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

Approaches to Improve the Translation of Safety, Pharmacokinetics and Therapeutic Index of ADCs

Antibody-drug conjugates (ADCs) are an important class of cancer therapies. They are complex molecules, comprising an antibody, a cytotoxic payload, and a linker. ADCs intend to confer high specificity by targeting a unique antigen expressed predominately on the surface of the tumor cells than on the normal cells and by releasing the potent cytotoxic drug inside the tumor causing cytotoxic cell death. Despite high specificity to tumor antigens, many ADCs are associated with off-target and on-target off-tumor toxicities, often leading to safety concerns before achieving the desirable clinical efficacy. Therefore, it is crucial to improve the therapeutic index (TI) of ADCs to enable the full potential of this important therapeutic modality.The review summarizes current approaches to improve the translation of safety, pharmacokinetics, and TI of ADCs. Common safety findings of ADCs resulting from off-target and on-target toxicities and nonclinical approaches to de-risk ADC safety will be discussed; multiple approaches of using preclinical and clinical dose and exposure data to calculate TI to guide clinical dosing will be elaborated; different approaches to improve TI of ADCs, including selecting the right target, right payload-linker and patients, optimizing physicochemical properties, and using fractionation dosing, will also be discussed.

Mathematical modeling in autoimmune diseases: from theory to clinical application

The research & development (R&D) of novel therapeutic agents for the treatment of autoimmune diseases is challenged by highly complex pathogenesis and multiple etiologies of these conditions. The number of targeted therapies available on the market is limited, whereas the prevalence of autoimmune conditions in the global population continues to rise. Mathematical modeling of biological systems is an essential tool which may be applied in support of decision-making across R&D drug programs to improve the probability of success in the development of novel medicines. Over the past decades, multiple models of autoimmune diseases have been developed. Models differ in the spectra of quantitative data used in their development and mathematical methods, as well as in the level of "mechanistic granularity" chosen to describe the underlying biology. Yet, all models strive towards the same goal: to quantitatively describe various aspects of the immune response. The aim of this review was to conduct a systematic review and analysis of mathematical models of autoimmune diseases focused on the mechanistic description of the immune system, to consolidate existing quantitative knowledge on autoimmune processes, and to outline potential directions of interest for future model-based analyses. Following a systematic literature review, 38 models describing the onset, progression, and/or the effect of treatment in 13 systemic and organ-specific autoimmune conditions were identified, most models developed for inflammatory bowel disease, multiple sclerosis, and lupus (5 models each). ≥70% of the models were developed as nonlinear systems of ordinary differential equations, others - as partial differential equations, integro-differential equations, Boolean networks, or probabilistic models. Despite covering a relatively wide range of diseases, most models described the same components of the immune system, such as T-cell response, cytokine influence, or the involvement of macrophages in autoimmune processes. All models were thoroughly analyzed with an emphasis on assumptions, limitations, and their potential applications in the development of novel medicines.

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.

An integrated PK/PD model investigating the impact of tumor size and systemic safety on animal survival in SW1990 pancreatic cancer xenograft

Survival is one of the most important endpoints in cancer therapy, and parametric survival analysis could comprehensively reveal the overall result of disease progression, drug efficacy, toxicity as well as their interactions. In this study we investigated the efficacy and toxicity of dexamethasone (DEX) combined with gemcitabine (GEM) in pancreatic cancer xenograft. Nude mice bearing SW1990 pancreatic cancer cells derived tumor were treated with DEX (4 mg/kg, i.g.) and GEM (15 mg/kg, i.v.) alone or in combination repeatedly (QD, Q3D, Q7D) until the death of animal or the end of study. Tumor volumes and net body weight (NBW) were assessed every other day. Taking NBW as a systemic safety indicator, an integrated pharmacokinetic/pharmacodynamic (PK/PD) model was developed to quantitatively describe the impact of tumor size and systemic safety on animal survival. The PK/PD models with time course data for tumor size and NBW were established, respectively, in a sequential manner; a parametric time-to-event (TTE) model was also developed based on the longitudinal PK/PD models to describe the survival results of the SW1990 tumor-bearing mice. These models were evaluated and externally validated. Only the mice with good tumor growth inhibition and relatively stable NBW had an improved survival result after DEX and GEM combination therapy, and the simulations based on the parametric TTE model showed that NBW played more important role in animals' survival compared with tumor size. The established model in this study demonstrates that tumor size was not always the most important reason for cancer-related death, and parametric survival analysis together with safety issues was also important in the evaluation of oncology therapies in preclinical studies.

Integration of Pharmacokinetics, Pharmacodynamics, Safety, and Efficacy into Model-Informed Dose Selection in Oncology First-in-Human Study: A Case of Roblitinib (FGF401)

Model-informed dose selection has been drawing increasing interest in oncology early clinical development. The current paper describes the example of FGF401, a selective fibroblast growth factor receptor 4 (FGFR4) inhibitor, in which a comprehensive modeling and simulation (M&S) framework, using both pharmacometrics and statistical methods, was established during its first-in-human clinical development using the totality of pharmacokinetics (PK), pharmacodynamic (PD) biomarkers, and safety and efficacy data in patients with cancer. These M&S results were used to inform FGF401 dose selection for future development. A two-compartment population PK (PopPK) model with a delayed 0-order absorption and linear elimination adequately described FGF401 PK. Indirect PopPK/PD models including a precursor compartment were independently established for two biomarkers: circulating FGF19 and 7α-hydroxy-4-cholesten-3-one (C4). Model simulations indicated a close-to-maximal PD effect achieved at the clinical exposure range. Time-to-progression was analyzed by Kaplan-Meier method which favored a trough concentration (C_trough )-driven efficacy requiring C_trough above a threshold close to the drug concentration producing 90% inhibition of phospho-FGFR4. Clinical tumor growth inhibition was described by a PopPK/PD model that reproduced the dose-dependent effect on tumor growth. Exposure-safety analyses on the expected on-target adverse events, including elevation of aspartate aminotransferase and diarrhea, indicated a lack of clinically relevant relationship with FGF401 exposure. Simulations from an indirect PopPK/PD model established for alanine aminotransferase, including a chain of three precursor compartments, further supported that maximal target inhibition was achieved and there was a lack of safety-exposure relationship. This M&S framework supported a dose selection of 120 mg once daily fasted or with a low-fat meal and provides a practical example that might be applied broadly in oncology early clinical development.

A Polymer Prodrug Strategy to Switch from Intravenous to Subcutaneous Cancer Therapy for Irritant/Vesicant Drugs

Chemotherapy is almost exclusively administered via the intravenous (IV) route, which has serious limitations (e.g., patient discomfort, long hospital stays, need for trained staff, high cost, catheter failures, infections). Therefore, the development of effective and less costly chemotherapy that is more comfortable for the patient would revolutionize cancer therapy. While subcutaneous (SC) administration has the potential to meet these criteria, it is extremely restrictive as it cannot be applied to most anticancer drugs, such as irritant or vesicant ones, for local toxicity reasons. Herein, we report a facile, general, and scalable approach for the SC administration of anticancer drugs through the design of well-defined hydrophilic polymer prodrugs. This was applied to the anticancer drug paclitaxel (Ptx) as a worst-case scenario due to its high hydrophobicity and vesicant properties (two factors promoting necrosis at the injection site). After a preliminary screening of well-established polymers used in nanomedicine, polyacrylamide (PAAm) was chosen as a hydrophilic polymer owing to its greater physicochemical, pharmacokinetic, and tumor accumulation properties. A small library of Ptx-based polymer prodrugs was designed by adjusting the nature of the linker (ester, diglycolate, and carbonate) and then evaluated in terms of rheological/viscosity properties in aqueous solutions, drug release kinetics in PBS and in murine plasma, cytotoxicity on two different cancer cell lines, acute local and systemic toxicity, pharmacokinetics and biodistribution, and finally their anticancer efficacy. We demonstrated that Ptx-PAAm polymer prodrugs could be safely injected subcutaneously without inducing local toxicity while outperforming Taxol, the commercial formulation of Ptx, thus opening the door to the safe transposition from IV to SC chemotherapy.

Growth‐rate model predicts in vivo tumor response from in vitro data

A major challenge in oncology drug development is to elucidate why drugs that show promising results in cancer cell lines in vitro fail in mouse studies or human trials. One of the fundamental steps toward solving this problem is to better predict how in vitro potency translates into in vivo efficacy. A common approach to infer whether a model will respond in vivo is based on in vitro half‐maximal inhibitory concentration values (IC₅₀), but yields limited quantitative comparison between cell lines and drugs, potentially because cell division and death rates differ between cell lines and in vivo models. Other methods based either on mechanistic modeling or machine learning require molecular insights or extensive training data, limiting their use for early drug development. To address these challenges, we propose a mathematical model integrating in vitro growth rate inhibition values with pharmacokinetic parameters to estimate in vivo drug response. Upon calibration with a drug‐specific factor, our model yields precise estimates of tumor growth rate inhibition for in vivo studies based on in vitro data. We then demonstrate how our model can be used to study dosing schedules and perform sensitivity analyses. In addition, it provides meaningful metrics to assess association with genotypes and guide clinical trial design. By relying on commonly collected data, our approach shows great promise for optimizing drug development, better characterizing the efficacy of novel molecules targeting proliferation, and identifying more robust biomarkers of sensitivity while limiting the number of in vivo experiments.

Optimized scaling of translational factors in oncology: from xenografts to RECIST

Purpose

Tumor growth inhibition (TGI) models are regularly used to quantify the PK–PD relationship between drug concentration and in vivo efficacy in oncology. These models are typically calibrated with data from xenograft mice and before being used for clinical predictions, translational methods have to be applied. Currently, such methods are commonly based on replacing model components or scaling of model parameters. However, difficulties remain in how to accurately account for inter-species differences. Therefore, more research must be done before xenograft data can fully be utilized to predict clinical response.

Method

To contribute to this research, we have calibrated TGI models to xenograft data for three drug combinations using the nonlinear mixed effects framework. The models were translated by replacing mice exposure with human exposure and used to make predictions of clinical response. Furthermore, in search of a better way of translating these models, we estimated an optimal way of scaling model parameters given the available clinical data.

Results

The predictions were compared with clinical data and we found that clinical efficacy was overestimated. The estimated optimal scaling factors were similar to a standard allometric scaling exponent of − 0.25.

Conclusions

We believe that given more data, our methodology could contribute to increasing the translational capabilities of TGI models. More specifically, an appropriate translational method could be developed for drugs with the same mechanism of action, which would allow for all preclinical data to be leveraged for new drugs of the same class. This would ensure that fewer clinically inefficacious drugs are tested in clinical trials.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00280-022-04458-8.

Cancer patient survival can be parametrized to improve trial precision and reveal time-dependent therapeutic effects

Individual participant data (IPD) from oncology clinical trials is invaluable for identifying factors that influence trial success and failure, improving trial design and interpretation, and comparing pre-clinical studies to clinical outcomes. However, the IPD used to generate published survival curves are not generally publicly available. We impute survival IPD from ~500 arms of Phase 3 oncology trials (representing ~220,000 events) and find that they are well fit by a two-parameter Weibull distribution. Use of Weibull functions with overall survival significantly increases the precision of small arms typical of early phase trials: analysis of a 50-patient trial arm using parametric forms is as precise as traditional, non-parametric analysis of a 90-patient arm. We also show that frequent deviations from the Cox proportional hazards assumption, particularly in trials of immune checkpoint inhibitors, arise from time-dependent therapeutic effects. Trial duration therefore has an underappreciated impact on the likelihood of success.

Analysis of more than 150 Phase 3 oncology clinical trials supports parametric statistical analysis, significantly increasing the precision of small early-phase trials and relating deviations from the Cox proportional hazards model to trial duration.

Interspecies comparison of plasma metabolism and sample stabilization for quantitative bioanalyses: Application to (R)-CE3F4 in preclinical development, including metabolite identification by high-resolution mass spectrometry

The CE3F4 is an inhibitor of the type 1 exchange protein directly activated by cAMP (EPAC1), which is involved in numerous signaling pathways. The inhibition of EPAC1 shows promising results in vitro and in vivo in different cardiac pathological situations like hypertrophic signaling, contributing to heart failure, or arrhythmia. An HPLC-UV method with a simple and fast sample treatment allowed the quantification of (R)-CE3F4. Sample treatment consisted of simple protein precipitation with 50 µL of ethanol and 150 µL of acetonitrile for a 50 µL biological sample. Two wavelengths were used according to the origin of plasma (220 or 250 nm for human samples and 250 nm for murine samples). Accuracy profile was evaluated for both wavelengths, and the method was in agreement with the criteria given by the EMA in the guideline for bioanalytical method validation for human and mouse plasma samples. The run time was 12 min allowing the detection of the (R)-CE3F4 and a metabolite. This study further permitted understanding the behavior of CE3F4 in plasma by highlighting an important difference between humans and rodents on plasma metabolism and may impact future in vivo studies related to this molecule and translation of results between animal models and humans. Using paraoxon as a metabolism inhibitor was crucial for the stabilization of (R)-CE3F4 in murine samples. HPLC-UV and HPLC-MS/MS studies were conducted to confirm metabolite structure and consequently, the main metabolic pathway in murine plasma.

Modeling approaches for reducing safety-related attrition in drug discovery and development: a review on myelotoxicity, immunotoxicity, cardiovascular toxicity, and liver toxicity

Introduction:Safety and tolerability is a critical area where improvements are needed to decrease the attrition rates during development of new drug candidates. Modeling approaches, when smartly implemented, can contribute to this aim.Areas covered:The focus of this review was on modeling approaches applied to four kinds of drug-induced toxicities: hematological, immunological, cardiovascular (CV) and liver toxicity. Papers, mainly published in the last 10 years, reporting models in three main methodological categories - computational models (e.g., quantitative structure-property relationships, machine learning approaches, neural networks, etc.), pharmacokinetic-pharmacodynamic (PK-PD) models, and quantitative system pharmacology (QSP) models - have been considered.Expert opinion:The picture observed in the four examined toxicity areas appears heterogeneous. Computational models are typically used in all areas as screening tools in the early stages of development for hematological, cardiovascular and liver toxicity, with accuracies in the range of 70-90%. A limited number of computational models, based on the analysis of drug protein sequence, was instead proposed for immunotoxicity. In the later stages of development, toxicities are quantitatively predicted with reasonably good accuracy using either semi-mechanistic PK-PD models (hematological and cardiovascular toxicity), or fully exploited QSP models (immuno-toxicity and liver toxicity).

Quantitative modeling predicts competitive advantages of a next generation anti-NKG2A monoclonal antibody over monalizumab for the treatment of cancer

A semimechanistic pharmacokinetic (PK)/receptor occupancy (RO) model was constructed to differentiate a next generation anti-NKG2A monoclonal antibody (KSQ mAb) from monalizumab, an immune checkpoint inhibitor in multiple clinical trials for the treatment of solid tumors. A three-compartment model incorporating drug PK, biodistribution, and NKG2A receptor interactions was parameterized using monalizumab PK, in vitro affinity measurements for both monalizumab and KSQ mAb, and receptor burden estimates from the literature. Following calibration against monalizumab PK data in patients with rheumatoid arthritis, the model successfully predicted the published PK and RO observed in gynecological tumors and in patients with squamous cell carcinoma of the head and neck. Simulations predicted that the KSQ mAb requires a 10-fold lower dose than monalizumab to achieve a similar RO over a 3-week period following q3w intravenous (i.v.) infusion dosing. A global sensitivity analysis of the model indicated that the drug-target binding affinity greatly affects the tumor RO and that an optimal affinity is needed to balance RO with enhanced drug clearance due to target mediated drug disposition. The model predicted that the KSQ mAb can be dosed over a less frequent regimen or at lower dose levels than the current monalizumab clinical dosing regimen of 10 mg/kg q2w. Either dosing strategy represents a competitive advantage over the current therapy. The results of this study demonstrate a key role for mechanistic modeling in identifying optimal drug parameters to inform and accelerate progression of mAb to clinical trials.

A Review on Drug-Induced Nephrotoxicity: Pathophysiological Mechanisms, Drug Classes, Clinical Management, and Recent Advances in Mathematical Modeling and Simulation Approaches

A variety of marketed drugs belonging to various therapeutic classes are known to cause nephrotoxicity. Nephrotoxicity can manifest itself in several forms depending on the specific site involved as well as the underlying pathophysiological mechanisms. As they often coexist with other pathophysiological conditions, the steps that can be taken to treat them are often limited. Thus, drug-induced nephrotoxicity remains a major clinical challenge. Prior knowledge of risk factors associated with special patient populations and specific classes of drugs, combined with early diagnosis, therapeutic drug monitoring with dose adjustments, as well as timely prospective treatments are essential to prevent and manage them better. Most incident drug-induced renal toxicity is reversible only if diagnosed at an early stage and treated promptly. Hence, diagnosis at an early stage is the need of the hour to counter it. Significant recent advances in the identification of novel early biomarkers of nephrotoxicity are not beyond limitations. In such a scenario, mathematical modeling and simulation (M&S) approaches may help to better understand and predict toxicities in a clinical setting. This review summarizes pathophysiological mechanisms of drug-induced nephrotoxicity, classes of nephrotoxic drugs, management, prevention, and diagnosis in clinics. Finally, it also highlights some of the recent advancements in mathematical M&S approaches that could be used to better understand and predict drug-induced nephrotoxicity.

Mechanistic Quantitative Pharmacology Strategies for the Early Clinical Development of Bispecific Antibodies in Oncology

Bispecific antibodies (bsAbs) have become an integral component of the therapeutic research strategy to treat cancer. In addition to clinically validated immune cell re‐targeting, bsAbs are being designed for tumor targeting and as dual immune modulators. Explorative preclinical and emerging clinical data indicate potential for enhanced efficacy and reduced systemic toxicity. However, bsAbs are a complex modality with challenges to overcome in early clinical trials, including selection of relevant starting doses using a minimal anticipated biological effect level approach, and predicting efficacious dose despite nonintuitive dose response relationships. Multiple factors can contribute to variability in the clinic, including differences in functional affinity due to avidity, receptor expression, effector to target cell ratio, and presence of soluble target. Mechanistic modeling approaches are a powerful integrative tool to understand the complexities and aid in clinical translation, trial design, and prediction of regimens and strategies to reduce dose limiting toxicities of bsAbs. In this tutorial, the use of mechanistic modeling to impact decision making for bsAbs is presented and illustrated using case study examples.

Quantification of Pharmacokinetic Profiles of PD-1/PD-L1 Antibodies by Validated ELISAs

Immunotherapy has changed the paradigm of cancer treatments. In this way, several combinatorial strategies based on monoclonal antibodies (mAb) such as anti (a)-PD-1 or anti (a)-PD-L1 are often reported to yield promising clinical benefits. However, the pharmacokinetic (PK) behavior of these mAbs is a critical issue that requires selective analytical techniques. Indeed, few publications report data on a-PD1/a-PD-L1 exposure and its relationship with therapeutic or toxic effects. In this regard, preclinical assays allow the time profiles of antibody plasma concentrations to be characterized rapidly and easily, which may help to increase PK knowledge. In this study, we have developed and validated two in-house ELISAs to quantify a-PD-1 and a-PD-L1 in plasma collected from tumor-bearing mice. The linear range for the a-PD-1 assay was 2.5–125 ng/mL and 0.11–3.125 ng/mL for the a-PD-L1 assay, whereas the intra-and inter-day precision was lower than 20% for both analytes. The PK characterization revealed a significant decrease in drug exposure after administration of multiple doses. Plasma half-life for a-PD-1 was slightly shorter (22.3 h) than for a-PD-L1 (46.7 h). To our knowledge, this is the first reported preclinical ELISA for these immune checkpoint inhibitors, which is sufficiently robust to be used in different preclinical models. These methods can help to understand the PK behavior of these antibodies under different scenarios and the relationship with response, thus guiding the choice of optimal doses in clinical settings.

Emerging siRNA Design Principles and Consequences for Biotransformation and Disposition in Drug Development

After two decades teetering at the intersection of laboratory tool and therapeutic reality, with two siRNA drugs now clinically approved, this modality has finally come into fruition. Consistent with other emerging modalities, initial proof-of-concept efforts concentrated on coupling pharmacologic efficacy with desirable safety profiles. Consequently, thorough investigations of siRNA absorption, distribution, metabolism, and excretion (ADME) properties are lacking. Advancing ADME knowledge will aid establishment of in vitro-in vivo correlations and pharmacokinetic-pharmacodynamic relationships to optimize candidate selection through discovery and translation. Here, we outline the emerging siRNA design principles and discuss the consequences for siRNA disposition and biotransformation. We propose a conceptual framework for siRNA ADME evaluation, contextualizing the site of biotransformation product formation with PK-PD modulation, and end with a discussion around safety and regulatory considerations and future directions for this modality.

Quantitative Spatial Analysis of Metabolic Heterogeneity Across in vivo and in vitro Tumor Models

Metabolic preferences of tumor cells vary within a single tumor, contributing to tumor heterogeneity, drug resistance, and patient relapse. However, the relationship between tumor treatment response and metabolically distinct tumor cell populations is not well-understood. Here, a quantitative approach was developed to characterize spatial patterns of metabolic heterogeneity in tumor cell populations within in vivo xenografts and 3D in vitro cultures (i.e., organoids) of head and neck cancer. Label-free images of cell metabolism were acquired using two-photon fluorescence lifetime microscopy of the metabolic co-enzymes NAD(P)H and FAD. Previous studies have shown that NAD(P)H mean fluorescence lifetimes can identify metabolically distinct cells with varying drug response. Thus, density-based clustering of the NAD(P)H mean fluorescence lifetime was used to identify metabolic sub-populations of cells, then assessed in control, cetuximab-, cisplatin-, and combination-treated xenografts 13 days post-treatment and organoids 24 h post-treatment. Proximity analysis of these metabolically distinct cells was designed to quantify differences in spatial patterns between treatment groups and between xenografts and organoids. Multivariate spatial autocorrelation and principal components analyses of all autofluorescence intensity and lifetime variables were developed to further improve separation between cell sub-populations. Spatial principal components analysis and Z-score calculations of autofluorescence and spatial distribution variables also visualized differences between models. This analysis captures spatial distributions of tumor cell sub-populations influenced by treatment conditions and model-specific environments. Overall, this novel spatial analysis could provide new insights into tumor growth, treatment resistance, and more effective drug treatments across a range of microscopic imaging modalities (e.g., immunofluorescence, imaging mass spectrometry).

Pharmacokinetic and Immunological Considerations for Expanding the Therapeutic Window of Next-Generation Antibody-Drug Conjugates

Antibody-drug conjugate (ADC) development has evolved greatly over the last 3 decades, including the Food and Drug Administration (FDA) approval of several new drugs. However, translating ADCs from the design stage and preclinical promise to clinical success has been a major hurdle for the field, particularly for solid tumors. The challenge in clinical development can be attributed to the difficulty in connecting the design of these multifaceted agents with the impact on clinical efficacy, especially with the accelerated development of 'next-generation' ADCs containing a variety of innovative biophysical developments. Given their complex nature, there is an urgent need to integrate holistic ADC characterization approaches. This includes comprehensive in vivo assessment of systemic, intratumoral and cellular pharmacokinetics, pharmacodynamics, toxicodynamics, and interactions with the immune system, with the aim of optimizing the ADC therapeutic window. Pharmacokinetic/pharmacodynamic factors influencing the ADC therapeutic window include (1) selecting optimal target and ADC components for prolonged and stable plasma circulation to increase tumoral uptake with minimal non-specific systemic toxicity, (2) balancing homogeneous intratumoral distribution with efficient cellular uptake, and (3) translating improved ADC potency to better clinical efficacy. Balancing beneficial immunological effects such as Fc-mediated and payload-mediated immune cell activation against harmful immunogenic/toxic effects is also an emerging concern for ADCs. Here, we review practical considerations for tracking ADC efficacy and toxicity, as aided by high-resolution biomolecular and immunological tools, quantitative pharmacology, and mathematical models, all of which can elucidate the relative contributions of the multitude of interactions governing the ADC therapeutic window.

Survival Prolongation Index as a Novel Metric to Assess Anti-Tumor Activity in Xenograft Models

A single efficacy metric quantifying anti-tumor activity in xenograft models is useful in evaluating different tumors' drug sensitivity and dose-response of an anti-tumor agent. Commonly used metrics include the ratio of tumor volume in treated vs. control mice (T/C), tumor growth inhibition (TGI), ratio of area under the curve (AUC), and growth rate inhibition (GRI). However, these metrics have some limitations. In particular, for biologics with long half-lives, tumor volume (TV) of treated xenografts displays a delay in volume reduction (and in some cases, complete regression) followed by a growth rebound. These observed data cannot be described by exponential functions, which is the underlying assumption of TGI and GRI, and the fit depends on how long the tumor volumes are monitored. On the other hand, T/C and TGI only utilizes information from one chosen time point. Here, we propose a new metric called Survival Prolongation Index (SPI), calculated as the time for drug-treated TV to reach a certain size (e.g., 600 mm³) divided by the time for control TV to reach 600mm³ and therefore not dependent on the chosen final time point t_f. Simulations were conducted under different scenarios (i.e., exponential vs. saturable growth, linear vs. nonlinear kill function). For all cases, SPI is the most linear and growth-rate independent metric. Subsequently, a literature analysis was conducted using 11 drugs to evaluate the correlation between pre-clinically obtained SPI and clinical overall response. This retrospective analysis of approved drugs suggests that a predicted SPI of 2 is necessary for clinical response.