Increasing serum half-life and extending cholesterol lowering in vivo by engineering antibody with pH-sensitive binding to PCSK9.

Target-mediated clearance and high antigen load can hamper the efficacy and dosage of many antibodies. We show for the first time that the mouse, cynomolgus, and human cross-reactive, antagonistic anti-proprotein convertase substilisin kexin type 9 (PCSK9) antibodies J10 and the affinity-matured and humanized J16 exhibit target-mediated clearance, resulting in dose-dependent pharmacokinetic profiles. These antibodies prevent the degradation of low density lipoprotein receptor, thus lowering serum levels of LDL-cholesterol and potently reducing serum cholesterol in mice, and selectively reduce LDL-cholesterol in cynomolgus monkeys. In order to increase the pharmacokinetic and efficacy of this promising therapeutic for hypercholesterolemia, we engineered pH-sensitive binding to mouse, cynomolgus, and human PCSK9 into J16, resulting in J17. This antibody shows prolonged half-life and increased duration of cholesterol lowering in two species in vivo by binding to endogenous PCSK9 in mice and cynomolgus monkeys, respectively. The proposed mechanism of this pH-sensitive antibody is that it binds with high affinity to PCSK9 in the plasma at pH 7.4, whereas the antibody-antigen complex dissociates at the endosomal pH of 5.5-6.0 in order to escape from target-mediated degradation. Additionally, this enables the antibody to bind to another PCSK9 and therefore increase the antigen-binding cycles. Furthermore, we show that this effect is dependent on the neonatal Fc receptor, which rescues the dissociated antibody in the endosome from degradation. Engineered pH-sensitive antibodies may enable less frequent or lower dosing of antibodies hampered by target-mediated clearance and high antigen load.

Quantitative systems pharmacology model of GITR-mediated T cell dynamics in tumor microenvironment.

T cell interaction in the tumor microenvironment is a key component of immuno-oncology therapy. Glucocorticoid-induced tumor necrosis factor receptor (TNFR)-related protein (GITR) is expressed on immune cells including regulatory T cells (Tregs) and effector T cells (Teffs). Preclinical data suggest that agonism of GITR in combination with Fc-γ receptor-mediated depletion of Tregs results in increased intratumoral Teff:Treg ratio and tumor shrinkage. A novel quantitative systems pharmacology (QSP) model was developed for the murine anti-GITR agonist antibody, DTA-1.mIgG2a, to describe the kinetics of intratumoral Tregs and Teffs in Colon26 and A20 syngeneic mouse tumor models. It adequately captured the time profiles of intratumoral Treg and Teff and serum DTA-1.mIgG2a and soluble GITR concentrations in both mouse models, and described the response differences between the two models. The QSP model provides a quantitative understanding of the trade-off between maximizing Treg depletion versus Teff agonism, and offers insights to optimize drug design and dose regimen.

Learning-dependent chromatin remodeling highlights noncoding regulatory regions linked to autism.

Autism spectrum disorder (ASD) is a prevalent neurodevelopmental disorder that is associated with genetic risk factors. Most human disease-associated single-nucleotide polymorphisms (SNPs) are not located in genes but rather are in regulatory regions that control gene expression. The function of regulatory regions is determined through epigenetic mechanisms. Parallels between the cellular basis of development and the formation of long-term memory have long been recognized, particularly the role of epigenetic mechanisms in both processes. We analyzed how learning alters chromatin accessibility in the mouse hippocampus using a new high-throughput sequencing bioinformatics strategy we call DEScan (differential enrichment scan). DEScan, which enabled the analysis of data from epigenomic experiments containing multiple replicates, revealed changes in chromatin accessibility at 2365 regulatory regions-most of which were promoters. Learning-regulated promoters were active during forebrain development in mice and were enriched in epigenetic modifications indicative of bivalent promoters. These promoters were disproportionally intronic, showed a complex relationship with gene expression and alternative splicing during memory consolidation and retrieval, and were enriched in the data set relative to known ASD risk genes. Genotyping in a clinical cohort within one of these promoters (SHANK3 promoter 6) revealed that the SNP rs6010065 was associated with ASD. Our data support the idea that learning recapitulates development at the epigenetic level and demonstrate that behaviorally induced epigenetic changes in mice can highlight regulatory regions relevant to brain disorders in patients.

A mechanistic tumor penetration model to guide antibody drug conjugate design.

Antibody drug conjugates (ADCs) represent novel anti-cancer modalities engineered to specifically target and kill tumor cells expressing corresponding antigens. Due to their large size and their complex kinetics, these therapeutic agents often face heterogeneous distributions in tumors, leading to large untargeted regions that escape therapy. We present a modeling framework which includes the systemic distribution, vascular permeability, interstitial transport, as well as binding and payload release kinetics of ADC-therapeutic agents in mouse xenografts. We focused, in particular, on receptor dynamics such as endocytic trafficking mechanisms within cancer cells, to simulate their impact on tumor mass shrinkage upon ADC administration. Our model identified undesirable tumor properties that can impair ADC tissue homogeneity, further compromising ADC success, and explored ADC design optimization scenarios to counteract upon such unfavorable intrinsic tumor tissue attributes. We further demonstrated the profound impact of cytotoxic payload release mechanisms and the role of bystander killing effects on tumor shrinkage. This model platform affords a customizable simulation environment which can aid with experimental data interpretation and the design of ADC therapeutic treatments.

Comprehensive mechanism-based antibody pharmacokinetic modeling.

Pharmacokinetic models of antibody distribution and dynamics are useful for predicting and optimizing therapeutic behavior. Targeted antigens are produced and distributed in various tissues in specific patterns in disease phenotypes. Existing models leave out significant mechanistic detail which would enable an understanding of how to modify therapeutics in an optimal manner to allow appropriate tissue penetration in either a healthy or diseased state. The model presented here incorporates additional complexity such as diffusion through endothelial barriers, differential transcytosis properties, FcRn-mediated recycling, and incorporates these properties in an organ-specific manner. This creates a platform which can be expanded upon to include understanding of the effect of target on therapeutic distribution and clearance, differences in dynamics during a diseased versus healthy state, differential dose strategies, and mechanistic translation between animal models and human disease state. This model represents a superior alternative to typical and potentially over-simplified scaling strategies utilized in most existing physiologically-based pharmacokinetic models. Ultimately, this will enable better therapeutic design and greater pharmacological effects.