Predicting the effects of 8C2, a monoclonal anti-topotecan antibody, on plasma and tissue disposition of topotecan

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.

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.

Bivalent engagement of endothelial surface antigens is critical to prolonged surface targeting and protein delivery in vivo

Targeted drug delivery to the endothelium has the potential to generate localized therapeutic effects at the blood-tissue interface. For some therapeutic cargoes, it is essential to maintain contact with the bloodstream to exert protective effects. The pharmacokinetics (PK) of endothelial surface-targeted affinity ligands and biotherapeutic cargo remain a largely unexplored area, despite obvious translational implications for this strategy. To bridge this gap, we site-specifically radiolabeled mono- (scFv) and bivalent (mAb) affinity ligands specific for the endothelial cell adhesion molecules, PECAM-1 (CD31) and ICAM-1 (CD54). Radiotracing revealed similar lung biodistribution at 30 minutes post-injection (79.3% ± 4.2% vs 80.4% ± 10.6% ID/g for αICAM and 58.9% ± 3.6% ID/g vs. 47.7% ± 5.8% ID/g for αPECAM mAb vs. scFv), but marked differences in organ residence time, with antibodies demonstrating an order of magnitude greater area under the lung concentration vs. time curve (AUC_inf 1698 ± 352 vs. 53.3 ± 7.9 ID/g*hrs for αICAM and 1023 ± 507 vs. 114 ± 37 ID/g*hrs for αPECAM mAb vs scFv). A physiologically based pharmacokinetic model, fit to and validated using these data, indicated contributions from both superior binding characteristics and prolonged circulation time supporting multiple binding-detachment cycles. We tested the ability of each affinity ligand to deliver a prototypical surface cargo, thrombomodulin (TM), using one-to-one protein conjugates. Bivalent mAb-TM was superior to monovalent scFv-TM in both pulmonary targeting and lung residence time (AUC_inf 141 ± 3.2 vs 12.4 ± 4.2 ID/g*hrs for ICAM and 188 ± 90 vs 34.7 ± 19.9 ID/g*hrs for PECAM), despite having similar blood PK, indicating that binding strength is more important parameter than the kinetics of binding. To maximize bivalent target engagement, we synthesized an oriented, end-to-end anti-ICAM mAb-TM conjugate and found that this therapeutic had the best lung residence time (AUC_inf 253 ± 18 ID/g*hrs) of all TM modalities. These observations have implications not only for the delivery of TM, but also potentially all therapeutics targeted to the endothelial surface.

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.

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.