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

Translational PK/PD framework for antibody-drug conjugates to inform drug discovery and development

(171/200)ADCs represent a transformative class of medicine that combines the specificity of monoclonal antibodies with the potency of highly cytotoxic agents through linkers, aiming to enhance the therapeutic index of cytotoxic drugs. Given the complex molecular structures of ADCs, combining the molecular characteristics of small-molecule drugs and those of large-molecule biotherapeutics, there are several unique considerations when designing nonclinical-to-clinical PK/PD translation strategies.This complexity also demands a thorough understanding of the ADC's components-antibody, linker, and payload-to the overall toxicological, PK/PD, and efficacy profile. ADC development is a multidisciplinary endeavor requiring a strategic integration of nonclinical safety, pharmacology, and PK/PD modeling to translate from bench to bedside successfully.The ADC development underscores the necessity for a robust scientific foundation, leveraging advanced analytical and modeling tools to predict human responses and optimize therapeutic outcomes.This review aims to provide an ADC translational PK/PD framework by discussing unique aspects of ADC nonclinical to clinical PK translation, starting dose determination, and leveraging PK/PD modeling for human efficacious dose prediction and potential safety mitigation.

Immune therapies of B-cell acute lymphoblastic leukaemia in children and adults

B-cell acute lymphoblastic leukaemia (B-cell ALL) is a common haematologic cancer in children and adults. About 10 percent of children and 50 percent of adults fail to achieve a histological complete remission or subsequently relapse despite current anti-leukaemia drug therapies and/or haematopoietic cell transplants. Several new immune therapies including monoclonal antibodies and chimeric antigen receptor (CAR)-T-cells are proved safe and effective in this setting. We review data on US Food and Drug Administration (FDA)-approved immune therapies for B-cell ALL in children and adults including blinatumomab, inotuzumab ozogamicin, tisagenlecleucel, and brexucabtagene autoleucel. We also summarize pharmaco-dynamics, pharmaco-kinetics, and pharmaco-economics of these interventions.

Towards a platform quantitative systems pharmacology (QSP) model for preclinical to clinical translation of antibody drug conjugates (ADCs)

A next generation multiscale quantitative systems pharmacology (QSP) model for antibody drug conjugates (ADCs) is presented, for preclinical to clinical translation of ADC efficacy. Two HER2 ADCs (trastuzumab-DM1 and trastuzumab-DXd) were used for model development, calibration, and validation. The model integrates drug specific experimental data including in vitro cellular disposition data, pharmacokinetic (PK) and tumor growth inhibition (TGI) data for T-DM1 and T-DXd, as well as system specific data such as properties of HER2, tumor growth rates, and volumes. The model incorporates mechanistic detail at the intracellular level, to account for different mechanisms of ADC processing and payload release. It describes the disposition of the ADC, antibody, and payload inside and outside of the tumor, including binding to off-tumor, on-target sinks. The resulting multiscale PK model predicts plasma and tumor concentrations of ADC and payload. Tumor payload concentrations predicted by the model were linked to a TGI model and used to describe responses following ADC administration to xenograft mice. The model was translated to humans and virtual clinical trial simulations were performed that successfully predicted progression free survival response for T-DM1 and T-DXd for the treatment of HER2+ metastatic breast cancer, including differential efficacy based upon HER2 expression status. In conclusion, the presented model is a step toward a platform QSP model and strategy for ADCs, integrating multiple types of data and knowledge to predict ADC efficacy. The model has potential application to facilitate ADC design, lead candidate selection, and clinical dosing schedule optimization.

Inotuzumab ozogamicin in B-cell precursor acute lymphoblastic leukemia: efficacy, toxicity, and practical considerations

Inotuzumab ozogamicin (InO) is an antibody drug conjugate composed of a humanized monoclonal antibody targeting the cell surface receptor CD22 coupled to a cytotoxic calicheamicin payload via an acid labile linker. InO has shown significant activity in relapsed and refractory B-cell precursor acute lymphoblastic leukemia (BCP-ALL) in both single agent and combination chemotherapy regimens in adult and pediatric trials. Its use in newly diagnosed elderly patients has also been established while clinical trials investigating its use in newly diagnosed pediatric patients and fit adults are ongoing. Notable toxicities include sinusoidal obstruction syndrome (SOS), particularly in patients who undergo hematopoietic stem cell transplantation (HSCT) after InO as well as myelosuppression and B-cell aplasia which confer increased infection risk, particularly in combination with cytotoxic chemotherapy. In the relapsed/refractory (R/R) setting, the planned subsequent curative therapy modality must be considered when using InO to mitigate SOS risk if proceeding to HSCT and account for potential B-cell aplasia if proceeding to chimeric antigen receptor CAR-T therapy. Studies exploring mechanisms of resistance or failure of InO are ongoing but modulation or loss CD22 expression, alternative CD22 splicing, and high Bcl-2 expression have been implicated. In this review, we will summarize the currently available data on InO, with an emphasis on pediatric trials, and explore future directions including combinatorial therapy.

'Targeting' Improved Outcomes with Antibody-Drug Conjugates in Non-Small Cell Lung Cancer-An Updated Review

Antibody-Drug conjugates (ADCs) are a relatively new class of drugs with a promise to improve the outcomes in specific cancers. By delivering the cytotoxic agent to tumor cells expressing specific antigens, ADCs achieve a better therapeutic index and more potency. ADCs have been approved for several hematological and solid malignancies, including breast, urothelial and gastric carcinoma. Recently, trastuzumab deruxtecan (TDXd) was the first ADC approved for previously treated metastatic HER2-mutant non-small cell lung cancer (NSCLC). Many promising ADCs are in the pipeline for clinical development in non-small cell lung cancer, including sacituzumab govitecan, patritumab deruxtecan, datopotamab deruxtecan and tusamitamab ravtansine. There is a hope that these drugs would cater to the unmet need of specific patient populations, including patients with currently untargetable mutations. We hope these drugs, e.g., TROP2 targeted ADCs, will also give more options for therapy in NSCLC to improve outcomes for patients. In this comprehensive review, we will be discussing the recent evidence including targets, efficacy and the safety of newer ADC candidates in NSCLC. We will also briefly discuss the specific toxicities, novel biomarkers, overcoming resistance mechanisms, challenges and the way forward, as these new ADCs and combinations find a way into the clinical practice.

Pharmacokinetics and Pharmacodynamics of Antibody-Drug Conjugates Administered via Subcutaneous and Intratumoral Routes

We hypothesize that different routes of administration may lead to altered pharmacokinetics/pharmacodynamics (PK/PD) behavior of antibody-drug conjugates (ADCs) and may help to improve their therapeutic index. To evaluate this hypothesis, here we performed PK/PD evaluation for an ADC administered via subcutaneous (SC) and intratumoral (IT) routes. Trastuzumab-vc-MMAE was used as the model ADC, and NCI-N87 tumor-bearing xenografts were used as the animal model. The PK of multiple ADC analytes in plasma and tumors, and the in vivo efficacy of ADC, after IV, SC, and IT administration were evaluated. A semi-mechanistic PK/PD model was developed to characterize all the PK/PD data simultaneously. In addition, local toxicity of SC-administered ADC was investigated in immunocompetent and immunodeficient mice. Intratumoral administration was found to significantly increase tumor exposure and anti-tumor activity of ADC. The PK/PD model suggested that the IT route may provide the same efficacy as the IV route at an increased dosing interval and reduced dose level. SC administration of ADC led to local toxicity and reduced efficacy, suggesting difficulty in switching from IV to SC route for some ADCs. As such, this manuscript provides unprecedented insight into the PK/PD behavior of ADCs after IT and SC administration and paves the way for clinical evaluation of these routes.

Detection of antibody-conjugate payload in cynomolgus monkey serum by a high throughput capture LC-MS/MS bioanalysis method

Antibody-drug conjugate (ADC) plays a vital role in oncology indications. The efficacy and toxicity of ADC generally depend on the concentration of the drugs in the body system, and physiologically-based pharmacokinetic (P.K.) is a quantitative tool to understand the drug concentration in the body. To characterize the whole drug carefully, sophisticated bioanalysis was required. ADC bioanalysis generally needs multiple analysis strategies, which can accurately quantify total antibody (TAb), antibody-drug conjugate (ADC), antibody-conjugate payload (ac-payload), and free-payload. In this work, we mainly described and validated a high throughput capture Liquid Chromatography tandem-Mass Spectrometry (LC-MS/MS) bioanalysis method to detect the concentrations of ac-payload (such as MMAE) in cynomolgus monkey serum. This method was allowed to determinate the Drug to Antibody Ratio (DAR), obtained by n of ac-payload/ n of TAb. In addition, the technique could significantly improve the throughput of the pre-coated antibody on a 96-well plate. Besides, this method had no interference or carryover in endogenous substances and showed linearity (R² ≥0.99) in the concentration range within 15.6-2000.0 ng/mL. The inter-run accuracy ranged from 75.8 % to 120.0 %, and precision was within ≤ 20.0 %. Meanwhile, selectivity and the benchtop stability of the method were also validated. This optimization method was successfully applied to the change of average DAR in P.K. study.

Switching from salvage chemotherapy to immunotherapy in adult B-cell acute lymphoblastic leukemia

About one-half of adults with acute B-cell lymphoblastic leukemia (B-ALL) who do not achieve molecular complete remission or who subsequently relapse are not cured by current chemo- or targeted therapies. Previously, the sole therapeutic option for such persons was a hematopoietic stem cell transplant. Recently, several immune therapies including monoclonal antibodies, bispecific T-cell engagers (BiTEs), antibody-drug conjugates (ADCs), and chimeric antigen receptor T-cells (CARs) have been shown safe and effective in this setting. In this manuscript, we summarize data on US FDA-approved immune therapies of advanced adult B-ALL including rituximab, blinatumomab, inotuzumab ozogamicin, tisagenlecleucel and brexucabtagene autoleucel. We consider the results of clinical trials focusing on efficacy, safety, and quality of life (QoL). Real-world evidence is presented as well. We also briefly discuss pharmacodynamics, pharmacokinetics, and pharmacoeconomics followed by risk-benefit analyses. Lastly, we present future directions of immune therapies for advanced B-ALL in adults.

Development of and insights from systems pharmacology models of antibody‐drug conjugates

Antibody‐drug conjugates (ADCs) have gained traction in the oncology space in the past few decades, with significant progress being made in recent years. Although the use of pharmacometric modeling is well‐established in the drug development process, there is an increasing need for a better quantitative biological understanding of the pharmacokinetic and pharmacodynamic relationships of these complex molecules. Quantitative systems pharmacology (QSP) approaches can assist in this endeavor; recent computational QSP models incorporate ADC‐specific mechanisms and use data‐driven simulations to predict experimental outcomes. Various modeling approaches and platforms have been developed at the in vitro, in vivo, and clinical scales, and can be further integrated to facilitate preclinical to clinical translation. These new tools can help researchers better understand the nature and mechanisms of these targeted therapies to help achieve a more favorable therapeutic window. This review delves into the world of systems pharmacology modeling of ADCs, discussing various modeling efforts in the field thus far.

Quantitative systems pharmacology modeling provides insight into inter‐mouse variability of Anti‐CTLA4 response

Clinical responses of immuno‐oncology therapies are highly variable among patients. Similar response variability has been observed in syngeneic mouse models. Understanding of the variability in the mouse models may shed light on patient variability. Using a murine anti‐CTLA4 antibody as a case study, we developed a quantitative systems pharmacology model to capture the molecular interactions of the antibody and relevant cellular interactions that lead to tumor cell killing. Nonlinear mixed effect modeling was incorporated to capture the inter‐animal variability of tumor growth profiles in response to anti‐CTLA4 treatment. The results suggested that intratumoral CD8+ T cell kinetics and tumor proliferation rate were the main drivers of the variability. In addition, simulations indicated that nonresponsive mice to anti‐CTLA4 treatment could be converted to responders by increasing the number of intratumoral CD8+ T cells. The model provides a mechanistic starting point for translation of CTLA4 inhibitors from syngeneic mice to the clinic.

Monoclonal antibodies for the treatment of acute lymphocytic leukemia: A literature review

BACKGROUND

Acute lymphocytic leukemia (ALL) is a type of blood cancer that is more prevalent in children. Several treatment methods are available for ALL, including chemotherapy, upfront treatment regimens, and pediatric-inspired regimens for adults. Monoclonal antibodies (Mabs) are the novel Food and Drug Administration (FDA) approved remedies for the relapsed/refractory (R/R) adult ALL. In this article, we aimed to review studies that investigated the efficacy and safety of Mabs on ALL.

METHODS

We gathered studies through a complete search with all proper related keywords in ISI Web of Science, SID, Scopus, Google Scholar, Science Direct, and PubMed for English language publications up to 2020.

RESULTS

The most commonly studied Mabs for ALL therapies are CD-19, CD-20, CD-22, and CD-52. The best results have been reported in the administration of blinatumomab, rituximab, ofatumumab, and inotuzumab with acceptable low side effects.

CONCLUSION

Appling personalized approach for achieving higher efficacy is one of the most important aspects of treatment. Moreover, we recommend that the wide use of these Mabs depends on designing further cost-effectiveness trials in this field.

Algorithmic multiscale analysis for the FcRn mediated regulation of antibody PK in human

A demonstration is provided on how algorithmic asymptotic analysis of multi-scale pharmacokinetics (PK) systems can provide (1) system level understanding and (2) predictions on the response of the model when parameters vary. Being algorithmic, this type of analysis is not hindered by the size or complexity of the model and requires no input from the investigator. The algorithm identifies the constraints that are generated by the fast part of the model and the components of the slow part of the model that drive the system within these constraints. The demonstration is based on a typical monoclonal antibody PK model. It is shown that the findings produced by the traditional methodologies, which require significant input by the investigator, can be produced algorithmically and more accurately. Moreover, additional insights are provided by the algorithm, which cannot be obtained by the traditional methodologies; notably, the dual influence of certain reactions depending on whether their fast or slow component dominates. The analysis reveals that the importance of physiological processes in determining the systemic exposure of monoclonal antibodies (mAb) varies with time. The analysis also confirms that the rate of mAb uptake by the cells, the binding affinity of mAb to neonatal Fc receptor (FcRn), and the intracellular degradation rate of mAb are the most sensitive parameters in determining systemic exposure of mAbs. The algorithmic framework for analysis introduced and the resulting novel insights can be used to engineer antibodies with desired PK properties.

Targeting Engineered Nanoparticles for Breast Cancer Therapy

Breast cancer (BC) is the second most common cancer in women globally after lung cancer. Presently, the most important approach for BC treatment consists of surgery, followed by radiotherapy and chemotherapy. The latter therapeutic methods are often unsuccessful in the treatment of BC because of their various side effects and the damage incurred to healthy tissues and organs. Currently, numerous nanoparticles (NPs) have been identified and synthesized to selectively target BC cells without causing any impairments to the adjacent normal tissues or organs. Based on an exploratory study, this comprehensive review aims to provide information on engineered NPs and their payloads as promising tools in the treatment of BC. Therapeutic drugs or natural bioactive compounds generally incorporate engineered NPs of ideal sizes and shapes to enhance their solubility, circulatory half-life, and biodistribution, while reducing their side effects and immunogenicity. Furthermore, ligands such as peptides, antibodies, and nucleic acids on the surface of NPs precisely target BC cells. Studies on the synthesis of engineered NPs and their impact on BC were obtained from PubMed, Science Direct, and Google Scholar. This review provides insights on the importance of engineered NPs and their methodology for validation as a next-generation platform with preventive and therapeutic effects against BC.

Semi-Mechanistic Model for the Antitumor Response of a Combination Cocktail of Immuno-Modulators in Non-Inflamed (Cold) Tumors

Simple Summary

The clinical efficacy of immunotherapies when treating cold tumors is still low, and different treatment combinations are needed when dealing with this challenging scenario. In this work, a middle-out strategy was followed to develop a model describing the antitumor efficacy of different immune-modulator combinations, including an antigen, a toll-like receptor-3 agonist, and an immune checkpoint inhibitor in mice treated with non-inflamed tumor cells. Our results support that clinical response requires antigen-presenting cell activation and also relies on the amount of CD8 T cells and tumor resistance mechanisms present. This mathematical model is a very useful platform to evaluate different immuno-oncology combinations in both preclinical and clinical settings.

Abstract

Immune checkpoint inhibitors, administered as single agents, have demonstrated clinical efficacy. However, when treating cold tumors, different combination strategies are needed. This work aims to develop a semi-mechanistic model describing the antitumor efficacy of immunotherapy combinations in cold tumors. Tumor size of mice treated with TC-1/A9 non-inflamed tumors and the drug effects of an antigen, a toll-like receptor-3 agonist (PIC), and an immune checkpoint inhibitor (anti-programmed cell death 1 antibody) were modeled using Monolix and following a middle-out strategy. Tumor growth was best characterized by an exponential model with an estimated initial tumor size of 19.5 mm³ and a doubling time of 3.6 days. In the treatment groups, contrary to the lack of response observed in monotherapy, combinations including the antigen were able to induce an antitumor response. The final model successfully captured the 23% increase in the probability of cure from bi-therapy to triple-therapy. Moreover, our work supports that CD8⁺ T lymphocytes and resistance mechanisms are strongly related to the clinical outcome. The activation of antigen-presenting cells might be needed to achieve an antitumor response in reduced immunogenic tumors when combined with other immunotherapies. These models can be used as a platform to evaluate different immuno-oncology combinations in preclinical and clinical scenarios.

The clinical development of antibody-drug conjugates - lessons from leukaemia

Advances in our understanding of cancer biology have enabled drug development to progress towards better targeted therapies that are both more effective and safer owing to their lack of off-target toxicities. In this regard, antibody-drug conjugates (ADCs), which have the potential to combine the selectivity of therapeutic antibodies with the cytotoxicity of highly toxic small molecules, are a rapidly developing drug class. The complex and unique structure of an ADC, composed of a monoclonal antibody conjugated to a potent cytotoxic payload via a chemical linker, is designed to selectively target a specific tumour antigen. The success of an ADC is highly dependent on the specific properties of its components, all of which have implications for the stability, cytotoxicity, pharmacokinetics and antitumour activity of the ADC. The development of therapeutic ADCs, including gemtuzumab ozogamicin and inotuzumab ozogamicin, provided great knowledge of the refinements needed for the optimization of such agents. In this Review, we describe the key components of ADC structure and function and focus on the clinical development and subsequent utilization of two leukaemia-directed ADCs - gemtuzumab ozogamicin and inotuzumab ozogamicin - as well as on the mechanisms of resistance and predictors of response to these two agents.

Reduction of quantitative systems pharmacology models using artificial neural networks

Quantitative systems pharmacology models are often highly complex and not amenable to further simulation and/or estimation analyses. Model-order reduction can be used to derive a mechanistically sound yet simpler model of the desired input-output relationship. In this study, we explore the use of artificial neural networks for approximating an input-output relationship within highly dimensional systems models. We illustrate this approach using a model of blood coagulation. The model consists of two components linked together through a highly dimensional discontinuous interface, which creates a difficulty for model reduction techniques. The proposed approach enables the development of an efficient approximation to complex models with the desired level of accuracy. The technique is applicable to a wide variety of models and provides substantial speed boost for use of such models in simulation and control purposes.

Antibody-Drug Conjugates: Patient and Treatment Selection

Antibody-drug conjugates (ADCs) are a promising drug platform designed to enhance the therapeutic index and minimize the toxicity of anticancer agents. ADCs have experienced substantial progress and technological growth over the past decades; however, several challenges to patient selection and treatment remain. Methods to optimally capture all patients who may benefit from a particular ADC are still largely unknown. Although target antigen expression remains a biomarker for patient selection, the impact of intratumor heterogeneity on antigen expression, as well as the dynamic changes in expression with treatment and disease progression, are important considerations in patient selection. Better understanding of these factors, as well as minimum levels of target antigen expression required to achieve therapeutic efficacy, will enable further optimization of selection strategies. Other important considerations include understanding mechanisms of primary and acquired resistance to ADCs. Ongoing efforts in the design of its constituent parts to possess the intrinsic ability to overcome these mechanisms, including use of the "bystander effect" to enhance efficacy in heterogeneous or low target antigen-expressing tumors, as well as modulation of the chemical and immunophenotypic properties of antibodies and linker molecules to improve payload sensitivity and therapeutic efficacy, are under way. These strategies may also lead to improved safety profiles. Similarly, combination strategies using ADCs with other cytotoxic or immunomodulatory agents are also under development. Great strides have been made in ADC technology. With further refinements, this therapeutic modality has the potential to make an important clinical impact on a wider range of tumor types.

Mechanistic Modeling of Intra-Tumor Spatial Distribution of Antibody-Drug Conjugates: Insights into Dosing Strategies in Oncology

Antibody drug conjugates (ADCs) provide targeted delivery of cytotoxic agents directly inside tumor cells. However, many ADCs targeting solid tumors have exhibited limited clinical efficacy, in part, due to insufficient penetration within tumors. To better understand the relationship between ADC tumor penetration and efficacy, previously applied Krogh cylinder models that explore tumor growth dynamics following ADC administration in preclinical species were expanded to a clinical framework by integrating clinical pharmacokinetics, tumor penetration, and tumor growth inhibition. The objective of this framework is to link ADC tumor penetration and distribution to clinical efficacy. The model was validated by comparing virtual patient population simulations to observed overall response rates from trastuzumab‐DM1 treated patients with metastatic breast cancer. To capture clinical outcomes, we expanded upon previous Krogh cylinder models to include the additional mechanism of heterogeneous tumor growth inhibition spatially across the tumor. This expansion mechanistically captures clinical response rates by describing heterogeneous ADC binding and tumor cell killing; high binding and tumor cell death close to capillaries vs. low binding, and high tumor cell proliferation far from capillaries. Sensitivity analyses suggest that clinical efficacy could be optimized through dose fractionation, and that clinical efficacy is primarily dependent on the ADC‐target affinity, payload potency, and tumor growth rate. This work offers a mechanistic basis to predict and optimize ADC clinical efficacy for solid tumors, allowing dosing strategy optimization to improve patient outcomes.

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

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

A novel semi-mechanistic tumor growth fraction model for translation of preclinical efficacy of anti-glypican 3 antibody drug conjugate to human

The growing fraction (GF) of tumor has been reported as one of the predictive markers of the efficacy of chemotherapeutics. Therefore, a semi-mechanistic model has been developed that describes tumor growth on the basis of cell cycle, allowing the incorporation of the GF of a tumor in pharmacokinetic/pharmacodynamic (PK/PD) modeling. Efficacy data of anti-glypican 3 (GPC3) antibody drug conjugate (ADC) in a hepatocellular carcinoma (HCC) patient derived xenograft (PDX) model was used for evaluation of this proposed model. Our model was able to describe the kinetics of growth inhibition of HCC PDX models following treatment with anti-GPC3 ADC remarkably well. The estimated tumurostatic concentrations were used in tandem with human PKs translated from cynomolgus monkey for prediction of the efficacious dose. The projected efficacious human dose of anti-GPC3 ADC was in the range 0.20-0.63 mg/kg for the Q3W dosing regimen, with a median dose of 0.50 mg/kg. This publication is the first step in evaluating the applicability of GF in PK/PD modeling of ADCs. The authors are hopeful that incorporation of GF will result in an improved translation of the preclinical efficacy of ADCs to clinical settings and thereby better prediction of the efficacious human dose.

Capturing the Magic Bullet: Pharmacokinetic Principles and Modeling of Antibody-Drug Conjugates

Over the past two decades, antibody-drug conjugates (ADCs) have emerged as a promising class of drugs for cancer therapy and have expanded to nononcology fields such as inflammatory diseases, atherosclerosis, and bacteremia. Eight ADCs are currently approved by FDA for clinical applications, with more novel ADCs under clinical development. Compared with traditional chemotherapy, ADCs combine the target specificity of antibodies with chemotherapeutic capabilities of cytotoxic drugs. The benefits include reduced systemic toxicity and enhanced therapeutic index for patients. However, the heterogeneous structures of ADCs and their dynamic changes following administration create challenges in their development. The understanding of ADC pharmacokinetics (PK) is crucial for the optimization of clinical dosing regimens when translating from animal to human. In addition, it contributes to the optimization of dose selection and clinical monitoring with regard to safety and efficacy. This manuscript reviews the PK characteristics of ADCs and summarizes the diverse approaches for PK modeling that can be used to evaluate an ADC at the preclinical and clinical stages to support their successful development. Despite the numerous available options, fit-for-purpose modeling approaches for the PK and PD of ADCs should be critically planned and well-thought-out to adequately support the development of an ADC.

Current Challenges in Providing Good Leukapheresis Products for Manufacturing of CAR-T Cells for Patients with Relapsed/Refractory NHL or ALL

Background: T lymphocyte collection through leukapheresis is an essential step for chimeric antigen receptor T (CAR-T) cell therapy. Timing of apheresis is challenging in heavily pretreated patients who suffer from rapid progressive disease and receive T cell impairing medication. Methods: A total of 75 unstimulated leukaphereses were analyzed including 45 aphereses in patients and 30 in healthy donors. Thereof, 41 adult patients with Non-Hodgkin’s lymphoma (85%) or acute lymphoblastic leukemia (15%) underwent leukapheresis for CAR-T cell production. Results: Sufficient lymphocytes were harvested from all patients even from those with low peripheral lymphocyte counts of 0.18/nL. Only four patients required a second leukapheresis session. Leukapheresis products contained a median of 98 × 10⁸ (9 - 341 × 10⁸) total nucleated cells (TNC) with 38 × 10⁸ (4 - 232 × 10⁸) CD3+ T cells. Leukapheresis products from healthy donors as well as from patients in complete remission were characterized by high TNC and CD3+ T lymphocyte counts. CAR-T cell products could be manufactured for all but one patient. Conclusions: Sufficient yield of lymphocytes for CAR-T cell production is feasible also for patients with low peripheral blood counts. Up to 12–15 L blood volume should be processed in patients with absolute lymphocyte counts ≤ 1.0/nL.

Evolution of the Systems Pharmacokinetics-Pharmacodynamics Model for Antibody-Drug Conjugates to Characterize Tumor Heterogeneity and In Vivo Bystander Effect

The objective of this work was to develop a systems pharmacokinetics-pharmacodynamics (PK-PD) model that can characterize in vivo bystander effect of antibody-drug conjugate (ADC) in a heterogeneous tumor. To accomplish this goal, a coculture xenograft tumor with 50% GFP-MCF7 (HER2-low) and 50% N87 (HER2-high) cells was developed. The relative composition of a heterogeneous tumor for each cell type was experimentally determined by immunohistochemistry analysis. Trastuzumab-vc-MMAE (T-vc-MMAE) was used as a tool ADC. Plasma and tumor PK of T-vc-MMAE was analyzed in N87, GFP-MCF7, and coculture tumor-bearing mice. In addition, tumor growth inhibition (TGI) studies were conducted in all three xenografts at different T-vc-MMAE dose levels. To characterize the PK of ADC in coculture tumors, our previously published tumor distribution model was evolved to account for different cell populations. The evolved tumor PK model was able to a priori predict the PK of all ADC analytes in the coculture tumors reasonably well. The tumor PK model was subsequently integrated with a PD model that used intracellular tubulin occupancy to drive ADC efficacy in each cell type. The final systems PK-PD model was able to simultaneously characterize all the TGI data reasonably well, with a common set of parameters for MMAE-induced cytotoxicity. The model was later used to simulate the effect of different dosing regimens and tumor compositions on the bystander effect of ADC. The model simulations suggested that dose-fractionation regimen may further improve overall efficacy and bystander effect of ADCs by prolonging the tubulin occupancy in each cell type. SIGNIFICANCE STATEMENT: A PK-PD analysis is presented to understand bystander effect of Trastuzumab-vc-MMAE ADC in antigen (Ag)-low, Ag-high, and coculture (i.e., Ag-high + Ag-low) xenograft mice. This study also describes a novel single cell-level systems PK-PD model to characterize in vivo bystander effect of ADCs. The proposed model can serve as a platform to mathematically characterize multiple cell populations and their interactions in tumor tissues. Our analysis also suggests that fractionated dosing regimen may help improve the bystander effect of ADCs.

Antibody-Drug Conjugates: Pharmacokinetic/Pharmacodynamic Modeling, Preclinical Characterization, Clinical Studies, and Lessons Learned

Antibody-drug conjugates are an emerging class of biopharmaceuticals changing the landscape of targeted chemotherapy. These conjugates combine the target specificity of monoclonal antibodies with the anti-cancer activity of small-molecule therapeutics. Several antibody-drug conjugates have received approval for the treatment of various types of cancer including gemtuzumab ozogamicin (Mylotarg^®), brentuximab vedotin (Adcetris^®), trastuzumab emtansine (Kadcyla^®), and inotuzumab ozogamicin, which recently received approval (Besponsa^®). In addition to these approved therapies, there are many antibody-drug conjugates in the drug development pipeline and in clinical trials, although these fall outside the scope of this article. Understanding the pharmacokinetics and pharmacodynamics of antibody-drug conjugates and the development of pharmacokinetic/pharmacodynamic models is indispensable, albeit challenging as there are many parameters to incorporate including the disposition of the intact antibody-drug conjugate complex, the antibody, and the drug agents following their dissociation in the body. In this review, we discuss how antibody-drug conjugates progressed over time, the challenges in their development, and how our understanding of their pharmacokinetics/pharmacodynamics led to greater strides towards successful targeted therapy programs.

Current status and future perspective on preclinical pharmacokinetic and pharmacodynamic (PK/PD) analysis: Survey in Japan pharmaceutical manufacturers association (JPMA)

Preclinical pharmacokinetic/pharmacodynamic (PK/PD) analysis is an efficient tool for the translational research and proof of mechanism/concept in animals. The questionnaire survey on the practice of preclinical PK/PD analysis was conducted in the member companies of the Japan Pharmaceutical Manufacturers Association (JPMA). According to the survey, 60% of companies conducted preclinical PK/PD analysis and its impact for drug development was different between each of the companies. The frequently analyzed therapeutic areas of preclinical PK/PD analysis were neurology, inflammation and metabolic disease, and those are different from the therapeutic area (infectious disease and oncology) in which PK/PD analysis was considered as effective by the present survey. Many companies which have used preclinical PK/PD analysis for the translation to human PK/PD and for the prediction of dose/regimen had good communication with other research & development (R&D) departments (e.g. pharmacology/clinical pharmacology). The increase in resources for preclinical PK/PD analysis including education was highly demanded. As a future perspective, the closer collaboration between pharmacokinetics scientists, pharmacologists, toxicologists and clinical pharmacologists and the increase in resources including upskilling and the comprehension of preclinical PK/PD analysis by the project team are considered to lead to efficient contributions to improve the success ratio of drug discovery and development.

A Cell-Level Systems PK-PD Model to Characterize In Vivo Efficacy of ADCs

Here, we have presented the development of a systems pharmacokinetics-pharmacodynamics (PK-PD) model for antibody-drug conjugates (ADCs), which uses intracellular target occupancy to drive in-vivo efficacy. The model is built based on PK and efficacy data generated using Trastuzumab-Valine-Citrulline-Monomethyl Auristatin E (T-vc-MMAE) ADC in N87 (high-HER2) and GFP-MCF7 (low-HER2) tumor bearing mice. It was observed that plasma PK of all ADC analytes was similar between the two tumor models; however, total trastuzumab, unconjugated MMAE, and total MMAE exposures were >10-fold, ~1.6-fold, and ~1.8-fold higher in N87 tumors. In addition, a prolonged retention of MMAE was observed within the tumors of both the mouse models, suggesting intracellular binding of MMAE to tubulin. A systems PK model, developed by integrating single-cell PK model with tumor distribution model, was able to capture all in vivo PK data reasonably well. Intracellular occupancy of tubulin predicted by the PK model was used to drive the efficacy of ADC using a novel PK-PD model. It was found that the same set of PD parameters was able to capture MMAE induced killing of GFP-MCF7 and N87 cells in vivo. These observations highlight the benefit of adopting a systems approach for ADC and provide a robust and predictive framework for successful clinical translation of ADCs.

Inotuzumab: from preclinical development to success in B-cell acute lymphoblastic leukemia

Inotuzumab ozogamicin (InO) is a recently US Food and Drug Administration-approved antibody-drug conjugate for the treatment of relapsed/refractory B-cell acute lymphoblastic leukemia (ALL). InO consists of a CD22-targeting immunoglobulin G4 humanized monoclonal antibody conjugated to calicheamicin. Although initially developed for the treatment of non-Hodgkin lymphoma (NHL) because of activity in preclinical models and high response rates in indolent lymphomas, a phase 3 trial was negative and further development focused on CD22+ ALL. Although results in NHL were disappointing, parallel testing in early-phase trials of CD22+ ALL demonstrated feasibility and efficacy. Subsequently, the randomized phase 3 Study Of Inotuzumab Ozogamicin Versus Investigator's Choice Of Chemotherapy In Patients With Relapsed Or Refractory Acute Lymphoblastic Leukemia trial showed that InO was superior to standard of care regimens with a significantly improved complete remission (CR) rate in patients with relapsed/refractory disease (80.7% vs 29.4%, P < .001). Patients achieving CR with InO also had a significantly higher rate of undetectable minimal residual disease compared with chemotherapy (78.4% vs 28.1%, P < .001). InO-specific side effects, including veno-occlusive disease, have been an ongoing area of concern, and consensus guidelines for minimizing toxicities are now available. Ongoing trials are investigating the combination of InO with other agents in the relapse setting and the addition of InO to frontline therapy. This review details the preclinical and clinical development of InO, focusing on how best to use it and future directions for further development.

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

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

Seek and destroy: improving PK/PD profiles of anticancer agents with nanoparticles

INTRODUCTION

The Pharmacokinetics/pharmacodynamics (PK/PD) relationships with cytotoxics are usually based on a steepening concentration-effect relationship; the greater the drug amount, the greater the effect. The Maximum Tolerated Dose paradigm, finding the balance between efficacy, while keeping toxicities at their manageable level, has been the rule of thumb for the last 50-years. Developing nanodrugs is an appealing strategy to help broaden this therapeutic window. The fact that efficacy and toxicity with cytotoxics are intricately linked is primarily due to the complete lack of specificity toward the tumor tissue during their distribution phase. Because nanoparticles are expected to better target tumor tissue while sparing healthy cells, accumulating large amounts of cytotoxics in tumors could be achieved in a safer way. Areas covered: This review aims at presenting how nanodrugs present unique features leading to reconsidering PK/PD relationships of anticancer agents. Expert commentary: The constant interplay between carrier PK, interactions with cancer cells, payload release, payload PK, target expression and target engagement, makes picturing the exact PK/PD relationships of nanodrugs particularly challenging. However, those improved PK/PD relationships now make the once contradictory higher efficacy and lower toxicities requirement an achievable goal in cancer patients.

Quantitative translational modeling to facilitate preclinical to clinical efficacy & toxicity translation in oncology

Significant scientific advances in biomedical research have expanded our knowledge of the molecular basis of carcinogenesis, mechanisms of cancer growth, and the importance of the cancer immunity cycle. However, despite scientific advances in the understanding of cancer biology, the success rate of oncology drug development remains the lowest among all therapeutic areas. In this review, some of the key translational drug development objectives in oncology will be outlined. The literature evidence of how mathematical modeling could be used to build a unifying framework to answer these questions will be summarized with recommendations on the strategies for building such a mathematical framework to facilitate the prediction of clinical efficacy and toxicity of investigational antineoplastic agents. Together, the literature evidence suggests that a rigorous and unifying preclinical to clinical translational framework based on mathematical models is extremely valuable for making go/no-go decisions in preclinical development, and for planning early clinical studies.

New Strategies Using Antibody Combinations to Increase Cancer Treatment Effectiveness

Antibodies have proven their high value in antitumor therapy over the last two decades. They are currently being used as the first-choice to treat some of the most frequent metastatic cancers, like HER2⁺ breast cancers or colorectal cancers, currently treated with trastuzumab (Herceptin) and bevacizumab (Avastin), respectively. The impressive therapeutic success of antibodies inhibiting immune checkpoints has extended the use of therapeutic antibodies to previously unanticipated tumor types. These anti-immune checkpoint antibodies allowed the cure of patients devoid of other therapeutic options, through the recovery of the patient’s own immune response against the tumor. In this review, we describe how the antibody-based therapies will evolve, including the use of antibodies in combinations, their main characteristics, advantages, and how they could contribute to significantly increase the chances of success in cancer therapy. Indeed, novel combinations will consist of mixtures of antibodies against either different epitopes of the same molecule or different targets on the same tumor cell; bispecific or multispecific antibodies able of simultaneously binding tumor cells, immune cells or extracellular molecules; immunomodulatory antibodies; antibody-based molecules, including fusion proteins between a ligand or a receptor domain and the IgG Fab or Fc fragments; autologous or heterologous cells; and different formats of vaccines. Through complementary mechanisms of action, these combinations could contribute to elude the current limitations of a single antibody which recognizes only one particular epitope. These combinations may allow the simultaneous attack of the cancer cells by using the help of the own immune cells and exerting wider therapeutic effects, based on a more specific, fast, and robust response, trying to mimic the action of the immune system.

Immunotherapy in adult acute leukemia

The treatment of acute myeloid leukemia (AML) did not evolve profoundly in the last decades. Some improvement has been made for acute lymphoblastic leukemia (ALL). Emerging new treatment modalities, such as immunotherapy, are now beginning to be available for acute leukemia, mostly for patients suffering from ALL. This review aims to give an overview of these new therapeutic approaches, especially those already available. The focus is on cell-based immunotherapy, or molecules using preexisting host cells. Underlying mechanisms are explained and an overview of clinical experience with phase 1-3 studies is given. Immunotherapies discussed are antibody-drug conjugates, bispecific T-cell engagers (BiTEs), chimeric antigen receptor T cells (CARTs) and immune checkpoint inhibitors (ICPIs). Most of the clinical studies reviewed are in ALL patients, usually in the relapse setting, but where available, studies on AML patients were also considered. This new general treatment approach offers hope to patients with until now dismal clinical outcome. Hopes are high that future developments, and moving these therapies to an earlier treatment phase, will improve the prognosis of patients suffering from acute leukemia.

Novel therapy for childhood acute lymphoblastic leukemia

INTRODUCTION

During recent decades, the prognosis of childhood acute lymphoblastic leukemia (ALL) has improved dramatically, nowadays, reaching a cure rate of almost 90%. These results are due to a better management and combination of old therapies, refined risk-group stratification and emergence of minimal residual disease (MRD) combined with treatment's intensification for high-risk subgroups. However, the subgroup of patients with refractory/relapsed ALL still presents a dismal prognosis indicating necessity for innovative therapeutic approaches. Areas covered: We performed an exhaustive review of current first-line therapies for childhood ALL in the worldwide main consortia, summarized the major advances for front-line and relapse treatment and highlighted recent and promising innovative therapies with an overview of the most promising ongoing clinical trials. Expert opinion: Two major avenues marked the beginning of 21^st century. First, is the introduction of tyrosine-kinase inhibitor coupled to chemotherapy for treatment of Philadelphia positive ALL opening new treatment possibilities for the recently identified subgroup of Ph-like ALL. Second, is the breakthrough of immunotherapy, notably CAR T-cell and specific antibody-based therapy, with remarkable success observed in initial studies. This review gives an insight on current knowledge in these innovative therapeutic directions, summarizes currently ongoing clinical trials and addresses challenges these approaches are faced with.

Current mathematical models for cancer drug discovery

INTRODUCTION

Pharmacometric models represent the most comprehensive approaches for extracting, summarizing and integrating information obtained in the often sparse, limited, and less-than-optimally designed experiments performed in the early phases of oncology drug discovery. Whilst empirical methodologies may be enough for screening and ranking candidate drugs, modeling approaches are needed for optimizing and making economically viable the learn-confirm cycles within an oncology research program and anticipating the dose regimens to be investigated in the subsequent clinical development. Areas covered: Papers appearing in the literature of approximately the last decade reporting modeling approaches applicable to anticancer drug discovery have been listed and commented. Papers were selected based on the interest in the proposed methodology or in its application. Expert opinion: The number of modeling approaches used in the discovery of anticancer drugs is consistently increasing and new models are developed based on the current directions of research of new candidate drugs. These approaches have contributed to a better understanding of new oncological targets and have allowed for the exploitation of the relatively sparse information generated by preclinical experiments. In addition, they are used in translational approaches for guiding and supporting the choice of dosing regimens in early clinical development.

New Paradigm for Translational Modeling to Predict Long-term Tuberculosis Treatment Response

Disappointing results of recent tuberculosis chemotherapy trials suggest that knowledge gained from preclinical investigations was not utilized to maximal effect. A mouse‐to‐human translational pharmacokinetics (PKs) – pharmacodynamics (PDs) model built on a rich mouse database may improve clinical trial outcome predictions. The model included Mycobacterium tuberculosis growth function in mice, adaptive immune response effect on bacterial growth, relationships among moxifloxacin, rifapentine, and rifampin concentrations accelerating bacterial death, clinical PK data, species‐specific protein binding, drug‐drug interactions, and patient‐specific pathology. Simulations of recent trials testing 4‐month regimens predicted 65% (95% confidence interval [CI], 55–74) relapse‐free patients vs. 80% observed in the REMox‐TB trial, and 79% (95% CI, 72–87) vs. 82% observed in the Rifaquin trial. Simulation of 6‐month regimens predicted 97% (95% CI, 93–99) vs. 92% and 95% observed in 2RHZE/4RH control arms, and 100% predicted and observed in the 35 mg/kg rifampin arm of PanACEA MAMS. These results suggest that the model can inform regimen optimization and predict outcomes of ongoing trials.

Translational Modeling to Guide Study Design and Dose Choice in Obesity Exemplified by AZD1979, a Melanin-concentrating Hormone Receptor 1 Antagonist

In this study, we present the translational modeling used in the discovery of AZD1979, a melanin‐concentrating hormone receptor 1 (MCHr1) antagonist aimed for treatment of obesity. The model quantitatively connects the relevant biomarkers and thereby closes the scaling path from rodent to man, as well as from dose to effect level. The complexity of individual modeling steps depends on the quality and quantity of data as well as the prior information; from semimechanistic body‐composition models to standard linear regression. Key predictions are obtained by standard forward simulation (e.g., predicting effect from exposure), as well as non‐parametric input estimation (e.g., predicting energy intake from longitudinal body‐weight data), across species. The work illustrates how modeling integrates data from several species, fills critical gaps between biomarkers, and supports experimental design and human dose‐prediction. We believe this approach can be of general interest for translation in the obesity field, and might inspire translational reasoning more broadly.

QSP Toolbox: Computational Implementation of Integrated Workflow Components for Deploying Multi-Scale Mechanistic Models

Quantitative systems pharmacology (QSP) modeling has become increasingly important in pharmaceutical research and development, and is a powerful tool to gain mechanistic insights into the complex dynamics of biological systems in response to drug treatment. However, even once a suitable mathematical framework to describe the pathophysiology and mechanisms of interest is established, final model calibration and the exploration of variability can be challenging and time consuming. QSP models are often formulated as multi-scale, multi-compartment nonlinear systems of ordinary differential equations. Commonly accepted modeling strategies, workflows, and tools have promise to greatly improve the efficiency of QSP methods and improve productivity. In this paper, we present the QSP Toolbox, a set of functions, structure array conventions, and class definitions that computationally implement critical elements of QSP workflows including data integration, model calibration, and variability exploration. We present the application of the toolbox to an ordinary differential equations-based model for antibody drug conjugates. As opposed to a single stepwise reference model calibration, the toolbox also facilitates simultaneous parameter optimization and variation across multiple in vitro, in vivo, and clinical assays to more comprehensively generate alternate mechanistic hypotheses that are in quantitative agreement with available data. The toolbox also includes scripts for developing and applying virtual populations to mechanistic exploration of biomarkers and efficacy. We anticipate that the QSP Toolbox will be a useful resource that will facilitate implementation, evaluation, and sharing of new methodologies in a common framework that will greatly benefit the community.

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

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