A mechanistic pharmacokinetic model elucidating the disposition of trastuzumab emtansine (T-DM1), an antibody-drug conjugate (ADC) for treatment of metastatic breast cancer

FZ-AD005, a Novel DLL3-Targeted Antibody-Drug Conjugate with Topoisomerase I Inhibitor, Shows Potent Antitumor Activity in Preclinical Models

Delta-like ligand 3 (DLL3) is overexpressed in small cell lung cancer (SCLC) and has been considered an attractive target for SCLC therapy. Rovalpituzumab tesirine was the first DLL3-targeted antibody-drug conjugate (ADC) to enter clinical studies. However, serious adverse events limited progress in the treatment of SCLC with rovalpituzumab tesirine. In this study, we developed a novel DLL3-targeted ADC, FZ-AD005, by using DXd with potent cytotoxicity and a relatively better safety profile to maximize the therapeutic index. FZ-AD005 was generated by a novel anti-DLL3 antibody, FZ-A038, and a valine-alanine (Val-Ala) dipeptide linker to conjugate DXd. Moreover, Fc-silencing technology was introduced in FZ-AD005 to avoid off-target toxicity mediated by FcγRs and showed negligible Fc-mediated effector functions in vitro. In preclinical evaluation, FZ-AD005 exhibited DLL3-specific binding and demonstrated efficient internalization, bystander killing, and excellent in vivo antitumor activities in cell line-derived xenograft and patient-derived xenograft models. FZ-AD005 was stable in circulation with acceptable pharmacokinetic profiles in cynomolgus monkeys. FZ-AD005 was well tolerated in rats and monkeys. The safety profile of FZ-AD005 was favorable, and the highest nonseverely toxic dose was 30 mg/kg in cynomolgus monkeys. In conclusion, FZ-AD005 has the potential to be a superior DLL3-targeted ADC with a wide therapeutic window and is expected to provide clinical benefits for the treatment of patients with SCLC.

A modelling approach to compare ADC deconjugation and systemic elimination rates of individual drug-load species using native ADC LC-MS data from human plasma

Native liquid chromatography mass spectrometry (LC-MS) is a commonly used approach for intact analysis of inter-chain cysteine conjugated antibody-drug conjugates (ADCs). Coupling native LC-MS with affinity capture provides a platform for intact ADC analysis from in vivo samples and characterisation of individual drug load species, specifically the impact of drug linker deconjugation, hydrolysis, and differential clearance in a biological system.This manuscript describes data generated from native LC-MS analysis of ADCs from human plasma, both in vitro incubations and clinical samples. It also details the pharmacokinetic (PK) model built to specifically characterise the disposition of individual drug load species from MMAE and MMAF interchain cysteine conjugated ADCs.In vitro deconjugation and hydrolysis rates were similar across both ADCs. Differential clearance of higher loaded species in vivo was pronounced for the MMAE conjugated ADC, while systemic elimination after accounting for deconjugation was similar across drug loads for the MMAF conjugated ADC. This is the first report of affinity capture native LC-MS analysis, and subsequent modelling of deconjugation, hydrolysis and clearance rates of individual drug load species using clinical data from cysteine conjugated ADCs.

Investigation of Antibody Pharmacokinetics in the Brain Following Intra-CNS Administration and Development of PBPK Model to Characterize the Data

Despite the promising potential of direct central nervous system (CNS) antibody administration to enhance brain exposure, there remains a significant gap in understanding the disposition of antibodies following different intra-CNS injection routes. To bridge this knowledge gap, this study quantitatively investigated the brain pharmacokinetics (PK) of antibodies following intra-CNS administration. The microdialysis samples from the striatum (ST), cerebrospinal fluid (CSF) samples through cisterna magna (CM) puncture, plasma, and brain homogenate samples were collected to characterize the pharmacokinetics (PK) profiles of a non-targeting antibody, trastuzumab, following intracerebroventricular (ICV), intracisternal (ICM), and intrastriatal (IST) administration. For a comprehensive analysis, these intra-CNS injection datasets were juxtaposed against our previously acquired intravenous (IV) injection data obtained under analogous experimental conditions. Our findings highlighted that direct CSF injections, either through ICV or ICM, resulted in ~ 5-6-fold higher interstitial fluid (ISF) drug exposure than IV administration. Additionally, the low bioavailability observed following IST administration indicates the existence of a local degradation process for antibody elimination in the brain ISF along with the ISF bulk flow. The study further refined a physiologically based pharmacokinetic (PBPK) model based on new observations by adding the perivascular compartments, oscillated CSF flow, and the nonspecific uptake and degradation of antibodies by brain parenchymal cells. The updated model can well characterize the antibody PK following systemic and intra-CNS administration. Thus, our research offers quantitative insight into antibody brain disposition pathways and paves the way for determining optimal dosing and administration strategies for antibodies targeting CNS disorders.

Application of physiologically-based pharmacokinetic models for therapeutic proteins and other novel modalities

The past two decades have seen diversification of drug development pipelines and approvals from traditional small molecule therapies to alternative modalities including monoclonal antibodies, engineered proteins, antibody drug conjugates (ADCs), oligonucleotides and gene therapies. At the same time, physiologically-based pharmacokinetic (PBPK) models for small molecules have seen increased industry and regulatory acceptance.This review focusses on the current status of the application of PBPK models to these newer modalities and give a perspective on the successes, challenges and future directions of this field.There is greatest experience in the development of PBPK models for therapeutic proteins, and PBPK models for ADCs benefit from prior experience for both therapeutic proteins and small molecules. For other modalities, the application of PBPK models is in its infancy.Challenges are discussed and a common theme is lack of availability of physiological and experimental data to characterise systems and drug parameters to enable a priori prediction of pharmacokinetics. Furthermore, sufficient clinical data are required to build confidence in developed models.The PBPK modelling approach provides a quantitative framework for integrating knowledge and data from multiple sources and can be built on as more data becomes available.

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.

A Simple Method for the Prediction of Human Concentration-Time Profiles and Pharmacokinetics of Antibody-Drug Conjugates (ADC) from Rats or Monkeys

Knowledge of human concentration-time profiles from animal data can be useful during early drug development. The objective of this study is to predict human concentration-time profiles of antibody-drug conjugates (ADCs) and subsequently predict pharmacokinetic parameters in humans from rats or monkeys. Eight methods with different exponents of volume of distribution (0.8-1) as well as exponents of clearance (0.85), along with the exponents of volume of distribution for 5 ADCs, were used to predict human concentration-time profiles. The PK parameters were also scaled to humans from monkeys or rats using fixed exponents and compared with the PK parameters predicted from predicted human concentration-time profiles. The results of the study indicated that the exponent 0.9 and the combination of exponents of 0.9 and 0.8 (two exponents, 0.8 and 0.9, were used) were the best method to predict human concentration-time profiles and, subsequently, human PK parameters. The predicted PK parameters from fixed exponents were comparable with the predicted PK parameters estimated from human concentration-time profiles. The proposed methods are applicable to rats or monkeys with the same degree of accuracy. Overall, the proposed methods are robust, accurate, and cost- and time-effective.

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.

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.

Integrated multiple analytes and semi-mechanistic population pharmacokinetic model of tusamitamab ravtansine, a DM4 anti-CEACAM5 antibody-drug conjugate

Tusamitamab ravtansine (SAR408701) is an antibody-drug conjugate (ADC), combining a humanized monoclonal antibody (IgG1) targeting carcinoembryonic antigen-related cell adhesion molecule 5 (CEACAM5) and a potent cytotoxic maytansinoid derivative, DM4, inhibiting microtubule assembly. SAR408701 is currently in clinical development for the treatment of advanced solid tumors expressing CEACAM5. It is administered intravenously as a conjugated antibody with an average Drug Antibody Ratio (DAR) of 3.8. During SAR408701 clinical development, four entities were measured in plasma: conjugated antibody (SAR408701), naked antibody (NAB), DM4 and its methylated metabolite (MeDM4), both being active. Average DAR and proportions of individual DAR species were also assessed in a subset of patients. An integrated and semi-mechanistic population pharmacokinetic model describing the time-course of all entities in plasma and DAR measurements has been developed. All DAR moieties were assumed to share the same drug disposition parameters, excepted for clearance which differed for DAR0 (i.e. NAB entity). The conversion of higher DAR to lower DAR resulted in a DAR-dependent ADC deconjugation and was represented as an irreversible first-order process. Each conjugated antibody was assumed to contribute to DM4 formation. All data were fitted simultaneously and the model developed was successful in describing the pharmacokinetic profile of each entity. Such a structural model could be translated to other ADCs and gives insight of mechanistic processes governing ADC disposition. This framework will further be expanded to evaluate covariates impact on SAR408701 pharmacokinetics and its derivatives, and thus can help identifying sources of pharmacokinetic variability and potential efficacy and safety pharmacokinetic drivers.

Development of a Physiologically-Based Pharmacokinetic Model for Whole-Body Disposition of MMAE Containing Antibody-Drug Conjugate in Mice

PURPOSE

To quantitate and mathematically characterize the whole-body pharmacokinetics (PK) of different ADC analytes following administration of an MMAE-conjugated ADC in tumor-bearing mice.

METHODS

The PK of different ADC analytes (total antibody, total drug, unconjugated drug) was measured following administration of an MMAE-conjugated ADC in tumor-bearing mice. The PK of ADC analytes was compared with the whole-body PK of the antibody and drug obtained following administration of these molecules alone. An ADC PBPK model was developed by linking antibody PBPK model with small-molecule PBPK model, where the drug was assumed to deconjugate in DAR-dependent manner.

RESULTS

Comparison of antibody biodistribution coefficient (ABC) values for total antibody suggests that conjugation of drug did not significantly affect the PK of antibody. Comparison of tissue:plasma AUC ratio (T/P) for the conjugated drug and total antibody suggests that in certain tissues (e.g., spleen) ADC may demonstrate higher deconjugation. It was observed that the tissue distribution profile of the drug can be altered following its conjugation to antibody. For example, MMAE distribution to the liver was found to increase while its distribution to the heart was found to decrease upon conjugation to antibody. MMAE exposure in the tumor was found to increase by ~20-fold following administration as conjugate (i.e., ADC). The PBPK model was able to a priori predict the PK of all three ADC analytes in plasma, tissues, and tumor reasonably well.

CONCLUSIONS

The ADC PBPK model developed here serves as a platform for translational and clinical investigations of MMAE containing ADCs.

Novel development strategies and challenges for anti-Her2 antibody-drug conjugates

Antibody-drug conjugates (ADCs) combining potent cytotoxicity of small-molecule drugs with the selectivity and excellent pharmacokinetic profile of monoclonal antibody (mAb) are promising therapeutic modalities for a diverse range of cancers. Owing to overexpression in a wide range of tumors, human epidermal growth factor receptor 2 (Her2) is one of the most utilized targeting antigens for ADCs to treat Her2-positive cancers. Owing to the high density of Her2 antigens on the tumor cells and high affinity and high internalization capacity of corresponding antibodies, 56 anti-Her2 ADCs which applied >10 different types of novel payloads had entered preclinical or clinical trials. Seven of 12 Food and Drug Administration (FDA)-approved ADCs including Polivy (2019), Padcev (2019), EnHertu (2019), Trodelvy (2020), Blenrep (2020), Zynlonta (2021), and Tivdak) (2021) have been approved by FDA in the past three years alone, indicating that the maturing of ADC technology brings more productive clinical outcomes. This review, focusing on the anti-Her2 ADCs in clinical trials or on the market, discusses the strategies to select antibody formats, the linkages between linker and mAb, and effective payloads with particular release and action mechanisms for a good clinical outcome.

Impact of Physiologically Based Pharmacokinetics, Population Pharmacokinetics and Pharmacokinetics/Pharmacodynamics in the Development of Antibody-Drug Conjugates

Antibody‐drug conjugates are important molecular entities in the treatment of cancer, with 8 antibody‐drug conjugates approved by the US Food and Drug Administration since 2000 and many more in early‐ and late‐stage clinical development. These conjugates combine the target specificity of monoclonal antibodies with the potent anticancer activity of small‐molecule therapeutics. The complex structure of antibody‐drug conjugates poses unique challenges to pharmacokinetic (PK) and pharmacodynamic (PD) characterization because it requires a quantitative understanding of the PK and PD properties of multiple different molecular species (eg, conjugate, total antibody, and unconjugated payload) in different tissues. Quantitative clinical pharmacology using mathematical modeling and simulation provides an excellent approach to overcome these challenges, as it can simultaneously integrate the disposition, PK, and PD of antibody‐drug conjugates and their components in a quantitative manner. In this review, we highlight diverse quantitative clinical pharmacology approaches, ranging from system models (eg, physiologically based pharmacokinetic [PBPK] modeling) to mechanistic and empirical models (eg, population PK/PD modeling for single or multiple analytes, exposure‐response modeling, platform modeling by pooling data across multiple antibody‐drug conjugates). The impact of these PBPK and PK/PD models to provide insights into clinical dosing justification and inform drug development decisions is also highlighted.

Clinical pharmacology of vc-MMAE antibody-drug conjugates in cancer patients: learning from eight first-in-human Phase 1 studies

vc-MMAE antibody–drug conjugates (ADCs) consist of a monoclonal antibody (mAb) covalently bound with a potent anti-mitotic toxin (MMAE) through a protease-labile valine-citrulline (vc) linker. The objective of this study was to characterize the pharmacokinetics (PK) and explore exposure–response relationships of eight vc-MMAE ADCs, against different targets and for diverse tumor indications, using data from eight first-in-human Phase 1 studies. PK parameters of the three analytes, namely antibody-conjugated MMAE (acMMAE), total antibody, and unconjugated MMAE, were estimated using non-compartmental approaches and compared across the eight vc-MMAE ADCs. Relationships between analytes were assessed by linear regression. Exposure–response relationships were explored with key efficacy (objective response rate) and safety (Grade 2+ peripheral neuropathy) endpoints. PK profiles of acMMAE, total antibody and unconjugated MMAE following the first dose of 2.4 mg/kg were comparable across the eight ADCs; the exposure differences between molecules were small relative to the inter-subject variability. acMMAE exposure was strongly correlated with total antibody exposure for all the eight ADCs, but such correlation was less evident between acMMAE and unconjugated MMAE exposure. For multiple ADCs evaluated, efficacy and safety endpoints appeared to correlate well with acMMAE exposure, but not with unconjugated MMAE over the doses tested. PK of vc-MMAE ADCs was well characterized and demonstrated remarkable similarity at 2.4 mg/kg across the eight ADCs. Results from analyte correlation and exposure–response relationship analyses suggest that measurement of acMMAE analyte alone might be adequate for vc-MMAE ADCs to support the clinical pharmacology strategy used during late-stage clinical development.

Antibody Co-Administration Can Improve Systemic and Local Distribution of Antibody-Drug Conjugates to Increase In Vivo Efficacy

Several antibody-drug conjugates (ADC) showing strong clinical responses in solid tumors target high expression antigens (HER2, TROP2, Nectin-4, and folate receptor alpha/FRα). Highly expressed tumor antigens often have significant low-level expression in normal tissues, resulting in the potential for target-mediated drug disposition (TMDD) and increased clearance. However, ADCs often do not cross-react with normal tissue in animal models used to test efficacy (typically mice), and the impact of ADC binding to normal tissue antigens on tumor response remains unclear. An antibody that cross-reacts with human and murine FRα was generated and tested in an animal model where the antibody/ADC bind both human tumor FRα and mouse FRα in normal tissue. Previous work has demonstrated that a "carrier" dose of unconjugated antibody can improve the tumor penetration of ADCs with high expression target-antigens. A carrier dose was employed to study the impact on cross-reactive ADC clearance, distribution, and efficacy. Co-administration of unconjugated anti-FRα antibody with the ADC-improved efficacy, even in low expression models where co-administration normally lowers efficacy. By reducing target-antigen-mediated clearance in normal tissue, the co-administered antibody increased systemic exposure, improved tumor tissue penetration, reduced target-antigen-mediated uptake in normal tissue, and increased ADC efficacy. However, payload potency and tumor antigen saturation are also critical to efficacy, as shown with reduced efficacy using too high of a carrier dose. The judicious use of higher antibody doses, either through lower DAR or carrier doses, can improve the therapeutic window by increasing efficacy while lowering target-mediated toxicity in normal tissue.

Use of translational modeling and simulation for quantitative comparison of PF-06804103, a new generation HER2 ADC, with Trastuzumab-DM1

A modeling and simulation approach was used for quantitative comparison of a new generation HER2 antibody drug conjugate (ADC, PF-06804103) with trastuzumab-DM1 (T-DM1). To compare preclinical efficacy, the pharmacokinetic (PK)/pharmacodynamic (PD) relationship of PF-06804103 and T-DM1 was determined across a range of mouse tumor xenograft models, using a tumor growth inhibition model. The tumor static concentration was assigned as the minimal efficacious concentration. PF-06804103 was concluded to be more potent than T-DM1 across cell lines studied. TSCs ranged from 1.0 to 9.8 µg/mL (n = 7) for PF-06804103 and from 4.7 to 29 µg/mL (n = 5) for T-DM1. Two experimental models which were resistant to T-DM1, responded to PF-06804103 treatment. A mechanism-based target mediated drug disposition (TMDD) model was used to predict the human PK of PF-06804103. This model was constructed and validated based on T-DM1 which has non-linear PK at doses administered in the clinic, driven by binding to shed HER2. Non-linear PK is predicted for PF-06804103 in the clinic and is dependent upon circulating HER2 extracellular domain (ECD) concentrations. The models were translated to human and suggested greater efficacy for PF-06804103 compared to T-DM1. In conclusion, a fit-for-purpose translational PK/PD strategy for ADCs is presented and used to compare a new generation HER2 ADC with T-DM1.

Nonclinical Development of Next-generation Site-specific HER2-targeting Antibody-drug Conjugate (ARX788) for Breast Cancer Treatment

Conventional antibody-drug conjugates (ADC) utilize native surface-exposed lysines or cysteines on the antibody of interest to conjugate cytotoxic payload. The nonspecific conjugation results in a mixture with variable drug-to-antibody ratios (DAR), conjugation sites, and ADCs that are often unstable in systemic circulation. ARX788 is an ADC consisting of a HER2-targeting antibody site-specifically conjugated with a potent antitubulin cytotoxic drug-linker, AS269. The site-specific conjugation is achieved by first incorporating the nonnatural amino acid, para-acetyl phenylalanine (pAF), into the antibody, followed by covalent conjugation of AS269 to the pAF to form a highly stable oxime bond resulting in a DAR 2 ADC. ARX788 exhibits significant, dose-dependent antitumor activity against HER2- expressing breast and gastric xenograft tumors. Pharmacokinetic (PK) studies in multiple species showed the highly stable nature of ARX788 with overlapping PK profiles for the intact ADC and total antibody. Metabolism studies demonstrated that pAF-AS269 was the sole major metabolite of ARX788, with no evidence for the release of free drug often observed in conventional ADCs and responsible for adverse side effects. Furthermore, ARX788 demonstrated a favorable safety profile in monkeys with a highest nonseverely toxic dose of 10 mg/kg, which was well above the efficacious dose level observed in preclinical tumor models, thus supporting clinical development of ARX788.

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.

Labeling Monoclonal Antibody with α-emitting 211At at High Activity Levels via a Tin Precursor

Background: In a previous clinical study, the authors evaluated the potential of antitenascin C monoclonal antibody (mAb) 81C6 labeled with ²¹¹At via the prosthetic agent N-succinimidyl 3-[²¹¹At]astatobenzoate (SAB) for the treatment of primary brain tumors. Although encouraging results were obtained, labeling chemistry failed while attempting to escalate the dose to 370 MBq. The goal of the current study was to develop a revised procedure less susceptible to radiolysis-mediated effects on ²¹¹At labeling that would be suitable for use at higher activity levels of this α-emitter. Materials and Methods: Addition of N-chlorosuccinimide to the methanol used to remove the ²¹¹At from the cryotrap after bismuth target distillation was done to thwart radiolytic decomposition of reactive ²¹¹At and the tin precursor. A series of 11 reactions were performed to produce SAB at initial ²¹¹At activity levels of 0.31-2.74 GBq from 50 μg of N-succinimidyl 3-trimethylstannylbenzoate (Me-STB), which was then reacted with murine 81C6 mAb without purification of the SAB intermediate. Radiochemical purity, immunoreactive fraction, sterility, and apyrogenicity of the ²¹¹At-labeled 81C6 preparations were evaluated. Results: Murine 81C6 mAb was successfully labeled with ²¹¹At using these revised procedures with improved radiochemical yields and decreased overall synthesis time compared with the original clinical labeling procedure. Conclusions: With 2.74 GBq of ²¹¹At, it was possible to produce 1.0 GBq of ²¹¹At-labeled 81C6 with an immunoreactive fraction of 92%. These revised procedures permit production of ²¹¹At-labeled mAbs suitable for use at clinically relevant activity levels.

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.

Development of a high yielding expression platform for the introduction of non-natural amino acids in protein sequences

The ability to genetically encode non-natural amino acids (nnAAs) into proteins offers an expanded tool set for protein engineering. nnAAs containing unique functional moieties have enabled the study of post-translational modifications, protein interactions, and protein folding. In addition, nnAAs have been developed that enable a variety of biorthogonal conjugation chemistries that allow precise and efficient protein conjugations. These are being studied to create the next generation of antibody-drug conjugates with improved efficacy, potency, and stability for the treatment of cancer. However, the efficiency of nnAA incorporation, and the productive yields of cell-based expression systems, have limited the utility and widespread use of this technology. We developed a process to isolate stable cell lines expressing a pyrrolysyl-tRNA synthetase/tRNApyl pair capable of efficient nnAA incorporation. Two different platform cell lines generated by these methods were used to produce IgG-expressing cell lines with normalized antibody titers of 3 g/L using continuous perfusion. We show that the antibodies produced by these platform cells contain the nnAA functionality that enables facile conjugations. Characterization of these highly active and robust platform hosts identified key parameters that affect nnAA incorporation efficiency. These highly efficient host platforms may help overcome the expression challenges that have impeded the developability of this technology for manufacturing proteins with nnAAs and represents an important step in expanding its utility.

Preclinical and translational pharmacokinetics of a novel THIOMAB™ antibody-antibiotic conjugate against Staphylococcus aureus

DSTA4637S, a novel THIOMAB™ antibody-antibiotic conjugate (TAC) against Staphylococcus aureus (S. aureus), is currently being investigated as a potential therapy for complicated S. aureus bloodstream infections. DSTA4637S is composed of a monoclonal THIOMAB^TM IgG1 recognizing S. aureus linked to a rifamycin-class antibiotic (dmDNA31) via a protease-cleavable linker. The pharmacokinetics (PK) of DSTA4637A (a liquid formulation of DSTA4637S) and its unconjugated antibody MSTA3852A were characterized in rats and monkeys. Systemic concentrations of three analytes, total antibody (TAb), antibody-conjugated dmDNA31 (ac-dmDNA31), and unconjugated dmDNA31, were measured to describe complex TAC PK in nonclinical studies. In rats and monkeys, following intravenous administration of a single dose of DSTA4637A, systemic concentration-time profiles of both TAb and ac-dmDNA31 were bi-exponential, characterized by a short distribution phase and a long elimination phase as expected for a monoclonal antibody-based therapeutic. Systemic exposures of both TAb and ac-dmDNA31 were dose proportional over the dose range tested, and ac-dmDNA31 cleared 2-3 times faster than TAb. Unconjugated dmDNA31 plasma concentrations were low (<4 ng/mL) in every study regardless of dose. In this report, an integrated semi-mechanistic PK model for two analytes (TAb and ac-dmDNA31) was successfully developed and was able to well describe the complicated DSTA4637A PK in mice, rats and monkeys. DSTA4637S human PK was predicted reasonably well using this model with allometric scaling of PK parameters from monkey data. This work provides insights into PK behaviors of DSTA4637A in preclinical species and informs clinical translatability of these observed results and further clinical development. Abbreviations: ADC: Antibody-drug conjugate; AUC_inf: time curve extrapolated to infinity; ac-dmDNA31: antibody-conjugated dmDNA31; C_max: maximum concentration observed; DAR: drug-to-antibody ratio; CL: clearance; CL_D: distribution clearance; CL₁: systemic clearance of all DAR species; k_DC: deconjugation rate constant; PK: Pharmacokinetics; IV: Intravenous; IgG: Immunoglobulin G; mAb: monoclonal antibody; S. aureus: Staphylococcus aureus; TAC: THIOMAB^TM antibody-antibiotic conjugate; TDC: THIOMAB^TM antibody-drug conjugate; TAb: total antibody; t_{1/2, λz}: terminal half-life; vc linker: valine-citrulline linker; V_ss: volume of distribution at steady state; V_c: volume of distribution for the central compartment; V_p: the volume of distribution for the peripheral compartment.

Conjugation of DM1 to anti-CD30 antibody has potential antitumor activity in CD30-positive hematological malignancies with lower systemic toxicity

An anti-CD30 antibody-drug conjugate incorporating the antimitotic agent DM1 and a stable SMCC linker, anti-CD30-MCC-DM1, was generated as a new antitumor drug candidate for CD30-positive hematological malignancies. Here, the in vitro and in vivo pharmacologic activities of anti-CD30-MCC-DM1 (also known as F0002-ADC) were evaluated and compared with ADCETRIS (brentuximab vedotin). Pharmacokinetics (PK) and the safety profiles in cynomolgus monkeys were assessed. Anti-CD30-MCC-DM1 was effective in in vitro cell death assays using CD30-positive lymphoma cell lines. We studied the properties of anti-CD30-MCC-DM1, including binding, internalization, drug release and actions. Unlike ADCETRIS, anti-CD30-MCC-DM1 did not cause a bystander effect in this study. In vivo, anti-CD30-MCC-DM1 was found to be capable of inducing tumor regression in subcutaneous inoculation of Karpas 299 (anaplastic large cell lymphoma), HH (cutaneous T-cell lymphoma) and L428 (Hodgkin's disease) cell models. The half-lives of 4 mg/kg and 12 mg/kg anti-CD30-MCC-DM1 were about 5 days in cynomolgus monkeys, and the tolerated dose was 30 mg/kg in non-human primates, supporting the tolerance of anti-CD30-MCC-DM1 in humans. These results suggest that anti-CD30-MCC-DM1 presents efficacy, safety and PK profiles that support its use as a valuable treatment for CD30-positive hematological malignancies.

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.

Review of approaches and examples for monitoring biotransformation in protein and peptide therapeutics by MS

Biotherapeutic drugs have emerged in quantity in pharmaceutical pipelines, and increasingly diverse biomolecules are progressed through preclinical and clinical development. As purification, separation, mass spectrometer detection and data processing capabilities improve, there is opportunity to monitor drug concentration by traditional ligand-binding assay or MS measurement and to monitor metabolism, catabolism or other biomolecular mass variants present in circulation. This review highlights approaches and examples of monitoring biotransformation of biotherapeutics by MS as these techniques are poised to add value to drug development in years to come. The increased use of such approaches, and the successful quantitation of biotherapeutic structural modifications, will provide insightful data for the benefit of both researchers and patients.

Prediction of non-linear pharmacokinetics in humans of an antibody-drug conjugate (ADC) when evaluation of higher doses in animals is limited by tolerability: Case study with an anti-CD33 ADC

For antibody-drug conjugates (ADCs) that carry a cytotoxic drug, doses that can be administered in preclinical studies are typically limited by tolerability, leading to a narrow dose range that can be tested. For molecules with non-linear pharmacokinetics (PK), this limited dose range may be insufficient to fully characterize the PK of the ADC and limits translation to humans. Mathematical PK models are frequently used for molecule selection during preclinical drug development and for translational predictions to guide clinical study design. Here, we present a practical approach that uses limited PK and receptor occupancy (RO) data of the corresponding unconjugated antibody to predict ADC PK when conjugation does not alter the non-specific clearance or the antibody-target interaction. We used a 2-compartment model incorporating non-specific and specific (target mediated) clearances, where the latter is a function of RO, to describe the PK of anti-CD33 ADC with dose-limiting neutropenia in cynomolgus monkeys. We tested our model by comparing PK predictions based on the unconjugated antibody to observed ADC PK data that was not utilized for model development. Prospective prediction of human PK was performed by incorporating in vitro binding affinity differences between species for varying levels of CD33 target expression. Additionally, this approach was used to predict human PK of other previously tested anti-CD33 molecules with published clinical data. The findings showed that, for a cytotoxic ADC with non-linear PK and limited preclinical PK data, incorporating RO in the PK model and using data from the corresponding unconjugated antibody at higher doses allowed the identification of parameters to characterize monkey PK and enabled human PK predictions.

Development of a Translational Physiologically Based Pharmacokinetic Model for Antibody-Drug Conjugates: a Case Study with T-DM1

Systems pharmacokinetic (PK) models that can characterize and predict whole body disposition of antibody-drug conjugates (ADCs) are needed to support (i) development of reliable exposure-response relationships for ADCs and (ii) selection of ADC targets with optimal tumor and tissue expression profiles. Towards this goal, we have developed a translational physiologically based PK (PBPK) model for ADCs, using T-DM1 as a tool compound. The preclinical PBPK model was developed using rat data. Biodistribution of DM1 in rats was used to develop the small molecule PBPK model, and the PK of conjugated trastuzumab (i.e., T-DM1) in rats was characterized using platform PBPK model for antibody. Both the PBPK models were combined via degradation and deconjugation processes. The degradation of conjugated antibody was assumed to be similar to a normal antibody, and the deconjugation of DM1 from T-DM1 in rats was estimated using plasma PK data. The rat PBPK model was translated to humans to predict clinical PK of T-DM1. The translation involved the use of human antibody PBPK model to characterize the PK of conjugated trastuzumab, use of allometric scaling to predict human clearance of DM1 catabolites, and use of monkey PK data to predict deconjugation of DM1 in the clinic. PBPK model-predicted clinical PK profiles were compared with clinically observed data. The PK of total trastuzumab and T-DM1 were predicted reasonably well, and slight systemic deviations were observed for the PK of DM1-containing catabolites. The ADC PBPK model presented here can serve as a platform to develop models for other ADCs.

Application of a PK-PD Modeling and Simulation-Based Strategy for Clinical Translation of Antibody-Drug Conjugates: a Case Study with Trastuzumab Emtansine (T-DM1)

Successful clinical translation of antibody-drug conjugates (ADCs) can be challenging due to complex pharmacokinetics and differences between preclinical and clinical tumors. To facilitate this translation, we have developed a general pharmacokinetic-pharmacodynamic (PK-PD) modeling and simulation (M&S)-based strategy for ADCs. Here we present the validation of this strategy using T-DM1 as a case study. A previously developed preclinical tumor disposition model for T-DM1 (Singh and Shah, AAPSJ. 2015; 18(4):861-875) was used to develop a PK-PD model that can characterize in vivo efficacy of T-DM1 in preclinical tumor models. The preclinical data was used to estimate the efficacy parameters for T-DM1. Human PK of T-DM1 was a priori predicted using allometric scaling of monkey PK parameters. The predicted human PK, preclinically estimated efficacy parameters, and clinically observed volume and growth parameters for breast cancer were combined to develop a translated clinical PK-PD model for T-DM1. Clinical trial simulations were performed using the translated PK-PD model to predict progression-free survival (PFS) and objective response rates (ORRs) for T-DM1. The model simulated PFS rates for HER2 1+ and 3+ populations were comparable to the rates observed in three different clinical trials. The model predicted only a modest improvement in ORR with an increase in clinically approved dose of T-DM1. However, the model suggested that a fractionated dosing regimen (e.g., front loading) may provide an improvement in the efficacy. In general, the PK-PD M&S-based strategy presented here is capable of a priori predicting the clinical efficacy of ADCs, and this strategy has been now retrospectively validated for all clinically approved ADCs.

Translational Model-Based Strategy to Guide the Choice of Clinical Doses for Antibody-Drug Conjugates

This work proposes a model-based approach to help select the phase 1 dosing regimen for the antibody-drug conjugate (ADC) SAR408701 leveraging the available data for 2 other ADCs of the same construct: SAR3419 and SAR566658. First, monkey and human pharmacokinetic (PK) data of SAR566658 and SAR3419 were used to establish the appropriate allometric approach to be applied to SAR408701 monkey PK data. Second, a population pharmacokinetics-pharmacodynamics (PK-PD) model was developed to describe tumor volume evolution following SAR408701 injection in mice. Third, allometric approaches identified for SAR566658 and SAR3419 were applied to SAR408701 monkey PK data to predict the human PK profile. Both SAR566658 and SAR3419 human and monkey PK were best described by a 2-compartment linear model. The relative difference was less than 10% between predicted and observed clearance using allometric exponents of 0.75 and 1, respectively. Tumor volume evolution following SAR408701 injection was best described by a full Simeoni model with a plasma concentration threshold of 4.6 μg/mL for eradication in mice. Both allometric exponents were used to predict SAR408701 PK in human from PK in monkey and to identify the potential effective dosing regimens. This translational strategy may be a valuable tool to design future clinical studies for ADCs, to support selection of the most appropriate dosing regimen, and to estimate the minimal dose required to assure antitumor activity, according to the schedule used.

Integrated Two-Analyte Population Pharmacokinetic Model for Antibody-Drug Conjugates in Patients: Implications for Reducing Pharmacokinetic Sampling

An integrated pharmacokinetics (PK) model that simultaneously describes concentrations of total antibody (Tab) and antibody‐conjugated monomethyl auristatin E (acMMAE) following administration of monomethyl auristatin E (MMAE)‐containing antibody–drug conjugates (ADCs) was developed based on phase I PK data with extensive sampling for two ADCs. Two linear two‐compartment models that shared all parameters were used to describe the PK of Tab and acMMAE, except that the deconjugation rate was an additional clearance pathway included in the acMMAE PK model compared to Tab. Further, the model demonstrated its ability to predict Tab concentrations and PK parameters based on observed acMMAE PK and various reduced or eliminated Tab PK sampling schemes of phase II data. Thus, this integrated model allows for the reduction of Tab PK sampling in late‐phase clinical development without compromising Tab PK characterization.

Development and Translational Application of an Integrated, Mechanistic Model of Antibody-Drug Conjugate Pharmacokinetics

Antibody drug conjugates (ADC), in which small molecule cytotoxic agents are non-specifically linked to antibodies, can enable targeted delivery of chemotherapeutics to tumor cells. ADCs are often produced and administered as a mixture of conjugated antibodies with different drug to antibody ratios (DAR) resulting in complex and heterogeneous disposition kinetics. We developed a mechanism-based platform model that can describe and predict the complex pharmacokinetic (PK) behavior of ADCs with protease-cleavable valine-citrulline (VC) linker linked to Monomethylmonomethyl auristatin F/E by incorporating known mechanisms of ADC disposition. The model includes explicit representation of all DAR species; DAR-dependent sequential deconjugation of the drug, resulting in the conversion of higher DAR to lower DAR species; and DAR-dependent antibody/ADC clearance. PK profiles of multiple analytes (total antibody, drug-conjugated antibody, and/or antibody-conjugated drug) for different ADC molecules and targets in rodents and cynomolgus monkeys were used for model development. The integrated cross-species model was successful in capturing the multi-analyte PK profiles after administration of purified ADCs with defined DAR species and ADCs with mixtures of DAR. Human PK predictions for DSTP3086S (anti-STEAP1-vc-MMAE) with the platform model agreed well with PK (total antibody and antibody-conjugated drug concentrations) measurements in the dose-ranging phase I clinical study. The integrated model is applicable to various other ADCs with different formats, conjugated drugs, and linkers, and provides a valuable tool for the exploration of mechanisms governing disposition of ADCs and enables translational predictions.

In-Depth Characterization of a Pro-Antibody-Drug Conjugate by LC-MS

Pro-antibody-drug conjugate (PDC) is a hybrid structural format of immunoconjugate, where the structural complexity of pro-antibody and intrinsic heterogeneity of ADCs impose a prominent analytical challenge to the in-depth characterization of PDCs. In the present study, we successfully prepared and characterized PanP-DM1 as a model of PDCs, which is an anti-EGFR pro-antibody following conjugation with DM1 at lysine residues. The drug-to-antibody ratio (DAR) of PanP-DM1 was determined by LC-MS after deglycosylation, and verified by UV/vis spectroscopy. Following reduction or IdeS digestion, the pro-antibody fragments linked with DM1 were investigated by middle-down mass spectrometry. Furthermore, more than 20 modified lysine conjugation sites were determined by peptide mapping after trypsin digestion. Additionally, more than ten glycoforms of PanP-DM1 were also identified and quantified. In summary, critical quality attributes (CQAs) of PDCs including DAR, drug load distribution, and conjugation sites were fully characterized, which would contribute to the development of other PDCs for cancer treatment.

In-depth structural characterization of Kadcyla® (ado-trastuzumab emtansine) and its biosimilar candidate

The biopharmaceutical industry has become increasingly focused on developing biosimilars as less expensive therapeutic products. As a consequence, the regulatory approval of 2 antibody-drug conjugates (ADCs), Kadcyla® and Adcetris® has led to the development of biosimilar versions by companies located worldwide. Because of the increased complexity of ADC samples that results from the heterogeneity of conjugation, it is imperative that close attention be paid to the critical quality attributes (CQAs) that stem from the conjugation process during ADC biosimilar development process. A combination of physicochemical, immunological, and biological methods are warranted in order to demonstrate the identity, purity, concentration, and activity (potency or strength) of ADC samples. As described here, we performed extensive characterization of a lysine conjugated ADC, ado-trastuzumab emtansine, and compared its CQAs between the reference product (Kadcyla®) and a candidate biosimilar. Primary amino acid sequences, drug-to-antibody ratios (DARs), conjugation sites and site occupancy data were acquired and compared by LC/MS methods. Furthermore, thermal stability, free drug content, and impurities were analyzed to further determine the comparability of the 2 ADCs. Finally, biological activities were compared between Kadcyla® and biosimilar ADCs using a cytotoxic activity assay and a HER2 binding assay. The in-depth characterization helps to establish product CQAs, and is vital for ADC biosimilars development to ensure their comparability with the reference product, as well as product safety.

Antibody–drug conjugate bioanalysis using LB-LC–MS/MS hybrid assays: strategies, methodology and correlation to ligand-binding assays

Background: Antibody–drug conjugates (ADCs) are complex drug constructs with multiple species in the heterogeneous mixture that contribute to their efficacy and toxicity. The bioanalysis of ADCs involves multiple assays and analytical platforms. Methods: A series of ligand binding and LC–MS/MS (LB-LC–MS/MS) hybrid assays, through different combinations of anti-idiotype (anti-Id), anti-payload, or generic capture reagents, and cathepsin-B or trypsin enzyme digestion, were developed and evaluated for the analysis of conjugated-payload as well as for species traditionally measured by ligand-binding assays, total-antibody and conjugated-antibody. Results & conclusion: Hybrid assays are complementary or viable alternatives to ligand-binding assay for ADC bioanalysis and PK/PD modeling. The fit-for-purpose choice of analytes, assays and platforms and an integrated strategy from Discovery to Development for ADC PK and bioanalysis are recommended.

Understanding How the Stability of the Thiol-Maleimide Linkage Impacts the Pharmacokinetics of Lysine-Linked Antibody-Maytansinoid Conjugates

Antibody-drug conjugates (ADCs) have become a widely investigated modality for cancer therapy, in part due to the clinical findings with ado-trastuzumab emtansine (Kadcyla). Ado-trastuzumab emtansine utilizes the Ab-SMCC-DM1 format, in which the thiol-functionalized maytansinoid cytotoxic agent, DM1, is linked to the antibody (Ab) via the maleimide moiety of the heterobifunctional SMCC linker. The pharmacokinetic (PK) data for ado-trastuzumab emtansine point to a faster clearance for the ADC than for total antibody. Cytotoxic agent release in plasma has been reported with nonmaytansinoid, cysteine-linked ADCs via thiol-maleimide exchange, for example, brentuximab vedotin. For Ab-SMCC-DM1 ADCs, however, the main catabolite reported is lysine-SMCC-DM1, the expected product of intracellular antibody proteolysis. To understand these observations better, we conducted a series of studies to examine the stability of the thiol-maleimide linkage, utilizing the EGFR-targeting conjugate, J2898A-SMCC-DM1, and comparing it with a control ADC made with a noncleavable linker that lacked a thiol-maleimide adduct (J2898A-(CH2)3-DM). We employed radiolabeled ADCs to directly measure both the antibody and the ADC components in plasma. The PK properties of the conjugated antibody moiety of the two conjugates, J2898A-SMCC-DM1 and J2898A-(CH2)3-DM (each with an average of 3.0 to 3.4 maytansinoid molecules per antibody), appear to be similar to that of the unconjugated antibody. Clearance values of the intact conjugates were slightly faster than those of the Ab components. Furthermore, J2898A-SMCC-DM1 clears slightly faster than J2898A-(CH2)3-DM, suggesting that there is a fraction of maytansinoid loss from the SMCC-DM1 ADC, possibly through a thiol-maleimide dependent mechanism. Experiments on ex vivo stability confirm that some loss of maytansinoid from Ab-SMCC-DM1 conjugates can occur via thiol elimination, but at a slower rate than the corresponding rate of loss reported for thiol-maleimide links formed at thiols derived by reduction of endogenous cysteine residues in antibodies, consistent with expected differences in thiol-maleimide stability related to thiol pKa. These findings inform the design strategy for future ADCs.

Multiscale Modeling of Antibody-Drug Conjugates: Connecting Tissue and Cellular Distribution to Whole Animal Pharmacokinetics and Potential Implications for Efficacy

Antibody-drug conjugates exhibit complex pharmacokinetics due to their combination of macromolecular and small molecule properties. These issues range from systemic concerns, such as deconjugation of the small molecule drug during the long antibody circulation time or rapid clearance from nonspecific interactions, to local tumor tissue heterogeneity, cell bystander effects, and endosomal escape. Mathematical models can be used to study the impact of these processes on overall distribution in an efficient manner, and several types of models have been used to analyze varying aspects of antibody distribution including physiologically based pharmacokinetic (PBPK) models and tissue-level simulations. However, these processes are quantitative in nature and cannot be handled qualitatively in isolation. For example, free antibody from deconjugation of the small molecule will impact the distribution of conjugated antibodies within the tumor. To incorporate these effects into a unified framework, we have coupled the systemic and organ-level distribution of a PBPK model with the tissue-level detail of a distributed parameter tumor model. We used this mathematical model to analyze new experimental results on the distribution of the clinical antibody-drug conjugate Kadcyla in HER2-positive mouse xenografts. This model is able to capture the impact of the drug-antibody ratio (DAR) on tumor penetration, the net result of drug deconjugation, and the effect of using unconjugated antibody to drive ADC penetration deeper into the tumor tissue. This modeling approach will provide quantitative and mechanistic support to experimental studies trying to parse the impact of multiple mechanisms of action for these complex drugs.

Integration of bioanalytical measurements using PK-PD modeling and simulation: implications for antibody-drug conjugate development

Recent technological advances have enabled precise quantitation of various bioanalytical measurements pertaining to antibody-drug conjugates (ADCs). However, availability of bioanalytical data alone cannot guarantee the provision of correct go/no-go decisions at different stages of ADC development. Integration and comprehension of all the available data at each stage of ADC development is necessary to make a well informed and objective decision about moving the ADC forward to the clinic. In this manuscript, we have reviewed the application of PK-PD modeling and simulation for quantitative integration of diverse bioanalytical data available from different stages of ADC development. We have also elaborated on how similar bioanalytical data can be characterized using different models to gain distinct insights into ADC development.

Evolution of Antibody-Drug Conjugate Tumor Disposition Model to Predict Preclinical Tumor Pharmacokinetics of Trastuzumab-Emtansine (T-DM1)

A mathematical model capable of accurately characterizing intracellular disposition of ADCs is essential for a priori predicting unconjugated drug concentrations inside the tumor. Towards this goal, the objectives of this manuscript were to: (1) evolve previously published cellular disposition model of ADC with more intracellular details to characterize the disposition of T-DM1 in different HER2 expressing cell lines, (2) integrate the improved cellular model with the ADC tumor disposition model to a priori predict DM1 concentrations in a preclinical tumor model, and (3) identify prominent pathways and sensitive parameters associated with intracellular activation of ADCs. The cellular disposition model was augmented by incorporating intracellular ADC degradation and passive diffusion of unconjugated drug across tumor cells. Different biomeasures and chemomeasures for T-DM1, quantified in the companion manuscript, were incorporated into the modified model of ADC to characterize in vitro pharmacokinetics of T-DM1 in three HER2+ cell lines. When the cellular model was integrated with the tumor disposition model, the model was able to a priori predict tumor DM1 concentrations in xenograft mice. Pathway analysis suggested different contribution of antigen-mediated and passive diffusion pathways for intracellular unconjugated drug exposure between in vitro and in vivo systems. Global and local sensitivity analyses revealed that non-specific deconjugation and passive diffusion of the drug across tumor cell membrane are key parameters for drug exposure inside a cell. Finally, a systems pharmacokinetic model for intracellular processing of ADCs has been proposed to highlight our current understanding about the determinants of ADC activation inside a cell.

Field Guide to Challenges and Opportunities in Antibody-Drug Conjugates for Chemists

Antibody-drug conjugates have attracted a great amount of attention as a therapeutic strategy for diseases where targeting specific tissues and cells are critical components, such as in cancer therapy. Although promising, the number of approved ADC drugs is relatively limited. This emanates from the challenges associated with generating the conjugates and the complexities associated with the stability requirements for these conjugates during circulation and after reaching the target. Here, we provide a comprehensive overview of the design challenges facing the ADC field. These challenges also provide several unique research and development opportunities, which are also highlighted throughout the review.