Heterogeneity in Vaccinal Immunity to SARS-CoV-2 Can Be Addressed by a Personalized Booster Strategy.

SARS-CoV-2 vaccinations were initially shown to substantially reduce risk of severe disease and death. However, pharmacokinetic (PK) waning and rapid viral evolution degrade neutralizing antibody (nAb) binding titers, causing loss of vaccinal protection. Additionally, there is inter-individual heterogeneity in the strength and durability of the vaccinal nAb response. Here, we propose a personalized booster strategy as a potential solution to this problem. Our model-based approach incorporates inter-individual heterogeneity in nAb response to primary SARS-CoV-2 vaccination into a pharmacokinetic/pharmacodynamic (PK/PD) model to project population-level heterogeneity in vaccinal protection. We further examine the impact of evolutionary immune evasion on vaccinal protection over time based on variant fold reduction in nAb potency. Our findings suggest viral evolution will decrease the effectiveness of vaccinal protection against severe disease, especially for individuals with a less durable immune response. More frequent boosting may restore vaccinal protection for individuals with a weaker immune response. Our analysis shows that the ECLIA RBD binding assay strongly predicts neutralization of sequence-matched pseudoviruses. This may be a useful tool for rapidly assessing individual immune protection. Our work suggests vaccinal protection against severe disease is not assured and identifies a potential path forward for reducing risk to immunologically vulnerable individuals.

A comprehensive regulatory and industry review of modeling and simulation practices in oncology clinical drug development.

Exposure-response (E-R) analyses are an integral component in the development of oncology products. Characterizing the relationship between drug exposure metrics and response allows the sponsor to use modeling and simulation to address both internal and external drug development questions (e.g., optimal dose, frequency of administration, dose adjustments for special populations). This white paper is the output of an industry-government collaboration among scientists with broad experience in E-R modeling as part of regulatory submissions. The goal of this white paper is to provide guidance on what the preferred methods for E-R analysis in oncology clinical drug development are and what metrics of exposure should be considered.

From Cold to Hot: Changing Perceptions and Future Opportunities for Quantitative Systems Pharmacology Modeling in Cancer Immunotherapy.

Immuno-oncology (IO) is a fast-expanding field due to recent success using IO therapies in treating cancer. As IO therapies do not directly kill tumor cells but rather act upon the patients' own immune cells either systemically or in the tumor microenvironment, new and innovative approaches are required to inform IO therapy research and development. Quantitative systems pharmacology (QSP) modeling describes the biological mechanisms of disease and the mode of action of drugs with mathematical equations, which has significant potential to address the big challenges in the IO field, from identifying patient populations that respond to different therapies to guiding the selection, dosing, and scheduling of combination therapy. To assess the perspectives of the community on the impact of QSP modeling in IO drug development and to understand current applications and challenges, the IO QSP working group-under the QSP Special Interest Group (SIG) of the International Society of Pharmacometrics (ISoP)-conducted a survey among QSP modelers, non-QSP modelers, and non-modeling IO program stakeholders. The survey results are presented here with discussions on how to address some of the findings. One of the findings is the differences in perception among these groups. To help bridge this perception gap, we present several case studies demonstrating the impact of QSP modeling in IO and suggest actions that can be taken in the future to increase the real and perceived impact of QSP modeling in IO drug research and development.

Navigating Between Right, Wrong, and Relevant: The Use of Mathematical Modeling in Preclinical Decision Making.

The goal of this mini-review is to summarize the collective experience of the authors for how modeling and simulation approaches have been used to inform various decision points from discovery to First-In-Human clinical trials. The article is divided into a high-level overview of the types of problems that are being aided by modeling and simulation approaches, followed by detailed case studies around drug design (Nektar Therapeutics, Genentech), feasibility analysis (Novartis Pharmaceuticals), improvement of preclinical drug design (Pfizer), and preclinical to clinical extrapolation (Merck, Takeda, and Amgen).

Using mixed-effects modeling to estimate decay kinetics of response to SARS-CoV-2 infection.

The duration of natural immunity in response to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a matter of some debate in the literature at present. For example, in a recent publication characterizing SARS-CoV-2 immunity over time, the authors fit pooled longitudinal data, using fitted slopes to infer the duration of SARS-CoV-2 immunity. In fact, such approaches can lead to misleading conclusions as a result of statistical model-fitting artifacts. To exemplify this phenomenon, we reanalyzed one of the markers (pseudovirus neutralizing titer) in the publication, using mixed-effects modeling, a methodology better suited to longitudinal datasets like these. Our findings showed that the half-life was both longer and more variable than reported by the authors. The example selected by us here illustrates the utility of mixed-effects modeling in provide more accurate estimates of the duration and heterogeneity of half-lives of molecular and cellular biomarkers of SARS-CoV-2 immunity.

Quantitative Systems Pharmacology Approaches for Immuno-Oncology: Adding Virtual Patients to the Development Paradigm.

Drug development in oncology commonly exploits the tools of molecular biology to gain therapeutic benefit through reprograming of cellular responses. In immuno-oncology (IO) the aim is to direct the patient's own immune system to fight cancer. After remarkable successes of antibodies targeting PD1/PD-L1 and CTLA4 receptors in targeted patient populations, the focus of further development has shifted toward combination therapies. However, the current drug-development approach of exploiting a vast number of possible combination targets and dosing regimens has proven to be challenging and is arguably inefficient. In particular, the unprecedented number of clinical trials testing different combinations may no longer be sustainable by the population of available patients. Further development in IO requires a step change in selection and validation of candidate therapies to decrease development attrition rate and limit the number of clinical trials. Quantitative systems pharmacology (QSP) proposes to tackle this challenge through mechanistic modeling and simulation. Compounds' pharmacokinetics, target binding, and mechanisms of action as well as existing knowledge on the underlying tumor and immune system biology are described by quantitative, dynamic models aiming to predict clinical results for novel combinations. Here, we review the current QSP approaches, the legacy of mathematical models available to quantitative clinical pharmacologists describing interaction between tumor and immune system, and the recent development of IO QSP platform models. We argue that QSP and virtual patients can be integrated as a new tool in existing IO drug development approaches to increase the efficiency and effectiveness of the search for novel combination therapies.

Progress and Opportunities to Advance Clinical Cancer Therapeutics Using Tumor Dynamic Models.

There is a need for new approaches and endpoints in oncology drug development, particularly with the advent of immunotherapies and the multiple drug combinations under investigation. Tumor dynamics modeling, a key component to oncology "model-informed drug development," has shown a growing number of applications and a broader adoption by drug developers and regulatory agencies in the past years to support drug development and approval in a variety of ways. Tumor dynamics modeling is also being investigated in personalized cancer therapy approaches. These models and applications are reviewed and discussed, as well as the limitations and issues open for further investigations. A close collaboration between stakeholders like clinical investigators, statisticians, and pharmacometricians is warranted to advance clinical cancer therapeutics.

A Systems Pharmacology Model for Drug Delivery to Solid Tumors by Antibody-Drug Conjugates: Implications for Bystander Effects.

Antibody-drug conjugates (ADCs) are cancer drugs composed of a humanized antibody linked to a cytotoxic payload, allowing preferential release of payload in cancer cells expressing the antibody-targeted antigen. Here, a systems pharmacology model is used to simulate ADC transport from blood to tumor tissue and ADC uptake by tumor cells. The model includes effects of spatial gradients in drug concentration in a three-dimensional network of tumor blood vessels with realistic geometry and accounts for diffusion of ADC in the tumor extracellular space, binding to antigen, internalization, intracellular processing, and payload efflux from cells. Cells that process an internalized ADC-antigen complex may release payload that can be taken up by other "bystander" cells. Such bystander effects are included in the model. The model is used to simulate conditions in previous experiments, showing good agreement with experimental results. Simulations are used to analyze the relationship of bystander effects to payload properties and single-dose administrations. The model indicates that exposure of payload to cells distant from vessels is sensitive to the free payload diffusivity in the extracellular space. When antigen expression is heterogeneous, the model indicates that the amount of payload accumulating in non-antigen-expressing cells increases linearly with dose but depends only weakly on the percentage of antigen-expressing cells. The model provides an integrated mechanistic framework for understanding the effects of spatial gradients on drug distribution using ADCs and for designing ADCs to achieve more effective payload distribution in solid tumors, thereby increasing the therapeutic index of the ADC.

Dose Optimization for Anticancer Drug Combinations: Maximizing Therapeutic Index via Clinical Exposure-Toxicity/Preclinical Exposure-Efficacy Modeling.

PURPOSE: Recommended phase II dose (RP2D) determination for combination therapy regimens is a constrained optimization problem of maximizing antitumor activity within the constraint of clinical tolerability to provide a wide therapeutic index. A methodology for addressing this problem was developed and tested using clinical and preclinical data from combinations of the investigational drugs TAK-117, a PI3Kα inhibitor, and TAK-228, a TORC1/2 dual inhibitor. EXPERIMENTAL DESIGN: Utilizing free fraction-corrected average concentrations, [Formula: see text] and [Formula: see text], which are the primary pharmacokinetic predictors of single-agent preclinical antitumor activity, a preclinical exposure-efficacy surface was characterized, allowing for nonlinear interactions between growth rate inhibition of the agents on a MDA-MB-361 cell line xenograft model. Logistic regression was used to generate an exposure-effect surface for [Formula: see text] and [Formula: see text] versus clinical toxicity outcomes [experiencing a dose-limiting toxicity (DLT)] in single-agent and combination dose-escalation studies. A maximum tolerated exposure curve was defined at which DLT probability was 25%; predicted antitumor activity along this curve was used to determine optimal RP2D. RESULTS: The toxicity constraint curve determined from early clinical data predicted that any clinically tolerable combination was unlikely to result in greater antitumor activity than either single-agent TAK-117 or TAK-228 administered at their respective MTDs. Similar results were obtained with 10 other cell lines, with one agent or the other predicted to outperform the combination. CONCLUSIONS: This methodology represents a general, principled way of evaluating and selecting optimal RP2D combinations in oncology. The methodology will be retested upon availability of clinical data from TAK-117/TAK-228 combination phase II studies.See related commentary by Mayawala et al., p. 6564.

Challenging the CNS Targeting Potential of Systemically Administered Nanoemulsion Delivery Systems: a Case Study with Rapamycin-Containing Fish Oil Nanoemulsions in Mice.

PURPOSE: Despite extensive preclinical investigations, in-vivo properties and formulation characteristics that improve CNS drug delivery following systemic dosing of nanoemulsions remain incompletely understood. METHODS: The CNS targeting potential of systemically administered nanoemulsions was evaluated by formulating rapamycin containing fish oil nanoemulsions, and testing the combined effect of formulation characteristics such as the circulation half-life and particle size distribution, on CNS delivery of rapamycin containing fish oil nanoemulsions in mice. RESULTS: Results generated with rapamycin nanoemulsions suggested that circulation half-life and particle size distribution did not impact the brain targeting efficiency of rapamycin containing fish oil nanoemulsions. Further, in the absence of any improvement in the systemic exposures of rapamycin, nanoemulsions did not outperform their aqueous counterpart with respect to the extent of CNS drug delivery. CONCLUSIONS: Our findings confirm that BBB penetration, which primarily depends on intrinsic drug-related properties, may not be significantly improved following encapsulation of drugs in nanoemulsions. Graphical Abstract The CNS targeting potential of systemically administered nanoemulsions was investigated by formulating various rapamycin containing fish oil nanoemulsions associated with different formulation characteristics such as the circulation half-life and particle size distribution. The targeting efficiency (TE) defined as the ratio of the brain exposures to the accompanying systemic exposures of rapamycin was estimated for each formulation following IV dosing in mice.

Mathematical Modeling and Simulation to Investigate the CNS Transport Characteristics of Nanoemulsion-Based Drug Delivery Following Intranasal Administration.

PURPOSE: Despite encouraging preclinical results, mechanisms of CNS drug delivery following intranasal dosing of nanoemulsions remain incompletely understood. Herein, the transport characteristics of intranasally administered nanoemulsions are investigated using mathematical modeling and simulation. METHODS: A compartmental model was developed to describe systemic and brain pharmacokinetics of drug solutions following intranasal dosing in rodents. The association between transport processes and CNS drug delivery was predicted using sensitivity analysis. Published pharmacokinetic data for four drugs; dosed as a nanoemulsion and aqueous solution were modeled to characterize differences in transport processes across formulations. RESULTS: The intranasal model structure performed in a drug agnostic fashion. Sensitivity analysis suggested that though the extent of CNS drug delivery depends on nasal bioavailability, the CNS targeting efficiency is only sensitive to changes in drug permeability across the nasal epithelium. Modeling results indicated that nanoemulsions primarily improve nasal bioavailability and drug permeability across the olfactory epithelium, with minimal effect on drug permeability across the non-olfactory epithelium. CONCLUSIONS: Using mathematical modeling we outlined dominant transport pathways following intranasal dosing, predicted the association between transport pathways and CNS drug delivery, predicted human CNS delivery after accounting for inter-species differences in nasal anatomy, and quantified the CNS delivery potential of different formulations in rodents.

Directional inconsistency between Response Evaluation Criteria in Solid Tumors (RECIST) time to progression and response speed and depth.

AIM: We seek to characterize how faster tumour shrinkage rate (k) can lead to paradoxically shorter Response Evaluation Criteria in Solid Tumors (RECIST) time to progression ('TTP20' - tumour size exceeding its minimum by 5 mm and 20%) [1] and, therefore, progression-free survival (PFS). Specifically, we investigate under what conditions this paradoxical behaviour occurs, what fraction of patients satisfy these conditions, whether this phenomenon can invert population-level PFS hazard ratio, and consistency of an alternative time-to-event benefit metric with k. METHODS: We use a mathematical model treating tumour burden as decreasing drug-sensitive and increasing drug-resistant cell subpopulations. We fit this model to data from several clinical trials with different indications [2]. We simulated a more effective treatment and recorded whether patients' TTP20 increased or decreased. We performed a study-level analysis to compare the relationship of speed and depth of response with TTP20 for both the administered 'control' and simulated 'more effective' drug. We propose and test an alternative benefit metric: the model-projected time that tumour size reaches 120% of baseline (TTB120). RESULTS: Depending on indication, 3-27% of patients are estimated to have a paradoxically inverse relationship between k and TTP20. Simulated head-to-head studies show that TTP20-based PFS can favour the less effective drug. In contrast, TTB120 always favours the more effective drug. CONCLUSION: We demonstrate the paradoxical behaviour of RECIST TTP20 - as an exemplar of percent-change-from-nadir based cancer progression criterion - both in theory and in observed patient data at the individual and trial level. We propose an alternative tumour size-based criterion (TTB120) that is directionally consistent with tumour shrinkage rate.

Exposure and Tumor Fn14 Expression as Determinants of Pharmacodynamics of the Anti-TWEAK Monoclonal Antibody RG7212 in Patients with Fn14-Positive Solid Tumors.

PURPOSE: The TWEAK-Fn14 pathway represents a novel anticancer target that is being actively investigated. Understanding the relationship between pharmacokinetics of anti-TWEAK therapeutics and tumor pharmacodynamics is critical. We investigated exposure-response relationships of RG7212, an anti-TWEAK mAb, in patients with Fn14-expressing tumors. EXPERIMENTAL DESIGN: Patients with Fn14-positive tumors (IHC ≥ 1+) treated in a phase I first-in-human study with ascending doses of RG7212 were the basis for this analysis. Pharmacokinetics of RG7212 and dynamics of TWEAK were determined, as were changes in tumor TWEAK-Fn14 signaling in paired pre- and posttreatment tumor biopsies. The objectives of the analysis were to define exposure-response relationships and the relationship between pretreatment tumor Fn14 expression and pharmacodynamic effect. Associations between changes in TWEAK-Fn14 signaling and clinical outcome were explored. RESULTS: Thirty-six patients were included in the analysis. RG7212 reduced plasma TWEAK to undetectable levels at all observed RG7212 exposures. In contrast, reductions in tumor Fn14 and TRAF1 protein expression were observed only at higher exposure (≥ 300 mg*h/mL). Significant reductions in tumor Ki-67 expression and early changes in serum concentrations of CCL-2 and MMP-9 were observed exclusively in patients with higher drug exposure who had high pretreatment tumor Fn14 expression. Pretreatment tumor Fn14 expression was not associated with outcome, but a trend toward longer time on study was observed with high versus low RG7212 exposure. CONCLUSIONS: RG7212 reduced tumor TWEAK-Fn14 signaling in a systemic exposure-dependent manner. In addition to higher exposure, relatively high Fn14 expression might be required for pharmacodynamic effect of anti-TWEAK monoclonal antibodies.

A phase I monotherapy study of RG7212, a first-in-class monoclonal antibody targeting TWEAK signaling in patients with advanced cancers.

PURPOSE: Tumor necrosis factor (TNF)-like weak inducer of apoptosis (TWEAK) and fibroblast growth factor-inducible molecule 14 (Fn14) are a ligand-receptor pair frequently overexpressed in solid tumors. TWEAK: Fn14 signaling regulates multiple oncogenic processes through MAPK, AKT, and NFκB pathway activation. A phase I study of RG7212, a humanized anti-TWEAK IgG1κ monoclonal antibody, was conducted in patients with advanced solid tumors expressing Fn14. EXPERIMENTAL DESIGN: Dose escalations, over a 200- to 7,200-mg range, were performed with patients enrolled in weekly (QW), bi-weekly (Q2W), or every-three-week (Q3W) schedules. Primary objectives included determination of dose and safety profile. Secondary endpoints included assessments related to inhibition of TWEAK: Fn14 signaling, tumor proliferation, tumor immune cell infiltration, and pharmacokinetics. RESULTS: In 192 treatment cycles administered to 54 patients, RG7212 was well-tolerated with no dose-limiting toxicities observed. More than 95% of related adverse events were limited to grade 1/2. Pharmacokinetics were dose proportional for all cohorts, with a t1/2 of 11 to 12 days. Pharmacodynamic changes included clearance of free and total TWEAK ligand and reductions in tumor Ki-67 and TRAF1. A patient with BRAF wild-type melanoma who received 36 weeks of RG7212 therapy had tumor regression and pharmacodynamic changes consistent with antitumor effects. Fifteen patients (28%) received 16 or more weeks of RG7212 treatment. CONCLUSION: RG7212 demonstrated excellent tolerability and favorable pharmacokinetics. Pharmacodynamic endpoints were consistent with reduced TWEAK: Fn14 signaling. Tumor regression was observed and prolonged stable disease was demonstrated in multiple heavily pretreated patients with solid tumors. These encouraging results support further study of RG7212. Clin Cancer Res; 21(2); 258-66. ©2014 AACR.

BCR-ABL transcript dynamics support the hypothesis that leukemic stem cells are reduced during imatinib treatment.

PURPOSE: Imatinib induces a durable response in most patients with Philadelphia chromosome-positive chronic myeloid leukemia, but it is currently unclear whether imatinib reduces the leukemic stem cell (LSC) burden, which may be an important step toward enabling safe discontinuation of therapy. In this article, we use mathematical models of BCR-ABL levels to make inferences on the dynamics of LSCs. EXPERIMENTAL DESIGN: Patients with at least 1 BCR-ABL transcript measurement on imatinib were included (N = 477). Maximum likelihood methods were used to test 3 potential hypotheses of the dynamics of BCR-ABL transcripts on imatinib therapy: (i) monoexponential, in which there is little, if any, decline in BCR-ABL transcripts; (ii) biexponential, in which patients have a rapid initial decrease in BCR-ABL transcripts followed by a more gradual response; and (iii) triexponential, in which patients first exhibit a biphasic decline but then have a third phase when BCR-ABL transcripts increase rapidly. RESULTS: We found that most patients treated with imatinib exhibit a biphasic decrease in BCR-ABL transcript levels, with a rapid decrease during the first few months of treatment, followed by a more gradual decrease that often continues over many years. CONCLUSIONS: We show that the only hypothesis consistent with current data on progenitor cell turnover and with the long-term, gradual decrease in the BCR-ABL levels seen in most patients is that these patients exhibit a continual, gradual reduction of the LSCs. This observation may explain the ability to discontinue imatinib therapy without relapse in some cases.

Preclinical cardiac safety assessment of pharmaceutical compounds using an integrated systems-based computer model of the heart.

Blockade of the delayed rectifier potassium channel current, I(Kr), has been associated with drug-induced QT prolongation in the electrocardiogram and life-threatening cardiac arrhythmias. However, it is increasingly clear that compound-induced interactions with multiple cardiac ion channels may significantly affect QT prolongation that would result from inhibition of only I(Kr) [Redfern, W.S., Carlsson, L., et al., 2003. Relationships between preclinical cardiac electrophysiology, clinical QT interval prolongation and torsade de pointes for a broad range of drugs: evidence for a provisional safety margin in drug development. Cardiovasc. Res. 58(1), 32-45]. Such an assessment may not be feasible in vitro, due to multi-factorial processes that are also time-dependent and highly non-linear. Limited preclinical data, I(Kr) hERG assay and canine Purkinje fiber (PF) action potentials (APs) [Gintant, G.A., Limberis, J.T., McDermott, J.S., Wegner, C.D., Cox, B.F., 2001. The canine Purkinje fiber: an in vitro model system for acquired long QT syndrome and drug-induced arrhythmogenesis. J. Cardiovasc. Pharmacol. 37(5), 607-618], were used for two test compounds in a systems-based modeling platform of cardiac electrophysiology [Muzikant, A.L., Penland, R.C., 2002. Models for profiling the potential QT prolongation risk of drugs. Curr. Opin. Drug. Discov. Dev. 5(1), 127-35] to: (i) convert a canine myocyte model to a PF model by training functional current parameters to the AP data; (ii) reverse engineer the compounds' effects on five channel currents other than I(Kr), predicting significant IC(50) values for I(Na+), sustained and I(Ca2+), L-type , which were subsequently experimentally validated; (iii) use the predicted (I(Na+), sustained and I(Ca2+), L-type) and measured (I(Kr)) IC(50) values to simulate dose-dependent effects of the compounds on APs in endocardial, mid-myocardial, and epicardiac ventricular cells; and (iv) integrate the three types of cellular responses into a tissue-level spatial model, which quantifiably predicted no potential for the test compounds to induce either QT prolongation or increased transmural dispersion of repolarization in a dose-dependent and reverse rate-dependent fashion, despite their inhibition of I(Kr) in vitro.

How nematode sperm crawl.

Sperm of the nematode, Ascaris suum, crawl using lamellipodial protrusion, adhesion and retraction, a process analogous to the amoeboid motility of other eukaryotic cells. However, rather than employing an actin cytoskeleton to generate locomotion, nematode sperm use the major sperm protein (MSP). Moreover, nematode sperm lack detectable molecular motors or the battery of actin-binding proteins that characterize actin-based motility. The Ascaris system provides a simple 'stripped down' version of a crawling cell in which to examine the basic mechanism of cell locomotion independently of other cellular functions that involve the cytoskeleton. Here we present a mechanochemical analysis of crawling in Ascaris sperm. We construct a finite element model wherein (a) localized filament polymerization and bundling generate the force for lamellipodial extension and (b) energy stored in the gel formed from the filament bundles at the leading edge is subsequently used to produce the contraction that pulls the rear of the cell forward. The model reproduces the major features of crawling sperm and provides a framework in which amoeboid cell motility can be analyzed. Although the model refers primarily to the locomotion of nematode sperm, it has important implications for the mechanics of actin-based cell motility.