation-guided, optimized crovalimab regimen (1,000 mg intravenously; four weekly, subcutaneous 340-mg doses; then 680 mg q4w from Day 29) was evaluated in Part 4. Confirming the model's predictions, mean proportions of large DTDCs in patients who switched from eculizumab to this optimized regimen decreased by > 50% by Day 22, and target crovalimab concentrations were maintained. No type III hypersensitivity reactions occurred in Part 4. Optimizing crovalimab dosing thus reduced the proportion of large DTDCs, ensured adequate complement inhibition, and may improve safety. Model-based dosing optimization to mitigate DTDC formation offers a useful strategy for patients switching to novel antibody treatments targeting soluble epitopes.
Subject(s)
Hemoglobinuria, Paroxysmal , Humans , Hemoglobinuria, Paroxysmal/drug therapy , Antibodies, Monoclonal , Complement Inactivating Agents/adverse effects , Complement C5ABSTRACT
BACKGROUND AND AIMS: RO7062931 is an N-acetylgalactosamine (GalNAc)-conjugated single-stranded locked nucleic acid oligonucleotide complementary to HBV RNA. GalNAc conjugation targets the liver through the asialoglycoprotein receptor (ASGPR). This two-part phase 1 study evaluated the safety, pharmacokinetics, and pharmacodynamics of RO7062931 in healthy volunteers and patients with chronic hepatitis B (CHB) who were virologically suppressed. APPROACH AND RESULTS: Part 1 was a single ascending dose study in healthy volunteers randomized to receive a single RO7062931 dose (0.1-4.0 mg/kg), or placebo. Part 2 was a multiple ascending dose study in patients with CHB randomized to receive RO7062931 at 0.5, 1.5, or 3.0 mg/kg or placebo every month for a total of 2 doses (Part 2a) or RO7062931 at 3.0 mg/kg every 2 weeks, 3.0 mg/kg every week (QW), or 4.0 mg/kg QW or placebo for a total of 3-5 doses (Part 2b). Sixty healthy volunteers and 59 patients received RO7062931 or placebo. The majority of adverse events (AEs) reported were mild in intensity. Common AEs included self-limiting injection site reactions and influenza-like illness. Supradose-proportional increases in RO7062931 plasma exposure and urinary excretion occurred at doses ≥3.0 mg/kg. In patients with CHB, RO7062931 resulted in dose-dependent and time-dependent reduction in HBsAg versus placebo. The greatest HBsAg declines from baseline were achieved with the 3.0 mg/kg QW dose regimen (mean nadir ~0.5 log10 IU/mL) independent of HBeAg status. CONCLUSIONS: RO7062931 is safe and well tolerated at doses up to 4.0 mg/kg QW. Supradose-proportional exposure at doses of 3.0-4.0 mg/kg was indicative of partial saturation of the ASGPR-mediated liver uptake system. Dose-dependent declines in HBsAg demonstrated target engagement with RO7062931.
Subject(s)
Acetylgalactosamine/therapeutic use , Hepatitis B, Chronic/drug therapy , Oligonucleotides, Antisense/therapeutic use , Oligonucleotides/therapeutic use , Acetylgalactosamine/analogs & derivatives , Adult , Asialoglycoprotein Receptor , Female , Healthy Volunteers , Hepatitis B Surface Antigens/blood , Hepatitis B virus/genetics , Hepatitis B, Chronic/blood , Humans , Male , Middle Aged , Oligonucleotides/genetics , Oligonucleotides, Antisense/genetics , RNA, Viral/genetics , Sustained Virologic ResponseABSTRACT
Within the challenging context of phase II dose-finding trials, longitudinal analyses may increase drug effect detection power compared to an end-of-treatment analysis. This work proposes cLRT-Mod, a pharmacometric adaptation of the MCP-Mod methodology, which allows the use of nonlinear mixed effect models to first detect a dose-response signal and then identify the doses for the confirmatory phase while accounting for model structure uncertainty. The method was evaluated through extensive clinical trial simulations of a hypothetical phase II dose-finding trial using different scenarios and comparing different methods such as MCP-Mod. The results show an increase in power using cLRT with longitudinal data compared to an EOT multiple contrast tests for scenarios with small sample size and weak drug effect while maintaining pre-specifiability of the models prior to data analysis and the nominal type I error. This work shows how model averaging provides better coverage probability of the drug effect in the prediction step, and avoids under-estimation of the size of the confidence interval. Finally, for illustration purpose cLRT-Mod was applied to the analysis of a real phase II dose-finding trial.
Subject(s)
Nonlinear Dynamics , Research Design , Clinical Trials, Phase II as Topic , Dose-Response Relationship, Drug , Humans , Sample Size , UncertaintyABSTRACT
In drug development, pharmacometric approaches consist in identifying via a model selection (MS) process the model structure that best describes the data. However, making predictions using a selected model ignores model structure uncertainty, which could impair predictive performance. To overcome this drawback, model averaging (MA) takes into account the uncertainty across a set of candidate models by weighting them as a function of an information criterion. Our primary objective was to use clinical trial simulations (CTSs) to compare model selection (MS) with model averaging (MA) in dose finding clinical trials, based on the AIC information criterion. A secondary aim of this analysis was to challenge the use of AIC by comparing MA and MS using five different information criteria. CTSs were based on a nonlinear mixed effect model characterizing the time course of visual acuity in wet age-related macular degeneration patients. Predictive performances of the modeling approaches were evaluated using three performance criteria focused on the main objectives of a phase II clinical trial. In this framework, MA adequately described the data and showed better predictive performance than MS, increasing the likelihood of accurately characterizing the dose-response relationship and defining the minimum effective dose. Moreover, regardless of the modeling approach, AIC was associated with the best predictive performances.
Subject(s)
Nonlinear Dynamics , Clinical Trials as Topic , Dose-Response Relationship, Drug , Drug Development , Humans , Macular Degeneration/drug therapy , Macular Degeneration/pathology , Visual AcuityABSTRACT
PURPOSE: This manuscript aims to precisely describe the natural disease progression of Parkinson's disease (PD) patients and evaluate approaches to increase the drug effect detection power. METHODS: An item response theory (IRT) longitudinal model was built to describe the natural disease progression of 423 de novo PD patients followed during 48 months while taking into account the heterogeneous nature of the MDS-UPDRS. Clinical trial simulations were then used to compare drug effect detection power from IRT and sum of item scores based analysis under different analysis endpoints and drug effects. RESULTS: The IRT longitudinal model accurately describes the evolution of patients with and without PD medications while estimating different progression rates for the subscales. When comparing analysis methods, the IRT-based one consistently provided the highest power. CONCLUSION: IRT is a powerful tool which enables to capture the heterogeneous nature of the MDS-UPDRS.