Regeneron

Evinacumab, an angiopoietin‐like 3 inhibitor, significantly reduces low‐density lipoprotein cholesterol (LDL‐C) in patients with homozygous familial hypercholesterolemia (HoFH). Herein, we report pharmacokinetic and efficacy analyses of evinacumab in < 5‐year‐old patients with HoFH. Population pharmacometric models characterizing evinacumab exposure and LDL‐C response accounting for lipoprotein apheresis effect in ≥ 5‐year‐old patients were adapted for growth and maturation to predict and compare evinacumab and LDL‐C concentrations across age/weight groups in virtual ≥ 6‐month‐old patients receiving 15 mg/kg evinacumab intravenous (iv) infusions every 4 weeks (q4w). As expected from allometric theory, weight‐based dosing resulted in decreasing evinacumab exposures with declining body weight. Consistent with trends observed in > 5‐year‐old patients, the predicted percent change from LDL‐C baseline (%∆LDL‐C) was generally comparable or even higher in < 5‐year‐old patients (63.0%–68.5%) than in 5‐ to < 18‐year‐old patients (61.3%–67.8%) or adults (51.7%), with the predicted percentages of patients achieving %∆LDL‐C > 50% also higher in < 5‐year‐old patients (82.0%–86.9%) versus 5‐ to < 18‐year‐old patients (72.0%–84.5%) and adults (54.8%). Through a managed access program, six 1‐ to < 5‐year‐old patients received between 5 and 23 iv infusions of 15 mg/kg evinacumab q4w. Rapid and clinically meaningful LDL‐C reductions were observed, with %∆LDL‐C at the last reported dose ranging from 41.3% to 77.3%. Based on the actual patient dosing and plasmapheresis history, model‐predicted evinacumab and LDL‐C concentrations were comparable to the observed data collected in the managed access program. Overall, this analysis provides evidence for the use of evinacumab 15 mg/kg iv q4w dosing regimen in 6‐month‐old to 5‐year‐old patients.

Evinacumab, an angiopoietin‐like 3 (ANGPTL3) inhibitor, significantly reduces low‐density lipoprotein cholesterol (LDL‐C), independent of low‐density lipoprotein receptor, in patients with homozygous familial hypercholesterolemia (HoFH). A population pharmacokinetic (PK)/pharmacodynamic (PD) model was previously developed to characterize evinacumab exposure and LDL‐C response in adolescents and adults. In this analysis, the PK/PD model was refined to include children aged 5 to < 12 years and to characterize the lipoprotein apheresis effect on LDL‐C reduction. The PK of evinacumab was characterized by a two‐compartment model with parallel linear and non‐linear elimination. Linear disposition parameters were allometrically scaled by body weight. Baseline ANGPTL3 concentrations and disease status (non‐HoFH vs. HoFH) influenced the maximum target‐mediated rate of elimination but had a minimal effect on evinacumab exposures at 15 mg/kg intravenous doses every 4 weeks across weight/age groups. In patients with HoFH, the LDL‐C reduction was adequately described by an indirect response model in which evinacumab inhibits the formation of LDL‐C and that includes a secondary elimination process quantifying the lipoprotein apheresis effect. Older age was associated with a decrease in baseline LDL‐C. An increase in body weight was associated with a reduction in the maximum inhibitory effect of evinacumab. Model‐based simulations showed that while evinacumab exposure is reduced with decreasing age/body weight, younger patients are predicted to have a comparable or greater magnitude of LDL‐C reduction than older patients at a dose of 15 mg/kg. Overall, the model adequately predicted the evinacumab exposure and LDL‐C reduction in children, adolescents, and adults with HoFH, aligning with clinically relevant observations.

Glucagon stimulates hepatic glucose production, in part by promoting the uptake and catabolism of amino acids. Inhibition of liver glucagon receptor (GCGR) results in elevated plasma amino acids, which triggers the proliferation of pancreatic alpha-cells, forming a liver-alpha cell loop. This study aims to delineate hepatic signaling molecules downstream of GCGR which mediate the liver-alpha cell loop. We knocked down liver GCGR, its G-coupled protein GNAS, and two GNAS downstream effectors, PKA and EPAC2 (RAPGEF4). Mice with GCGR, GNAS, and PKA knockdown had similar suppression of hepatic amino acid catabolism genes, hyperaminoacidemia, and alpha cell hyperplasia, but EPAC2 knockdown did not. We then demonstrated that activating liver PKA was sufficient to reverse hyperaminoacidemia and alpha cell hyperplasia caused by GCGR blockade. These results suggest that liver GCGR signals through PKA to control amino acid metabolism, and that hepatic PKA plays a critical role in the liver-alpha cell loop.

Therapeutic drug monitoring (TDM) for dose modification of biologics has the potential to improve patient outcomes. The US Food and Drug Administration (FDA) and the American Association of Pharmaceutical Scientists (AAPS) hosted the first US-based public workshop on TDM of biologics with contributions from a broad array of interested parties including healthcare providers, clinical pharmacologists, test developers, bioanalysis and immunogenicity scientists, health economics and outcomes research (HEOR) experts and regulators. The key insight was that despite a body of evidence to support TDM in certain therapeutic areas, there remain substantial challenges to widespread clinical implementation. There is a lack of consensus regarding the integration of TDM in clinical guidelines, and a lack of consensus on the cost-effectiveness of TDM; both factors contribute to the difficulty that healthcare providers face in obtaining reimbursement for TDM (both coverage of testing itself, and coverage of potential dosing modifications). The HEOR experts outlined alternative routes to obtaining reimbursement and suggested advocating for changes in coverage policies to promote TDM use in the clinic. Reaching alignment across policy makers, patients and advocacy groups, payers, and healthcare providers, on specific treatment settings where TDM will be clearly beneficial, was identified as an important step to advancing TDM implementation for the benefit of patients.

The method of power prior has long been used as a tool for leveraging external data to augment a traditional clinical study. More recently, it has been found that integrating propensity scoring into its application has the potential for improved operating characteristics. In this paper, we introduce a new propensity score‐integrated power prior strategy which uses propensity score weighting and is distinctive from other such proposals in the literature. This strategy replaces the sufficient statistic in the original expression of power prior with a propensity score weighted version of it. A simulation study shows that the operating characteristics of the proposed weighting strategy compare favorably to those of the original power prior method when there is covariate imbalance, like the stratification strategy we first introduced.

In clinical trials of patients with atopic dermatitis (AD), conjunctivitis and keratitis occurred more frequently with dupilumab than placebo. Studies using real-world data have also shown a higher incidence with dupilumab but did not use validated algorithms to identify the population and outcomes. The objective of this study was to investigate the incidence of conjunctivitis and keratitis among patients with moderate-to-severe AD treated with dupilumab relative to dupilumab-naïve patients in a real-world setting using validated algorithms. A retrospective observational cohort study was conducted in an insurance claims database using validated algorithms for moderate-to-severe AD and ocular outcomes. Initiators of dupilumab were identified and propensity score (PS) matched with dupilumab-naïve patients with moderate-to-severe AD. Incidence rate ratios (IRRs) with 95% confidence intervals (CIs) for conjunctivitis and keratitis during follow-up were estimated using Poisson regression. Among 13,790 patients in the moderate-to-severe AD study population, 2175 dupilumab initiators and 2189 dupilumab-naïve patients were included in the analysis. Dupilumab was associated with an increased risk of conjunctivitis (IRR = 1.86 [95% CI 1.51–2.30]) and keratitis (IRR = 4.06 [95% CI 1.70–9.68]) compared to patients with moderate-to-severe AD not receiving dupilumab. The cumulative 1-year risk of conjunctivitis was 15.8% and 8.4% in the dupilumab and dupilumab-naïve cohorts, respectively; the cumulative 1-year risk of keratitis was 1.3% and 0.2%, respectively. Study findings are consistent with safety data from clinical trials and existing literature. However, a few keratitis events were observed, and post hoc analyses suggested residual confounding might be present. The known benefit-risk profile for dupilumab remains unchanged.

Aims Brigimadlin (BI 907828) is a potent, oral MDM2‐p53 antagonist under clinical investigation for the treatment of advanced solid tumours. A brigimadlin exposure–tumour growth inhibition (E‐TGI) model was developed to support the recommended phase II dose (RP2D) selection of brigimadlin in future clinical trials. Methods Population modelling was applied to analyse longitudinal tumour size (sum of longest diameters, SLD) data of 151 patients from a phase I trial treated with 5–80 mg brigimadlin every third or fourth week (q3w/q4w). The impact of brigimadlin exposure on tumour shrinkage was assessed and the effects of patient‐ and tumour‐related covariates on model parameters were explored. The final E‐TGI model was used to simulate the effect of brigimadlin treatment on longitudinal SLD. The probability of dropout from tumour assessments were characterized via logistic regression and included in simulations to allow for realistic predictions of tumour shrinkage over time. Results The E‐TGI model adequately characterized the observed SLD data over time. Simulations demonstrated a substantially stronger tumour shrinkage with higher dose, based on the identified exposure–response relationship. For patients with the most common tumour (dedifferentiated liposarcoma) and standard body weight (70 kg) and remaining in the study for 1 year, the median relative change from baseline in tumour size was 0.141%, −4.48%, −10.8% and −17.4%, for treatment with 20, 30, 45 and 60 mg brigimadlin q3w doses, respectively. Conclusions The developed E‐TGI model predicted that higher doses of brigimadlin resulted in a substantially stronger tumour shrinkage. These results contributed to selecting 45 mg brigimadlin q3w dose as RP2D in subsequent clinical trials.

Atrial fibrillation (AF) is the most common heart rhythm abnormality and is a leading cause of heart failure and stroke. This large-scale meta-analysis of genome-wide association studies increased the power to detect single-nucleotide variant associations and found more than 350 AF-associated genetic loci. We identified candidate genes related to muscle contractility, cardiac muscle development and cell–cell communication at 139 loci. Furthermore, we assayed chromatin accessibility using assay for transposase-accessible chromatin with sequencing and histone H3 lysine 4 trimethylation in stem cell-derived atrial cardiomyocytes. We observed a marked increase in chromatin accessibility for our sentinel variants and prioritized genes in atrial cardiomyocytes. Finally, a polygenic risk score (PRS) based on our updated effect estimates improved AF risk prediction compared to the CHARGE-AF clinical risk score and a previously reported PRS for AF. The doubling of known risk loci will facilitate a greater understanding of the pathways underlying AF.

Selection of lead therapeutic molecules is often driven predominantly by pharmacological efficacy and safety. Candidate developability, such as biophysical properties that affect the formulation of the molecule into a product, is usually evaluated only toward the end of the drug development pipeline. The ability to evaluate developability properties early in the process of antibody therapeutic development could accelerate the timeline from discovery to clinic and save considerable resources. In silico predictive approaches, such as machine learning models, which map molecular features to predictions of developability properties could offer a cost-effective and high-throughput alternative to experiments for antibody developability assessment. We developed a computational framework, PROPERMAB (PROPERties of Monoclonal AntiBodies), for large-scale and efficient in silico prediction of developability properties for monoclonal antibodies, using custom molecular features and machine learning modeling. We demonstrate the power of PROPERMAB by using it to develop models to predict antibody hydrophobic interaction chromatography retention time and high-concentration viscosity. We further show that structure-derived features can be rapidly and accurately predicted directly from sequences by pre-training simple models for molecular features, thus providing the ability to scale these approaches to repertoire-scale sequence datasets.

Background Mutations in cancer cells can result in the production of neoepitopes that can be recognized by T cells and trigger an immune response. A reliable pipeline to identify such immunogenic neoepitopes for a given tumor would be beneficial for the design of cancer immunotherapies. Current methods, such as the pipeline proposed by the Tumor Neoantigen Selection Alliance (TESLA), aim to select short peptides with the highest likelihood to be MHC-I restricted minimal epitopes. Typically, only a small percentage of these predicted epitopes are recognized by T cells when tested experimentally. This is particularly problematic as the limited amount of sample available from patients that are acutely sick restricts the number of peptides that can be tested in practice. This led our group to develop an in-house pipeline termed Identify-Prioritize-Validate (IPV) that identifies long peptides that cover both CD4 and CD8 epitopes. Methods Here, we systematically compared how IPV performs compared to the TESLA pipeline. Patient peripheral blood mononuclear cells were cultured in vitro with their corresponding candidate peptides, and immune recognition was measured using cytokine-secretion assays. Results The IPV pipeline consistently outperformed the TESLA pipeline in predicting neoepitopes that elicited an immune response in our assay. This was primarily due to the inclusion of longer peptides in IPV compared to TESLA. Conclusions Our work underscores the improved predictive ability of IPV in comparison to TESLA in this assay system and highlights the need to clearly define which experimental metrics are used to evaluate bioinformatic epitope predictions.

Heart failure (HF) is a major contributor to global morbidity and mortality. While distinct clinical subtypes, defined by etiology and left ventricular ejection fraction, are well recognized, their genetic determinants remain inadequately understood. In this study, we report a genome-wide association study of HF and its subtypes in a sample of 1.9 million individuals. A total of 153,174 individuals had HF, of whom 44,012 had a nonischemic etiology (ni-HF). A subset of patients with ni-HF were stratified based on left ventricular systolic function, where data were available, identifying 5,406 individuals with reduced ejection fraction and 3,841 with preserved ejection fraction. We identify 66 genetic loci associated with HF and its subtypes, 37 of which have not previously been reported. Using functionally informed gene prioritization methods, we predict effector genes for each identified locus, and map these to etiologic disease clusters through phenome-wide association analysis, network analysis and colocalization. Through heritability enrichment analysis, we highlight the role of extracardiac tissues in disease etiology. We then examine the differential associations of upstream risk factors with HF subtypes using Mendelian randomization. These findings extend our understanding of the mechanisms underlying HF etiology and may inform future approaches to prevention and treatment.