PK/TD modeling for prediction of the effects of 8C2, an anti-topotecan mAb, on topotecan-induced toxicity in mice

Use of Payload Binding Selectivity Enhancers to Improve Therapeutic Index of Maytansinoid-Antibody-Drug Conjugates

Systemic exposure to released cytotoxic payload contributes to the dose-limiting off-target toxicities of anticancer antibody-drug conjugates (ADC). In this work, we present an "inverse targeting" strategy to optimize the therapeutic selectivity of maytansinoid-conjugated ADCs. Several anti-maytansinoid sdAbs were generated via phage-display technology with binding IC50 values between 10 and 60 nmol/L. Co-incubation of DM4 with the anti-maytansinoid sdAbs shifted the IC50 value of DM4 up to 250-fold. Tolerability and efficacy of 7E7-DM4 ADC, an anti-CD123 DM4-conjugated ADC, were assessed in healthy and in tumor-bearing mice, with and without co-administration of an anti-DM4 sdAb. Co-administration with anti-DM4 sdAb reduced 7E7-DM4-induced weight loss, where the mean values of percentage weight loss at nadir for mice receiving ADC+saline and ADC+sdAb were 7.9% ± 3% and 3.8% ± 1.3% (P < 0.05). In tumor-bearing mice, co-administration of the anti-maytansinoid sdAb did not negatively affect the efficacy of 7E7-DM4 on tumor growth or survival following dosing of the ADC at 1 mg/kg (P = 0.49) or at 10 mg/kg (P = 0.9). Administration of 7E7-DM4 at 100 mg/kg led to dramatic weight loss, with 80% of treated mice succumbing to toxicity before the appearance of mortality relating to tumor growth in control mice. However, all mice receiving co-dosing of 100 mg/kg 7E7-DM4 with anti-DM4 sdAb were able to tolerate the treatment, which enabled reduction in tumor volume to undetectable levels and to dramatic improvements in survival. In summary, we have demonstrated the utility and feasibility of the application of anti-payload antibody fragments for inverse targeting to improve the selectivity and efficacy of anticancer ADC therapy.

Payload-Binding Fab Fragments Increase the Therapeutic Index of MMAE Antibody-Drug Conjugates

Monomethyl auristatin E (MMAE) is a potent tubulin inhibitor that is used as the payload for four FDA-approved antibody-drug conjugates (ADC). Deconjugated MMAE readily diffuses into untargeted cells, resulting in off-target toxicity. Here, we report the development and evaluation of a humanized Fab fragment (ABC3315) that enhances the therapeutic selectivity of MMAE ADCs. ABC3315 increased the IC50 of MMAE against human cancer cell lines by > 500-fold with no impact on the cytotoxicity of MMAE ADCs, including polatuzumab vedotin (PV) and trastuzumab-vc-MMAE (TvcMMAE). Coadministration of ABC3315 did not reduce the efficacy of PV or TvcMMAE in xenograft tumor models. Coadministration of ABC3315 with 80 mg/kg TvcMMAE significantly (P < 0.0001) increased the cumulative amount of MMAE that was excreted in urine 0 to 4 days after administration from 789.4±19.0 nanograms (TvcMMAE alone) to 2625±206.8 nanograms (for mice receiving TvcMMAE with coadministration of ABC3315). Mice receiving 80 mg/kg TvcMMAE and PBS exhibited a significant drop in white blood cell counts (P = 0.025) and red blood cell counts (P = 0.0083) in comparison with control mice. No significant differences, relative to control mice, were found for white blood cell counts (P = 0.15) or for red blood cell counts (P = 0.23) for mice treated with 80 mg/kg TvcMMAE and ABC3315. Coadministration of ABC3315 with 120 mg/kg PV significantly (P = 0.045) decreased the percentage body weight loss at nadir for treated mice from 11.9%±7.0% to 4.1%±2.1%. Our results demonstrate that ABC3315, an anti-MMAE Fab fragment, decreases off-target toxicity while not decreasing antitumor efficacy, increasing the therapeutic window of MMAE ADCs.

Mechanisms of ADC Toxicity and Strategies to Increase ADC Tolerability

Anti-cancer antibody-drug conjugates (ADCs) aim to expand the therapeutic index of traditional chemotherapy by employing the targeting specificity of monoclonal antibodies (mAbs) to increase the efficiency of the delivery of potent cytotoxic agents to malignant cells. In the past three years, the number of ADCs approved by the Food and Drug Administration (FDA) has tripled. Although several ADCs have demonstrated sufficient efficacy and safety to warrant FDA approval, the clinical use of all ADCs leads to substantial toxicity in treated patients, and many ADCs have failed during clinical development due to their unacceptable toxicity profiles. Analysis of the clinical data has demonstrated that dose-limiting toxicities (DLTs) are often shared by different ADCs that deliver the same cytotoxic payload, independent of the antigen that is targeted and/or the type of cancer that is treated. DLTs are commonly associated with cells and tissues that do not express the targeted antigen (i.e., off-target toxicity), and often limit ADC dosage to levels below those required for optimal anti-cancer effects. In this manuscript, we review the fundamental mechanisms contributing to ADC toxicity, we summarize common ADC treatment-related adverse events, and we discuss several approaches to mitigating ADC toxicity.

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.

Research Progress on Quantification Methods of Drug Concentration of Monoclonal Antibodies

Background:

With the development of monoclonal antibodies (mAbs) from the first generation of mice to the fourth generation of human origin, the efficacy and safety in the treatment of many diseases have been continuously improved. MAbs have been widely used in the treatment of cancer, chronic inflammatory diseases, and so on. However, the treatment response of mAbs varies greatly among individuals, and drug exposure may be affected by a variety of physiological and pathological factors, such as combined use of drugs and progression of disease. Therefore, studies tend to recommend therapeutic drug monitoring and individualized treatment strategies.

Objective:

In this paper, the commonly used methods of quantification of monoclonal antibodies were reviewed, especially liquid chromatography- mass spectrometry (LC-MS/MS) and enzyme-linked immunosorbent assay (ELISA), to provide technical support for therapeutic drug detection and individualize dosing for patients.

Conclusion:

For patients achieving mAbs treatment, it is necessary to carry out therapeutic drug monitoring and take it as a routine monitoring index. We recommend that for pharmaceutical laboratories in hospitals, establishing an appropriate assay formats, such as ELISA and LC-MS/MS is critical to determine drug concentration and antidrug antibody (ADA) for mAbs.

Toxicity and Immunogenicity of a Tardigrade Cytosolic Abundant Heat Soluble Protein in Mice

Tardigrades are microscopic animals well-known for their stress tolerance, including the ability to survive desiccation. This survival requires cytosolic abundant heat soluble (CAHS) proteins. CAHS D protects enzymes from desiccation- and lyophilization-induced inactivation in vitro and has the potential to stabilize protein-based therapeutics, including vaccines. Here, we investigate whether purified recombinant CAHS D causes hemolysis or a toxic or immunogenic response following intraperitoneal injection in mice. CAHS D did not cause hemolysis, and all mice survived the 28-day monitoring period. The mice gained weight normally and developed anti-CAHS D antibodies but did not show upregulation of the inflammatory cytokines interleukin-6 and tumor necrosis factor alpha. In summary, CAHS D is not toxic and does not promote an inflammatory immune response in mice under the conditions used here, suggesting the reasonability of further study for use as stabilizers of protein-based therapeutics.

Physiologically-based modeling of monoclonal antibody pharmacokinetics in drug discovery and development

Over the past few decades, monoclonal antibodies (mAbs) have become one of the most important and fastest growing classes of therapeutic molecules, with applications in a wide variety of disease areas. As such, understanding of the determinants of mAb pharmacokinetic (PK) processes (absorption, distribution, metabolism, and elimination) is crucial in developing safe and efficacious therapeutics. In the present review, we discuss the use of physiologically-based pharmacokinetic (PBPK) models as an approach to characterize the in vivo behavior of mAbs, in the context of the key PK processes that should be considered in these models. Additionally, we discuss current and potential future applications of PBPK in the drug discovery and development timeline for mAbs, spanning from identification of potential target molecules to prediction of potential drug-drug interactions. Finally, we conclude with a discussion of currently available PBPK models for mAbs that could be implemented in the drug development process.

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.

"Catch-and-Release" Anti-Carcinoembryonic Antigen Monoclonal Antibody Leads to Greater Plasma and Tumor Exposure in a Mouse Model of Colorectal Cancer

In this study, we examined the effects of target expression, neonatal Fc receptor (FcRn) expression in tumors, and pH-dependent target binding on the disposition of monoclonal antibodies (mAbs) in murine models of colorectal cancer. A panel of anti-carcinoembryonic antigen (CEA) mAbs was developed via standard hybridoma technology and then evaluated for pH-dependent CEA binding. Binding was assessed via immunoassay and radioligand binding assays. 10H6, a murine IgG1 mAb with high affinity for CEA at pH = 7.4 (K_D = 12.6 ± 1.7 nM) and reduced affinity at pH = 6.0 (K_D = 144.6 ± 21.8 nM), and T84.66, which exhibits pH-independent CEA binding (K_D = 1.1 ± 0.11 and 1.4 ± 0.16 nM at pH 7.4 and 6.0), were selected for pharmacokinetic investigations. We evaluated pharmacokinetics after intravenous administration to control mice and to mice bearing tumors with (MC38^CEA+, LS174T) and without (MC38^CEA-) CEA expression and with or without expression of murine FcRn, at doses of 0.1, 1, and 10 mg/kg. 10H6 displayed linear pharmacokinetics in mice bearing MC38^CEA+ or MC38^CEA- tumors. T84.66 displayed linear pharmacokinetics in mice with MC38^CEA- tumors but dose-dependent nonlinear pharmacokinetics in mice bearing MC38^CEA+ In addition to the improved plasma pharmacokinetic profile (i.e., linear pharmacokinetics, longer terminal half-life), 10H6 exhibited improved exposure in MC38^CEA+ tumors relative to T84.66. In mice bearing tumors with CEA expression, but lacking expression of murine FcRn (LS174T), 10H6 demonstrated nonlinear pharmacokinetics, with rapid clearance at low dose. These data are consistent with the hypothesis that pH-dependent CEA binding allows mAb dissociation from target in acidified endosomes, enabling FcRn-mediated protection from target-mediated elimination in mice bearing MC38^CEA+ tumors.

Investigation of the Influence of Protein-Losing Enteropathy on Monoclonal Antibody Pharmacokinetics in Mice

Protein losing enteropathy (PLE), which is characterized by substantial loss of plasma proteins into the gastrointestinal (GI) tract, is a complication of a variety of GI diseases, including inflammatory bowel disease. Clinical studies have found that the clearance of monoclonal antibodies (mAb) is often increased in subjects with diseases known to cause PLE; however, direct relationships between PLE and mAb pharmacokinetics have not been demonstrated. This study employed a murine model of colitis to examine the influence of PLE on mAb pharmacokinetics. Mice were given dextran sodium sulfate (DSS, 2% w/v) supplemented tap water as drinking source for 6 days to induce colitis and PLE. Mice were then intravenously injected with 8C2, a murine IgG1 mAb. 8C2 plasma concentrations were measured up to 14 days post injection. Fecal alpha-1-antitrypsin (A1AT) clearance was measured as biomarker for PLE. DSS-treated mice developed PLE of clinically relevant severity. They also showed a transient increase in 8C2 plasma clearance and a decrease in 8C2 plasma exposure. The area under the 8C2 plasma concentration-time curve for the length of the study (AUC_0-14d) reduced from 1368 ± 255 to 594 ± 224 day μg/ml following DSS treatment (p = 0.001). A quantitative relationship between A1AT clearance and 8C2 clearance was obtained via population pharmacokinetic modeling. DSS treatment substantially increased 8C2 clearance and reduced 8C2 exposure. Increased mAb plasma clearance was highly correlated with A1AT fecal clearance, suggesting the possible utility of A1AT fecal clearance as a mechanistic biomarker to predict the pharmacokinetics of therapeutic antibodies.

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.

Systems pharmacology and enhanced pharmacodynamic models for understanding antibody-based drug action and toxicity

Pharmacokinetic (PK) and pharmacodynamic (PD) models seek to describe the temporal pattern of drug exposures and their associated pharmacological effects produced at micro- and macro-scales of organization. Antibody-based drugs have been developed for a large variety of diseases, with effects exhibited through a comprehensive range of mechanisms of action. Mechanism-based PK/PD and systems pharmacology models can play a major role in elucidating and integrating complex antibody pharmacological properties, such as nonlinear disposition and dynamical intracellular signaling pathways triggered by ligation to their cognate targets. Such complexities can be addressed through the use of robust computational modeling techniques that have proven powerful tools for pragmatic characterization of experimental data and for theoretical exploration of antibody efficacy and adverse effects. The primary objectives of such multi-scale mathematical models are to generate and test competing hypotheses and to predict clinical outcomes. In this review, relevant systems pharmacology and enhanced PD (ePD) models that are used as predictive tools for antibody-based drug action are reported. Their common conceptual features are highlighted, along with approaches used for modeling preclinical and clinically available data. Key examples illustrate how systems pharmacology and ePD models codify the interplay among complex biology, drug concentrations, and pharmacological effects. New hybrid modeling concepts that bridge cutting-edge systems pharmacology models with established PK/ePD models will be needed to anticipate antibody effects on disease in subpopulations and individual patients.

Physiologically based pharmacokinetic models of small molecules and therapeutic antibodies: a mini-review on fundamental concepts and applications

The mechanisms of absorption, distribution, metabolism and elimination of small and large molecule therapeutics differ significantly from one another and can be explored within the framework of a physiologically based pharmacokinetic (PBPK) model. This paper briefly reviews fundamental approaches to PBPK modeling, in which drug kinetics within tissues and organs are explicitly represented using physiologically meaningful parameters. The differences in PBPK models applied to small/large molecule drugs are highlighted, thus elucidating differences in absorption, distribution and elimination properties between these two classes of drugs in a systematic manner. The absorption of small and large molecules differs with respect to their common extravascular routes of delivery (oral versus subcutaneous). The role of the lymphatic system in drug distribution, and the involvement of tissues as sites of elimination (through catabolism and target mediated drug disposition) are unique features of antibody distribution and elimination that differ from small molecules, which are commonly distributed into the tissues but are eliminated primarily by liver metabolism. Fundamental differences exist in the ability to predict human pharmacokinetics based upon preclinical data due to differing mechanisms governing small and large molecule disposition. These differences have influence on the evolving utilization of PBPK modeling in the discovery and development of small and large molecule therapeutics.

Unraveling the pharmacokinetic interaction of ticagrelor and MEDI2452 (Ticagrelor antidote) by mathematical modeling

The investigational ticagrelor‐neutralizing antibody fragment, MEDI2452, is developed to rapidly and specifically reverse the antiplatelet effects of ticagrelor. However, the dynamic interaction of ticagrelor, the ticagrelor active metabolite (TAM), and MEDI2452, makes pharmacokinetic (PK) analysis nontrivial and mathematical modeling becomes essential to unravel the complex behavior of this system. We propose a mechanistic PK model, including a special observation model for post‐sampling equilibration, which is validated and refined using mouse in vivo data from four studies of combined ticagrelor‐MEDI2452 treatment. Model predictions of free ticagrelor and TAM plasma concentrations are subsequently used to drive a pharmacodynamic (PD) model that successfully describes platelet aggregation data. Furthermore, the model indicates that MEDI2452‐bound ticagrelor is primarily eliminated together with MEDI2452 in the kidneys, and not recycled to the plasma, thereby providing a possible scenario for the extrapolation to humans. We anticipate the modeling work to improve PK and PD understanding, experimental design, and translational confidence.

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.

Application of Pharmacokinetic-Pharmacodynamic Modeling and Simulation for Antibody-Drug Conjugate Development

Characterization and prediction of the pharmacokinetics (PK) and pharmacodynamics (PD) of Antibody-Drug Conjugates (ADCs) is challenging, since it requires simultaneous quantitative understanding about the PK-PD properties of three different molecular species i.e., the monoclonal antibody, the drug, and the conjugate. Mathematical modeling and simulation provides an excellent tool to overcome these challenges, as it can simultaneously integrate the PK-PD of ADCs and their components in a quantitative manner. Additionally, the computational PK-PD models can also serve as a cornerstone for the model-based drug development and preclinical-to-clinical translation of ADCs. To provide an overview of this subject matter, this manuscript reviews the PK-PD models applicable to ADCs. Additionally, the usage of these models during different drug development stages (i.e., discovery, preclinical development, and clinical development) is also emphasized. The importance of PK-PD modeling and simulation in making rationale go/no-go decisions throughout the drug development process is also highlighted. There is an array of PK-PD models available, ranging from the systems models specifically developed for ADCs to the empirical models applicable to all chemotherapeutic agents, which one can employ for ADCs. The decision about which model to choose depends on the questions to be answered, time at hand, and resources available.

Toward sensitive and accurate analysis of antibody biotherapeutics by liquid chromatography coupled with mass spectrometry

Remarkable methodological advances in the past decade have expanded the application of liquid chromatography coupled with mass spectrometry (LC/MS) analysis of biotherapeutics. Currently, LC/MS represents a promising alternative or supplement to the traditional ligand binding assay (LBA) in the pharmacokinetic, pharmacodynamic, and toxicokinetic studies of protein drugs, owing to the rapid and cost-effective method development, high specificity and reproducibility, low sample consumption, the capacity of analyzing multiple targets in one analysis, and the fact that a validated method can be readily adapted across various matrices and species. While promising, technical challenges associated with sensitivity, sample preparation, method development, and quantitative accuracy need to be addressed to enable full utilization of LC/MS. This article introduces the rationale and technical challenges of LC/MS techniques in biotherapeutics analysis and summarizes recently developed strategies to alleviate these challenges. Applications of LC/MS techniques on quantification and characterization of antibody biotherapeutics are also discussed. We speculate that despite the highly attractive features of LC/MS, it will not fully replace traditional assays such as LBA in the foreseeable future; instead, the forthcoming trend is likely the conjunction of biochemical techniques with versatile LC/MS approaches to achieve accurate, sensitive, and unbiased characterization of biotherapeutics in highly complex pharmaceutical/biologic matrices. Such combinations will constitute powerful tools to tackle the challenges posed by the rapidly growing needs for biotherapeutics development.