Preclinical to Clinical Translation of Antibody Drug Conjugates Using a Quantitative Systems Pharmacology (QSP) Model: A Case Study with Disitamab Vedotin

Preclinical to Clinical Translation of Antibody Drug Conjugates Using a Quantitative Systems Pharmacology (QSP) Model: A Case Study with Disitamab Vedotin | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Preclinical to Clinical Translation of Antibody Drug Conjugates Using a Quantitative Systems Pharmacology (QSP) Model: A Case Study with Disitamab Vedotin Qiaoning Li, Ling Wang, Baiyang Wu, Xinting Ma, Guorui Zhao, Zhihao Liu, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4543078/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Background : The multiscale quantitative systems pharmacology (QSP) model, which includes biomolecule, cellular and tissue data, was used for the preclinical to clinical translation of ADC efficacy. Here, we validated the feasibility of the model using the marketed drug disitamab vedotin (DV). Methods : Model integrated pharmacokinetics (PK), in vitro tumor cell disposition data and tumor growth inhibition (TGI) data were obtained. This mechanistically explains how the ADC exerted efficacy. Based on the in vivo efficacy data, which were obtained from studies conducted using two cell-derived xenograft models (NCI-N87 and SKOV3), a QSP model was established to characterize the efficacy, and the inherent sensitivity of the ADC between patient populations with different target expression levels was further simulated. For preclinical to clinical transitioning, human PK behavior was predicted with allometric scaling using the data of the monkeys’ PK parameters. Key parameters impacting the QSP model, such as TGI, tumor volume, and target expression level of tumors, were replaced with clinically relevant data to predict the effective dose of DV in patients. The clinically approved Q2W dosing intervals were used to simulate the effective dose. Results : After continuous administration of 1.5-2 mg/kg Q2W for five months, patients with HER2-2+ and HER2-3+ tumors achieved an objective response rate (ORR), but progressive disease was observed in HER2-1+ patients, which was consistent with clinically observed efficacy outcomes. Conclusions : The model has promising applications in ADC screening, the optimization of clinical dosing regimens and other related areas. Disitamab vedotin quantitative systems pharmacology model efficacy preclinical to clinical translation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Antibody‒drug conjugates (ADCs) are unique therapeutic monoclonal antibodies that have great promise in the field of cancer treatment. ADCs consist of a monoclonal antibody (mAb) targeting specific antigens, a payload that has the ability to kill tumor cells and a linker conjugating payload and mAb [1] . ADC binds to antigens on the surface of tumor cells and is internalized into the cells. Once inside the cells, the conjugated payload is released through the degradation of the antibody or linker by the lysosomes. The released payload, which possesses tumor-killing activity, has therapeutic efficacy [2] . Due to the complexity of the structure and mechanism of action of ADCs, they exhibit unique behaviors. For example, 1) the molecule is heterogeneous, 2) the payload in tumor cells needs to undergo a complex release process, and 3) it has the PK characteristics of a mAb and small molecules, and its metabolic behavior is complex [3] . Consequently, the development of a pharmacokinetic/pharmacodynamics (PK/PD) model to characterize the relationship between the metabolic behavior of both mAbs and the payload simultaneously and their effects is particularly important for ADC drugs. The quantitative systems pharmacology (QSP) model can simulate complex biological mechanisms and link them to the exposure response. It has advantages over traditional PK/PD models in translating biological effects between species and assessing the mechanism of action of drugs. In recent years, QSP models have been increasingly used for target antigen selection, biomarker identification, and clinical pharmacodynamic prediction [4] . Due to the complexity of the structure and mechanism of action (MOA) of ADCs, the use of QSP models is highly valuable for drug discovery and development. To date, the QSP model using preclinical data to predict the therapeutic index of the ADC in the clinic has not yet been fully established. Although there are several challenges, modeling and simulation are needed for the quantitative understanding of ADCs, which are difficult to replace by any other methods. Shah et al. first reported a mechanistic model for translating the efficacy of ADCs from preclinical to clinical [5] . Brentuximab-vedotin (cAC10-vc-MMAE, SGN-35), which is marketed for the treatment of Hodgkin's lymphoma and anaplastic large cell lymphoma, was used for modeling and simulation. The PK of the ADC at the cellular level was first integrated with the PK/PD model. The in vitro biomeasures (e.g., internalization rate, receptor number per cell, exit rate of monomethyl auristatin E (MMAE) from the cell, binding affinity) were used to characterize the fate of brentuximab-vedotin. Recently, Shah et al. published an article to validate the model's ability to predict the ADC and payload concentration in tumors and evaluate the model's sensitivity parameters [2] . Betts et al. optimized tumor penetration and tumor growth inhibition models on the basis of Shah et al. to translate clinical data from preclinical data [6] . Singh et al. optimized a previously published cellular disposition model of an ADC that incorporated intracellular ADC degradation and passive diffusion of the unconjugated payload across tumor cells [7] . Recently, Singh et al. used an optimized PK/PD model for clinical translation with trastuzumab emtansine [8] . In recent years, Singh et al. refined the model in which the intracellular occupancy of tubulin was used to drive the efficacy of ADCs [9] . However, this model has not been used for clinical translation. This manuscript was built on previous research [9] and validated the ability of the model to predict clinical efficacy using preclinical research data. We conducted PK/PD studies in animals, PK studies at the tumor cellular level and drug-specific biomolecular level studies in vitro. The system-specific parameters are cited in the literature. By integrating the above data, we established the system QSP model. According to the differences between humans and animals, the key parameters of the model are corrected to predict the clinical efficacy of DV, which is the first ADC drug approved for marketing in China, and compare it with data obtained clinically. Materials and methods Cell lines The high and middle HER2-expressing cell lines NCI-N87 (human gastric cancer cells) and SKOV3 (human ovarian adenocarcinoma cells) were chosen. They exhibited reproducible growth curves as tumor xenografts. Cell lines were obtained from ATCC. Tool ADCs DV is a novel recombinant human anti-HER2 monoclonal antibody conjugated with a microtubule inhibitor (MMAE) via a cleavable linker, which was provided by RemeGen Co., Ltd. It was synthesized and characterized in house. [10] . The average drug-to-antibody ratio (DAR) was ~ 4. Biomeasures The binding coefficients of DV (KD and ) for HER2, the antigen-antibody internalization rate ( ), and the number of HER2 receptors on cancer cells were measured in-house. The results are shown in Fig S1. The detection methods used were described in previous literature [11] . The rest of the parameters were either extracted from the literature or estimated using the model, the details of which are provided in Table 1. Cellular Disposition The cellular disposition of the ADC was previously described [12] . We characterized the cellular disposition of DVs with reference to this method. Briefly, ten million NCI-N87 cells, a high-Her2 expression cell line, were cultured in a T-75 flask and treated with 10 μg/mL (66.7 nM) DV. Cell and medium samples were collected at different time points (2 h, 12 h, 24 h, 48 h, 72 h and 96 h) following continuous and 2 h of exposure. Three analytes, namely, unconjugated MMAE, total MMAE and total antibody, in the medium and cell lysate were analyzed to characterize the cellular disposition. Mouse PD studies Thirty-six female nude mice (BALB/c nude) aged 6-7 weeks were purchased from Shanghai Sippr BK Laboratory Animals Ltd. The license numbers were SCXK (Hu) 2008-0016. Nude BALB/c mice were inoculated subcutaneously with NCI-N87 or SKOV3 cells. When the initial tumor volume reached approximately 100-200 mm 3 , the animals were administered 1.1, 3.3, or 10 mg/kg via intravenous injection with DV. Tumors were measured twice a week. Tumor volume was calculated as mm 3 =0.5 × (tumor width 2 ) × (tumor length). Mouse PK studies Eighteen female nude mice (BALB/c nude) aged 6-7 weeks were purchased from Shanghai Slac Laboratory Animals Co., Ltd. The license numbers were SCXK (Hu) 2007-0005. The PK of male BT474 tumor-bearing mice was evaluated. DV was administered to mice at 1.5 and 5 mg/kg via intravenous injection. At 0.083, 4, 24, 48, 72, 96 and 168 h after dosing, three animals from each group were sacrificed to collect blood samples, which were immediately processed to extract serum. Cynomolgus Monkey Pharmacokinetics Eighteen Cynomolgus monkeys were purchased from Beijing Institute of Xieerxin Biology Resource Co., Ltd. (the production license for the experimental animals was SCXK (Jing) 2010-0007). DV was administered to monkeys at 2.5, 5, or 10 mg/kg (n=6/group, half male and half female) via intravenous infusion. Blood samples were collected predose and at 0.167, 0.33, 0.5 (Immediately after the end of administration), 2, 6, 12, 24, 48, 72, 96, 120, 144, 168, 192, 240, 288 and 336 h postdose. Blood samples were immediately processed to extract serum. Bioanalytical Method of Total Antibody and Conjugated Antibody The DVs of total antibody and conjugated antibody were detected using an enzyme-linked immunosorbent assay (ELISA). The same analytical method was used for different matrices, including mouse serum, monkey serum, cell medium and cell lysate. HER2 ECD protein (G&P Biosciences, Santa Clara, CA, USA), at final concentrations of 0.1 μg/mL (total antibody) and 0.5 μg/mL (conjugated antibody), was used as coated reagent. The minimum required dilution of total antibody and conjugated antibody was 25 and 10, respectively. 1.0 mg/ml goat anti-human IgG-H+L HRP monkey adsorbed (Bethyl Laboratories, Montgomery, Texas, USA) was added to detect the total antibody. Mouse anti-MMAE monoclonal antibody (4.1 μg/mL; RemeGen Co., Ltd., Yantai, Shandong, China) as the detection antibody and goat anti-mouse IgG HRP as the enzyme-labeled antibody (Multisciences, Hangzhou, Zhejiang, China) were used to detect the conjugated antibody. 3,3′,5,5′-Tetramethylbenzidine was used as the substrate for the colorimetric readout. The reaction was stopped by the addition of 1 M phosphoric acid, and the absorbance was measured at a wavelength of 450 nm using a microplate reader (Molecular Devices Inc., San Jose, CA, USA). The calibration curve was fitted using a four-parameter logistic fit regression model. Bioanalytical Method of MMAE A Qtrap 6500+ LC‒MS/MS system was used to analyze the concentration of MMAE. For the detection of MMAE, an ACQUITY HPLC BEH C18 column (Waters®) was used with a mobile phase of 0.1% FA in water (A) and ACN with 0.1% FA (B) at a flow rate of 0.5 mL/min. The following gradient elution program was used: 0–0.8 min, 10% B; 0.8–2.1 min, 10–98% B; and 2.1–3 min, 98–10% B. The total time of analysis was 3 mins. Two MRM scans (718.5/686.6 and 718.5/154.1 amu) were monitored, and the samples were analyzed by positive ion electrospray under multiple reaction monitoring modes. The general parameters of MMAE and MMAE-D8 were as follows: curtain gas (CUR)=25 psi, collision gas (CAD)=725 psi, ion spray voltage (IS)=550025 psi, temperature (TEM)=550°C, ion gas source 1 (GS1)=50 psi, and ion gas source 2 psi (GS2)=50. The samples were treated with the same volume of ACN (V:V=1:1). The sample treatment method for total MMAE has been published previously [12] . Translational parameters For preclinical to clinical translation, we hypothesized that the tumor receptor number, tumor volume and tumor growth rate are key parameters that differ between humans and animals. Other parameters, including the cellular disposition of the drug, the tumor disposition of the drug and the drug effect parameters, were kept consistent in the mouse TGI model. The changes of key difference parameters were as follows: 1) tumor receptor numbers of 100,000, 500,000 and 1000,000/cell were used to represent HER2 1+, 2+, and 3+ patients, respectively [13, 14] , and 2) the tumor doubling time was replaced by clinically observed values from the literature [15, 16] . The mean tumor doubling times of patients with gastric cancer and Her-2+ breast cancer were 2.04 and 1.7 months, respectively. Therefore, 2 months was used for the simulation. 3) The initial tumor volume was replaced by that published in Patient [17] . A tumor diameter of 2 cm was used for simulation. DV treatment at doses ranging from 0.5 to 2.5 mg/kg given Q2W for 5 months was evaluated. The ORR is the criterion for efficacy, including complete response (tumor undetected, assuming a diameter of 30% decrease in tumor diameter but still detectable) according to the Response Evaluation Criteria in Solid Tumors (RECIST) guidelines. Modeling and Simulation Phoenix NLME software (Version 8.3.3.33, Certara) was used to develop the QSP model with a naive pooled algorithm. This model has previously been used for characterizing the in vivo pharmacodynamics of ADC drugs in animal models. The quantitative systems pharmacology (QSP) model consists of four components: the PK model for the drug in serum, a tumor distribution model, a single-cell disposition model and a tumor growth inhibition model. The model was developed step by step. First, the PK model parameters were estimated by the concentration of total antibody, ADC and payload in the circulatory system and then fixed. Second, parameters for the tumor distribution model were obtained from previous literature and fixed. Third, the single-cell level disposition model was used to characterize the penetration of the ADC and payload into the tumor interstitium, entry into tumor cells, and release within tumor cells, and the parameters were fixed. Finally, the occupancy of microtubules by MMAE within tumor cells is considered a driver of tumor growth inhibition. Tumor growth inhibition was characterized by a cell distribution model . The relevant parameters are estimated. These components are integrated to form a complete QSP model. The specific structure of the QSP model is described in Fig 1. The equation systems are detailed in the additional files. Results Serum PK modeling Fig 2 shows the concentration-time profiles of total antibody, conjugated antibody and payload observed and the model described after intravenous injection of DV in BT474 tumor-bearing mice and monkeys. The total antibody, conjugated antibody and payload were characterized by a two-compartment linear model. The conjugated antibody released into the central compartment through elimination and nonspecific dissociation (K dec ). During model development, the modeling process was as follows: Step 1: Relevant PK parameters for the total antibody concentration were estimated. Step 2: The distribution volumes and clearance of the total antibody and conjugated antibody were assumed to be the same and fixed. The difference between the ADC and total antibody elimination is mainly due to the nonspecific dissociation of the payload from the ADC (K dec ). The nonspecific dissociation (K dec ) was estimated. Step 3: The PK parameters of the conjugated antibody were fixed, and the PK parameters of the payload were estimated. The model fitting results are shown in Fig 2, the estimated mouse PK parameter results are detailed in Table 1, and the estimated monkey PK parameter results are detailed in Table 2. Cell-level disposition modeling The PK behavior of the ADC and payload in tumor cells was characterized by a single-cell-level disposition model [12] . This model mechanism describes the dynamic processes of the drug within tumor cells. The cell-level disposition model was developed using in vitro data. Model parameters were derived from in-house or literature. The target receptor counts on tumor cells, , and were acquired from in-house, while , , and were obtained from the literature. The intracellular ADC degradation ( ) was estimated. The parameters are shown in Table 1, and the fitting plots of the observations and predictions are shown in Fig 3. The results showed that the predicted values of the model were consistent with the observed values for total antibody, total MMAE and free MMAE in the cellular space and medium after continuous and 2 h of exposure to 75 nM DV in NCI-N87 cells. Tumor growth inhibition in mice Fig 4 shows the mean tumor growth curves of the NCI-N87 and SKOV3 tumor-bearing mice following intravenous injection of DV. These parameters represented the efficacy and sensitivity of DV in HER2 3+ and 2+ tumor cells, respectively. The results indicated that tumor cell growth continued with the administration of 1.1 mg/kg via injection, while there was a noticeable decrease in tumor size with the administration of 3.3 mg/kg and 10 mg/kg via injection. The tumor growth curves were characterized using a previously reported cell distribution model, with the occupancy of microtubules by MMAE within tumor cells serving as the driving factor for tumor growth inhibition [9] . The estimated parameters related to tumor growth inhibition can be found in Table 1. Prediction of human PK The human PK behavior was obtained by allometric scaling from the monkey PK data. Fig 2b shows the concentration-time profiles of total antibody, conjugated antibody and payload observed and the model described after intravenous injection of DV in cynomolgus monkeys. Modeling was consistent with mouse PK, and the estimated PK parameter results are detailed in Table 2. The allometric scaling exponent for the volume of distribution and clearance of conjugated antibody PK parameters was 1, while the allometric scaling exponent for the volume of distribution of payload was 1, and the exponent of clearance of payload was 0.75. The nonspecific degradation rate, K deg , was assumed to be consistent between cynomolgus monkeys and humans. The predicted human PK parameters are shown in Table 2. Prediction of clinical efficacy Fig 5 shows the simulated tumor diameters of patients with different HER2 expression levels after 5 months of DV treatment with a dosing regimen of 0.5-2.5 mg/kg every 2 weeks (Q2W). The results indicated that for HER2 3+ and 2+ patients, an optimal ORR was achieved with a dosage range of 1.5-2 mg/kg Q2W. No observable drug efficacy was noted for HER2 1+ patients under dosing regimens below 2.5 mg/kg Q2W. It is worth noting that we have only two preclinical mouse model data points, and the availability of more preclinical data would strengthen this prediction. Discussion In this study, the established QSP model was used to characterize the pharmacodynamics of DV in mice. The key parameters in the model were adjusted based on relevant clinical data from patients, such as initial tumor volume, tumor doubling time, and tumor target receptor density. Monkey PK characteristics were scaled allometrically to those of humans. By integrating preclinical and clinical data from different modalities and dimensions, this approach mechanistically describes how drugs exert their pharmacological effects, enabling the prospective design of optimal clinical initiation strategies. This is achieved by constructing mathematical models that cover multiple scales, including biomolecules, cells and tissues, facilitating more efficient and precise model-guided clinical drug development. Step 1: To describe the PK behavior of the ADC, we incorporated different analytes from the ADC (total antibody, conjugated antibody, and payload) into the model. The payload was considered the driving factor for efficacy. We established a PK model using preclinical data and predicted clinical PK behavior through allometric scaling. For ADC molecules, allometric scaling of PK parameters in monkeys provided a good prior estimation for human PK parameters, with an allometric scaling exponent of 1 [18, 19] . For the payload, a classic allometric scaling exponent of 0.75 was applied to predict its PK parameters in humans [20] . Here, we lacked sufficient evidence to prove the relevance of monkeys as a sensitive species for humans, which may introduce bias in predicting the PK parameters of the payload. Model parameters should be adjusted after obtaining clinical data to more accurately describe the PK behavior of the ADC. Step 2: When describing the disposition of the ADC in tumor cells, we assumed that there was only a difference in the HER2 expression levels between SKOV3 and NCI-N87 cells and between SKOV3 (600,000) and NCI-N87 (988,000) cells. Therefore, during model fitting, in addition to differences in receptor expression on tumor cells, all other parameters remained consistent. Step 3: In the tumor killing model, assume that the occupancy of microtubules by the payload drives tumor cytotoxicity. The study indicated that the estimated K max and KC50 in the model were highly consistent with in vitro experimental results [21] , so the parameters were fixed. The tumor growth rate (K g ) and tumor-killing-related parameters τ and γ were estimated. These findings also provide a basis for early drug screening through in vitro experiments. The differences in tumor-killing-related parameters for two tumor cell lines were utilized to simulate inherent drug sensitivity variations across different patient populations. As interindividual variability among animals did not reflect patient variability, individual variability among animals was not considered during model fitting. Step 4: When transitioned from preclinical to clinical stages, the key parameters reflecting differences between humans and animals in the model were adjusted. Sensitivity analysis of these parameters revealed that tumor volume, tumor doubling time, and the expression level of antigens on the tumor surface are sensitive parameters in the model. [7, 12] These parameters were replaced with actual patient data to better reflect the real conditions of the patients. In this study, the initial tumor volume and tumor doubling time for patients were obtained from the clinical data of breast cancer patients and gastric cancer patients, while the expression levels of tumor surface antigens were obtained from previous relevant reports [14] . The other PD parameters were consistent with the preclinical data. We only used preclinical data for modeling and simulation, excluding clinical data, aiming to validate the model's accuracy in predicting clinical dosage. In the future, when clinical data are available, modifying model parameters and introducing variability between individual patients will better guide clinical drug delivery design. DV is the first ADC drug approved for commercial use in China. Current clinical results indicate that DV demonstrates favorable clinical efficacy at a dose of 2 mg/kg Q2W in the treatment of indications such as gastric cancer, breast cancer, and urothelial carcinoma [22, 23] . This finding was closely consistent with the clinical efficacy predicted based on preclinical data, highlighting the significant utility of the model in preclinical to clinical translation. However, further cases are still needed to validate the accuracy of the model. However, the model still leaves something to be desired. During modeling, we only incorporated PK data from single-dose administration in cynomolgus monkeys and mice, without including indicators such as immunogenicity that may affect drug efficacy. This could overestimate the clinical efficacy of the drug. After conducting clinical studies, it was essential to adjust the PK model based on actual clinical data to more accurately describe PK behavior in patients. While the simulation results closely matched the clinically approved dosages, they did not account for the impact of bystander killing effects, potentially underestimating the efficacy of the drug. In the future, it will be necessary to supplement relevant in vitro bystander killing data to further refine the model. The QSP model mechanistically explains how the ADC exerts efficacy. Conclusion In summary, by constructing a QSP model that spans multiple scales, including biomolecules, cells and tumor tissues, we validated the feasibility of the model's preclinical and clinical translation using DVs. To successfully predict the clinical results of DVs, this model is a promising quantitative tool for supporting the design, selection, and optimization of ADC clinical dosing regimens. Declarations Funding This work was partly supported by the Yantai Science and Technology Plan (No. 2021XDHZ083) and the Taishan Industrial Innovation Leading Talent Project Competing Interests The authors have no relevant financial or non-financial interests to disclose Authors' contributions The first draft of the manuscript was written by Qiaoning Li and Ling Wang. The study design was performed by Ling Wang and Jing jiang. The data analysis was performed by Qiaoning Li and Baiyang Wu. The study was conducted by Baiyang Wu, Xinting Ma, Guorui Zhao and Juncheng Wang. All authors read and approved the final manuscript. Acknowledgments The authors would like to thank members of the United-Power Pharma Tech Co. Ltd. team who completed the Cynomolgus monkey PK studies. Data Availability All the data needed to evaluate the conclusions in the paper are presented in the paper and the Additional files. 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Tables Table 1 Estimated or Literature-Derived Parameter Values Used for Simulating the ADC QSP Model [9, 12] Parameter Unit Description Value Source in Vitro Cellular Parameters 1/h 1 st order rate of proteases induced intracellular ADC degradation 0.353 Estimated 1/h 1 st order net antibody-HER2 complex internalization rate 0.13 In-house 1/nM/h 2 nd order association rates of DV binding to HER2 0.025 In-house 1/h 1 st order dissociation rates of DV binding to HER2 0.1 In-house Ag Unitless Number of HER2 receptors on N87 and SKOV3 cells, respectively 988000, 600000 In-house 1/h 1 st order influx rate constant for MMAE from extracellular space to intracellular space 8.330 Derived from [12] 1/h 1 st order efflux rate constants for MMAE from intracellular space to extracellular space 0.199 Derived from [12] 1/nM/h 2 nd order association of MMAE binding to tubulin 0.0183 Derived from [12] 1/h 1 st order dissociation rates of MMAE binding to tubulin 0.545 Derived from [12] SF Unitless Scaling factor to convert the number of molecules to nanoMoles. 1.70E-15 Derived from [12] V cell Liters Volume of cell 3.12E-12 Derived from [12] DT h Doubling time 40.1 Derived from [12] Mouse PK Parameters CL ADC L/h/kg Plasma clearance of ADC 0.0026(16.2%) Estimated CLD ADC L/h/kg Distribution clearance of ADC 0.0090 (18.4%) Estimated V1 ADC L/kg ADC volume of distribution for central compartment 0.1218(7.07%) Estimated V2 ADC L/kg ADC volume of distribution for peripheral compartment 0.1499(15.4%) Estimated 1/h Dissociation rate of payload from ADC 0.0064(51.8%) Estimated CL drug L/h/kg Plasma clearance of payload 1.2046(7.6%) Estimated CLD drug L/h/kg Distribution clearance of payload 1.2188(10.6%) Estimated V1 drug L/kg Payload volume of distribution for central compartment 0.0341(13.4%) Estimated V2 drug L/kg Payload volume of distribution for peripheral compartment 0.5173 (13.0%) Estimated Mouse PD Parameters DT h Doubling time of NCI-N87 and SKOV4 tumors 307 (0.23%), 204 (0.08%) Estimated K max 1/h First order killing rate of MMAE in each tumor cell 0.043 Derived from [9] KC50 Percentage Percentage of intracellular occupancy to tubulin by MMAE which leads to 50% of maximum killing 97 Derived from [9] τ h Transit time associated with the killing of NCI-N87 and SKOV3 7.03 (21.5%), 18.52 (43.4%) Estimated γ Unitless Curve-fitting parameter associated with sigmoidal tubulin occupancy-killing relationship of NCI-N87 and SKOV3 32.28 (12.1%), 16.68 (12.0%) Estimated Table 2 Estimated or predicted PK parameters. Parameter Unit Description Monkey PK Predicted human PK CL ADC L/h/kg Plasma clearance of ADC 0.0016(3.6%) 0.0016 CLD ADC L/h/kg Distribution clearance of ADC 0.0023(11.7%) 0.0023 V1 ADC L/kg ADC volume of distribution for central compartment 0.0390(3.7%) 0.0390 V2 ADC L/kg ADC volume of distribution for peripheral compartment 0.0154(10.1%) 0.0154 1/h Dissociation rate of payload from ADC 0.0097(33.8%) 0.0097 CL drug L/h/kg Plasma clearance of payload 2.4677(6.5%) 1.1721 CLD drug L/h/kg Distribution clearance of payload 42.1332(51.4%) 19.8079 V1 drug L/kg Payload volume of distribution for central compartment 19.6476(22.7%) 19.6476 V2 drug L/kg Payload volume of distribution for peripheral compartment 84.2451(7.6%) 84.2451 Additional Declarations No competing interests reported. Supplementary Files 3SI.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 09 Jul, 2024 Reviews received at journal 09 Jul, 2024 Reviewers agreed at journal 20 Jun, 2024 Reviewers agreed at journal 17 Jun, 2024 Reviewers agreed at journal 17 Jun, 2024 Reviewers invited by journal 17 Jun, 2024 Editor assigned by journal 16 Jun, 2024 Submission checks completed at journal 16 Jun, 2024 First submitted to journal 06 Jun, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4543078","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":320994987,"identity":"4a096ac4-37b0-430b-b896-e51e71f52e38","order_by":0,"name":"Qiaoning Li","email":"","orcid":"","institution":"RemeGen, Ltd","correspondingAuthor":false,"prefix":"","firstName":"Qiaoning","middleName":"","lastName":"Li","suffix":""},{"id":320994989,"identity":"9517f550-9e05-44de-b21c-6bc0e6215716","order_by":1,"name":"Ling Wang","email":"","orcid":"","institution":"RemeGen, Ltd","correspondingAuthor":false,"prefix":"","firstName":"Ling","middleName":"","lastName":"Wang","suffix":""},{"id":320994991,"identity":"43380138-ce90-4b3a-bfd2-cc50ad50f9e2","order_by":2,"name":"Baiyang Wu","email":"","orcid":"","institution":"Binzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Baiyang","middleName":"","lastName":"Wu","suffix":""},{"id":320994994,"identity":"6597e9a9-4db9-417a-992f-bf81535b62c9","order_by":3,"name":"Xinting Ma","email":"","orcid":"","institution":"RemeGen, Ltd","correspondingAuthor":false,"prefix":"","firstName":"Xinting","middleName":"","lastName":"Ma","suffix":""},{"id":320994996,"identity":"e470b1aa-569b-4bf2-bd3e-92c739635b23","order_by":4,"name":"Guorui Zhao","email":"","orcid":"","institution":"RemeGen, Ltd","correspondingAuthor":false,"prefix":"","firstName":"Guorui","middleName":"","lastName":"Zhao","suffix":""},{"id":320994997,"identity":"a8babcf1-cb88-4fb6-b7e8-9cc810e6c10f","order_by":5,"name":"Zhihao Liu","email":"","orcid":"","institution":"RemeGen, Ltd","correspondingAuthor":false,"prefix":"","firstName":"Zhihao","middleName":"","lastName":"Liu","suffix":""},{"id":320994999,"identity":"46964f6c-e4fa-4166-b299-9da8a9f22a74","order_by":6,"name":"Juncheng Wang","email":"","orcid":"","institution":"RemeGen, Ltd","correspondingAuthor":false,"prefix":"","firstName":"Juncheng","middleName":"","lastName":"Wang","suffix":""},{"id":320995000,"identity":"b7223dbd-8e57-40da-b396-ac330e04cba7","order_by":7,"name":"Huifang Liu","email":"","orcid":"","institution":"RemeGen, Ltd","correspondingAuthor":false,"prefix":"","firstName":"Huifang","middleName":"","lastName":"Liu","suffix":""},{"id":320995001,"identity":"4fa3f491-5eeb-48c0-a1bd-0db1981a4f76","order_by":8,"name":"Jing Jiang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/ElEQVRIiWNgGAWjYBACxmYQySMBYjY+/sNjw8BGghbmZgMemTTCWpAAe5sEj81hwuqY23kPv2CQsZA351/YJiGRc96eT7r5AcOPim14HMaXZgF0mOHOGQ+bLQzO3E5skzlmwNhz5jYeLTxmBkAtjBtuHGy8kdhzO4FNIsGAmbGNsBZ7oJYGiYP/ztmzSaR/IKTF+AFQS+KG841Nkg08BxjbJHII2wIK5OQNNxibjRl4khOBWgoO4vOLYf8Z4w+MPXW2G84ff/iYgcfOXn5G+sYHPyrwaGlgYJP+2wNkSSQgRA/gVA8E8sCo+cDwA8jix6tuFIyCUTAKRjIAAL4YUy8PfmFCAAAAAElFTkSuQmCC","orcid":"","institution":"Binzhou Medical University","correspondingAuthor":true,"prefix":"","firstName":"Jing","middleName":"","lastName":"Jiang","suffix":""}],"badges":[],"createdAt":"2024-06-07 02:59:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4543078/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4543078/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":60352731,"identity":"b8788359-fbde-4e37-bbd0-3be7c9eddc43","added_by":"auto","created_at":"2024-07-15 23:37:07","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":104319,"visible":true,"origin":"","legend":"\u003cp\u003eA schematic diagram of the ADC QSP model [10] used to characterize the PK/PD of DV in xenograft mice. A combined PK model consisting of 2 integrated two-compartment models with linear clearance from the central compartment was used to characterize the PK of the ADC and released payload simultaneously. The surface and vascular exchange were used to characterize how the drug entered the tumor extracellular space. The single-cell disposition model was used to describe the mechanism of action of the drug in tumors. The occupancy of intracellular tubulin by MMAE drove tumor killing.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4543078/v1/2be6e60cf2aa9e81855fb152.jpg"},{"id":60352732,"identity":"37c69253-6272-4d02-b74e-91cd24337b99","added_by":"auto","created_at":"2024-07-15 23:37:08","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":70618,"visible":true,"origin":"","legend":"\u003cp\u003eThe observed (symbols) and model described (lines) plasma PK profiles of total antibody, apparent diffusion coefficient (ADC) and free MMAE (a). following single IV dose administration to mice at 1.5 and 5 mg/kg of DV and (b). following a single IV dose of 2.5, 5 or 10 mg/kg DV to cynomolgus monkeys.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4543078/v1/a1458ccc5f5355894a9488c8.jpg"},{"id":60352733,"identity":"c55b4811-cbb8-4e9b-a971-1761fd5463e6","added_by":"auto","created_at":"2024-07-15 23:37:08","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":51096,"visible":true,"origin":"","legend":"\u003cp\u003eThe observed (symbols) and model-described (lines) tumor growth inhibition data for SKOV3 (a) and NCI-N87 (b) xenograft tumor-bearing mice following a single IV injection of 1.1, 3.3 or 10 mg/kg of DV.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4543078/v1/ec13fc85f0b37191c2fec894.jpg"},{"id":60353869,"identity":"c3ba45cc-22f1-4afa-8fcc-964db64efc51","added_by":"auto","created_at":"2024-07-15 23:45:08","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":40745,"visible":true,"origin":"","legend":"\u003cp\u003eObserved (symbols) and model-generated (lines) profiles of total antibody, total MMAE and free MMAE in the cellular space and media after continuous (a) and 2 h (b) exposure of NCI-N87 cells to 75 nM DV.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4543078/v1/40c464898cd2dec310f76c9c.jpg"},{"id":60352736,"identity":"6184ea3d-fd26-4784-88b3-0bc80655a3dc","added_by":"auto","created_at":"2024-07-15 23:37:08","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":63331,"visible":true,"origin":"","legend":"\u003cp\u003eModel predictions of DV efficacy (tumor growth inhibition) after key parameters were replaced by patients with different HER2 expression levels following IV administration at 0.5–2.5 mg/kg. (a). simulation with tumor-killing parameters of NCI-N87 cells. (b). simulation with tumor-killing parameters of SKOV3 cells.\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4543078/v1/5c21ecb30e52f5aad59ba8cc.jpg"},{"id":60353882,"identity":"1cbe697e-6f5a-444c-8daa-dae6496dc40e","added_by":"auto","created_at":"2024-07-15 23:45:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":896318,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4543078/v1/54d426fc-33e2-4f9e-84c1-057fc0f44943.pdf"},{"id":60352735,"identity":"70523d18-35e3-4714-8b39-9c66dafae68c","added_by":"auto","created_at":"2024-07-15 23:37:08","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":54320,"visible":true,"origin":"","legend":"","description":"","filename":"3SI.docx","url":"https://assets-eu.researchsquare.com/files/rs-4543078/v1/41bc35c9bebdbd52be37c695.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Preclinical to Clinical Translation of Antibody Drug Conjugates Using a Quantitative Systems Pharmacology (QSP) Model: A Case Study with Disitamab Vedotin","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAntibody‒drug conjugates (ADCs) are unique therapeutic monoclonal antibodies that have great promise in the field of cancer treatment. ADCs consist of a monoclonal antibody (mAb) targeting specific antigens, a payload that has the ability to kill tumor cells and a linker conjugating payload and mAb \u003cstrong\u003e[1]\u003c/strong\u003e. ADC binds to antigens on the surface of tumor cells and is internalized into the cells. Once inside the cells, the conjugated payload is released through the degradation of the antibody or linker by the lysosomes. The released payload, which possesses tumor-killing activity, has therapeutic efficacy \u003cstrong\u003e[2]\u003c/strong\u003e. Due to the complexity of the structure and mechanism of action of ADCs, they exhibit unique behaviors. For example, 1) the molecule is heterogeneous, 2) the payload in tumor cells needs to undergo a complex release process, and 3) it has the PK characteristics of a mAb and small molecules, and its metabolic behavior is complex \u003cstrong\u003e[3]\u003c/strong\u003e. Consequently, the development of a pharmacokinetic/pharmacodynamics (PK/PD) model to characterize the relationship between the metabolic behavior of both mAbs and the payload simultaneously and their effects is particularly important for ADC drugs.\u003c/p\u003e\n\u003cp\u003eThe quantitative systems pharmacology (QSP) model can simulate complex biological mechanisms and link them to the exposure response. It has advantages over traditional PK/PD models in translating biological effects between species and assessing the mechanism of action of drugs. In recent years, QSP models have been increasingly used for target antigen selection, biomarker identification, and clinical pharmacodynamic prediction\u0026nbsp;\u003cstrong\u003e[4]\u003c/strong\u003e. Due to the complexity of the structure and mechanism of action (MOA) of ADCs, the use of QSP models is highly valuable for drug discovery and development. To date, the QSP model using preclinical data to predict the therapeutic index of the ADC in the clinic has not yet been fully established. Although there are several challenges, modeling and simulation are needed for the quantitative understanding of ADCs, which are difficult to replace by any other methods. Shah et al. first reported a mechanistic model for translating the efficacy of ADCs from preclinical to clinical\u0026nbsp;\u003cstrong\u003e[5]\u003c/strong\u003e. Brentuximab-vedotin (cAC10-vc-MMAE, SGN-35), which is marketed for the treatment of Hodgkin\u0026apos;s lymphoma and anaplastic large cell lymphoma, was used for modeling and simulation. The PK of the ADC at the cellular level was first integrated with the PK/PD model. The in vitro biomeasures (e.g., internalization rate, receptor number per cell, exit rate of monomethyl auristatin E (MMAE) from the cell, binding affinity) were used to characterize the fate of brentuximab-vedotin. Recently, Shah et al. published an article to validate the model\u0026apos;s ability to predict the ADC and payload concentration in tumors and evaluate the model\u0026apos;s sensitivity parameters\u003cstrong\u003e[2]\u003c/strong\u003e. Betts et al. optimized tumor penetration and tumor growth inhibition models on the basis of Shah et al. to translate clinical data from preclinical data \u003cstrong\u003e[6]\u003c/strong\u003e. Singh et al. optimized a previously published cellular disposition model of an ADC that incorporated intracellular ADC degradation and passive diffusion of the unconjugated payload across tumor cells \u003cstrong\u003e[7]\u003c/strong\u003e. Recently, Singh et al. used an optimized PK/PD model for clinical translation with trastuzumab emtansine \u003cstrong\u003e[8]\u003c/strong\u003e. In recent years, Singh et al. refined the model in which the intracellular occupancy of tubulin was used to drive the efficacy of ADCs \u003cstrong\u003e[9]\u003c/strong\u003e. However, this model has not been used for clinical translation.\u003c/p\u003e\n\u003cp\u003eThis manuscript was built on previous research \u003cstrong\u003e[9]\u003c/strong\u003e and validated the ability of the model to predict clinical efficacy using preclinical research data. We conducted PK/PD studies in animals, PK studies at the tumor cellular level and drug-specific biomolecular level studies in vitro. The system-specific parameters are cited in the literature. By integrating the above data, we established the system QSP model. According to the differences between humans and animals, the key parameters of the model are corrected to predict the clinical efficacy of DV, which is the first ADC drug approved for marketing in China, and compare it with data obtained clinically.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003e\u003cstrong\u003eCell lines\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe high and middle HER2-expressing cell lines NCI-N87 (human gastric cancer cells) and SKOV3 (human ovarian adenocarcinoma cells) were chosen. They exhibited reproducible growth curves as tumor xenografts. Cell lines were obtained from ATCC.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTool ADCs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDV is a novel recombinant human anti-HER2 monoclonal antibody conjugated with a microtubule inhibitor (MMAE) via a cleavable linker, which was provided by RemeGen Co., Ltd. It was synthesized and characterized in house. \u003cstrong\u003e[10]\u003c/strong\u003e. The average drug-to-antibody ratio (DAR) was ~ 4.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBiomeasures\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe binding coefficients of DV (KD and \u0026nbsp;) for HER2, the antigen-antibody internalization rate (\u0026nbsp;), and the number of HER2 receptors on cancer cells were measured in-house. The results are shown in Fig S1. The detection methods used were described in previous literature \u003cstrong\u003e[11]\u003c/strong\u003e. The rest of the parameters were either extracted from the literature or estimated using the model, the details of which are provided in Table 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCellular\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Disposition\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe cellular disposition of the ADC was previously described \u003cstrong\u003e[12]\u003c/strong\u003e. We characterized the cellular disposition of DVs with reference to this method. Briefly, ten million NCI-N87 cells, a high-Her2 expression cell line, were cultured in a T-75 flask and treated with 10 \u0026mu;g/mL (66.7 nM) DV. Cell and medium samples were collected at different time points (2 h, 12 h, 24 h, 48 h, 72 h and 96 h) following continuous and 2 h of exposure. Three analytes, namely, unconjugated MMAE, total MMAE and total antibody, in the medium and cell lysate were analyzed to characterize the cellular disposition.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMouse PD studies\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThirty-six female \u003cem\u003enude mice (BALB/c nude)\u003c/em\u003e aged 6-7 weeks were purchased from Shanghai Sippr BK Laboratory Animals Ltd. The license numbers were SCXK (Hu) 2008-0016. \u003cem\u003eNude\u0026nbsp;\u003c/em\u003e\u003cem\u003eBALB/c mice\u003c/em\u003e were inoculated subcutaneously with NCI-N87 or SKOV3 cells. When the initial tumor volume reached approximately 100-200 mm\u003csup\u003e3\u003c/sup\u003e, the animals were administered 1.1, 3.3, or 10 mg/kg via intravenous injection with DV. Tumors were measured twice a week.\u003c/p\u003e\n\u003cp\u003eTumor volume was calculated as mm\u003csup\u003e3\u003c/sup\u003e=0.5 \u0026times; (tumor width\u003csup\u003e2\u003c/sup\u003e) \u0026times; (tumor length).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMouse PK studies\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEighteen female \u003cem\u003enude mice (BALB/c nude)\u003c/em\u003e aged 6-7 weeks were purchased from Shanghai Slac Laboratory Animals Co., Ltd. The license numbers were SCXK (Hu) 2007-0005. The PK of male BT474 tumor-bearing mice was evaluated.\u0026nbsp;DV\u0026nbsp;was administered to mice at 1.5 and 5 mg/kg via intravenous injection.\u0026nbsp;At 0.083, 4, 24, 48, 72, 96 and 168 h after\u0026nbsp;dosing, three animals from each group were sacrificed to collect blood samples, which were immediately processed to extract serum.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCynomolgus Monkey Pharmacokinetics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEighteen \u003cem\u003eCynomolgus monkeys\u003c/em\u003e were purchased from Beijing Institute of Xieerxin Biology Resource Co., Ltd. (the production license for the experimental animals was SCXK (Jing) 2010-0007). DV was administered to monkeys at 2.5, 5, or 10 mg/kg (n=6/group, half male and half female) via intravenous infusion. Blood samples were collected predose and at 0.167, 0.33, 0.5 (Immediately after the end of administration), 2, 6, 12, 24, 48, 72, 96, 120, 144, 168, 192, 240, 288 and 336 h postdose. Blood samples were immediately processed to extract serum.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBioanalytical Method of Total Antibody and Conjugated Antibody\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe DVs of total antibody and conjugated antibody were detected using an enzyme-linked immunosorbent assay (ELISA). The same analytical method was used for different matrices, including mouse serum, monkey serum, cell medium and cell lysate. HER2 ECD protein (G\u0026amp;P Biosciences, Santa Clara,\u0026nbsp;CA, USA), at final concentrations of 0.1 \u0026mu;g/mL (total antibody) and 0.5 \u0026mu;g/mL (conjugated antibody), was used as coated reagent. The\u0026nbsp;minimum required dilution\u0026nbsp;of total antibody and conjugated antibody was 25 and 10, respectively.\u0026nbsp;1.0 mg/ml goat anti-human IgG-H+L HRP monkey adsorbed (Bethyl Laboratories, Montgomery, Texas, USA) was added to detect the total antibody. Mouse anti-MMAE monoclonal antibody (4.1 \u0026mu;g/mL; RemeGen Co., Ltd., Yantai, Shandong, China) as the detection antibody and goat anti-mouse IgG HRP as the enzyme-labeled antibody (Multisciences, Hangzhou, Zhejiang, China) were used to detect the conjugated antibody. 3,3\u0026prime;,5,5\u0026prime;-Tetramethylbenzidine was used as the substrate for the colorimetric readout. The reaction was stopped by the addition of 1 M phosphoric acid, and the absorbance was measured at a wavelength of 450 nm using a microplate reader (Molecular Devices Inc., San Jose, CA, USA). The calibration curve was fitted using a four-parameter logistic fit regression model.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBioanalytical Method of MMAE\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA Qtrap 6500+ LC‒MS/MS system was used to analyze the concentration of MMAE. For the detection of MMAE, an ACQUITY HPLC BEH C18 column (Waters\u0026reg;) was used with a mobile phase of 0.1% FA in water (A) and ACN with 0.1% FA (B) at a flow rate of 0.5 mL/min. The following gradient elution\u0026nbsp;program was used: 0\u0026ndash;0.8\u0026nbsp;min,\u0026nbsp;10%\u0026nbsp;B; 0.8\u0026ndash;2.1 min,\u0026nbsp;10\u0026ndash;98% B; and 2.1\u0026ndash;3\u0026nbsp;min, 98\u0026ndash;10% B. The total time of analysis was 3 mins.\u0026nbsp;Two MRM scans (718.5/686.6 and 718.5/154.1 amu) were monitored, and the samples were analyzed by positive ion electrospray under multiple reaction monitoring modes. The general parameters of MMAE and MMAE-D8 were as follows: curtain gas (CUR)=25 psi, collision gas (CAD)=725 psi, ion spray voltage (IS)=550025 psi, temperature (TEM)=550\u0026deg;C, ion gas source 1 (GS1)=50 psi, and ion gas source 2 psi\u0026nbsp;(GS2)=50. The samples were treated with the same volume of ACN (V:V=1:1). The sample treatment method for total MMAE has been published previously \u003cstrong\u003e[12]\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTranslational parameters\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor preclinical to clinical translation, we hypothesized that the tumor receptor number, tumor volume and tumor growth rate are key parameters that differ between humans and animals. Other parameters, including the cellular disposition of the drug, the tumor disposition of the drug and the drug effect parameters, were kept consistent in the mouse TGI model. The changes of key difference parameters were as follows: 1) tumor receptor numbers of 100,000, 500,000 and 1000,000/cell were used to represent HER2 1+, 2+, and 3+ patients, respectively \u003cstrong\u003e[13, 14]\u003c/strong\u003e, and 2) the tumor doubling time was replaced by clinically observed values from the literature \u003cstrong\u003e[15, 16]\u003c/strong\u003e. The mean tumor doubling times of patients with gastric cancer and Her-2+ breast cancer were 2.04 and 1.7 months, respectively. Therefore, 2 months was used for the simulation. 3) The initial tumor volume was replaced by that published in Patient \u003cstrong\u003e[17]\u003c/strong\u003e. A tumor diameter of 2 cm was used for simulation. DV treatment at doses ranging from 0.5 to 2.5 mg/kg given Q2W for 5 months was evaluated. The ORR is the criterion for efficacy, including complete response (tumor undetected, assuming a diameter of \u0026lt;0.5 cm) and partial response (\u0026gt;30% decrease in tumor diameter but still detectable) according to the Response Evaluation Criteria in Solid Tumors (RECIST) guidelines.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eModeling and Simulation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePhoenix NLME software (Version 8.3.3.33, Certara) was used to develop the QSP model with a naive pooled algorithm. This model has previously been used for characterizing the in vivo pharmacodynamics of ADC drugs in animal models. The quantitative systems pharmacology (QSP) model consists of four components: the PK model for the drug in serum, a tumor distribution model, a single-cell disposition model and a tumor growth inhibition model. The model was developed step by step. First, the PK model parameters were estimated by the concentration of total antibody, ADC and payload in the circulatory system and then fixed. Second, parameters for the tumor distribution model were obtained from previous literature and fixed. Third, the single-cell level disposition model was used to characterize the penetration of the ADC and payload into the tumor interstitium, entry into tumor cells, and release within tumor cells, and the parameters were fixed. Finally, the occupancy of microtubules by MMAE within tumor cells is considered a driver of tumor growth inhibition. Tumor growth inhibition was characterized by a cell distribution model\u003cstrong\u003e.\u003c/strong\u003e The relevant parameters are estimated. These components are integrated to form a complete QSP model. The specific structure of the QSP model is described in Fig 1. The equation systems are detailed in the additional files.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eSerum PK modeling\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFig 2 shows the concentration-time profiles of total antibody, conjugated antibody and payload observed and the model described after intravenous injection of DV in BT474 tumor-bearing mice and monkeys. The total antibody, conjugated antibody and payload were characterized by a two-compartment linear model. The conjugated antibody released into the central compartment through elimination and nonspecific dissociation (K\u003csub\u003edec\u003c/sub\u003e). During model development, the modeling process was as follows: Step 1: Relevant PK parameters for the total antibody concentration were estimated. Step 2: The distribution volumes and clearance of the total antibody and conjugated antibody were assumed to be the same and fixed. The difference between the ADC and total antibody elimination is mainly due to the nonspecific dissociation of the payload from the ADC (K\u003csub\u003edec\u003c/sub\u003e). The nonspecific dissociation (K\u003csub\u003edec\u003c/sub\u003e) was estimated. Step 3: The PK parameters of the conjugated antibody were fixed, and the PK parameters of the payload were estimated. The model fitting results are shown in Fig 2, the estimated mouse PK parameter results are detailed in Table 1, and the estimated monkey PK parameter results are detailed in Table 2.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCell-level disposition modeling\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe PK behavior of the ADC and payload in tumor cells was characterized by a single-cell-level disposition model \u003cstrong\u003e[12]\u003c/strong\u003e. This model mechanism describes the dynamic processes of the drug within tumor cells. The cell-level disposition model was developed using in vitro data. Model parameters were derived from in-house or literature. The target receptor counts on tumor cells, \u0026nbsp;, \u0026nbsp; and \u0026nbsp; were acquired from in-house, while \u0026nbsp;, \u0026nbsp;, \u0026nbsp; and \u0026nbsp;were obtained from the literature. The intracellular ADC degradation (\u0026nbsp;) was estimated. The parameters are shown in Table 1, and the fitting plots of the observations and predictions are shown in Fig 3. The results showed that the predicted values of the model were consistent with the observed values for total antibody, total MMAE and free MMAE in the cellular space and medium after continuous and 2 h of exposure to 75 nM DV in NCI-N87 cells.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTumor growth inhibition in mice\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFig 4 shows the mean tumor growth curves of the NCI-N87 and SKOV3 tumor-bearing mice following intravenous injection of DV. These parameters represented the efficacy and sensitivity of DV in HER2 3+ and 2+ tumor cells, respectively. The results indicated that tumor cell growth continued with the administration of 1.1 mg/kg via injection, while there was a noticeable decrease in tumor size with the administration of 3.3 mg/kg and 10 mg/kg via injection. The tumor growth curves were characterized using a previously reported cell distribution model, with the occupancy of microtubules by MMAE within tumor cells serving as the driving factor for tumor growth inhibition \u003cstrong\u003e[9]\u003c/strong\u003e. The estimated parameters related to tumor growth inhibition can be found in Table 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrediction of human PK\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe human PK behavior was obtained by allometric scaling from the monkey PK data. Fig 2b shows the concentration-time profiles of total antibody, conjugated antibody and payload observed and the model described after intravenous injection of DV in cynomolgus monkeys. Modeling was consistent with mouse PK, and the estimated PK parameter results are detailed in Table 2. The allometric scaling exponent for the volume of distribution and clearance of conjugated antibody PK parameters was 1, while the allometric scaling exponent for the volume of distribution of payload was 1, and the exponent of clearance of payload was 0.75. The nonspecific degradation rate, K\u003csub\u003edeg\u003c/sub\u003e, was assumed to be consistent between cynomolgus monkeys and humans. The predicted human PK parameters are shown in Table 2.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrediction of clinical efficacy\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFig 5 shows the simulated tumor diameters of patients with different HER2 expression levels after 5 months of DV treatment with a dosing regimen of 0.5-2.5 mg/kg every 2 weeks (Q2W). The results indicated that for HER2 3+ and 2+ patients, an optimal ORR was achieved with a dosage range of 1.5-2 mg/kg Q2W. No observable drug efficacy was noted for HER2 1+ patients under dosing regimens below 2.5 mg/kg Q2W. It is worth noting that we have only two preclinical mouse model data points, and the availability of more preclinical data would strengthen this prediction.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, the established QSP model was used to characterize the pharmacodynamics of DV in mice. The key parameters in the model were adjusted based on relevant clinical data from patients, such as initial tumor volume, tumor doubling time, and tumor target receptor density. Monkey PK characteristics were scaled allometrically to those of humans. By integrating preclinical and clinical data from different modalities and dimensions, this approach mechanistically describes how drugs exert their pharmacological effects, enabling the prospective design of optimal clinical initiation strategies. This is achieved by constructing mathematical models that cover multiple scales, including biomolecules, cells and tissues, facilitating more efficient and precise model-guided clinical drug development.\u003c/p\u003e\n\u003cp\u003eStep 1: To describe the PK behavior of the ADC, we incorporated different analytes from the ADC (total antibody, conjugated antibody, and payload) into the model. The payload was considered the driving factor for efficacy. We established a PK model using preclinical data and predicted clinical PK behavior through allometric scaling. For ADC molecules, allometric scaling of PK parameters in monkeys provided a good prior estimation for human PK parameters, with an allometric scaling exponent of 1 \u003cstrong\u003e[18, 19]\u003c/strong\u003e. For the payload, a classic allometric scaling exponent of 0.75 was applied to predict its PK parameters in humans \u003cstrong\u003e[20]\u003c/strong\u003e. Here, we lacked sufficient evidence to prove the relevance of monkeys as a sensitive species for humans, which may introduce bias in predicting the PK parameters of the payload. Model parameters should be adjusted after obtaining clinical data to more accurately describe the PK behavior of the ADC. Step 2: When describing the disposition of the ADC in tumor cells, we assumed that there was only a difference in the HER2 expression levels between SKOV3 and NCI-N87 cells and between SKOV3 (600,000) and NCI-N87 (988,000) cells. Therefore, during model fitting, in addition to differences in receptor expression on tumor cells, all other parameters remained consistent. Step 3: In the tumor killing model, assume that the occupancy of microtubules by the payload drives tumor cytotoxicity. The study indicated that the estimated K\u003csub\u003emax\u003c/sub\u003e and KC50 in the model were highly consistent with in vitro experimental results \u003cstrong\u003e[21]\u003c/strong\u003e, so the parameters were fixed. The tumor growth rate (K\u003csub\u003eg\u003c/sub\u003e) and tumor-killing-related parameters \u0026tau; and \u0026gamma; were estimated. These findings also provide a basis for early drug screening through in vitro experiments. The differences in tumor-killing-related parameters for two tumor cell lines were utilized to simulate inherent drug sensitivity variations across different patient populations. As interindividual variability among animals did not reflect patient variability, individual variability among animals was not considered during model fitting. Step 4: When transitioned from preclinical to clinical stages, the key parameters reflecting differences between humans and animals in the model were adjusted. Sensitivity analysis of these parameters revealed that tumor volume, tumor doubling time, and the expression level of antigens on the tumor surface are sensitive parameters in the model.\u003cstrong\u003e[7, 12]\u003c/strong\u003e These parameters were replaced with actual patient data to better reflect the real conditions of the patients. In this study, the initial tumor volume and tumor doubling time for patients were obtained from the clinical data of breast cancer patients and gastric cancer patients, while the expression levels of tumor surface antigens were obtained from previous relevant reports \u003cstrong\u003e[14]\u003c/strong\u003e. The other PD parameters were consistent with the preclinical data. We only used preclinical data for modeling and simulation, excluding clinical data, aiming to validate the model\u0026apos;s accuracy in predicting clinical dosage. In the future, when clinical data are available, modifying model parameters and introducing variability between individual patients will better guide clinical drug delivery design. DV is the first ADC drug approved for commercial use in China. Current clinical results indicate that DV demonstrates favorable clinical efficacy at a dose of 2 mg/kg Q2W in the treatment of indications such as gastric cancer, breast cancer, and urothelial carcinoma \u003cstrong\u003e[22, 23]\u003c/strong\u003e. This finding was closely consistent with the clinical efficacy predicted based on preclinical data, highlighting the significant utility of the model in preclinical to clinical translation. However, further cases are still needed to validate the accuracy of the model.\u003c/p\u003e\n\u003cp\u003eHowever, the model still leaves something to be desired. During modeling, we only incorporated PK data from single-dose administration in cynomolgus monkeys and mice, without including indicators such as immunogenicity that may affect drug efficacy. This could overestimate the clinical efficacy of the drug. After conducting clinical studies, it was essential to adjust the PK model based on actual clinical data to more accurately describe PK behavior in patients. While the simulation results closely matched the clinically approved dosages, they did not account for the impact of bystander killing effects, potentially underestimating the efficacy of the drug. In the future, it will be necessary to supplement relevant in vitro bystander killing data to further refine the model. The QSP model mechanistically explains how the ADC exerts efficacy.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary, by constructing a QSP model that spans multiple scales, including biomolecules, cells and tumor tissues, we validated the feasibility of the model\u0026apos;s preclinical and clinical translation using DVs. To successfully predict the clinical results of DVs, this model is a promising quantitative tool for supporting the design, selection, and optimization of ADC clinical dosing regimens.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis work was partly supported by the Yantai Science and Technology Plan (No. 2021XDHZ083) and the Taishan Industrial Innovation Leading Talent Project\u003c/p\u003e\n\u003cp\u003eCompeting Interests\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose\u003c/p\u003e\n\u003cp\u003eAuthors\u0026apos; contributions\u003c/p\u003e\n\u003cp\u003eThe first draft of the manuscript was written by Qiaoning Li and Ling Wang. The study design was performed by Ling Wang and Jing jiang. The data analysis was performed by Qiaoning Li and Baiyang Wu. The study was conducted by Baiyang Wu, Xinting Ma, Guorui Zhao and Juncheng Wang. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003eAcknowledgments\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank members of the United-Power Pharma Tech Co. Ltd. team who completed the \u003cem\u003eCynomolgus monkey\u003c/em\u003e PK studies.\u003c/p\u003e\n\u003cp\u003eData Availability\u003c/p\u003e\n\u003cp\u003eAll the data needed to evaluate the conclusions in the paper are presented in the paper and the Additional files.\u003c/p\u003e\n\u003cp\u003eEthics approval\u003c/p\u003e\n\u003cp\u003eThe animal protocols were approved by the Institutional Animal Care and Use Committee. The animal protocols were carried out in strict accordance with the National Guidelines for Animal Usage in Research (China).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eDucry L, Stump B. Antibody-drug conjugates: linking cytotoxic payloads to monoclonal antibodies. Bioconjug Chem. 2010;21(1):5-13. doi: 10.1021/bc9002019.\u003c/li\u003e\n\u003cli\u003eShah DK, King LE, Han X, Wentland JA, Zhang Y, Lucas J, et al. A priori prediction of tumor payload concentrations: preclinical case study with an auristatin-based anti-5T4 antibody-drug conjugate. AAPS J. 2014;16(3):452-63. doi: 10.1208/s12248-014-9576-9.\u003c/li\u003e\n\u003cli\u003eHaraya K, Tsutsui H, Komori Y, Tachibana T. Recent Advances in Translational Pharmacokinetics and Pharmacodynamics Prediction of Therapeutic Antibodies Using Modeling and Simulation. Pharmaceuticals (Basel). 2022;15(5). doi: 10.3390/ph15050508.\u003c/li\u003e\n\u003cli\u003eNijsen M, Wu F, Bansal L, Bradshaw-Pierce E, Chan JR, Liederer BM, et al. Preclinical QSP Modeling in the Pharmaceutical Industry: An IQ Consortium Survey Examining the Current Landscape. CPT Pharmacometrics Syst Pharmacol. 2018;7(3):135-46. doi: 10.1002/psp4.12282.\u003c/li\u003e\n\u003cli\u003eShah DK, Haddish-Berhane N, Betts A. 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J Pharm Sci. 2019;108(7):2465-75. doi: 10.1016/j.xphs.2019.01.034.\u003c/li\u003e\n\u003cli\u003eSheng X, Yan X, Wang L, Shi Y, Yao X, Luo H, et al. Open-label, Multicenter, Phase II Study of RC48-ADC, a HER2-Targeting Antibody-Drug Conjugate, in Patients with Locally Advanced or Metastatic Urothelial Carcinoma. Clin Cancer Res. 2021;27(1):43-51. doi: 10.1158/1078-0432.CCR-20-2488.\u003c/li\u003e\n\u003cli\u003eShi F, Liu Y, Zhou X, Shen P, Xue R, Zhang M. Disitamab vedotin: a novel antibody-drug conjugates for cancer therapy. Drug Deliv. 2022;29(1):1335-44. doi: 10.1080/10717544.2022.2069883.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1 Estimated or Literature-Derived Parameter Values Used for Simulating the ADC QSP Model \u003cstrong\u003e[9, 12]\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"591\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.859560067681896%\" valign=\"top\"\u003e\n \u003cp\u003eParameter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.336717428087987%\" valign=\"top\"\u003e\n \u003cp\u003eUnit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.76311336717428%\" valign=\"top\"\u003e\n \u003cp\u003eDescription\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.07445008460237%\" valign=\"top\"\u003e\n \u003cp\u003eValue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.96615905245347%\" valign=\"top\"\u003e\n \u003cp\u003eSource\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003ein Vitro Cellular Parameters\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.859560067681896%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.336717428087987%\" valign=\"top\"\u003e\n \u003cp\u003e1/h\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.76311336717428%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003csup\u003est\u003c/sup\u003e order rate of proteases induced intracellular ADC degradation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.07445008460237%\" valign=\"top\"\u003e\n \u003cp\u003e0.353\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.96615905245347%\" valign=\"top\"\u003e\n \u003cp\u003eEstimated\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.859560067681896%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.336717428087987%\" valign=\"top\"\u003e\n \u003cp\u003e1/h\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.76311336717428%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003csup\u003est\u003c/sup\u003e order net antibody-HER2\u003c/p\u003e\n \u003cp\u003ecomplex internalization rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.07445008460237%\" valign=\"top\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.96615905245347%\" valign=\"top\"\u003e\n \u003cp\u003eIn-house\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.859560067681896%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.336717428087987%\" valign=\"top\"\u003e\n \u003cp\u003e1/nM/h\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.76311336717428%\" valign=\"top\"\u003e\n \u003cp\u003e2\u003csup\u003end\u003c/sup\u003e order association rates of DV binding to HER2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.07445008460237%\" valign=\"top\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.96615905245347%\" valign=\"top\"\u003e\n \u003cp\u003eIn-house\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.859560067681896%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.336717428087987%\" valign=\"top\"\u003e\n \u003cp\u003e1/h\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.76311336717428%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003csup\u003est\u003c/sup\u003e order dissociation rates of DV binding to HER2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.07445008460237%\" valign=\"top\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.96615905245347%\" valign=\"top\"\u003e\n \u003cp\u003eIn-house\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.859560067681896%\"\u003e\n \u003cp\u003eAg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.336717428087987%\" valign=\"top\"\u003e\n \u003cp\u003eUnitless\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.76311336717428%\" valign=\"top\"\u003e\n \u003cp\u003eNumber of HER2 receptors on N87 and SKOV3 cells, respectively\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.07445008460237%\" valign=\"top\"\u003e\n \u003cp\u003e988000,\u003c/p\u003e\n \u003cp\u003e600000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.96615905245347%\" valign=\"top\"\u003e\n \u003cp\u003eIn-house\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.859560067681896%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.336717428087987%\" valign=\"top\"\u003e\n \u003cp\u003e1/h\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.76311336717428%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003csup\u003est\u003c/sup\u003e order influx rate constant for MMAE from extracellular space to intracellular space\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.07445008460237%\" valign=\"top\"\u003e\n \u003cp\u003e8.330\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.96615905245347%\" valign=\"top\"\u003e\n \u003cp\u003eDerived from \u003cstrong\u003e[12]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.859560067681896%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.336717428087987%\" valign=\"top\"\u003e\n \u003cp\u003e1/h\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.76311336717428%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003csup\u003est\u003c/sup\u003e order efflux rate constants for MMAE from intracellular space to\u003c/p\u003e\n \u003cp\u003eextracellular space\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.07445008460237%\" valign=\"top\"\u003e\n \u003cp\u003e0.199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.96615905245347%\" valign=\"top\"\u003e\n \u003cp\u003eDerived from \u003cstrong\u003e[12]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.859560067681896%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.336717428087987%\" valign=\"top\"\u003e\n \u003cp\u003e1/nM/h\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.76311336717428%\" valign=\"top\"\u003e\n \u003cp\u003e2\u003csup\u003end\u003c/sup\u003e order association of MMAE binding to tubulin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.07445008460237%\" valign=\"top\"\u003e\n \u003cp\u003e0.0183\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.96615905245347%\" valign=\"top\"\u003e\n \u003cp\u003eDerived from \u003cstrong\u003e[12]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.859560067681896%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.336717428087987%\" valign=\"top\"\u003e\n \u003cp\u003e1/h\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.76311336717428%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003csup\u003est\u003c/sup\u003e order dissociation rates of\u003c/p\u003e\n \u003cp\u003eMMAE binding to tubulin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.07445008460237%\" valign=\"top\"\u003e\n \u003cp\u003e0.545\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.96615905245347%\" valign=\"top\"\u003e\n \u003cp\u003eDerived from \u003cstrong\u003e[12]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.859560067681896%\"\u003e\n \u003cp\u003eSF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.336717428087987%\" valign=\"top\"\u003e\n \u003cp\u003eUnitless\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.76311336717428%\" valign=\"top\"\u003e\n \u003cp\u003eScaling factor to convert the number of molecules to nanoMoles.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.07445008460237%\" valign=\"top\"\u003e\n \u003cp\u003e1.70E-15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.96615905245347%\" valign=\"top\"\u003e\n \u003cp\u003eDerived from \u003cstrong\u003e[12]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.859560067681896%\"\u003e\n \u003cp\u003eV\u003csub\u003ecell\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.336717428087987%\" valign=\"top\"\u003e\n \u003cp\u003eLiters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.76311336717428%\" valign=\"top\"\u003e\n \u003cp\u003eVolume of cell\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.07445008460237%\" valign=\"top\"\u003e\n \u003cp\u003e3.12E-12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.96615905245347%\" valign=\"top\"\u003e\n \u003cp\u003eDerived from \u003cstrong\u003e[12]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.859560067681896%\"\u003e\n \u003cp\u003eDT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.336717428087987%\" valign=\"top\"\u003e\n \u003cp\u003eh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.76311336717428%\" valign=\"top\"\u003e\n \u003cp\u003eDoubling time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.07445008460237%\" valign=\"top\"\u003e\n \u003cp\u003e40.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.96615905245347%\" valign=\"top\"\u003e\n \u003cp\u003eDerived from \u003cstrong\u003e[12]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"5\"\u003e\n \u003cp\u003eMouse PK Parameters\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.859560067681896%\"\u003e\n \u003cp\u003eCL\u003csub\u003eADC\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.336717428087987%\" valign=\"top\"\u003e\n \u003cp\u003eL/h/kg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.76311336717428%\" valign=\"top\"\u003e\n \u003cp\u003ePlasma clearance of ADC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.07445008460237%\" valign=\"top\"\u003e\n \u003cp\u003e0.0026(16.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.96615905245347%\" valign=\"top\"\u003e\n \u003cp\u003eEstimated\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.859560067681896%\"\u003e\n \u003cp\u003eCLD\u003csub\u003eADC\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.336717428087987%\" valign=\"top\"\u003e\n \u003cp\u003eL/h/kg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.76311336717428%\" valign=\"top\"\u003e\n \u003cp\u003eDistribution clearance of ADC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.07445008460237%\" valign=\"top\"\u003e\n \u003cp\u003e0.0090 (18.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.96615905245347%\" valign=\"top\"\u003e\n \u003cp\u003eEstimated\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.859560067681896%\"\u003e\n \u003cp\u003eV1\u003csub\u003eADC\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.336717428087987%\" valign=\"top\"\u003e\n \u003cp\u003eL/kg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.76311336717428%\" valign=\"top\"\u003e\n \u003cp\u003eADC volume of distribution for central compartment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.07445008460237%\" valign=\"top\"\u003e\n \u003cp\u003e0.1218(7.07%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.96615905245347%\" valign=\"top\"\u003e\n \u003cp\u003eEstimated\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.859560067681896%\"\u003e\n \u003cp\u003eV2\u003csub\u003eADC\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.336717428087987%\" valign=\"top\"\u003e\n \u003cp\u003eL/kg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.76311336717428%\" valign=\"top\"\u003e\n \u003cp\u003eADC volume of distribution for peripheral compartment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.07445008460237%\" valign=\"top\"\u003e\n \u003cp\u003e0.1499(15.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.96615905245347%\" valign=\"top\"\u003e\n \u003cp\u003eEstimated\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.859560067681896%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.336717428087987%\" valign=\"top\"\u003e\n \u003cp\u003e1/h\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.76311336717428%\" valign=\"top\"\u003e\n \u003cp\u003eDissociation rate of payload from ADC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.07445008460237%\" valign=\"top\"\u003e\n \u003cp\u003e0.0064(51.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.96615905245347%\" valign=\"top\"\u003e\n \u003cp\u003eEstimated\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.859560067681896%\"\u003e\n \u003cp\u003eCL\u003csub\u003edrug\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.336717428087987%\" valign=\"top\"\u003e\n \u003cp\u003eL/h/kg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.76311336717428%\" valign=\"top\"\u003e\n \u003cp\u003ePlasma clearance of payload\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.07445008460237%\" valign=\"top\"\u003e\n \u003cp\u003e1.2046(7.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.96615905245347%\" valign=\"top\"\u003e\n \u003cp\u003eEstimated\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.859560067681896%\"\u003e\n \u003cp\u003eCLD\u003csub\u003edrug\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.336717428087987%\" valign=\"top\"\u003e\n \u003cp\u003eL/h/kg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.76311336717428%\" valign=\"top\"\u003e\n \u003cp\u003eDistribution clearance of payload\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.07445008460237%\" valign=\"top\"\u003e\n \u003cp\u003e1.2188(10.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.96615905245347%\" valign=\"top\"\u003e\n \u003cp\u003eEstimated\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.859560067681896%\"\u003e\n \u003cp\u003eV1\u003csub\u003edrug\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.336717428087987%\" valign=\"top\"\u003e\n \u003cp\u003eL/kg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.76311336717428%\" valign=\"top\"\u003e\n \u003cp\u003ePayload volume of distribution for central compartment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.07445008460237%\" valign=\"top\"\u003e\n \u003cp\u003e0.0341(13.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.96615905245347%\" valign=\"top\"\u003e\n \u003cp\u003eEstimated\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.859560067681896%\"\u003e\n \u003cp\u003eV2\u003csub\u003edrug\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.336717428087987%\" valign=\"top\"\u003e\n \u003cp\u003eL/kg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.76311336717428%\" valign=\"top\"\u003e\n \u003cp\u003ePayload volume of distribution for peripheral compartment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.07445008460237%\" valign=\"top\"\u003e\n \u003cp\u003e0.5173 (13.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.96615905245347%\" valign=\"top\"\u003e\n \u003cp\u003eEstimated\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"5\"\u003e\n \u003cp\u003eMouse PD Parameters\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.859560067681896%\"\u003e\n \u003cp\u003eDT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.336717428087987%\" valign=\"top\"\u003e\n \u003cp\u003eh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.76311336717428%\" valign=\"top\"\u003e\n \u003cp\u003eDoubling time of NCI-N87 and SKOV4 tumors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.07445008460237%\" valign=\"top\"\u003e\n \u003cp\u003e307 (0.23%), 204 (0.08%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.96615905245347%\" valign=\"top\"\u003e\n \u003cp\u003eEstimated\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.859560067681896%\"\u003e\n \u003cp\u003eK\u003csub\u003emax\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.336717428087987%\" valign=\"top\"\u003e\n \u003cp\u003e1/h\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.76311336717428%\" valign=\"top\"\u003e\n \u003cp\u003eFirst order killing rate of MMAE in each tumor cell\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.07445008460237%\" valign=\"top\"\u003e\n \u003cp\u003e0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.96615905245347%\" valign=\"top\"\u003e\n \u003cp\u003eDerived from \u003cstrong\u003e[9]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.859560067681896%\"\u003e\n \u003cp\u003eKC50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.336717428087987%\" valign=\"top\"\u003e\n \u003cp\u003ePercentage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.76311336717428%\" valign=\"top\"\u003e\n \u003cp\u003ePercentage of intracellular occupancy to tubulin by MMAE which leads to 50% of maximum killing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.07445008460237%\" valign=\"top\"\u003e\n \u003cp\u003e97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.96615905245347%\" valign=\"top\"\u003e\n \u003cp\u003eDerived from \u003cstrong\u003e[9]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.859560067681896%\"\u003e\n \u003cp\u003e\u0026tau;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.336717428087987%\" valign=\"top\"\u003e\n \u003cp\u003eh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.76311336717428%\" valign=\"top\"\u003e\n \u003cp\u003eTransit time associated with the killing of NCI-N87 and SKOV3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.07445008460237%\" valign=\"top\"\u003e\n \u003cp\u003e7.03 (21.5%), 18.52 (43.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.96615905245347%\" valign=\"top\"\u003e\n \u003cp\u003eEstimated\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.859560067681896%\"\u003e\n \u003cp\u003e\u0026gamma;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.336717428087987%\" valign=\"top\"\u003e\n \u003cp\u003eUnitless\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.76311336717428%\" valign=\"top\"\u003e\n \u003cp\u003eCurve-fitting parameter associated with sigmoidal tubulin occupancy-killing relationship of NCI-N87 and SKOV3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.07445008460237%\" valign=\"top\"\u003e\n \u003cp\u003e32.28 (12.1%),\u003c/p\u003e\n \u003cp\u003e16.68 (12.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.96615905245347%\" valign=\"top\"\u003e\n \u003cp\u003eEstimated\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 2 Estimated or predicted PK parameters.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.35676625659051%\" valign=\"top\"\u003e\n \u003cp\u003eParameter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.787346221441124%\" valign=\"top\"\u003e\n \u003cp\u003eUnit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.85237258347979%\" valign=\"top\"\u003e\n \u003cp\u003eDescription\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.926186291739896%\" valign=\"top\"\u003e\n \u003cp\u003eMonkey PK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.077328646748683%\" valign=\"top\"\u003e\n \u003cp\u003ePredicted human PK\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.35676625659051%\"\u003e\n \u003cp\u003eCL\u003csub\u003eADC\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.787346221441124%\" valign=\"top\"\u003e\n \u003cp\u003eL/h/kg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.85237258347979%\" valign=\"top\"\u003e\n \u003cp\u003ePlasma clearance of ADC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.926186291739896%\" valign=\"top\"\u003e\n \u003cp\u003e0.0016(3.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.077328646748683%\" valign=\"top\"\u003e\n \u003cp\u003e0.0016\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.35676625659051%\"\u003e\n \u003cp\u003eCLD\u003csub\u003eADC\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.787346221441124%\" valign=\"top\"\u003e\n \u003cp\u003eL/h/kg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.85237258347979%\" valign=\"top\"\u003e\n \u003cp\u003eDistribution clearance of ADC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.926186291739896%\" valign=\"top\"\u003e\n \u003cp\u003e0.0023(11.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.077328646748683%\" valign=\"top\"\u003e\n \u003cp\u003e0.0023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.35676625659051%\"\u003e\n \u003cp\u003eV1\u003csub\u003eADC\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.787346221441124%\" valign=\"top\"\u003e\n \u003cp\u003eL/kg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.85237258347979%\" valign=\"top\"\u003e\n \u003cp\u003eADC volume of distribution for central compartment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.926186291739896%\" valign=\"top\"\u003e\n \u003cp\u003e0.0390(3.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.077328646748683%\" valign=\"top\"\u003e\n \u003cp\u003e0.0390\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.35676625659051%\"\u003e\n \u003cp\u003eV2\u003csub\u003eADC\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.787346221441124%\" valign=\"top\"\u003e\n \u003cp\u003eL/kg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.85237258347979%\" valign=\"top\"\u003e\n \u003cp\u003eADC volume of distribution for peripheral compartment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.926186291739896%\" valign=\"top\"\u003e\n \u003cp\u003e0.0154(10.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.077328646748683%\" valign=\"top\"\u003e\n \u003cp\u003e0.0154\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.35676625659051%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.787346221441124%\" valign=\"top\"\u003e\n \u003cp\u003e1/h\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.85237258347979%\" valign=\"top\"\u003e\n \u003cp\u003eDissociation rate of payload from ADC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.926186291739896%\" valign=\"top\"\u003e\n \u003cp\u003e0.0097(33.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.077328646748683%\" valign=\"top\"\u003e\n \u003cp\u003e0.0097\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.35676625659051%\"\u003e\n \u003cp\u003eCL\u003csub\u003edrug\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.787346221441124%\" valign=\"top\"\u003e\n \u003cp\u003eL/h/kg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.85237258347979%\" valign=\"top\"\u003e\n \u003cp\u003ePlasma clearance of payload\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.926186291739896%\" valign=\"top\"\u003e\n \u003cp\u003e2.4677(6.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.077328646748683%\" valign=\"top\"\u003e\n \u003cp\u003e1.1721\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.35676625659051%\"\u003e\n \u003cp\u003eCLD\u003csub\u003edrug\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.787346221441124%\" valign=\"top\"\u003e\n \u003cp\u003eL/h/kg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.85237258347979%\" valign=\"top\"\u003e\n \u003cp\u003eDistribution clearance of payload\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.926186291739896%\" valign=\"top\"\u003e\n \u003cp\u003e42.1332(51.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.077328646748683%\" valign=\"top\"\u003e\n \u003cp\u003e19.8079\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.35676625659051%\"\u003e\n \u003cp\u003eV1\u003csub\u003edrug\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.787346221441124%\" valign=\"top\"\u003e\n \u003cp\u003eL/kg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.85237258347979%\" valign=\"top\"\u003e\n \u003cp\u003ePayload volume of distribution for central compartment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.926186291739896%\" valign=\"top\"\u003e\n \u003cp\u003e19.6476(22.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.077328646748683%\" valign=\"top\"\u003e\n \u003cp\u003e19.6476\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.35676625659051%\"\u003e\n \u003cp\u003eV2\u003csub\u003edrug\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.787346221441124%\" valign=\"top\"\u003e\n \u003cp\u003eL/kg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.85237258347979%\" valign=\"top\"\u003e\n \u003cp\u003ePayload volume of distribution for peripheral compartment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.926186291739896%\" valign=\"top\"\u003e\n \u003cp\u003e84.2451(7.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.077328646748683%\" valign=\"top\"\u003e\n \u003cp\u003e84.2451\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"investigational-new-drugs","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"drug","sideBox":"Learn more about [Investigational New Drugs](https://www.springer.com/journal/10637)","snPcode":"10637","submissionUrl":"https://submission.nature.com/new-submission/10637/3","title":"Investigational New Drugs","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Disitamab vedotin, quantitative systems pharmacology model, efficacy, preclinical to clinical translation","lastPublishedDoi":"10.21203/rs.3.rs-4543078/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4543078/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: The multiscale quantitative systems pharmacology (QSP) model, which includes biomolecule, cellular and tissue data, was used for the preclinical to clinical translation of ADC efficacy. Here, we validated the feasibility of the model using the marketed drug disitamab vedotin (DV).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: Model integrated pharmacokinetics (PK), in vitro tumor cell disposition data and tumor growth inhibition (TGI) data were obtained. This mechanistically explains how the ADC exerted efficacy. Based on the in vivo efficacy data, which were obtained from studies conducted using two cell-derived xenograft models (NCI-N87 and SKOV3), a QSP model was established to characterize the efficacy, and the inherent sensitivity of the ADC between patient populations with different target expression levels was further simulated. For preclinical to clinical transitioning, human PK behavior was predicted with allometric scaling using the data of the monkeys’ PK parameters. Key parameters impacting the QSP model, such as TGI, tumor volume, and target expression level of tumors, were replaced with clinically relevant data to predict the effective dose of DV in patients. The clinically approved Q2W dosing intervals were used to simulate the effective dose. \u003cstrong\u003eResults\u003c/strong\u003e: After continuous administration of 1.5-2 mg/kg Q2W for five months, patients with HER2-2+ and HER2-3+ tumors achieved an objective response rate (ORR), but progressive disease was observed in HER2-1+ patients, which was consistent with clinically observed efficacy outcomes.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e: The model has promising applications in ADC screening, the optimization of clinical dosing regimens and other related areas.\u003c/p\u003e","manuscriptTitle":"Preclinical to Clinical Translation of Antibody Drug Conjugates Using a Quantitative Systems Pharmacology (QSP) Model: A Case Study with Disitamab Vedotin","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-15 23:37:03","doi":"10.21203/rs.3.rs-4543078/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-07-09T15:24:50+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-07-09T15:09:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"107991263460841960866728085932800383851","date":"2024-06-20T17:50:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"339342522595223895215248103880769291256","date":"2024-06-17T08:31:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"146309177481022951908457009514276129865","date":"2024-06-17T07:45:31+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-06-17T07:31:30+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-06-17T01:27:59+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-06-17T01:26:15+00:00","index":"","fulltext":""},{"type":"submitted","content":"Investigational New Drugs","date":"2024-06-07T02:57:25+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"investigational-new-drugs","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"drug","sideBox":"Learn more about [Investigational New Drugs](https://www.springer.com/journal/10637)","snPcode":"10637","submissionUrl":"https://submission.nature.com/new-submission/10637/3","title":"Investigational New Drugs","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"c0345c95-15eb-4040-963e-0378e3e8e304","owner":[],"postedDate":"July 15th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-09-18T10:58:42+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-15 23:37:03","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4543078","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4543078","identity":"rs-4543078","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}