Design considerations for early-phase clinical trials of immune-oncology agents

Adaptive Bayesian information borrowing methods for finding and optimizing subgroup-specific doses

In precision oncology, integrating multiple cancer patient subgroups into a single master protocol allows for the simultaneous assessment of treatment effects in these subgroups and promotes the sharing of information between them, ultimately reducing sample sizes and costs and enhancing scientific validity. However, the safety and efficacy of these therapies may vary across different subgroups, resulting in heterogeneous outcomes. Therefore, identifying subgroup-specific optimal doses in early-phase clinical trials is crucial for the development of future trials. In this article, we review various innovative Bayesian information-borrowing strategies that aim to determine and optimize subgroup-specific doses. Specifically, we discuss Bayesian hierarchical modeling, Bayesian clustering, Bayesian model averaging or selection, pairwise borrowing, and other relevant approaches. By employing these Bayesian information-borrowing methods, investigators can gain a better understanding of the intricate relationships between dose, toxicity, and efficacy in each subgroup. This increased understanding significantly improves the chances of identifying an optimal dose tailored to each specific subgroup. Furthermore, we present several practical recommendations to guide the design of future early-phase oncology trials involving multiple subgroups when using the Bayesian information-borrowing methods.

Local continual reassessment methods for dose finding and optimization in drug-combination trials

Due to the limited sample size and large dose exploration space, obtaining a desirable dose combination is a challenging task in the early development of combination treatments for cancer patients. Most existing designs for optimizing the dose combination are model-based, requiring significant efforts to elicit parameters or prior distributions. Model-based designs also rely on intensive model calibration and may yield unstable performance in the case of model misspecification or sparse data. We propose to employ local, underparameterized models for dose exploration to reduce the hurdle of model calibration and enhance the design robustness. Building upon the framework of the partial ordering continual reassessment method, we develop local data-based continual reassessment method designs for identifying the maximum tolerated dose combination, using toxicity only, and the optimal biological dose combination, using both toxicity and efficacy, respectively. The local data-based continual reassessment method designs only model the local data from neighboring dose combinations. Therefore, they are flexible in estimating the local space and circumventing unstable characterization of the entire dose-exploration surface. Our simulation studies show that our approach has competitive performance compared to widely used methods for finding maximum tolerated dose combination, and it has advantages over existing model-based methods for optimizing optimal biological dose combination.

Surv-CRM-12: A Bayesian phase I/II survival CRM for right-censored toxicity endpoints with competing disease progression

The growing interest in new classes of anti-cancer agents, such as molecularly-targeted therapies and immunotherapies with modes of action different from those of cytotoxic chemotherapies, has changed the dose-finding paradigm. In this setting, the observation of late-onset toxicity endpoints may be precluded by treatment and trial discontinuation due to disease progression, defining a competing event to toxicity. Trial designs where dose-finding is modeled in the framework of a survival competing risks model appear particularly well-suited. We aim to provide a phase I/II dose-finding design that allows dose-limiting toxicity (DLT) outcomes to be delayed or unobserved due to competing progression within the possibly long observation window. The proposed design named the Survival-continual reassessment method-12, uses survival models for right-censored DLT and progression endpoints. In this competing risks framework, cause-specific hazards for DLT and progression-free of DLT were considered, with model parameters estimated using Bayesian inference. It aims to identify the optimal dose (OD), by minimizing the cumulative incidence of disease progression, given an acceptable toxicity threshold. In a simulation study, design operating characteristics were evaluated and compared to the TITE-BOIN-ET design and a nonparametric benchmark approach. The performance of the proposed method was consistent with the complexity of scenarios as assessed by the nonparametric benchmark. We found that the proposed design presents satisfying operating characteristics in selecting the OD and safety.

A risk stratification model for toxicities in phase 1 immunotherapy trials

INTRODUCTION

Despite the increased number of novel immunotherapy (IO) agents under current development, their toxicity profile remains to be fully elucidated.

METHODS

An IO risk stratification model was developed based on 5 different variables: treatment-related deaths; rate of grade ≥3 treatment-related adverse events or treatment-emergent adverse events; grade ≥2 encephalopathy or central nervous system toxicity; grade ≥2 cytokine release syndrome; and the number and type of dose-limiting toxicity. Phase 1 IO trials published from January 2014 to December 2020 were reviewed and categorised based on our risk stratification model into three categories: low-, intermediate- and high-risk. Clinical trial variables were associated with the high-risk category. To review the quality of reporting across phase 1 IO trials, a subset of studies was further examined by the use of the ASCO/SITC Trial Reporting in Immuno-Oncology (TRIO) standards.

RESULTS

Different IO compounds demonstrated diverse risk profiles. In multivariable analysis, combination versus IO single agent treatment, and testing IO agents different from anti-programmed death-1/programmed death ligand-1 (anti-PD1/L1), anti-cytotoxic t-lymphocyte antigen-4 (anti-CTLA4) antibodies and anti-cancer vaccines were associated with a higher toxicity risk. None of the studies examined in our dataset reported all the items included in the TRIO standards.

CONCLUSIONS

Our results have important implications for future clinical trial design. Additionally, standards for reporting are urgently needed.

Recent advances and clinical pharmacology aspects of Chimeric Antigen Receptor (CAR) T-cellular therapy development

Advances in immuno-oncology have provided a variety of novel therapeutics that harness the innate immune system to identify and destroy neoplastic cells. It is noteworthy that acceptable safety profiles accompany the development of these targeted therapies, which result in efficacious cancer treatment with higher survival rates and lower toxicities. Adoptive cellular therapy (ACT) has shown promising results in inducing sustainable remissions in patients suffering from refractory diseases. Two main types of ACT include engineered Chimeric Antigen Receptor (CAR) T cells and T cell receptor (TCR) T cells. The application of these immuno-therapies in the last few years has been successful and has demonstrated a safe and rapid treatment regimen for solid and non-solid tumors. The current review presents an insight into the clinical pharmacology aspects of immuno-therapies, especially CAR-T cells. Here, we summarize the current knowledge of TCR and CAR-T cell immunotherapy with particular focus on the structure of CAR-T cells, the effects and toxicities associated with these therapies in clinical trials, risk mitigation strategies, dose selection approaches, and cellular kinetics. Finally, the quantitative approaches and modeling techniques used in the development of CAR-T cell therapies are described.

Outcomes and endpoints in clinical trials supporting the marketing authorisation of treatments in paediatric acute lymphoblastic leukaemia

The improvement in acute lymphoblastic leukaemia (ALL) treatment has led research efforts to focus on the unmet medical needs of an increasingly smaller patient cohort with resistant leukaemia and to develop more-targeted agents. Survival and response rates remain the most-prevalent endpoints in paediatric ALL research, but other intermediate clinical endpoints and molecular biomarkers for efficacy and mid- and long-term safety endpoints are also being investigated. The success of current ALL treatment appears to be driving new paradigms to optimise clinical drug development, while at the same time, regulatory tools in place are supporting meaningful drug development in the area.

Optimizing the future: how mathematical models inform treatment schedules for cancer

For decades, mathematical models have influenced how we schedule chemotherapeutics. More recently, mathematical models have leveraged lessons from ecology, evolution, and game theory to advance predictions of optimal treatment schedules, often in a personalized medicine manner. We discuss both established and emerging therapeutic strategies that deviate from canonical standard-of-care regimens, and how mathematical models have contributed to the design of such schedules. We first examine scheduling options for single therapies and review the advantages and disadvantages of various treatment plans. We then consider the challenge of scheduling multiple therapies, and review the mathematical and clinical support for various conflicting treatment schedules. Finally, we propose how a consilience of mathematical and clinical knowledge can best determine the optimal treatment schedules for patients.

Immunotherapeutic early-phase clinical trials and malignant gliomas: A single-center experience and comprehensive immunophenotyping of circulating leukocytes

Background

Immunotherapeutic early-phase clinical trials (ieCTs) increasingly adopt large expansion cohorts exploring novel agents across different tumor types. High-grade glioma (HGG) patients are usually excluded from these trials.

Methods

Data of patients with recurrent HGGs treated within multicohort ieCTs between February 2014 and August 2019 (experimental group, EG) at our Phase I Unit were retrospectively reviewed and compared to a matched control group (CG) of patients treated with standard therapies. We retrospectively evaluated clinical, laboratory, and molecular parameters through univariate and multivariate analysis. A prospective characterization of circulating leukocyte subpopulations was performed in the latest twenty patients enrolled in the EG, with a statistical significance cutoff of P < .1.

Results

Thirty HGG patients were treated into six ieCTs. Fifteen patients received monotherapies (anti-PD-1, anti-CSF-1R, anti-TGFβ, anti-cereblon), fifteen patients combination regimens (anti-PD-L1 + anti-CD38, anti-PD-1 + anti-CSF-1R). In the EG, median progression-free survival and overall survival (OS) from treatment initiation were 1.8 and 8.6 months; twelve patients survived more than 12 months, and two of them more than 6 years. Univariate analysis identified O⁶-methylguanine DNA methyltransferase (MGMT) promoter methylation and total protein value at six weeks as significantly correlated with a better outcome. Decreased circulating neutrophils and increased conventional dendritic cells levels lead to significantly better OS.

Conclusions

A subgroup of EG patients achieved remarkably durable disease control. MGMT promoter methylation identifies patients who benefit more from immunotherapy. Monitoring dynamic changes of innate immune cell populations may help to predict clinical outcomes.

Encorafenib, binimetinib plus pembrolizumab triplet therapy in patients with advanced BRAFV600 mutant melanoma: safety and tolerability results from the phase I IMMU-TARGET trial

BACKGROUND

Combination of immune checkpoint inhibitors and mitogen-activated protein kinase (MAPK) pathway inhibitors (MAPKi) has been proposed to enhance the durability of anti-tumour responses induced by MAPKi. Here, we present phase I safety results from an open-label, phase I/II study of pembrolizumab (PEM), encorafenib (ENC) and binimetinib (BIN) triplet therapy in advanced, B-Raf proto-oncogene serine/threonine kinase (BRAF)V600-mutated melanoma (IMMU-TARGET, NCT02902042).

METHODS

The dose finding phase I part used a 3 + 3 design, starting with the approved doses of PEM (200 mg every three weeks), ENC (450 mg once daily [QD]) and BIN (45 mg twice daily [BID]) as dose level (DL) 0. Reduction of the ENC and BIN doses (300 mg QD and 30 mg BID at DL-1 and 200 mg QD and 30 mg BID at DL-2) was preplanned in case of ≥2 dose-limiting toxicities (DLTs). Primary objectives were to estimate the recommended phase II dose of the triplet combination, DLT and safety. As per the sponsor's decision, the study was terminated after the phase I part, as the clinical efficacy of the combination is currently being investigated in a pivotal, placebo-controlled (PEM mono), double-blinded phase III trial (STARBOARD,NCT04657991).

RESULTS

Fifteen patients were enrolled. DLTs of DL0 were creatine phosphokinase (CPK) elevation plus cytokine release syndrome (n = 1) and gamma glutamyl transferase (GGT) increase (n = 1). No DLT was observed in further 3 + 3 patients at DL-1. One (isolated GGT elevations) DLT of DL0 was questionable, as the patient had further episodes of isolated GGT elevations after treatment discontinuation. Hence, further 6 patients were enrolled at DL0: here, no DLT occurred. In total, 13 of 15 patients (87%) experienced a treatment-related adverse event (TRAE) and 8 patients (53%), a grade ≥III TRAE; there were no TRAE-related deaths. Increases in aspartate aminotransferases, GGT (6/15 patients) and CPK elevations (4/15) were the most common grade III-IV TRAE. In median, patients received triplet therapy for 24 weeks (interquartile range [IQR], 12-45). Of the 14 patients evaluable for efficacy, the overall response rate was 64% (95% confidence interval [CI], 35-87). At a median follow-up of 25 months (IQR, 9-28), progression-free survival at 12 months was 41% (95% CI, 13-68).

CONCLUSIONS

Triplet therapy with PEM, ENC and BIN as used in the study was feasible and safe and led to clinically meaningful disease control.

Healthy volunteers in first-in-human oncology drug development for small molecules

This review provides tools to consider the inclusion of healthy volunteers (HVs) in first-in-human (FIH) oncology clinical trials with small molecules, including targeted and immunomodulatory agents, a strategy that was not envisioned with classic chemotherapy. To enable an FIH oncology trial in HVs compared to cancer patients (CPs), a robust nonclinical package must be generated, which includes toxicokinetic and pharmacokinetic studies, as well as more extensive safety pharmacology, toxicology and genotoxicity studies. This strategy could provide an early clinical characterization of the pharmacokinetic parameters and clinical safety profile in the absence of comorbidities and concomitant medication. It also avoids the ethical issue of administrating subtherapeutic doses to CPs, and could potentially help to accelerate the timelines of clinical drug development for patient care. That being said, stakeholders involved in these studies need to proceed with caution, fully understand the regulatory guidance and thoroughly evaluate the benefits and risks. This paper serves to address the regulatory guidance and other considerations needed when using healthy volunteers in early oncology trials.

Concentration optimization of combinatorial drugs using Markov chain-based models

BACKGROUND

Combinatorial drug therapy for complex diseases, such as HSV infection and cancers, has a more significant efficacy than single-drug treatment. However, one key challenge is how to effectively and efficiently determine the optimal concentrations of combinatorial drugs because the number of drug combinations increases exponentially with the types of drugs.

RESULTS

In this study, a searching method based on Markov chain is presented to optimize the combinatorial drug concentrations. In this method, the searching process of the optimal drug concentrations is converted into a Markov chain process with state variables representing all possible combinations of discretized drug concentrations. The transition probability matrix is updated by comparing the drug responses of the adjacent states in the network of the Markov chain and the drug concentration optimization is turned to seek the state with maximum value in the stationary distribution vector. Its performance is compared with five stochastic optimization algorithms as benchmark methods by simulation and biological experiments. Both simulation results and experimental data demonstrate that the Markov chain-based approach is more reliable and efficient in seeking global optimum than the benchmark algorithms. Furthermore, the Markov chain-based approach allows parallel implementation of all drug testing experiments, and largely reduces the times in the biological experiments.

CONCLUSION

This article provides a versatile method for combinatorial drug screening, which is of great significance for clinical drug combination therapy.

Correlation Between Early Endpoints and Overall Survival in Non-Small-Cell Lung Cancer: A Trial-Level Meta-Analysis

Early endpoints, such as progression-free survival (PFS), are increasingly used as surrogates for overall survival (OS) to accelerate approval of novel oncology agents. Compiling trial-level data from randomized controlled trials (RCTs) could help to develop a predictive framework to ascertain correlation trends between treatment effects for early and late endpoints. Through trial-level correlation and random-effects meta-regression analysis, we assessed the relationship between hazard ratio (HR) OS and (1) HR PFS and (2) odds ratio (OR) PFS at 4 and 6 months, stratified according to the mechanism of action of the investigational product. Using multiple source databases, we compiled a data set including 81 phase II-IV RCTs (35 drugs and 156 observations) of patients with non-small-cell lung cancer. Low-to-moderate correlations were generally observed between treatment effects for early endpoints (based on PFS) and HR OS across trials of agents with different mechanisms of action. Moderate correlations were seen between treatment effects for HR PFS and HR OS across all trials, and in the programmed cell death-1/programmed cell death ligand-1 and epidermal growth factor receptor trial subsets. Although these results constitute an important step, caution is advised, as there are some limitations to our evaluation, and an additional patient-level analysis would be needed to establish true surrogacy.

A two-stage Bayesian adaptive design for minimum effective dose (MinED)-based dosing-finding trials

Conventional phase I designs for finding a phase II recommended dose (P2RD) based on toxicity alone is problematic because the maximum tolerated dose (MTD) is not necessarily the optimal dose. Instead, recently attention has been given to find the minimum effective dose (MinED) - defined as the lowest effective dose. Traditional paradigms for the MinED studies are conducted as dose-ranging or dose-response trials which involve several doses and randomize patients among doses to find the MinED. An alternative approach for the MinED study is the so-called MinED-based dose-finding study, in which instead of conducting hypothesis testings and without power analysis, this kind of trial conduct dose escalation/de-escalation to target a pre-set MinED target. In this study, we propose a new Bayesian two-stage adaptive design schema based on framework of the interval-based phase I method. The proposed method is model-free without curve pre-specifications, which is suitable for various dose-efficacy relationships. The proposed method shows desirable theoretical finite property of semi-coherence and large sample property of consistency. A random scenario generative algorithm for the MinED has also been proposed for extensive simulation studies, which demonstrated desirable performances of the proposed method. An R package "MinEDfind" and a Shiny app have been developed for implementing the method.

Recruitment, outcomes, and toxicity trends in phase I oncology trials: Six-year experience in a large institution

Background

With the rapid influx of novel anti‐cancer agents, phase I clinical trials in oncology are evolving. Historically, response rates on early phase trials have been modest with the clinical benefit and ethics of enrolment debated. However, there is a paucity of real‐world data in this setting.

Aim

To better understand the changing landscape of phase I oncology trials, we performed a retrospective review at our institution to examine patient and trial characteristics, screening outcomes, and treatment outcomes.

Methods and results

We analyzed all consecutive adult patients with advanced solid organ malignancies who were screened across phase I trials from January 2013 to December 2018 at a single institution. During this period, 242 patients were assessed for 28 different trials. Median age was 64 years (range 30–89) with an equal sex distribution. Among 257 screening visits, the overall screen failure rate was 18%, resulting in 212 patients being enrolled onto a study. Twenty‐six trials (93%) involved immunotherapeutic agents or molecular targeted agents either alone or in combination, with only two trials of cytotoxic agents (7%). Twenty‐two (13.4%) of the 209 treated patients experienced a total of 33 grade 3 or higher treatment‐related adverse events. There was one treatment‐related death (0.5%). Of 190 response‐evaluable patients, 7 (4%) had a complete response, 34 (18%) a partial response, and 59 (31%) experienced stable disease for a disease control rate of 53%. The median overall survival for our cohort was 8.0 (95% CI: 6.8–9.2) months.

Conclusion

The profile of phase I trials at our institution are consistent with the changing early drug development landscape. Response rates and overall survival in our cohort are superior to historically reported rates and comparable to contemporaneous studies. Severe treatment‐related toxicity was relatively uncommon, and treatment‐related mortality was rare.

Seamless phase I/II design for novel anticancer agents with competing disease progression

Molecularly targeted agents and immunotherapies have prolonged administration and complicated toxicity and efficacy profiles requiring longer toxicity observation windows and the inclusion of efficacy information to identify the optimal dose. Methods have been proposed to either jointly model toxicity and efficacy, or for prolonged observation windows. However, it is inappropriate to address these issues individually in the setting of dose-finding because longer toxicity windows increase the risk of patients experiencing disease progression and discontinuing the trial, with progression defining a competing event to toxicity, and progression-free survival being a commonly used efficacy endpoint. No method has been proposed to address this issue in a competing risk framework. We propose a seamless phase I/II design, namely the competing risks continual reassessment method (CR-CRM). Given an observation window, the objective is to recommend doses that minimize the progression probability, among a set of tolerable doses in terms of toxicity risk. In toxicity-centered stage of the design, doses are assigned based on toxicity alone, and in optimization stage of the design, doses are assigned integrating both toxicity and progression information. Design operating characteristics were examined in a simulation study compared with benchmark performances, including sensitivity to time-varying hazards and correlated events. The method performs well in selecting doses with acceptable toxicity risk and minimum progression risk across a wide range of scenarios.

Convection enhanced delivery of light responsive antigen capturing oxygen generators for chemo-phototherapy triggered adaptive immunity

In recent years, combination therapy has emerged as the cornerstone of clinical practice in treating glioblastoma multiforme. However, their ability to trigger and leverage the body's adaptive immunity has rarely been studied. Tumour heterogeneity, the presence of the blood-brain barrier, and an immunosuppressive tumor microenvironment play a crucial role in the 90% local tumor recurrence post-treatment. Herein, we report an improved combination therapy approach capable of stimulating an immune response that utilizes Light responsive antigen-capturing oxygen generators (LAGs). The engineered LAGs loaded with a non-genotoxic molecule, Nutlin-3a, and a photosensitizer, Protoporphyrin IX, can release the payload on-demand when exposed to light of a specific wavelength. The in-situ oxygen generation capability of LAGs enables tumor oxygenation enhancement, thereby alleviating the tumor hypoxia and enhancing the efficacy of chemo-photodynamic therapy. Furthermore, by modulating the surface properties of LAGs, we demonstrated that the tumor-derived protein antigens released can be captured and retained in-situ, which improves antigen uptake and presentation by the antigen-presenting cells. Dual drug-loaded LAGs (DD-LAGs) upregulated the expression of cell surface CD83 maturation and CD86 costimulatory markers on monocyte-derived-dendritic cells, suggesting intrinsic immune adjuvancy. In the presence of 3D printed hypoxic U87 spheroids (h-U87), DD-LAGs induced cancer cell death, upregulated IL-1β, and downregulated IL-10 resulting in CD3⁺, helper CD4⁺, and cytotoxic CD8⁺ proliferation. Finally, we have investigated convection-enhanced delivery as a potential route of administration for DD-LAGs. Our work presents a novel strategy to induce tumor cell death both during and post-treatment, thereby reducing the possibility of recurrence.

Moving Beyond 3+3: The Future of Clinical Trial Design

Misgivings have been raised about the operating characteristics of the canonical 3+3 dose-escalation phase I clinical trial design. Yet, the traditional 3+3 design is still the most commonly used. Although it has been implied that adhering to this design is due to a stubborn reluctance to adopt change despite other designs performing better in hypothetical computer-generated simulation models, the continued adherence to 3+3 dose-escalation phase I strategies is more likely because these designs perform the best in the real world, pinpointing the correct dose and important side effects with an acceptable degree of precision. Beyond statistical simulations, there are little data to refute the supposed shortcomings ascribed to the 3+3 method. Even so, to address the unique nuances of gene- and immune-targeted compounds, a variety of inventive phase 1 trial designs have been suggested. Strategies for developing these therapies have launched first-in-human studies devised to acquire a breadth of patient data that far exceed the size of a typical phase I design and blur the distinction between dose selection and efficacy evaluation. Recent phase I trials of promising cancer therapies assessed objective tumor response and durability at various doses and schedules as well as incorporated multiple expansion cohorts spanning a variety of histology or biomarker-defined tumor subtypes, sometimes resulting in U.S. Food and Drug Administration approval after phase I. This article reviews recent innovations in phase I design from the perspective of multiple stakeholders and provides recommendations for future trials.

Which factors matter the most? Revisiting and dissecting antibody therapeutic doses

Factors such as antibody clearance and target affinity can influence antibodies' effective doses for specific indications. However, these factors vary considerably across antibody classes, precluding direct and quantitative comparisons. Here, we apply a dimensionless metric, the therapeutic exposure affinity ratio (TEAR), which normalizes the therapeutic doses by antibody bioavailability, systemic clearance and target-binding property to enable direct and quantitative comparisons of therapeutic doses. Using TEAR, we revisited and dissected the doses of up to 60 approved antibodies. We failed to detect a significant influence of target baselines, turnovers or anatomical locations on antibody therapeutic doses, challenging the traditional perceptions. We highlight the importance of antibodies' modes of action for therapeutic doses and dose selections; antibodies that work through neutralizing soluble targets show higher TEARs than those working through other mechanisms. Overall, our analysis provides insights into the factors that influence antibody doses, and the factors that are crucial for antibodies' pharmacological effects.

Addressing Recent Failures in Immuno-Oncology Trials to Guide Novel Immunotherapeutic Treatment Strategies

The incorporation of checkpoint inhibitors into the treatment armamentarium of oncologic therapeutics has revolutionized the course of disease in many cancers. This has spurred the evaluation of other novel immunotherapy agents in clinical trials with varying levels of success. This review explores possible explanations for differences in efficacy in clinical outcomes among currently US FDA-approved immunotherapy agents, lessons learned from clinical trial failures of investigational immunotherapies, and methods to improve success in the future. An inherent challenge of early phase immunotherapy trials is identifying the maximum tolerated dose and improving understanding of the pharmacokinetics/pharmacodynamics of immunotherapies as they exert their effects indirectly via T cells rather than directly via dose-dependent cytotoxic activity. The wide heterogeneity of the immune system among patients and within an individual patient over time largely affects the results of optimal dose- and toxicity-finding studies as well as the effectiveness of immunotherapy. Therefore, optimization of phase I/II study design is crucial for clinical trial success. These differences may also help elucidate the lack of immunotherapy benefit in certain disease subtypes despite the presence of specific biomarkers. Broader investigation of the tumor microenvironment and its dynamic nature can help in the identification of alternative pathways for targeted therapies, mechanisms of immunotherapy resistance, and more correlative biomarkers. Finally, manipulation of the tumor microenvironment via a single agonist or antagonist may be inadequate, so combination therapies and sequencing of agents must be further assessed while balancing cumulative toxicity risk.

Quantifying the limits of CAR T-cell delivery in mice and men

CAR (Chimeric Antigen Receptor) T cells have demonstrated clinical success for the treatment of multiple lymphomas and leukaemias, but not for various solid tumours, despite promising data from murine models. Lower effective CAR T-cell delivery rates to human solid tumours compared to haematological malignancies in humans and solid tumours in mice might partially explain these divergent outcomes. We used anatomical and physiological data for human and rodent circulatory systems to calculate the typical perfusion of healthy and tumour tissues, and estimated the upper limits of immune cell delivery rates across different organs, tumour types and species. Estimated maximum delivery rates were up to 10 000-fold greater in mice than humans yet reported CAR T-cell doses are typically only 10-100-fold lower in mice, suggesting that the effective delivery rates of CAR T cells into tumours in clinical trials are far lower than in corresponding mouse models. Estimated delivery rates were found to be consistent with published positron emission tomography data. Results suggest that higher effective human doses may be needed to drive efficacy comparable to mouse solid tumour models, and that lower doses should be tested in mice. We posit that quantitation of species and organ-specific delivery and homing of engineered T cells will be key to unlocking their potential for solid tumours.

Change-point joint model for identification of plateau of activity in early phase trials

This article presents a phase I/II trial design for targeted therapies and immunotherapies, with the objective of identifying the optimal dose (OD). We employ a joint modeling technique for discrete time-to-event toxicity data and repeated and continuous biomarker measurements. For the biomarker measurements, we implement a change point linear mixed effects skeleton model. This model can accommodate both plateauing and nonplateauing dose-activity relationships. For each new cohort of patients, we estimate the maximum tolerated dose (MTD) taking toxicity as a cumulative endpoint, over six treatment cycles. Then, we select the OD using two different criteria. The OD is a dose that is equally active to the MTD or a dose located on the beginning of the plateau of the dose-activity relationship. Joint modeling allows us to take into account informative censoring due to toxicities or lack of activity and we also consider consent withdrawal and intermittent missing responses. Model estimation relies on likelihood inference.

Considerations when treating high-grade pediatric glioma patients with immunotherapy

INTRODUCTION

Children with high-grade gliomas (pHGGs) represent a clinical population in substantial need of new therapeutic options given the inefficacy and toxicity of current standard-of-care modalities. Although immunotherapy has emerged as a promising modality, it has yet to elicit a significant survival benefit for pHGG patients. While preclinical studies address a variety of underlying challenges, translational clinical trial design and management also need to reflect the most updated progress and lessons from the field.

AREAS COVERED

The authors will focus our discussion on the design of clinical trials, the management of potential toxicities, immune monitoring, and novel biomarkers. Clinical trial design should integrate appropriate patient populations, novel, and preclinically optimized trial design, and logical treatment combinations, particularly those which synergize with standard of care modalities. However, there are caveats due to the nature of immunotherapy trials, such as patient selection bias, evidenced by the frequent exclusion of patients on high-dose corticosteroids. Robust immune-modulating effects of modern immunotherapy can have toxicities. As such, it is important to understand and manage these, especially in pHGG patients.

EXPERT OPINION

Adequate integration of these considerations should allow us to effectively gain insights on biological activity, safety, and biomarkers associated with benefits for patients.

Designing Dose-Finding Phase I Clinical Trials: Top 10 Questions That Should Be Discussed With Your Statistician

In recent years, the landscape in clinical trial development has changed to involve many molecularly targeted agents, immunotherapies, or radiotherapy, as a single agent or in combination. Given their different mechanisms of action and lengths of administration, these agents have different toxicity profiles, which has resulted in numerous challenges when applying traditional designs such as the 3 + 3 design in dose-finding clinical trials. Novel methods have been proposed to address these design challenges such as combinations of therapies or late-onset toxicities. However, their design and implementation require close collaboration between clinicians and statisticians to ensure that the appropriate design is selected to address the aims of the study and that the design assumptions are pertinent to the study drug. The goal of this paper is to provide guidelines for appropriate questions that should be considered early in the design stage to facilitate the interactions between clinical and statistical teams and to improve the design of dose-finding clinical trials for novel anticancer agents.

Optimal biological dose: a systematic review in cancer phase I clinical trials

BACKGROUND

Classical phase 1 dose-finding designs based on a single toxicity endpoint to assess the maximum tolerated dose were initially developed in the context of cytotoxic drugs. With the emergence of molecular targeted agents and immunotherapies, the concept of optimal biological dose (OBD) was subsequently introduced to account for efficacy in addition to toxicity. The objective was therefore to provide an overview of published phase 1 cancer clinical trials relying on the concept of OBD.

METHODS

We performed a systematic review through a computerized search of the MEDLINE database to identify early phase cancer clinical trials that relied on OBD. Relevant publications were selected based on a two-step process by two independent readers. Relevant information (phase, type of therapeutic agents, objectives, endpoints and dose-finding design) were collected.

RESULTS

We retrieved 37 articles. OBD was clearly mentioned as a trial objective (primary or secondary) for 22 articles and was traditionally defined as the smallest dose maximizing an efficacy criterion such as biological target: biological response, immune cells count for immunotherapies, or biological cell count for targeted therapies. Most trials considered a binary toxicity endpoint defined in terms of the proportion of patients who experienced a dose-limiting toxicity. Only two articles relied on an adaptive dose escalation design.

CONCLUSIONS

In practice, OBD should be a primary objective for the assessment of the recommended phase 2 dose (RP2D) for a targeted therapy or immunotherapy phase I cancer trial. Dose escalation designs have to be adapted accordingly to account for both efficacy and toxicity.

A review of vaccine effects on women in light of the COVID-19 pandemic

The pandemic situation triggered by the spread of COVID-19 has caused great harm worldwide. More than six million people have been infected, and more than 360,000 of them have died. This is the worst catastrophe suffered by mankind in recent history. In the face of this severe disaster, people all over the world are frightened of the prospect of facing an outbreak or an annual recurrence. However, the development of a vaccine will help control the impact of COVID-19. Women in particular have been more seriously affected by the pandemic. Since the pressure and physical load they suffer are often greater than what men endure, women are more threatened by COVID-19. Though women have a poorer quality of life and work and face worse economic conditions, they also tend to have better physiological immunity than men, which can ease the effect of COVID-19. The early development of a vaccine against COVID-19 is an important issue that must take into consideration women's better immune response to the virus along with the technique of hormone regulation. Relevant research has been conducted on female-specific vaccines in the past, and women's issues were considered during those clinical trials to ensure that complications and antibody responses were positive and effective in women. National policies should also propose good strategies for women to be vaccinated. This could improve consciousness, give women a better vaccination experience, enhance their willingness to vaccinate, and protect them from COVID-19 infection.

Clinical Trial Subinvestigator: An Emerging Role for Oncology Nurse Practitioners

Phase 1 clinical trials are essential to improving outcomes in cancer care. The investigational agents in these trials may be associated with adverse events that can contribute to symptom burden and declining performance status for trial participants. The emerging role for oncology nurse practitioners (ONPs) as subinvestigators offers a unique practice setting for advanced practice nurses. In this role, ONPs provide expert oncology care, are responsible for swift recognition and management of adverse events, and ensure adherence to the clinical trial protocol.

Novel strategies in immune checkpoint inhibitor drug development: How far are we from the paradigm shift?

The development of immune checkpoint inhibitors (ICI) represents a major milestone in immune-oncology. Over the years these agents have demonstrated efficacy in an increasing array of malignancies. Despite this success however, significant challenges remain. Novel approaches to both drug development and trial design are required to incorporate the unique pharmacokinetic and pharmacodynamic properties of ICIs. Further, it has also been established that the benefit of ICIs is limited to only a subset of patients. The molecular interactions between native immune cells and tumorigenesis and progression represent an active area of biomarker research, and elucidating the mechanisms of response and resistance is crucial to develop rational trial designs for the next wave of immune-oncology (IO) clinical trials, particularly in patients with primary and/or acquired resistance. Efforts are now being made to integrate both biological and clinical information using novel multi-omic approaches which are now being developed to further elucidate the molecular signatures associated with IO treatment response and resistance and enable rational drug development and trial design processes. As such, precision IO and the ability to deliver patient-specific choices for ICI monotherapies or combination therapies has become an increasingly tangible goal. We herein describe the current landscape in ICI drug development and discuss the challenges and future directions in this exciting and evolving era in immune-oncology.

Chimeric antigen receptor T-cell therapies: Optimising the dose

Lymphocytes such as T-cells can be genetically transduced to express a synthetic chimeric antigen receptor (CAR) that re-directs their cytotoxic activity against a tumour-expressed antigen of choice. Autologous (patient-derived) CAR T-cells have been licensed to treat certain relapsed and refractory B-cell malignancies, and numerous CAR T-cell products are in clinical development. As living gene-modified cells, CAR T-cells exhibit unique pharmacokinetics, typically proliferating within the recipient during the first 14 days after administration before contracting in number, and sometimes exhibiting long-term persistence. The relationship between CAR T-cell dose and exposure is highly variable, and may be influenced by CAR design, patient immune function at the time of T-cell harvest, phenotype of the CAR T-cell product, disease burden, lymphodepleting chemotherapy and subsequent immunomodulatory therapies. Recommended CAR T-cell doses are typically established for a specific product and indication, although for some products, stratification of dose based on disease burden may mitigate toxicity while maintaining efficacy. Re-evaluation of CAR T-cell dosing may be necessary following changes to the lymphodepleting regimen, for different disease indications, and following significant manufacturing changes, if product comparability cannot be demonstrated. Dose escalation trials have typically employed 3 + 3 designs, although this approach has limitations, and alternative phase I trial designs may facilitate the identification of CAR T-cell doses that strike an optimal balance of safety, efficacy and manufacturing feasibility.

Master protocols in immuno-oncology: do novel drugs deserve novel designs?

The rapid rise to fame of immuno-oncology (IO) drugs has generated unprecedented interest in the industry, patients and doctors, and has had a major impact in the treatment of most cancers. An interesting aspect in the clinical development of many IO agents is the increasing reliance on nonconventional trial design, including the so-called 'master protocols' that incorporate various adaptive features and often heavily rely on biomarkers to select patient populations most likely to benefit. These novel designs promise to maximize the clinical benefit that can be reaped from clinical research, but are not without costs. Their acceptance as solid evidence basis for use outside of the research context requires profound cultural changes by multiple stakeholders, including regulatory bodies, decision-makers, statisticians, researchers, doctors and, most importantly, patients. Here we review characteristics of recent and ongoing trials testing IO drugs with unconventional design, and we highlight trends and critical aspects.

Tailoring early-phase clinical trial design to address multiple research objectives

INTRODUCTION

In contemporary oncology drug development, implementation of novel early-phase designs with the ability to address multiple research objectives is needed to better refine regimens. This paper describes an adaptive design strategy for identifying a range of optimal regimens based on two endpoints within multiple cohorts. The proposed design was developed to address objectives in an early-phase trial of cancer vaccines in combination with agonistic antibodies to CD40 and CD27.

MATERIALS AND METHODS

We describe a model-based design strategy that was developed for a trial evaluating the safety and immunogenicity of vaccination with (1) peptides plus CD40 antibody and TLR3 ligand, (2) systemic administration of an agonistic CD27 antibody, and (3) to assess immune response from (1) and (2) compared to optimal controls in participants with stage IIB-IV melanoma.

RESULTS AND CONCLUSIONS

The proposed design is a practical adaptive method for use with combined immunotherapy regimens with multiple objectives within multiple cohorts of interest. Further advances in the effectiveness of cancer immunotherapies will require new approaches that include redefining optimal strategies to take multiple regimens forward into later phases, incorporating additional endpoints in the dose selection process and testing drug combination therapies to improve efficacy and reduce toxicity. Our goal is to facilitate the acceptance and application of more novel designs in contemporary early development trials.

Challenges and shifting paradigms in clinical trials in oncology: the case for immunological and targeted therapies

The advent of immunotherapy has undoubtedly changed the current standard for cancer treatment. Immunotherapy offers the possibility of achieving excellent results—a new alternative for patients with advanced-stage or relapsed disease. Nowadays, the progress made in tumour biology has led to multiple advances in clinical and translational cancer research. Many oncogenic pathways responsible for tumour growth and metastases have been described and, consequently, multiple new cancer therapeutic agents have been developed and are under current investigation. Due to this rapid increase in knowledge and pharmaceutical development, traditional clinical trials designs have encountered major limitations. The pharmacological differences (in toxicity profiles and effectiveness patterns) between immunotherapy and chemotherapy have caused traditional clinical trials to evolve in order to meet this emerging need. This review focuses on the different options pertaining to clinical trial design that have arisen in the field of immuno-oncology, as well as the challenges of accurately interpreting traditional survival analyses within this novel area of cancer medicine.

Quantitative Mechanistic Modeling in Support of Pharmacological Therapeutics Development in Immuno-Oncology

Following the approval, in recent years, of the first immune checkpoint inhibitor, there has been an explosion in the development of immuno-modulating pharmacological modalities for the treatment of various cancers. From the discovery phase to late-stage clinical testing and regulatory approval, challenges in the development of immuno-oncology (IO) drugs are multi-fold and complex. In the preclinical setting, the multiplicity of potential drug targets around immune checkpoints, the growing list of immuno-modulatory molecular and cellular forces in the tumor microenvironment-with additional opportunities for IO drug targets, the emergence of exploratory biomarkers, and the unleashed potential of modality combinations all have necessitated the development of quantitative, mechanistically-oriented systems models which incorporate key biology and patho-physiology aspects of immuno-oncology and the pharmacokinetics of IO-modulating agents. In the clinical setting, the qualification of surrogate biomarkers predictive of IO treatment efficacy or outcome, and the corresponding optimization of IO trial design have become major challenges. This mini-review focuses on the evolution and state-of-the-art of quantitative systems models describing the tumor vs. immune system interplay, and their merging with quantitative pharmacology models of IO-modulating agents, as companion tools to support the addressing of these challenges.

Emerging strategies in cancer therapy combining chemotherapy with immunotherapy

Cancer immunotherapy holds great potential to battle cancer by exerting a durable immunity effect. However, this process might be limited by various constraints existing in the tumor microenvironment (TME), such as the lack of available neoantigen, insufficient T cells from the naive repertoire, or immunosuppressive networks in which immunogenic tissue is protected from immune attacks. Certain chemotherapeutic drugs could elicit immune-potentiating effects by either inducing immunogenicity or relieving tumor-induced immunosuppression. Some also leave tumors directly susceptible to cytotoxic T cell attacks. Mounting evidence accumulated from preclinical and clinical studies suggests that these two treatment modalities might be mutually reinforcing as an effective "chemo-immunotherapy" strategy. Herein, we reviewed the latest advances in cancer immunotherapy and related mechanisms involved in chemotherapeutic-mediated immune activation. The emerging combination strategies and synergistic effects in response to chemo-immunotherapy are highlighted. We also discuss the challenges and critical considerations in its future development.