Strategies for clinical dose optimization of T cell-engaging therapies in oncology.

Innovative approaches in the design of T cell-engaging (TCE) molecules are ushering in a new wave of promising immunotherapies for the treatment of cancer. Their mechanism of action, which generates an in trans interaction to create a synthetic immune synapse, leads to complex and interconnected relationships between the exposure, efficacy, and toxicity of these drugs. Challenges thus arise when designing optimal clinical dose regimens for TCEs with narrow therapeutic windows, with a variety of dosing strategies being evaluated to mitigate key side effects such as cytokine release syndrome, neurotoxicity, and on-target off-tumor toxicities. This review evaluates the current approaches to dose optimization throughout the preclinical and clinical development of TCEs, along with perspectives for improvement of these strategies. Quantitative approaches used to aid the understanding of dose-exposure-response relationships are highlighted, along with opportunities to guide the rational design of next-generation TCE molecules, and optimize their dose regimens in patients.

Population Pharmacokinetics and Exposure-Response for Dapirolizumab Pegol From a Phase 2b Trial in Patients With Systemic Lupus Erythematosus.

Systemic lupus erythematosus (SLE) is a systemic, autoimmune disease characterized by chronic inflammation and organ damage. Dapirolizumab pegol inhibits CD40 ligand (CD40L) and is currently undergoing phase 3 trials for the treatment of SLE. To describe the pharmacokinetic characteristics of dapirolizumab pegol and the relationship between exposure and probability of achieving a British Isles Lupus Assessment Group-based Composite Lupus Assessment (BICLA) response, a population pharmacokinetic (popPK) model and an exposure-response model were developed, based on results of the phase 2b trial (RISE; NCT02804763) of dapirolizumab pegol in SLE. Dapirolizumab pegol pharmacokinetics were found to be dose proportional and well described by a 2-compartment model with first-order elimination from the central compartment. In the popPK model, body weight was the only significant covariate. The average concentration of dapirolizumab pegol, derived from the popPK model, was incorporated into the exposure-response model. Overall, the exposure-response model showed that treatment with dapirolizumab pegol increased the probability of transitioning from BICLA "Nonresponder" to "Responder." No significant covariates on BICLA responder status were identified. Notably, the half maximal effective concentration was greater for the transition from "Responder" to "Nonresponder" (150 µg/mL) than the transition from "Nonresponder" to "Responder" (12 µg/mL), indicating that sustained dapirolizumab pegol concentrations may be required to maintain BICLA response. In conclusion, dapirolizumab pegol pharmacokinetics were as expected for a PEGylated molecule and results from the exposure-response model indicate that a favorable dapirolizumab pegol effect was identified for both BICLA "Nonresponder" to "Responder" and "Responder" to "Nonresponder" transition probabilities.

Bimekizumab, a Novel Humanized IgG1 Antibody That Neutralizes Both IL-17A and IL-17F.

Interleukin (IL)-17A is a key driver of inflammation and the principal target of anti-IL-17 therapeutic monoclonal antibodies. IL-17A, and its structurally similar family member IL-17F, have been shown to be functionally dysregulated in certain human immune-mediated inflammatory diseases such as psoriasis, psoriatic arthritis, and axial spondyloarthritis. Given the overlapping biology of these two cytokines, we postulated that dual neutralization of IL-17A and IL-17F may provide a greater depth of clinical response in IL-17-mediated diseases than IL-17A inhibition alone. We identified 496.g1, a humanized antibody with strong affinity for IL-17A but poor affinity for IL-17F. Affinity maturation of 496.g1 to 496.g3 greatly enhanced the affinity of the Fab fragment for IL-17F while retaining strong binding to IL-17A. As an IgG1, the affinity for IL-17A and IL-17F was 3.2 pM and 23 pM, respectively. Comparison of 496.g3 IgG1 with the commercially available anti-IL-17A monoclonal antibodies ixekizumab and secukinumab, by surface plasmon resonance and in a human in vitro IL-17A functional assay, showed that 496.g3 and ixekizumab display equivalent affinity for IL-17A, and that both antibodies are markedly more potent than secukinumab. In contrast to ixekizumab and secukinumab, 496.g3 exhibited the unique feature of also being able to neutralize the biological activity of IL-17F. Therefore, antibody 496.g3 was selected for clinical development for its ability to neutralize the biologic function of both IL-17A and IL-17F and was renamed bimekizumab (formerly UCB4940). Early clinical data in patients with psoriasis, in those with psoriatic arthritis, and from the Phase 2 studies in psoriasis, psoriatic arthritis, and ankylosing spondylitis, are encouraging and support the targeted approach of dual neutralization of IL-17A and IL-17F. Taken together, these findings provide the rationale for the continued clinical evaluation of bimekizumab in patients with immune-mediated inflammatory diseases.

Randomized study of the safety and pharmacodynamics of inhaled interleukin-13 monoclonal antibody fragment VR942.

BACKGROUND: Interleukin-13 (IL-13) is a key mediator of T-helper-cell-type-2 (Th-2)-driven asthma, the inhibition of which may improve treatment outcomes. We examined the safety, pharmacokinetics, pharmacodynamics, and immunogenicity of VR942, a dry-powder formulation containing CDP7766, a high-affinity anti-human-IL-13 antigen-binding antibody fragment being developed for the treatment of asthma. METHODS: We conducted a phase 1, randomized, double-blind, placebo-controlled, ascending-dose study at Hammersmith Medicines Research, London, UK, which is now complete. Healthy adults aged 18-50 years (n = 40) were randomized 3:1 to a single inhaled dose of VR942 0.5, 1.0, 5.0, 10, or 20 mg, or placebo. Adults aged 18-50 years who were diagnosed with asthma for ≥6 months before screening, and had forced expiratory volume in 1 s (FEV1) and forced vital capacity (FVC) values ≥70% of the predicted values at screening (n = 45), were randomized to once-daily inhaled VR942 0.5 or 10 mg, or placebo (2:2:1), or VR942 20 mg or placebo (3:2), for 10 days. All participants were randomized to receive VR942 or placebo based on a randomization list prepared by an independent HMR statistician using SAS® software (SAS Institute, Cary, NC). The primary outcome was safety and tolerability of VR942 (safety population, defined as all who received at least one dose of VR942 or placebo). This study is listed on ClinicalTrials.gov (NCT02473939). FINDINGS: In the VR942 and placebo groups, treatment-emergent adverse events (TEAEs) were reported in 10/30 (33%) and 0/10 (0%) healthy participants, and in 16/29 (55%) and 9/16 (56%) participants with asthma, respectively. Mild intermittent wheezing occurred in 7 participants (VR942 20 mg, n = 4; corresponding placebo, n = 3), resolving spontaneously within 1 h. All TEAEs were mild or moderate; there were no deaths, serious adverse events, or clinically significant changes in vital signs, electrocardiograms, or laboratory parameters. There was no clinically significant immunogenicity, with only one participant with asthma considered positive for treatment-related immunogenicity for CDP7766. INTERPRETATION: This study, considered to be the only example of a dry powder anti-IL-13 fragment antibody being administered via inhalation, demonstrated that single and repeat doses were well tolerated over a period of up to 10 days in duration. Rapid and durable inhibition of fractional exhaled nitric oxide (FeNO) (secondary outcome) provided evidence of pharmacological engagement with the IL-13 target in the airways of participants diagnosed with mild asthma. These data, together with the numerical improvements observed for predose FEV1, justify further clinical evaluation of VR942 in a broader population of patients with asthma, and continue to support the development of an inhaled anti-IL-13 antibody fragment as a potential future treatment that is alternative to monoclonal antibodies delivered via the parenteral route. FUNDING: Study funding and funding for the medical writing and editorial support for preparation of the manuscript were split equally between the two study co-funders (Vectura Ltd and UCB Pharma).

Dual IL-17A and IL-17F neutralisation by bimekizumab in psoriatic arthritis: evidence from preclinical experiments and a randomised placebo-controlled clinical trial that IL-17F contributes to human chronic tissue inflammation.

OBJECTIVE: Interleukin (IL)-17A has emerged as pivotal in driving tissue pathology in immune-mediated inflammatory diseases. The role of IL-17F, sharing 50% sequence homology and overlapping biological function, remains less clear. We hypothesised that IL-17F, together with IL-17A, contributes to chronic tissue inflammation, and that dual neutralisation may lead to more profound suppression of inflammation than inhibition of IL-17A alone. METHODS: Preclinical experiments assessed the role of IL-17A and IL-17F in tissue inflammation using disease-relevant human cells. A placebo-controlled proof-of-concept (PoC) clinical trial randomised patients with psoriatic arthritis (PsA) to bimekizumab (n=39) or placebo (n=14). Safety, pharmacokinetics and clinical efficacy of multiple doses (weeks 0, 3, 6 (240 mg/160 mg/160 mg; 80 mg/40 mg/40 mg; 160 mg/80 mg/80 mg and 560 mg/320 mg/320 mg)) of bimekizumab, a humanised monoclonal IgG1 antibody neutralising both IL-17A and IL-17F, were investigated. RESULTS: IL-17F induced qualitatively similar inflammatory responses to IL-17A in skin and joint cells. Neutralisation of IL-17A and IL-17F with bimekizumab more effectively suppressed in vitro cytokine responses and neutrophil chemotaxis than inhibition of IL-17A or IL-17F alone. The PoC trial met both prespecified efficacy success criteria and showed rapid, profound responses in both joint and skin (pooled top three doses vs placebo at week 8: American College of Rheumatology 20% response criteria 80.0% vs 16.7% (posterior probability >99%); Psoriasis Area and Severity Index 100% response criteria 86.7% vs 0%), sustained to week 20, without unexpected safety signals. CONCLUSIONS: These data support IL-17F as a key driver of human chronic tissue inflammation and the rationale for dual neutralisation of IL-17A and IL-17F in PsA and related conditions. TRIAL REGISTRATION NUMBER: NCT02141763; Results.

First-in-human randomized study of bimekizumab, a humanized monoclonal antibody and selective dual inhibitor of IL-17A and IL-17F, in mild psoriasis.

AIMS: To assess safety, pharmacokinetics (PK) and clinical efficacy of bimekizumab (formerly UCB4940), a novel humanized monoclonal antibody and dual inhibitor of interleukin (IL)-17A and IL-17F, in subjects with mild plaque psoriasis. METHODS: Randomized, double-blind, first-in-human study of bimekizumab in 39 subjects who received single-dose intravenous bimekizumab (8-640 mg) or placebo (NCT02529956). RESULTS: Bimekizumab demonstrated dose-proportional linear PK and was tolerated across the dose range assessed. No subject discontinued due to treatment-emergent adverse events and no severe adverse events were reported. Bimekizumab demonstrated fast onset of clinically-meaningful effects on skin of patients with mild psoriasis as early as Week 2. Maximal improvements (100% or near 100% reductions from baseline) in all measures of disease activity were observed between Weeks 8-12 in subjects receiving 160-640 mg bimekizumab. The duration of effect at doses ≥160 mg was evident up to Weeks 12-20 after a single intravenous dose, dependent on endpoint. CONCLUSIONS: This is the first study to demonstrate the safety, tolerability and clinical efficacy of a dual IL-17A and IL-17F inhibitor, in subjects with mild psoriasis. Bimekizumab showed fast onset of clinically-meaningful efficacy by Week 2, with a maximal or near-maximal magnitude of response that was maintained up to study Weeks 12-20. These findings support the continued clinical development of bimekizumab for diseases mediated by both IL-17A and IL-17F, including psoriasis.

Does age affect gastric emptying time? A model-based meta-analysis of data from premature neonates through to adults.

PURPOSE: Gastric emptying (GE) is often reported to be slower and more irregular in premature neonates than in older children and adults. The aim of this study was to investigate the impact of age and other covariates on the rate of GE. METHODS: The effect of age on the mean gastric residence times (MGRT) of liquid and solid food was assessed by analysing 49 published studies of 1457 individuals, aged from 28 weeks gestation to adults. The data were modelled using the nonlinear mixed-effects approach within NONMEM version 7.2 (ICON, Dublin, Ireland), with evaluation of postnatal age, gestational age and meal type as covariates. A double Weibull function was selected as a suitable model since it could account for the typical biphasic nature of GE. RESULTS: Age was not a significant covariate for GE but meal type was. Aqueous solutions were associated with the fastest emptying time (mean simulated gastric residence time of 45 min) and solid food was associated with the slowest (98 min). CONCLUSIONS: These findings challenge the assertion that GE is different in neonates, as compared with older children and adults due to age, and they reinforce the significance of food type in modulating GE.

Precision criteria to derive sample size when designing pediatric pharmacokinetic studies: which measure of variability should be used?

A new approach for calculation of sample size in pediatric clinical pharmacokinetic studies was suggested based on desired precision for a pharmacokinetic parameter of interest. The estimate of variability for sample size calculations could be obtained from different sources. It is not known whether these sources constantly show higher/lower variability across compounds and age groups. We obtained estimates of variability for clearance, volume of distribution and area under the plasma concentration-time curve for 5 drugs from adult/pediatric classic clinical pharmacokinetic studies, and physiologically based pharmacokinetics (PBPK) combined with in vitro-in vivo extrapolation. Estimates were applied to the proposal methodology for non-compartmental analysis. Sample size was different for each drug based on various estimates of variability from different pharmacokinetic parameters and depending on the age. Overall, there was no consistent discrepancy in sample size calculated according to the source of variability. A conservative approach should be taken when using "precision based methodology" knowing that various sources of initial estimates of variability will not lead to similar sample size calculations. Although PBPK simulations could be used for estimating variability, further work is required to investigate the best approach to estimate variability of pharmacokinetic parameters in pediatric populations and hence sample size calculations.

A generalisation of T-optimality for discriminating between competing models with an application to pharmacokinetic studies.

The T-optimality criterion is used in optimal design to derive designs for model selection. To set up the method, it is required that one of the models is considered to be true. We term this local T-optimality. In this work, we propose a generalisation of T-optimality (termed robust T-optimality) that relaxes the requirement that one of the candidate models is set as true. We then show an application to a nonlinear mixed effects model with two candidate non-nested models and combine robust T-optimality with robust D-optimality. Optimal design under local T-optimality was found to provide adequate power when the a priori assumed true model was the true model but poor power if the a priori assumed true model was not the true model. The robust T-optimality method provided adequate power irrespective of which model was true. The robust T-optimality method appears to have useful properties for nonlinear models, where both the parameter values and model structure are required to be known a priori, and the most likely model that would be applied to any new experiment is not known with certainty.

Introduction to pharmacokinetics in clinical toxicology.

In clinical toxicology, a better understanding of the pharmacokinetics of the drugs may be useful in both risk assessment and formulating treatment guidelines for patients. Pharmacokinetics describes the time course of drug concentrations and is a driver for the time course of drug effects. In this chapter pharmacokinetics is described from a mathematical modeling perspective as applied to clinical toxicology. The pharmacokinetics of drugs are described using a combination of input and disposition (distribution and elimination) phases. A description of the time course of the input and disposition of drugs in overdose provides a basis for understanding the time course of effects of drugs in overdose. Relevant clinical toxicology examples are provided to explain various pharmacokinetic principles. Throughout this chapter we have taken a pragmatic approach to understanding and interpreting the time course of drug effects.

A reduction in between subject variability is not mandatory for selecting a new covariate.

Population pharmacokinetic-pharmacodynamic analysis involves nonlinear hierarchical modelling where the mean response in a population and the variability in response from different sources are studied. It generally consists of two model hierarchies: a model for residual error and a model for heterogeneity termed between subject variance (BSV). The overall variability in a parameter within a population termed population parameter variance (PPV) consists of within subject variance (WSV) and BSV. Both these variances can further be split into random and predictable components. The predictable component of BSV (termed BSVP) is explained by covariates, individual characteristics e.g. weight. As BSVP increases, the remaining unpredictable (or random) between subject variability (BSVR) decreases since BSV = BSVP + BSVR, and BSV is a constant in any given data set. Since BSV and BSVR are estimated from the base and full covariate models, respectively, then BSVP = BSV-BSVR. The aim of this study was to explore the hypothesis, that a significant covariate may not always decrease BSVR. The specific aims were: (1) to explore circumstances where BSVR may not be reduced when adding a significantly correlated covariate and (2) to explore whether specific models for covariates may eliminate this anomaly when assessing BSVR. Simulations were performed using MATLAB (2011a) and estimation using NONMEM (ver 7.2) with FOCE and INTERACTION. A 1-compartment intravenous bolus PK model was used for simulation following a single unit dose (d = 1). The BSV of clearance [BSV(CL)] was described according to a log-normal distribution model with mean zero and variance ω². An additive random unexplained variability was assumed. Initially, we show through a simple simulation that BSVR can increase when a significantly correlated covariate is added to the model. We follow this with five simulation scenarios, A to E, that have various levels of correlation between the continuous covariate (Z) and CL ranging from 0 to 100 %. Each simulated scenario was replicated 100 times and estimated by a base model (i.e. without covariate addition) and six covariate models (M1-M6) which included non-nested (M1), nested (M2), and two types of interaction models for each of M1 and M2; non-nested interaction (M3, M5), nested interaction (M4, M6). Initially, through a motivating example we show that BSVR may not reduce even when there is 50 % correlation between the covariate Z and CL. It was found that with 0 % correlation M1, the non-nested covariate model (NNCM) resulted in negative BSVP (inflated BSVR) whereas M2, the nested covariate model (NCM), resulted in a calculated BSVP of zero. NNCM (M1) shows negative BSVP (BSVR > BSV) with correlation as high as 50 % and this model needs a minimum of 75 % correlation to show a positive BSVP. NCM (M2) shows positive but downwardly biased BSVP with 25, 50 and 75 % correlations. However, inclusion of a covariate-eta interaction term for both types of covariate models resulted in greater BSVP for 25, 50 and 75 % correlation scenarios compared to NNCM and NCM respectively. For 100 % correlation, it was found that covariate-eta interaction models show the same BSVP as the models without the interaction term, i.e. under perfect positive correlation all models perform similarly and correctly. It was found that a significantly correlated covariate may not reduce BSVR and in fact it may inflate the BSVR due to statistical misspecification of the covariate model. Incorporating statistical models that account for the covariate-eta interaction may be useful diagnostically in identifying the variability explained by covariates.