Interspecies Brain PBPK Modeling Platform to Predict Passive Transport through the Blood-Brain Barrier and Assess Target Site Disposition

The high failure rate of central nervous system (CNS) drugs is partly associated with an insufficient understanding of target site exposure. Blood-brain barrier (BBB) permeability evaluation tools are needed to explore drugs' ability to access the CNS. An outstanding aspect of physiologically based pharmacokinetic (PBPK) models is the integration of knowledge on drug-specific and system-specific characteristics, allowing the identification of the relevant factors involved in target site distribution. We aimed to qualify a PBPK platform model to be used as a tool to predict CNS concentrations when significant transporter activity is absent and human data are sparse or unavailable. Data from the literature on the plasma and CNS of rats and humans regarding acetaminophen, oxycodone, lacosamide, ibuprofen, and levetiracetam were collected. Human BBB permeability values were extrapolated from rats using inter-species differences in BBB surface area. The percentage of predicted AUC and Cmax within the 1.25-fold criterion was 85% and 100% for rats and humans, respectively, with an overall GMFE of <1.25 in all cases. This work demonstrated the successful application of the PBPK platform for predicting human CNS concentrations of drugs passively crossing the BBB. Future applications include the selection of promising CNS drug candidates and the evaluation of new posologies for existing drugs.

Preclinical Pharmacokinetics and Translational Pharmacokinetic/Pharmacodynamic Modeling of M8891, a Potent and Reversible Inhibitor of Methionine Aminopeptidase 2

INTRODUCTION

M8891 is a selective and reversible inhibitor of methionine aminopeptidase 2 (MetAP2). We describe translational research to define the target pharmacokinetics (PK) of M8891 and associated pharmacodynamic (PD) levels, which were used to support efficacious dose selection in humans.

METHODS

In vitro and in vivo PK characteristics were investigated in animal species, and data integrated using in vitro-in vivo correlation and allometric methods to predict the clearance, volume of distribution, and absorption parameters of M8891 in humans. In parallel, inhibition of MetAP2 activity by M8891 was studied in renal cancer xenografts in mice by measuring accumulation of Met-EF1α, a substrate of MetAP2. The corresponding PD effect was described by a turnover and effect compartment model. This model was used to simulate PD at the M8891 dose showing in vivo efficacy, i.e. significant tumor growth inhibition. Simulations of M8891 PK and associated PD in humans were conducted by integrating predicted human PK parameters into the preclinical PK/PD model.

RESULTS

The target minimum PD level associated with efficacy was determined to be 125 µg Met-EF1α per mg protein. Integrating predicted human PK parameters into the preclinical PK/PD model defined a minimal M8891 concentration at steady-state (Ctrough) of 1500 ng/mL (3.9 µM) in humans as being required to produce the corresponding minimum target Met-EF1a level (125 µg per mg protein).

CONCLUSION

The defined target PK and PD levels supported the design of the clinical Phase Ia dose escalation study of M8891 (NCT03138538) and selection of the recommended Phase II dose.

Characterization of concurrent target suppression by JNJ-61178104, a bispecific antibody against human tumor necrosis factor and interleukin-17A

Tumor necrosis factor (TNF) and interleukin (IL)-17A are pleiotropic cytokines implicated in the pathogenesis of several autoimmune diseases including rheumatoid arthritis (RA) and psoriatic arthritis (PsA). JNJ-61178104 is a novel human anti-TNF and anti-IL-17A monovalent, bispecific antibody that binds to both human TNF and human IL-17A with high affinities and blocks the binding of TNF and IL-17A to their receptors in vitro. JNJ-61178104 also potently neutralizes TNF and IL-17A-mediated downstream effects in multiple cell-based assays. In vivo, treatment with JNJ-61178104 resulted in dose-dependent inhibition of cellular influx in a human IL-17A/TNF-induced murine lung neutrophilia model and the inhibitory effects of JNJ-61178104 were more potent than the treatment with bivalent parental anti-TNF or anti-IL-17A antibodies. JNJ-61178104 was shown to engage its targets, TNF and IL-17A, in systemic circulation measured as drug/target complex formation in normal cynomolgus monkeys (cyno). Surprisingly, quantitative target engagement assessment suggested lower apparent in vivo target-binding affinities for JNJ-61178104 compared to its bivalent parental antibodies, despite their similar in vitro target-binding affinities. The target engagement profiles of JNJ-61178104 in humans were in general agreement with the predicted profiles based on cyno data, suggesting similar differences in the apparent in vivo target-binding affinities. These findings show that in vivo target engagement of monovalent bispecific antibody does not necessarily recapitulate that of the molar-equivalent dose of its bivalent parental antibody. Our results also offer valuable insights into the understanding of the pharmacokinetics/pharmacodynamics and target engagement of other bispecific biologics against dimeric and/or trimeric soluble targets in vivo.

A Translational Quantitative Systems Pharmacology Model for CD3 Bispecific Molecules: Application to Quantify T Cell-Mediated Tumor Cell Killing by P-Cadherin LP DART®

CD3 bispecific antibody constructs recruit cytolytic T cells to kill tumor cells, offering a potent approach to treat cancer. T cell activation is driven by the formation of a trimolecular complex (trimer) between drugs, T cells, and tumor cells, mimicking an immune synapse. A translational quantitative systems pharmacology (QSP) model is proposed for CD3 bispecific molecules capable of predicting trimer concentration and linking it to tumor cell killing. The model was used to quantify the pharmacokinetic (PK)/pharmacodynamic (PD) relationship of a CD3 bispecific targeting P-cadherin (PF-06671008). It describes the disposition of PF-06671008 in the central compartment and tumor in mouse xenograft models, including binding to target and T cells in the tumor to form the trimer. The model incorporates T cell distribution to the tumor, proliferation, and contraction. PK/PD parameters were estimated for PF-06671008 and a tumor stasis concentration (TSC) was calculated as an estimate of minimum efficacious trimer concentration. TSC values ranged from 0.0092 to 0.064 pM across mouse tumor models. The model was translated to the clinic and used to predict the disposition of PF-06671008 in patients, including the impact of binding to soluble P-cadherin. The predicted terminal half-life of PF-06671008 in the clinic was approximately 1 day, and P-cadherin expression and number of T cells in the tumor were shown to be sensitive parameters impacting clinical efficacy. A translational QSP model is presented for CD3 bispecific molecules, which integrates in silico, in vitro and in vivo data in a mechanistic framework, to quantify and predict efficacy across species.

Translational PBPK Modeling of the Protein Therapeutic and CD95L Inhibitor Asunercept to Develop Dose Recommendations for Its First Use in Pediatric Glioblastoma Patients

The protein therapeutic and CD95L inhibitor asunercept is currently under clinical investigation for the treatment of glioblastoma and myelodysplastic syndrome. The purpose of this study was to predict the asunercept pharmacokinetics in children and to give dose recommendations for its first use in pediatric glioblastoma patients. A physiologically-based pharmacokinetic (PBPK) model of asunercept in healthy and diseased adults was successfully developed using the available clinical Phase I and Phase II study data. This model was then extrapolated to different pediatric populations, to predict the asunercept exposure in children and to find equivalent starting doses. Simulation of the asunercept serum concentration-time curves in children between 1–18 years of age shows that a dosing regimen based on body weight results in a similar asunercept steady-state exposure in all patients (pediatric or adult) above 12 years of age. For children between 1–12 years, higher doses per kg body weight are recommended, with the highest dose for the very young patients. Translational PBPK modeling is strongly encouraged by regulatory agencies to help with the initial dose selection for pediatric trials. To our knowledge, this is the first report of pediatric PBPK to support the dose selection of a therapeutic protein before its administration to children.

Modeling of prolactin response following dopamine D2 receptor antagonists in rats: can it be translated to clinical dosing?

Prolactin release is a side effect of antipsychotic therapy with dopamine antagonists, observed in rats as well as humans. We examined whether two semimechanistic models could describe prolactin response in rats and subsequently be translated to predict pituitary dopamine D₂ receptor occupancy and plasma prolactin concentrations in humans following administration of paliperidone or remoxipride. Data on male Wistar rats receiving single or multiple doses of risperidone, paliperidone, or remoxipride was described by two semimechanistic models, the precursor pool model and the agonist–antagonist interaction model. Using interspecies scaling approaches, human D₂ receptor occupancy and plasma prolactin concentrations were predicted for a range of clinical paliperidone and remoxipride doses. The predictions were compared with corresponding observations described in literature as well as with predictions from published models developed on human data. The pool model could predict D₂ receptor occupancy and prolactin response in humans following single doses of paliperidone and remoxipride. Tolerance of prolactin release was predicted following multiple doses. The interaction model underpredicted both D₂ receptor occupancy and prolactin response. Prolactin elevation may be deployed as a suitable biomarker for interspecies translation and can inform the clinical safe and effective dose range of antipsychotic drugs. While the pool model was more predictive than the interaction model, it overpredicted tolerance on multiple dosing. Shortcomings of the translations reflect the need for better mechanistic models.

A Novel Experimental and Modelling Strategy for Nanoparticle Toxicity Testing Enabling the Use of Small Quantities

Metallic nanoparticles (NPs) differ from other metal forms with respect to their large surface to volume ratio and subsequent inherent reactivity. Each new modification to a nanoparticle alters the surface to volume ratio, fate and subsequently the toxicity of the particle. Newly-engineered NPs are commonly available only in low quantities whereas, in general, rather large amounts are needed for fate characterizations and effect studies. This challenge is especially relevant for those NPs that have low inherent toxicity combined with low bioavailability. Therefore, within our study, we developed new testing strategies that enable working with low quantities of NPs. The experimental testing method was tailor-made for NPs, whereas we also developed translational models based on different dose-metrics allowing to determine dose-response predictions for NPs. Both the experimental method and the predictive models were verified on the basis of experimental effect data collected using zebrafish embryos exposed to metallic NPs in a range of different chemical compositions and shapes. It was found that the variance in the effect data in the dose-response predictions was best explained by the minimal diameter of the NPs, whereas the data confirmed that the predictive model is widely applicable to soluble metallic NPs. The experimental and model approach developed in our study support the development of (eco)toxicity assays tailored to nano-specific features.

In Vivo Systems Response Profiling and Multivariate Classification of CNS Active Compounds: A Structured Tool for CNS Drug Discovery

This paper describes the application of in vivo systems response profiling in CNS drug discovery by a process referred to as the Integrative Screening Process. The biological response profile, treated as an array, is used as major outcome for selection of candidate drugs. Dose-response data, including ex vivo brain monoaminergic biomarkers and behavioral descriptors, are systematically collected and analyzed by principal component analysis (PCA) and partial least-squares (PLS) regression, yielding multivariate characterization across compounds. The approach is exemplified by assessing a new class of CNS active compounds, the dopidines, compared to other monoamine modulating compounds including antipsychotics, antidepressants, and procognitive agents. Dopidines display a distinct phenotypic profile which has prompted extensive further preclinical and clinical investigations. In summary, in vivo profiles of CNS compounds are mapped, based on dose response studies in the rat. Applying a systematic and standardized work-flow, a database of in vivo systems response profiles is compiled, enabling comparisons and classification. This creates a framework for translational mapping, a crucial component in CNS drug discovery.