Alison Betts

Bench to bedside translation of antibody drug conjugates using a multiscale mechanistic PK/PD model: a case study with brentuximab-vedotin

To build a multiscale mechanism based pharmacokinetic–pharmacodynamic (PK/PD) model for antibody drug conjugates (ADCs), using brentuximab-vedotin as an example, for preclinical to clinical translation of ADC efficacy. Brentuximab-vedotin experimental data, collected from diverse publications, were employed in the following steps to build and validate the model: (1) characterization of ADC and payload PK at the cellular level, (2) characterization of payload PK in plasma and tumor tissue of xenograft mouse, (3) characterization of ADC PK in mouse plasma, (4) prediction of the tumor payload concentrations in xenograft mouse by integrating parameters obtained from steps 1–3 with the novel tumor disposition model for ADC, (5) characterization of preclinical brentuximab-vedotin tumor growth inhibition data using the novel PK/PD model, (6) characterization of ADC and payload PK in cancer patients, and (7) prediction of clinical responses of brentuximab-vedotin using the PK/PD model, by integrating PK parameters obtained from step 6, and translated mouse parameters from step 5; and comparing them with clinical trial results. The tumor disposition model was able to accurately predict xenograft tumor and plasma payload concentrations. PK/PD model predicted progression free survival rates and complete response rates for brentuximab-vedotin in patients were comparable to the observed clinical results. It is essential to understand and characterize the disposition of ADC and payload, at the cellular and physiological level, to predict the clinical outcome of ADC. A first of its kind mechanistic model has been developed for ADCs, which can integrate preclinical biomeasures and PK/PD data, to predict clinical response.

The Application of Target Information and Preclinical Pharmacokinetic/Pharmacodynamic Modeling in Predicting Clinical Doses of a Dickkopf-1 Antibody for Osteoporosis

PF-04840082 is a humanized prototype anti-Dickkopf-1 (Dkk-1) immunoglobulin isotype G2 (IgG2) antibody for the treatment of osteoporosis. In vitro, PF-04840082 binds to human, monkey, rat, and mouse Dkk-1 with high affinity. After administration of PF-04840082 to rat and monkey, free Dkk-1 concentrations decreased rapidly and returned to baseline in a dose-dependent manner. In rat and monkey, PF-04840082 exhibited nonlinear pharmacokinetics (PK) and a target-mediated drug disposition (TMDD) model was used to characterize PF-04840082 versus Dkk-1 concentration response relationship. PK/pharmacodynamic (PK/PD) modeling enabled estimation of antibody non-target-mediated elimination, Dkk-1 turnover, complex formation, and complex elimination. The TMDD model was translated to human to predict efficacious dose and minimum anticipated biological effect level (MABEL) by incorporating information on typical IgG2 human PK, antibody-target association/dissociation rates, Dkk-1 expression, and turnover rates. The PK/PD approach to MABEL was compared with the standard “no adverse effect level” (NOAEL) approach to calculating clinical starting doses and a pharmacological equilibrium method. The NOAEL method gave estimates of dose that were too high to ensure safety of clinical trials. The pharmacological equilibrium approach calculated receptor occupancy (RO) based on equilibrium dissociation constant alone and did not take into account rate of turnover of the target or antibody–target complex kinetics and, as a result, it likely produced a substantial overprediction of RO at a given dose. It was concluded that the calculation of MABEL according to the TMDD model was the most appropriate means for ensuring safety and efficacy in clinical studies.

Antibody biodistribution coefficients: inferring tissue concentrations of monoclonal antibodies based on the plasma concentrations in several preclinical species and human.

Tissue vs. plasma concentration profiles have been generated from a physiologically-based pharmacokinetic model of monoclonal antibody (mAb). Based on the profiles, we hypothesized that a linear relationship between the plasma and tissue concentrations of non-binding mAbs could exist; and that the relationship may be generally constant irrespective of the absolute mAb concentration, time, and animal species being analyzed. The hypothesis was verified for various tissues in mice, rat, monkey, and human using mAb or antibody-drug conjugate tissue distribution data collected from diverse literature. The relationship between the plasma and various tissue concentrations was mathematically characterized using the antibody biodistribution coefficient (ABC). Estimated ABC values suggest that typically the concentration of mAb in lung is 14.9%, heart 10.2%, kidney 13.7%, muscle 3.97%, skin 15.7%, small intestine 5.22%, large intestine 5.03%, spleen 12.8%, liver 12.1%, bone 7.27%, stomach 4.98%, lymph node 8.46%, adipose 4.78%, brain 0.351%, pancreas 6.4%, testes 5.88%, thyroid 67.5% and thymus is 6.62% of the plasma concentration. The validity of using the ABC to predict mAb concentrations in different tissues of mouse, rat, monkey, and human species was evaluated by generating validation data sets, which demonstrated that predicted concentrations were within 2-fold of the observed concentrations. The use of ABC to infer tissue concentrations of mAbs and related molecules provides a valuable tool for investigating preclinical or clinical disposition of these molecules. It can also help eliminate or optimize biodistribution studies, and interpret efficacy or toxicity of the drug in a particular tissue.

A Priori Prediction of Tumor Payload Concentrations: Preclinical Case Study with an Auristatin-Based Anti-5T4 Antibody-Drug Conjugate

The objectives of this investigation were as follows: (a) to validate a mechanism-based pharmacokinetic (PK) model of ADC for its ability to a priori predict tumor concentrations of ADC and released payload, using anti-5T4 ADC A1mcMMAF, and (b) to analyze the PK model to find out main pathways and parameters model outputs are most sensitive to. Experiential data containing biomeasures, and plasma and tumor concentrations of ADC and payload, following A1mcMMAF administration in two different xenografts, were used to build and validate the model. The model performed reasonably well in terms of a priori predicting tumor exposure of total antibody, ADC, and released payload, and the exposure of released payload in plasma. Model predictions were within two fold of the observed exposures. Pathway analysis and local sensitivity analysis were conducted to investigate main pathways and set of parameters the model outputs are most sensitive to. It was discovered that payload dissociation from ADC and tumor size were important determinants of plasma and tumor payload exposure. It was also found that the sensitivity of the model output to certain parameters is dose-dependent, suggesting caution before generalizing the results from the sensitivity analysis. Model analysis also revealed the importance of understanding and quantifying the processes responsible for ADC and payload disposition within tumor cell, as tumor concentrations were sensitive to these parameters. Proposed ADC PK model provides a useful tool for a priori predicting tumor payload concentrations of novel ADCs preclinically, and possibly translating them to the clinic.

Preclinical and clinical pharmacokinetic/pharmacodynamic considerations for antibody–drug conjugates

Antibody-drug conjugates (ADCs) represent a promising therapeutic modality for the clinical management of cancer. Here we discuss the clinical pharmacology and safety of ADCs that are either approved or in late stages of clinical development. We have taken examples of ADCs employing either DNA damaging payloads (such as calicheamicin) or tubulin depolymerizing agents (such as auristatins and maytansinoids) to discuss the impact of dose and dosage intervals on pharmacokinetics/pharmacodynamics (PK/PD) and safety of ADCs. We also discuss the development of PK/PD models that were validated using preclinical and clinical data from two approved ADCs (ado-trastuzumab emtansine (T-DM1, Kadcyla™) and brentuximab vedotin (SGN-35, Adcetris™). These models could be used to predict clinical efficacious doses of ADCs.

A PTK7-targeted antibody-drug conjugate reduces tumor-initiating cells and induces sustained tumor regressions

PTK7 is a tumor-initiating cell antigen, which can be targeted with an antibody-drug conjugate to confer sustained tumor regressions. Initiating an antitumor attack Cancer is notorious for relapsing after treatment, making it difficult to eradicate from a patient’s body. Such relapses are driven by tumor-initiating cells, a type of stem cells that give rise to tumors. Damelin et al. determined that a protein called PTK7 is frequently present on tumor-initiating cells and developed an antibody-drug conjugate for targeting it. The authors demonstrated the effectiveness of this therapy in mouse models of several tumor types and confirmed that it reduces tumor-initiating cells and outperforms standard chemotherapy. The antibody-drug conjugate also had some unexpected benefits, reducing tumor angiogenesis and promoting antitumor immunity, all of which may contribute to its effectiveness. Disease relapse after treatment is common in triple-negative breast cancer (TNBC), ovarian cancer (OVCA), and non–small cell lung cancer (NSCLC). Therapies that target tumor-initiating cells (TICs) should improve patient survival by eliminating the cells that can drive tumor recurrence and metastasis. We demonstrate that protein tyrosine kinase 7 (PTK7), a highly conserved but catalytically inactive receptor tyrosine kinase in the Wnt signaling pathway, is enriched on TICs in low-passage TNBC, OVCA, and NSCLC patient–derived xenografts (PDXs). To deliver a potent anticancer drug to PTK7-expressing TICs, we generated a targeted antibody-drug conjugate (ADC) composed of a humanized anti-PTK7 monoclonal antibody, a cleavable valine-citrulline–based linker, and Aur0101, an auristatin microtubule inhibitor. The PTK7-targeted ADC induced sustained tumor regressions and outperformed standard-of-care chemotherapy. Moreover, the ADC specifically reduced the frequency of TICs, as determined by serial transplantation experiments. In addition to reducing the TIC frequency, the PTK7-targeted ADC may have additional antitumor mechanisms of action, including the inhibition of angiogenesis and the stimulation of immune cells. Together, these preclinical data demonstrate the potential for the PTK7-targeted ADC to improve the long-term survival of cancer patients.

Quantitative Prediction of Human Pharmacokinetics for mAbs Exhibiting Target-Mediated Disposition

Prediction of human pharmacokinetics (PK) can be challenging for monoclonal antibodies (mAbs) exhibiting target-mediated drug disposition (TMDD). In this study, we performed a quantitative analysis of a diverse set of six mAbs exhibiting TMDD to explore translational rules that can be utilized to predict human PK. A TMDD model with rapid-binding approximation was utilized to fit PK and PD (i.e., free and/or total target levels) data, and average absolute fold error (AAFE) was calculated for each model parameter. Based on the comparative analysis, translational rules were developed and applied to a test antibody not included in the original analysis. AAFE of less than two-fold was observed between monkey and human for baseline target levels (R0), body-weight (BW) normalized central elimination rate (Kel/BW−0.25) and central volume (Vc/BW1.0). AAFE of less than three-fold was estimated for the binding affinity constant (KD). The other four parameters, i.e., complex turnover rate (Kint), target turnover rate (Kdeg), central to peripheral distribution rate constant (Kpt) and peripheral to central rate constant (Ktp) were poorly correlated between monkey and human. The projected human PK of test antibody based on the translation rules was in good agreement with the observed nonlinear PK. In conclusion, we recommend a TMDD model-based prediction approach that integrates in vitro human biomeasures and in vivo preclinical data using translation rules developed in this study.

Preclinical to Clinical Translation of Antibody-Drug Conjugates Using PK/PD Modeling: a Retrospective Analysis of Inotuzumab Ozogamicin

ABSTRACTA mechanism-based pharmacokinetic/pharmacodynamic (PK/PD) model was used for preclinical to clinical translation of inotuzumab ozogamicin, a CD22-targeting antibody-drug conjugate (ADC) for B cell malignancies including non-Hodgkin’s lymphoma (NHL) and acute lymphocytic leukemia (ALL). Preclinical data was integrated in a PK/PD model which included (1) a plasma PK model characterizing disposition and clearance of inotuzumab ozogamicin and its released payload N-Ac-γ-calicheamicin DMH, (2) a tumor disposition model describing ADC diffusion into the tumor extracellular environment, (3) a cellular model describing inotuzumab ozogamicin binding to CD22, internalization, intracellular N-Ac-γ-calicheamicin DMH release, binding to DNA, or efflux from the tumor cell, and (4) tumor growth and inhibition in mouse xenograft models. The preclinical model was translated to the clinic by incorporating human PK for inotuzumab ozogamicin and clinically relevant tumor volumes, tumor growth rates, and values for CD22 expression in the relevant patient populations. The resulting stochastic models predicted progression-free survival (PFS) rates for inotuzumab ozogamicin in patients comparable to the observed clinical results. The model suggested that a fractionated dosing regimen is superior to a conventional dosing regimen for ALL but not for NHL. Simulations indicated that tumor growth is a highly sensitive parameter and predictive of successful outcome. Inotuzumab ozogamicin PK and N-Ac-γ-calicheamicin DMH efflux are also sensitive parameters and would be considered more useful predictors of outcome than CD22 receptor expression. In summary, a multiscale, mechanism-based model has been developed for inotuzumab ozogamicin, which can integrate preclinical biomeasures and PK/PD data to predict clinical response.

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

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

In vitro and in vivo SAR of pyrido[3,4-d]pyramid-4-ylamine based mGluR1 antagonists.

The SAR of a series of novel pyrido[3,4-d]pyramid-4-ylamine mGluR1 antagonists is described. The multiple of the unbound K(i) in cerebrospinal fluid necessary to give morphine like analgesic effects in an electromyograph pinch model in rodents is determined and the effect of structure on CNS penetration examined.