Subcutaneous Administration of Monoclonal Antibodies: Pharmacology, Delivery, Immunogenicity, and Learnings From Applications to Clinical Development.

Subcutaneous (s.c.) administration of monoclonal antibodies (mAbs) can reduce treatment burden for patients and healthcare systems compared with intravenous (i.v.) infusion through shorter administration times, made possible by convenient, patient-centric devices. A deeper understanding of clinical pharmacology principles related to efficacy and safety of s.c.-administered mAbs over the past decade has streamlined s.c. product development. This review presents learnings from key constituents of the s.c. mAb development pathway, including pharmacology, administration variables, immunogenicity, and delivery devices. Restricted mAb transportation through the hypodermis explains their incomplete absorption at a relatively slow rate (pharmacokinetic (PK)) and may impact mAb-cellular interactions and/or onset and magnitude of physiological responses (pharmacodynamic). Injection volumes, formulation, rate and site of injection, and needle attributes may affect PKs and the occurrence/severity of adverse events like injection-site reactions or pain, with important consequences for treatment adherence. A review of immunogenicity data for numerous compounds reveals that incidence of anti-drug antibodies (ADAs) is generally comparable across i.v. and s.c. routes, and complementary factors including response magnitude (ADA titer), persistence over time, and neutralizing antibody presence are needed to assess clinical impact. Finally, four case studies showcase how s.c. biologics have been clinically developed: (i) by implementation of i.v./s.c. bridging strategies to streamline PD-1/PD-L1 inhibitor development, (ii) through co-development with i.v. presentations for anti-severe acute respiratory syndrome-coronavirus 2 antibodies to support rapid deployment of both formulations, (iii) as the lead route for bispecific T cell engagers (BTCEs) to mitigate BTCE-mediated cytokine release syndrome, and (iv) for pediatric patients in the case of dupilumab.

Multivariate modeling of magnetic resonance biomarkers and clinical outcome measures for Duchenne muscular dystrophy clinical trials.

Although regulatory agencies encourage inclusion of imaging biomarkers in clinical trials for Duchenne muscular dystrophy (DMD), industry receives minimal guidance on how to use these biomarkers most beneficially in trials. This study aims to identify the optimal use of muscle fat fraction biomarkers in DMD clinical trials through a quantitative disease-drug-trial modeling and simulation approach. We simultaneously developed two multivariate models quantifying the longitudinal associations between 6-minute walk distance (6MWD) and fat fraction measures from vastus lateralis and soleus muscles. We leveraged the longitudinal individual-level data collected for 10 years through the ImagingDMD study. Age of the individuals at assessment was chosen as the time metric. After the longitudinal dynamic of each measure was modeled separately, the selected univariate models were combined using correlation parameters. Covariates, including baseline scores of the measures and steroid use, were assessed using the full model approach. The nonlinear mixed-effects modeling was performed in Monolix. The final models showed reasonable precision of the parameter estimates. Simulation-based diagnostics and fivefold cross-validation further showed the model's adequacy. The multivariate models will guide drug developers on using fat fraction assessment most efficiently using available data, including the widely used 6MWD. The models will provide valuable information about how individual characteristics alter disease trajectories. We will extend the multivariate models to incorporate trial design parameters and hypothetical drug effects to inform better clinical trial designs through simulation, which will facilitate the design of clinical trials that are both more inclusive and more conclusive using fat fraction biomarkers.

Assessing the Combined Public Health Impact of Pharmaceutical Interventions on Pandemic Transmission and Mortality: An Example in SARS CoV-2.

To assess the combined role of anti-viral monoclonal antibodies (mAbs) and vaccines in reducing severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) transmission and mortality in the United States, an agent-based model was developed that accounted for social contacts, movement/travel, disease progression, and viral shedding. The model was calibrated to coronavirus disease 2019 (COVID-19) mortality between October 2020 and April 2021 (aggressive pandemic phase), and projected an extended outlook to estimate mortality during a less aggressive phase (April-August 2021). Simulated scenarios evaluated mAbs for averting infections and deaths in addition to vaccines and aggregated non-pharmaceutical interventions. Scenarios included mAbs as a treatment of COVID-19 and for passive immunity for postexposure prophylaxis (PEP) during a period when variants were susceptible to the mAbs. Rapid diagnostic testing paired with mAbs was evaluated as an early treatment-as-prevention strategy. Sensitivity analyses included increasing mAb supply and vaccine rollout. Allocation of mAbs for use only as PEP averted up to 14% more infections than vaccine alone, and targeting individuals ≥ 65 years averted up to 37% more deaths. Rapid testing for earlier diagnosis and mAb use amplified these benefits. Doubling the mAb supply further reduced infections and mortality. mAbs provided benefits even as proportion of the immunized population increased. Model projections estimated that ~ 42% of expected deaths between April and August 2021 could be averted. Assuming sensitivity to mAbs, their use as early treatment and PEP in addition to vaccines would substantially reduce SARS-CoV-2 transmission and mortality even as vaccination increases and mortality decreases. These results provide a template for informing public health policy for future pandemic preparedness.

Development of a model-based clinical trial simulation platform to optimize the design of clinical trials for Duchenne muscular dystrophy.

Early clinical trials of therapies to treat Duchenne muscular dystrophy (DMD), a fatal genetic X-linked pediatric disease, have been designed based on the limited understanding of natural disease progression and variability in clinical measures over different stages of the continuum of the disease. The objective was to inform the design of DMD clinical trials by developing a disease progression model-based clinical trial simulation (CTS) platform based on measures commonly used in DMD trials. Data were integrated from past studies through the Duchenne Regulatory Science Consortium founded by the Critical Path Institute (15 clinical trials and studies, 1505 subjects, 27,252 observations). Using a nonlinear mixed-effects modeling approach, longitudinal dynamics of five measures were modeled (NorthStar Ambulatory Assessment, forced vital capacity, and the velocities of the following three timed functional tests: time to stand from supine, time to climb 4 stairs, and 10 meter walk-run time). The models were validated on external data sets and captured longitudinal changes in the five measures well, including both early disease when function improves as a result of growth and development and the decline in function in later stages. The models can be used in the CTS platform to perform trial simulations to optimize the selection of inclusion/exclusion criteria, selection of measures, and other trial parameters. The data sets and models have been reviewed by the US Food and Drug Administration and the European Medicines Agency; have been accepted into the Fit-for-Purpose and Qualification for Novel Methodologies pathways, respectively; and will be submitted for potential endorsement by both agencies.

Open Data Revolution in Clinical Research: Opportunities and Challenges.

Efforts for sharing individual clinical data are gaining momentum due to a heightened recognition that integrated data sets can catalyze biomedical discoveries and drug development. Among the benefits are the fact that data sharing can help generate and investigate new research hypothesis beyond those explored in the original study. Despite several accomplishments establishing public systems and guidance for data sharing in clinical trials, this practice is not the norm. Among the reasons are ethical challenges, such as privacy of individuals, data ownership, and control. This paper creates awareness of the potential benefits and challenges of sharing individual clinical data, how to overcome these challenges, and how as a clinical pharmacology community we can shape future directions in this field.

Machine Learning in Drug Discovery and Development Part 1: A Primer.

Artificial intelligence, in particular machine learning (ML), has emerged as a key promising pillar to overcome the high failure rate in drug development. Here, we present a primer on the ML algorithms most commonly used in drug discovery and development. We also list possible data sources, describe good practices for ML model development and validation, and share a reproducible example. A companion article will summarize applications of ML in drug discovery, drug development, and postapproval phase.

Hippocampal Neuroimaging-Informed Clinical Trial Enrichment Tool for Amnestic Mild Cognitive Impairment Using Open Data.

Our goal was to assess the enrichment utility of hippocampal volume (HV) as an enrichment biomarker in amnestic mild cognitive impairment (aMCI) clinical trials, and, hence, develop an HV neuroimaging-informed clinical trial enrichment tool. Modeling of integrated longitudinal patient-level data came from open-access natural history studies in patients diagnosed with aMCI-the Alzheimer's Disease Neuroimaging Initiative (ADNI)-1 and ADNI-2-and indicated that a decrease of 1 cm3 with respect to the analysis dataset median baseline intracranial volume-adjusted HV (ICV-HV; ~ 5 cm3 ) is associated with > 50% increase in disease progression rate as measured by the Clinical Dementia Rating Scale-Sum of Boxes. Clinical trial simulations showed that the inclusion of aMCI subjects with baseline ICV-HV below the 84th or 50th percentile allowed an approximate reduction in trial size of at least 26% and 55%, respectively. This clinical trial enrichment tool can help design more efficient and informative clinical trials.

Challenges in Alzheimer's Disease Drug Discovery and Development: The Role of Modeling, Simulation, and Open Data.

Alzheimer's disease (AD) is the leading cause of dementia worldwide. With 35 million people over 60 years of age with dementia, there is an urgent need to develop new treatments for AD. To streamline this process, it is imperative to apply insights and learnings from past failures to future drug development programs. In the present work, we focus on how modeling and simulation tools can leverage open data to address drug development challenges in AD.

Development of a Disease Progression Model for Leucine-Rich Repeat Kinase 2 in Parkinson's Disease to Inform Clinical Trial Designs.

A quantitative assessment of Parkinson's disease (PD) progression is critical for optimizing clinical trials design. Disease progression model was developed using pooled data from the Progression Marker Initiative study and the Incidence of Cognitive Impairment in Cohorts with Longitudinal Evaluation in Parkinson's Disease study. Age, gender, concomitant medication, and study arms were predictors of baseline. A mutation in the leucine-rich repeat kinase 2 (LRRK2) encoding gene was associated with the disease progression rate. The progression rate in subjects with PD who carried LRRK2 mutation was slightly slower (~0.170 points/month) than that in PD subjects without the mutation (~0.222 points/month). For a nonenriched placebo-controlled clinical trial, approximately 70 subjects/arm would be required to detect a drug effect of 50% reduction in the progression rate with 80% probability, whereas 85, 93, and 100 subjects/arm would be required for an enriched clinical trial with 30%, 50%, and 70% subjects with LRRK2 mutations, respectively.

The Qualification of an Enrichment Biomarker for Clinical Trials Targeting Early Stages of Parkinson's Disease.

As therapeutic trials target early stages of Parkinson's disease (PD), appropriate patient selection based purely on clinical criteria poses significant challenges. Members of the Critical Path for Parkinson's Consortium formally submitted documentation to the European Medicines Agency (EMA) supporting the use of Dopamine Transporter (DAT) neuroimaging in early PD. Regulatory documents included a comprehensive literature review, a proposed analysis plan of both observational and clinical trial data, and an assessment of biomarker reproducibility and reliability. The research plan included longitudinal analysis of the Parkinson Research Examination of CEP-1347 Trial (PRECEPT) and the Parkinson's Progression Markers Initiative (PPMI) study to estimate the degree of enrichment achieved and impact on future trials in subjects with early motor PD. The presence of reduced striatal DAT binding based on visual reads of single photon emission tomography (SPECT) scans in early motor PD subjects was an independent predictor of faster decline in UPDRS Parts II and III as compared to subjects with scans without evidence of dopaminergic deficit (SWEDD) over 24 months. The EMA issued in 2018 a full Qualification Opinion for the use of DAT as an enrichment biomarker in PD trials targeting subjects with early motor symptoms. Exclusion of SWEDD subjects in future clinical trials targeting early motor PD subjects aims to enrich clinical trial populations with idiopathic PD patients, improve statistical power, and exclude subjects who are unlikely to progress clinically from being exposed to novel test therapeutics.

ß2 -Adrenergic Receptor Gene Affects the Heart Rate Response of ß-Blockers: Evidence From 3 Clinical Studies.

ß-Blockers' heart rate (HR)-lowering effect is an important determinant of the effectiveness for this class of drugs, yet it is variable among ß-blocker-treated patients. To date, genetic studies have revealed several genetic signals associated with HR response to ß-blockers. However, these genetic signals have not been consistently replicated across multiple independent cohorts. Here we sought to use data from 3 hypertension clinical trials to validate single-nucleotide polymorphisms (SNPs) previously associated with the HR response to ß-blockers. Using linear regression analysis, we investigated the effects of 6 SNPs in 3 genes, including ADRB1, ADRB2, and GNB3, relative to the HR response following ß-blocker used in the PEAR (n = 757), PEAR-2 (n = 368), and INVEST (n = 1401) trials, adjusting for baseline HR, age, sex, and ancestry. Atenolol was used in PEAR and INVEST, and metoprolol was used in PEAR-2. We found that rs1042714 and rs1042713 in ADRB2 were significantly associated with HR response to both ß-blockers in whites (rs1042714 C-allele carriers, meta-analysis ß = -0.95 beats per minute [bpm], meta-analysis P = 3×10-4 ; rs1042713 A-allele carriers, meta-analysis ß = -1.15 bpm, meta-analysis P = 2×10-3 ). In conclusion, the results of our analyses provide strong evidence to support the hypothesis that rs1042714 and rs1042713 in the ADRB2 gene are important predictors of HR response to cardioselective ß-blockade in hypertensive patient cohorts.

Towards regulatory endorsement of drug development tools to promote the application of model-informed drug development in Duchenne muscular dystrophy.

Drug development for rare diseases is challenged by small populations and limited data. This makes development of clinical trial protocols difficult and contributes to the uncertainty around whether or not a potential therapy is efficacious. The use of data standards to aggregate data from multiple sources, and the use of such integrated databases to develop statistical models can inform protocol development and reduce the risks in developing new therapies. Achieving regulatory endorsement of such models through defined pathways at the US Food and Drug Administration and European Medicines Authority allows such tools to be used by the drug development community for defined contexts of use without further need for discussion of the underlying model(s). The Duchenne Regulatory Science Consortium (D-RSC) has brought together multiple stakeholders to develop a clinical trial simulation tool for Duchenne muscular dystrophy using such an approach. Here we describe the work of D-RSC as an example of how such an approach may be effective at reducing uncertainty in drug development for rare diseases, and thus bringing effective therapies to patients faster.

Midazolam Dose Optimization in Critically Ill Pediatric Patients With Acute Respiratory Failure: A Population Pharmacokinetic-Pharmacogenomic Study.

OBJECTIVES: To develop a pharmacokinetic-pharmacogenomic population model of midazolam in critically ill children with primary respiratory failure. DESIGN: Prospective pharmacokinetic-pharmacogenomic observational study. SETTING: Thirteen PICUs across the United States. PATIENTS: Pediatric subjects mechanically ventilated for acute respiratory failure, weight greater than or equal to 7 kg, receiving morphine and/or midazolam continuous infusions. INTERVENTIONS: Serial blood sampling for drug quantification and a single blood collection for genomic evaluation. MEASUREMENTS AND MAIN RESULTS: Concentrations of midazolam, the 1' (1`-hydroxymidazolam metabolite) and 4' (4`-hydroxymidazolam metabolite) hydroxyl, and the 1' and 4' glucuronide metabolites were measured. Subjects were genotyped using the Illumina HumanOmniExpress genome-wide single nucleotide polymorphism chip. Nonlinear mixed effects modeling was performed to develop the pharmacokinetic-pharmacogenomic model. Body weight, age, hepatic and renal functions, and the UGT2B7 rs62298861 polymorphism are relevant predictors of midazolam pharmacokinetic variables. The estimated midazolam clearance was 0.61 L/min/70kg. Time to reach 50% complete mature midazolam and 1`-hydroxymidazolam metabolite/4`-hydroxymidazolam metabolite clearances was 1.0 and 0.97 years postmenstrual age. The final model suggested a decrease in midazolam clearance with increase in alanine transaminase and a lower clearance of the glucuronide metabolites with a renal dysfunction. In the pharmacogenomic analysis, rs62298861 and rs28365062 in the UGT2B7 gene were in high linkage disequilibrium. Minor alleles were associated with a higher 1`-hydroxymidazolam metabolite clearance in Caucasians. In the pharmacokinetic-pharmacogenomic model, clearance was expected to increase by 10% in heterozygous and 20% in homozygous for the minor allele with respect to homozygous for the major allele. CONCLUSIONS: This work leveraged available knowledge on nonheritable and heritable factors affecting midazolam pharmacokinetic in pediatric subjects with primary respiratory failure requiring mechanical ventilation, providing the basis for a future implementation of an individual-based approach to sedation.

Genome-Wide Association Approach Identified Novel Genetic Predictors of Heart Rate Response to ß-Blockers.

BACKGROUND: For many indications, the negative chronotropic effect of ß-blockers is important to their efficacy, yet the heart rate (HR) response to ß-blockers varies. Herein, we sought to use a genome-wide association approach to identify novel single nucleotide polymorphisms (SNPs) associated with HR response to ß-blockers. METHODS AND RESULTS: We first performed 4 genome-wide association analyses for HR response to atenolol (a ß1-adrenergic receptor blocker) as: (1) monotherapy or (2) add-on therapy, in 426 whites and 273 blacks separately from the PEAR (Pharmacogenomic Evaluation of Antihypertensive Responses) study. A meta-analysis was then performed between the genome-wide association analysis performed in PEAR atenolol monotherapy and add-on therapy, in each race separately, using the inverse variance method assuming fixed effects. From this analysis, SNPs associated with HR response to atenolol at a P<1E-05 were tested for replication in whites (n=200) and blacks (n=168) treated with metoprolol (a ß1-adrenergic receptor blocker). From the genome-wide association meta-analyses, SNP rs17117817 near olfactory receptor family10 subfamily-p-member1 (OR10P1), and SNP rs2364349 in sorting nexin-9 (SNX9) replicated in blacks. The combined studies meta-analysis P values for the rs17117817 and rs2364349 reached genome-wide significance (rs17117817G-allele; Meta-ß=5.53 beats per minute, Meta-P=2E-09 and rs2364349 A-allele; Meta-ß=3.5 beats per minute, Meta-P=1E-08). Additionally, SNPs in the OR10P1 and SNX9 gene regions were also associated with HR response in whites. CONCLUSIONS: This study highlights OR10P1 and SNX9 as novel genes associated with changes in HR in response to ß-blockers. CLINICAL TRIAL REGISTRATION: URL: http://www.clinicaltrials.gov. Unique identifier: NCT00246519.

Dopamine Transporter Neuroimaging as an Enrichment Biomarker in Early Parkinson's Disease Clinical Trials: A Disease Progression Modeling Analysis.

Given the recognition that disease-modifying therapies should focus on earlier Parkinson's disease stages, trial enrollment based purely on clinical criteria poses significant challenges. The goal herein was to determine the utility of dopamine transporter neuroimaging as an enrichment biomarker in early motor Parkinson's disease clinical trials. Patient-level longitudinal data of 672 subjects with early-stage Parkinson's disease in the Parkinson's Progression Markers Initiative (PPMI) observational study and the Parkinson Research Examination of CEP-1347 Trial (PRECEPT) clinical trial were utilized in a linear mixed-effects model analysis. The rate of worsening in the motor scores between subjects with or without a scan without evidence of dopamine transporter deficit was different both statistically and clinically. The average difference in the change from baseline of motor scores at 24 months between biomarker statuses was -3.16 (90% confidence interval [CI] = -0.96 to -5.42) points. Dopamine transporter imaging could identify subjects with a steeper worsening of the motor scores, allowing trial enrichment and 24% reduction of sample size.

Open innovation: Towards sharing of data, models and workflows.

Sharing of resources across organisations to support open innovation is an old idea, but which is being taken up by the scientific community at increasing speed, concerning public sharing in particular. The ability to address new questions or provide more precise answers to old questions through merged information is among the attractive features of sharing. Increased efficiency through reuse, and increased reliability of scientific findings through enhanced transparency, are expected outcomes from sharing. In the field of pharmacometrics, efforts to publicly share data, models and workflow have recently started. Sharing of individual-level longitudinal data for modelling requires solving legal, ethical and proprietary issues similar to many other fields, but there are also pharmacometric-specific aspects regarding data formats, exchange standards, and database properties. Several organisations (CDISC, C-Path, IMI, ISoP) are working to solve these issues and propose standards. There are also a number of initiatives aimed at collecting disease-specific databases - Alzheimer's Disease (ADNI, CAMD), malaria (WWARN), oncology (PDS), Parkinson's Disease (PPMI), tuberculosis (CPTR, TB-PACTS, ReSeqTB) - suitable for drug-disease modelling. Organized sharing of pharmacometric executable model code and associated information has in the past been sparse, but a model repository (DDMoRe Model Repository) intended for the purpose has recently been launched. In addition several other services can facilitate model sharing more generally. Pharmacometric workflows have matured over the last decades and initiatives to more fully capture those applied to analyses are ongoing. In order to maximize both the impact of pharmacometrics and the knowledge extracted from clinical data, the scientific community needs to take ownership of and create opportunities for open innovation.

Predicting the probability of successful efficacy of a dissociated agonist of the glucocorticoid receptor from dose-response analysis.

PF-04171327 is a dissociated agonist of the glucocorticoid receptor (DAGR) being developed to retain anti-inflammatory efficacy while reducing unwanted effects. Our aim was to conduct a longitudinal dose-response analysis to identify the DAGR doses with efficacy similar to or greater than prednisone 10 mg once daily (QD). The data included were from a Phase 2, randomized, double-blind, parallel-group study in 323 subjects with active rheumatoid arthritis on a background of methotrexate. Subjects received DAGR 1, 5, 10 or 15 mg, prednisone 5 or 10 mg, or placebo QD for 8 weeks. The Disease Activity Score 28-4 calculated using C-Reactive Protein (DAS28-4 CRP) was the efficacy endpoint utilized in this dose-response model. For DAGR, the maximum effect (Emax) on DAS28-4 CRP was estimated to be -1.2 points (95 % CI -1.7, -0.84), and the evaluated dose range provided 31-87 % of the Emax; for prednisone 5 and 10 mg, the estimated effects were -0.27 (95 % CI -0.55, 0.006) and -0.94 point (95 % CI -1.3, -0.59), respectively. Stochastic simulations indicated that the DAGR 1, 5, 10 and 15 mg have probabilities of 0.9, 29, 54 and 62 %, respectively, to achieve efficacy greater than prednisone 10 mg at week 8. DAGR 9 mg estimated probability was 50 % suggesting that DAGR ≥9 mg QD has an effect on DAS28-4 CRP comparable to or greater than prednisone 10 mg QD. This work informs dose selection for late-stage confirmatory trials.

An updated Alzheimer's disease progression model: incorporating non-linearity, beta regression, and a third-level random effect in NONMEM.

Our objective was to expand our understanding of the predictors of Alzheimer's disease (AD) progression to help design a clinical trial on a novel AD medication. We utilized the Coalition Against Major Diseases AD dataset consisting of control-arm data (both placebo and stable background AD medication) from 15 randomized double-blind clinical trials in mild-to-moderate AD patients (4,495 patients; July 2013). Our ADAS-cog longitudinal model incorporates a beta-regression with between-study, -subject, and -residual variability in NONMEM; it suggests that faster AD progression is associated with younger age and higher number of apolipoprotein E type 4 alleles (APOE*4), after accounting for baseline disease severity. APOE*4, in particular, seems to be implicated in the AD pathogenesis. In addition, patients who are already on stable background AD medications appear to have a faster progression relative to those who are not receiving AD medication. The current knowledge does not support a causality relationship between use of background AD medications and higher rate of disease progression, and the correlation is potentially due to confounding covariates. Although causality has not necessarily been demonstrated, this model can inform inclusion criteria and stratification, sample size, and trial duration.