Unmodified rabies mRNA vaccine elicits high cross-neutralizing antibody titers and diverse B cell memory responses.

Licensed rabies virus vaccines based on whole inactivated virus are effective in humans. However, there is a lack of detailed investigations of the elicited immune response, and whether responses can be improved using novel vaccine platforms. Here we show that two doses of a lipid nanoparticle-formulated unmodified mRNA vaccine encoding the rabies virus glycoprotein (RABV-G) induces higher levels of RABV-G specific plasmablasts and T cells in blood, and plasma cells in the bone marrow compared to two doses of Rabipur in non-human primates. The mRNA vaccine also generates higher RABV-G binding and neutralizing antibody titers than Rabipur, while the degree of somatic hypermutation and clonal diversity of the response are similar for the two vaccines. The higher overall antibody titers induced by the mRNA vaccine translates into improved cross-neutralization of related lyssavirus strains, suggesting that this platform has potential for the development of a broadly protective vaccine against these viruses.

COVID-19 acute respiratory distress syndrome: A simulation study of the effects of combination therapy with tocilizumab and siltuximab.

AIMS: To assess the potential of interleukin-6 (IL-6) signalling blockade in the lung to treat SARS-CoV-2 infection via model-based simulation by exploring soluble IL-6 receptor (sIL-6R) sequestration by tocilizumab (TCZ) and IL-6 sequestration by siltuximab (SIL). METHODS: Literature values of IL-6, IL-6 antagonist SIL, sIL-6R, IL-6R antagonist TCZ and their respective binding constants were used to develop a model to predict the impact of treatment on IL-6 signalling. Models were used to generate simulated bronchoalveolar lavage fluid concentrations for normal subjects, subjects at risk of developing acute respiratory distress syndrome (ARDS), and subjects with ARDS under 4 conditions: without treatment; treatment with TCZ; treatment with SIL; and treatment with TCZ + SIL. RESULTS: With TCZ intervention, IL-6 levels are unaffected and sIL-6R is reduced somewhat below the Normal case. IL-6:sIL-6R complex only slightly decreased relative to the no-intervention case. With SIL intervention, sIL-6R levels are unaffected and IL-6 is greatly reduced below the Normal case. IL-6:sIL-6R complex is greatly decreased relative to the no-intervention case. With TCZ + SIL intervention, IL-6 and sIL-6R levels are reduced below the Normal case and achieve suppression equivalent to monotherapy results for their respective targets. IL-6:sIL-6R complex reduction is predicted to be greater than that achieved with monotherapy. This reflects sequestration of both components of the complex and the nonlinear binding equilibrium. CONCLUSION: Coadministration of both IL-6 and IL-6R sequestering products such as SIL and TCZ may be necessary to effectively treat COVID-19 patients who have or are at risk of developing ARDS.

Model-informed drug repurposing: Viral kinetic modelling to prioritize rational drug combinations for COVID-19.

AIM: We hypothesized that viral kinetic modelling could be helpful to prioritize rational drug combinations for COVID-19. The aim of this research was to use a viral cell cycle model of SARS-CoV-2 to explore the potential impact drugs, or combinations of drugs, that act at different stages in the viral life cycle might have on various metrics of infection outcome relevant in the early stages of COVID-19 disease. METHODS: Using a target-cell limited model structure that has been used to characterize viral load dynamics from COVID-19 patients, we performed simulations to inform on the combinations of therapeutics targeting specific rate constants. The endpoints and metrics included viral load area under the curve (AUC), duration of viral shedding and epithelial cells infected. Based on the known kinetics of the SARS-CoV-2 life cycle, we rank ordered potential targeted approaches involving repurposed, low-potency agents. RESULTS: Our simulations suggest that targeting multiple points central to viral replication within infected host cells or release from those cells is a viable strategy for reducing both viral load and host cell infection. In addition, we observed that the time-window opportunity for a therapeutic intervention to effect duration of viral shedding exceeds the effect on sparing epithelial cells from infection or impact on viral load AUC. Furthermore, the impact on reduction on duration of shedding may extend further in patients who exhibit a prolonged shedder phenotype. CONCLUSIONS: Our work highlights the use of model-informed drug repurposing approaches to better rationalize effective treatments for COVID-19.

Accelerating Clinical Evaluation of Repurposed Combination Therapies for COVID-19.

As the global COVID-19 pandemic continues, unabated and clinical trials demonstrate limited effective pharmaceutical interventions, there is a pressing need to accelerate treatment evaluations. Among options for accelerated development is the evaluation of drug combinations in the absence of prior monotherapy data. This approach is appealing for a number of reasons. First, combining two or more drugs with related or complementary therapeutic effects permits a multipronged approach addressing the variable pathways of the disease. Second, if an individual component of a combination offers a therapeutic effect, then in the absence of antagonism, a trial of combination therapy should still detect individual efficacy. Third, this strategy is time saving. Rather than taking a stepwise approach to evaluating monotherapies, this strategy begins with testing all relevant therapeutic options. Finally, given the severity of the current pandemic and the absence of treatment options, the likelihood of detecting a treatment effect with combination therapy maintains scientific enthusiasm for evaluating repurposed treatments. Antiviral combination selection can be facilitated by insights regarding SARS-CoV-2 pathophysiology and cell cycle dynamics, supported by infectious disease and clinical pharmacology expert advice. We describe a clinical evaluation strategy using adaptive combination platform trials to rapidly test combination therapies to treat COVID-19.

Impact of Target-Mediated Elimination on the Dose and Regimen of Evolocumab, a Human Monoclonal Antibody Against Proprotein Convertase Subtilisin/Kexin Type 9 (PCSK9).

Understanding the pharmacokinetic (PK) and pharmacodynamic (PD) relationship of a therapeutic monoclonal antibody against proprotein convertase subtilisin/kexin type 9 (PCSK9) exhibiting target-mediated drug disposition (TMDD) is critical for selecting optimal dosing regimens. We describe the PK/PD relationship of evolocumab using a mathematical model that captures evolocumab binding and removal of unbound PCSK9 as well as reduction in circulating low-density lipoprotein cholesterol (LDL-C). Data were pooled from 2 clinical studies: a single-dose escalation study in healthy subjects (7-420 mg SC; n = 44) and a multiple-dose escalation study in statin-treated hypercholesterolemic patients (14 mg weekly to 420 mg monthly [QM] SC; n = 57). A TMDD model described the time course of unbound evolocumab concentrations and removal of unbound PCSK9. The estimated linear clearance and volume of evolocumab were 0.256 L/day and 2.66 L, respectively, consistent with other monoclonal antibodies. The time course of LDL-C reduction was described by an indirect response model with the elimination rate of LDL-C being modulated by unbound PCSK9. The concentration of unbound PCSK9 associated with half-maximal inhibition (IC50 ) of LDL-C elimination was 1.46 nM. Based on simulations, 140 mg every 2 weeks (Q2W) and 420 mg QM were predicted to achieve a similar time-averaged effect of 69% reduction in LDL-C in patients on statin therapy, suggesting that an approximate 3-fold dose increase is required for a 2-fold extension in the dosing interval. Evolocumab dosing regimens of 140 mg Q2W or 420 mg QM were predicted to result in comparable reductions in LDL-C over a monthly period, consistent with results from recently completed phase 3 studies.

A model-based meta-analysis of monoclonal antibody pharmacokinetics to guide optimal first-in-human study design.

The objectives of this retrospective analysis were (1) to characterize the population pharmacokinetics (popPK) of four different monoclonal antibodies (mAbs) in a combined analysis of individual data collected during first-in-human (FIH) studies and (2) to provide a scientific rationale for prospective design of FIH studies with mAbs. The data set was composed of 171 subjects contributing a total of 2716 mAb serum concentrations, following intravenous (IV) and subcutaneous (SC) doses. mAb PK was described by an open 2-compartment model with first-order elimination from the central compartment and a depot compartment with first-order absorption. Parameter values obtained from the popPK model were further used to generate optimal sampling times for a single dose study. A robust fit to the combined data from four mAbs was obtained using the 2-compartment model. Population parameter estimates for systemic clearance and central volume of distribution were 0.20 L/day and 3.6 L with intersubject variability of 31% and 34%, respectively. The random residual error was 14%. Differences (> 2-fold) in PK parameters were not apparent across mAbs. Rich designs (22 samples/subject), minimal designs for popPK (5 samples/subject), and optimal designs for non-compartmental analysis (NCA) and popPK (10 samples/subject) were examined by stochastic simulation and estimation. Single-dose PK studies for linear mAbs executed using the optimal designs are expected to yield high-quality model estimates, and accurate capture of NCA estimations. This model-based meta-analysis has determined typical popPK values for four mAbs with linear elimination and enabled prospective optimization of FIH study designs, potentially improving the efficiency of FIH studies for this class of therapeutics.

Immune activation in advanced cancer patients treated with recombinant IL-21: multianalyte profiling of serum proteins.

PURPOSE: Recombinant interleukin-21 (rIL-21) is an immune stimulating cytokine recently tested in two Phase 1 trials for immune responsive cancers. A secondary objective of these trials was to characterize pharmacodynamic responses to rIL-21 in patients. Here, we report the effects of systemic rIL-21 on serum markers of immune stimulation. EXPERIMENTAL DESIGN: Recombinant IL-21 was administered by intravenous bolus injection at dose levels from 1 to 100 microg/kg using two distinct treatment regimens: thrice weekly ('3/w') for 6 weeks; or once daily for five consecutive days followed by nine dose-free days ('5 + 9'). In the absence of dose limiting toxicity, additional cycles of dosing were initiated immediately following the nine dose-free days. An array of 70 different proteins was profiled in subject serum samples from several time points during the course of the study. Hierarchical clustering analysis was performed on a normalized subset of these data. RESULTS: Systemic administration of rIL-21 affected the serum levels of several cytokines, chemokines, acute-phase proteins and cell adhesion proteins. The magnitude and duration of response were dose dependent for a subset of these biomarkers. The 5 + 9 dosing regimen generally produced cyclic changes that were of greater magnitude, as compared to a more chronic stimulation with the 3/w dosing regimen. Despite these differences, rIL-21 effects on many analytes were similar between regimens when averaged over the time of treatment. Based on similar temporal, between-subject and dose response changes, groups of analytes were identified that exhibited distinct components of the rIL-21-mediated immune activation. Biomarkers indicative of lymphocyte activation (increased IL-16, decreased RANTES), acute phase response (increased CRP, ferritin), myeloid activation (increased MDC, MIP-1 alpha), and leukocyte chemotaxis/trafficking (increased sCAMs, MCP-1) were strongly modulated in subjects treated with rIL-21. CONCLUSIONS: Administration of rIL-21 resulted in activation of multiple cell types and immune response pathways. The changes observed in serum proteins were consistent with coincident processes of lymphoid and myeloid cell activation and trafficking, and acute phase response.

Population pharmacokinetics of recombinant factor XIII in cynomolgus monkeys.

Hemostasis in humans and other animals is a complex process that controls blood loss after a vascular injury. Factor XIII (FXIII) stabilizes clots primarily by cross-linking fibrin, thus protecting a newly formed clot from fibrinolysis by plasmin. Congenital deficiencies in humans involving FXIII are associated with delayed bleeding and wound healing and severe spontaneous hemorrhaging. These symptoms can be alleviated by intravenous administration of enriched FXIII plasma fractions. Circulating plasma FXIII is found as a heterotetramer that dissociates in the presence of calcium and thrombin into an active dimer and 2 inactive monomers. The recombinant FXIII under investigation is the active dimer alone. A 3-compartment, nonlinear population pharmacokinetic model was implemented in NONMEM V and then used to analyze data from preclinical studies in cynomolgus monkeys. The model simultaneously describes endogenous production of dimer (0.622 microg kg(-1) hr(-1)) and monomer (12.1 microg kg(-1) hr(-1)), and the administration of recombinant dimer. The model incorporates the rate and extent of complexation of recombinant dimer with available endogenous monomer (6.59 mg(-1) kg hr(-1)) to form the heterotetramer. Half-lives for dimer, heterotetramer, and monomer (3.33 hours, 2.83 days, and 3.94 hours for A(2), A(2)B(2), and B, respectively) were estimated, along with their variability in the population studied.

Robust population pharmacokinetic experiment design.

The population approach to estimating mixed effects model parameters of interest in pharmacokinetic (PK) studies has been demonstrated to be an effective method in quantifying relevant population drug properties. The information available for each individual is usually sparse. As such, care should be taken to ensure that the information gained from each population experiment is as efficient as possible by designing the experiment optimally, according to some criterion. The classic approach to this problem is to design "good" sampling schedules, usually addressed by the D-optimality criterion. This method has the drawback of requiring exact advanced knowledge (expected values) of the parameters of interest. Often, this information is not available. Additionally, if such prior knowledge about the parameters is misspecified, this approach yields designs that may not be robust for parameter estimation. In order to incorporate uncertainty in the prior parameter specification, a number of criteria have been suggested. We focus on ED-optimality. This criterion leads to a difficult numerical problem, which is made tractable here by a novel approximation of the expectation integral usually solved by stochastic integration techniques. We present two case studies as evidence of the robustness of ED-optimal designs in the face of misspecified prior information. Estimates from replicate simulated population data show that such misspecified ED-optimal designs recover parameter estimates that are better than similarly misspecified D-optimal designs, and approach estimates gained from D-optimal designs where the parameters are correctly specified.

Integrated model of hepatic and peripheral glucose regulation for estimation of endogenous glucose production during the hot IVGTT.

We have developed a new model to describe endogenous glucose kinetics during a labeled (hot) intravenous glucose tolerance test (IVGTT) to derive a time profile of endogenous glucose production (EGP). We reanalyzed data from a previously published study (P. Vicini, J. J. Zachwieja, K. E. Yarasheski, D. M. Bier, A. Caumo, and C. Cobelli. Am J Physiol Endocrinol Metab 276: E285-E294, 1999), in which insulin-modified [6,6-2H2]glucose-labeled IVGTTs (0.33 g/kg glucose) were performed in 10 normal subjects. In addition, a second tracer ([U-13C]glucose) was infused in a variable rate to clamp the endogenous glucose tracer-to-tracee ratio (TTR). Our new model describing endogenous glucose kinetics was incorporated into the two-compartment hot minimal-model structure. The model gave estimates of glucose effectiveness [1.54 +/- 0.31 (SE) ml x kg(-1) x min(-1)], insulin sensitivity (37.74 +/- 5.23 10(4) dl x kg(-1) x min(-1) x microU(-1) x ml), and a new parameter describing the sensitivity of EGP to the inhibitory effect of insulin (IC50 = 0.0195 +/- 0.0046 min(-1)). The model additionally provided an estimate of the time course of EGP showing almost immediate inhibition, followed by a secondary inhibitory effect caused by infusion of insulin, and a large overshoot as EGP returns to its basal value. Our estimates show very good agreement with those obtained via deconvolution and the model-independent TTR clamp technique. These results suggest that the new integrated model can serve as a simple one-step approach to obtain metabolic indexes while also providing a parametric description of EGP.

Assessing convergence of Markov chain Monte Carlo simulations in hierarchical Bayesian models for population pharmacokinetics.

Advances in computer hardware and the associated computer-intensive algorithms made feasible by these advances [like Markov chain Monte Carlo (MCMC) data analysis techniques] have made possible the application of hierarchical full Bayesian methods in analyzing pharmacokinetic and pharmacodynamic (PK-PD) data sets that are multivariate in nature. Pharmacokinetic data analysis in particular has been one area that has seized upon this technology to refine estimates of drug parameters from sparse data gathered in a large, highly variable population of patients. A drawback in this type of analysis is that it is difficult to quantitatively assess convergence of the Markov chains to a target distribution, and thus, it is sometimes difficult to assess the reliability of estimates gained from this procedure. Another complicating factor is that, although the application of MCMC methods to population PK-PD problems has been facilitated by new software designed for the PK-PD domain (specifically PKBUGS), experts in PK-PD may not have the necessary experience with MCMC methods to detect and understand problems with model convergence. The objective of this work is to provide an example of a set of diagnostics useful to investigators, by analyzing in detail three convergence criteria (namely the Raftery and Lewis, Geweke, and Heidelberger and Welch methods) on a simulated problem and with a rule of thumb of 10,000 chain elements in the Markov chain. We used two publicly available software packages to assess convergence of MCMC parameter estimates; the first performs Bayesian parameter estimation (PKBUGS/WinBUGS), and the second is focused on posterior analysis of estimates (BOA). The main message that seems to emerge is that accurately estimating confidence regions for the parameters of interest is more demanding than estimating the parameter means. Together, these tools provide numerical means by which an investigator can establish confidence in convergence and thus in the estimated parameters derived from hierarchical full Bayesian pharmacokinetic data analysis.

An evaluation of population D-optimal designs via pharmacokinetic simulations.

One goal of large scale clinical trials is to determine how a drug is processed by, and cleared from, the human body [i.e., its pharmacokinetic (PK) properties] and how these PK properties differ between individuals in a population (i.e., its population PK properties). Due to the high cost of these studies and the limited amount of data (e.g., blood samples) available from each study subject, it would be useful to know how many measurements are needed and when those measurements should be taken to accurately quantify population PK model parameters means and variances. Previous studies have looked at optimal design strategies of population PK experiments by developing an optimal design for an individual study (i.e., no interindividual variability was considered in the design), and then applying that design to each individual in a population study (where interindividual variability is present). A more algorithmically and informationally intensive approach is to develop a population optimal design, which inherently includes the assessment of interindividual variability. We present a simulation-based evaluation of these two design methods based on nonlinear Gaussian population PK models. Specifically, we compute standard individual and population D-optimal designs and compare population PK model parameter estimates based on simulated optimal design measurements. Our results show that population and standard D-optimal designs are not significantly different when both designs have the same number of samples per individual. However, population optimal designs allow for sampling schedules where the number of samples per individual is less than the number of model parameters, the theoretical limit allowed in standard optimal design. These designs with a low number of samples per individual are shown to be nearly as robust in parameter estimation as standard D-optimal designs. In the limit of just one sample per individual, however, population D-optimal designs are shown to be inadequate.