A mechanistic tumor penetration model to guide antibody drug conjugate design

Know your molecule: pharmacological characterization of drug candidates to enhance efficacy and reduce late-stage attrition

Despite advances in chemical, computational and biological sciences, the rate of attrition of drug candidates in clinical development is still high. A key point in the small-molecule discovery process that could provide opportunities to help address this challenge is the pharmacological characterization of hit and lead compounds, culminating in the selection of a drug candidate. Deeper characterization is increasingly important, because the 'quality' of drug efficacy, at least for G protein-coupled receptors (GPCRs), is now understood to be much more than activation of commonly evaluated pathways such as cAMP signalling, with many more 'efficacies' of ligands that could be harnessed therapeutically. Such characterization is being enabled by novel assays to characterize the complex behaviour of GPCRs, such as biased signalling and allosteric modulation, as well as advances in structural biology, such as cryo-electron microscopy. This article discusses key factors in the assessments of the pharmacology of hit and lead compounds in the context of GPCRs as a target class, highlighting opportunities to identify drug candidates with the potential to address limitations of current therapies and to improve the probability of them succeeding in clinical development.

Single-Domain Antibodies as Antibody-Drug Conjugates: From Promise to Practice-A Systematic Review

BACKGROUND

Antibody-drug conjugates (ADCs) represent potent cancer therapies that deliver highly toxic drugs to tumor cells precisely, thus allowing for targeted treatment and significantly reducing off-target effects. Despite their effectiveness, ADCs can face limitations due to acquired resistance and potential side effects.

OBJECTIVES

This study focuses on advances in various ADC components to improve both the efficacy and safety of these agents, and includes the analysis of several novel ADC formats. This work assesses whether the unique features of VHHs-such as their small size, enhanced tissue penetration, stability, and cost-effectiveness-make them a viable alternative to conventional antibodies for ADCs and reviews their current status in ADC development.

METHODS

Following PRISMA guidelines, this study focused on VHHs as components of ADCs, examining advancements and prospects from 1 January 2014 to 30 June 2024. Searches were conducted in PubMed, Cochrane Library, ScienceDirect and LILACS using specific terms related to ADCs and single-domain antibodies. Retrieved articles were rigorously evaluated, excluding duplicates and non-qualifying studies. The selected peer-reviewed articles were analyzed for quality and synthesized to highlight advancements, methods, payloads, and future directions in ADC research.

RESULTS

VHHs offer significant advantages for drug conjugation over conventional antibodies due to their smaller size and structure, which enhance tissue penetration and enable access to previously inaccessible epitopes. Their superior stability, solubility, and manufacturability facilitate cost-effective production and expand the range of targetable antigens. Additionally, some VHHs can naturally cross the blood-brain barrier or be easily modified to favor their penetration, making them promising for targeting brain tumors and metastases. Although no VHH-drug conjugates (nADC or nanoADC) are currently in the clinical arena, preclinical studies have explored various conjugation methods and linkers.

CONCLUSIONS

While ADCs are transforming cancer treatment, their unique mechanisms and associated toxicities challenge traditional views on bioavailability and vary with different tumor types. Severe toxicities, often linked to compound instability, off-target effects, and nonspecific blood cell interactions, highlight the need for better understanding. Conversely, the rapid distribution, tumor penetration, and clearance of VHHs could be advantageous, potentially reducing toxicity by minimizing prolonged exposure. These attributes make single-domain antibodies strong candidates for the next generation of ADCs, potentially enhancing both efficacy and safety.

Advancing cancer drug development with mechanistic mathematical modeling: bridging the gap between theory and practice

Quantitative predictive modeling of cancer growth, progression, and individual response to therapy is a rapidly growing field. Researchers from mathematical modeling, systems biology, pharmaceutical industry, and regulatory bodies, are collaboratively working on predictive models that could be applied for drug development and, ultimately, the clinical management of cancer patients. A plethora of modeling paradigms and approaches have emerged, making it challenging to compile a comprehensive review across all subdisciplines. It is therefore critical to gauge fundamental design aspects against requirements, and weigh opportunities and limitations of the different model types. In this review, we discuss three fundamental types of cancer models: space-structured models, ecological models, and immune system focused models. For each type, it is our goal to illustrate which mechanisms contribute to variability and heterogeneity in cancer growth and response, so that the appropriate architecture and complexity of a new model becomes clearer. We present the main features addressed by each of the three exemplary modeling types through a subjective collection of literature and illustrative exercises to facilitate inspiration and exchange, with a focus on providing a didactic rather than exhaustive overview. We close by imagining a future multi-scale model design to impact critical decisions in oncology drug development.

Translational PK/PD framework for antibody-drug conjugates to inform drug discovery and development

(171/200)ADCs represent a transformative class of medicine that combines the specificity of monoclonal antibodies with the potency of highly cytotoxic agents through linkers, aiming to enhance the therapeutic index of cytotoxic drugs. Given the complex molecular structures of ADCs, combining the molecular characteristics of small-molecule drugs and those of large-molecule biotherapeutics, there are several unique considerations when designing nonclinical-to-clinical PK/PD translation strategies.This complexity also demands a thorough understanding of the ADC's components-antibody, linker, and payload-to the overall toxicological, PK/PD, and efficacy profile. ADC development is a multidisciplinary endeavor requiring a strategic integration of nonclinical safety, pharmacology, and PK/PD modeling to translate from bench to bedside successfully.The ADC development underscores the necessity for a robust scientific foundation, leveraging advanced analytical and modeling tools to predict human responses and optimize therapeutic outcomes.This review aims to provide an ADC translational PK/PD framework by discussing unique aspects of ADC nonclinical to clinical PK translation, starting dose determination, and leveraging PK/PD modeling for human efficacious dose prediction and potential safety mitigation.

Improving Intracellular Delivery of an Antibody-Drug Conjugate Targeting Carcinoembryonic Antigen Increases Efficacy at Clinically Relevant Doses In Vivo

Solid tumor antibody-drug conjugates (ADC) have experienced more clinical success in the last 5 years than the previous 18-year span since the first ADC approval in 2000. While recent advances in protein engineering, linker design, and payload variations have played a role in this success, high expression and readily internalized targets have also been crucial to solid tumor therapy. However, these factors are also paradoxically connected to poor tissue penetration and lower efficacy. Previous work shows that potent ADCs can benefit from slower internalization under subsaturating doses to improve tissue penetration and increase tumor response. In contrast, faster internalization is predicted to increase efficacy under higher, tumor saturating doses. In this work, the intracellular delivery of SN-38 conjugated to an anti-carcinoembryonic antigen (anti-CEA) antibody (Ab) is increased by coadministering a noncompeting (cross-linking) anti-CEA Ab to improve efficacy in a colorectal carcinoma animal model. The SN-38 payload enables broad tumor saturation with clinically-tolerable doses, and under these saturating conditions, using a second CEA receptor cross-linking Ab yields faster internalization, which increases tumor killing efficacy. Our spheroid results show indirect bystander killing can also occur, but the more efficient direct cell killing from targeted intracellular payload release drives a greater tumor response. These results provide a strategy to increase therapeutic effectiveness with improved intracellular delivery under tumor saturating doses with the potential to expand the ADC target repertoire.

Targeted Nanofitin-drug Conjugates Achieve Efficient Tumor Delivery and Therapeutic Effect in an EGFRpos Mouse Xenograft Model

Adjusting the molecular size, the valency and the pharmacokinetics of drug conjugates are as many leverages to improve their therapeutic window, notably by affecting tumor penetration, renal clearance, and short systemic exposure. In that regard, small tumor-targeting ligands are gaining attention. In this study, we demonstrate the benefits of the small Nanofitin alternative scaffolds (7 kDa) as selective tumor-targeting modules for the generation of drug conjugates, focusing on Nanofitins B10 and D8 directed against the EGFR. Owing to their small size and monovalent format, the two Nanofitins displayed a fast and deep tumor penetration in EGFR-positive A431 xenografts in BALB/c nude mice after intravenous administration, yielding to a targeting of respectively 67.9% ± 14.1 and 98.9% ± 0.7 of the tumor cells as demonstrated by IHC. Conjugation with the monomethyl auristatin E toxin provided homogeneous Nanofitin-drug conjugates, with an overall yield of ≥97%, for in vivo assessment in a curative xenograft model using bioluminescent, EGFR-positive, A431 cells in BALB/c nude mice. Internalization was found critical for efficient release of the toxin. Hence, the intravenous administration of the D8-based construct showed significant antitumor effect in vivo as determined by monitoring tumor volumes and bioluminescence levels over 2 months.

Towards a platform quantitative systems pharmacology (QSP) model for preclinical to clinical translation of antibody drug conjugates (ADCs)

A next generation multiscale quantitative systems pharmacology (QSP) model for antibody drug conjugates (ADCs) is presented, for preclinical to clinical translation of ADC efficacy. Two HER2 ADCs (trastuzumab-DM1 and trastuzumab-DXd) were used for model development, calibration, and validation. The model integrates drug specific experimental data including in vitro cellular disposition data, pharmacokinetic (PK) and tumor growth inhibition (TGI) data for T-DM1 and T-DXd, as well as system specific data such as properties of HER2, tumor growth rates, and volumes. The model incorporates mechanistic detail at the intracellular level, to account for different mechanisms of ADC processing and payload release. It describes the disposition of the ADC, antibody, and payload inside and outside of the tumor, including binding to off-tumor, on-target sinks. The resulting multiscale PK model predicts plasma and tumor concentrations of ADC and payload. Tumor payload concentrations predicted by the model were linked to a TGI model and used to describe responses following ADC administration to xenograft mice. The model was translated to humans and virtual clinical trial simulations were performed that successfully predicted progression free survival response for T-DM1 and T-DXd for the treatment of HER2+ metastatic breast cancer, including differential efficacy based upon HER2 expression status. In conclusion, the presented model is a step toward a platform QSP model and strategy for ADCs, integrating multiple types of data and knowledge to predict ADC efficacy. The model has potential application to facilitate ADC design, lead candidate selection, and clinical dosing schedule optimization.

Shark IgNAR: The Next Broad Application Antibody in Clinical Diagnoses and Tumor Therapies?

Antibodies represent a relatively mature detection means and serve as therapeutic drug carriers in the clinical diagnosis and treatment of cancer-among which monoclonal antibodies (mAbs) currently occupy a dominant position. However, the emergence and development of small-molecule monodomain antibodies are inevitable due to the many limitations of mAbs, such as their large size, complex structure, and sensitivity to extreme temperature, and tumor microenvironments. Thus, since first discovered in Chondroid fish in 1995, IgNAR has become an alternative therapeutic strategy through which to replace monoclonal antibodies, thus entailing that this novel type of immunoglobulin has received wide attention with respect to clinical diagnoses and tumor therapies. The variable new antigen receptor (VNAR) of IgNAR provides an advantage for the development of new antitumor drugs due to its small size, high stability, high affinity, as well as other structural and functional characteristics. In that respect, a better understanding of the unique characteristics and therapeutic potential of IgNAR/VNAR in clinical and anti-tumor treatment is needed. This article reviews the advantages of its unique biochemical conditions and molecular structure for clinical diagnoses and novel anti-tumor drugs. At the same time, the main advantages of the existing conjugated drugs, which are based on single-domain antibodies, are introduced here, thereby providing new ideas and methods for the development of clinical diagnoses and anti-tumor therapies in the future.

Discovery of novel cMET-targeting antibody Fab drug conjugates as potential treatment for solid tumors with highly expressed cMET

BACKGROUND

Solid tumors are becoming prevalent affecting both old and young populations. Numerous solid tumors are associated with high cMET expression. The complexity of solid tumors combined with the highly interconnected nature of the cMET/HGF pathway with other cellular pathways make the pursuit of finding an effective treatment extremely challenging. The current standard of care for these malignancies is mostly small molecule-based chemotherapy. Antibody-based therapeutics as well as antibody drug conjugates are promising emerging classes against cMET-overexpressing solid tumors.

RESEARCH DESIGN AND METHODS

In this study, we described the design, synthesis, in vitro and in vivo characterization of cMET-targeting Fab drug conjugates (FDCs) as an alternative therapeutic strategy. The format is comprised of a Fab conjugated to a potent cytotoxic drug via a cleavable linker employing lysine-based and cysteine-based conjugation chemistries.

RESULTS

We found that the FDCs have potent anti-tumor efficacies in cancer cells with elevated overexpression of cMET. Moreover, they demonstrated a remarkable anti-tumor effect in a human gastric xenograft mouse model.

CONCLUSIONS

The FDC format has the potential to overcome some of the challenges presented by the other classes of therapeutics. This study highlights the promise of antibody fragment-based drug conjugate formats for the treatment of solid tumors.

Passive Membrane Permeability of Sizable Acyclic β-Hairpin Peptides

The recent shift toward increasingly larger drug modalities has created a significant demand for novel classes of compounds with high membrane permeability that can inhibit intracellular protein-protein interactions (PPIs). While major advances have been made in the design of cell-permeable helices, stapled β-sheets, and cyclic peptides, the development of large acyclic β-hairpins lags far behind. Therefore, we investigated a series of 26 β-hairpins (MW > 1.6 kDa) belonging to a chemical space far beyond the Lipinski "rule of five" (fbRo5) and showed that, in addition to their innate plasticity, the lipophilicity of these peptides (log D 7.4 ≈ 0 ± 0.7) can be tuned to drastically improve the balance between aqueous solubility and passive membrane permeability.

Antibody-Drug Conjugate Sacituzumab Govitecan Drives Efficient Tissue Penetration and Rapid Intracellular Drug Release

Antibody-drug conjugates (ADC) are a rapidly growing class of targeted cancer treatments, but the field has experienced significant challenges from their complex design. This study examined the multiscale distribution of sacituzumab govitecan (SG; Trodelvy), a recently clinically approved ADC, to clarify the mechanism(s) of efficacy given its unique design strategy. We employed a multiscale quantitative pharmacokinetic approach, including near-infrared fluorescence imaging, single-cell flow cytometry measurements, payload distribution via γH2AX pharmacodynamic staining, and a novel dual-labeled fluorescent technique to track the ADC and payload in a high trophoblast cell-surface antigen 2 expression xenograft model of gastric cancer (NCI-N87). We found that rapid release of the SN-38 payload from the hydrolysable linker inside cells imparts more DNA damage in vitro and in vivo than an ADC with a more stable enzyme cleavable linker. With SG, little to no extracellular payload release in the tumor was observed using a dual-labeled fluorescence technique, although bystander effects were detected. The high dosing regimen allowed the clinical dose to reach the majority of cancer cells, which has been linked to improved efficacy. In addition, the impact of multiple doses (day 1 and day 8) of a 21-day cycle was found to further improve tissue penetration despite not changing tumor uptake [percent injected dose per gram (%ID/g)] of the ADC. These results show increased ADC efficacy with SG can be attributed to efficient tumor penetration and intracellular linker cleavage after ADC internalization. This quantitative approach to study multiscale delivery can be used to inform the design of next-generation ADCs and prodrugs for other targets.

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

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

ADME and DMPK considerations for the discovery and development of antibody drug conjugates (ADCs)

 The therapeutic concept of antibody drug conjugates (ADCs) is to selectively target tumour cells with small molecule cytotoxic drugs to maximise cell kill benefit and minimise healthy tissue toxicity.An ADC generally consists of an antibody that targets a protein on the surface of tumour cells chemically linked to a warhead small molecule cytotoxic drug.To deliver the warhead to the tumour cell, the antibody must bind to the target protein and in general be internalised into the cell. Following internalisation, the cytotoxic agent can be released in the endosomal or lysosomal compartment (via different mechanisms). Diffusion or transport out of the endosome or lysosome allows the cytotoxic drug to express its cell-killing pharmacology. Alternatively, some ADCs (e.g. EDB-ADCs) rely on extracellular cleavage releasing membrane permeable warheads.One potentially important aspect of the ADC mechanism is the 'bystander effect' whereby the cytotoxic drug released in the targeted cell can diffuse out of that cell and into other (non-target expressing) tumour cells to exert its cytotoxic effect. This is important as solid tumours tend to be heterogeneous and not all cells in a tumour will express the targeted protein.The combination of large and small molecule aspects in an ADC poses significant challenges to the disposition scientist in describing the ADME properties of the entire molecule.This article will review the ADC landscape and the ADME properties of successful ADCs, with the aim of outlining best practice and providing a perspective of how the field can further facilitate the discovery and development of these important therapeutic modalities.

Development of and insights from systems pharmacology models of antibody‐drug conjugates

Antibody‐drug conjugates (ADCs) have gained traction in the oncology space in the past few decades, with significant progress being made in recent years. Although the use of pharmacometric modeling is well‐established in the drug development process, there is an increasing need for a better quantitative biological understanding of the pharmacokinetic and pharmacodynamic relationships of these complex molecules. Quantitative systems pharmacology (QSP) approaches can assist in this endeavor; recent computational QSP models incorporate ADC‐specific mechanisms and use data‐driven simulations to predict experimental outcomes. Various modeling approaches and platforms have been developed at the in vitro, in vivo, and clinical scales, and can be further integrated to facilitate preclinical to clinical translation. These new tools can help researchers better understand the nature and mechanisms of these targeted therapies to help achieve a more favorable therapeutic window. This review delves into the world of systems pharmacology modeling of ADCs, discussing various modeling efforts in the field thus far.

Recent Advances in Translational Pharmacokinetics and Pharmacodynamics Prediction of Therapeutic Antibodies Using Modeling and Simulation

Therapeutic monoclonal antibodies (mAbs) have been a promising therapeutic approach for several diseases and a wide variety of mAbs are being evaluated in clinical trials. To accelerate clinical development and improve the probability of success, pharmacokinetics and pharmacodynamics (PKPD) in humans must be predicted before clinical trials can begin. Traditionally, empirical-approach-based PKPD prediction has been applied for a long time. Recently, modeling and simulation (M&S) methods have also become valuable for quantitatively predicting PKPD in humans. Although several models (e.g., the compartment model, Michaelis–Menten model, target-mediated drug disposition model, and physiologically based pharmacokinetic model) have been established and used to predict the PKPD of mAbs in humans, more complex mechanistic models, such as the quantitative systemics pharmacology model, have been recently developed. This review summarizes the recent advances and future direction of M&S-based approaches to the quantitative prediction of human PKPD for mAbs.

A highly stable human single-domain antibody-drug conjugate exhibits superior penetration and treatment of solid tumors

The inefficient tumor penetration of therapeutic antibodies has hampered their effective use in treating solid tumors. Here, we report the identification of a fully human single-domain antibody (UdAb), designated as n501, targeting the oncofetal antigen 5T4. The high-resolution crystal structure indicates that n501 adopts a compact structure very similar to that of camelid nanobodies, and binds tightly to all eight leucine-rich repeats of 5T4. Furthermore, the UdAb n501 exhibits exceptionally high stability, with no apparent activity changes over 4 weeks of storage at various temperatures. Importantly, the UdAb-based antibody-drug conjugate (n501-SN38) showed much deeper tumor penetration, significantly higher tumor uptake, and faster accumulation at tumor sites than conventional IgG1-based antibody-drug conjugate (m603-SN38), resulting in improved tumor inhibition. These results highlight the potential of UdAb-based antibody-drug conjugates as a potential class of antitumor therapeutics with characteristics of high stability and strong tumor penetration for the effective treatment of solid tumors.

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

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

Targeted Mass Spectrometry-Based Approach for the Determination of Intrinsic Internalization Kinetics of Cell-Surface Membrane Protein Targets

Successful development of targeted therapeutics aimed at the elimination of diseased cells relies on the target properties and the therapeutics that target them. Currently, target properties have been evaluated through antibody-dependent semiquantitative approaches such as flow cytometry, Western blotting, or microscopy. Since antibodies can alter target properties following binding, antibody-dependent approaches provide at best skewed measurements for target intrinsic properties. To circumvent, here we attempted to develop an antibody-free targeted mass spectrometry-based (ATM) strategy to measure the surface densities and the intrinsic rates (K_int) of CD38 internalization in multiple myeloma cell lines. Using cell-surface biotinylation in conjunction with differential mass tagging to separate inward CD38 molecules from the outbound and nascent ones, the ATM approach revealed diversities in measured CD38 K_int values of 0.239 min^-1 S.E. ± 0.076, 0.109 min^-1 S.E. ± 0.032, and 0.058 min^-1 S.E. ± 0.001 for LP1, NCIH929, and MOLP8 cell lines, respectively. Together with CD38 surface densities, intrinsic K_int values aligned well with the tumor penetration model and supported the outcomes for tumor regression in mouse xenografts upon drug treatment. Additionally, the ATM approach can evaluate molecules with fast K_int as we determined for CTLA4 protein. We believe that the ATM approach has the potential to evaluate diverse cell-surface targets as part of the pharmacological assessment in drug discovery.

Homogeneous tumor targeting with a single dose of HER2-targeted albumin-binding domain-fused nanobody-drug conjugates results in long-lasting tumor remission in mice

Background: The non-homogenous distribution of antibody-drug conjugates (ADCs) within solid tumors is a major limiting factor for their wide clinical application. Nanobodies have been shown to rapidly penetrate into xenografts, achieving more homogeneous tumor targeting. However, their rapid renal clearance can hamper their application as nanobody drug conjugates (NDCs). Here, we evaluate whether half-life extension via non-covalent interaction with albumin can benefit the efficacy of a HER2-targeted NDC.

Methods: HER2-targeted nanobody 11A4 and the irrelevant nanobody R2 were genetically fused to an albumin-binding domain (ABD) at their C-terminus. Binding to both albumin and tumor cells was determined by ELISA-based assays. The internalization potential as well as the in vitro efficacy of NDCs were tested on HER2 expressing cells. Serum half-life of iodinated R2 and R2-ABD was studied in tumor-free mice. The distribution of fluorescently labelled 11A4 and 11A4-ABD was assessed in vitro in 3D spheroids. Subsequently, the in vivo distribution was evaluated by optical molecular imaging and ex vivo by tissue biodistribution and tumor immunohistochemical analysis after intravenous injection of IRDye800-conjugated nanobodies in mice bearing HER2-positive subcutaneous xenografts. Finally, efficacy studies were performed in HER2-positive NCI-N87 xenograft-bearing mice intravenously injected with a single dose (250 nmol/kg) of nanobodies conjugated to auristatin F (AF) either via a maleimide or the organic Pt(II)‑based linker, coined Lx^®.

Results: 11A4-ABD was able to bind albumin and HER2 and was internalized by HER2 expressing cells, irrespective of albumin presence. Interaction with albumin did not alter its distribution through 3D spheroids. Fusion to ABD resulted in a 14.8-fold increase in the serum half-life, as illustrated with the irrelevant nanobody. Furthermore, ABD fusion prolonged the accumulation of 11A4-ABD in HER2-expressing xenografts without affecting the expected homogenous intratumoral distribution. Next to that, reduced kidney retention of ABD-fused nanobodies was observed. Finally, a single dose administration of either 11A4-ABD-maleimide-AF or 11A4-ABD-Lx-AF led to long-lasting tumor remission in HER2-positive NCI-N87 xenograft-bearing mice.

Conclusion: Our results demonstrate that genetic fusion of a nanobody to ABD can significantly extend serum half-life, resulting in prolonged and homogenous tumor accumulation. Most importantly, as supported by the impressive anti-tumor efficacy observed after a single dose administration of 11A4-ABD-AF, our data reveal that monovalent internalizing ABD-fused nanobodies have potential for the development of highly effective NDCs.

What makes a good antibody-drug conjugate?

Introduction: Antibody-Drug Conjugates (ADCs) are becoming increasingly important weapons in the fight against cancer, as evidenced by the growing number of approved products. The complex nature of an ADC means that there is a vast array of choices to consider in the design of such drugs.Areas covered: We provide an overview of developments in each facet of ADC structure: the antibody, linker, and payload. Looking at the current clinical landscape, we discuss trends that have led to the evolution of ADC design.Expert opinion:Following a history of setbacks and high discontinuation rates, the understanding of the ADC field has grown. If developers can obtain a firm grasp of the structure-function relationship of their molecule, we expect the advances in ADC design to translate to improved clinical success. Moreover, the breadth of ADC applications will continue to expand to target new indications with novel targets and payloads.

The Evolution of Antibody-Drug Conjugates: A Positive Inflexion Point

In 2019, an important inflection point occurred when the U.S. Food and Drug Administration approved three new antibody-drug conjugates (ADCs) for the treatment of malignancies, including urothelial cancer (enfortumab vedotin-ejfv), diffuse large B-cell lymphoma (polatuzumab vedotin-piiq), and HER2 breast cancer (fam-trastuzumab deruxtecan-nxki), and expanded the indication for ado-trastuzumab emtansine to early breast cancer. This near doubling in the number of approved ADCs within 1 year validates the ADC platform and represents a successful evolution over the past 30 years. ADCs were born in an era when systemic therapy for cancer was largely cytotoxic chemotherapy. Many of the investigational cytotoxic agents were determined to be too toxic for oral and intravenous use. The agents were especially potent, with inhibitory concentrations that inhibited 50% of cells in the nanomolar and picomolar range but had poor therapeutic indexes when administered systemically. Now, over the last 30 years, we have seen an evolution of the many aspects of this complex platform with better antigen target selection, more sophisticated chemistry for the linkers, a growing diversity of payloads from cytotoxic chemotherapy to targeted therapies and immunostimulants, and, with the recent series of regulatory approvals, a buoyed sense of optimism for the technology. Nonetheless, we have not fully realized the full potential of this platform. In this review, the many components of ADCs will be discussed, the difficulties encountered will be highlighted, the innovative strategies that are being used to improve them will be assessed, and the direction that the field is going will be considered.

Mechanistic Modeling of Intra-Tumor Spatial Distribution of Antibody-Drug Conjugates: Insights into Dosing Strategies in Oncology

Antibody drug conjugates (ADCs) provide targeted delivery of cytotoxic agents directly inside tumor cells. However, many ADCs targeting solid tumors have exhibited limited clinical efficacy, in part, due to insufficient penetration within tumors. To better understand the relationship between ADC tumor penetration and efficacy, previously applied Krogh cylinder models that explore tumor growth dynamics following ADC administration in preclinical species were expanded to a clinical framework by integrating clinical pharmacokinetics, tumor penetration, and tumor growth inhibition. The objective of this framework is to link ADC tumor penetration and distribution to clinical efficacy. The model was validated by comparing virtual patient population simulations to observed overall response rates from trastuzumab‐DM1 treated patients with metastatic breast cancer. To capture clinical outcomes, we expanded upon previous Krogh cylinder models to include the additional mechanism of heterogeneous tumor growth inhibition spatially across the tumor. This expansion mechanistically captures clinical response rates by describing heterogeneous ADC binding and tumor cell killing; high binding and tumor cell death close to capillaries vs. low binding, and high tumor cell proliferation far from capillaries. Sensitivity analyses suggest that clinical efficacy could be optimized through dose fractionation, and that clinical efficacy is primarily dependent on the ADC‐target affinity, payload potency, and tumor growth rate. This work offers a mechanistic basis to predict and optimize ADC clinical efficacy for solid tumors, allowing dosing strategy optimization to improve patient outcomes.

Capturing the Magic Bullet: Pharmacokinetic Principles and Modeling of Antibody-Drug Conjugates

Over the past two decades, antibody-drug conjugates (ADCs) have emerged as a promising class of drugs for cancer therapy and have expanded to nononcology fields such as inflammatory diseases, atherosclerosis, and bacteremia. Eight ADCs are currently approved by FDA for clinical applications, with more novel ADCs under clinical development. Compared with traditional chemotherapy, ADCs combine the target specificity of antibodies with chemotherapeutic capabilities of cytotoxic drugs. The benefits include reduced systemic toxicity and enhanced therapeutic index for patients. However, the heterogeneous structures of ADCs and their dynamic changes following administration create challenges in their development. The understanding of ADC pharmacokinetics (PK) is crucial for the optimization of clinical dosing regimens when translating from animal to human. In addition, it contributes to the optimization of dose selection and clinical monitoring with regard to safety and efficacy. This manuscript reviews the PK characteristics of ADCs and summarizes the diverse approaches for PK modeling that can be used to evaluate an ADC at the preclinical and clinical stages to support their successful development. Despite the numerous available options, fit-for-purpose modeling approaches for the PK and PD of ADCs should be critically planned and well-thought-out to adequately support the development of an ADC.

Recent advances in physiologically based pharmacokinetic and pharmacodynamic models for anticancer nanomedicines

Nanoparticles (NPs) have distinct pharmacokinetic (PK) properties and can potentially improve the absorption, distribution, metabolism, and elimination (ADME) of small-molecule drugs loaded therein. Owing to the unwanted toxicities of anticancer agents in healthy organs and tissues, their precise delivery to the tumor is an essential requirement. There have been numerous advancements in the development of nanomedicines for cancer therapy. Physiologically based PK (PBPK) models serve as excellent tools for describing and predicting the ADME properties and the efficacy and toxicity of drugs, in combination with pharmacodynamic (PD) models. The recent preliminary application of these modeling approaches to NPs demonstrated their potential benefits in research and development processes relevant to the ADME and pharmacodynamics of NPs and nanomedicines. Here, we comprehensively review the pharmacokinetics of NPs, the developed PBPK models for anticancer NPs, and the developed PD model for anticancer agents.

Increased Tumor Penetration of Single-Domain Antibody-Drug Conjugates Improves In Vivo Efficacy in Prostate Cancer Models

Targeted delivery of chemotherapeutics aims to increase efficacy and lower toxicity by concentrating drugs at the site-of-action, a method embodied by the seven current FDA-approved antibody-drug conjugates (ADC). However, a variety of pharmacokinetic challenges result in relatively narrow therapeutic windows for these agents, hampering the development of new drugs. Here, we use a series of prostate-specific membrane antigen-binding single-domain (Humabody) ADC constructs to demonstrate that tissue penetration of protein-drug conjugates plays a major role in therapeutic efficacy. Counterintuitively, a construct with lower in vitro potency resulted in higher in vivo efficacy than other protein-drug conjugates. Biodistribution data, tumor histology images, spheroid experiments, in vivo single-cell measurements, and computational results demonstrate that a smaller size and slower internalization rate enabled higher tissue penetration and more cell killing. The results also illustrate the benefits of linking an albumin-binding domain to the single-domain ADCs. A construct lacking an albumin-binding domain was rapidly cleared, leading to lower tumor uptake (%ID/g) and decreased in vivo efficacy. In conclusion, these results provide evidence that reaching the maximum number of cells with a lethal payload dose correlates more strongly with in vivo efficacy than total tumor uptake or in vitro potency alone for these protein-drug conjugates. Computational modeling and protein engineering can be used to custom design an optimal framework for controlling internalization, clearance, and tissue penetration to maximize cell killing. SIGNIFICANCE: A mechanistic study of protein-drug conjugates demonstrates that a lower potency compound is more effective in vivo than other agents with equal tumor uptake due to improved tissue penetration and cellular distribution.

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.

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.

Synthesis and Evaluation of Camptothecin Antibody-Drug Conjugates

Antibody-drug conjugates (ADCs) that incorporate the exatecan derivative DXd in their payload are showing promising clinical results in solid tumor indications. The payload has an F-ring that also contains a second chiral center, both of which complicate its synthesis and derivatization. Here we report on new camptothecin-ADCs that do not have an F-ring in their payloads yet behave similarly to DXd-bearing conjugates in vitro and in vivo. This simplification allows easier derivatization of camptothecin A and B rings for structure-activity relationship studies and payload optimization. ADCs having different degrees of bystander killing and the ability to release hydroxyl or thiol-bearing metabolites following peptide linker cleavage were investigated.

Quantitative Systems Pharmacology: An Exemplar Model-Building Workflow With Applications in Cardiovascular, Metabolic, and Oncology Drug Development

Quantitative systems pharmacology (QSP), a mechanistically oriented form of drug and disease modeling, seeks to address a diverse set of problems in the discovery and development of therapies. These problems bring a considerable amount of variability and uncertainty inherent in the nonclinical and clinical data. Likewise, the available modeling techniques and related software tools are manifold. Appropriately, the development, qualification, application, and impact of QSP models have been similarly varied. In this review, we describe the progressive maturation of a QSP modeling workflow: a necessary step for the efficient, reproducible development and qualification of QSP models, which themselves are highly iterative and evolutive. Furthermore, we describe three applications of QSP to impact drug development; one supporting new indications for an approved antidiabetic clinical asset through mechanistic hypothesis generation, one highlighting efficacy and safety differentiation within the sodium‐glucose cotransporter‐2 inhibitor drug class, and one enabling rational selection of immuno‐oncology drug combinations.

Modeling to capture bystander-killing effect by released payload in target positive tumor cells

Background

Antibody-drug conjugates (ADCs) are intended to bind to specific positive target antigens and eradicate only tumor cells from an intracellular released payload through the lysosomal protease. Payloads, such as MMAE, have the capacity to kill adjacent antigen-negative (Ag–) tumor cells, which is called the bystander-killing effect, as well as directly kill antigen-positive (Ag+) tumor cells. We propose that a dose-response curve should be independently considered to account for target antigen-positive/negative tumor cells.

Methods

A model was developed to account for the payload in Ag+/Ag– cells and the associated parameters were applied. A tumor growth inhibition (TGI) effect was explored based on an ordinary differential equation (ODE) after substituting the payload concentration in Ag+/Ag– cells into an Emax model, which accounts for the dose-response curve. To observe the bystander-killing effects based on the amount of Ag+/Ag– cells, the Emax model is used independently. TGI models based on ODE are unsuitable for describing the initial delay through a tumor–drug interaction. This was solved using an age-structured model based on the stochastic process.

Results

β∈(0,1] is a fraction parameter that determines the proportion of cells that consist of Ag+/Ag– cells. The payload concentration decreases when the ratio of efflux to influx increases. The bystander-killing effect differs with varying amounts of Ag+ cells. The larger β is, the less bystander-killing effect. The decrease of the bystander-killing effect becomes stronger as Ag+ cells become larger than the Ag– cells. Overall, the ratio of efflux to influx, the amount of released payload, and the proportion of Ag+ cells determine the efficacy of the ADC. The tumor inhibition delay through a payload-tumor interaction, which goes through several stages, may be solved using an age-structured model.

Conclusions

The bystander-killing effect, one of the most important topics of ADCs, has been explored in several studies without the use of modeling. We propose that the bystander-killing effect can be captured through a mathematical model when considering the Ag+ and Ag– cells. In addition, the TGI model based on the age-structure can capture the initial delay through a drug interaction as well as the bystander-killing effect.

Electronic supplementary material

The online version of this article (10.1186/s12885-019-5336-7) contains supplementary material, which is available to authorized users.

Pharmacokinetic and Immunological Considerations for Expanding the Therapeutic Window of Next-Generation Antibody-Drug Conjugates

Antibody-drug conjugate (ADC) development has evolved greatly over the last 3 decades, including the Food and Drug Administration (FDA) approval of several new drugs. However, translating ADCs from the design stage and preclinical promise to clinical success has been a major hurdle for the field, particularly for solid tumors. The challenge in clinical development can be attributed to the difficulty in connecting the design of these multifaceted agents with the impact on clinical efficacy, especially with the accelerated development of 'next-generation' ADCs containing a variety of innovative biophysical developments. Given their complex nature, there is an urgent need to integrate holistic ADC characterization approaches. This includes comprehensive in vivo assessment of systemic, intratumoral and cellular pharmacokinetics, pharmacodynamics, toxicodynamics, and interactions with the immune system, with the aim of optimizing the ADC therapeutic window. Pharmacokinetic/pharmacodynamic factors influencing the ADC therapeutic window include (1) selecting optimal target and ADC components for prolonged and stable plasma circulation to increase tumoral uptake with minimal non-specific systemic toxicity, (2) balancing homogeneous intratumoral distribution with efficient cellular uptake, and (3) translating improved ADC potency to better clinical efficacy. Balancing beneficial immunological effects such as Fc-mediated and payload-mediated immune cell activation against harmful immunogenic/toxic effects is also an emerging concern for ADCs. Here, we review practical considerations for tracking ADC efficacy and toxicity, as aided by high-resolution biomolecular and immunological tools, quantitative pharmacology, and mathematical models, all of which can elucidate the relative contributions of the multitude of interactions governing the ADC therapeutic window.

DNA damaging agent-based antibody-drug conjugates for cancer therapy

Currently, four antibody-drug conjugates (ADCs) are approved by the Food and Drug Administration or the European Medicine Agency to treat cancer patients. More than 60 ADCs are in clinical development for cancer therapy. More than 60% of ADCs in clinical trials employ microtubule inhibitors as their payloads. A better understanding of payloads other than microtubule inhibitors, especially DNA-damaging agents, is important for further development of ADCs. In this review, we highlight an emerging trend of using DNA-damaging agents as payloads for ADCs. This review summarizes recent advances in our understanding gained from ongoing clinical studies; it will help to define the utility of DNA-damaging payloads for ADCs as cancer therapeutics. Future directions of the development of ADCs are also discussed, focusing on targeting drug resistance and combination treatment with immunotherapy.

89 Zr-ImmunoPET companion diagnostics and their impact in clinical drug development

Therapeutic monoclonal antibodies have been used in cancer treatment for 30 years, with around 24 mAb and mAb:drug conjugates approved by the FDA to date. Despite their specificity, efficacy has remained limited, which, in part, derails nascent initiatives towards precision medicine. An image-guided approach to reinforce treatment decisions using immune positron emission tomography (immunoPET) companion diagnostic is warranted. This review provides a general overview of current translational research using Zr-89 immunoPET and opportunities for utilizing and harnessing this tool to its full potential. Patient case studies are cited to illustrate immunoPET probes as tools for profiling molecular signatures. Discussions on its utility in reinforcing clinical decisions as it relates to histopathological tumor assessment and standard diagnostic methods, and its potential as predictive biomarkers, are presented. We finally conclude with an overview of practical considerations to its utility in the clinic.

Influence of the dissociation rate constant on the intra-tumor distribution of antibody-drug conjugate against tissue factor

Antibody-drug conjugates (ADCs) are currently considered to be promising agents for cancer therapy. However, especially in solid tumors, the uneven distribution of ADCs would decrease their efficacy in clinical studies. We suggest that in addition to optimizing ADC components, such as the linker structure and anticancer agent, it is necessary to consider the distribution of the ADC within tumor tissue. In this study, we established three kinds of anti-tissue factor (TF) ADCs: 1849ADC with a low k_d, 444ADC with an intermediate k_d, and 1084ADC with a high k_d. All three of the anti-TF ADCs exhibited almost the same in vitro cytotoxicity and pharmacological and biochemical characteristics, although the binding kinetics parameters differed. In vivo, all ADCs exerted equivalent antitumor effects against small BxPC3 tumors. However, on larger BxPC3 tumors, 1084ADC (higher k_d) exerted higher antitumor activity than 1849ADC (lower k_d). Furthermore, immunofluorescence staining indicated that 1084ADC was distributed throughout the whole tumor, whereas 1849ADC was mainly localized close to tumor vessels. We conclude that the ADC with a higher k_d increased the antitumor effect of because it penetrated and distributed evenly throughout the entire solid tumor. These findings highlight the importance of the k_d of a mAb in ADC design.

Peptide and protein nanoparticle conjugates: versatile platforms for biomedical applications

Peptide- and protein-nanoparticle conjugates have emerged as powerful tools for biomedical applications, enabling the treatment, diagnosis, and prevention of disease. In this review, we focus on the key roles played by peptides and proteins in improving, controlling, and defining the performance of nanotechnologies. Within this framework, we provide a comprehensive overview of the key sequences and structures utilised to provide biological and physical stability to nano-constructs, direct particles to their target and influence their cellular and tissue distribution, induce and control biological responses, and form polypeptide self-assembled nanoparticles. In doing so, we highlight the great advances made by the field, as well as the challenges still faced in achieving the clinical translation of peptide- and protein-functionalised nano-drug delivery vehicles, imaging species, and active therapeutics.

Antibody-Drug Conjugates as Cancer Therapeutics: Past, Present, and Future

Antibody-drug conjugates (ADCs) represent an innovative therapeutic approach that provides novel treatment options and hope for patients with cancer. By coupling monoclonal antibodies (mAbs) to cytotoxic small-molecule payloads with a plasma-stable linker, ADCs offer the potential for increased drug specificity and fewer off-target effects than systemic chemotherapy. As evidence for the potential of these therapies, many new ADCs are in various stages of clinical development. Because their structure poses unique challenges to pharmacokinetic and pharmacodynamic characterization, it is critical to recognize the differences between ADCs and conventional chemotherapy in the design of ADC clinical development strategies. Although some properties may be determined mainly by either the mAb or the small-molecule portion, the behavior of these agents is not always predictable. Furthermore, because the absorption, distribution, metabolism, and excretion (ADME) of ADCs are influenced by all 3 of its components (mAb, linker, and payload), it is important to characterize the intact molecule, any target-mediated catabolic clearance of the mAb, and the ADME properties of the small-molecule payload. Here we describe key issues in the clinical development of ADCs, including considerations for designing first-in-human studies for ADCs. We discuss some difficulties of ADC pharmacokinetic characterization and current approaches to overcoming these challenges. Finally, we consider all aspects of clinical pharmacology assessment required during drug development, using examples from the literature to illustrate the discussion.

Small-Format Drug Conjugates: A Viable Alternative to ADCs for Solid Tumours?

Antibody-Drug Conjugates (ADCs) have been through multiple cycles of technological innovation since the concept was first practically demonstrated ~40 years ago. Current technology is focusing on large, whole immunoglobulin formats (of which there are approaching 100 in clinical development), many with site-specifically conjugated payloads numbering 2 or 4. Despite the success of trastuzumab-emtansine in breast cancer, ADCs have generally failed to have an impact in solid tumours, leading many to explore alternative, smaller formats which have better penetrating properties as well as more rapid pharmacokinetics (PK). This review describes research and development progress over the last ~10 years obtained from the primary literature or conferences covering over a dozen different smaller format-drug conjugates from 80 kDa to around 1 kDa in total size. In general, these agents are potent in vitro, particularly more recent ones incorporating ultra-potent payloads such as auristatins or maytansinoids, but this potency profile changes when testing in vivo due to the more rapid clearance. Strategies to manipulate the PK properties, whilst retaining the more effective tumour penetrating properties could at last make small-format drug conjugates viable alternative therapeutics to the more established ADCs.

Mechanistic Modeling of Antibody-Drug Conjugate Internalization at the Cellular Level Reveals Inefficient Processing Steps

Antibody-drug conjugates (ADC) offer an avenue for specific drug delivery to target cells. Here, parameters with important roles in the cellular processing of ADCs were quantitatively measured for Ab033, an antibody against EGFR. In EGFR-overexpressing cancer cell lines, Ab033 internalized at rates of 0.047/min and 0.15/min for A431 and H441 cells, respectively. Once internalized, Ab033 either trafficked to the lysosome or was recycled; up to 45% of internalized Ab033 returned to the cell surface. Despite such recycling, intracellular accumulation of Ab033 continually increased over 24 hours. Ab033 was conjugated to form a dual toxin ADC containing both cleavable and non-cleavable linker-drug payloads for release rate comparisons. Intracellular concentrations of freed drug from cleavable linker were greater than from non-cleavable linker and exceeded 5 × 10⁶ drug molecules per A431 cell after 24 hours. Compared with intracellular antibody accumulation, formation of released drug was delayed, likely due to the time needed for endo-lysosomal trafficking and subsequent linker/antibody proteolysis. Informed by the quantitative data, a cellular ADC model was constructed and used to summarize processing inefficiencies. Modeling simulations were conducted to determine parameter sensitivity on intracellular drug concentrations, with rates of EGFR internalization and recycling as well as ADC trafficking found to be the most sensitive toward final intracellular drug concentrations. Overall, this study shows Ab033 ADCs to be a viable strategy for delivery of cytotoxic drugs into tumor cells with subsequent modeling efforts able to highlight key processing steps to be improved for increased drug delivery. Mol Cancer Ther; 17(6); 1341-51. ©2018 AACR.

Tissue transport affects how treatment scheduling increases the efficacy of chemotherapeutic drugs

A method to predict the effect of tissue transport on the scheduling of chemotherapeutic treatment could increase efficacy. Many drugs with desirable pharmacokinetic properties fail in vivo due to poor transport through tissue. To predict the effect of treatment schedule on drug efficacy we developed an in silico method that integrates diffusion through tissue and cell binding into a pharmacokinetic model. The model was evaluated with an array of theoretical drugs that had different rates of diffusivity, binding, and clearance. The efficacy of each drug, quantified as the fraction of cells killed, was calculated for twenty dosage schedules. Simulations showed that efficacy strongly depended on tissue transport, with a range of 0.00 to 99.99%, despite each drug having equal plasma areas under the curve (AUC). For most drugs, schedules that increased exposure also increased efficacy. Drugs with fast clearance benefited the most from increasing the number of doses and this was most effective for those with intermediary binding. All drugs with slow diffusivity were ineffective. For a subset of drugs, increasing the number of doses decreased efficacy. This phenomenon was unexpected because, when considering uptake into tissue, sustained plasma levels from multiple doses are generally assumed to be more effective. This counterintuitive decrease in efficacy was caused by drug retention within tumor tissue. These results established a set of rules that suggests how transport parameters affect the efficacy of drugs at different schedules. The two most predominant rules are (1) multiple doses improve efficacy for drugs with fast clearance, fast diffusivity and low to intermediate cell binding; and (2) one dose is most effective for drugs with slow clearance, slow diffusivity or strong cell binding. Understanding the role of tissue transport when determining drug treatment schedules would improve the outcome of preclinical animal experiments and early clinical trials.

Transport of drugs from blood vessels to tumour tissue

The effectiveness of anticancer drugs in treating a solid tumour is dependent on delivery of the drug to virtually all cancer cells in the tumour. The distribution of drug in tumour tissue depends on the plasma pharmacokinetics, the structure and function of the tumour vasculature and the transport properties of the drug as it moves through microvessel walls and in the extravascular tissue. The aim of this Review is to provide a broad, balanced perspective on the current understanding of drug transport to tumour cells and on the progress in developing methods to enhance drug delivery. First, the fundamental processes of solute transport in blood and tissue by convection and diffusion are reviewed, including the dependence of penetration distance from vessels into tissue on solute binding or uptake in tissue. The effects of the abnormal characteristics of tumour vasculature and extravascular tissue on these transport properties are then discussed. Finally, methods for overcoming limitations in drug transport and thereby achieving improved therapeutic results are surveyed.

Pharmacokinetic Considerations for Antibody-Drug Conjugates against Cancer

Antibody-drug conjugates (ADCs) are ushering in the next era of targeted therapy against cancer. An ADC for cancer therapy consists of a potent cytotoxic payload that is attached to a tumour-targeted antibody by a chemical linker, usually with an average drug-to-antibody ratio (DAR) of 3.5-4. The theory is to deliver potent cytotoxic payloads directly to tumour cells while sparing healthy cells. However, practical application has proven to be more difficult. At present there are only two ADCs approved for clinical use. Nevertheless, in the last decade there has been an explosion of options for ADC engineering to optimize target selection, Fc receptor interactions, linker, payload and more. Evaluation of these strategies requires an understanding of the mechanistic underpinnings of ADC pharmacokinetics. Development of ADCs for use in cancer further requires an understanding of tumour properties and kinetics within the tumour environment, and how the presence of cancer as a disease will impact distribution and elimination. Key pharmacokinetic considerations for the successful design and clinical application of ADCs in oncology are explored in this review, with a focus on the mechanistic determinants of distribution and elimination.

A novel individual-cell-based mathematical model based on multicellular tumour spheroids for evaluating doxorubicin-related delivery in avascular regions

BACKGROUND AND PURPOSE

Effective drug delivery in the avascular regions of tumours, which is crucial for the promising antitumour activity of doxorubicin-related therapy, is governed by two inseparable processes: intercellular diffusion and intracellular retention. To accurately evaluate doxorubicin-related delivery in the avascular regions, these two processes should be assessed together. Here we describe a new approach to such an assessment.

EXPERIMENTAL APPROACH

An individual-cell-based mathematical model based on multicellular tumour spheroids was developed that describes the different intercellular diffusion and intracellular retention kinetics of doxorubicin in each cell layer. The different effects of a P-glycoprotein inhibitor (LY335979) and a hypoxia inhibitor (YC-1) were quantitatively evaluated and compared, in vitro (tumour spheroids) and in vivo (HepG2 tumours in mice). This approach was further tested by evaluating in these models, an experimental doxorubicin derivative, INNO 206, which is in Phase II clinical trials.

KEY RESULTS

Inhomogeneous, hypoxia-induced, P-glycoprotein expression compromised active transport of doxorubicin in the central area, that is, far from the vasculature. LY335979 inhibited efflux due to P-glycoprotein but limited levels of doxorubicin outside the inner cells, whereas YC-1 co-administration specifically increased doxorubicin accumulation in the inner cells without affecting the extracellular levels. INNO 206 exhibited a more effective distribution profile than doxorubicin.

CONCLUSIONS AND IMPLICATIONS

The individual-cell-based mathematical model accurately evaluated and predicted doxorubicin-related delivery and regulation in the avascular regions of tumours. The described framework provides a mechanistic basis for the proper development of doxorubicin-related drug co-administration profiles and nanoparticle development and could avoid unnecessary clinical trials.

QSP Toolbox: Computational Implementation of Integrated Workflow Components for Deploying Multi-Scale Mechanistic Models

Quantitative systems pharmacology (QSP) modeling has become increasingly important in pharmaceutical research and development, and is a powerful tool to gain mechanistic insights into the complex dynamics of biological systems in response to drug treatment. However, even once a suitable mathematical framework to describe the pathophysiology and mechanisms of interest is established, final model calibration and the exploration of variability can be challenging and time consuming. QSP models are often formulated as multi-scale, multi-compartment nonlinear systems of ordinary differential equations. Commonly accepted modeling strategies, workflows, and tools have promise to greatly improve the efficiency of QSP methods and improve productivity. In this paper, we present the QSP Toolbox, a set of functions, structure array conventions, and class definitions that computationally implement critical elements of QSP workflows including data integration, model calibration, and variability exploration. We present the application of the toolbox to an ordinary differential equations-based model for antibody drug conjugates. As opposed to a single stepwise reference model calibration, the toolbox also facilitates simultaneous parameter optimization and variation across multiple in vitro, in vivo, and clinical assays to more comprehensively generate alternate mechanistic hypotheses that are in quantitative agreement with available data. The toolbox also includes scripts for developing and applying virtual populations to mechanistic exploration of biomarkers and efficacy. We anticipate that the QSP Toolbox will be a useful resource that will facilitate implementation, evaluation, and sharing of new methodologies in a common framework that will greatly benefit the community.

AFIR: A Dimensionless Potency Metric for Characterizing the Activity of Monoclonal Antibodies

For monoclonal antibody (mAb) drugs, soluble targets may accumulate several thousand fold after binding to the drug. Time course data of mAb and total target is often collected and, although free target is more closely related to clinical effect, it is difficult to measure. Therefore, mathematical models of this data are used to predict target engagement. In this article, a “potency factor” is introduced as an approximation for the model‐predicted target inhibition. This potency factor is defined to be the time‐Averaged Free target concentration to Initial target concentration Ratio (AFIR), and it depends on three key quantities: the average drug concentration at steady state; the binding affinity; and the degree of target accumulation. AFIR provides the intuition for how changes in dosing regimen and binding affinity affect target capture and AFIR can be used to predict the druggability of new targets and the expected benefits of more potent, second‐generation mAbs.