gPKPDSim: a SimBiology®-based GUI application for PKPD modeling in drug development.

Modeling and simulation (M&S) is increasingly used in drug development to characterize pharmacokinetic-pharmacodynamic (PKPD) relationships and support various efforts such as target feasibility assessment, molecule selection, human PK projection, and preclinical and clinical dose and schedule determination. While model development typically require mathematical modeling expertise, model exploration and simulations could in many cases be performed by scientists in various disciplines to support the design, analysis and interpretation of experimental studies. To this end, we have developed a versatile graphical user interface (GUI) application to enable easy use of any model constructed in SimBiology® to execute various common PKPD analyses. The MATLAB®-based GUI application, called gPKPDSim, has a single screen interface and provides functionalities including simulation, data fitting (parameter estimation), population simulation (exploring the impact of parameter variability on the outputs of interest), and non-compartmental PK analysis. Further, gPKPDSim is a user-friendly tool with capabilities including interactive visualization, exporting of results and generation of presentation-ready figures. gPKPDSim was designed primarily for use in preclinical and translational drug development, although broader applications exist. gPKPDSim is a MATLAB®-based open-source application and is publicly available to download from MATLAB® Central™. We illustrate the use and features of gPKPDSim using multiple PKPD models to demonstrate the wide applications of this tool in pharmaceutical sciences. Overall, gPKPDSim provides an integrated, multi-purpose user-friendly GUI application to enable efficient use of PKPD models by scientists from various disciplines, regardless of their modeling expertise.

Ocular Pharmacokinetics of Therapeutic Antibodies Given by Intravitreal Injection: Estimation of Retinal Permeabilities Using a 3-Compartment Semi-Mechanistic Model.

Intravitreally (IVT) injected macromolecules for the treatment of age-related macular degeneration must permeate through the inner limiting membrane (ILM) into the retina and through the retinal pigment epithelium (RPE) to enter the choroid. A quantitative understanding of intraocular transport mechanisms, elimination pathways, and the effect of molecular size is currently incomplete. We present a semimechanistic, 3-compartment (retina, vitreous, and aqueous) pharmacokinetic (PK) model, expressed using linear ordinary differential equations (ODEs), to describe the molecular concentrations following a single IVT injection. The model was fit to experimental rabbit data, with Fab, Fc, IgG, and IgG null antibodies and antibody fragments, to estimate key ocular pharmacokinetic parameters. The model predicts an ocular half-life, t1/2, which is the same for all compartments and dependent on the hydrodynamic radius (Rh) of the respective molecules, consistent with observations from the experimental data. Estimates of the permeabilities of the RPE and ILM are derived for Rh values ranging from 2.5 to 4.9 nm, and are found to be in good agreement with ex-vivo measurements from bovine eyes. We show that the ratio of these permeabilities largely determines the ratio of the molecular concentrations in the retina and vitreal compartments and their dependence on Rh. The model further provides estimates for the ratio of fluxes corresponding to the elimination pathways from the eye, i.e., aqueous humor to retina/choroid, which increase from 5:1 to 7:1 as Rh decreases. Our semimechanistic model provides a quantitative framework for interpreting ocular PK and the effects of molecule size on rate-determining parameters. We have shown that intraocular permeabilities can be reasonably estimated from 3-compartment ocular PK data and can determine how these parameters influence the half-life, retinal permeation, and elimination of intravitreally injected molecules from the eye.

Evaluation of HDL-modulating interventions for cardiovascular risk reduction using a systems pharmacology approach.

The recent failures of cholesteryl ester transport protein inhibitor drugs to decrease CVD risk, despite raising HDL cholesterol (HDL-C) levels, suggest that pharmacologic increases in HDL-C may not always reflect elevations in reverse cholesterol transport (RCT), the process by which HDL is believed to exert its beneficial effects. HDL-modulating therapies can affect HDL properties beyond total HDL-C, including particle numbers, size, and composition, and may contribute differently to RCT and CVD risk. The lack of validated easily measurable pharmacodynamic markers to link drug effects to RCT, and ultimately to CVD risk, complicates target and compound selection and evaluation. In this work, we use a systems pharmacology model to contextualize the roles of different HDL targets in cholesterol metabolism and provide quantitative links between HDL-related measurements and the associated changes in RCT rate to support target and compound evaluation in drug development. By quantifying the amount of cholesterol removed from the periphery over the short-term, our simulations show the potential for infused HDL to treat acute CVD. For the primary prevention of CVD, our analysis suggests that the induction of ApoA-I synthesis may be a more viable approach, due to the long-term increase in RCT rate.

Mechanism-Based Pharmacokinetic/Pharmacodynamic Model for THIOMAB™ Drug Conjugates.

PURPOSE: THIOMAB™ drug conjugates (TDCs) with engineered cysteine residues allow site-specific drug conjugation and defined Drug-to-Antibody Ratios (DAR). In order to help elucidate the impact of drug-loading, conjugation site, and subsequent deconjugation on pharmacokinetics and efficacy, we have developed an integrated mathematical model to mechanistically characterize pharmacokinetic behavior and preclinical efficacy of MMAE conjugated TDCs with different DARs. General applicability of the model structure was evaluated with two different TDCs. METHOD: Pharmacokinetics studies were conducted for unconjugated antibody and purified TDCs with DAR-1, 2 and 4 for trastuzumab TDC and Anti-STEAP1 TDC in mice. Total antibody concentrations and individual DAR fractions were measured. Efficacy studies were performed in tumor-bearing mice. RESULTS: An integrated model consisting of distinct DAR species (DAR0-4), each described by a two-compartment model was able to capture the experimental data well. Time series measurements of each Individual DAR species allowed for the incorporation of site-specific drug loss through deconjugation and the results suggest a higher deconjugation rate from heavy chain site HC-A114C than the light chain site LC-V205C. Total antibody concentrations showed multi-exponential decline, with a higher clearance associated with higher DAR species. The experimentally observed effects of TDC on tumor growth kinetics were successfully described by linking pharmacokinetic profiles to DAR-dependent killing of tumor cells. CONCLUSION: Results from the integrated model evaluated with two different TDCs highlight the impact of DAR and site of conjugation on pharmacokinetics and efficacy. The model can be used to guide future drug optimization and in-vivo studies.

A Minimal PBPK/PD Model with Expansion-Enhanced Target-Mediated Drug Disposition to Support a First-in-Human Clinical Study Design for a FLT3L-Fc Molecule.

FLT3L-Fc is a half-life extended, effectorless Fc-fusion of the native human FLT3-ligand. In cynomolgus monkeys, treatment with FLT3L-Fc leads to a complex pharmacokinetic/pharmacodynamic (PK/PD) relationship, with observed nonlinear PK and expansion of different immune cell types across different dose levels. A minimal physiologically based PK/PD model with expansion-enhanced target-mediated drug disposition (TMDD) was developed to integrate the molecule's mechanism of action, as well as the complex preclinical and clinical PK/PD data, to support the preclinical-to-clinical translation of FLT3L-Fc. In addition to the preclinical PK data of FLT3L-Fc in cynomolgus monkeys, clinical PK and PD data from other FLT3-agonist molecules (GS-3583 and CDX-301) were used to inform the model and project the expansion profiles of conventional DC1s (cDC1s) and total DCs in peripheral blood. This work constitutes an essential part of our model-informed drug development (MIDD) strategy for clinical development of FLT3L-Fc by projecting PK/PD in healthy volunteers, determining the first-in-human (FIH) dose, and informing the efficacious dose in clinical settings. Model-generated results were incorporated in regulatory filings to support the rationale for the FIH dose selection.

Systems-based digital twins to help characterize clinical dose-response and propose predictive biomarkers in a Phase I study of bispecific antibody, mosunetuzumab, in NHL.

Phase I oncology clinical trials often comprise a limited number of patients representing different disease subtypes who are divided into cohorts receiving treatment(s) at different dosing levels and schedules. Here, we leverage a previously developed quantitative systems pharmacology model of the anti-CD20/CD3 T-cell engaging bispecific antibody, mosunetuzumab, to account for different dosing regimens and patient heterogeneity in the phase I study to inform clinical dose/exposure-response relationships and to identify biological determinants of clinical response. We developed a novel workflow to generate digital twins for each patient, which together form a virtual population (VPOP) that represented variability in biological, pharmacological, and tumor-related parameters from the phase I trial. Simulations based on the VPOP predict that an increase in mosunetuzumab exposure increases the proportion of digital twins with at least a 50% reduction in tumor size by day 42. Simulations also predict a left-shift of the exposure-response in patients diagnosed with indolent compared to aggressive non-Hodgkin's lymphoma (NHL) subtype; this increased sensitivity in indolent NHL was attributed to the lower inferred values of tumor proliferation rate and baseline T-cell infiltration in the corresponding digital twins. Notably, the inferred digital twin parameters from clinical responders and nonresponders show that the potential biological difference that can influence response include tumor parameters (tumor size, proliferation rate, and baseline T-cell infiltration) and parameters defining the effect of mosunetuzumab on T-cell activation and B-cell killing. Finally, the model simulations suggest intratumor expansion of pre-existing T-cells, rather than an influx of systemically expanded T-cells, underlies the antitumor activity of mosunetuzumab.

A TREM2-activating antibody with a blood-brain barrier transport vehicle enhances microglial metabolism in Alzheimer's disease models.

Loss-of-function variants of TREM2 are associated with increased risk of Alzheimer's disease (AD), suggesting that activation of this innate immune receptor may be a useful therapeutic strategy. Here we describe a high-affinity human TREM2-activating antibody engineered with a monovalent transferrin receptor (TfR) binding site, termed antibody transport vehicle (ATV), to facilitate blood-brain barrier transcytosis. Upon peripheral delivery in mice, ATV:TREM2 showed improved brain biodistribution and enhanced signaling compared to a standard anti-TREM2 antibody. In human induced pluripotent stem cell (iPSC)-derived microglia, ATV:TREM2 induced proliferation and improved mitochondrial metabolism. Single-cell RNA sequencing and morphometry revealed that ATV:TREM2 shifted microglia to metabolically responsive states, which were distinct from those induced by amyloid pathology. In an AD mouse model, ATV:TREM2 boosted brain microglial activity and glucose metabolism. Thus, ATV:TREM2 represents a promising approach to improve microglial function and treat brain hypometabolism found in patients with AD.

CD98hc is a target for brain delivery of biotherapeutics.

Brain exposure of systemically administered biotherapeutics is highly restricted by the blood-brain barrier (BBB). Here, we report the engineering and characterization of a BBB transport vehicle targeting the CD98 heavy chain (CD98hc or SLC3A2) of heterodimeric amino acid transporters (TVCD98hc). The pharmacokinetic and biodistribution properties of a CD98hc antibody transport vehicle (ATVCD98hc) are assessed in humanized CD98hc knock-in mice and cynomolgus monkeys. Compared to most existing BBB platforms targeting the transferrin receptor, peripherally administered ATVCD98hc demonstrates differentiated brain delivery with markedly slower and more prolonged kinetic properties. Specific biodistribution profiles within the brain parenchyma can be modulated by introducing Fc mutations on ATVCD98hc that impact FcγR engagement, changing the valency of CD98hc binding, and by altering the extent of target engagement with Fabs. Our study establishes TVCD98hc as a modular brain delivery platform with favorable kinetic, biodistribution, and safety properties distinct from previously reported BBB platforms.

Quantitative systems pharmacology model-based investigation of adverse gastrointestinal events associated with prolonged treatment with PI3-kinase inhibitors.

Several PI3K inhibitors are in clinical development for the treatment of various forms of cancers, including pan-PI3K inhibitors targeting all four PI3K isoforms (α, ß, γ, and δ), and isoform-selective inhibitors. Diarrhea and immune-mediated colitis are among the adverse events observed with PI3K inhibition which limits the maximal tolerated dose. A quantitative systems pharmacology model was developed to investigate PI3K-inhibitor-induced colitis. The effects of individual PI3K isoforms on relevant cellular pathways were incorporated into a mechanistic representation of mucosal inflammation. A virtual clinical population captures the observed clinical variability in the onset timing and rates of diarrhea and colitis for seven clinically tested PI3K inhibitors. Model-based analysis suggests that colitis development is governed by both the inhibition of PI3Kδ, which drives T cell differentiation and proliferation, and PI3Kα, which regulates epithelial barrier integrity. Specifically, when PI3Kα is inhibited below a given threshold, epithelial barrier dysfunction precipitates an exaggerated T effector response due to PI3Kδ-inhibition, leading to risk of diarrhea and colitis. This synergy explains why the lowest diarrhea and colitis rates are seen with the weakest PI3Kδ inhibition (alpelisib), and higher rates are seen with strong PI3Kδ inhibition if PI3Kα is even mildly inhibited (e.g., idelalisib), whereas strong PI3Kδ inhibition in the absence of PI3Kα inhibition does not result in high colitis rates (umbralisib). Thus, the model-based analysis suggests that PI3Kα and δ inhibition play unique but synergistic roles in driving colitis. Finally, we explore if and how dose-regimen might influence colitis rates for molecules that inhibit both PI3Kα and PI3Kδ.

Integrated systems modeling of severe asthma: Exploration of IL-33/ST2 antagonism.

Asthma is a complex, heterogeneous disease with a high unmet medical need, despite therapies targeting a multitude of pathways. The ability to quantitatively integrate preclinical and clinical data on these pathways could aid in the development and testing of novel targets and therapeutics. In this work, we develop a computational model of asthma biology, including key cell types and mediators, and create a virtual population capturing clinical heterogeneity. The simulated responses to therapies targeting IL-13, IL-4Rα, IL-5, IgE, and TSLP demonstrate agreement with clinical endpoints and biomarkers of type 2 (T2) inflammation, including blood eosinophils, FEV1, IgE, and FeNO. We use the model to explore the potential benefit of targeting the IL-33 pathway with anti-IL-33 and anti-ST2. Model predictions are compared with data on blood eosinophils, FeNO, and FEV1 from recent anti-IL-33 and anti-ST2 trials and used to interpret trial results based on pathway biology and pharmacology. Results of sensitivity analyses on the contributions of IL-33 to the predicted biomarker changes suggest that anti-ST2 therapy reduces circulating blood eosinophil levels primarily through its impact on eosinophil progenitor maturation and IL-5-dependent survival, and induces changes in FeNO and FEV1 through its effect on immune cells involved in T2 cytokine production. Finally, we also investigate the impact of ST2 genetics on the conferred benefit of anti-ST2. The model includes representation of a wide array of biologic mechanisms and interventions that will provide mechanistic insight and support clinical program design for a wide range of novel therapies during drug development.

FDA-Industry Scientific Exchange on assessing quantitative systems pharmacology models in clinical drug development: a meeting report, summary of challenges/gaps, and future perspective.

The pharmaceutical industry is actively applying quantitative systems pharmacology (QSP) to make internal decisions and guide drug development. To facilitate the eventual development of a common framework for assessing the credibility of QSP models for clinical drug development, scientists from US Food and Drug Administration and the pharmaceutical industry organized a full-day virtual Scientific Exchange on July 1, 2020. An assessment form was used to ensure consistency in the evaluation process. Among the cases presented, QSP was applied to various therapeutic areas. Applications mostly focused on phase 2 dose selection. Model transparency, including details on expert knowledge and data used for model development, was identified as a major factor for robust model assessment. The case studies demonstrated some commonalities in the workflow of QSP model development, calibration, and validation but differ in the size, scope, and complexity of QSP models, in the acceptance criteria for model calibration and validation, and in the algorithms/approaches used for creating virtual patient populations. Though efforts are being made to build the credibility of QSP models and the confidence is increasing in applying QSP for internal decisions at the clinical stages of drug development, there are still many challenges facing QSP application to late stage drug development. The QSP community needs a strategic plan that includes the ability and flexibility to Adapt, to establish Common expectations for model Credibility needed to inform drug Labeling and patient care, and to AIM to achieve the goal (ACCLAIM).

gQSPSim: A SimBiology-Based GUI for Standardized QSP Model Development and Application.

Quantitative systems pharmacology (QSP) models are often implemented using a wide variety of technical workflows and methodologies. To facilitate reproducibility, transparency, portability, and reuse for QSP models, we have developed gQSPSim, a graphical user interface-based MATLAB application that performs key steps in QSP model development and analyses. The capabilities of gQSPSim include (i) model calibration using global and local optimization methods, (ii) development of virtual subjects to explore variability and uncertainty in the represented biology, and (iii) simulations of virtual populations for different interventions. gQSPSim works with SimBiology-built models using components such as species, doses, variants, and rules. All functionalities are equipped with an interactive visualization interface and the ability to generate presentation-ready figures. In addition, standardized gQSPSim sessions can be shared and saved for future extension and reuse. In this work, we demonstrate gQSPSim's capabilities with a standard target-mediated drug disposition model and a published model of anti-proprotein convertase subtilisin/kexin type 9 (PCSK9) treatment of hypercholesterolemia.

Mitigating the risk of cytokine release syndrome in a Phase I trial of CD20/CD3 bispecific antibody mosunetuzumab in NHL: impact of translational system modeling.

Mosunetuzumab, a T-cell dependent bispecific antibody that binds CD3 and CD20 to drive T-cell mediated B-cell killing, is currently being tested in non-Hodgkin lymphoma. However, potent immune stimulation with T-cell directed therapies poses the risk of cytokine release syndrome, potentially limiting dose and utility. To understand mechanisms behind safety and efficacy and explore safety mitigation strategies, we developed a novel mechanistic model of immune and antitumor responses to the T-cell bispecifics (mosunetuzumab and blinatumomab), including the dynamics of B- and T-lymphocytes in circulation, lymphoid tissues, and tumor. The model was developed and validated using mosunetuzumab nonclinical and blinatumomab clinical data. Simulations delineated mechanisms contributing to observed cell and cytokine (IL6) dynamics and predicted that initial step-fractionated dosing limits systemic T-cell activation and cytokine release without compromising tumor response. These results supported a change to a step-fractionated treatment schedule of mosunetuzumab in the ongoing Phase I clinical trial, enabling safer administration of higher doses.

Avidity-based binding to HER2 results in selective killing of HER2-overexpressing cells by anti-HER2/CD3.

A primary barrier to the success of T cell-recruiting bispecific antibodies in the treatment of solid tumors is the lack of tumor-specific targets, resulting in on-target off-tumor adverse effects from T cell autoreactivity to target-expressing organs. To overcome this, we developed an anti-HER2/CD3 T cell-dependent bispecific (TDB) antibody that selectively targets HER2-overexpressing tumor cells with high potency, while sparing cells that express low amounts of HER2 found in normal human tissues. Selectivity is based on the avidity of two low-affinity anti-HER2 Fab arms to high target density on HER2-overexpressing cells. The increased selectivity to HER2-overexpressing cells is expected to mitigate the risk of adverse effects and increase the therapeutic index. Results included in this manuscript not only support the clinical development of anti-HER2/CD3 1Fab-immunoglobulin G TDB but also introduce a potentially widely applicable strategy for other T cell-directed therapies. The potential of this discovery has broad applications to further enable consideration of solid tumor targets that were previously limited by on-target, but off-tumor, autoimmunity.

The virtual NOD mouse: applying predictive biosimulation to research in type 1 diabetes.

Type 1 diabetes is a complex, multifactorial disease characterized by T cell-mediated autoimmune destruction of insulin-secreting pancreatic beta cells. To facilitate research in type 1 diabetes, a large-scale dynamic mathematical model of the female non-obese diabetic (NOD) mouse was developed. In this model, termed the Entelos Type 1 Diabetes PhysioLab platform, virtual NOD mice are constructed by mathematically representing components of the immune system and islet beta cell physiology important for the pathogenesis of type 1 diabetes. This report describes the scope of the platform and illustrates some of its capabilities. Specifically, using two virtual NOD mice with either average or early diabetes-onset times, we demonstrate the reproducibility of experimentally observed dynamics involved in diabetes progression, therapeutic responses to exogenous IL-10, and heterogeneity in disease onset. Additionally, we use the Type 1 Diabetes PhysioLab platform to investigate the impact of disease heterogeneity on the effectiveness of exogenous IL-10 therapy to prevent diabetes onset. Results indicate that the inability of a previously published IL-10 therapy protocol to protect NOD mice who exhibit early diabetes onset is due to high levels of pancreatic lymph node (PLN) inflammation, islet infiltration, and beta cell destruction at the time of treatment initiation. Further, simulation indicates that earlier administration of the treatment protocol can prevent NOD mice from developing diabetes by initiating treatment during the period when the disease is still sensitive to IL-10's protective function.

Dosing and timing effects of anti-CD40L therapy: predictions from a mathematical model of type 1 diabetes.

Several publications describing the use of anti-CD40L monoclonal antibodies (anti-CD40L) for the treatment of type 1 diabetes in non-obese diabetic (NOD) mice have reported different treatment responses to similar protocols. The Entelos Type 1 Diabetes PhysioLab platform, a dynamic large-scale mathematical model of the pathogenesis of type 1 diabetes, was used to study the effects of anti-CD40L therapy in silico. An examination of the impact of pharmacokinetic variability and the heterogeneity of disease progression rate on therapeutic outcome provided insights that could reconcile the apparently conflicting data. Optimal treatment protocols were identified by exploring the dynamics of key pathophysiological pathways.

Erratum: Clinical responses to ERK inhibition in BRAFV600E-mutant colorectal cancer predicted using a computational model.

[This corrects the article DOI: 10.1038/s41540-017-0016-1.].

Clinical responses to ERK inhibition in BRAFV600E-mutant colorectal cancer predicted using a computational model.

Approximately 10% of colorectal cancers harbor BRAFV600E mutations, which constitutively activate the MAPK signaling pathway. We sought to determine whether ERK inhibitor (GDC-0994)-containing regimens may be of clinical benefit to these patients based on data from in vitro (cell line) and in vivo (cell- and patient-derived xenograft) studies of cetuximab (EGFR), vemurafenib (BRAF), cobimetinib (MEK), and GDC-0994 (ERK) combinations. Preclinical data was used to develop a mechanism-based computational model linking cell surface receptor (EGFR) activation, the MAPK signaling pathway, and tumor growth. Clinical predictions of anti-tumor activity were enabled by the use of tumor response data from three Phase 1 clinical trials testing combinations of EGFR, BRAF, and MEK inhibitors. Simulated responses to GDC-0994 monotherapy (overall response rate = 17%) accurately predicted results from a Phase 1 clinical trial regarding the number of responding patients (2/18) and the distribution of tumor size changes ("waterfall plot"). Prospective simulations were then used to evaluate potential drug combinations and predictive biomarkers for increasing responsiveness to MEK/ERK inhibitors in these patients.

Development and Translational Application of an Integrated, Mechanistic Model of Antibody-Drug Conjugate Pharmacokinetics.

Antibody drug conjugates (ADC), in which small molecule cytotoxic agents are non-specifically linked to antibodies, can enable targeted delivery of chemotherapeutics to tumor cells. ADCs are often produced and administered as a mixture of conjugated antibodies with different drug to antibody ratios (DAR) resulting in complex and heterogeneous disposition kinetics. We developed a mechanism-based platform model that can describe and predict the complex pharmacokinetic (PK) behavior of ADCs with protease-cleavable valine-citrulline (VC) linker linked to Monomethylmonomethyl auristatin F/E by incorporating known mechanisms of ADC disposition. The model includes explicit representation of all DAR species; DAR-dependent sequential deconjugation of the drug, resulting in the conversion of higher DAR to lower DAR species; and DAR-dependent antibody/ADC clearance. PK profiles of multiple analytes (total antibody, drug-conjugated antibody, and/or antibody-conjugated drug) for different ADC molecules and targets in rodents and cynomolgus monkeys were used for model development. The integrated cross-species model was successful in capturing the multi-analyte PK profiles after administration of purified ADCs with defined DAR species and ADCs with mixtures of DAR. Human PK predictions for DSTP3086S (anti-STEAP1-vc-MMAE) with the platform model agreed well with PK (total antibody and antibody-conjugated drug concentrations) measurements in the dose-ranging phase I clinical study. The integrated model is applicable to various other ADCs with different formats, conjugated drugs, and linkers, and provides a valuable tool for the exploration of mechanisms governing disposition of ADCs and enables translational predictions.