Translational two-pore PBPK model to characterize whole-body disposition of different-size endogenous and exogenous proteins.

Two-pore physiologically based pharmacokinetic (PBPK) modeling has demonstrated its potential in describing the pharmacokinetics (PK) of different-size proteins. However, all existing two-pore models lack either diverse proteins for validation or interspecies extrapolation. To fill the gap, here we have developed and optimized a translational two-pore PBPK model that can characterize plasma and tissue disposition of different-size proteins in mice, rats, monkeys, and humans. Datasets used for model development include more than 15 types of proteins: IgG (150 kDa), F(ab)2 (100 kDa), minibody (80 kDa), Fc-containing proteins (205, 200, 110, 105, 92, 84, 81, 65, or 60 kDa), albumin conjugate (85.7 kDa), albumin (67 kDa), Fab (50 kDa), diabody (50 kDa), scFv (27 kDa), dAb2 (23.5 kDa), proteins with an albumin-binding domain (26, 23.5, 22, 16, 14, or 13 kDa), nanobody (13 kDa), and other proteins (110, 65, or 60 kDa). The PBPK model incorporates: (i) molecular weight (MW)-dependent extravasation through large and small pores via diffusion and filtration, (ii) MW-dependent renal filtration, (iii) endosomal FcRn-mediated protection from catabolism for IgG and albumin-related modalities, and (iv) competition for FcRn binding from endogenous IgG and albumin. The finalized model can well characterize PK of most of these proteins, with area under the curve predicted within two-fold error. The model also provides insights into contribution of renal filtration and lysosomal degradation towards total elimination of proteins, and contribution of paracellular convection/diffusion and transcytosis towards extravasation. The PBPK model presented here represents a cross-modality, cross-species platform that can be used for development of novel biologics.

Preclinical Assessment of a MUC12-Targeted BiTE (Bispecific T-cell Engager) Molecule.

MUC12 is a transmembrane mucin that is highly expressed in >50% of primary and metastatic colorectal tumors. MUC12 is also expressed by normal epithelial cells of the colon and small intestine. Although MUC12 localization in normal epithelial cells is restricted to the apical membrane, expression in tumors is depolarized and shows broad membrane localization. The differential localization of MUC12 in tumor cells as compared with normal cells makes it a potential therapeutic target. Here, we evaluated targeting of MUC12 with a BiTE (bispecific T-cell engager) molecule. We generated a panel of proof-of-concept half-life extended (HLE) BiTE molecules that bind MUC12 on tumor cells and CD3 on T cells. We prioritized one molecule based on in vitro activity for further characterization in vivo In vitro, the MUC12 HLE BiTE molecule mediated T-cell-redirected lysis of MUC12-expressing cells with half-maximal lysis of 4.4 ± 0.9 to 117 ± 78 pmol/L. In an exploratory cynomolgus monkey toxicology study, the MUC12 HLE BiTE molecule administered at 200 µg/kg with a step dose to 1,000 µg/kg was tolerated with minimal clinical observations. However, higher doses were not tolerated, and there was evidence of damage in the gastrointestinal tract, suggesting dose levels projected to be required for antitumor activity may be associated with on-target toxicity. Together, these data demonstrate that the apically restricted expression of MUC12 in normal tissues is accessible to BiTE molecule target engagement and highlight the difficult challenge of identifying tumor-selective antigens for solid tumor T-cell engagers.

GIPR antagonist antibodies conjugated to GLP-1 peptide are bispecific molecules that decrease weight in obese mice and monkeys.

Glucose-dependent insulinotropic polypeptide (GIP) and glucagon-like peptide-1 (GLP-1) regulate glucose and energy homeostasis. Targeting both pathways with GIP receptor (GIPR) antagonist antibody (GIPR-Ab) and GLP-1 receptor (GLP-1R) agonist, by generating GIPR-Ab/GLP-1 bispecific molecules, is an approach for treating obesity and its comorbidities. In mice and monkeys, these molecules reduce body weight (BW) and improve many metabolic parameters. BW loss is greater with GIPR-Ab/GLP-1 than with GIPR-Ab or a control antibody conjugate, suggesting synergistic effects. GIPR-Ab/GLP-1 also reduces the respiratory exchange ratio in DIO mice. Simultaneous receptor binding and rapid receptor internalization by GIPR-Ab/GLP-1 amplify endosomal cAMP production in recombinant cells expressing both receptors. This may explain the efficacy of the bispecific molecules. Overall, our GIPR-Ab/GLP-1 molecules promote BW loss, and they may be used for treating obesity.

Optimal Affinity of a Monoclonal Antibody: Guiding Principles Using Mechanistic Modeling.

Affinity optimization of monoclonal antibodies (mAbs) is essential for developing drug candidates with the highest likelihood of clinical success; however, a quantitative approach for setting affinity requirements is often lacking. In this study, we computationally analyzed the in vivo mAb-target binding kinetics to delineate general principles for defining optimal equilibrium dissociation constant ([Formula: see text]) of mAbs against soluble and membrane-bound targets. Our analysis shows that in general [Formula: see text] to achieve 90% coverage for a soluble target is one tenth of its baseline concentration ([Formula: see text]), and is independent of the dosing interval, target turnover rate or the presence of competing ligands. For membrane-bound internalizing targets, it is equal to the ratio of internalization rate of mAb-target complex and association rate constant ([Formula: see text]). In cases where soluble and membrane-bound forms of the target co-exist, [Formula: see text] lies within a range determined by the internalization rate ([Formula: see text]) of the mAb-membrane target complex and the ratio of baseline concentrations of soluble and membrane-bound forms ([Formula: see text]). Finally, to demonstrate practical application of these general rules, we collected target expression and turnover data to project [Formula: see text] for a number of marketed mAbs against soluble (TNFα, RANKL, and VEGF) and membrane-bound targets (CD20, EGFR, and HER2).

A cell-level model of pharmacodynamics-mediated drug disposition.

We aimed to develop a cell-level pharmacodynamics-mediated drug disposition (PDMDD) model to analyze in vivo systems where the PD response to a drug has an appreciable effect on the pharmacokinetics (PK). An existing cellular level model of PD stimulation was combined with the standard target-mediated drug disposition (TMDD) model and the resulting model structure was parametrically identifiable from typical in vivo PK and PD data. The PD model of the cell population was controlled by the production rate k in and elimination rate k out which could be stimulated or inhibited by the number of bound receptors on a single cell. Simulations were performed to assess the impact of single and repeated dosing on the total drug clearance. The clinical utility of the cell-level PDMDD model was demonstrated by fitting published data on the stimulatory effects of filgrastim on absolute neutrophil counts in healthy subjects. We postulated repeated dosing as a means of detecting and quantifying PDMDD as a single dose might not be sufficient to elicit the cellular response capable of altering the receptor pool to visibly affect drug disposition. In the absence of any PD effect, the model reduces down to the standard TMDD model. The applications of this model can be readily extended to include chemotherapy-induced cytopenias affecting clearance of endogenous hematopoietic growth factors, different monoclonal antibodies and immunogenicity effects on PK.

Simulations of site-specific target-mediated pharmacokinetic models for guiding the development of bispecific antibodies.

Bispecific antibodies (BAbs) are novel constructs that are under development and show promise as new therapeutic modalities for cancer and autoimmune disorders. The aim of this study is to develop a semi-mechanistic modeling approach to elucidate the disposition of BAbs in plasma and possible sites of action in humans. Here we present two case studies that showcase the use of modeling to guide BAb development. In case one, a BAb is directed against a soluble and a membrane-bound ligand for treating systemic lupus erythematosus, and in case two, a BAb targets two soluble ligands as a potential treatment for ulcerative colitis and asthma. Model simulations revealed important differences between plasma and tissues, when evaluated for drug disposition and target suppression. Target concentrations at tissue sites and type (soluble vs membrane-bound), tissue-site binding, and binding affinity are all major determinants of BAb disposition and subsequently target suppression. For the presented case studies, higher doses and/or frequent dosing regimens are required to achieve 80 % target suppression in site specific tissue (the more relevant matrix) as compared to plasma. Site-specific target-mediated models may serve to guide the selection of first-in-human doses for new BAbs.

Ubiquity: a framework for physiological/mechanism-based pharmacokinetic/pharmacodynamic model development and deployment.

Practitioners of pharmacokinetic/pharmacodynamic modeling routinely employ various software packages that enable them to fit differential equation based mechanistic or empirical models to biological/pharmacological data. The availability and choice of different analytical tools, while enabling, can also pose a significant challenge in terms of both, implementation and transferability. A package has been developed that addresses these issues by creating a simple text-based format, which provides methods to reduce coding complexity and enables the modeler to describe the components of the model based on the underlying physiochemical processes. A Perl script builds the system for multiple formats (ADAPT, MATLAB, Berkeley Madonna, etc.), enabling analysis across several software packages and reducing the chance for transcription error. Workflows can then be built around this package, which can increase efficiency and model availability. As a proof of concept, tools are included that allow models constructed in this format to be run with MATLAB both at the scripting level and through a generic graphical application that can be compiled and run as a stand-alone application.

Combinatorial chemotherapeutic efficacy in non-Hodgkin lymphoma can be predicted by a signaling model of CD20 pharmacodynamics.

Combination chemotherapy represents the standard-of-care for non-Hodgkin lymphoma. However, the development of new therapeutic regimens is empirical and this approach cannot be used prospectively to identify novel or optimal drug combinations. Quantitative system pharmacodynamic models could promote the discovery and development of combination regimens based upon first principles. In this study, we developed a mathematical model that integrates temporal patterns of drug exposure, receptor occupancy, and signal transduction to predict the effects of the CD20 agonist rituximab in combination with rhApo2L/TNF-related apoptosis inducing ligand or fenretinide, a cytotoxic retinoid, upon growth kinetics in non-Hodgkin lymphoma xenografts. The model recapitulated major regulatory mechanisms, including target-mediated disposition of rituximab, modulation of proapoptotic intracellular signaling induced by CD20 occupancy, and the relative efficacy of death receptor isoforms. The multiscale model coupled tumor responses to individual anticancer agents with their mechanisms of action in vivo, and the changes in Bcl-xL and Fas induced by CD20 occupancy were linked to explain the synergy of these drugs. Tumor growth profiles predicted by the model agreed with cell and xenograft data, capturing the apparent pharmacologic synergy of these agents with fidelity. Together, our findings provide a mechanism-based platform for exploring new regimens with CD20 agonists.

Interspecies scaling of receptor-mediated pharmacokinetics and pharmacodynamics of type I interferons.

PURPOSE: To develop an integrated mechanism-based modeling approach for the interspecies scaling of pharmacokinetic (PK) and pharmacodynamic (PD) properties of type I interferons (IFNs) that exhibit target-mediated drug disposition (TMDD). METHODS: PK and PD profiles of human IFN-beta1a, IFN-beta1b, and IFN-alpha2a in humans, monkeys, rats, and mice from nine studies were extracted from the literature by digitization. Concentration-time profiles from different species were fitted simultaneously using various allometric relationships to scale model-specific parameters. RESULTS: PK/PD profiles of IFN-beta1a in humans and monkeys were successfully characterized by utilizing the same rate constant parameters and scaling the volume of the central compartment to body weight using an allometric exponent of 1. Concentration and effect profiles of other IFNs were also well described by changing only the affinity of the drug to its receptor. PK profiles in rodents were simulated using an allometric exponent of -0.25 for the first-order elimination rate constant, and no receptor-binding was included given the lack of cross-reactivity. CONCLUSIONS: An integrated TMDD PK/PD model was successfully combined with classic allometric scaling techniques and showed good predictive performance. Several parameters obtained from one IFN can be effectively shared to predict the kinetic behavior of other IFN subtypes.

Differential pharmacodynamic effects of paclitaxel formulations in an intracranial rat brain tumor model.

Nano- and microparticulate carriers can exert a beneficial impact on the pharmacodynamics of anticancer agents. To investigate the relationships between carrier and antitumor pharmacodynamics, paclitaxel incorporated in liposomes (L-pac) was compared with the clinical standard formulated in Cremophor-EL/ethanol (Cre-pac) in a rat model of advanced primary brain cancer. Three maximum-tolerated-dose regimens given by intravenous administration were investigated: 50 mg/kg on day 8 (d8) after implantation of 9L gliosarcoma tumors; 40 mg/kg on d8 and d15; 20 mg/kg on d8, d11, and d15. Body weight change and neutropenia were assessed as pharmacodynamic markers of toxicity. The pharmacodynamic markers of antitumor efficacy were increase in lifespan (ILS) and tumor volume progression, measured noninvasively by magnetic resonance imaging. At equivalent doses, neutropenia was similar for both formulations, but weight loss was more severe for Cre-pac. No regimen of Cre-pac extended survival, whereas L-pac at 40 mg/kg x2 doses was well tolerated and mediated 26% ILS (p < 0.0002) compared with controls. L-pac at a lower cumulative dose (20 mg/kg x3) was even more effective (40% ILS; p < 0.0001). In striking contrast, the identical regimen of Cre-pac was lethal. Development of a novel semimechanistic pharmacodynamic model permitted quantitative hypothesis testing with the tumor volume progression data, and suggested the existence of a transient treatment effect that was consistent with sensitization or "priming" of tumors by more frequent L-pac dosing schedules. Therefore, improved antitumor responses of carrier-based paclitaxel formulations can arise both from dose escalation, because of reduced toxicity, and from novel carrier-mediated alterations of antitumor pharmacodynamic effects.

Control-relevant modeling of the antitumor effects of 9-nitrocamptothecin in SCID mice bearing HT29 human colon xenografts.

The mathematical model structure selected to describe system behavior is at least partially dependent on the proposed use of the model. In this paper, a pharmacokinetic(PK)/pharmacodynamic (PD) model for use in drug delivery algorithm synthesis is developed. The antitumor agent 9-nitrocamptothecin (9NC) was administered orally to severe combined immunodeficient (SCID) mice bearing subcutaneously implanted HT29 human colon xenografts, and the effect of 9NC on those xenografts was characterized. Different PK model structures were considered in characterizing the dynamics of the drug concentration in the plasma. Akaike's Information Criterion (AIC) was used to select the model structure maximizing fit accuracy while simultaneously minimizing the number of model parameters. The resulting PK model was a set of coupled linear ordinary differential equations able to describe the nonlinear dynamic behavior (e.g. plateauing, etc.) of the drug concentrations observed in the plasma. Pharmacodynamics were modeled by characterizing tumor growth in both the untreated and drug-treated animals. The resulting PK/PD model related drug administration to effect, and this model has a structure that facilitates future control algorithm synthesis. Control algorithms in this context would directly utilize PK/PD model predictions. These predictions would be used to determine the amount and frequency of drug administration in order to reduce the tumor burden without violating clinically relevant constraints. This methodology could then be used to aid the clinician in selecting dose levels and schedules, and extension to patient tailored treatment may eventually be feasible with this approach.