A Physiologically-Based Pharmacokinetic Model for the Prediction of "Half-Life Extension" and "Catch and Release" Monoclonal Antibody Pharmacokinetics.

Monoclonal antibodies (mAbs) can be engineered to have "extended half-life" and "catch and release" properties to improve target coverage. We have developed a mAb physiologically-based pharmacokinetic model that describes intracellular trafficking, neonatal Fc receptor (FcRn) recycling, and nonspecific clearance of mAbs. We extended this model to capture target binding as a function of target affinity, expression, and turnover. For mAbs engineered to have an extended half-life, the model was able to accurately predict the terminal half-life (82% within 2-fold error of the observed value) in the human FcRn transgenic (Tg32) homozygous mouse and human. The model also accurately captures the trend in pharmacokinetic and target coverage data for a set of mAbs with differing catch and release properties in the Tg32 mouse. The mechanistic nature of this model allows us to explore different engineering techniques early in drug discovery, potentially expanding the number of "druggable" targets.

Use of translational modeling and simulation for quantitative comparison of PF-06804103, a new generation HER2 ADC, with Trastuzumab-DM1.

A modeling and simulation approach was used for quantitative comparison of a new generation HER2 antibody drug conjugate (ADC, PF-06804103) with trastuzumab-DM1 (T-DM1). To compare preclinical efficacy, the pharmacokinetic (PK)/pharmacodynamic (PD) relationship of PF-06804103 and T-DM1 was determined across a range of mouse tumor xenograft models, using a tumor growth inhibition model. The tumor static concentration was assigned as the minimal efficacious concentration. PF-06804103 was concluded to be more potent than T-DM1 across cell lines studied. TSCs ranged from 1.0 to 9.8 µg/mL (n = 7) for PF-06804103 and from 4.7 to 29 µg/mL (n = 5) for T-DM1. Two experimental models which were resistant to T-DM1, responded to PF-06804103 treatment. A mechanism-based target mediated drug disposition (TMDD) model was used to predict the human PK of PF-06804103. This model was constructed and validated based on T-DM1 which has non-linear PK at doses administered in the clinic, driven by binding to shed HER2. Non-linear PK is predicted for PF-06804103 in the clinic and is dependent upon circulating HER2 extracellular domain (ECD) concentrations. The models were translated to human and suggested greater efficacy for PF-06804103 compared to T-DM1. In conclusion, a fit-for-purpose translational PK/PD strategy for ADCs is presented and used to compare a new generation HER2 ADC with T-DM1.

A Quantitative Systems Pharmacology Model of Gaucher Disease Type 1 Provides Mechanistic Insight Into the Response to Substrate Reduction Therapy With Eliglustat.

Gaucher's disease type 1 (GD1) leads to significant morbidity and mortality through clinical manifestations, such as splenomegaly, hematological complications, and bone disease. Two types of therapies are currently approved for GD1: enzyme replacement therapy (ERT), and substrate reduction therapy (SRT). In this study, we have developed a quantitative systems pharmacology (QSP) model, which recapitulates the effects of eliglustat, the only first-line SRT approved for GD1, on treatment-naïve or patients with ERT-stabilized adult GD1. This multiscale model represents the mechanism of action of eliglustat that leads toward reduction of spleen volume. Model capabilities were illustrated through the application of the model to predict ERT and eliglustat responses in virtual populations of adult patients with GD1, representing patients across a spectrum of disease severity as defined by genotype-phenotype relationships. In summary, the QSP model provides a mechanistic computational platform for predicting treatment response via different modalities within the heterogeneous GD1 patient population.

Quantitative Systems Pharmacology Modeling of Acid Sphingomyelinase Deficiency and the Enzyme Replacement Therapy Olipudase Alfa Is an Innovative Tool for Linking Pathophysiology and Pharmacology.

Acid sphingomyelinase deficiency (ASMD) is a rare lysosomal storage disorder with heterogeneous clinical manifestations, including hepatosplenomegaly and infiltrative pulmonary disease, and is associated with significant morbidity and mortality. Olipudase alfa (recombinant human acid sphingomyelinase) is an enzyme replacement therapy under development for the non-neurological manifestations of ASMD. We present a quantitative systems pharmacology (QSP) model supporting the clinical development of olipudase alfa. The model is multiscale and mechanistic, linking the enzymatic deficiency driving the disease to molecular-level, cellular-level, and organ-level effects. Model development was informed by natural history, and preclinical and clinical studies. By considering patient-specific pharmacokinetic (PK) profiles and indicators of disease severity, the model describes pharmacodynamic (PD) and clinical end points for individual patients. The ASMD QSP model provides a platform for quantitatively assessing systemic pharmacological effects in adult and pediatric patients, and explaining variability within and across these patient populations, thereby supporting the extrapolation of treatment response from adults to pediatrics.

Systems Pharmacology Model of Gastrointestinal Damage Predicts Species Differences and Optimizes Clinical Dosing Schedules.

Gastrointestinal (GI) adverse events (AEs) are frequently dose limiting for oncology agents, requiring extensive clinical testing of alternative schedules to identify optimal dosing regimens. Here, we develop a translational mathematical model to predict these clinical AEs starting from preclinical GI toxicity data. The model structure incorporates known biology and includes stem cells, daughter cells, and enterocytes. Published data, including cellular numbers and division times, informed the system parameters for humans and rats. The drug-specific parameters were informed with preclinical histopathology data from rats treated with irinotecan. The model fit the rodent irinotecan-induced pathology changes well. The predicted time course of enterocyte loss in patients treated with weekly doses matched observed AE profiles. The model also correctly predicts a lower level of AEs for every 3 weeks (Q3W), as compared to the weekly schedule.

Dynamic Optical Tuning of Interlayer Interactions in the Transition Metal Dichalcogenides.

Modulation of weak interlayer interactions between quasi-two-dimensional atomic planes in the transition metal dichalcogenides (TMDCs) provides avenues for tuning their functional properties. Here we show that above-gap optical excitation in the TMDCs leads to an unexpected large-amplitude, ultrafast compressive force between the two-dimensional layers, as probed by in situ measurements of the atomic layer spacing at femtosecond time resolution. We show that this compressive response arises from a dynamic modulation of the interlayer van der Waals interaction and that this represents the dominant light-induced stress at low excitation densities. A simple analytic model predicts the magnitude and carrier density dependence of the measured strains. This work establishes a new method for dynamic, nonequilibrium tuning of correlation-driven dispersive interactions and of the optomechanical functionality of TMDC quasi-two-dimensional materials.

Preclinical to Clinical Translation of Antibody-Drug Conjugates Using PK/PD Modeling: a Retrospective Analysis of Inotuzumab Ozogamicin.

A mechanism-based pharmacokinetic/pharmacodynamic (PK/PD) model was used for preclinical to clinical translation of inotuzumab ozogamicin, a CD22-targeting antibody-drug conjugate (ADC) for B cell malignancies including non-Hodgkin's lymphoma (NHL) and acute lymphocytic leukemia (ALL). Preclinical data was integrated in a PK/PD model which included (1) a plasma PK model characterizing disposition and clearance of inotuzumab ozogamicin and its released payload N-Ac-γ-calicheamicin DMH, (2) a tumor disposition model describing ADC diffusion into the tumor extracellular environment, (3) a cellular model describing inotuzumab ozogamicin binding to CD22, internalization, intracellular N-Ac-γ-calicheamicin DMH release, binding to DNA, or efflux from the tumor cell, and (4) tumor growth and inhibition in mouse xenograft models. The preclinical model was translated to the clinic by incorporating human PK for inotuzumab ozogamicin and clinically relevant tumor volumes, tumor growth rates, and values for CD22 expression in the relevant patient populations. The resulting stochastic models predicted progression-free survival (PFS) rates for inotuzumab ozogamicin in patients comparable to the observed clinical results. The model suggested that a fractionated dosing regimen is superior to a conventional dosing regimen for ALL but not for NHL. Simulations indicated that tumor growth is a highly sensitive parameter and predictive of successful outcome. Inotuzumab ozogamicin PK and N-Ac-γ-calicheamicin DMH efflux are also sensitive parameters and would be considered more useful predictors of outcome than CD22 receptor expression. In summary, a multiscale, mechanism-based model has been developed for inotuzumab ozogamicin, which can integrate preclinical biomeasures and PK/PD data to predict clinical response.

Rapid Method To Determine Intracellular Drug Concentrations in Cellular Uptake Assays: Application to Metformin in Organic Cation Transporter 1-Transfected Human Embryonic Kidney 293 Cells.

Because of the importance of intracellular unbound drug concentrations in the prediction of in vivo concentrations that are determinants of drug efficacy and toxicity, a number of assays have been developed to assess in vitro unbound concentrations of drugs. Here we present a rapid method to determine the intracellular unbound drug concentrations in cultured cells, and we apply the method along with a mechanistic model to predict concentrations of metformin in subcellular compartments of stably transfected human embryonic kidney 293 (HEK293) cells. Intracellular space (ICS) was calculated by subtracting the [(3)H]-inulin distribution volume (extracellular space, ECS) from the [(14)C]-urea distribution volume (total water space, TWS). Values obtained for intracellular space (mean ± S.E.M.; µl/10(6) cells) of monolayers of HEK cells (HEK-empty vector [EV]) and cells overexpressing human organic cation transporter 1 (HEK-OCT1), 1.21± 0.07 and 1.25±0.06, respectively, were used to determine the intracellular metformin concentrations. After incubation of the cells with 5 µM metformin, the intracellular concentrations were 26.4 ± 7.8 µM and 268 ± 11.0 µM, respectively, in HEK-EV and HEK-OCT1. In addition, intracellular metformin concentrations were lower in high K(+) buffer (140 mM KCl) compared with normal K(+) buffer (5.4 mM KCl) in HEK-OCT1 cells (54.8 ± 3.8 µM and 198.1 ± 11.2 µM, respectively; P < 0.05). Our mechanistic model suggests that, depending on the credible range of assumed physiologic values, the positively charged metformin accumulates to particularly high levels in endoplasmic reticulum and/or mitochondria. This method together with the computational model can be used to determine intracellular unbound concentrations and to predict subcellular accumulation of drugs in other complex systems such as primary cells.

Electronic cooling via interlayer Coulomb coupling in multilayer epitaxial graphene.

In van der Waals bonded or rotationally disordered multilayer stacks of two-dimensional (2D) materials, the electronic states remain tightly confined within individual 2D layers. As a result, electron-phonon interactions occur primarily within layers and interlayer electrical conductivities are low. In addition, strong covalent in-plane intralayer bonding combined with weak van der Waals interlayer bonding results in weak phonon-mediated thermal coupling between the layers. We demonstrate here, however, that Coulomb interactions between electrons in different layers of multilayer epitaxial graphene provide an important mechanism for interlayer thermal transport, even though all electronic states are strongly confined within individual 2D layers. This effect is manifested in the relaxation dynamics of hot carriers in ultrafast time-resolved terahertz spectroscopy. We develop a theory of interlayer Coulomb coupling containing no free parameters that accounts for the experimentally observed trends in hot-carrier dynamics as temperature and the number of layers is varied.

Modeling population heterogeneity in viral dynamics for chronic hepatitis C infection: Insights from Phase 3 telaprevir clinical studies.

Viral dynamic modelling has proven useful for designing clinical studies and predicting treatment outcomes for patients infected with the hepatitis C virus. Generally these models aim to capture and predict the on-treatment viral load dynamics from a small study of individual patients. Here, we explored extending these models (1) to clinical studies with numerous patients and (2) by incorporating additional data types, including sequence data and prior response to interferon. Data from Phase 3 clinical studies of the direct-acting antiviral telaprevir (T; total daily dose of 2250 mg) combined with pegylated-interferon alfa and ribavirin (PR) were used for the analysis. The following data in the treatment-naïve population were reserved to verify the model: (1) a T/PR regimen where T was dosed every 8 h for 8 weeks (T8(q8h)/PR) and (2) a T/PR regimen where T was dosed twice daily for 12 weeks (T12(b.i.d.)/PR). The resulting model accurately predicted (1) sustained virologic response rates for both of these dosing regimens and (2) viral breakthrough characteristics of the T8(q8h)/PR regimen. Since the observed viral variants depend on the T exposure, the second verification suggested that the model was correctly sensitive to the different T regimen even though the model was developed using data from another T regimen. Furthermore, the model predicted that b.i.d. T dosing was comparable to q8h T dosing in the PR-experienced population, a comparison that has not been made in a controlled clinical study. The methods developed in this work to estimate the variability occurring below the limit of detection for the viral load were critical for making accurate predictions.

Bridging the gap between in vitro and in vivo: Dose and schedule predictions for the ATR inhibitor AZD6738.

Understanding the therapeutic effect of drug dose and scheduling is critical to inform the design and implementation of clinical trials. The increasing complexity of both mono, and particularly combination therapies presents a substantial challenge in the clinical stages of drug development for oncology. Using a systems pharmacology approach, we have extended an existing PK-PD model of tumor growth with a mechanistic model of the cell cycle, enabling simulation of mono and combination treatment with the ATR inhibitor AZD6738 and ionizing radiation. Using AZD6738, we have developed multi-parametric cell based assays measuring DNA damage and cell cycle transition, providing quantitative data suitable for model calibration. Our in vitro calibrated cell cycle model is predictive of tumor growth observed in in vivo mouse xenograft studies. The model is being used for phase I clinical trial designs for AZD6738, with the aim of improving patient care through quantitative dose and scheduling prediction.

Band offset and negative compressibility in graphene-MoS2 heterostructures.

We use electron transport to characterize monolayer graphene-multilayer MoS2 heterostructures. Our samples show ambipolar characteristics and conductivity saturation on the electron branch that signals the onset of MoS2 conduction band population. Surprisingly, the carrier density in graphene decreases with gate bias once MoS2 is populated, demonstrating negative compressibility in MoS2. We are able to interpret our measurements quantitatively by accounting for disorder and using the random phase approximation (RPA) for the exchange and correlation energies of both Dirac and parabolic-band two-dimensional electron gases. This interpretation allows us to extract the energetic offset between the conduction band edge of MoS2 and the Dirac point of graphene.