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

Suborgan Level Quantitation of Proteins in Tissues Delivered by Polymeric Nanocarriers

Amidst the rapid growth of protein therapeutics as a drug class, there is an increased focus on designing systems to effectively deliver proteins to target organs. Quantitative monitoring of protein distributions in tissues is essential for optimal development of delivery systems; however, existing strategies can have limited accuracy, making it difficult to assess suborgan dosing. Here, we describe a quantitative imaging approach that utilizes metal-coded mass tags and laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) to quantify the suborgan distributions of proteins in tissues that have been delivered by polymeric nanocarriers. Using this approach, we measure nanomole per gram levels of proteins as delivered by guanidinium-functionalized poly(oxanorborneneimide) (PONI) polymers to various tissues, including the alveolar region of the lung. Due to the multiplexing capability of the LA-ICP-MS imaging, we are also able to simultaneously quantify protein and polymer distributions, obtaining valuable information about the relative excretion pathways of the protein cargo and carrier. This imaging approach will facilitate quantitative correlations between nanocarrier properties and protein cargo biodistributions.

Development and validation of bioanalytical methods to support clinical study of disitamab vedotin

Disitamab vedotin (RC48), a humanized anti-HER2 antibody conjugated with monomethyl auristatin E (MMAE), is the first antibody-drug conjugate in China with an approved biological license application. A bioanalytical method was established for three analytes (total antibody, conjugate antibody and free payload) to help characterize their pharmacokinetic behavior in clinical settings. The bioanalytical methods were validated according to M10 guidance. Electrochemiluminescence assay methods were used for the quantitative measurement of total antibody and conjugated antibody in human serum. A LC-MS/MS method was used to quantify the concentration of MMAE in human serum. The method had high specificity and sensitivity with a quantitative range of 19.531-1250.000 ng/ml (total antibody), 39.063-5000.000 ng/ml (conjugated antibody) and 0.04-10.0 ng/ml (MMAE), respectively.

Potent antitumor activity of anti-HER2 antibody-topoisomerase I inhibitor conjugate based on self-immolative dendritic dimeric-linker

Antibody-drug conjugates (ADCs) are a rapidly expanding class of anticancer therapeutics, with 14 ADCs already approved worldwide. We developed unique linker technologies for the bioconjugation of drug molecules with controlled-release applications. We synthesized cathepsin-cleavable ADCs using a dimeric prodrug system based on a self-immolative dendritic scaffold, resulting in a high drug-antibody ratio (DAR) with the potential to reach 16 payloads due to its dendritic structure, increased stability in the circulation and efficient release profile of a highly cytotoxic payload at the targeted site. Using our novel cleavable linker technologies, we conjugated the anti-human epidermal growth factor receptor 2 (anti-HER2) antibody, trastuzumab, with topoisomerase I inhibitors, exatecan or belotecan. The newly synthesized ADCs were tested in vitro on mammary carcinoma cells overexpressing human HER2, demonstrating a substantial inhibitory effect on the proliferation of HER2-positive cells. Importantly, a single dose of our trastuzumab-based ADCs administered in vivo to mice bearing HER2-positive tumors, showed a dose-dependent inhibition of tumor growth and survival benefit, with the most potent antitumor effects observed at 10 mg/kg, which resulted in complete tumor regression and survival of 100% of the mice. Overall, our novel dendritic technologies using the protease-cleavable Val-Cit linker present an opportunity for the development of highly selective and potent controlled-released therapeutic payloads. This strategy could potentially lead to the development of novel and effective ADC technologies for patients diagnosed with HER2-positive cancers. Moreover, our proposed ADC linker technology can be implemented in additional medical conditions such as other malignancies as well as autoimmune diseases that overexpress targets, other than HER2.

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.

Current Approaches of Building Mechanistic Pharmacodynamic Drug-Target Binding Models

Mechanistic pharmacodynamic models that incorporate the binding kinetics of drug-target interactions have several advantages in understanding target engagement and the efficacy of a drug dose. However, guidelines on how to build and interpret mechanistic pharmacodynamic drug-target binding models considering both biological and computational factors are still missing in the literature. In this chapter, current approaches of building mechanistic PD models and their advantages are discussed. We also present a methodology on how to select a suitable model considering both biological and computational perspectives, as well as summarize the challenges of current mechanistic PD models.

Analytical Methods for the Detection and Quantification of ADCs in Biological Matrices

Understanding pharmacokinetics and biodistribution of antibody–drug conjugates (ADCs) is a one of the critical steps enabling their successful development and optimization. Their complex structure combining large and small molecule characteristics brought out multiple bioanalytical methods to decipher the behavior and fate of both components in vivo. In this respect, these methods must provide insights into different key elements including half-life and blood stability of the construct, premature release of the drug, whole-body biodistribution, and amount of the drug accumulated within the targeted pathological tissues, all of them being directly related to efficacy and safety of the ADC. In this review, we will focus on the main strategies enabling to quantify and characterize ADCs in biological matrices and discuss their associated technical challenges and current limitations.

Opportunities for Quantitative Translational Modeling in Oncology

A 2-day meeting was held by members of the UK Quantitative Systems Pharmacology Network () in November 2018 on the topic of Translational Challenges in Oncology. Participants from a wide range of backgrounds were invited to discuss current and emerging modeling applications in nonclinical and clinical drug development, and to identify areas for improvement. This resulting perspective explores opportunities for impactful quantitative pharmacology approaches. Four key themes arose from the presentations and discussions that were held, leading to the following recommendations: Evaluate the predictivity and reproducibility of animal cancer models through precompetitive collaboration. Apply mechanism of action (MoA) based mechanistic models derived from nonclinical data to clinical trial data. Apply MoA reflective models across trial data sets to more robustly quantify the natural history of disease and response to differing interventions. Quantify more robustly the dose and concentration dependence of adverse events through mathematical modelling techniques and modified trial design.

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.

Multi-scale modeling of drug binding kinetics to predict drug efficacy

Optimizing drug therapies for any disease requires a solid understanding of pharmacokinetics (the drug concentration at a given time point in different body compartments) and pharmacodynamics (the effect a drug has at a given concentration). Mathematical models are frequently used to infer drug concentrations over time based on infrequent sampling and/or in inaccessible body compartments. Models are also used to translate drug action from in vitro to in vivo conditions or from animal models to human patients. Recently, mathematical models that incorporate drug-target binding and subsequent downstream responses have been shown to advance our understanding and increase predictive power of drug efficacy predictions. We here discuss current approaches of modeling drug binding kinetics that aim at improving model-based drug development in the future. This in turn might aid in reducing the large number of failed clinical trials.

Recent advances in physiologically based pharmacokinetic and pharmacodynamic models for anticancer nanomedicines

Nanoparticles (NPs) have distinct pharmacokinetic (PK) properties and can potentially improve the absorption, distribution, metabolism, and elimination (ADME) of small-molecule drugs loaded therein. Owing to the unwanted toxicities of anticancer agents in healthy organs and tissues, their precise delivery to the tumor is an essential requirement. There have been numerous advancements in the development of nanomedicines for cancer therapy. Physiologically based PK (PBPK) models serve as excellent tools for describing and predicting the ADME properties and the efficacy and toxicity of drugs, in combination with pharmacodynamic (PD) models. The recent preliminary application of these modeling approaches to NPs demonstrated their potential benefits in research and development processes relevant to the ADME and pharmacodynamics of NPs and nanomedicines. Here, we comprehensively review the pharmacokinetics of NPs, the developed PBPK models for anticancer NPs, and the developed PD model for anticancer agents.

Determination of ADC Concentration by Ligand-Binding Assays

Total antibody, conjugated antibody or antibody-conjugated drug, and free drug are key analytes required to establish exposure-response relationships for ADCs. Therefore, bioanalytical strategies for ADCs include ligand-binding assays (LBA) and LC-MS/MS methods. Here we describe detailed methodology to develop a solid-phase-based enzyme-linked immunosorbent assay (ELISA), which is the most widely used LBA to quantify large-molecule components of ADC in biological matrices such as plasma, serum, tumor, or tissue homogenates. The approach presented here is designed to quantify total antibody concentrations in ADC containing samples, and can be easily adapted to quantify conjugated antibody concentrations.