Optimizing Solid Tumor Treatment with Antibody-drug Conjugates Using Agent-Based Modeling: Considering the Role of a Carrier Dose and Payload Class.

INTRODUCTION: Antibody-drug conjugates (ADCs) show significant clinical efficacy in the treatment of solid tumors, but a major limitation to their success is poor intratumoral distribution. Adding a carrier dose improves both distribution and overall drug efficacy of ADCs, but the optimal carrier dose has not been outlined for different payload classes. OBJECTIVE: In this work, we study two carrier dose regimens: 1) matching payload potency to cellular delivery but potentially not reaching cells farther away from blood vessels, or 2) dosing to tumor saturation but risking a reduction in cell killing from a lower amount of payload delivered per cell. METHODS: We use a validated computational model to test four different payloads conjugated to trastuzumab to determine the optimal carrier dose as a function of target expression, ADC dose, and payload potency. RESULTS: We find that dosing to tumor saturation is more efficacious than matching payload potency to cellular delivery for all payloads because the increase in the number of cells targeted by the ADC outweighs the loss in cell killing on targeted cells. An important exception exists if the carrier dose reduces the payload uptake per cell to the point where all cell killing is lost. Likewise, receptor downregulation can mitigate the benefits of a carrier dose. CONCLUSIONS: Because tumor saturation and in vitro potency can be measured early in ADC design, these results provide insight into maximizing ADC efficacy and demonstrate the benefits of using simulation to guide ADC design.

Characterizing heterogeneous single-cell dose responses computationally and experimentally using threshold inhibition surfaces and dose-titration assays.

Single cancer cells within a tumor exhibit variable levels of resistance to drugs, ultimately leading to treatment failures. While tumor heterogeneity is recognized as a major obstacle to cancer therapy, standard dose-response measurements for the potency of targeted kinase inhibitors aggregate populations of cells, obscuring intercellular variations in responses. In this work, we develop an analytical and experimental framework to quantify and model dose responses of individual cancer cells to drugs. We first explore the connection between population and single-cell dose responses using a computational model, revealing that multiple heterogeneous populations can yield nearly identical population dose responses. We demonstrate that a single-cell analysis method, which we term a threshold inhibition surface, can differentiate among these populations. To demonstrate the applicability of this method, we develop a dose-titration assay to measure dose responses in single cells. We apply this assay to breast cancer cells responding to phosphatidylinositol-3-kinase inhibition (PI3Ki), using clinically relevant PI3Kis on breast cancer cell lines expressing fluorescent biosensors for kinase activity. We demonstrate that MCF-7 breast cancer cells exhibit heterogeneous dose responses with some cells requiring over ten-fold higher concentrations than the population average to achieve inhibition. Our work reimagines dose-response relationships for cancer drugs in an emerging paradigm of single-cell tumor heterogeneity.

In vivo Auto-tuning of Antibody-Drug Conjugate Delivery for Effective Immunotherapy using High-Avidity, Low-Affinity Antibodies.

Antibody-drug conjugates (ADCs) have experienced a surge in clinical approvals in the past five years. Despite this success, a major limitation to ADC efficacy in solid tumors is poor tumor penetration, which leaves many cancer cells untargeted. Increasing antibody doses or co-administering ADC with an unconjugated antibody can improve tumor penetration and increase efficacy when target receptor expression is high. However, it can also reduce efficacy in low-expression tumors where ADC delivery is limited by cellular uptake. This creates an intrinsic problem because many patients express different levels of target between tumors and even within the same tumor. Here, we generated High-Avidity, Low-Affinity (HALA) antibodies that can automatically tune the cellular ADC delivery to match the local expression level. Using HER2 ADCs as a model, HALA antibodies were identified with the desired HER2 expression-dependent competitive binding with ADCs in vitro. Multi-scale distribution of trastuzumab emtansine and trastuzumab deruxtecan co-administered with the HALA antibody were analyzed in vivo, revealing that the HALA antibody increased ADC tumor penetration in high-expression systems with minimal reduction in ADC uptake in low-expression tumors. This translated to greater ADC efficacy in immunodeficient mouse models across a range of HER2 expression levels. Furthermore, Fc-enhanced HALA antibodies showed improved Fc-effector function at both high and low expression levels and elicited a strong response in an immunocompetent mouse model. These results demonstrate that HALA antibodies can expand treatment ranges beyond high expression targets and leverage strong immune responses.

Optimizing tuberculosis treatment efficacy: Comparing the standard regimen with Moxifloxacin-containing regimens.

Tuberculosis (TB) continues to be one of the deadliest infectious diseases in the world, causing ~1.5 million deaths every year. The World Health Organization initiated an End TB Strategy that aims to reduce TB-related deaths in 2035 by 95%. Recent research goals have focused on discovering more effective and more patient-friendly antibiotic drug regimens to increase patient compliance and decrease emergence of resistant TB. Moxifloxacin is one promising antibiotic that may improve the current standard regimen by shortening treatment time. Clinical trials and in vivo mouse studies suggest that regimens containing moxifloxacin have better bactericidal activity. However, testing every possible combination regimen with moxifloxacin either in vivo or clinically is not feasible due to experimental and clinical limitations. To identify better regimens more systematically, we simulated pharmacokinetics/pharmacodynamics of various regimens (with and without moxifloxacin) to evaluate efficacies, and then compared our predictions to both clinical trials and nonhuman primate studies performed herein. We used GranSim, our well-established hybrid agent-based model that simulates granuloma formation and antibiotic treatment, for this task. In addition, we established a multiple-objective optimization pipeline using GranSim to discover optimized regimens based on treatment objectives of interest, i.e., minimizing total drug dosage and lowering time needed to sterilize granulomas. Our approach can efficiently test many regimens and successfully identify optimal regimens to inform pre-clinical studies or clinical trials and ultimately accelerate the TB regimen discovery process.

A framework for multi-scale intervention modeling: virtual cohorts, virtual clinical trials, and model-to-model comparisons.

Computational models of disease progression have been constructed for a myriad of pathologies. Typically, the conceptual implementation for pathology-related in-silico intervention studies has been ad-hoc and similar in design to experimental studies. We introduce a multi-scale interventional design (MID) framework toward two key goals: tracking of disease dynamics from within-body to patient to population scale; and tracking impact(s) of interventions across these same spatial scales. Our MID framework prioritizes investigation of impact on individual patients within virtual pre-clinical trials, instead of replicating the design of experimental studies. We apply a MID framework to develop, organize, and analyze a cohort of virtual patients for the study of tuberculosis (TB) as an example disease. For this study, we use HostSim: our next-generation whole patient-scale computational model of individuals infected with Mycobacterium tuberculosis. HostSim captures infection within lungs by tracking multiple granulomas, together with dynamics occurring with blood and lymph node compartments, the compartments involved during pulmonary TB. We extend HostSim to include a simple drug intervention as an example of our approach and use our MID framework to quantify the impact of treatment at cellular and tissue (granuloma), patient (lungs, lymph nodes and blood), and population scales. Sensitivity analyses allow us to determine which features of virtual patients are the strongest predictors of intervention efficacy across scales. These insights allow us to identify patient-heterogeneous mechanisms that drive outcomes across scales.

Concomitant immunity to M. tuberculosis infection.

Some persistent infections provide a level of immunity that protects against reinfection with the same pathogen, a process referred to as concomitant immunity. To explore the phenomenon of concomitant immunity during Mycobacterium tuberculosis infection, we utilized HostSim, a previously published virtual host model of the immune response following Mtb infection. By simulating reinfection scenarios and comparing with data from non-human primate studies, we propose a hypothesis that the durability of a concomitant immune response against Mtb is intrinsically tied to levels of tissue resident memory T cells (Trms) during primary infection, with a secondary but important role for circulating Mtb-specific T cells. Further, we compare HostSim reinfection experiments to observational TB studies from the pre-antibiotic era to predict that the upper bound of the lifespan of resident memory T cells in human lung tissue is likely 2-3 years. To the authors' knowledge, this is the first estimate of resident memory T-cell lifespan in humans. Our findings are a first step towards demonstrating the important role of Trms in preventing disease and suggest that the induction of lung Trms is likely critical for vaccine success.

Pharmacokinetics and Pharmacodynamics of TAK-164 Antibody Drug Conjugate Coadministered with Unconjugated Antibody.

The development of new antibody-drug conjugates (ADCs) has led to the approval of 7 ADCs by the FDA in 4 years. Given the impact of intratumoral distribution on efficacy of these therapeutics, coadministration of unconjugated antibody with ADC has been shown to improve distribution and efficacy of several ADCs in high and moderately expressed tumor target systems by increasing tissue penetration. However, the benefit of coadministration in low expression systems is less clear. TAK-164, an ADC composed of an anti-GCC antibody (5F9) conjugated to a DGN549 payload, has demonstrated heterogeneous distribution and bystander killing. Here, we evaluated the impact of 5F9 coadministration on distribution and efficacy of TAK-164 in a primary human tumor xenograft mouse model. Coadministration was found to improve the distribution of TAK-164 within the tumor, but it had no significant impact (increase or decrease) on efficacy. Experimental and computational evidence indicates that this was not a result of tumor saturation, increased binding to perivascular cells, or compensatory bystander effects. Rather, the cellular potency of DGN549 was matched with the single-cell uptake of TAK-164 making its IC50 close to its equilibrium binding affinity (KD), and as such, coadministration dilutes total DGN549 in cells below the maximum cytotoxic concentration, thereby offsetting an increased number of targeted cells with decreased ability to kill each cell. These results provide new insights on matching payload potency to ADC delivery to help identify when increasing tumor penetration is beneficial for improving ADC efficacy and demonstrate how mechanistic simulations can be leveraged to design clinically effective ADCs.

Computational modeling implicates protein scaffolding in p38 regulation of Akt.

Cells process environmental cues by activating intracellular signaling pathways with numerous interconnections and opportunities for cross-regulation. We employed a systems biology approach to investigate intersections of kinase p38, a context-dependent tumor suppressor or promoter, with Akt and ERK, two kinases known to promote cell survival, proliferation, and drug resistance in cancer. Using live, single cell microscopy, multiplexed fluorescent reporters of p38, Akt, and ERK activities, and a custom automated image-processing pipeline, we detected marked heterogeneity of signaling outputs in breast cancer cells stimulated with chemokine CXCL12 or epidermal growth factor (EGF). Basal activity of p38 correlated inversely with amplitude of Akt and ERK activation in response to either ligand. Remarkably, small molecule inhibitors of p38 immediately decreased basal activities of Akt and ERK but increased the proportion of cells with high amplitude ligand-induced activation of Akt signaling. To identify mechanisms underlying cross-talk of p38 with Akt signaling, we developed a computational model incorporating subcellular compartmentalization of signaling molecules by scaffold proteins. Dynamics of this model revealed that subcellular scaffolding of Akt accounted for observed regulation by p38. The model also predicted that differences in the amount of scaffold protein in a subcellular compartment captured the observed single cell heterogeneity in signaling. Finally, our model predicted that reduction in kinase signaling can be accomplished by both scaffolding and direct kinase inhibition. However, scaffolding inhibition can potentiate future kinase activity by redistribution of pathway components, potentially amplifying oncogenic signaling. These studies reveal how computational modeling can decipher mechanisms of cross-talk between the p38 and Akt signaling pathways and point to scaffold proteins as central regulators of signaling dynamics and amplitude.

Co-optimization of therapeutic antibody affinity and specificity using machine learning models that generalize to novel mutational space.

Therapeutic antibody development requires selection and engineering of molecules with high affinity and other drug-like biophysical properties. Co-optimization of multiple antibody properties remains a difficult and time-consuming process that impedes drug development. Here we evaluate the use of machine learning to simplify antibody co-optimization for a clinical-stage antibody (emibetuzumab) that displays high levels of both on-target (antigen) and off-target (non-specific) binding. We mutate sites in the antibody complementarity-determining regions, sort the antibody libraries for high and low levels of affinity and non-specific binding, and deep sequence the enriched libraries. Interestingly, machine learning models trained on datasets with binary labels enable predictions of continuous metrics that are strongly correlated with antibody affinity and non-specific binding. These models illustrate strong tradeoffs between these two properties, as increases in affinity along the co-optimal (Pareto) frontier require progressive reductions in specificity. Notably, models trained with deep learning features enable prediction of novel antibody mutations that co-optimize affinity and specificity beyond what is possible for the original antibody library. These findings demonstrate the power of machine learning models to greatly expand the exploration of novel antibody sequence space and accelerate the development of highly potent, drug-like antibodies.

A virtual host model of Mycobacterium tuberculosis infection identifies early immune events as predictive of infection outcomes.

Tuberculosis (TB), caused by infection with Mycobacterium tuberculosis (Mtb), is one of the world's deadliest infectious diseases and remains a significant global health burden. TB disease and pathology can present clinically across a spectrum of outcomes, ranging from total sterilization of infection to active disease. Much remains unknown about the biology that drives an individual towards various clinical outcomes as it is challenging to experimentally address specific mechanisms driving clinical outcomes. Furthermore, it is unknown whether numbers of immune cells in the blood accurately reflect ongoing events during infection within human lungs. Herein, we utilize a systems biology approach by developing a whole-host model of the immune response to Mtb across multiple physiologic and time scales. This model, called HostSim, tracks events at the cellular, granuloma, organ, and host scale and represents the first whole-host, multi-scale model of the immune response following Mtb infection. We show that this model can capture various aspects of human and non-human primate TB disease and predict that biomarkers in the blood may only faithfully represent events in the lung at early time points after infection. We posit that HostSim, as a first step toward personalized digital twins in TB research, offers a powerful computational tool that can be used in concert with experimental approaches to understand and predict events about various aspects of TB disease and therapeutics.

Simulating the Selection of Resistant Cells with Bystander Killing and Antibody Coadministration in Heterogeneous Human Epidermal Growth Factor Receptor 2-Positive Tumors.

Intratumoral heterogeneity is a leading cause of treatment failure resulting in tumor recurrence. For the antibody-drug conjugate (ADC) ado-trastuzumab emtansine (T-DM1), two major types of resistance include changes in human epidermal growth factor receptor 2 (HER2) expression and reduced payload sensitivity, which is often exacerbated by heterogenous HER2 expression and ADC distribution during treatment. ADCs with bystander payloads, such as trastuzumab-monomethyl auristatin E (T-MMAE), can reach and kill adjacent cells with lower receptor expression that cannot be targeted directly with the ADC. Additionally, coadministration of T-DM1 with its unconjugated antibody, trastuzumab, can improve distribution and minimize heterogeneous delivery. However, the effectiveness of trastuzumab coadministration and ADC bystander killing in heterogenous tumors in reducing the selection of resistant cells is not well understood. Here, we use an agent-based model to predict outcomes with these different regimens. The simulations demonstrate that both T-DM1 and T-MMAE benefit from trastuzumab coadministration for tumors with high average receptor expression (up to 70% and 40% decrease in average tumor volume, respectively), with greater benefit for nonbystander payloads. However, the benefit decreases as receptor expression is reduced, reversing at low concentrations (up to 360% and 430% increase in average tumor volume for T-DM1 and T-MMAE, respectively) for this mechanism that impacts both ADC distribution and efficacy. For tumors with intrinsic payload resistance, coadministration uniformly exhibits better efficacy than ADC monotherapy (50%-70% and 19%-36% decrease in average tumor volume for T-DM1 and T-MMAE, respectively). Finally, we demonstrate that several regimens select for resistant cells at clinical tolerable doses, which highlights the need to pursue other mechanisms of action for durable treatment responses. SIGNIFICANCE STATEMENT: Experimental evidence demonstrates heterogeneity in the distribution of both the antibody-drug conjugate and the target receptor in the tumor microenvironment, which can promote the selection of resistant cells and lead to recurrence. This study quantifies the impact of increasing the antibody dose and utilizing bystander payloads in heterogeneous tumors. Alternative cell-killing mechanisms are needed to avoid enriching resistant cell populations.

Corrigendum: Spatial Organization and Recruitment of Non-Specific T Cells May Limit T Cell-Macrophage Interactions Within Mycobacterium tuberculosis Granulomas.

[This corrects the article DOI: 10.3389/fimmu.2020.613638.].

Neutrophil Dynamics Affect Mycobacterium tuberculosis Granuloma Outcomes and Dissemination.

Neutrophil infiltration into tuberculous granulomas is often associated with higher bacteria loads and severe disease but the basis for this relationship is not well understood. To better elucidate the connection between neutrophils and pathology in primate systems, we paired data from experimental studies with our next generation computational model GranSim to identify neutrophil-related factors, including neutrophil recruitment, lifespan, and intracellular bacteria numbers, that drive granuloma-level outcomes. We predict mechanisms underlying spatial organization of neutrophils within granulomas and identify how neutrophils contribute to granuloma dissemination. We also performed virtual deletion and depletion of neutrophils within granulomas and found that neutrophils play a nuanced role in determining granuloma outcome, promoting uncontrolled bacterial growth in some and working to contain bacterial growth in others. Here, we present three key results: We show that neutrophils can facilitate local dissemination of granulomas and thereby enable the spread of infection. We suggest that neutrophils influence CFU burden during both innate and adaptive immune responses, implying that they may be targets for therapeutic interventions during later stages of infection. Further, through the use of uncertainty and sensitivity analyses, we predict which neutrophil processes drive granuloma severity and structure.

CaliPro: A Calibration Protocol That Utilizes Parameter Density Estimation to Explore Parameter Space and Calibrate Complex Biological Models.

INTRODUCTION: Mathematical and computational modeling have a long history of uncovering mechanisms and making predictions for biological systems. However, to create a model that can provide relevant quantitative predictions, models must first be calibrated by recapitulating existing biological datasets from that system. Current calibration approaches may not be appropriate for complex biological models because: 1) many attempt to recapitulate only a single aspect of the experimental data (such as a median trend) or 2) Bayesian techniques require specification of parameter priors and likelihoods to experimental data that cannot always be confidently assigned. A new calibration protocol is needed to calibrate complex models when current approaches fall short. METHODS: Herein, we develop CaliPro, an iterative, model-agnostic calibration protocol that utilizes parameter density estimation to refine parameter space and calibrate to temporal biological datasets. An important aspect of CaliPro is the user-defined pass set definition, which specifies how the model might successfully recapitulate experimental data. We define the appropriate settings to use CaliPro. RESULTS: We illustrate the usefulness of CaliPro through four examples including predator-prey, infectious disease transmission, and immune response models. We show that CaliPro works well for both deterministic, continuous model structures as well as stochastic, discrete models and illustrate that CaliPro can work across diverse calibration goals. CONCLUSIONS: We present CaliPro, a new method for calibrating complex biological models to a range of experimental outcomes. In addition to expediting calibration, CaliPro may be useful in already calibrated parameter spaces to target and isolate specific model behavior for further analysis.

Pre-existing Cell States Control Heterogeneity of Both EGFR and CXCR4 Signaling.

INTRODUCTION: CXCR4 and epidermal growth factor receptor (EGFR) represent two major families of receptors, G-protein coupled receptors and receptor tyrosine kinases, with central functions in cancer. While utilizing different upstream signaling molecules, both CXCR4 and EGFR activate kinases ERK and Akt, although single-cell activation of these kinases is markedly heterogeneous. One hypothesis regarding the origin of signaling heterogeneity proposes that intercellular variations arise from differences in pre-existing intracellular states set by extrinsic noise. While pre-existing cell states vary among cells, each pre-existing state defines deterministic signaling outputs to downstream effectors. Understanding causes of signaling heterogeneity will inform treatment of cancers with drugs targeting drivers of oncogenic signaling. METHODS: We built a single-cell computational model to predict Akt and ERK responses to CXCR4- and EGFR-mediated stimulation. We investigated signaling heterogeneity through these receptors and tested model predictions using quantitative, live-cell time-lapse imaging. RESULTS: We show that the pre-existing cell state predicts single-cell signaling through both CXCR4 and EGFR. Computational modeling reveals that the same set of pre-existing cell states explains signaling heterogeneity through both EGFR and CXCR4 at multiple doses of ligands and in two different breast cancer cell lines. The model also predicts how phosphatidylinositol-3-kinase (PI3K) targeted therapies potentiate ERK signaling in certain breast cancer cells and that low level, combined inhibition of MEK and PI3K ablates potentiated ERK signaling. CONCLUSIONS: Our data demonstrate that a conserved motif exists for EGFR and CXCR4 signaling and suggest potential clinical utility of the computational model to optimize therapy.

A multi-scale pipeline linking drug transcriptomics with pharmacokinetics predicts in vivo interactions of tuberculosis drugs.

Tuberculosis (TB) is the deadliest infectious disease worldwide. The design of new treatments for TB is hindered by the large number of candidate drugs, drug combinations, dosing choices, and complex pharmaco-kinetics/dynamics (PK/PD). Here we study the interplay of these factors in designing combination therapies by linking a machine-learning model, INDIGO-MTB, which predicts in vitro drug interactions using drug transcriptomics, with a multi-scale model of drug PK/PD and pathogen-immune interactions called GranSim. We calculate an in vivo drug interaction score (iDIS) from dynamics of drug diffusion, spatial distribution, and activity within lesions against various pathogen sub-populations. The iDIS of drug regimens evaluated against non-replicating bacteria significantly correlates with efficacy metrics from clinical trials. Our approach identifies mechanisms that can amplify synergistic or mitigate antagonistic drug interactions in vivo by modulating the relative distribution of drugs. Our mechanistic framework enables efficient evaluation of in vivo drug interactions and optimization of combination therapies.

Computational methods for characterizing and learning from heterogeneous cell signaling data.

Heterogeneity in cell signaling pathways is increasingly appreciated as a fundamental feature of cell biology and a driver of clinically relevant disease phenotypes. Understanding the causes of heterogeneity, the cellular mechanisms used to control heterogeneity, and the downstream effects of heterogeneity in single cells are all key obstacles for manipulating cellular populations and treating disease. Recent advances in genetic engineering, including multiplexed fluorescent reporters, have provided unprecedented measurements of signaling heterogeneity, but these vast data sets are often difficult to interpret, necessitating the use of computational techniques to extract meaning from the data. Here, we review recent advances in computational methods for extracting meaning from these novel data streams. In particular, we evaluate how machine learning methods related to dimensionality reduction and classification can identify structure in complex, dynamic datasets, simplifying interpretation. We also discuss how mechanistic models can be merged with heterogeneous data to understand the underlying differences between cells in a population. These methods are still being developed, but the work reviewed here offers useful applications of specific analysis techniques that could enable the translation of single-cell signaling data to actionable biological understanding.

A computational model tracks whole-lung Mycobacterium tuberculosis infection and predicts factors that inhibit dissemination.

Mycobacterium tuberculosis (Mtb), the causative infectious agent of tuberculosis (TB), kills more individuals per year than any other infectious agent. Granulomas, the hallmark of Mtb infection, are complex structures that form in lungs, composed of immune cells surrounding bacteria, infected cells, and a caseous necrotic core. While granulomas serve to physically contain and immunologically restrain bacteria growth, some granulomas are unable to control Mtb growth, leading to bacteria and infected cells leaving the granuloma and disseminating, either resulting in additional granuloma formation (local or non-local) or spread to airways or lymph nodes. Dissemination is associated with development of active TB. It is challenging to experimentally address specific mechanisms driving dissemination from TB lung granulomas. Herein, we develop a novel hybrid multi-scale computational model, MultiGran, that tracks Mtb infection within multiple granulomas in an entire lung. MultiGran follows cells, cytokines, and bacterial populations within each lung granuloma throughout the course of infection and is calibrated to multiple non-human primate (NHP) cellular, granuloma, and whole-lung datasets. We show that MultiGran can recapitulate patterns of in vivo local and non-local dissemination, predict likelihood of dissemination, and predict a crucial role for multifunctional CD8+ T cells and macrophage dynamics for preventing dissemination.

Both Pharmacokinetic Variability and Granuloma Heterogeneity Impact the Ability of the First-Line Antibiotics to Sterilize Tuberculosis Granulomas.

Tuberculosis (TB) remains as one of the world's deadliest infectious diseases despite the use of standardized antibiotic therapies. Recommended therapy for drug-susceptible TB is up to 6 months of antibiotics. Factors that contribute to lengthy regimens include antibiotic underexposure in lesions due to poor pharmacokinetics (PK) and complex granuloma compositions, but it is difficult to quantify how individual antibiotics are affected by these factors and to what extent these impact treatments. We use our next-generation multi-scale computational model to simulate granuloma formation and function together with antibiotic pharmacokinetics and pharmacodynamics, allowing us to predict conditions leading to granuloma sterilization. In this work, we focus on how PK variability, determined from human PK data, and granuloma heterogeneity each quantitatively impact granuloma sterilization. We focus on treatment with the standard regimen for TB of four first-line antibiotics: isoniazid, rifampin, ethambutol, and pyrazinamide. We find that low levels of antibiotic concentration due to naturally occurring PK variability and complex granulomas leads to longer granuloma sterilization times. Additionally, the ability of antibiotics to distribute in granulomas and kill different subpopulations of bacteria contributes to their specialization in the more efficacious combination therapy. These results can inform strategies to improve antibiotic therapy for TB.

An Agent-Based Systems Pharmacology Model of the Antibody-Drug Conjugate Kadcyla to Predict Efficacy of Different Dosing Regimens.

The pharmaceutical industry has invested significantly in antibody-drug conjugates (ADCs) with five FDA-approved therapies and several more showing promise in late-stage clinical trials. The FDA-approved therapeutic Kadcyla (ado-trastuzumab emtansine or T-DM1) can extend the survival of patients with tumors overexpressing HER2. However, tumor histology shows that most T-DM1 localizes perivascularly, but coadministration with its unconjugated form (trastuzumab) improves penetration of the ADC into the tumor and subsequent treatment efficacy. ADC dosing schedule, e.g., dose fractionation, has also been shown to improve tolerability. However, it is still not clear how coadministration with carrier doses impacts efficacy in terms of receptor expression, dosing regimens, and payload potency. Here, we develop a hybrid agent-based model (ABM) to capture ADC and/or antibody delivery and to predict tumor killing and growth kinetics. The results indicate that a carrier dose improves efficacy when the increased number of cells targeted by the ADC outweighs the reduced fractional killing of the targeted cells. The threshold number of payloads per cell required for killing plays a pivotal role in defining this cutoff. Likewise, fractionated dosing lowers ADC efficacy due to lower tissue penetration from a reduced maximum plasma concentration. It is only beneficial when an increase in tolerability from fractionation allows a higher ADC/payload dose that more than compensates for the loss in efficacy from fractionation. Overall, the multiscale model enables detailed depictions of heterogeneous ADC delivery, cancer cell death, and tumor growth to show how carrier dosing impacts efficacy to design the most efficacious regimen.