A quantitative systems pharmacology model of plasma kallikrein-kinin system dysregulation in hereditary angioedema.

Hereditary angioedema (HAE) due to C1-inhibitor deficiency is a rare, debilitating, genetic disorder characterized by recurrent, unpredictable, attacks of edema. The clinical symptoms of HAE arise from excess bradykinin generation due to dysregulation of the plasma kallikrein-kinin system (KKS). A quantitative systems pharmacology (QSP) model that mechanistically describes the KKS and its role in HAE pathophysiology was developed based on HAE attacks being triggered by autoactivation of factor XII (FXII) to activated FXII (FXIIa), resulting in kallikrein production from prekallikrein. A base pharmacodynamic model was constructed and parameterized from literature data and ex vivo assays measuring inhibition of kallikrein activity in plasma of HAE patients or healthy volunteers who received lanadelumab. HAE attacks were simulated using a virtual patient population, with attacks recorded when systemic bradykinin levels exceeded 20 pM. The model was validated by comparing the simulations to observations from lanadelumab and plasma-derived C1-inhibitor clinical trials. The model was then applied to analyze the impact of nonadherence to a daily oral preventive therapy; simulations showed a correlation between the number of missed doses per month and reduced drug effectiveness. The impact of reducing lanadelumab dosing frequency from 300 mg every 2 weeks (Q2W) to every 4 weeks (Q4W) was also examined and showed that while attack rates with Q4W dosing were substantially reduced, the extent of reduction was greater with Q2W dosing. Overall, the QSP model showed good agreement with clinical data and could be used for hypothesis testing and outcome predictions.

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.

A Physiologically-Based Pharmacokinetic Model for the Prediction of Monoclonal Antibody Pharmacokinetics From In Vitro Data.

Monoclonal antibody (mAb) pharmacokinetics (PK) have largely been predicted via allometric scaling with little consideration for cross-species differences in neonatal Fc receptor (FcRn) affinity or clearance/distribution mechanisms. To address this, we developed a mAb physiologically-based PK model that describes the intracellular trafficking and FcRn recycling of mAbs in a human FcRn transgenic homozygous mouse and human. This model uses mAb-specific in vitro data together with species-specific FcRn tissue expression, tissue volume, and blood-flow physiology to predict mAb in vivo linear PK a priori. The model accurately predicts the terminal half-life of 90% of the mAbs investigated within a twofold error. The mechanistic nature of this model allows us to not only predict linear PK from in vitro data but also explore the PK and target binding of mAbs engineered to have pH-dependent binding to its target or FcRn and could aid in the selection of mAbs with optimal PK and pharmacodynamic properties.

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.

DNA migration and separation on surfaces with a microscale dielectrophoretic trap array.

We studied the surface migration of DNA chains driven by a dc electric field across localized dielectrophoretic traps. By adjusting the length scale of the trap array, separation of a selected band of DNA was accomplished with a scaling exponent between mobility and number of base pairs similar to that obtained in capillary electrophoresis. We then provided a model, which predicts the trapping and extension of DNA chains at a dielectrophoretic trap responsible for the surface mobility and separation.

Separation of DNA with different configurations on flat and nanopatterned surfaces.

We demonstrate that electrophoresis on a flat Si substrate is an effective method in separation of DNA with different configurations, e.g., linear, supercoiled, and relaxed or DNA of different length, e.g., supercoiled DNA ladder. The surface separation arises from the different number of contacts due to the conformational differences between adsorbed DNA chains. Imposing a Au nanopattern on the Si surface further improves the separation effect. The simulation of electric field on this patterned surface by the finite element method shows that Au nanodots act as local pinning points for DNA segments due to dielectrophoretic force. The results of molecular dynamics simulation showed that the conformational differences between adsorbed polymer chains were amplified on the patterned surface and enhanced separations were achieved, which are consistent with the experimental results.

Influence of electric field intensity, ionic strength, and migration distance on the mobility and diffusion in DNA surface electrophoresis.

In order to increase the separation rate of surface electrophoresis while preserving the resolution for large DNA chains, e.g., genomic DNA, the mobility and diffusion of Lambda DNA chains adsorbed on flat silicon substrate under an applied electric field, as a function of migration distance, ionic strength, and field intensity, were studied using laser fluorescence microscope. The mobility was found to follow a power law with the field intensity beyond a certain threshold. The detected DNA peak width was shown to be constant with migration distance, slightly smaller with stronger field intensity, but significantly decreased with higher ionic strength. The molecular dynamics simulation demonstrated that the peak width was strongly related with the conformation of DNA chains adsorbed onto surface. The results also implied that there was no diffusion of DNA during migration on surface. Therefore, the Nernst-Einstein relation is not valid in the surface electrophoresis and the separation rate could be improved without losing resolution by decreasing separation distance, increasing buffer concentration, and field intensity. The results indicate the fast separation of genomic DNA chains by surface electrophoresis is possible.

Modeling the dynamics of DNA electrophoresis on a flat surface.

We use molecular dynamics simulations to study the mechanism by which a flat, homogeneous surface can serve as an electrophoretic separation medium for DNA. We find that the mobility of DNA on the surface is a function of the conformation of the adsorbed DNA molecule, and that this mobility is controlled by the attraction between the DNA and the surface. Our results will provide guidelines for the fabrication of surfaces that can be used to separate DNA in a wide size range.