Physiologically Based Pharmacokinetic Modeling as a Tool to Predict Drug Interactions for Antibody-Drug Conjugates

Application of Pharmacometrics in Advancing the Clinical Research of Antibody-Drug Conjugates: Principles and Modeling Strategies

Antibody-drug conjugates (ADCs) have become a pivotal area in the research and development of antitumor drugs. They provide innovative possibilities for tumor therapy by integrating the tumor-targeting capabilities of monoclonal antibodies with the cytotoxic effect of small molecule drugs. Pharmacometrics, an important discipline, facilitates comprehensive understanding of the pharmacokinetic characteristics of ADCs by integrating clinical trial data through modeling and simulation. However, due to the complex structure of ADCs, their modeling approaches are still unclear. In this review, we analyzed published population pharmacokinetic models for ADCs and classified them into single-analyte, two-analyte, and three-analyte models. We also described the benefits, limitations, and recommendations for each model. Furthermore, we suggested that the development of population pharmacokinetic models for ADCs should be rigorously considered and established based on four key aspects: (1) research objectives; (2) available in vitro and animal data; (3) accessible clinical information; and (4) the capability of bioanalytical methods. This review offered insights to guide the application of pharmacometrics in the clinical research of ADCs, thereby contributing to more effective therapeutic development.

Dose Optimization Informed by PBPK Modeling: State-of-the Art and Future

Model-informed drug development (MIDD) is a powerful quantitative approach that plays an integral role in drug development and regulatory review. While applied throughout the life cycle of the development of new drugs, a key application of MIDD is to inform clinical trial design including dose selection and optimization. To date, physiologically-based pharmacokinetic (PBPK) modeling, an established component of the MIDD toolkit, has mainly been used for assessment of drug-drug interactions (DDIs) and consequential dose adjustments in regulatory submissions. As a result of recent scientific advances and growing confidence in the utility of the approach, PBPK models are being increasingly applied to provide dose recommendations for subjects with differing ages, genetics, and disease states. In this review, we present our perspective on the current landscape of regulatory acceptance of PBPK applications supported by relevant case studies. We also discuss the recent progress and future challenges associated with expanding the utility of PBPK models into emerging areas for regulatory decision making, especially dose optimization in highly vulnerable and understudied populations and facilitating diversity in clinical trials.

Prediction of CYP Down Regulation after Tusamitamab Ravtansine Administration (a DM4-Conjugate), Based on an In Vitro-In Vivo Extrapolation Approach

Tusamitamab ravtansine is an antibody-drug conjugate (ADC) composed of a humanized monoclonal antibody (IgG1) and DM4 payload. Even if DM4 and its main metabolite methyl-DM4 (Me-DM4) circulate at low concentrations after ADC administration, their potential as perpetrators of cytochrome P450 mediated drug-drug interaction was assessed. In vitro studies in human hepatocytes indicated that Me-DM4 elicited a clear concentration-dependent down regulation of cytochrome P450 enzymes (CYP3A4, 1A2, and 2B6). Because DM4 was unstable under the incubation conditions studied, the in vitro constants could not be determined for this entity. Thus, to predict the clinical relevance of this observed downregulation, an in vitro-in vivo extrapolation (IVIVE) pharmacokinetic (PK) based approach was developed. To mitigate model prediction errors and because of their similar inhibitory effect on tubulin polymerization, the same downregulation constants were used for DM4 and Me-DM4. This approach describes the time course of decreasing CYP3A4, 1A2, and 2B6 enzyme amounts as a function of circulating concentrations of DM4 and Me-DM4 predicted from a population PK model. The developed IVIVE-PK model showed that the highest CYP abundance decrease was observed for CYP3A4, with a transient reduction of < 10% from baseline. The impact on midazolam exposure, as probe substrate of CYP3A, was then simulated based on a physiologically-based PK static method. The maximal CYP3A4 abundance reduction was associated with a predicted midazolam area under the curve (AUC) ratio of 1.14. To conclude, the observed in vitro downregulation of CYPs by Me-DM4 is not expected to have relevant clinical impact.

Physiologically based pharmacokinetic model to predict drug-drug interactions with the antibody-drug conjugate enfortumab vedotin

Enfortumab vedotin is an antibody-drug conjugate (ADC) comprised of a Nectin-4-directed antibody and monomethyl auristatin E (MMAE), which is primarily eliminated through P-glycoprotein (P-gp)-mediated excretion and cytochrome P450 3A4 (CYP3A4)-mediated metabolism. A physiologically based pharmacokinetic (PBPK) model was developed to predict effects of combined P-gp with CYP3A4 inhibitor/inducer (ketoconazole/rifampin) on MMAE exposure when coadministered with enfortumab vedotin and study enfortumab vedotin with CYP3A4 (midazolam) and P-gp (digoxin) substrate exposure. A PBPK model was built for enfortumab vedotin and unconjugated MMAE using the PBPK simulator ADC module. A similar model was developed with brentuximab vedotin, an ADC with the same valine-citrulline-MMAE linker as enfortumab vedotin, for MMAE drug-drug interaction (DDI) verification using clinical data. The DDI simulation predicted a less-than-2-fold increase in MMAE exposure with enfortumab vedotin plus ketoconazole (MMAE geometric mean ratio [GMR] for maximum concentration [Cmax], 1.15; GMR for area under the time-concentration curve from time 0 to last quantifiable concentration [AUClast], 1.38). Decreased MMAE exposure above 50% but below 80% was observed with enfortumab vedotin plus rifampin (MMAE GMR Cmax, 0.72; GMR AUClast, 0.47). No effect of enfortumab vedotin on midazolam or digoxin systemic exposure was predicted. Results suggest that combination enfortumab vedotin, P-gp, and a CYP3A4 inhibitor may result in increased MMAE exposure and patients should be monitored for potential adverse effects. Combination P-gp and a CYP3A4 inducer may result in decreased MMAE exposure. No exposure change is expected for CYP3A4 or P-gp substrates when combined with enfortumab vedotin.ClinicalTrials.gov identifier Not applicable.

Development of a Physiologically-Based Pharmacokinetic Model for Whole-Body Disposition of MMAE Containing Antibody-Drug Conjugate in Mice

PURPOSE

To quantitate and mathematically characterize the whole-body pharmacokinetics (PK) of different ADC analytes following administration of an MMAE-conjugated ADC in tumor-bearing mice.

METHODS

The PK of different ADC analytes (total antibody, total drug, unconjugated drug) was measured following administration of an MMAE-conjugated ADC in tumor-bearing mice. The PK of ADC analytes was compared with the whole-body PK of the antibody and drug obtained following administration of these molecules alone. An ADC PBPK model was developed by linking antibody PBPK model with small-molecule PBPK model, where the drug was assumed to deconjugate in DAR-dependent manner.

RESULTS

Comparison of antibody biodistribution coefficient (ABC) values for total antibody suggests that conjugation of drug did not significantly affect the PK of antibody. Comparison of tissue:plasma AUC ratio (T/P) for the conjugated drug and total antibody suggests that in certain tissues (e.g., spleen) ADC may demonstrate higher deconjugation. It was observed that the tissue distribution profile of the drug can be altered following its conjugation to antibody. For example, MMAE distribution to the liver was found to increase while its distribution to the heart was found to decrease upon conjugation to antibody. MMAE exposure in the tumor was found to increase by ~20-fold following administration as conjugate (i.e., ADC). The PBPK model was able to a priori predict the PK of all three ADC analytes in plasma, tissues, and tumor reasonably well.

CONCLUSIONS

The ADC PBPK model developed here serves as a platform for translational and clinical investigations of MMAE containing ADCs.

Antibody-drug conjugates: Resurgent anticancer agents with multi-targeted therapeutic potential

Antibody-drug conjugates (ADCs) constitute a relatively new group of anticancer agents, whose first appearance took place about two decades ago, but a renewed interest occurred in recent years, following the success of anti-cancer immunotherapy with monoclonal antibodies. Indeed, an ADC combines the selectivity of a monoclonal antibody with the cell killing properties of a chemotherapeutic agent (payload), joined together through an appropriate linker. The antibody moiety targets a specific cell surface antigen expressed by tumor cells and/or cells of the tumor microenvironment and acts as a carrier that delivers the cytotoxic payload within the tumor mass. Despite advantages in terms of selectivity and potency, the development of ADCs is not devoid of challenges, due to: i) low tumor selectivity when the target antigens are not exclusively expressed by cancer cells; ii) premature release of the cytotoxic drug into the bloodstream as a consequence of linker instability; iii) development of tumor resistance mechanisms to the payload. All these factors may result in lack of efficacy and/or in no safety improvement compared to unconjugated cytotoxic agents. Nevertheless, the development of antibodies engineered to remain inert until activated in the tumor (e.g., antibodies activated proteolytically after internalization or by the acidic conditions of the tumor microenvironment) together with the discovery of innovative targets and cytotoxic or immunomodulatory payloads, have allowed the design of next-generation ADCs that are expected to possess improved therapeutic properties. This review provides an overview of approved ADCs, with related advantages and limitations, and of novel targets exploited by ADCs that are presently under clinical investigation.

Synthesis, Characterization, and Preclinical Evaluation of a Small-Molecule Prostate-Specific Membrane Antigen-Targeted Monomethyl Auristatin E Conjugate

Prostate cancer is the second most common type of cancer among men. Its main method of treatment is chemotherapy, which has a wide range of side effects. One of the solutions to this challenge is targeted delivery to prostate cancer cells. Here we synthesized a novel small-molecule PSMA-targeted conjugate based on the monomethyl auristatin E. Its structure and conformational properties were investigated by NMR spectroscopy. Cytotoxicity, intracellular reactive oxygen species induction, and stability under liver microsomes and P450-cytochrome species were investigated for this conjugate. The conjugate demonstrated 77-85% tumor growth inhibition levels on 22Rv1 (PSMA (+)) xenografts, compared with a 37% inhibition level on PC-3 (PSMA (-)) xenografts, in a single dose of 0.3 mg/kg and a sufficiently high therapeutic index of 21. Acute, chronic, and subchronic toxicities and pharmacokinetics have shown that the synthesized conjugate is a promising potential agent for the chemotherapy of prostate cancer.

Current Practices, Gap Analysis, and Proposed Workflows for PBPK Modeling of Cytochrome P450 Induction: An Industry Perspective

The International Consortium for Innovation and Quality (IQ) Physiologically Based Pharmacokinetic (PBPK) Modeling Induction Working Group (IWG) conducted a survey across participating companies around general strategies for PBPK modeling of induction, including experience with its utility to address various questions, regulatory interactions, and regulatory acceptance. The results highlight areas where PBPK modeling is used with high confidence and identifies opportunities where confidence is lower and further evaluation is needed. To enhance the survey results, the PBPK-IWG also collected case studies and analyzed recent literature examples where PBPK models were applied to predict CYP3A induction-mediated drug-drug interactions. PBPK modeling of induction has evolved and progressed significantly, proving to have great potential to accelerate drug discovery and development. With the aim of enabling optimal use for new molecular entities that are either substrates and/or inducers of CYP3A, the PBPK-IWG proposes initial workflows for PBPK application, discusses future trends, and identifies gaps that need to be addressed.

Risk-Based Pharmacokinetic and Drug-Drug Interaction Characterization of Antibody-Drug Conjugates in Oncology Clinical Development: An International Consortium for Innovation and Quality in Pharmaceutical Development Perspective

Antibody-drug conjugates (ADCs) represent a rapidly evolving area of drug development and hold significant promise. To date, nine ADCs have been approved by the US Food and Drug Administration (FDA). These conjugates combine the target specificity of monoclonal antibodies with the anticancer activity of small-molecule therapeutics (also referred to as payload). Due to the complex structure, three analytes, namely ADC conjugate, total antibody, and unconjugated payload, are typically quantified during drug development; however, the benefits of measuring all three analytes at later stages of clinical development are not clear. The cytotoxic payloads, upon release from the ADC, are considered to behave like small molecules. Given the relatively high potency and low systemic exposure of cytotoxic payloads, drug-drug interaction (DDI) considerations for ADCs might be different from traditional small molecule therapeutics. The International Consortium for Innovation and Quality in Pharmaceutical Development (IQ Consortium) convened an ADC working group to create an IQ ADC database that includes 26 ADCs with six unique payloads. The analysis of the ADC data in the IQ database, as well as nine approved ADCs, supports the strategy of pharmacokinetic characterization of all three analytes in early-phase development and progressively minimizing the number of analytes to be measured in the late-phase studies. The systemic concentrations of unconjugated payload are usually too low to serve as a DDI perpetrator; however, the potential for unconjugated payloads as a victim still exists. A data-driven and risk-based decision tree was developed to guide the assessment of a circulating payload as a victim of DDI.

Impact of Physiologically Based Pharmacokinetics, Population Pharmacokinetics and Pharmacokinetics/Pharmacodynamics in the Development of Antibody-Drug Conjugates

Antibody‐drug conjugates are important molecular entities in the treatment of cancer, with 8 antibody‐drug conjugates approved by the US Food and Drug Administration since 2000 and many more in early‐ and late‐stage clinical development. These conjugates combine the target specificity of monoclonal antibodies with the potent anticancer activity of small‐molecule therapeutics. The complex structure of antibody‐drug conjugates poses unique challenges to pharmacokinetic (PK) and pharmacodynamic (PD) characterization because it requires a quantitative understanding of the PK and PD properties of multiple different molecular species (eg, conjugate, total antibody, and unconjugated payload) in different tissues. Quantitative clinical pharmacology using mathematical modeling and simulation provides an excellent approach to overcome these challenges, as it can simultaneously integrate the disposition, PK, and PD of antibody‐drug conjugates and their components in a quantitative manner. In this review, we highlight diverse quantitative clinical pharmacology approaches, ranging from system models (eg, physiologically based pharmacokinetic [PBPK] modeling) to mechanistic and empirical models (eg, population PK/PD modeling for single or multiple analytes, exposure‐response modeling, platform modeling by pooling data across multiple antibody‐drug conjugates). The impact of these PBPK and PK/PD models to provide insights into clinical dosing justification and inform drug development decisions is also highlighted.

Clinical Pharmacology of Antibody-Drug Conjugates

Antibody-drug conjugates (ADCs) are biopharmaceutical products where a monoclonal antibody is linked to a biologically active drug (a small molecule) forming a conjugate. Since the approval of first ADC (Gemtuzumab ozogamicin (trade name: Mylotarg)) for the treatment of CD33-positive acute myelogenous leukemia, several ADCs have been developed for the treatment of cancer. The goal of an ADC as a cancer agent is to release the cytotoxic drug to kill the tumor cells without harming the normal or healthy cells. With time, it is being realized that ADCS can also be used to manage or cure other diseases such as inflammatory diseases, atherosclerosis, and bacteremia and some research in this direction is ongoing. The focus of this review is on the clinical pharmacology aspects of ADC development. From the selection of an appropriate antibody to the finished product, the entire process of the development of an ADC is a difficult and challenging task. Clinical pharmacology is one of the most important tools of drug development since this tool helps in finding the optimum dose of a product, thus preserving the safety and efficacy of the product in a patient population. Unlike other small or large molecules where only one moiety and/or metabolite(s) is generally measured for the pharmacokinetic profiling, there are several moieties that need to be measured for characterizing the PK profiles of an ADC. Therefore, knowledge and understanding of clinical pharmacology of ADCs is vital for the selection of a safe and efficacious dose in a patient population.

Clinical pharmacology strategies in supporting drug development and approval of antibody-drug conjugates in oncology

Antibody–drug conjugates (ADCs) are important molecular entities in the treatment of cancer. These conjugates combine the target specificity of monoclonal antibodies with the potent anti-cancer activity of small-molecule therapeutics. The complex structure of ADCs poses unique challenges to characterize the drug’s pharmacokinetics (PKs) and pharmacodynamics (PDs) since it requires a quantitative understanding of the PK and PD properties of multiple different molecular species (e.g., ADC conjugate, total antibody and unconjugated cytotoxic drug). As a result, clinical pharmacology strategy of an ADC is rather unique and dependent on the linker/cytotoxic drug technology, heterogeneity of the ADC, PK and safety/efficacy profile of the specific ADC in clinical development. In this review, we summarize the clinical pharmacology strategies in supporting development and approval of ADCs using the approved ADCs as specific examples to illustrate the customized approach to clinical pharmacology assessments in their clinical development.

Whole-Body Pharmacokinetics and Physiologically Based Pharmacokinetic Model for Monomethyl Auristatin E (MMAE)

Monomethyl auristatin E (MMAE) is one of the most commonly used payloads for developing antibody–drug conjugates (ADC). However, limited studies have comprehensively evaluated the whole-body disposition of MMAE. Consequently, here, we have investigated the whole-body pharmacokinetics (PK) of MMAE in tumor-bearing mice. We show that while MMAE is rapidly eliminated from the plasma, it shows prolonged and extensive distribution in tissues, blood cells, and tumor. Highly perfused tissues (e.g., lung, kidney, heart, liver, and spleen) demonstrated tissue-to-plasma area under the concentration curve (AUC) ratios > 20, and poorly perfused tissues (e.g., fat, pancreas, skin, bone, and muscle) had ratios from 1.3 to 2.4. MMAE distribution was limited in the brain, and tumor had 8-fold higher exposure than plasma. A physiological-based pharmacokinetic (PBPK) model was developed to characterize the whole-body PK of MMAE, which accounted for perfusion/permeability-limited transfer of drug in the tissue, blood cell distribution of the drug, tissue/tumor retention of the drug, and plasma protein binding. The model was able to characterize the PK of MMAE in plasma, tissues, and tumor simultaneously, and model parameters were estimated with good precision. The MMAE PBPK model presented here can facilitate the development of a platform PBPK model for MMAE containing ADCs and help with their preclinical-to-clinical translation and clinical dose optimization.

Application of a Two-Analyte Integrated Population Pharmacokinetic Model to Evaluate the Impact of Intrinsic and Extrinsic Factors on the Pharmacokinetics of Polatuzumab Vedotin in Patients with Non-Hodgkin Lymphoma

PURPOSE

The established two-analyte integrated population pharmacokinetic model was applied to assess the impact of intrinsic/extrinsic factors on the pharmacokinetics (PK) of polatuzumab vedotin (pola) in patients with non-Hodgkin lymphoma (NHL) following bodyweight-based dosing.

METHODS

Model simulations based on individual empirical Bayes estimates were used to evaluate the impact of intrinsic/extrinsic factors as patient subgroups on Cycle 6 exposures. Intrinsic factors included bodyweight, age, sex, hepatic and renal functions. Extrinsic factors included rituximab/obinutuzumab or bendamustine combination with pola and manufacturing process. The predicted impact on exposures along with the established exposure-response relationships were used to assess clinical relevance.

RESULTS

No clinically meaningful differences in Cycle 6 pola exposures were found for the following subgroups: bodyweight 100-146 kg versus 38-<100 kg, age ≥ 65 years versus <65 years, female versus male, mild hepatic impairment versus normal, mild-to-moderate renal impairment versus normal. Co-administration of rituximab/obinutuzumab or bendamustine, and change in the pola manufacturing process, also had no meaningful impact on PK.

CONCLUSIONS

In patients with NHL, bodyweight-based dosing is adequate, and no further dose adjustment is recommended for the heavier subgroup (100-146 kg). In addition, no dose adjustments are recommended for other subgroups based on intrinsic/extrinsic factors evaluated.

Physiologically Based Pharmacokinetic Model-Informed Drug Development for Polatuzumab Vedotin: Label for Drug-Drug Interactions Without Dedicated Clinical Trials

Model-informed drug development (MIDD) has become an important approach to improving clinical trial efficiency, optimizing drug dosing, and proposing drug labeling in the absence of dedicated clinical trials. For the first time, we developed a physiologically based pharmacokinetic (PBPK) model-based approach to assess CYP3A-mediated drug-drug interaction (DDI) risk for polatuzumab vedotin (Polivy), an anti-CD79b-vc-monomethyl auristatin E (MMAE) antibody-drug conjugate (ADC). The model was developed and verified using data from the existing clinical DDI study for brentuximab vedotin, a similar vc-MMAE ADC. Analogous to the brentuximab vedotin clinical study, polatuzumab vedotin at the proposed labeled dose was predicted to have a limited drug interaction potential with strong CYP3A inhibitor and inducer. Polatuzumab vedotin was also predicted to neither inhibit nor induce CYP3A. The present work demonstrated a high-impact application using a PBPK MIDD approach to predict the CYP3A-mediated DDI to enable drug labeling in the absence of any dedicated clinical DDI study. The key considerations for the PBPK report included in the Biologics License Application/Marketing Authorization Application submission, as well as the strategy and responses to address some of the critical and challenging questions from the health authorities following the submission are also discussed. Our experience and associated perspective using a PBPK approach to ultimately enable a drug interaction label claim for polatuzumab vedotin in lieu of a dedicated clinical DDI study, as well as the interactions with the regulatory agencies, further provides confidence in applying MIDD to accelerate the registration and approval of new drug therapies.

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.

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.

Pharmacokinetics of polatuzumab vedotin in combination with R/G-CHP in patients with B-cell non-Hodgkin lymphoma

Purpose

The phase Ib/II open-label study (NCT01992653) evaluated the antibody-drug conjugate polatuzumab vedotin (pola) plus rituximab/obinutuzumab, cyclophosphamide, doxorubicin, and prednisone (R/G-CHP) as first-line therapy for B-cell non-Hodgkin lymphoma (B-NHL). We report the pharmacokinetics (PK) and drug–drug interaction (DDI) for pola.

Methods

Six or eight cycles of pola 1.0–1.8 mg/kg were administered intravenously every 3 weeks (q3w) with R/G-CHP. Exposures of pola [including antibody-conjugated monomethyl auristatin E (acMMAE) and unconjugated MMAE] and R/G-CHP were assessed by non-compartmental analysis and/or descriptive statistics with cross-cycle comparisons to cycle 1 and/or after multiple cycles. Pola was evaluated as a potential victim and perpetrator of a PK drug–drug interaction with R/G-CHP. Population PK (popPK) analysis assessed the impact of prior treatment status (naïve vs. relapsed/refractory) on pola PK.

Results

Pola PK was similar between treatment arms and independent of line of therapy. Pola PK was dose proportional from 1.0 to 1.8 mg/kg with R/G-CHP. Geometric mean volume of distribution and clearance of acMMAE ranged from 57.3 to 95.6 mL/kg and 12.7 to 18.2 mL/kg/day, respectively. acMMAE exhibited multi-exponential decay (elimination half-life ~ 1 week). Unconjugated MMAE exhibited formation rate-limited kinetics. Exposures of pola with R/G-CHP were similar to those in the absence of CHP; exposures of R/G-CHP in the presence of pola were comparable to those in the absence of pola.

Conclusions

Pola PK was well characterized with no clinically meaningful DDIs with R/G-CHP. Findings are consistent with previous studies of pola + R/G, and support pola + R/G-CHP use in previously untreated diffuse large B-cell lymphoma.

Electronic supplementary material

The online version of this article (10.1007/s00280-020-04054-8) contains supplementary material, which is available to authorized users.

Entering the era of computationally driven drug development

Historically, failure rates in drug development are high; increased sophistication and investment throughout the process has shifted the reasons for attrition, but the overall success rates have remained stubbornly and consistently low. Only 8% of new entities entering clinical testing gain regulatory approval, indicating that significant obstacles still exist for efficient therapeutic development. The continued high failure rate can be partially attributed to the inability to link drug exposure with the magnitude of observed safety and efficacy-related pharmacodynamic (PD) responses; frequently, this is a result of nonclinical models exhibiting poor prediction of human outcomes across a wide range of disease conditions, resulting in faulty evaluation of drug toxicology and efficacy. However, the increasing quality and standardization of experimental methods in preclinical stages of testing has created valuable data sets within companies that can be leveraged to further improve the efficiency and accuracy of preclinical prediction for both pharmacokinetics (PK) and PD. Models of Quantitative structure-activity relationships (QSAR), physiologically based pharmacokinetics (PBPK), and PK/PD relationships have also improved efficiency. Founded on a core understanding of biochemistry and physiological interactions of xenobiotics, these in silico methods have the potential to increase the probability of compound success in clinical trials. Integration of traditional computational methods with machine-learning approaches and existing internal pharma databases stands to make a fundamental impact on the speed and accuracy of predictions during the process of drug development and approval.

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.

Hydrophilic Small Molecules That Harness Transthyretin To Enhance the Safety and Efficacy of Targeted Chemotherapeutic Agents

The hydrophobicity of many chemotherapeutic agents usually results in their nonselective passive distribution into healthy cells and organs causing collateral toxicity. Ligand-targeted drugs (LTDs) are a promising class of targeted anticancer agents. The hydrophilicity of the targeting ligands in LTDs limits its nonselective passive tissue distribution and toxicity to healthy cells. In addition, the small size of LTDs allows for better tumor penetration, especially in the case of solid tumors. However, the short circulation half-life of LTDs, due to their hydrophilicity and small size, remains a significant challenge for achieving their full therapeutic potential. Therefore, extending the circulation half-life of targeted chemotherapeutic agents while maintaining their hydrophilicity and small size will represent a significant advance toward effective and safe cancer treatment. Here, we present a new approach for enhancing the safety and efficacy of targeted chemotherapeutic agents. By endowing hydrophobic chemotherapeutic agents with a targeting moiety and a hydrophilic small molecule that binds reversibly to the serum protein transthyretin, we generated small hydrophilic drug conjugates that displayed enhanced circulation half-life in rodents and selectivity to cancer cells. To the best of our knowledge, this is the first demonstration of a successful approach that maintains the small size and hydrophilicity of targeted anticancer agents containing hydrophobic payloads while at the same time extending their circulation half-life. This was demonstrated by the superior in vivo efficacy and lower toxicity of our conjugates in xenograft mouse models of metastatic prostate cancer.

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

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

Utility of physiologically based pharmacokinetic (PBPK) modeling in oncology drug development and its accuracy: a systematic review

PURPOSE

Physiologically based pharmacokinetic (PBPK) modeling, a mathematical modeling approach which uses a pharmacokinetic model to mimick human physiology to predict drug concentration-time profiles, has been used for the discover and development of drugs in various fields, including oncology, since 2000. There have been a few general review articles on the utilization of PBPK in the development of oncology drugs, but these do not include an evaluation of model prediction accuracy. We therefore conducted a systematic review to define the accuracy of PBPK model prediction and its utility throughout all the developmental phases of oncology drugs.

METHODS

A systematic search was performed in the PubMed, PubMed Central and Cochrane Library databases from 1980 to February 2017 for articles (1) written in English, (2) focused on oncology or antineoplastic or anticancer drugs, tumor or cancer or anticancer drugs listed in the U.S. National Institutes of Health and (3) involving a PBPK model. The absolute-average-folding-errors (AAFEs) of the area under the curve (AUC) between predicted and observed values in each article were calculated to assess model prediction accuracy.

RESULTS

Of the 2341 articles initially identified by our search of the databases, 40 were included in the review analysis. These articles reported on six types of studies, i.e. in vivo (n = 4), first-in-human (n = 5), phase II/III clinical trials (n = 9), organ impairment (n = 3), pediatrics (n = 4) and drug-drug interactions (n = 15). AAFEs of the predicted AUC for all groups of studies were within 1.3-fold of each other despite variations in experimental methodologies.

CONCLUSION

PBPK modeling is a potential tool which can be effectively applied throughout all phases of oncology drug development. The number of experimental animals and human participants enrolled in the studies can be reduced using PBPK modeling and PBPK-population-PK modeling. The limited number of publications of unsuccessful model application to date may contribute to bias toward the usefulness of modeling.

Antibody-Drug Conjugates as Cancer Therapeutics: Past, Present, and Future

Antibody-drug conjugates (ADCs) represent an innovative therapeutic approach that provides novel treatment options and hope for patients with cancer. By coupling monoclonal antibodies (mAbs) to cytotoxic small-molecule payloads with a plasma-stable linker, ADCs offer the potential for increased drug specificity and fewer off-target effects than systemic chemotherapy. As evidence for the potential of these therapies, many new ADCs are in various stages of clinical development. Because their structure poses unique challenges to pharmacokinetic and pharmacodynamic characterization, it is critical to recognize the differences between ADCs and conventional chemotherapy in the design of ADC clinical development strategies. Although some properties may be determined mainly by either the mAb or the small-molecule portion, the behavior of these agents is not always predictable. Furthermore, because the absorption, distribution, metabolism, and excretion (ADME) of ADCs are influenced by all 3 of its components (mAb, linker, and payload), it is important to characterize the intact molecule, any target-mediated catabolic clearance of the mAb, and the ADME properties of the small-molecule payload. Here we describe key issues in the clinical development of ADCs, including considerations for designing first-in-human studies for ADCs. We discuss some difficulties of ADC pharmacokinetic characterization and current approaches to overcoming these challenges. Finally, we consider all aspects of clinical pharmacology assessment required during drug development, using examples from the literature to illustrate the discussion.

Development of a Translational Physiologically Based Pharmacokinetic Model for Antibody-Drug Conjugates: a Case Study with T-DM1

Systems pharmacokinetic (PK) models that can characterize and predict whole body disposition of antibody-drug conjugates (ADCs) are needed to support (i) development of reliable exposure-response relationships for ADCs and (ii) selection of ADC targets with optimal tumor and tissue expression profiles. Towards this goal, we have developed a translational physiologically based PK (PBPK) model for ADCs, using T-DM1 as a tool compound. The preclinical PBPK model was developed using rat data. Biodistribution of DM1 in rats was used to develop the small molecule PBPK model, and the PK of conjugated trastuzumab (i.e., T-DM1) in rats was characterized using platform PBPK model for antibody. Both the PBPK models were combined via degradation and deconjugation processes. The degradation of conjugated antibody was assumed to be similar to a normal antibody, and the deconjugation of DM1 from T-DM1 in rats was estimated using plasma PK data. The rat PBPK model was translated to humans to predict clinical PK of T-DM1. The translation involved the use of human antibody PBPK model to characterize the PK of conjugated trastuzumab, use of allometric scaling to predict human clearance of DM1 catabolites, and use of monkey PK data to predict deconjugation of DM1 in the clinic. PBPK model-predicted clinical PK profiles were compared with clinically observed data. The PK of total trastuzumab and T-DM1 were predicted reasonably well, and slight systemic deviations were observed for the PK of DM1-containing catabolites. The ADC PBPK model presented here can serve as a platform to develop models for other ADCs.

Integration of bioanalytical measurements using PK-PD modeling and simulation: implications for antibody-drug conjugate development

Recent technological advances have enabled precise quantitation of various bioanalytical measurements pertaining to antibody-drug conjugates (ADCs). However, availability of bioanalytical data alone cannot guarantee the provision of correct go/no-go decisions at different stages of ADC development. Integration and comprehension of all the available data at each stage of ADC development is necessary to make a well informed and objective decision about moving the ADC forward to the clinic. In this manuscript, we have reviewed the application of PK-PD modeling and simulation for quantitative integration of diverse bioanalytical data available from different stages of ADC development. We have also elaborated on how similar bioanalytical data can be characterized using different models to gain distinct insights into ADC development.

Recent Advances in Development and Application of Physiologically-Based Pharmacokinetic (PBPK) Models: a Transition from Academic Curiosity to Regulatory Acceptance

There is a renewed surge of interest in applications of physiologically-based pharmacokinetic (PBPK) models by the pharmaceutical industry and regulatory agencies. Developing PBPK models within a systems pharmacology context allows separation of the parameters pertaining to the animal or human body (the system) from that of the drug and the study design which is essential to develop generic drug-independent models used to extrapolate PK/PD properties in various healthy and patient populations. This has expanded the classical paradigm to a ‘predict-learn-confirm-apply’ concept. Recently, a number of drug labels are informed by simulation results generated using PBPK models. These cases show that either the simulations are used in lieu of conducting clinical studies or have informed the drug label that otherwise would have been silent in some specific situations. It will not be surprising to see applications of these models in implementing precision dosing at the point of care in the near future.

Simultaneous determination of itraconazole, hydroxy itraconazole, keto itraconazole and N-desalkyl itraconazole concentration in human plasma using liquid chromatography with tandem mass spectrometry

A high-performance liquid chromatography tandem mass spectrometry (LC-MS/MS) assay was developed and validated for simultaneous determination of itraconazole (ITZ), hydroxy-itraconazole (OH-ITZ), keto-itraconazole (keto-ITZ) and N-desalkyl itraconazole (ND-ITZ) concentration in human plasma. One hundred and fifty microliters of human plasma were extracted using a solid-supported liquid extraction (SLE) method and the final extracts were analyzed using reverse-phase chromatography and positive electrospray ionization mass spectrometry. The standard curve range is 5-2500 ng/mL for ITZ and OH-ITZ and 0.4-200 ng/mL for keto-ITZ and ND-ITZ. The curve was fitted to a 1/x(2) weighted linear regression model for all analytes. The precision and accuracy of the LC-MS/MS assay based on the five analytical quality control (QC) levels were well within the acceptance criteria from both FDA and EMA guidance for bioanalytical method validation. Average extraction recovery was 97.4% for ITZ, 112.9% for OH-ITZ, 103.4% for keto-ITZ, and 102.3% for ND-ITZ across their respective curve range. Matrix factor was close to 1.0 at both high and low QC levels of all 4 analytes, which indicates minimal ion suppression or enhancement in our validated assay. Itraconazole and all three metabolites are stable in human plasma for 145 days stored at -70 °C freezers. The validated assay was successfully applied to a clinical study, which has a drug-drug interaction (DDI) arm using ITZ as a cytochrome P450, family 3, subfamily A (CYP3A) inhibitor.

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

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

Semi-mechanistic Multiple-Analyte Pharmacokinetic Model for an Antibody-Drug-Conjugate in Cynomolgus Monkeys

Purpose

A semi-mechanistic multiple-analyte population pharmacokinetics (PK) model was developed to describe the complex relationship between the different analytes of monomethyl auristatin E (MMAE) containing antibody-drug conjugates (ADCs) and to provide insight regarding the major pathways of conjugate elimination and unconjugated MMAE release in vivo.

Methods

For an anti-CD79b-MMAE ADC the PK of total antibody (Tab), conjugate (evaluated as antibody conjugated MMAE or acMMAE), and unconjugated MMAE were quantified in cynomolgus monkeys for single (0.3, 1, or 3 mg/kg), and multiple doses (3 or 5 mg/kg, every-three-weeks for 4 doses). The PK data of MMAE in cynomolgus monkeys, after intravenous administration of MMAE at single doses (0.03 or 0.063 mg/kg), was included in the analysis. A semi-mechanistic model was developed and parameter estimates were obtained by simultaneously fitting the model to all PK data using a hybrid ITS-MCPEM method.

Results

The final model well described the observed Tab, acMMAE and unconjugated MMAE concentration-time profiles. Analysis suggested that conjugate is lost via both proteolytic degradation and deconjugation, while unconjugated MMAE in systemic circulation appears to be mainly released via proteolytic degradation of the conjugate.

Conclusions

Our model improves the understanding of ADC catabolism, which may provide useful insights when designing future ADCs.

Electronic supplementary material

The online version of this article (doi:10.1007/s11095-014-1585-y) contains supplementary material, which is available to authorized users.