Accelerating therapeutics development during a pandemic: population pharmacokinetics of the long-acting antibody combination AZD7442 (tixagevimab/cilgavimab) in the prophylaxis and treatment of COVID-19

AZD7442 is a combination of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-neutralizing antibodies, tixagevimab and cilgavimab, developed for pre-exposure prophylaxis (PrEP) and treatment of coronavirus disease 2019 (COVID-19). Using data from eight clinical trials, we describe a population pharmacokinetic (popPK) model of AZD7442 and show how modeling of "interim" data accelerated decision-making during the COVID-19 pandemic. The final model was a two-compartmental distribution model with first-order absorption and elimination, including standard allometric exponents for the effect of body weight on clearance and volume. Other covariates included were as follows: sex, age >65 years, body mass index ≥30 kg/m2, and diabetes on absorption rate; diabetes on clearance; Black race on central volume; and intramuscular (IM) injection site on bioavailability. Simulations indicated that IM injection site and body weight had > 20% effects on AZD7442 exposure, but no covariates were considered to have a clinically relevant impact requiring dose adjustment. The pharmacokinetics of AZD7442, cilgavimab, and tixagevimab were comparable and followed linear kinetics with extended half-lives (median 78.6 days for AZD7442), affording prolonged protection against susceptible SARS-CoV-2 variants. Comparison of popPK simulations based on "interim data" with a target concentration based on 80% viral inhibition and assuming 1.81% partitioning into the nasal lining fluid supported a decision to double the PrEP dosage from 300 mg to 600 mg to prolong protection against Omicron variants. Serum AZD7442 concentrations in adolescents weighing 40-95 kg were predicted to be only marginally different from those observed in adults, supporting authorization for use in adolescents before clinical data were available. In these cases, popPK modeling enabled accelerated clinical decision-making.

Potassium homeostasis and therapeutic intervention with sodium zirconium cyclosilicate: A model-informed drug development case study

Potassium (K+ ) is the main intracellular cation in the body. Elevated K+ levels (hyperkalemia) increase the risk of life-threatening arrhythmias and sudden cardiac death. However, the details of K+ homeostasis and the effects of orally administered K+ binders, such as sodium zirconium cyclosilicate (SZC), on K+ redistribution and excretion in patients remain incompletely understood. We built a fit-for-purpose systems pharmacology model to describe K+ homeostasis in hyperkalemic subjects and capture serum K+ (sK+ ) dynamics in response to acute and chronic administration of SZC. The resulting model describes K+ distribution in the gastrointestinal (GI) tract, blood, and extracellular and intracellular spaces of tissue, renal clearance of K+ , and K+ -SZC binding and excretion in the GI tract. The model, which was fit to time-course sK+ data for individual patients from two clinical trials, accounts for bolus delivery of K+ in meals and oral doses of SZC. The virtual population of patients derived from fitting the model to these trials was then modified to predict the SZC dose-response and inform clinical trial design in two new applications: emergency lowering of sK+ in severe hyperkalemia and prevention of hyperkalemia between dialysis sessions in patients with end-stage chronic kidney disease. In both cases, the model provided novel and useful insight that was borne out by the now completed clinical trials, providing a concrete case study of fit-for-purpose, model-informed drug development after initial approval of a drug.

Assessing reproducibility and utility of clustering of patients with type 2 diabetes and established CV disease (SAVOR -TIMI 53 trial)

OBJECTIVE

To assess the reproducibility and clinical utility of clustering-based subtyping of patients with type 2 diabetes (T2D) and established cardiovascular (CV) disease.

METHODS

The cardiovascular outcome trial SAVOR-TIMI 53 (n = 16,492) was used. Analyses focused on T2D patients with established CV disease. Unsupervised machine learning technique called "k-means clustering" was used to divide patients into subtypes. K-means clustering including HbA1c, age of diagnosis, BMI, HOMA2-IR and HOMA2-B was used to assign clusters to the following diabetes subtypes: severe insulin deficient diabetes (SIDD); severe insulin-resistant diabetes (SIRD); mild obesity-related diabetes (MOD); mild age-related diabetes (MARD). We refer these subtypes as "clustering-based diabetes subtypes". A simulation study using randomly generated data was conducted to understand how correlations between the above variables influence the formation of the cluster-based diabetes subtypes. The predictive utility of clustering-based diabetes subtypes for CV events (3-point MACE), renal function reduction (eGFR decrease >30%) and diabetic disease progression (introduction of additional anti-diabetic medication) were compared with conventional risk scores. Hazard ratios (HR) were estimated by Cox-proportional hazard models.

RESULTS

In the SAVOR-TIMI 53 trial based dataset, the percentage of the clustering-based T2D subtypes were; SIDD (18%), SIRD (17%), MOD (29%), MARD (37%). Using the simulated dataset, the diabetes subtypes could be largely reproduced from a log-normal distribution when including known correlations between variables. The predictive utility of clustering-based diabetic subtypes on CV events, renal function reduction, and diabetic disease progression did not show an advantage compared to conventional risk scores.

CONCLUSIONS

The consistent reproduction of four clustering-based T2D subtypes can be explained by the correlations between the variables used for clustering. Subtypes of T2D based on clustering had limited advantage compared to conventional risk scores to predict clinical outcome in patients with T2D and established CV disease.

Cardiovascular and renal safety of metformin in patients with diabetes and moderate or severe chronic kidney disease: Observations from the EXSCEL and SAVOR-TIMI 53 cardiovascular outcomes trials

AIM

To provide evidence on the cardiovascular and renal safety of metformin in chronic kidney disease (CKD) stages 3 to 4.

MATERIALS AND METHODS

This post hoc analysis compared participants with an estimated glomerular filtration rate (eGFR) of 15 to 59 mL/min/1.73m² in the Exenatide Study of Cardiovascular Event Lowering (EXSCEL) and the Saxagliptin and Cardiovascular Outcomes in Patients With Type 2 Diabetes Mellitus (SAVOR-TIMI 53) trials taking metformin, with those not exposed to metformin during these trials, using a propensity-matching approach. Adjusted Cox proportional hazards models were used to assess risk of major adverse cardiovascular events (MACE) and all-cause mortality (ACM). Metformin effect on eGFR slope was calculated using a mixed-model repeated measures analysis, and the number of lactic acidosis events was tabulated.

RESULTS

No strong trend for lower metformin doses with lower eGFR values was observed in either the EXSCEL or SAVOR-TIMI 53 trials. In the 1745 metformin-using participants matched to non-metformin users, metformin had neutral effects on MACE (hazard ratio [HR] 0.91, 95% confidence interval [CI] 0.76-1.08; P = 0.28) and ACM (HR 0.86, 95% CI 0.70-1.07; P = 0.18), with no interaction by CKD stage, or with use of exenatide or saxagliptin. An improvement in eGFR slope was observed with metformin in the CKD stage 3B cohort in SAVOR-TIMI 53, but not in other groups.

CONCLUSIONS

This analysis of participants with CKD stages 3 to 4 from two cardiovascular outcomes trials supports the cardiorenal safety of metformin, but does not suggest a consistent benefit on MACE, ACM, or eGFR slope across this population.

Effect of once-weekly exenatide on estimated glomerular filtration rate slope depends on baseline renal risk: A post hoc analysis of the EXSCEL trial

The effects of glucagon-like peptide-1 receptor agonists (GLP-1RAs) on renal outcomes in patients with type 2 diabetes at high cardiovascular risk are modest or neutral. However, GLP-1RAs may confer clinical benefits in those at high risk of progressive renal function loss. We examined the effects of once-weekly exenatide (EQW) on estimated glomerular filtration rate (eGFR) slope and urinary albumin:creatinine ratio (UACR) as a function of baseline UACR in 3503 EXSCEL participants (23.7%) with eGFR data available and 2828 participants (19.2%) with UACR change data available. EQW improved eGFR slope assessed via mixed model repeated measures, compared with placebo, in participants with baseline UACR >100 mg/g (0.79 mL/min/1.73 m² /year [95% confidence interval {CI} 0.24-1.34]) and UACR >200 mg/g (1.32 mL/min/1.73 m² /year [95% CI 0.57-2.06]), but not at lower UACR thresholds. EQW reduced UACR, compared with placebo, assessed via analysis of covariance, consistently across subgroups with baseline UACR >30 mg/g (28.2% reduction), baseline UACR >100 mg (22.5% reduction) and baseline UACR >200 mg (34.5% reduction). This post hoc EXSCEL analysis suggests that EQW reduces UACR, with improvement in eGFR slope specifically in participants with elevated baseline UACR.

Prediction and validation of exenatide risk marker effects on progression of renal disease: Insights from EXSCEL

AIM

To assess whether the previously developed multivariable risk prediction framework (PRE score) could predict the renal effects observed in the EXSCEL cardiovascular outcomes trial using short-term changes in cardio-renal risk markers.

MATERIALS AND METHODS

Changes from baseline to 6 months in HbA1c, systolic blood pressure (SBP), body mass index (BMI), haemoglobin, total cholesterol, and new micro- or macroalbuminuria were evaluated. The renal outcomes were defined as a composite of a sustained 30% or 40% decline in estimated glomerular filtration rate (eGFR) or end-stage renal disease (ESRD). Relationships between risk markers and long-term renal outcomes were determined in patients with type 2 diabetes from the ALTITUDE study using multivariable Cox regression analysis, and then applied to short-term changes in risk markers observed in EXSCEL to predict the exenatide-induced impact on renal outcomes.

RESULTS

Compared with placebo, mean HbA1c, BMI, SBP and total cholesterol were lower at 6 months with exenatide, as was the incidence of new microalbuminuria. The PRE score predicted a relative risk reduction for the 30% eGFR decline + ESRD endpoint of 11.3% (HR 0.89; 95% CI 0.83-0.94), compared with 12.7% (HR 0.87; 0.77-0.99) observed risk reduction. For the 40% eGFR decline + ESRD endpoint, the predicted and observed risk reductions were 11.0% (HR 0.89; 0.82-0.97) and 13.7% (HR 0.86, 0.72-1.04), respectively.

CONCLUSIONS

Integrating short-term risk marker changes into a multivariable risk score predicted the magnitude of renal risk reduction observed in EXSCEL.

Effects of exenatide and open-label SGLT2 inhibitor treatment, given in parallel or sequentially, on mortality and cardiovascular and renal outcomes in type 2 diabetes: insights from the EXSCEL trial

BACKGROUND

Sodium-glucose cotransporter-2 inhibitors (SGLT2i) and glucagon-like peptide-1 receptor agonists (GLP-1 RA) improve cardiovascular and renal outcomes in patients with type 2 diabetes through distinct mechanisms. However, evidence on clinical outcomes in patients treated with both GLP-1 RA and SGLT2i is lacking. We aim to provide insight into the effects of open-label SGLT2i use in parallel with or shortly after once-weekly GLP-1 RA exenatide (EQW) on cardiorenal outcomes.

METHODS

In the EXSCEL cardiovascular outcomes trial EQW arm, SGLT2i drop-in occurred in 8.7% of participants. These EQW+SGLT2i users were propensity-matched to: (1) placebo-arm participants not taking SGLT2i (n = 572 per group); and to (2) EQW-arm participants not taking SGLT2i (n = 575), based on their last measured characteristics before SGLT2i initiation, and equivalent study visit in comparator groups. Time-to-first major adverse cardiovascular event (MACE) and all-cause mortality (ACM) were compared using Cox regression analyses. eGFR slopes were quantified using mixed model repeated measurement analyses.

RESULTS

In adjusted analyses, the risk for MACE with combination EQW+SGLT2i use was numerically lower compared with both placebo (adjusted hazard ratio 0.68, 95% CI 0.39-1.17) and EQW alone (0.85, 0.48-1.49). Risk of ACM was nominally significantly reduced compared with placebo (0.38, 0.16-0.90) and compared with EQW (0.41, 0.17-0.95). Combination EQW+SGLT2i use also nominally significantly improved estimated eGFR slope compared with placebo (+ 1.94, 95% CI 0.94-2.94 mL/min/1.73 m²/year) and EQW alone (+ 2.38, 1.40-3.35 mL/min/1.73 m²/year).

CONCLUSIONS

This post hoc analysis supports the hypothesis that combinatorial EQW and SGLT2i therapy may provide benefit on cardiovascular outcomes and mortality. Trial registration Clinicaltrials.gov, Identifying number: NCT01144338, Date of registration: June 15, 2010.

Reduction of Cardiovascular Risk and Improved Estimated Glomerular Filtration Rate by SGLT2 Inhibitors, Including Dapagliflozin, Is Consistent Across the Class: An Analysis of the Placebo Arm of EXSCEL

OBJECTIVE

The sodium-glucose cotransporter 2 inhibitors (SGLT2i) empagliflozin and canagliflozin reduce the incidence of major adverse cardiovascular events (MACE), all-cause mortality (ACM), and renal events in cardiovascular outcomes trials, with observational real-world evidence suggesting class effect benefits that include dapagliflozin. We examined the placebo arm of the Exenatide Study of Cardiovascular Event Lowering (EXSCEL) to determine whether the effects of drop-in open-label dapagliflozin on MACE, ACM, and estimated glomerular filtration rate (eGFR) were consistent with the SGLT2i class as a whole.

RESEARCH DESIGN AND METHODS

SGLT2i drop-in therapy occurred in 10.6% of EXSCEL participants, with 5.2% taking dapagliflozin. Propensity-matched cohorts of SGLT2i users and nonusers (n = 709 per group) were generated on the basis of their characteristics before open-label SGLT2i drop-in or at baseline for participants taking SGLT2i at enrollment and an equivalent study visit for non-SGLT2i users. Time to first adjudicated MACE and ACM was analyzed using Cox regression. eGFR slopes were compared between matched cohorts using a mixed-model repeated-measures analysis.

RESULTS

In adjusted analyses, SGLT2i users (compared with nonusers) had a numerically lower risk of MACE (adjusted hazard ratio 0.79 [95% CI 0.49-1.28]), as did dapagliflozin users (0.55 [0.26-1.15]). SGLT2i users had a significantly lower ACM risk (0.51 [0.27-0.95]; dapagliflozin: 0.66 [0.25-1.72]). Compared with nonusers, eGFR slope was significantly better for SGLT2i users overall (+1.78 [95% CI 0.87-2.69] mL/min/1.73 m² per year) and for dapagliflozin users (+2.28 [1.01-3.54] mL/min/1.73 m² per year).

CONCLUSIONS

This post hoc analysis of the placebo arm of EXSCEL supports a beneficial class effect for all SGLT2i, including dapagliflozin, for reduced ACM and less eGFR decline.

Arterialized collateral capillaries progress from nonreactive to capable of increasing perfusion in an ischemic arteriolar tree

OBJECTIVE

CCA, outward remodeling of capillaries that anastomose 2 arteriolar trees with different parent feed arteries, may represent a therapeutic target for patients who lack collaterals. ACCs can reperfuse an ischemic tree, but their functional capacity is unknown. Therefore, we determined whether ACCs mature into resistance vessels that regulate blood flow following arterial occlusion.

METHODS

We ligated the lateral spinotrapezius feed artery in Balb/C mice, which induces CCA. At days 7 and 21 following occlusion, we measured vasodilation of ACCs using intravital microscopy and blood flow in the ischemic tree using LSF. We determined the presence of ACCs and neurovascular alignment with immunofluorescence.

RESULTS

At day 7, ACCs do not vasodilate following muscle contraction and have reduced responses to endothelial- and smooth muscle-dependent agents. By day 21, ACCs exhibit normal vasodilation, accompanied by normalized increases in relative blood flow to the ischemic zone. Although functioning as resistance vessels by regulating blood flow, ACCs do not appear to be innervated.

CONCLUSIONS

ACCs mature into resistance vessels that regulate blood flow to the downstream tissue. Therefore, induction of mature ACCs may be a target for reducing ischemia in patients who lack collateral networks.

A computational analysis of pro-angiogenic therapies for peripheral artery disease

Inducing therapeutic angiogenesis to effectively form hierarchical, non-leaky networks of perfused vessels in tissue engineering applications and ischemic disease remains an unmet challenge, despite extensive research and multiple clinical trials. Here, we use a previously-developed, multi-scale, computational systems pharmacology model of human peripheral artery disease to screen a diverse array of promising pro-angiogenic strategies, including gene therapy, biomaterials, and antibodies. Our previously-validated model explicitly accounts for VEGF immobilization, Neuropilin-1 binding, and weak activation of VEGF receptor 2 (VEGFR2) by the "VEGFxxxb" isoforms. First, we examine biomaterial-based delivery of VEGF engineered for increased affinity to the extracellular matrix. We show that these constructs maintain VEGF close to physiological levels and extend the duration of VEGFR2 activation. We demonstrate the importance of sub-saturating VEGF dosing to prevent angioma formation. Second, we examine the potential of ligand- or receptor-based gene therapy to normalize VEGF receptor signaling. Third, we explore the potential for antibody-based pro-angiogenic therapy. Our model supports recent observations that improvement in perfusion following treatment with anti-VEGF165b in mice is mediated by VEGF-receptor 1, not VEGFR2. Surprisingly, the model predicts that the approved anti-VEGF cancer drug, bevacizumab, may actually improve signaling of both VEGFR1 and VEGFR2 via a novel 'antibody swapping' effect that we demonstrate here. Altogether, this model provides insight into the mechanisms of action of several classes of pro-angiogenic strategies within the context of the complex molecular and physiological processes occurring in vivo. We identify molecular signaling similarities between promising approaches and key differences between promising and ineffective strategies.

Systems Pharmacology of VEGF165b in Peripheral Artery Disease

We built a whole‐body computational model to study the role of the poorly understood vascular endothelial growth factor (VEGF)_165b splice isoform in peripheral artery disease (PAD). This model was built and validated using published and new experimental data from cells, mice, and humans, and explicitly accounts for known properties of VEGF_165b: lack of extracellular matrix (ECM)‐binding and weak phosphorylation of vascular endothelial growth factor receptor‐2 (VEGFR2) in vitro. The resulting model captures all known information about VEGF_165b distribution and signaling in human PAD, and provides novel, nonintuitive insight into VEGF_165b mechanism of action in vivo. Although VEGF_165a and VEGF_165b compete for VEGFR2 in vitro, simulations show that these isoforms do not compete for VEGFR2 at much lower physiological concentrations. Instead, reduced VEGF_165a may drive impaired VEGFR2 signaling. The model predicts that VEGF_165b does compete for binding to VEGFR1, supporting a VEGFR1‐mediated response to anti‐VEGF_165b. The model predicts a key role for VEGF_165b in PAD, but in a different way than previously hypothesized.

A computational analysis of in vivo VEGFR activation by multiple co-expressed ligands

The splice isoforms of vascular endothelial growth A (VEGF) each have different affinities for the extracellular matrix (ECM) and the coreceptor NRP1, which leads to distinct vascular phenotypes in model systems expressing only a single VEGF isoform. ECM-immobilized VEGF can bind to and activate VEGF receptor 2 (VEGFR2) directly, with a different pattern of site-specific phosphorylation than diffusible VEGF. To date, the way in which ECM binding alters the distribution of isoforms of VEGF and of the related placental growth factor (PlGF) in the body and resulting angiogenic signaling is not well-understood. Here, we extend our previous validated cell-level computational model of VEGFR2 ligation, intracellular trafficking, and site-specific phosphorylation, which captured differences in signaling by soluble and immobilized VEGF, to a multi-scale whole-body framework. This computational systems pharmacology model captures the ability of the ECM to regulate isoform-specific growth factor distribution distinctly for VEGF and PlGF, and to buffer free VEGF and PlGF levels in tissue. We show that binding of immobilized growth factor to VEGF receptors, both on endothelial cells and soluble VEGFR1, is likely important to signaling in vivo. Additionally, our model predicts that VEGF isoform-specific properties lead to distinct profiles of VEGFR1 and VEGFR2 binding and VEGFR2 site-specific phosphorylation in vivo, mediated by Neuropilin-1. These predicted signaling changes mirror those observed in murine systems expressing single VEGF isoforms. Simulations predict that, contrary to the 'ligand-shifting hypothesis,' VEGF and PlGF do not compete for receptor binding at physiological concentrations, though PlGF is predicted to slightly increase VEGFR2 phosphorylation when over-expressed by 10-fold. These results are critical to design of appropriate therapeutic strategies to control VEGF availability and signaling in regenerative medicine applications.

Molecular mechanism matters: Benefits of mechanistic computational models for drug development

Making drug development a more efficient and cost-effective process will have a transformative effect on human health. A key, yet underutilized, tool to aid in this transformation is mechanistic computational modeling. By incorporating decades of hard-won prior knowledge of molecular interactions, cellular signaling, and cellular behavior, mechanistic models can achieve a level of predictiveness that is not feasible using solely empirical characterization of drug pharmacodynamics. These models can integrate diverse types of data from cell culture and animal experiments, including high-throughput systems biology experiments, and translate the results into the context of human disease. This provides a framework for identification of new drug targets, measurable biomarkers for drug action in target tissues, and patient populations for which a drug is likely to be effective or ineffective. Additionally, mechanistic models are valuable in virtual screening of new therapeutic strategies, such as gene or cell therapy and tissue regeneration, identifying the key requirements for these approaches to succeed in a heterogeneous patient population. These capabilities, which are distinct from and complementary to those of existing drug development strategies, demonstrate the opportunity to improve success rates in the drug development pipeline through the use of mechanistic computational models.

Systems biology of the microvasculature

The vascular network carries blood throughout the body, delivering oxygen to tissues and providing a pathway for communication between distant organs. The network is hierarchical and structured, but also dynamic, especially at the smaller scales. Remodeling of the microvasculature occurs in response to local changes in oxygen, gene expression, cell-cell communication, and chemical and mechanical stimuli from the microenvironment. These local changes occur as a result of physiological processes such as growth and exercise, as well as acute and chronic diseases including stroke, cancer, and diabetes, and pharmacological intervention. While the vasculature is an important therapeutic target in many diseases, drugs designed to inhibit vascular growth have achieved only limited success, and no drug has yet been approved to promote therapeutic vascular remodeling. This highlights the challenges involved in identifying appropriate therapeutic targets in a system as complex as the vasculature. Systems biology approaches provide a means to bridge current understanding of the vascular system, from detailed signaling dynamics measured in vitro and pre-clinical animal models of vascular disease, to a more complete picture of vascular regulation in vivo. This will translate to an improved ability to identify multi-component biomarkers for diagnosis, prognosis, and monitoring of therapy that are easy to measure in vivo, as well as better drug targets for specific disease states. In this review, we summarize systems biology approaches that have advanced our understanding of vascular function and dysfunction in vivo, with a focus on computational modeling.