I-SPY 2--A Glimpse of the Future of Phase 2 Drug Development?

A platform trial approach to proof-of-concept (POC) studies in autism spectrum disorder: Autism spectrum POC initiative (ASPI)

Background

Over the past decade, autism spectrum disorder (ASD) research has blossomed, and multiple clinical trials have tested potential interventions, with varying results and no clear demonstration of efficacy. Lack of clarity concerning appropriate biological mechanisms to target and lack of sensitive, objective tools to identify subgroups and measure symptom changes have hampered the efforts to develop treatments. A platform trial for proof-of-concept studies in ASD could help address these issues. A major goal of a platform trial is to find the best treatment in the most expeditious manner, by simultaneously investigating multiple treatments, using specialized statistical tools for allocation and analysis. We describe the setup of a platform trial and perform simulations to evaluate the operating characteristics under several scenarios. We use the Autism Behavior Inventory (ABI), a psychometrically validated web-based rating scale to measure the change in ASD core and associated symptoms.

Methods

Detailed description of the setup, conduct, and decision-making rules of a platform trial are explained. Simulations of a virtual platform trial for several scenarios are performed to compare operating characteristics. The success and futility criteria for treatments are based on a Bayesian posterior probability model.

Results

Overall, simulation results show the potential gain in terms of statistical properties especially for improved decision-making ability, while careful planning is needed due to the complexities of a platform trial.

Conclusions

Autism research, shaped particularly by its heterogeneity, may benefit from the platform trial approach for POC clinical studies.

A Bayesian adaptive design for clinical trials of rare efficacy outcomes with multiple definitions

INTRODUCTION

Bayesian adaptive designs for clinical trials have gained popularity in the recent years due to the flexibility and efficiency that they offer. We consider the scenario where the outcome of interest comprises events with relatively low risk of occurrence and different case definitions resulting in varying control group risk assumptions. This is a scenario that occurs frequently for infectious diseases in global health research.

METHODS

We propose a Bayesian adaptive design that incorporates different case definitions of the outcome of interest that vary in stringency. A set of stopping rules are proposed where superiority and futility may be concluded with respect to different outcome definitions and therefore maintain a realistic probability of stopping in trials with low event rates. Through a simulation study, a variety of stopping rules and design configurations are compared.

RESULTS

The simulation results are provided in an interactive web application that allows the user to explore and compare the design operating characteristics for a variety of assumptions and design parameters with respect to different outcome definitions. The results for select simulation scenarios are provided in the article.

DISCUSSION

Bayesian adaptive designs offer the potential for maximizing the information learned from the data collected through clinical trials. The proposed design enables monitoring and utilizing multiple composite outcomes based on rare events to optimize the trial design operating characteristics.

New Insights into Clinical and Mechanistic Heterogeneity of the Acute Respiratory Distress Syndrome: Summary of the Aspen Lung Conference 2021

Clinical and molecular heterogeneity are common features of human disease. Understanding the basis for heterogeneity has led to major advances in therapy for many cancers and pulmonary diseases such as cystic fibrosis and asthma. Although heterogeneity of risk factors, disease severity, and outcomes in survivors are common features of the acute respiratory distress syndrome (ARDS), many challenges exist in understanding the clinical and molecular basis for disease heterogeneity and using heterogeneity to tailor therapy for individual patients. This report summarizes the proceedings of the 2021 Aspen Lung Conference, which was organized to review key issues related to understanding clinical and molecular heterogeneity in ARDS. The goals were to review new information about ARDS phenotypes, to explore multicellular and multisystem mechanisms responsible for heterogeneity, and to review how best to account for clinical and molecular heterogeneity in clinical trial design and assessment of outcomes. The report concludes with recommendations for future research to understand the clinical and basic mechanisms underlying heterogeneity in ARDS to advance the development of new treatments for this life-threatening critical illness.

Development and Validation of a Nomogram to Predict the Probability of Breast Cancer Pathologic Complete Response after Neoadjuvant Chemotherapy: A Retrospective Cohort Study

Background

The methods used to predict the pathologic complete response (pCR) after neoadjuvant chemotherapy (NAC) have some limitations. In this study, we aimed to develop a nomogram to predict breast cancer pCR after NAC based on convenient and economical multi-system hematological indicators and clinical characteristics.

Materials and Methods

Patients diagnosed from July 2017 to July 2019 served as the training group (N = 114), and patients diagnosed in from July 2019 to July 2021 served as the validation group (N = 102). A nomogram was developed according to eight indices, including body mass index, platelet distribution width, monocyte count, albumin, cystatin C, phosphorus, hemoglobin, and D-dimer, which were determined by multivariate logistic regression. Internal and external validation curves are used to calibrate the nomogram.

Results

The area under the receiver operating characteristic curve was 0.942 (95% confidence interval 0.892–0.992), and the concordance index indicated that the nomogram had good discrimination. The Hosmer–Lemeshow test and calibration curve showed that the model was well-calibrated.

Conclusion

The nomogram developed in this study can help clinicians accurately predict the possibility of patients achieving the pCR after NAC. This information can be used to decide the most effective treatment strategies for patients.

The Evolving Role of Neoadjuvant Therapy for Operable Breast Cancer

The role of neoadjuvant therapy (NAT) for localized breast cancer has evolved tremendously over the past several years. Currently, NAT is the preferred option for high-risk early triple-negative (TN) and HER2-positive (HER2+) breast cancers and is indicated for some estrogen receptor-positive (ER+) breast cancers. In addition to traditional absolute indications for NAT, relative indications such as the assessment of outcomes at the time of surgery and guidance of treatment escalation and de-escalation have greatly evolved in recent years. Pathologic complete response (pCR) and the Residual Cancer Burden (RCB) index are highly prognostic for disease recurrence and survival, mainly in patients with TN or HER2+ disease. Furthermore, post-NAT escalation strategies have been shown to improve long-term outcomes of patients who do not achieve pCR. Additionally, by allowing the direct assessment of drug effect on the tumor, the neoadjuvant setting has become an attractive setting for the exploration of novel agents and the identification of predictive biomarkers. Neoadjuvant trial design has also evolved, using adaptive treatment approaches that enable treatment de-escalation or escalation based on response. However, despite multiple practice-changing neoadjuvant trials and the addition of various new agents to the neoadjuvant setting for early breast cancer, many key questions remain. For example, patient selection for neoadjuvant immunotherapy in TN breast cancer, de-escalation methods in HER2+ breast cancer, and the use of gene expression profiles to guide NAT recommendations in ER+ breast cancer. This article reviews the current approach for NAT in localized breast cancer as well as evolving NAT strategies, the key remaining challenges, and the ongoing work in the field.

Ganitumab and metformin plus standard neoadjuvant therapy in stage 2/3 breast cancer

I-SPY2 is an adaptively randomized phase 2 clinical trial evaluating novel agents in combination with standard-of-care paclitaxel followed by doxorubicin and cyclophosphamide in the neoadjuvant treatment of breast cancer. Ganitumab is a monoclonal antibody designed to bind and inhibit function of the type I insulin-like growth factor receptor (IGF-1R). Ganitumab was tested in combination with metformin and paclitaxel (PGM) followed by AC compared to standard-of-care alone. While pathologic complete response (pCR) rates were numerically higher in the PGM treatment arm for hormone receptor-negative, HER2-negative breast cancer (32% versus 21%), this small increase did not meet I-SPY's prespecified threshold for graduation. PGM was associated with increased hyperglycemia and elevated hemoglobin A1c (HbA1c), despite the use of metformin in combination with ganitumab. We evaluated several putative predictive biomarkers of ganitumab response (e.g., IGF-1 ligand score, IGF-1R signature, IGFBP5 expression, baseline HbA1c). None were specific predictors of response to PGM, although several signatures were associated with pCR in both arms. Any further development of anti-IGF-1R therapy will require better control of anti-IGF-1R drug-induced hyperglycemia and the development of more predictive biomarkers.

PRoBE the cloud toolkit: finding the best biomarkers of drug response within a breast cancer clinical trial

Objectives

In this paper, we discuss leveraging cloud-based platforms to collect, visualize, analyze, and share data in the context of a clinical trial. Our cloud-based infrastructure, Patient Repository of Biomolecular Entities (PRoBE), has given us the opportunity for uniform data structure, more efficient analysis of valuable data, and increased collaboration between researchers.

Materials and Methods

We utilize a multi-cloud platform to manage and analyze data generated from the clinical Investigation of Serial Studies to Predict Your Therapeutic Response with Imaging And moLecular Analysis 2 (I-SPY 2 TRIAL). A collaboration with the Institute for Systems Biology Cancer Gateway in the Cloud has additionally given us access to public genomic databases. Applications to I-SPY 2 data have been built using R Shiny, while leveraging Google's BigQuery tables and SQL commands for data mining.

Results

We highlight the implementation of PRoBE in several unique case studies including prediction of biomarkers associated with clinical response, access to the Pan-Cancer Atlas, and integrating pathology images within the cloud. Our data integration pipelines, documentation, and all codebase will be placed in a Github repository.

Discussion and conclusion

We are hoping to develop risk stratification diagnostics by integrating additional molecular, magnetic resonance imaging, and pathology markers into PRoBE to better predict drug response. A robust cloud infrastructure and tool set can help integrate these large datasets to make valuable predictions of response to multiple agents. For that reason, we are continuously improving PRoBE to advance the way data is stored, accessed, and analyzed in the I-SPY 2 clinical trial.

A nomogram based on clinicopathological features and serological indicators predicting breast pathologic complete response of neoadjuvant chemotherapy in breast cancer

A single tumor marker is not enough to predict the breast pathologic complete response (bpCR) after neoadjuvant chemotherapy (NAC) in breast cancer patients. We aimed to establish a nomogram based on multiple clinicopathological features and routine serological indicators to predict bpCR after NAC in breast cancer patients. Data on clinical factors and laboratory indices of 130 breast cancer patients who underwent NAC and surgery in First Affiliated Hospital of Xi'an Jiaotong University from July 2017 to July 2019 were collected. Multivariable logistic regression analysis identified 11 independent indicators: body mass index, carbohydrate antigen 125, total protein, blood urea nitrogen, cystatin C, serum potassium, serum phosphorus, platelet distribution width, activated partial thromboplastin time, thrombin time, and hepatitis B surface antibodies. The nomogram was established based on these indicators. The 1000 bootstrap resampling internal verification calibration curve and the GiViTI calibration belt showed that the model was well calibrated. The Brier score of 0.095 indicated that the nomogram had a high accuracy. The area under the curve (AUC) of receiver operating characteristic (ROC) curve was 0.941 (95% confidence interval: 0.900-0.982) showed good discrimination of the model. In conclusion, this nomogram showed high accuracy and specificity and did not increase the economic burden of patients, thereby having a high clinical application value.

The effects of releasing early results from ongoing clinical trials

Most trials do not release interim summaries on efficacy and toxicity of the experimental treatments being tested, with this information only released to the public after the trial has ended. While early release of clinical trial data to physicians and patients can inform enrollment decision making, it may also affect key operating characteristics of the trial, statistical validity and trial duration. We investigate the public release of early efficacy and toxicity results, during ongoing clinical studies, to better inform patients about their enrollment options. We use simulation models of phase II glioblastoma (GBM) clinical trials in which early efficacy and toxicity estimates are periodically released accordingly to a pre-specified protocol. Patients can use the reported interim efficacy and toxicity information, with the support of physicians, to decide which trial to enroll in. We describe potential effects on various operating characteristics, including the study duration, selection bias and power.

Future Approaches to Precision Oncology-Based Clinical Trials

The last 2 decades have seen a rapid advance of the precision oncology paradigm-from its early singular successes to becoming the prevailing model of cancer therapy. As the treatment of cancer moves away from traditional chemotherapy, so too will oncology clinical trials have to move away from the traditional model of phase I to phase III progression of drug development. Achieving this goal of individualized care will involve a concerted effort by the entire cancer care community to fundamentally change the design and implementation of oncology clinical trials. We envision that the next 2 decades will be a period of evolution in precision oncology clinical trials through scientific and technologic advances, transformation of clinical trial infrastructure, and changes in the kind of evidence required for regulatory approval.

The world is waiting, use sequential analysis and get us the evidence-based treatment we need for COVID-19

In spite of the relatively high morbidity and mortality, there is no approved medication yet for COVID-19. There are more than 200 ongoing trials on different drugs or vaccines, but new medications may take until 2021 to develop. Defining the optimal number of patients to be included in a study is a considerable challenge in these interventional researches. Ethical considerations prompt researchers to minimize the number of patients included in a trial. This gains particular importance when the disease is rare or lethal which is particularly so in the case of COVID-19. It is of paramount importance to explore some of the available tools that could help accelerate the adoption of any or some of the many proposed modalities for the treatment of diseases. These tools should be effective, yet efficient, for rapid testing of such treatments. Sequential analysis has not been frequently used in many clinical trials where it should have been used. None of the authors in published literature, as far as we know, used sequential analysis techniques to test potential drugs for COVID-19. In addition to its usefulness when the results of new forms of treatment are quickly needed, other important benefit of sequential analysis includes the ability to reach a similar conclusion about the utility of a new drug without unduly exposing more patients to the side effect of the old drug, in particularly, for the treatment of a rare disease.

Model Informed Drug Development: Novel Oncology Agents are Lost in Translation

Excitement around and investment in oncology drug development are at unprecedented levels. To maximize the health impact and productivity of this research and development investment, quantitative modeling should impact key decisions in early clinical oncology including Go/No-Go decisions based on early clinical data, and dose selection for late stage studies.See related article by Bottino et al., p. 6633.

Comparison of methods for control allocation in multiple arm studies using response adaptive randomization

BACKGROUND/AIMS

Response adaptive randomization has many polarizing properties in two-arm settings comparing control to a single treatment. The generalization of these features to the multiple arm setting has been less explored, and existing comparisons in the literature reach disparate conclusions. We investigate several generalizations of two-arm response adaptive randomization methods relating to control allocation in multiple arm trials, exploring how critiques of response adaptive randomization generalize to the multiple arm setting.

METHODS

We perform a simulation study to investigate multiple control allocation schemes within response adaptive randomization, comparing the designs on metrics such as power, arm selection, mean square error, and the treatment of patients within the trial.

RESULTS

The results indicate that the generalization of two-arm response adaptive randomization concerns is variable and depends on the form of control allocation employed. The concerns are amplified when control allocation may be reduced over the course of the trial but are mitigated in the methods considered when control allocation is maintained or increased during the trial. In our chosen example, we find minimal advantage to increasing, as opposed to maintaining, control allocation; however, this result reflects an extremely limited exploration of methods for increasing control allocation.

CONCLUSION

Selection of control allocation in multiple arm response adaptive randomization has a large effect on the performance of the design. Some disparate comparisons of response adaptive randomization to alternative paradigms may be partially explained by these results. In future comparisons, control allocation for multiple arm response adaptive randomization should be chosen to keep in mind the appropriate match between control allocation in response adaptive randomization and the metric or metrics of interest.

Progress in adjuvant systemic therapy for breast cancer

The prognosis of patients with early stage breast cancer has greatly improved in the past three decades. Following the first adjuvant endocrine therapy and chemotherapy trials, continuous improvements of clinical outcomes have been achieved through intense therapeutic escalation, albeit with increased health-care costs and treatment-related toxicities. In contrast to the advances achieved in surgery or radiotherapy, the identification of the patient subgroups that will derive clinical benefit from therapeutic escalation has proved to be a daunting process hindered by a lack of collaboration between scientific groups and by the pace of drug development. In the past few decades, initiatives towards de-escalation of systemic adjuvant treatment have achieved success. Herein, we summarize attempts to escalate and de-escalate adjuvant systemic treatment for patients with breast cancer and argue that new, creative trial designs focused on patients' actual needs rather than on maximizing drug market size are needed. Ultimately, the adoption of effective treatments that do not needlessly expose patients and health-care systems to harm demands extensive international collaboration between academic groups, governments, and pharmaceutical companies.

Learning health systems, clinical equipoise and the ethics of response adaptive randomisation

To give substance to the rhetoric of 'learning health systems', a variety of novel trial designs are being explored to more seamlessly integrate research with medical practice, reduce study duration and reduce the number of participants allocated to ineffective interventions. Many of these designs rely on response adaptive randomisation (RAR). However, critics charge that RAR is unethical on the grounds that it violates the principle of equipoise. In this paper, I reconstruct critiques of RAR as holding that it is inconsistent with five important ethical principles. I then argue that these criticisms rest on a faulty view of equipoise encouraged by the idea that a RAR study models the beliefs of a single rational agent about the relative merits of the interventions being studied. I outline a view in which RAR models an idealised health system in which diverse communities of fully informed experts shrink or grow as their constituent members update their expert opinions in light of reliable medical evidence. I show how a proper understanding of clinical equipoise can reconcile this conception of RAR with these five ethical principles. This analysis removes an in-principle objection to RAR and sheds important light on the relationship between clinical equipoise and transient diversity in the scientific community.

Sequential analysis in neonatal research-systematic review

As more new drugs are discovered, traditional designs come at their limits. Ten years after the adoption of the European Paediatric Regulation, we performed a systematic review on the US National Library of Medicine and Excerpta Medica database of sequential trials involving newborns. Out of 326 identified scientific reports, 21 trials were included. They enrolled 2832 patients, of whom 2099 were analyzed: the median number of neonates included per trial was 48 (IQR 22-87), median gestational age was 28.7 (IQR 27.9-30.9) weeks. Eighteen trials used sequential techniques to determine sample size, while 3 used continual reassessment methods for dose-finding. In 16 studies reporting sufficient data, the sequential design allowed to non-significantly reduce the number of enrolled neonates by a median of 24 (31%) patients (IQR - 4.75 to 136.5, p = 0.0674) with respect to a traditional trial. When the number of neonates finally included in the analysis was considered, the difference became significant: 35 (57%) patients (IQR 10 to 136.5, p = 0.0033).

CONCLUSION

Sequential trial designs have not been frequently used in Neonatology. They might potentially be able to reduce the number of patients in drug trials, although this is not always the case. What is known: • In evaluating rare diseases in fragile populations, traditional designs come at their limits. About 20% of pediatric trials are discontinued, mainly because of recruitment problems. What is new: • Sequential trials involving newborns were infrequently used and only a few (n = 21) are available for analysis. • The sequential design allowed to non-significantly reduce the number of enrolled neonates by a median of 24 (31%) patients (IQR - 4.75 to 136.5, p = 0.0674).

Designing Clinical Trials That Accept New Arms: An Example in Metastatic Breast Cancer

Purpose

The majority of randomized oncology trials are two-arm studies that test the efficacy of new therapies against a standard of care, thereby assigning a large proportion of patients to nonexperimental therapies. In contrast, multiarm studies efficiently share a common control arm while evaluating multiple experimental therapies. A major bottleneck for traditional multiarm trials is the requirement that all therapies—often drugs from different companies—have to be available at the same time when the trial starts. We evaluate the potential gains of a platform design—the rolling-arms design—that adds and removes arms on a rolling basis.

Methods

We define the rolling-arms design with the goal of minimizing the complexity of random assignment and data analyses of a platform trial. We then evaluate its potential advantages in hormone receptor–positive, human epidermal growth factor receptor 2–negative advanced breast cancer. Multiple pharmaceutical companies currently test CDK4/6 inhibitors in combination with letrozole in independent two-arm trials. We conducted a simulation study to quantify the reduction in sample size, number of patients treated with the standard of care, and the average time to treatment discovery if these therapies had been tested in a rolling-arms trial.

Results

A rolling-arms platform design with two to five experimental treatments can reduce the overall sample size requirement by up to 30% compared with standard two-arm studies. It assigns up to 60% fewer patients to the control arm compared with five independent trials that test distinct treatments. Moreover, under realistic scenarios, effective experimental treatments are discovered up to 15 months earlier compared with separate two-arm trials.

Conclusion

The rolling-arms platform design is applicable to a broad variety of diseases, and under realistic scenarios, it is substantially more efficient than standard two-arm randomized trials.

Re-inventing drug development: A case study of the I-SPY 2 breast cancer clinical trials program

BACKGROUND

In this case study, we profile the I-SPY 2 TRIAL (Investigation of Serial studies to Predict Your Therapeutic Response with Imaging And molecular anaLysis 2), a unique breast cancer clinical trial led by researchers at 20 leading cancer centers across the US, and examine its potential to serve as a model of drug development for other disease areas. This multicenter collaboration launched in 2010 to reengineer the drug development process to be more efficient and patient-centered.

METHODS

We conduct several interviews with the I-SPY leadership as well as a literature review of relevant publications to assess the I-SPY 2 initiative.

RESULTS

To date, six drugs have graduated from I-SPY 2, identified as excellent candidates for phase 3 trials in their corresponding tumor subtype, and several others have been or are still being evaluated. These trials are also more efficient, typically involving fewer subjects and reaching conclusions more quickly, and candidates have more than twice the predicted likelihood of success in a smaller phase 3 setting compared to traditional trials.

CONCLUSIONS

We observe that I-SPY 2 possesses several novel features that could be used as a template for more efficient and cost effective drug development, namely its adaptive trial design; precompetitive network of stakeholders; and flexible infrastructure to accommodate innovation.