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

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

INTRODUCTION

Antibody-drug conjugates (ADCs) show significant clinical efficacy in the treatment of solid tumors, but a major limitation to their success is poor intratumoral distribution. Adding a carrier dose improves both distribution and overall drug efficacy of ADCs, but the optimal carrier dose has not been outlined for different payload classes.

OBJECTIVE

In this work, we study two carrier dose regimens: 1) matching payload potency to cellular delivery but potentially not reaching cells farther away from blood vessels, or 2) dosing to tumor saturation but risking a reduction in cell killing from a lower amount of payload delivered per cell.

METHODS

We use a validated computational model to test four different payloads conjugated to trastuzumab to determine the optimal carrier dose as a function of target expression, ADC dose, and payload potency.

RESULTS

We find that dosing to tumor saturation is more efficacious than matching payload potency to cellular delivery for all payloads because the increase in the number of cells targeted by the ADC outweighs the loss in cell killing on targeted cells. An important exception exists if the carrier dose reduces the payload uptake per cell to the point where all cell killing is lost. Likewise, receptor downregulation can mitigate the benefits of a carrier dose.

CONCLUSIONS

Because tumor saturation and in vitro potency can be measured early in ADC design, these results provide insight into maximizing ADC efficacy and demonstrate the benefits of using simulation to guide ADC design.

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.

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

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

Spatiotemporal Quantification of HER2-targeting Antibody-Drug Conjugate Bystander Activity and Enhancement of Solid Tumor Penetration

PURPOSE

Antibody-drug conjugate (ADC) has had a transformative effect on the treatment of many solid tumors, yet it remains unclear how ADCs exert bystander activity in the tumor microenvironment.

EXPERIMENTAL DESIGN

Here, we directly visualized and spatiotemporally quantified the intratumor biodistribution and pharmacokinetics of different ADC components by developing dual-labeled fluorescent probes.

RESULTS

Mechanistically, we found that tumor penetration of ADCs is distinctly affected by their ability to breach the binding site barrier (BSB) in perivascular regions of tumor vasculature, and bystander activity of ADC can only partially breach BSB. Furthermore, bystander activity of ADCs can work in synergy with coadministration of their parental antibodies, leading to fully bypassing BSBs and enhancing tumor penetration via a two-step process.

CONCLUSIONS

These promising preclinical data allowed us to initiate a phase I/II clinical study of coadministration of RC48 and trastuzumab in patients with malignant stomach cancer to further evaluate this treatment strategy in humans.

Integrated PK/PD Modeling Relates Smoothened Inhibitor Biomarkers to The Heterogeneous Intratumor Disposition of Cetuximab in Pancreatic Cancer Tumor Models

Therapeutic antibodies have shown little efficacy in the treatment of pancreatic ductal adenocarcinomas (PDAC). Tumor desmoplasia, hypovascularity, and poor perfusion result in insufficient tumor cell exposure, contributing to treatment failure. Smoothened inhibitors of hedgehog signaling (sHHi) increase PDAC tumor permeability, perfusion, and drug delivery, and provide a tool to develop a quantitative, mechanistic understanding as to how the temporal dynamics of tumor priming can impact intratumor distribution of monoclonal antibodies (mAb). A linked pharmacokinetic (PK)/pharmacodynamic (PD) model was developed to integrate the plasma and tumor PK of a sHHi priming agent with its effects upon downstream stromal biomarkers Gli1, hyaluronic acid, and interstitial fluid pressure in PDAC patient-derived xenograft (PDX) tumors. In parallel, in situ tumor concentrations of cetuximab (CTX: anti-epidermal growth factor receptor; EGFR) were quantified as a marker for tumor delivery of mAb or antibody-drug conjugates. A minimal, physiologically-based pharmacokinetic (mPBPK) model was constructed to link sHHi effects upon mechanistic effectors of tumor barrier compromise with the intratumor distribution of CTX, and CTX occupancy of EGFR in tumors. Integration of the mPBPK model of mAb deposition and intratumor distribution with the PK/PD model of tumor responses to priming not only identified physiological parameters that are critical for tumor antibody distribution, but also provides insight into dosing regimens that could achieve maximal tumor disposition of therapeutic antibodies under conditions of transient PDAC tumor permeability barrier compromise that mechanistically-diverse tumor priming strategies may achieve.

Current status and prospects for improved targeted delivery approaches for cancer

Millions of individuals worldwide suffer from the complicated disease known as cancer. Though they frequently have serious side effects and can harm healthy cells, the current cancer treatments, such as radiation therapy and chemotherapy, are effective in many cases. Targeted drug delivery systems have emerged as a promising new paradigm in cancer treatment because they can deliver drugs directly to cancer cells with minimal harm to healthy cells. This review aims to give a broad overview of the state of targeted drug delivery systems for cancer treatment and investigate the technology's potential in the future. We'll go through the various kinds of targeted drug delivery systems, their drawbacks, the most recent developments, and possible future paths for further study and creation. This review aims to provide an overview of the current status of targeted drug delivery systems for cancer treatment, including the different types of targeted drug delivery systems, their limitations, recent advancements, and potential future directions for research and development. By examining the field's current state and exploring prospects, this review aims to highlight the potential of targeted drug delivery systems for improving cancer treatment and ultimately enhancing patient outcomes.

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.

An agent-based model of monocyte differentiation into tumour-associated macrophages in chronic lymphocytic leukemia

Monocyte-derived macrophages help maintain tissue homeostasis and defend the organism against pathogens. In tumors, recent studies have uncovered complex macrophage populations, including tumor-associated macrophages, which support tumorigenesis through cancer hallmarks such as immunosuppression, angiogenesis, or matrix remodeling. In the case of chronic lymphocytic leukemia, these macrophages are known as nurse-like cells (NLCs) and they protect leukemic cells from spontaneous apoptosis, contributing to their chemoresistance. We propose an agent-based model of monocyte differentiation into NLCs upon contact with leukemic B cells in vitro. We performed patient-specific model optimization using cultures of peripheral blood mononuclear cells from patients. Using our model, we were able to reproduce the temporal survival dynamics of cancer cells in a patient-specific manner and to identify patient groups related to distinct macrophage phenotypes. Our results show a potentially important role of phagocytosis in the polarization process of NLCs and in promoting cancer cells' enhanced survival.

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.

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.

Design of high avidity and low affinity antibodies for in situ control of antibody drug conjugate targeting

Antibody-Drug Conjugates (ADCs) have rapidly expanded in the clinic, with 7 new approvals in 3 years. For solid tumors, high doses of ADCs improve tissue penetration and efficacy. These doses are enabled by lower drug-to-antibody ratios and/or co-administration of unconjugated antibody carrier doses to avoid payload toxicity. While effective for highly expressed targets, these strategies may not maintain efficacy with lower target expression. To address this issue, a carrier dose that adjusts binding in situ according to cellular expression was designed using computational modeling. Previous studies demonstrated that coadministration of unconjugated antibody with the corresponding ADC at an 8:1 ratio improves ADCs efficacy in high HER2 expressing tumors. By designing a High Avidity, Low Affinity (HALA) carrier antibody, ADC binding is partially blocked in high expression cells, improving tissue penetration. In contrast, the HALA antibody cannot compete with the ADC in low expressing cells, allowing ADC binding to the majority of receptors. Thus, the amount of competition from the carrier dose automatically adjusts to expression levels, allowing tailored competition between different patients/metastases. The computational model highlights two dimensionless numbers, the Thiele modulus and a newly defined competition number, to design an optimal HALA antibody carrier dose for any target.

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.

In Vitro and In Vivo Fluorescence Imaging of Antibody-Drug Conjugate-Induced Tumor Apoptosis Using Annexin V-EGFP Conjugated Quantum Dots

Antibody-drug conjugates (ADCs) are conjugates of a monoclonal antibody and a cytotoxic drug that induce tumor apoptosis. The evaluation of ADC-induced tumor apoptosis is crucial for the development of ADCs for cancer therapy. To evaluate the efficacy of ADCs, we present in vitro and in vivo fluorescence imaging techniques for ADC-induced tumor apoptosis using annexin V-EGFP (EGFP: enhanced green fluorescent protein) conjugated quantum dots (annexin V-EGFP-QDs). This probe emits visible (VIS) and near-infrared (NIR) dual fluorescence at 515 nm (EGFP emission) and 850 nm (QD emission), which can be used for the detection of tumor apoptosis at the cellular and whole-body levels. By using annexin V-EGFP-QDs, we achieved VIS and NIR fluorescence imaging of human epidermal growth factor receptor 2-positive breast tumor apoptosis induced by an ADC, Kadcyla (trastuzumab emtansine). The results show that the in vitro and in vivo fluorescence imaging of ADC-induced tumor apoptosis using annexin V-EGFP-QDs is a useful tool to evaluate the efficacy of ADCs for cancer therapy.

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.

Key metrics to expanding the pipeline of successful antibody-drug conjugates

Although the recent FDA approval of six new antibody-drug conjugates (ADCs) is promising, attrition of ADCs during clinical development remains high. The inherent complexity of ADCs is a double-edged sword that provides opportunities for perfecting therapeutic action while also increasing confounding factors in therapeutic failures. ADC design drives their pharmacokinetics and pharmacodynamics, and requires deeper analysis than the commonly used C_max and area under the curve (AUC) metrics to scale dosing to the clinic. Common features of current FDA-approved ADCs targeting solid tumors include humanized IgG1 antibody domains, highly expressed tumor receptors, and large antibody doses. The potential consequences of these shared features for clinical pharmacokinetics and mechanism of action are discussed, and key design aspects for successful solid tumor ADCs are highlighted.

Transient Competitive Inhibition Bypasses the Binding Site Barrier to Improve Tumor Penetration of Trastuzumab and Enhance T-DM1 Efficacy

Poor penetration of mAbs in solid tumors is explained, in part, by the binding site barrier hypothesis. Following extravasation, mAbs rapidly bind cellular antigens, leading to the observation that, at subsaturating doses, therapeutic antibody in solid tumors localizes around tumor vasculature. Here we report a unique strategy to overcome the binding site barrier through transient competitive inhibition of antibody-antigen binding. The anti-trastuzumab single domain antibody 1HE was identified through in vitro binding assays as a model inhibitor. Coadministration of 1HE did not alter the plasma pharmacokinetics of trastuzumab or ado-trastuzumab emtansine (T-DM1) in vivo. Administration of 1HE alone was rapidly eliminated with a terminal plasma half-life of 1.2 hours, while coadministrations of 1HE with trastuzumab had a terminal half-life of 56 hours. In mice harboring SKOV3 xenografts, coadministration of 1HE with trastuzumab led to significant increases in both penetration of trastuzumab from vasculature and the percentage of tumor area that stained positive for trastuzumab. 1HE coadministered with a single dose of T-DM1 to NCI-N87 xenograft-bearing mice significantly enhanced T-DM1 efficacy, increasing median survival. These results support the hypothesis that transient competitive inhibition can improve therapeutic antibody distribution in solid tumors and enhance antibody efficacy. SIGNIFICANCE: This study describes the development of a transient competitive inhibition strategy that enhances the tumor penetration and efficacy of anticancer antibodies.See related commentary by van Dongen, p. 3956.

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.

Synthesis, characterization, and targeted chemotherapy of SCT200-linker-monomethyl auristatin E conjugates

Antibody-drug conjugates (ADCs) are currently among the most successful and important strategies for treating patients with solid tumors. ADCs are composed of a monoclonal antibody and warhead, which are conjugated via a linker. Currently, monomethyl auristatin E (MMAE) is the most widely applied warhead in the development of ADCs. However, MMAE-based ADCs are generally constructed using the MC-VC-PABC linker, and this design has limited structural diversity and some disadvantages. Accordingly, in this study, we generated three types of novel linker-MMAE (with alterations in the spacer, catabolizing area, and self-immolative compared with MC-VC-PABC-MMAE) in ADCs, termed SCT200-linker-MMAE conjugates, and then evaluated the linker-drug plasma stability and the rate of drug release by cathepsin B. The binding ability, internalization rates, and efficacy of all SCT200-linker-MMAE ADCs were systematically studied, and the expression of apoptosis-associated proteins and the therapeutic efficacies of SCT200-M-2, -C-2, and -C-4 were evaluated. The results showed that the activities of some of these ADCs were increased for epidermal growth factor receptor-positive tumors. Moreover, the novel linkers designed in this study can be linked with other antibodies to treat other types of cancer. Overall, these findings provide important insights into the application of SCT200-based linkers in ADCs.

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.