Therapeutic antibodies administered intravitreally are the current standard of care to treat retinal diseases. The ocular half-life (t 1/2) is a key determinant of the duration of target suppression. To support the development of novel, longer-acting drugs, a reliable determination of t 1/2 is needed together with an improved understanding of the factors that influence it. A model-based meta-analysis was conducted in humans and nonclinical species (rat, rabbit, monkey, and pig) to determine consensus values for the ocular t 1/2 of IgG antibodies and Fab fragments. Results from multiple literature and in-house pharmacokinetic studies are presented within a mechanistic framework that assumes diffusion-controlled drug elimination from the vitreous. Our analysis shows, both theoretically and experimentally, that the ocular t 1/2 increases in direct proportion to the product of the hydrodynamic radius of the macromolecule (3.0 nm for Fab and 5.0 nm for IgG) and the square of the radius of the vitreous globe, which varies approximately 24-fold from the rat to the human. Interspecies differences in the proportionality factors are observed and discussed in mechanistic terms. In addition, mathematical formulae are presented that allow prediction of the ocular t 1/2 for molecules of interest. The utility of these formulae is successfully demonstrated in case studies of aflibercept, brolucizumab, and PEGylated Fabs, where the predicted ocular t 1/2 values are found to be in reasonable agreement with the experimental data available for these molecules.

One of the main aims of early phase clinical trials is to identify a safe dose with an indication of therapeutic benefit to administer to subjects in further studies. Ideally therefore, dose-limiting events (DLEs) and responses indicative of efficacy should be considered in the dose-escalation procedure. Several methods have been suggested for incorporating both DLEs and efficacy responses in early phase dose-escalation trials. In this paper, we describe and evaluate a Bayesian adaptive approach based on one binary response (occurrence of a DLE) and one continuous response (a measure of potential efficacy) per subject. A logistic regression and a linear log-log relationship are used respectively to model the binary DLEs and the continuous efficacy responses. A gain function concerning both the DLEs and efficacy responses is used to determine the dose to administer to the next cohort of subjects. Stopping rules are proposed to enable efficient decision making. Simulation results shows that our approach performs better than taking account of DLE responses alone. To assess the robustness of the approach, scenarios where the efficacy responses of subjects are generated from an Emax model, but modelled by the linear log-log model are also considered. This evaluation shows that the simpler log-log model leads to robust recommendations even under this model showing that it is a useful approximation to the difficulty in estimating Emax model. Additionally, we find comparable performance to alternative approaches using efficacy and safety for dose-finding.

The main purpose of dose-escalation trials is to identify the dose(s) that is/are safe and efficacious for further investigations in later studies. In this paper, we introduce dose-escalation designs that incorporate both the dose-limiting events and dose-limiting toxicities (DLTs) and indicative responses of efficacy into the procedure. A flexible nonparametric model is used for modelling the continuous efficacy responses while a logistic model is used for the binary DLTs. Escalation decisions are based on the combination of the probabilities of DLTs and expected efficacy through a gain function. On the basis of this setup, we then introduce 2 types of Bayesian adaptive dose-escalation strategies. The first type of procedures, called "single objective," aims to identify and recommend a single dose, either the maximum tolerated dose, the highest dose that is considered as safe, or the optimal dose, a safe dose that gives optimum benefit risk. The second type, called "dual objective," aims to jointly estimate both the maximum tolerated dose and the optimal dose accurately. The recommended doses obtained under these dose-escalation procedures provide information about the safety and efficacy profile of the novel drug to facilitate later studies. We evaluate different strategies via simulations based on an example constructed from a real trial on patients with type 2 diabetes, and the use of stopping rules is assessed. We find that the nonparametric model estimates the efficacy responses well for different underlying true shapes. The dual-objective designs give better results in terms of identifying the 2 real target doses compared to the single-objective designs.

AIMSThis study aimed at identifying pharmacological factors such as pharmacogenetics and drug exposure as new predictive biomarkers for delayed graft function (DGF), acute rejection (AR) and/or subclinical rejection (SCR). METHODSAdult renal transplant recipients (n = 361) on cyclosporine-based immunosuppression were followed for the first 6 months after transplantation. The incidence of DGF and AR were documented as well as the prevalence of SCR at 6 months in surveillance biopsies. Demographic, transplant-related factors, pharmacological and pharmacogenetic factors (ABCB1, CYP3A5, CYP3A4, CYP2C8, NR1I2, PPP3CA and PPP3CB) were analysed in a combined approach in relation to the occurrence of DGF, AR and prevalence of SCR at month 6 using a proportional odds model and time to event model. RESULTSFourteen per cent of the patients experienced at least one clinical rejection episode and only DGF showed a significant effect on the time to AR. The incidence of DGF correlated with a deceased donor kidney transplant (27% vs. 0.6% of living donors). Pharmacogenetic factors were not associated with risk for DGF, AR or SCR. A deceased donor kidney and acute rejection history were the most important determinants for SCR, resulting in a 52% risk of SCR at 6 months (vs. 11% average). In a sub-analysis of the patients with AR, those treated with rejection treatment including ATG, significantly less frequent SCR was found in the 6-month biopsy (13% vs. 50%). CONCLUSIONSTransplant-related factors remain the most important determinants of DGF, AR and SCR. Furthermore, rejection treatment with depleting antibodies effectively prevented SCR in 6-month surveillance biopsies. British Journal of Clinical Pharmacology WHAT IS ALREADY KNOW ABOUT THIS SUBJECT• Acute rejection rates have decreased dramatically in the past decades. However, long-term outcome has not improved accordingly.• Transplant-related factors are important determinants of delayed graft function, acute rejection and subclinical rejection, which in its turn leads to progressive fibrosis and loss of graft function.• Pharmacogenetics can have an influence on cyclosporine A pharmacokinetics, but whether this also influences delayed graft function, acute rejection and subclinical rejection is subject to debate. WHAT THIS STUDY ADDS• The combined approach of analysing a wide range of time-varying and time-constant pharmacological factors in addition to known transplant-related factors did not identify new factors suitable for prediction of delayed graft function, acute rejection and subclinical rejection.• Transplant-related factors currently remain the most important determinants of delayed graft function, acute rejection and subclinical rejection in this therapeutic drug monitoring (TDM)-guided setting. Furthermore, rejection treatment with ATG prevented SCR.• Pharmacogenetic factors, although theoretically plausible, are not suitable as predictive factors for delayed graft function, acute rejection and subclinical rejection and subsequently long-term graft survival.

AIMRecent publications indicate a strong interest in applying Bayesian adaptive designs in first time in humans (FTIH) studies outside of oncology. The objective of the present work was to assess the performance of a new approach that includes Bayesian adaptive design in single ascending dose (SAD) trials conducted in healthy volunteers, in comparison with a more traditional approach. METHODSA trial simulation approach was used and seven different scenarios of dose-response were tested. RESULTSThe new approach provided less biased estimates of maximum tolerated dose (MTD). In all scenarios, the number of subjects needed to define a MTD was lower with the new approach than with the traditional approach. With respect to duration of the trials, the two approaches were comparable. In all scenarios, the number of subjects exposed to a dose greater than the actual MTD was lower with the new approach than with the traditional approach. CONCLUSIONSThe new approach with Bayesian adaptive design shows a very good performance in the estimation of MTD and in reducing the total number of healthy subjects. It also reduces the number of subjects exposed to doses greater than the actual MTD. WHAT IS ALREADY KNOWN ABOUT THIS SUBJECT• Bayesian adaptive designs in phase 1 oncology trials have been used for more than two decades.• Outside of oncology, these model-based approaches are very rarely used in phase 1 studies.• Recent publications indicate an interest to find better and more efficient approaches in the conduct of single ascending dose trials. WHAT THIS STUDY ADDS• An approach with Bayesian adaptive design shows a very good performance in the estimation of maximum tolerated dose (MTD) and in reducing the total number of healthy subjects.• This approach reduces the number of subjects exposed to doses greater than the actual MTD.