A mechanistic compartmental model for total antibody uptake in tumors

A mechanistic PK/PD model of AZD0171 (anti-LIF) to support Phase II dose selection

AZD0171 (INN: Falbikitug) is being developed as a humanized monoclonal antibody (mAb), immunoglobulin G subclass 1 (IgG1), which binds specifically to the immunosuppressive human cytokine leukemia inhibitory factor (LIF) and inhibits downstream signaling by blocking recruitment of glycoprotein 130 (gp130) to the LIF receptor (LIFR) subunit (gp190) and the phosphorylation of signal transducer and activator of transcription 3 (STAT3) and is intended to treat adult participants with advanced solid tumors. LIF is a pleiotropic cytokine (and a member of the IL-6 family of cytokines) involved in many physiological and pathological processes and is highly expressed in a subset of solid tumors, including non-small cell lung cancer (NSCLC), colon, ovarian, prostate, and pancreatic cancer. The aim of this work was to develop a mechanistic PK/PD model to investigate the effect of AZD0171 on tumor LIF levels, predict the level of downstream signaling complex (LIF:LIFR:gp130) inhibition, and examine the dose-response relationship to support dose selection for a Phase II clinical study. Modeling results show that tumor LIF is inhibited in a dose-dependent manner with >90% inhibition for 95% of patients at the Phase II clinical dose of 1500 mg Q2W.

Clinical translation of antibody drug conjugate dosing in solid tumors from preclinical mouse data

Antibody drug conjugates (ADCs) have made impressive strides in the clinic in recent years with 11 Food and Drug Administration approvals, including 6 for the treatment of patients with solid tumors. Despite this success, the development of new agents remains challenging with a high failure rate in the clinic. Here, we show that current approved ADCs for the treatment of patients with solid tumors can all show substantial efficacy in some mouse models when administered at a similar weight-based [milligrams per kilogram (mg/kg)] dosing in mice that is tolerated in the clinic. Mechanistically, equivalent mg/kg dosing results in a similar drug concentration in the tumor and a similar tissue penetration into the tumor due to the unique delivery features of ADCs. Combined with computational approaches, which can account for the complex distribution within the tumor microenvironment, these scaling concepts may aid in the evaluation of new agents and help design therapeutics with maximum clinical efficacy.

On the uncertainty of the correlation between nanoparticle avidity and biodistribution

The specific delivery of a drug to its site of action also known as targeted drug delivery is a topic in the field of pharmaceutics studied for decades. One approach extensively investigated in this context is the use ligand functionalized nanoparticles. These particles are modified to carry receptor specific ligands, enabling them to accumulate at a desired target site. However, while this concept initially appears straightforward to implement, in-depth research has revealed several challenges hindering target site specific particle accumulation - some of which remain unresolved to this day. One of these challenges consists in the still incomplete understanding of how nanoparticles interact with biological systems. This knowledge gap significantly compromises the predictability of particle distribution in biological systems, which is critical for therapeutic efficacy. One of the most crucial steps in delivery is the attachment of nanoparticles to cells at the target site. This attachment occurs via the formation of multiple ligand receptor bonds. A process also referred to as multivalent interaction. While multivalency has been described extensively for individual molecules and macromolecules respectively, little is known on the multivalent binding of nanoparticles to cells. Here, we will specifically introduce the concept of avidity as a measure for favorable particle membrane interactions. Also, an overview about nanoparticle and membrane properties affecting avidity will be given. Thereafter, we provide a thorough review on literature investigating the correlation between nanoparticle avidity and success in targeted particle delivery. In particular, we want to analyze the currently uncertain data on the existence and nature of the correlation between particle avidity and biodistribution.

Advances and challenges in immunoPET methodology

Immuno-positron emission tomography (immunoPET) enables imaging of specific targets that play a role in targeted therapy and immunotherapy, such as antigens on cell membranes, targets in the disease microenvironment, or immune cells. The most common immunoPET applications use a monoclonal antibody labeled with a relatively long-lived positron emitter such as 89Zr (T 1/2 = 78.4 h), but smaller antibody-based constructs labeled with various other positron emitting radionuclides are also being investigated. This molecular imaging technique can thus guide the development of new drugs and may have a pivotal role in selecting patients for a particular therapy. In early phase immunoPET trials, multiple imaging time points are used to examine the time-dependent biodistribution and to determine the optimal imaging time point, which may be several days after tracer injection due to the slow kinetics of larger molecules. Once this has been established, usually only one static scan is performed and semi-quantitative values are reported. However, total PET uptake of a tracer is the sum of specific and nonspecific uptake. In addition, uptake may be affected by other factors such as perfusion, pre-/co-administration of the unlabeled molecule, and the treatment schedule. This article reviews imaging methodologies used in immunoPET studies and is divided into two parts. The first part summarizes the vast majority of clinical immunoPET studies applying semi-quantitative methodologies. The second part focuses on a handful of studies applying pharmacokinetic models and includes preclinical and simulation studies. Finally, the potential and challenges of immunoPET quantification methodologies are discussed within the context of the recent technological advancements provided by long axial field of view PET/CT scanners.

Non-specific irreversible 89Zr-mAb uptake in tumours: evidence from biopsy-proven target-negative tumours using 89Zr-immuno-PET

BACKGROUND

Distribution of mAbs into tumour tissue may occur via different processes contributing differently to the 89Zr-mAb uptake on PET. Target-specific binding in tumours is of main interest; however, non-specific irreversible uptake may also be present, which influences quantification. The aim was to investigate the presence of non-specific irreversible uptake in tumour tissue using Patlak linearization on 89Zr-immuno-PET data of biopsy-proven target-negative tumours. Data of two studies, including target status obtained from biopsies, were retrospectively analysed, and Patlak linearization provided the net rate of irreversible uptake (Ki).

RESULTS

Two tumours were classified as CD20-negative and two as CD20-positive. Four tumours were classified as CEA-negative and nine as CEA-positive. Ki values of CD20-negative (0.43 µL/g/h and 0.92 µL/g/h) and CEA-negative tumours (mdn = 1.97 µL/g/h, interquartile range (IQR) = 1.50-2.39) were higher than zero. Median Ki values of target-negative tumours were lower than CD20-positive (1.87 µL/g/h and 1.90 µL/g/h) and CEA-positive tumours (mdn = 2.77 µL/g/h, IQR = 2.11-3.65).

CONCLUSION

Biopsy-proven target-negative tumours showed irreversible uptake of 89Zr-mAbs measured in vivo using 89Zr-immuno-PET data, which suggests the presence of non-specific irreversible uptake in tumours. Consequently, for 89Zr-immuno-PET, even if the target is absent, a tumour-to-plasma ratio always increases over time.

Mechanistically Weighted Metric to Predict In Vivo Antibody-Receptor Occupancy: An Analytical Approach

In situ clinical measurement of receptor occupancy (RO) is challenging, particularly for solid tumors, necessitating the use of mathematical models that predict tumor receptor occupancy to guide dose decisions. A potency metric, average free tissue target to initial target ratio (AFTIR), was previously described based on a mechanistic compartmental model and is informative for near-saturating dose regimens. However, the metric fails at clinically relevant subsaturating antibody doses, as compartmental models cannot capture the spatial heterogeneity of distribution faced by some antibodies in solid tumors. Here we employ a partial differential equation (PDE) Krogh cylinder model to simulate spatiotemporal receptor occupancy and derive an analytical solution, a mechanistically weighted global AFTIR, that can better predict receptor occupancy regardless of dosing regimen. In addition to the four key parameters previously identified, a fifth key parameter, the absolute receptor density (targets/cell), is incorporated into the mechanistic AFTIR metric. Receptor density can influence equilibrium intratumoral drug concentration relative to whether the dose is saturating or not, thereby influencing the tumor penetration depth of the antibody. We derive mechanistic RO predictions based on distinct patterns of antibody tumor penetration, presented as a global AFTIR metric guided by a Thiele Modulus and a local saturation potential (drug equivalent of binding potential for positron emissions tomography imaging) and validate the results using rigorous global and local sensitivity analysis. This generalized AFTIR serves as a more accurate analytical metric to aid clinical dose decisions and rational design of antibody-based therapeutics without the need for extensive PDE simulations. SIGNIFICANCE STATEMENT: Determining antibody-receptor occupancy (RO) is critical for dosing decisions in pharmaceutical development, but direct clinical measurement of RO is often challenging and invasive, particularly for solid tumors. Significant efforts have been made to develop mathematical models and simplified analytical metrics of RO, but these often require complex computer simulations. Here we present a mathematically rigorous but simplified analytical model to accurately predict RO across a range of affinities, doses, drug, and tumor properties.

A comprehensive overview on antibody-drug conjugates: from the conceptualization to cancer therapy

Antibody-Drug Conjugates (ADCs) represent an innovative class of potent anti-cancer compounds that are widely used in the treatment of hematologic malignancies and solid tumors. Unlike conventional chemotherapeutic drug-based therapies, that are mainly associated with modest specificity and therapeutic benefit, the three key components that form an ADC (a monoclonal antibody bound to a cytotoxic drug via a chemical linker moiety) achieve remarkable improvement in terms of targeted killing of cancer cells and, while sparing healthy tissues, a reduction in systemic side effects caused by off-tumor toxicity. Based on their beneficial mechanism of action, 15 ADCs have been approved to date by the market approval by the Food and Drug Administration (FDA), the European Medicines Agency (EMA) and/or other international governmental agencies for use in clinical oncology, and hundreds are undergoing evaluation in the preclinical and clinical phases. Here, our aim is to provide a comprehensive overview of the key features revolving around ADC therapeutic strategy including their structural and targeting properties, mechanism of action, the role of the tumor microenvironment and review the approved ADCs in clinical oncology, providing discussion regarding their toxicity profile, clinical manifestations and use in novel combination therapies. Finally, we briefly review ADCs in other pathological contexts and provide key information regarding ADC manufacturing and analytical characterization.

Discovery and Preclinical Characterization of XMT-1660, an Optimized B7-H4-Targeted Antibody-Drug Conjugate for the Treatment of Cancer

Antibody-drug conjugates (ADC) achieve targeted drug delivery to a tumor and have demonstrated clinical success in many tumor types. The activity and safety profile of an ADC depends on its construction: antibody, payload, linker, and conjugation method, as well as the number of payload drugs per antibody [drug-to-antibody ratio (DAR)]. To allow for ADC optimization for a given target antigen, we developed Dolasynthen (DS), a novel ADC platform based on the payload auristatin hydroxypropylamide, that enables precise DAR-ranging and site-specific conjugation. We used the new platform to optimize an ADC that targets B7-H4 (VTCN1), an immune-suppressive protein that is overexpressed in breast, ovarian, and endometrial cancers. XMT-1660 is a site-specific DS DAR 6 ADC that induced complete tumor regressions in xenograft models of breast and ovarian cancer as well as in a syngeneic breast cancer model that is refractory to PD-1 immune checkpoint inhibition. In a panel of 28 breast cancer PDXs, XMT-1660 demonstrated activity that correlated with B7-H4 expression. XMT-1660 has recently entered clinical development in a phase I study (NCT05377996) in patients with cancer.

Antibody-drug conjugates: the paradigm shifts in the targeted cancer therapy

Cancer is one of the deadliest diseases, causing million of deaths each year globally. Conventional anti-cancer therapies are non-targeted and have systemic toxicities limiting their versatile applications in many cancers. So, there is an unmet need for more specific therapeutic options that will be effective as well as free from toxicities. Antibody-drug conjugates (ADCs) are suitable alternatives with the right potential and improved therapeutic index for cancer therapy. The ADCs are highly precise new class of biopharmaceutical products that covalently linked a monoclonal antibody (mAb) (binds explicitly to a tumor-associated surface antigen) with a customized cytotoxic drug (kills cancer cells) and tied via a chemical linker (releases the drug). Due to its precise design, it brings about the target cell killing sparing the normal counterpart and free from the toxicities of conventional chemotherapy. It has never been so easy to develop potential ADCs for successful therapeutic usage. With relentless efforts, it took almost a century for scientists to advance the formula and design ADCs for its current clinical applications. Until now, several ADCs have passed successfully through preclinical and clinical trials and because of proven efficacy, a few are approved by the FDA to treat various cancer types. Even though ADCs posed some shortcomings like adverse effects and resistance at various stages of development, with continuous efforts most of these limitations are addressed and overcome to improve their efficacy. In this review, the basics of ADCs, physical and chemical properties, the evolution of design, limitations, and future potentials are discussed.

Antibody-Drug Conjugates in Lung Cancer: Recent Advances and Implementing Strategies

Antibody-drug conjugates (ADCs) are one of the fastest-growing oncology therapeutics, merging the cytotoxic effect of conjugated payload with the high specific ability and selectivity of monoclonal antibody targeted on a specific cancer cell membrane antigen. The main targets for ADC development are antigens commonly expressed by lung cancer cells, but not in normal tissues. They include human epidermal growth factor receptor 2, human epidermal growth factor receptor 3, trophoblast cell surface antigen 2, c-MET, carcinoembryonic antigen-related cell adhesion molecule 5, and B7-H3, each with one or more specific ADCs that showed encouraging results in the lung cancer field, more in non-small-cell lung cancer than in small-cell lung cancer histology. To date, multiple ADCs are under evaluation, alone or in combination with different molecules (eg, chemotherapy agents or immune checkpoint inhibitors), and the optimal strategy for selecting patients who may benefit from the treatment is evolving, including an improvement of biomarker understanding, involving markers of resistance or response to the payload, besides the antibody target. In this review, we discuss the available evidence and future perspectives on ADCs for lung cancer treatment, including a comprehensive discussion on structure-based drug design, mechanism of action, and resistance concepts. Data were summarized by specific target antigen, biology, efficacy, and safety, differing among ADCs according to the ADC payload and their pharmacokinetics and pharmacodynamics properties.

Pharmacokinetics and Pharmacodynamics of Antibody-Drug Conjugates Administered via Subcutaneous and Intratumoral Routes

We hypothesize that different routes of administration may lead to altered pharmacokinetics/pharmacodynamics (PK/PD) behavior of antibody-drug conjugates (ADCs) and may help to improve their therapeutic index. To evaluate this hypothesis, here we performed PK/PD evaluation for an ADC administered via subcutaneous (SC) and intratumoral (IT) routes. Trastuzumab-vc-MMAE was used as the model ADC, and NCI-N87 tumor-bearing xenografts were used as the animal model. The PK of multiple ADC analytes in plasma and tumors, and the in vivo efficacy of ADC, after IV, SC, and IT administration were evaluated. A semi-mechanistic PK/PD model was developed to characterize all the PK/PD data simultaneously. In addition, local toxicity of SC-administered ADC was investigated in immunocompetent and immunodeficient mice. Intratumoral administration was found to significantly increase tumor exposure and anti-tumor activity of ADC. The PK/PD model suggested that the IT route may provide the same efficacy as the IV route at an increased dosing interval and reduced dose level. SC administration of ADC led to local toxicity and reduced efficacy, suggesting difficulty in switching from IV to SC route for some ADCs. As such, this manuscript provides unprecedented insight into the PK/PD behavior of ADCs after IT and SC administration and paves the way for clinical evaluation of these routes.

Site-specific encoding of photoactivity and photoreactivity into antibody fragments

Design of biomolecules that perform two or more distinct functions in response to light remains challenging. Here, we have introduced concurrent photoactivity and photoreactivity into an epidermal growth factor receptor (EGFR)-targeting antibody fragment, 7D12. This was achieved by site-specific incorporation of photocaged tyrosine (pcY) for photoactivity and p-benzoyl-ʟ-phenylalanine (Bpa) for photoreactivity into 7D12. We identified a position for installing Bpa in 7D12 that has minimal effect on 7D12-EGFR binding affinity in the absence of light. Upon exposure to 365-nm light, this Bpa-containing 7D12 mutant forms a covalent bond with EGFR in an antigen-specific manner. We then developed a method for site-specific incorporation of pcY and Bpa at two distinct sites in 7D12. Finally, we demonstrated that in the absence of light, this pcY- and Bpa-containing mutant of 7D12 does not bind to EGFR, but irradiation with 365-nm light activates (1) specific binding and (2) covalent bond formation with EGFR.

The emerging landscape of novel 4-1BB (CD137) agonistic drugs for cancer immunotherapy

The clinical development of 4-1BB agonists for cancer immunotherapy has raised substantial interest during the past decade. The first generation of 4-1BB agonistic antibodies entering the clinic, urelumab (BMS-663513) and utomilumab (PF-05082566), failed due to (liver) toxicity or lack of efficacy, respectively. The two antibodies display differences in the affinity and the 4-1BB receptor epitope recognition, as well as the isotype, which determines the Fc-gamma-receptor (FcγR) crosslinking activity. Based on this experience a very diverse landscape of second-generation 4-1BB agonists addressing the liabilities of first-generation agonists has recently been developed, with many entering clinical Phase 1 and 2 studies. This review provides an overview focusing on differences and their scientific rationale, as well as challenges foreseen during the clinical development of these molecules.

A Predictive Pharmacokinetic Model for Immune Cell-Mediated Uptake and Retention of Nanoparticles in Tumors

A promise of cancer nanomedicine is the "targeted" delivery of therapeutic agents to tumors by the rational design of nanostructured materials. During the past several decades, a realization that in vitro and in vivo preclinical data are unreliable predictors of successful clinical translation has motivated a reexamination of this approach. Mathematical models of drug pharmacokinetics (PK) and biodistribution (BD) are essential tools for small-molecule drugs development. A key assumption underlying these models is that drug-target binding kinetics dominate blood clearance, hence recognition by host innate immune cells is not explicitly included. Nanoparticles circulating in the blood are conspicuous to phagocytes, and inevitable interactions typically trigger active biological responses to sequester and remove them from circulation. Our recent findings suggest that, instead of referring to nanoparticles as designed for active or passive "tumor targeting", we ought rather to refer to immune cells residing in the tumor microenvironment (TME) as active or passive actors in an essentially "cell-mediated tumor retention" process that competes with active removal by other phagocytes. Indeed, following intravenous injection, nanoparticles induce changes in the immune compartment of the TME because of nanoparticle uptake, irrespective of the nature of tumor targeting moieties. In this study, we propose a 6-compartment PK model as an initial mathematical framework for modeling this tumor-associated immune cell-mediated retention. Published in vivo PK and BD results obtained with bionized nanoferrite® (BNF®) nanoparticles were combined with results from in vitro internalization experiments with murine macrophages to guide simulations. As a preliminary approximation, we assumed that tumor-associated macrophages (TAMs) are solely responsible for active retention in the TME. We model the TAM approximation by relating in vitro macrophage uptake to an effective macrophage avidity term for the BNF® nanoparticles under consideration.

Dynamics of tumor-associated macrophages in a quantitative systems pharmacology model of immunotherapy in triple-negative breast cancer

Quantitative systems pharmacology (QSP) modeling is an emerging mechanistic computational approach that couples drug pharmacokinetics/pharmacodynamics and the course of disease progression. It has begun to play important roles in drug development for complex diseases such as cancer, including triple-negative breast cancer (TNBC). The combination of the anti-PD-L1 antibody atezolizumab and nab-paclitaxel has shown clinical activity in advanced TNBC with PD-L1-positive tumor-infiltrating immune cells. As tumor-associated macrophages (TAMs) serve as major contributors to the immuno-suppressive tumor microenvironment, we incorporated the dynamics of TAMs into our previously published QSP model to investigate their impact on cancer treatment. We show that through proper calibration, the model captures the macrophage heterogeneity in the tumor microenvironment while maintaining its predictive power of the trial results at the population level. Despite its high mechanistic complexity, the modularized QSP platform can be readily reproduced, expanded for new species of interest, and applied in clinical trial simulation.

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A mechanistic model of quantitative systems pharmacology in immuno-oncology

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Dynamics of tumor-associated macrophages are integrated into our previous work

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Conducting in silico clinical trials to predict clinical response to cancer therapy

Potential of antibody-drug conjugates (ADCs) for cancer therapy

The primary purpose of ADCs is to increase the efficacy of anticancer medications by minimizing systemic drug distribution and targeting specific cells. Antibody conjugates (ADCs) have changed the way cancer is treated. However, because only a tiny fraction of patients experienced long-term advantages, current cancer preclinical and clinical research has been focused on combination trials. The complex interaction of ADCs with the tumor and its microenvironment appear to be reliant on the efficacy of a certain ADC, all of which have significant therapeutic consequences. Several clinical trials in various tumor types are now underway to examine the potential ADC therapy, based on encouraging preclinical results. This review tackles the potential use of ADCs in cancer therapy, emphasizing the essential processes underlying their positive therapeutic impacts on solid and hematological malignancies. Additionally, opportunities are explored to understand the mechanisms of ADCs action, the mechanism of resistance against ADCs, and how to overcome potential resistance following ADCs administration. Recent clinical findings have aroused interest, leading to a large increase in the number of ADCs in clinical trials. The rationale behind ADCs, as well as their primary features and recent research breakthroughs, will be discussed. We then offer an approach for maximizing the potential value that ADCs can bring to cancer patients by highlighting key ideas and distinct strategies.

Accum™ Technology: A Novel Conjugable Primer for Onco-Immunotherapy

Compromised activity is a common impediment for biologics requiring endosome trafficking into target cells. In cancer cells, antibody-drug conjugates (ADCs) are trapped in endosomes or subsequently pumped extracellularly, leading to a reduction in intracellular accumulation. In subsets of dendritic cells (DCs), endosome-engulfed antigens face non-specific proteolysis and collateral damage to epitope immunogenicity before proteasomal processing and subsequent surface presentation. To bypass these shortcomings, we devised Accum™, a conjugable biotechnology harboring cholic acid (ChAc) and a nuclear localization signal (NLS) sequence for endosome escape and prompt nuclear targeting. Combined, these mechanisms culminate in enhanced intracellular accumulation and functionalization of coupled biologics. As proof-of-principle, we have biochemically characterized Accum, demonstrating its adaptability to ADCs or antigens in different cancer settings. Additionally, we have validated that endosome escape and nuclear routing are indispensable for effective intracellular accumulation and guaranteed target cell selectivity. Importantly, we have demonstrated that the unique mechanism of action of Accum translates into enhanced tumor cytotoxicity when coupled to ADCs, and durable therapeutic and prophylactic anti-cancer immunogenicity when coupled to tumor antigens. As more pre-clinical evidence accumulates, the adaptability, unique mechanism of action, and high therapeutic potency of Accum signal a promising transition into clinical investigations in the context of onco-immunotherapy.

Mathematical Models to Characterize the Absorption, Distribution, Metabolism, and Excretion of Protein Therapeutics

Therapeutic proteins (TPs) have ranked among the most important and fastest-growing classes of drugs in the clinic, yet the development of successful TPs is often limited by unsatisfactory efficacy. Understanding pharmacokinetic (PK) characteristics of TPs is key to achieving sufficient and prolonged exposure at the site of action, which is a prerequisite for eliciting desired pharmacological effects. PK modeling represents a powerful tool to investigate factors governing in vivo disposition of TPs. In this mini-review, we discuss many state-of-the-art models that recapitulate critical processes in each of the absorption, distribution, metabolism/catabolism, and excretion pathways of TPs, which can be integrated into the physiologically-based pharmacokinetic framework. Additionally, we provide our perspectives on current opportunities and challenges for evolving the PK models to accelerate the discovery and development of safe and efficacious TPs. SIGNIFICANCE STATEMENT: This minireview provides an overview of mechanistic pharmacokinetic (PK) models developed to characterize absorption, distribution, metabolism, and elimination (ADME) properties of therapeutic proteins (TPs), which can support model-informed discovery and development of TPs. As the next-generation of TPs with diverse physicochemical properties and mechanism-of-action are being developed rapidly, there is an urgent need to better understand the determinants for the ADME of TPs and evolve existing platform PK models to facilitate successful bench-to-bedside translation of these promising drug molecules.

Virtual Populations for Quantitative Systems Pharmacology Models

Quantitative systems pharmacology (QSP) places an emphasis on dynamic systems modeling, incorporating considerations from systems biology modeling and pharmacodynamics. The goal of QSP is often to quantitatively predict the effects of clinical therapeutics, their combinations, and their doses on clinical biomarkers and endpoints. In order to achieve this goal, strategies for incorporating clinical data into model calibration are critical. Virtual population (VPop) approaches facilitate model calibration while faced with challenges encountered in QSP model application, including modeling a breadth of clinical therapies, biomarkers, endpoints, utilizing data of varying structure and source, capturing observed clinical variability, and simulating with models that may require more substantial computational time and resources than often found in pharmacometrics applications. VPops are frequently developed in a process that may involve parameterization of isolated pathway models, integration into a larger QSP model, incorporation of clinical data, calibration, and quantitative validation that the model with the accompanying, calibrated VPop is suitable to address the intended question or help with the intended decision. Here, we introduce previous strategies for developing VPops in the context of a variety of therapeutic and safety areas: metabolic disorders, drug-induced liver injury, autoimmune diseases, and cancer. We introduce methodological considerations, prior work for sensitivity analysis and VPop algorithm design, and potential areas for future advancement. Finally, we give a more detailed application example of a VPop calibration algorithm that illustrates recent progress and many of the methodological considerations. In conclusion, although methodologies have varied, VPop strategies have been successfully applied to give valid clinical insights and predictions with the assistance of carefully defined and designed calibration and validation strategies. While a uniform VPop approach for all potential QSP applications may be challenging given the heterogeneity in use considerations, we anticipate continued innovation will help to drive VPop application for more challenging cases of greater scale while developing new rigorous methodologies and metrics.

Predictive Simulations in Preclinical Oncology to Guide the Translation of Biologics

Preclinical in vivo studies form the cornerstone of drug development and translation, bridging in vitro experiments with first-in-human trials. However, despite the utility of animal models, translation from the bench to bedside remains difficult, particularly for biologics and agents with unique mechanisms of action. The limitations of these animal models may advance agents that are ineffective in the clinic, or worse, screen out compounds that would be successful drugs. One reason for such failure is that animal models often allow clinically intolerable doses, which can undermine translation from otherwise promising efficacy studies. Other times, tolerability makes it challenging to identify the necessary dose range for clinical testing. With the ability to predict pharmacokinetic and pharmacodynamic responses, mechanistic simulations can help advance candidates from in vitro to in vivo and clinical studies. Here, we use basic insights into drug disposition to analyze the dosing of antibody drug conjugates (ADC) and checkpoint inhibitor dosing (PD-1 and PD-L1) in the clinic. The results demonstrate how simulations can identify the most promising clinical compounds rather than the most effective in vitro and preclinical in vivo agents. Likewise, the importance of quantifying absolute target expression and antibody internalization is critical to accurately scale dosing. These predictive models are capable of simulating clinical scenarios and providing results that can be validated and updated along the entire development pipeline starting in drug discovery. Combined with experimental approaches, simulations can guide the selection of compounds at early stages that are predicted to have the highest efficacy in the clinic.

Development and Evaluation of Competitive Inhibitors of Trastuzumab-HER2 Binding to Bypass the Binding-Site Barrier

Our group has developed and experimentally validated a strategy to increase antibody penetration in solid tumors through transient inhibition of antibody-antigen binding. In prior work, we demonstrated that 1HE, an anti-trastuzumab single domain antibody that transiently inhibits trastuzumab binding to HER2, increased the penetration of trastuzumab and increased the efficacy of ado-trastuzumab emtansine (T-DM1) in HER2+ xenograft bearing mice. In the present work, 1HE variants were developed using random mutagenesis and phage display to enable optimization of tumor penetration and efficacy of trastuzumab-based therapeutics. To guide the rational selection of a particular 1HE mutant for a specific trastuzumab-therapy, we developed a mechanistic pharmacokinetic (PK) model to predict within-tumor exposure of trastuzumab/T-DM1. A pharmacodynamic (PD) component was added to the model to predict the relationship between intratumor exposure to T-DM1 and the corresponding therapeutic effect in HER2+ xenografts. To demonstrate the utility of the competitive inhibition approach for immunotoxins, PK parameters specific for a recombinant immunotoxin were incorporated into the model structure. Dissociation half-lives for variants ranged from 1.1 h (for variant LG11) to 107.9 h (for variant HE10). Simulations predicted that 1HE co-administration can increase the tumor penetration of T-DM1, with inhibitors with longer trastuzumab binding half-lives relative to 1HE (15.5 h) further increasing T-DM1 penetration at the expense of total tumor uptake of T-DM1. The PK/PD model accurately predicted the response of NCI-N87 xenografts to treatment with T-DM1 or T-DM1 co-administered with 1HE. Model predictions indicate that the 1HE mutant HF9, with a trastuzumab binding half-life of 51.1 h, would be the optimal inhibitor for increasing T-DM1 efficacy with a modest extension in the median survival time relative to T-DM1 with 1HE. Model simulations predict that LG11 co-administration will dramatically increase immunotoxin penetration within all tumor regions. We expect that the mechanistic model structure and the wide range of inhibitors developed in this work will enable optimization of trastuzumab-cytotoxin penetration and efficacy in solid tumors.

An affinity threshold for maximum efficacy in anti-PD-1 immunotherapy

Monoclonal antibodies targeting the programmed cell death protein 1 (PD-1) remain the most prevalent cancer immunotherapy both as a monotherapy and in combination with additional therapies. Despite the extensive success of anti-PD-1 monoclonal antibodies in the clinic, the experimental relationship between binding affinity and functional potency for anti-PD-1 antibodies in vivo has not been reported. Anti-PD-1 antibodies with higher and lower affinity than nivolumab or pembrolizumab are entering the clinic and show varied preclinical efficacy. Here, we explore the role of broad-ranging affinity variation within a single lineage in a syngeneic immunocompetent mouse model. By developing a panel of murine anti-PD-1 antibodies with varying affinity (ranging from KD = 20 pM - 15 nM), we find that there is a threshold affinity required for maximum efficacy at a given dose in the treatment of the MC38 adenocarcinoma model with anti-PD-1 immunotherapy. Physiologically based pharmacokinetic modeling complements interpretation of the experimental results and highlights the direct relationship between dose, affinity, and PD-1 target saturation in the tumor.

Fluorescence grid analysis for the evaluation of piecemeal surgery in sinonasal inverted papilloma: a proof-of-concept study

Purpose

Local recurrence occurs in ~ 19% of sinonasal inverted papilloma (SNIP) surgeries and is strongly associated with incomplete resection. During surgery, it is technically challenging to visualize and resect all SNIP tissue in this anatomically complex area. Proteins that are overexpressed in SNIP, such as vascular endothelial growth factor (VEGF), may serve as a target for fluorescence molecular imaging to guide surgical removal of SNIP. A proof-of-concept study was performed to investigate if the VEGF-targeted near-infrared fluorescent tracer bevacizumab-800CW specifically localizes in SNIP and whether it could be used as a clinical tool to guide SNIP surgery.

Methods

In five patients diagnosed with SNIP, 10 mg of bevacizumab-800CW was intravenously administered 3 days prior to surgery. Fluorescence molecular imaging was performed in vivo during surgery and ex vivo during the processing of the surgical specimen. Fluorescence signals were correlated with final histopathology and VEGF-A immunohistochemistry. We introduced a fluorescence grid analysis to assess the fluorescence signal in individual tissue fragments, due to the nature of the surgical procedure (i.e., piecemeal resection) allowing the detection of small SNIP residues and location of the tracer ex vivo.

Results

In all patients, fluorescence signal was detected in vivo during endoscopic SNIP surgery. Using ex vivo fluorescence grid analysis, we were able to correlate bevacizumab-800CW fluorescence of individual tissue fragments with final histopathology. Fluorescence grid analysis showed substantial variability in mean fluorescence intensity (FI_mean), with SNIP tissue showing a median FI_mean of 77.54 (IQR 50.47–112.30) compared to 35.99 (IQR 21.48–57.81) in uninvolved tissue (p < 0.0001), although the diagnostic ability was limited with an area under the curve of 0.78.

Conclusions

A fluorescence grid analysis could serve as a valid method to evaluate fluorescence molecular imaging in piecemeal surgeries. As such, although substantial differences were observed in fluorescence intensities, VEGF-A may not be the ideal target for SNIP surgery.

Trial registration

NCT03925285.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00259-021-05567-x.

Analyze impact of tumor-associated kinetics on antibody delivery in solid tumors with a physiologically based pharmacokinetics/pharmacodynamics model

Monoclonal antibody (mAb)-based drugs are critical anti-cancer therapies. Unfortunately, therapeutic efficacy can be compromised by spatially heterogeneous intratumoral Ab deposition. Binding-site barriers arising from Ab and tumor-associated kinetics often underlie this phenomenon. Quantitative insight into these issues may lead to more efficient drug delivery. Difficulties in addressing this issue include (1) lack of techniques to quantify critical kinetic events, (2) lack of a pharmacokinetic/pharmacodynamic (PK/PD) model to assess important parameters for specific tumor types, and (3) uncertainty or variability of critical kinetic factors even within a single tumor type. This study developed a mechanism-based PK/PD model to profile heterogeneous distribution of Ab within tumors and tested this model using real-life experimental data. Model simulations incorporating several uncertainties were used to determine how mAb and tumor-associated kinetics influence receptor occupancy. Simulations were also used to predict the potential impact of these findings in preclinical tumor models and human tumors. We found significant differences in tumor-associated kinetics between groups in which mAb therapy was effective versus groups in which it was ineffective. These kinetic differences included rates of tumor-associated antigen (TAA) degradation, TAA expression, apparent flow rates of interstitial fluid, and ratios of Ab-TAA complex internalization to TAA degradation. We found less significant differences in mAb kinetics, including rates of clearance or affinity for target antigens. In conclusion, our mechanism-based PK/PD model suggests that TAA-associated kinetic factors participate more significantly than those associated with the Ab in generating barriers to mAb delivery and distribution in tumors.

Generation and Characterization of Nanobodies Against Tomato Leaf Curl Sudan Virus

Begomoviruses infect food, fiber, and vegetable crop plants, including tomato, potato, bean, cotton, cucumber, and pumpkin, and damage many economically important crop plants worldwide. Tomato leaf curl Sudan virus (ToLCSDV) is the most widespread tomato-infecting begomovirus in Saudi Arabia. Using phage display technology, this study isolated two camel-derived nanobodies against purified ToLCSDV virions from a library of antigen-binding fragments (VHH or nanobody) of heavy-chain antibodies built from an immunized camel. The isolated nanobodies also cross-reacted with purified tomato yellow leaf curl virus virions and showed significant enzyme-linked immunosorbent assay reactivity with extracts from plants with typical begomovirus infection symptoms. The results can pave the way to developing diagnostics for begomovirus detection, design, and characterization of novel nanomaterials based on virus-like particles, in addition to nanobody-mediated begomovirus resistance in economically important crops, such as tomato, potato, and cucumber.

Potential and pitfalls of 89Zr-immuno-PET to assess target status: 89Zr-trastuzumab as an example

Background

⁸⁹Zirconium-immuno-positron emission tomography (⁸⁹Zr-immuno-PET) is used for assessment of target status to guide antibody-based therapy. We aim to determine the relation between antibody tumor uptake and target concentration to improve future study design and interpretation.

Methods

The relation between tumor uptake and target concentration was predicted by mathematical modeling of ⁸⁹Zr-labeled antibody disposition in the tumor. Literature values for trastuzumab kinetics were used to provide an example.

Results

⁸⁹Zr-trastuzumab uptake initially increases with increasing target concentration, until it levels off to a constant value. This is determined by the total administered mass dose of trastuzumab. For a commonly used imaging dose of 50 mg ⁸⁹Zr-trastuzumab, uptake can discriminate between immunohistochemistry score (IHC) 0 versus 1–2–3.

Conclusion

The example for ⁸⁹Zr-trastuzumab illustrates the potential to assess target expression. The pitfall of false-positive findings depends on the cut-off to define clinical target positivity (i.e., IHC 3) and the administered mass dose.

Unlocking the potential of antibody-drug conjugates for cancer therapy

Nine different antibody-drug conjugates (ADCs) are currently approved as cancer treatments, with dozens more in preclinical and clinical development. The primary goal of ADCs is to improve the therapeutic index of antineoplastic agents by restricting their systemic delivery to cells that express the target antigen of interest. Advances in synthetic biochemistry have ushered in a new generation of ADCs, which promise to improve upon the tissue specificity and cytotoxicity of their predecessors. Many of these drugs have impressive activity against treatment-refractory cancers, although hurdles impeding their broader use remain, including systemic toxicity, inadequate biomarkers for patient selection, acquired resistance and unknown benefit in combination with other cancer therapies. Emerging evidence indicates that the efficacy of a given ADC depends on the intricacies of how the antibody, linker and payload components interact with the tumour and its microenvironment, all of which have important clinical implications. In this Review, we discuss the current state of knowledge regarding the design, mechanism of action and clinical efficacy of ADCs as well as the apparent limitations of this treatment class. We then propose a path forward by highlighting several hypotheses and novel strategies to maximize the potential benefit that ADCs can provide to patients with cancer.

Drug Exposure to Establish Pharmacokinetic-Response Relationships in Oncology

In the oncology field, understanding the relationship between the dose administered and the exerted effect is particularly important because of the narrow therapeutic index associated with anti-cancer drugs and the high interpatient variability. Therefore, in this review, we provide a critical perspective of the different methods of characterising treatment exposure in the oncology setting. The increasing number of modelling applications in oncology reflects the applicability and the impact of pharmacometrics on all phases of the drug development process and patient management as well. Pharmacometric modelling is a worthy component within the current paradigm of model-based drug development, but pharmacometric modelling techniques are also accessible for the clinician in the optimisation of current oncology therapies. Consequently, the application of population models in a hospital setting by generating close collaborations between physicians and pharmacometricians is highly recommended, providing a systematic means of developing and assessing model-based metrics as 'drivers' for various responses to treatments, which can then be evaluated as predictors for treatment success. Characterising the key determinants of variability in exposure is of particular importance for anticancer agents, as efficacy and toxicity are associated with exposure. We present the different strategies to describe and predict drug exposure that can be applied depending on the data available, with the objective of obtaining the most useful information in the patients' favour throughout the full drug cycle. Therefore, the objective of the present article is to review the different approaches used to characterise a patient's exposure to oncology drugs, which will result in a better understanding of the time course of the response and the magnitude of interpatient variability.

A mechanistic systems pharmacology modeling platform to investigate the effect of PD-L1 expression heterogeneity and dynamics on the efficacy of PD-1 and PD-L1 blocking antibodies in cancer

Tumors have developed multitude of ways to evade immune response and suppress cytotoxic T cells. Programed cell death protein 1 (PD-1) and programed cell death ligand 1 (PD-L1) are immune checkpoints that when activated, rapidly inactivate the cytolytic activity of T cells. Expression heterogeneity of PD-L1 and the surface receptor dynamics of both PD-1 and PD-L1 may be important parameters in modulating the immune response. PD-L1 is expressed on both tumor and non-tumor immune cells and this differential expression reflects different aspects of anti-tumor immunity. Here, we developed a mechanistic computational model to investigate the role of PD-1 and PD-L1 dynamics in modulating the efficacy of PD-1 and PD-L1 blocking antibodies. Our model incorporates immunological synapse restricted interaction of PD-1 and PD-L1, basal parameters for receptor dynamics, and T cell interaction with tumor and non-tumor immune cells. Simulations predict the existence of a threshold in PD-1 expression above which there is no efficacy for both anti-PD-1 and anti-PD-L1. Model also predicts that anti-tumor response is more sensitive to PD-L1 expression on non-tumor immune cells than tumor cells. New combination strategies are suggested that may enhance efficacy in resistant cases such as combining anti-PD-1 with a low dose of anti-PD-L1 or with inhibitors of PD-L1 recycling and synthesis. Another combination strategy suggested by the model is the combination of anti-PD-1 and anti-PD-L1 with enhancers of PD-L1 degradation rate. Virtual patients are then generated to test specific biomarkers of response. Intriguing predictions that emerge from the virtual patient simulations are that PD-1 blocking antibody results in higher response rate than PD-L1 blockade and that PD-L1 expression density on non-tumor immune cells rather than tumor cells is a predictor of response.

Modeling Pharmacokinetics and Pharmacodynamics of Therapeutic Antibodies: Progress, Challenges, and Future Directions

With more than 90 approved drugs by 2020, therapeutic antibodies have played a central role in shifting the treatment landscape of many diseases, including autoimmune disorders and cancers. While showing many therapeutic advantages such as long half-life and highly selective actions, therapeutic antibodies still face many outstanding issues associated with their pharmacokinetics (PK) and pharmacodynamics (PD), including high variabilities, low tissue distributions, poorly-defined PK/PD characteristics for novel antibody formats, and high rates of treatment resistance. We have witnessed many successful cases applying PK/PD modeling to answer critical questions in therapeutic antibodies’ development and regulations. These models have yielded substantial insights into antibody PK/PD properties. This review summarized the progress, challenges, and future directions in modeling antibody PK/PD and highlighted the potential of applying mechanistic models addressing the development questions.

Pivotal Dose of Pembrolizumab: A Dose-Finding Strategy for Immuno-Oncology

Despite numerous publications emphasizing the value of dose finding, drug development in oncology is dominated by the mindset that higher dose provides higher efficacy. Examples of dose finding implemented by biopharmaceutical firms can change this mindset. The purpose of this article is to outline a pragmatic dose selection strategy for immuno-oncology (IO) and other targeted monoclonal antibodies (mAbs). The approach was implemented for pembrolizumab. Selecting a recommended phase II dose (RP2D) with a novel mechanism of action is often challenging due to uncertain relationships between pharmacodynamics measurements and clinical end points. Additionally, phase I efficacy and safety data are generally inadequate for RP2D selection for IO mAbs. Here, the RP2D was estimated based on phase I (clinical study KN001 A and A2) pharmacokinetics data as the dose required for target saturation, which represents a surrogate for maximal pharmacological effect for antagonist mAbs. Due to limitations associated with collecting and analyzing tumor biopsies, characterizing intratumoral target engagement (TE) is challenging. To overcome this gap, a physiologically-based pharmacokinetic model was implemented to predict intratumoral TE. As tumors are spatially heterogeneous, TE was predicted in well-vascularized and poorly vascularized tumor regions. Additionally, impact of differences in target expression, for example, due to interindividual variability and cancer type, was simulated. Simulations showed that 200 mg every 3 weeks can achieve ≥ 90% TE in clinically relevant scenarios, resulting in the recommendation of 200 mg every 3 weeks as the RP2D. Randomized dose comparison studies (KN001 B2 and D) showing similar efficacy over a fivefold dose/exposure range confirmed the RP2D as the pivotal dose.

Co-administered antibody improves penetration of antibody-dye conjugate into human cancers with implications for antibody-drug conjugates

Poor tissue penetration remains a major challenge for antibody-based therapeutics of solid tumors, but proper dosing can improve the tissue penetration and thus therapeutic efficacy of these biologics. Due to dose-limiting toxicity of the small molecule payload, antibody-drug conjugates (ADCs) are administered at a much lower dose than their parent antibodies, which further reduces tissue penetration. We conducted an early-phase clinical trial (NCT02415881) and previously reported the safety of an antibody-dye conjugate (panitumumab-IRDye800CW) as primary outcome. Here, we report a retrospective exploratory analysis of the trial to evaluate whether co-administration of an unconjugated antibody could improve the intratumoral distribution of the antibody-dye conjugate in patients. By measuring the multiscale distribution of the antibody-dye conjugate, this study demonstrates improved microscopic antibody distribution without increasing uptake (toxicity) in healthy tissue when co-administered with the parent antibody, supporting further clinical investigation of the co-administration dosing strategy to improve the tumor penetration of ADCs.

The Therapeutic Potential of Nanobodies

Today, bio-medical efforts are entering the subcellular level, which is witnessed with the fast-developing fields of nanomedicine, nanodiagnostics and nanotherapy in conjunction with the implementation of nanoparticles for disease prevention, diagnosis, therapy and follow-up. Nanoparticles or nanocontainers offer advantages including high sensitivity, lower toxicity and improved safety—characteristics that are especially valued in the oncology field. Cancer cells develop and proliferate in complex microenvironments leading to heterogeneous diseases, often with a fatal outcome for the patient. Although antibody-based therapy is widely used in the clinical care of patients with solid tumours, its efficiency definitely needs improvement. Limitations of antibodies result mainly from their big size and poor penetration in solid tissues. Nanobodies are a novel and unique class of antigen-binding fragments, derived from naturally occurring heavy-chain-only antibodies present in the serum of camelids. Their superior properties such as small size, high stability, strong antigen-binding affinity, water solubility and natural origin make them suitable for development into next-generation biodrugs. Less than 30 years after the discovery of functional heavy-chain-only antibodies, the nanobody derivatives are already extensively used by the biotechnology research community. Moreover, a number of nanobodies are under clinical investigation for a wide spectrum of human diseases including inflammation, breast cancer, brain tumours, lung diseases and infectious diseases. Recently, caplacizumab, a bivalent nanobody, received approval from the European Medicines Agency (EMA) and the US Food and Drug Administration (FDA) for treatment of patients with thrombotic thrombocytopenic purpura.

Cetuximab-Triptolide Conjugate Suppresses the Growth of EGFR-Overexpressing Lung Cancers through Targeting RNA Polymerase II

To overcome poor pharmacokinetics and toxicity of triptolide (TPL), a natural compound that exhibits potent anticancer activities, we developed a novel antibody-drug conjugate (ADC) to specifically deliver TPL to epidermal growth factor receptor (EGFR)-overexpressing non-small cell lung cancer (NSCLC) and others. The ADC (Cet-TPL) is made by conjugation of TPL to lysine residues of cetuximab (Cet), a clinically available anti-EGFR monoclonal antibody. Studies of antitumor efficacy demonstrated that Cet-TPL drastically suppressed in vitro proliferation and in vivo growth of these EGFR-overexpressing cancers, including NSCLC A549 and H1299 cells and two patient-derived xenografts, and head and neck squamous carcinoma UM-SCC6 cell, while it did not inhibit the proliferation and growth of NSCLC H520 that rarely expresses EGFR. Furthermore, immunofluorescence analysis revealed that Cet-TPL was effectively internalized and transported into lysosomes of EGFR-overexpressing cells. Cet-TPL effectively led to degradation of RNA polymerase II (Pol II) and demethylation of histone H3 lysines, and significantly induced apoptosis in these EGFR-overexpressing cancers. Compared with TPL, Cet, or their combination, Cet-TPL displayed higher target-specific cytotoxicity against EGFR-expressing cancers and much lower in vivo toxicity. In addition, Cet-TPL efficiently suppressed the activated EGFR pathway in UM-SCC6 cancer cells. Taken together, Cet-TPL represents a potent targeting therapeutic agent against EGFR-overexpressing NSCLC and others.

Tumor-targeted 4-1BB agonists for combination with T cell bispecific antibodies as off-the-shelf therapy

Endogenous costimulatory molecules on T cells such as 4-1BB (CD137) can be leveraged for cancer immunotherapy. Systemic administration of agonistic anti-4-1BB antibodies, although effective preclinically, has not advanced to phase 3 trials because they have been hampered by both dependency on Fcγ receptor-mediated hyperclustering and hepatotoxicity. To overcome these issues, we engineered proteins simultaneously targeting 4-1BB and a tumor stroma or tumor antigen: FAP-4-1BBL (RG7826) and CD19-4-1BBL. In the presence of a T cell receptor signal, they provide potent T cell costimulation strictly dependent on tumor antigen-mediated hyperclustering without systemic activation by FcγR binding. We could show targeting of FAP-4-1BBL to FAP-expressing tumor stroma and lymph nodes in a colorectal cancer-bearing rhesus monkey. Combination of FAP-4-1BBL with tumor antigen-targeted T cell bispecific (TCB) molecules in human tumor samples led to increased IFN-γ and granzyme B secretion. Further, combination of FAP- or CD19-4-1BBL with CEA-TCB (RG7802) or CD20-TCB (RG6026), respectively, resulted in tumor remission in mouse models, accompanied by intratumoral accumulation of activated effector CD8⁺ T cells. FAP- and CD19-4-1BBL thus represent an off-the-shelf combination immunotherapy without requiring genetic modification of effector cells for the treatment of solid and hematological malignancies.

MMAE Delivery Using the Bicycle Toxin Conjugate BT5528

The EphA2 receptor is found at high levels in tumors and low levels in normal tissue and high EphA2 expression in biopsies is a predictor of poor outcome in patients. Drug discovery groups have therefore sought to develop EphA2-based therapies using small molecule, peptide, and nanoparticle-based approaches (1-3). However, until now only EphA2-targeting antibody-drug conjugates (ADC) have entered clinical development. For example, MEDI-547 is an EphA2-targeting ADC that displayed encouraging antitumor activity in preclinical models and progressed to phase I clinical testing in man. Here we describe the development of BT5528, a bicyclic peptide ("Bicycle") conjugated to the auristatin derivative maleimidocaproyl-monomethyl auristatin E to generate the Bicycle toxin conjugate BT5528. The report compares and contrasts the Pharmacokinetics (PK) characteristics of antibody and Bicycle-based targeting systems and discusses how the PK and payload characteristics of different delivery systems impact the efficacy-toxicity trade off which is key to the development of successful cancer therapies. We show that BT5528 gives rise to rapid update into tumors and fast renal elimination followed by persistent toxin levels in tumors without prolonged exposure of parent drug in the vasculature. This fast in, fast out kinetics gave rise to more favorable toxicology findings in rats and monkeys than were observed with MEDI-547 in preclinical and clinical studies.Graphical Abstract: http://mct.aacrjournals.org/content/molcanther/19/7/1385/F1.large.jpg.

From tumour perfusion to drug delivery and clinical translation of in silico cancer models

In silico cancer models have demonstrated great potential as a tool to improve drug design, optimise the delivery of drugs to target sites in the host tissue and, hence, improve therapeutic efficacy and patient outcome. However, there are significant barriers to the successful translation of in silico technology from bench to bedside. More precisely, the specification of unknown model parameters, the necessity for models to adequately reflect in vivo conditions, and the limited amount of pertinent validation data to evaluate models' accuracy and assess their reliability, pose major obstacles in the path towards their clinical translation. This review aims to capture the state-of-the-art in in silico cancer modelling of vascularised solid tumour growth, and identify the important advances and barriers to success of these models in clinical oncology. Particular emphasis has been put on continuum-based models of cancer since they - amongst the class of mechanistic spatio-temporal modelling approaches - are well-established in simulating transport phenomena and the biomechanics of tissues, and have demonstrated potential for clinical translation. Three important avenues in in silico modelling are considered in this contribution: first, since systemic therapy is a major cancer treatment approach, we start with an overview of the tumour perfusion and angiogenesis in silico models. Next, we present the state-of-the-art in silico work encompassing the delivery of chemotherapeutic agents to cancer nanomedicines through the bloodstream, and then review continuum-based modelling approaches that demonstrate great promise for successful clinical translation. We conclude with a discussion of what we view to be the key challenges and opportunities for in silico modelling in personalised and precision medicine.

Predictive Model for Delivery Efficiency: Erythrocyte Membrane-Camouflaged Magnetofluorescent Nanocarriers Study

Delivery efficiencies of theranostic nanoparticles (NPs) based on passive tumor targeting strongly depend either on their blood circulation time or on appropriate modulations of the tumor microenvironment. Therefore, predicting the NP delivery efficiency before and after a tumor microenvironment modulation is highly desirable. Here, we present a new erythrocyte membrane-camouflaged magnetofluorescent nanocarrier (MMFn) with long blood circulation time (92 h) and high delivery efficiency (10% ID for Ehrlich murine tumor model). MMFns owe their magnetic and fluorescent properties to the incorporation of manganese ferrite nanoparticles (MnFe₂O₄ NPs) and IR-780 (a lipophilic indocyanine fluorescent dye), respectively, to their erythrocyte membrane-derived camouflage. MMFn composition, morphology, and size, as well as optical absorption, zeta potential, and fluorescent, magnetic, and magnetothermal properties, are thoroughly examined in vitro. We then present an analytical pharmacokinetic (PK) model capable of predicting the delivery efficiency (DE) and the time of peak tumor uptake (t_max), as well as changes in DE and t_max due to modulations of the tumor microenvironment, for potentially any nanocarrier. Experimental PK data sets (blood and tumor amounts of MMFns) are simultaneously fit to the model equations using the PK modeling software Monolix. We then validate our model analytical solutions with the numerical solutions provided by Monolix. We also demonstrate how our a priori nonmechanistic model for passive targeting relates to a previously reported mechanistic model for active targeting. All in vivo PK studies, as well as in vivo and ex vivo biodistribution studies, were conducted using two noninvasive techniques, namely, fluorescence molecular tomography (FMT) and alternating current biosusceptometry (ACB). Finally, histopathology corroborates our PK and biodistribution results.

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.

Quantitative Systems Pharmacology Model of a Masked, Tumor-Activated Antibody

PROBODY therapeutics (Pb‐Tx) are protease‐activatable prodrugs of monoclonal antibodies (mAbs) designed to target tumors where protease activity is elevated while avoiding normal tissue. They are composed of a parental mAb, a mask that inhibits antibody binding to target, and a protease‐cleavable substrate between the mask and the mAb. We report a quantitative systems pharmacology model for the rational design and clinical translation of Pb‐Tx. The model adequately described monkey pharmacokinetic data following the administration of six anti‐CD166 Pb‐Tx of varying mask strength and substrate cleavability and captured the trend of decreasing Pb‐Tx systemic clearance with increasing mask strength. Projections to humans suggested both higher levels of Pb‐Tx in tumor relative to parental mAb and an optimal mask strength for maximizing tumor receptor–mediated uptake. Simulations further suggested the majority of circulating species in humans would be intact/masked Pb‐Tx, with no significant flux of cleaved/activated species from tumor to the systemic compartment.

Influence of size and charge of unstructured polypeptides on pharmacokinetics and biodistribution of targeted fusion proteins

Alternative non-IgG binding proteins developed for therapy are small in size and, thus, are rapidly cleared from the circulation by renal filtration. To avoid repeated injection or continuous infusion for the maintenance of therapeutic serum concentrations, extensions of unfolded polypeptides have been developed to prolong serum half-life, but systematic, comparative studies investigating the influence of their size and charge on serum half-life, extravasation, tumor localization and excretion mechanisms have so far been lacking. Here we used a high-affinity Designed Ankyrin Repeat Protein (DARPin) targeting the tumor marker epithelial cell adhesion molecule (EpCAM) in a preclinical tumor xenograft model in mice, and fused it with a series of defined unstructured polypeptides. We used three different sizes of two previously described polypeptides, an uncharged one consisting of only Pro, Ala and Ser (termed PAS) and a charged one consisting of Pro, Ala, Ser, Thr, Gly, Glu (termed XTEN) and performed for the first time a precise comparative localization, distribution and extravasation study. Pharmacokinetic analysis showed a clear linear relationship between hydrodynamic radius and serum half-life across both polypeptides, reaching a half-life of up to 21 h in mice. Tumor uptake was EpCAM-dependent and directly proportional to half-life and size, showing an even tumor penetration for all fusion proteins without unspecific accumulation in non-target tissue. Unexpectedly, charge had no influence on any parameter, neither tumor nor tissue accumulation nor kidney elimination kinetics. Thus, both polypeptide types have a very similar potential for precise half-life modification and tumor targeting.

A Translational Quantitative Systems Pharmacology Model for CD3 Bispecific Molecules: Application to Quantify T Cell-Mediated Tumor Cell Killing by P-Cadherin LP DART®

CD3 bispecific antibody constructs recruit cytolytic T cells to kill tumor cells, offering a potent approach to treat cancer. T cell activation is driven by the formation of a trimolecular complex (trimer) between drugs, T cells, and tumor cells, mimicking an immune synapse. A translational quantitative systems pharmacology (QSP) model is proposed for CD3 bispecific molecules capable of predicting trimer concentration and linking it to tumor cell killing. The model was used to quantify the pharmacokinetic (PK)/pharmacodynamic (PD) relationship of a CD3 bispecific targeting P-cadherin (PF-06671008). It describes the disposition of PF-06671008 in the central compartment and tumor in mouse xenograft models, including binding to target and T cells in the tumor to form the trimer. The model incorporates T cell distribution to the tumor, proliferation, and contraction. PK/PD parameters were estimated for PF-06671008 and a tumor stasis concentration (TSC) was calculated as an estimate of minimum efficacious trimer concentration. TSC values ranged from 0.0092 to 0.064 pM across mouse tumor models. The model was translated to the clinic and used to predict the disposition of PF-06671008 in patients, including the impact of binding to soluble P-cadherin. The predicted terminal half-life of PF-06671008 in the clinic was approximately 1 day, and P-cadherin expression and number of T cells in the tumor were shown to be sensitive parameters impacting clinical efficacy. A translational QSP model is presented for CD3 bispecific molecules, which integrates in silico, in vitro and in vivo data in a mechanistic framework, to quantify and predict efficacy across species.

Guiding dose selection of monoclonal antibodies using a new parameter (AFTIR) for characterizing ligand binding systems

Guiding the dose selection for monoclonal antibody oncology drugs is often done using methods for predicting the receptor occupancy of the drug in the tumor. In this manuscript, previous work on characterizing target inhibition at steady state using the AFIR metric (Stein and Ramakrishna in CPT Pharmacomet Syst Pharmacol 6(4):258-266, 2017) is extended to include a "target-tissue" compartment and the shedding of membrane-bound targets. A new potency metric average free tissue target to initial target ratio (AFTIR) at steady state is derived, and it depends on only four key quantities: the equilibrium binding constant, the fold-change in target expression at steady state after binding to drug, the biodistribution of target from circulation to target tissue, and the average drug concentration in circulation. The AFTIR metric is useful for guiding dose selection, for efficiently performing sensitivity analyses, and for building intuition for more complex target mediated drug disposition models. In particular, reducing the complex, physiological model to four key parameters needed to predict target inhibition helps to highlight specific parameters that are the most important to estimate in future experiments to guide drug development.

A quantitative in silico platform for simulating cytotoxic and nanoparticle drug delivery to solid tumours

The role of tumour-host mechano-biology and the mechanisms involved in the delivery of anti-cancer drugs have been extensively studied using in vitro and in vivo models. A complementary approach is offered by in silico models, which can also potentially identify the main factors affecting the transport of tumour-targeting molecules. Here, we present a generalized three-dimensional in silico modelling framework of dynamic solid tumour growth, angiogenesis and drug delivery. Crucially, the model allows for drug properties-such as size and binding affinity-to be explicitly defined, hence facilitating investigation into the interaction between the changing tumour-host microenvironment and cytotoxic and nanoparticle drugs. We use the model to qualitatively recapitulate experimental evidence of delivery efficacy of cytotoxic and nanoparticle drugs on matrix density (and hence porosity). Furthermore, we predict a highly heterogeneous distribution of nanoparticles after delivery; that nanoparticles require a high porosity extracellular matrix to cause tumour regression; and that post-injection transvascular fluid velocity depends on matrix porosity, and implicitly on the size of the drug used to treat the tumour. These results highlight the utility of predictive in silico modelling in better understanding the factors governing efficient cytotoxic and nanoparticle drug delivery.

Miniaturised 'antibody'-drug conjugates for solid tumours?

With Antibody-Drug Conjugate strategies firmly focussed on the precise conjugation to the large protein Immunoglobulin-G format, it is easy to miss the more recent technological innovations in small-format drug conjugates. Here, the targeting ligand can be at 50-95% reduced in size, or even smaller if peptidic in nature. Antibody domains or alternative binding scaffolds, chemically-modified with ultra-potent cytotoxic payloads offer an alternative approach for oncology therapeutics, promising a wider therapeutic window by virtue of superior solid tumour penetration properties and more rapid system clearance. Many of the traditional ADC concepts still apply, but as these miniaturised ADCs enter the clinic over the next 2-3 years, we will learn whether these new features translate to patient benefits.

In-silico dynamic analysis of cytotoxic drug administration to solid tumours: Effect of binding affinity and vessel permeability

The delivery of blood-borne therapeutic agents to solid tumours depends on a broad range of biophysical factors. We present a novel multiscale, multiphysics, in-silico modelling framework that encompasses dynamic tumour growth, angiogenesis and drug delivery, and use this model to simulate the intravenous delivery of cytotoxic drugs. The model accounts for chemo-, hapto- and mechanotactic vessel sprouting, extracellular matrix remodelling, mechano-sensitive vascular remodelling and collapse, intra- and extravascular drug transport, and tumour regression as an effect of a cytotoxic cancer drug. The modelling framework is flexible, allowing the drug properties to be specified, which provides realistic predictions of in-vivo vascular development and structure at different tumour stages. The model also enables the effects of neoadjuvant vascular normalisation to be implicitly tested by decreasing vessel wall pore size. We use the model to test the interplay between time of treatment, drug affinity rate and the size of the vessels’ endothelium pores on the delivery and subsequent tumour regression and vessel remodelling. Model predictions confirm that small-molecule drug delivery is dominated by diffusive transport and further predict that the time of treatment is important for low affinity but not high affinity cytotoxic drugs, the size of the vessel wall pores plays an important role in the effect of low affinity but not high affinity drugs, that high affinity cytotoxic drugs remodel the tumour vasculature providing a large window for the normalisation of the vascular architecture, and that the combination of large pores and high affinity enhances cytotoxic drug delivery efficiency. These results have implications for treatment planning and methods to enhance drug delivery, and highlight the importance of in-silico modelling in investigating the optimisation of cancer therapy on a personalised setting.

One of the main challenges in optimising cancer therapy is understanding the in-vivo cancer environment and how it changes over time. The efficacy of chemotherapeutic drugs is known to be strongly dependent on blood vessel wall properties and the architecture of the developing tumour vasculature, which in turn are dependent on biochemical and mechanical interactions between cancer cells and their microenvironment. Here we present a novel in-silico modelling framework of dynamic tumour growth, angiogenesis and drug delivery, and we use it to explore biophysical factors governing the efficient delivery of cytotoxic drugs to solid tumours. We find that the time of treatment and vessel permeability are important factors for the efficacy of chemical agents with low binding affinity, that high affinity drugs can impact the tumour vasculature remodelling and bring vascular structure back to a more normalised state, and that the combination of large-sized vessel wall pores and high affinity enhances cytotoxic drug delivery and efficacy. These results have implications for treatment planning and optimisation, and show how in-silico models can be used to help understand and optimise cancer therapy.

Heterogeneous distribution of trastuzumab in HER2-positive xenografts and metastases: role of the tumor microenvironment

Most HER2-positive metastatic breast cancer patients continue to relapse. Incomplete access to all target HER2-positive cells in metastases and tumor tissues is a potential mechanism of resistance to trastuzumab. The location of locally bound trastuzumab was evaluated in HER2-positive tissues in vivo and as in vivo xenografts or metastases models in mice. Microenvironmental elements of tumors were related to bound trastuzumab using immunohistochemical staining and include tight junctions, vasculature, vascular maturity, vessel patency, hypoxia and HER2 to look for correlations. Trastuzumab was evaluated alone and in combination with bevacizumab. Dynamic contrast-enhanced magnetic resonance imaging parameters of overall vascular function, perfusion and apparent permeability were compared with matched histological images of trastuzumab distribution and vascular patency. Trastuzumab distribution is highly heterogeneous in all models examined, including avascular micrometastases of the brain and lung. Trastuzumab distributes well through the extravascular compartment even in conditions of high HER2 expression and poor convective flow in vivo. Microregional patterns of trastuzumab distribution in vivo do not consistently correlate with vascular density, patency, function or maturity; areas of poor trastuzumab access are not necessarily those with poor vascular supply. The number of vessels with perivascular trastuzumab increases with time and higher doses and dramatically decreases when pre-treated with bevacizumab. Areas of HER2-positive tissue without bound trastuzumab persist in all conditions. These data directly demonstrate tissue- and vessel-level barriers to trastuzumab distribution in vivo that can effectively limit access of the drug to target cells in brain metastases and elsewhere.

Preclinical optimization of antibody-based radiopharmaceuticals for cancer imaging and radionuclide therapy-Model, vector, and radionuclide selection

Intact antibodies and their truncated counterparts (eg, Fab, scFv fragments) are generally exquisitely specific and selective vectors, enabling recognition of individual cancer-associated molecular phenotypes against a complex and dynamic biomolecular background. Complementary alignment of these advantages with unique properties of radionuclides is a defining paradigm in both radioimmunoimaging and radioimmunotherapy, which remain some of the most adept and promising tools for cancer diagnosis and treatment. This review discusses how translational potency can be maximized through rational selection of antibody-nuclide couples for radioimmunoimaging/therapy in preclinical models.