All-comers versus enrichment design strategy in phase II trials

Facts and Hopes on Biomarkers for Successful Early Clinical Immunotherapy Trials: Innovative Patient Enrichment Strategies

Despite the clinical validation and unequivocal benefit to patients, the development of cancer immunotherapies is facing some key challenges and the attrition rate in early phases of development remains high. Identifying the appropriate patient population that would benefit most from the drug is on the critical path for successful clinical development. We believe that a systematic implementation of patient enrichment strategies early in the drug development process and trial design, is the basis for an innovative, more efficient, and leaner clinical development to achieve earlier a clear proof of concept or proof of failure. In this position article, we will describe and propose key considerations for the implementation of patient enrichment strategies as an opportunity to provide decision-enabling data earlier in the drug development process. We introduce an innovative multidimensional tool for immuno-oncology drug development that focuses on facilitating the identification and prioritization of enrichment-relevant biomarkers, based on the drug mechanism of action. To illustrate its utility, we discuss patient enrichment examples and use a case in the field of cancer immunotherapy, together with technical and regulatory considerations. Overall, we propose to implement fit for purpose enrichment strategies for all investigational drugs as early as possible in the development process. We believe that this will increase the success rate of immuno-oncology clinical trials, and eventually bring new and better medicines to patients faster.

Combining explainable machine learning, demographic and multi-omic data to inform precision medicine strategies for inflammatory bowel disease

Inflammatory bowel diseases (IBDs), including ulcerative colitis and Crohn’s disease, affect several million individuals worldwide. These diseases are heterogeneous at the clinical, immunological and genetic levels and result from complex host and environmental interactions. Investigating drug efficacy for IBD can improve our understanding of why treatment response can vary between patients. We propose an explainable machine learning (ML) approach that combines bioinformatics and domain insight, to integrate multi-modal data and predict inter-patient variation in drug response. Using explanation of our models, we interpret the ML models’ predictions to infer unique combinations of important features associated with pharmacological responses obtained during preclinical testing of drug candidates in ex vivo patient-derived fresh tissues. Our inferred multi-modal features that are predictive of drug efficacy include multi-omic data (genomic and transcriptomic), demographic, medicinal and pharmacological data. Our aim is to understand variation in patient responses before a drug candidate moves forward to clinical trials. As a pharmacological measure of drug efficacy, we measured the reduction in the release of the inflammatory cytokine TNFα from the fresh IBD tissues in the presence/absence of test drugs. We initially explored the effects of a mitogen-activated protein kinase (MAPK) inhibitor; however, we later showed our approach can be applied to other targets, test drugs or mechanisms of interest. Our best model predicted TNFα levels from demographic, medicinal and genomic features with an error of only 4.98% on unseen patients. We incorporated transcriptomic data to validate insights from genomic features. Our results showed variations in drug effectiveness (measured by ex vivo assays) between patients that differed in gender, age or condition and linked new genetic polymorphisms to patient response variation to the anti-inflammatory treatment BIRB796 (Doramapimod). Our approach models IBD drug response while also identifying its most predictive features as part of a transparent ML precision medicine strategy.

Randomised Phase 1 clinical trials in oncology

The aims of Phase 1 trials in oncology have broadened considerably from simply demonstrating that the agent/regimen of interest is well tolerated in a relatively heterogeneous patient population to addressing multiple objectives under the heading of early-phase trials and, if possible, obtaining reliable evidence regarding clinical activity to lead to drug approvals via the Accelerated Approval approach or Breakthrough Therapy designation in cases where the tumours are rare, prognosis is poor or where there might be an unmet therapeutic need. Constructing a Phase 1 design that can address multiple objectives within the context of a single trial is not simple. Randomisation can play an important role, but carrying out such randomisation according to the principles of equipoise is a significant challenge in the Phase 1 setting. If the emerging data are not sufficient to definitively address the aims early on, then a proper design can reduce biases, enhance interpretability, and maximise information so that the Phase 1 data can be more compelling. This article outlines objectives and design considerations that need to be adhered to in order to respect ethical and scientific principles required for research in human subjects in early phase clinical trials.

Application of pharmacogenomics and bioinformatics to exemplify the utility of human ex vivo organoculture models in the field of precision medicine

Here we describe a collaboration between industry, the National Health Service (NHS) and academia that sought to demonstrate how early understanding of both pharmacology and genomics can improve strategies for the development of precision medicines. Diseased tissue ethically acquired from patients suffering from chronic obstructive pulmonary disease (COPD), was used to investigate inter-patient variability in drug efficacy using ex vivo organocultures of fresh lung tissue as the test system. The reduction in inflammatory cytokines in the presence of various test drugs was used as the measure of drug efficacy and the individual patient responses were then matched against genotype and microRNA profiles in an attempt to identify unique predictors of drug responsiveness. Our findings suggest that genetic variation in CYP2E1 and SMAD3 genes may partly explain the observed variation in drug response.

Bayesian adaptive basket trial design using model averaging

In this article, we develop a Bayesian adaptive design methodology for oncology basket trials with binary endpoints using a Bayesian model averaging framework. Most existing methods seek to borrow information based on the degree of homogeneity of estimated response rates across all baskets. In reality, an investigational product may only demonstrate activity for a subset of baskets, and the degree of activity may vary across the subset. A key benefit of our Bayesian model averaging approach is that it explicitly accounts for the possibility that any subset of baskets may have similar activity and that some may not. Our proposed approach performs inference on the basket-specific response rates by averaging over the complete model space for the response rates, which can include thousands of models. We present results that demonstrate that this computationally feasible Bayesian approach performs favorably compared to existing state-of-the-art approaches, even when held to stringent requirements regarding false positive rates.

Critical Review of Umbrella, Basket, and Platform Designs for Oncology Clinical Trials

The successful development of new drugs with a companion diagnostic based on genomic alteration of an oncogene has led to rethinking of all phases on clinical development of cancer drugs. We critically review some of the new clinical trial designs for biomarker-based cancer drug development. We try to clarify the objectives of the new designs and examine completed trials using these designs to evaluate what has been learned about these designs.

Exposure-response evaluations of venetoclax efficacy and safety in patients with non-Hodgkin lymphoma

Exposure-response analyses were performed for a venetoclax monotherapy study in 106 patients with varying subtypes of non-Hodgkin lymphoma (NHL) (NCT01328626). Logistic regression, time-to-event, and progression-free survival (PFS) analyses were used to evaluate the relationship between venetoclax exposure, NHL subtype and response, PFS, or occurrence of serious adverse events. Trends for small increases in the probability of response with increasing venetoclax exposures were identified, and became more evident when assessed by NHL subtype. Trends in exposure-PFS were shown for the mantle cell lymphoma (MCL) subtype, but not other subtypes. There was no increase in the probability of experiencing a serious adverse event with increasing exposure. Overall, the results indicate that venetoclax doses of 800-1200 mg as a single agent may be appropriate to maximize efficacy in MCL, follicular lymphoma, and diffuse large B-cell lymphoma subtypes with no expected negative impact on safety.

Biomarker-Guided Non-Adaptive Trial Designs in Phase II and Phase III: A Methodological Review

Biomarker-guided treatment is a rapidly developing area of medicine, where treatment choice is personalised according to one or more of an individual’s biomarker measurements. A number of biomarker-guided trial designs have been proposed in the past decade, including both adaptive and non-adaptive trial designs which test the effectiveness of a biomarker-guided approach to treatment with the aim of improving patient health. A better understanding of them is needed as challenges occur both in terms of trial design and analysis. We have undertaken a comprehensive literature review based on an in-depth search strategy with a view to providing the research community with clarity in definition, methodology and terminology of the various biomarker-guided trial designs (both adaptive and non-adaptive designs) from a total of 211 included papers. In the present paper, we focus on non-adaptive biomarker-guided trial designs for which we have identified five distinct main types mentioned in 100 papers. We have graphically displayed each non-adaptive trial design and provided an in-depth overview of their key characteristics. Substantial variability has been observed in terms of how trial designs are described and particularly in the terminology used by different authors. Our comprehensive review provides guidance for those designing biomarker-guided trials.

Strategies to design clinical studies to identify predictive biomarkers in cancer research

The discovery of reliable biomarkers to predict efficacy and toxicity of anticancer drugs remains one of the key challenges in cancer research. Despite its relevance, no efficient study designs to identify promising candidate biomarkers have been established. This has led to the proliferation of a myriad of exploratory studies using dissimilar strategies, most of which fail to identify any promising targets and are seldom validated. The lack of a proper methodology also determines that many anti-cancer drugs are developed below their potential, due to failure to identify predictive biomarkers. While some drugs will be systematically administered to many patients who will not benefit from them, leading to unnecessary toxicities and costs, others will never reach registration due to our inability to identify the specific patient population in which they are active. Despite these drawbacks, a limited number of outstanding predictive biomarkers have been successfully identified and validated, and have changed the standard practice of oncology. In this manuscript, a multidisciplinary panel reviews how those key biomarkers were identified and, based on those experiences, proposes a methodological framework-the DESIGN guidelines-to standardize the clinical design of biomarker identification studies and to develop future research in this pivotal field.

Methods for identification and confirmation of targeted subgroups in clinical trials: A systematic review

Important objectives in the development of stratified medicines include the identification and confirmation of subgroups of patients with a beneficial treatment effect and a positive benefit-risk balance. We report the results of a literature review on methodological approaches to the design and analysis of clinical trials investigating a potential heterogeneity of treatment effects across subgroups. The identified approaches are classified based on certain characteristics of the proposed trial designs and analysis methods. We distinguish between exploratory and confirmatory subgroup analysis, frequentist, Bayesian and decision-theoretic approaches and, last, fixed-sample, group-sequential, and adaptive designs and illustrate the available trial designs and analysis strategies with published case studies.

Improving the evidence base in palliative care to inform practice and policy: thinking outside the box

The adoption of evidence-based hierarchies and research methods from other disciplines may not completely translate to complex palliative care settings. The heterogeneity of the palliative care population, complexity of clinical presentations, and fluctuating health states present significant research challenges. The aim of this narrative review was to explore the debate about the use of current evidence-based approaches for conducting research, such as randomized controlled trials and other study designs, in palliative care, and more specifically to (1) describe key myths about palliative care research; (2) highlight substantive challenges of conducting palliative care research, using case illustrations; and (3) propose specific strategies to address some of these challenges. Myths about research in palliative care revolve around evidence hierarchies, sample heterogeneity, random assignment, participant burden, and measurement issues. Challenges arise because of the complex physical, psychological, existential, and spiritual problems faced by patients, families, and service providers. These challenges can be organized according to six general domains: patient, system/organization, context/setting, study design, research team, and ethics. A number of approaches for dealing with challenges in conducting research fall into five separate domains: study design, sampling, conceptual, statistical, and measures and outcomes. Although randomized controlled trials have their place whenever possible, alternative designs may offer more feasible research protocols that can be successfully implemented in palliative care. Therefore, this article highlights "outside the box" approaches that would benefit both clinicians and researchers in the palliative care field. Ultimately, the selection of research designs is dependent on a clearly articulated research question, which drives the research process.

Can a combined screening/treatment programme prevent premature failure of renal transplants due to chronic rejection in patients with HLA antibodies: study protocol for the multicentre randomised controlled OuTSMART trial

BACKGROUND

Renal transplantation is the best treatment for kidney failure, in terms of length and quality of life and cost-effectiveness. However, most transplants fail after 10 to 12 years, consigning patients back onto dialysis. Damage by the immune system accounts for approximately 50% of failing transplants and it is possible to identify patients at risk by screening for the presence of antibodies against human leukocyte antigens. However, it is not clear how best to treat patients with antibodies. This trial will test a combined screening and treatment protocol in renal transplant recipients.

METHODS/DESIGN

Recipients>1 year post-transplantation, aged 18 to 70 with an estimated glomerular filtration rate>30 mL/min will be randomly allocated to blinded or unblinded screening arms, before being screened for the presence of antibodies. In the unblinded arm, test results will be revealed. Those with antibodies will have biomarker-led care, consisting of a change in their anti-rejection drugs to prednisone, tacrolimus and mycophenolate mofetil. In the blinded arm, screening results will be double blinded and all recruits will remain on current therapy (standard care). In both arms, those without antibodies will be retested every 8 months for 3 years. The primary outcome is the 3-year kidney failure rate for the antibody-positive recruits, as measured by initiation of long-term dialysis or re-transplantation, predicted to be approximately 20% in the standard care group but <10% in biomarker-led care. The secondary outcomes include the rate of transplant dysfunction, incidence of infection, cancer and diabetes mellitus, an analysis of adherence with medication and a health economic analysis of the combined screening and treatment protocol. Blood samples will be collected and stored every 4 months and will form the basis of separately funded studies to identify new biomarkers associated with the outcomes.

DISCUSSION

We have evidence that the biomarker-led care regime will be effective at preventing graft dysfunction and expect this to feed through to graft survival. This trial will confirm the benefit of routine screening and lead to a greater understanding of how to keep kidney transplants working longer.

TRIAL REGISTRATION

Current Controlled Trials ISRCTN46157828.

Generation and validation of a normative, age-specific reference curve for lumbar spine trabecular bone score (TBS) in French women

Age-related changes in lumbar vertebral microarchitecture are evaluated, as assessed by trabecular bone score (TBS), in a cohort of 5,942 French women. The magnitude of TBS decline between 45 and 85 years of age is piecewise linear in the spine and averaged 14.5%. TBS decline rate increases after 65 years by 50%.

INTRODUCTION

This study aimed to evaluate age-related changes in lumbar vertebral microarchitecture, as assessed by TBS, in a cohort of French women aged 45-85 years.

METHODS

An all-comers cohort of French Caucasian women was selected from two clinical centers. Data obtained from these centers were cross-calibrated for TBS and bone mineral density (BMD). BMD and TBS were evaluated at L1-L4 and for all lumbar vertebrae combined using GE-Lunar Prodigy densitometer images. Weight, height, and body mass index (BMI) also were determined. To validate our all-comers cohort, the BMD normative data of our cohort and French Prodigy data were compared.

RESULTS

A cohort of 5,942 French women aged 45 to 85 years was created. Dual-energy X-ray absorptiometry normative data obtained for BMD from this cohort were not significantly different from French prodigy normative data (p = 0.15). TBS values at L1-L4 were poorly correlated with BMI (r = -0.17) and weight (r = -0.14) and not correlated with height. TBS values obtained for all lumbar vertebra combined (L1, L2, L3, L4) decreased with age. The magnitude of TBS decline at L1-L4 between 45 and 85 years of age was piecewise linear in the spine and averaged 14.5%, but this rate increased after 65 years by 50%. Similar results were obtained for other region of interest in the lumbar spine. As opposed to BMD, TBS was not affected by spinal osteoarthrosis.

CONCLUSION

The age-specific reference curve for TBS generated here could therefore be used to help clinicians to improve osteoporosis patient management and to monitor microarchitectural changes related to treatment or other diseases in routine clinical practice.

Considerations for the successful co-development of targeted cancer therapies and companion diagnostics

As diagnostic tests become increasingly important for optimizing the use of drugs to treat cancers, the co-development of a targeted therapy and its companion diagnostic test is becoming more prevalent and necessary. In July 2011, the US Food and Drug Administration released a draft guidance that gave the agency's formal definition of companion diagnostics and introduced a drug-diagnostic co-development process for gaining regulatory approval. Here, we identify areas of drug-diagnostic co-development that were either not covered by the guidance or that would benefit from increased granularity, including how to determine when clinical studies should be limited to biomarker-positive patients, defining the diagnostically selected patient population in which to use a companion diagnostic, and defining and clinically validating a biomarker signature for assays that use more than one biomarker. We propose potential approaches that sponsors could use to deal with these challenges and provide strategies to help guide the future co-development of drugs and diagnostics.

A review of phase II trial designs for initial marker validation

Phase II clinical trials aim to identify promising experimental regimens for further testing in phase III trials. In this review article, we focus on phase II designs for initial predictive biomarker validation to determine if a drug should be developed for an unselected patient population or for a biomarker-defined patient subset only. Several prospective designs for biomarker-directed therapy have been proposed, differing primarily in the study population, or randomization scheme, or both. The design choice is driven by scientific rationale, marker prevalence, strength of preliminary evidence, assay performance, and turn-around times for marker assessment. The enrichment design is most appropriate when compelling preliminary evidence suggests treatment benefit in only certain marker-defined subgroups, the all-comers design is useful when preliminary evidence regarding treatment effects in marker subgroups is unclear, and adaptive designs have the most potential in the setting of multiple treatment options and multiple marker-defined subgroups. We recently proposed a 2-stage phase II design that has the option for direct assignment (i.e., stop randomization and assign all patients to the experimental arm in stage 2) based on interim analysis (IA) results. This design not only recognizes the need for randomization but also acknowledges the possibility of promising but inconclusive results after pre-planned IA. Simulation studies demonstrated that the direct assignment-option design has minimal power loss, marginal increase in type I error rates, and reasonable robustness to population shift effects. Systematic evaluation and implementation of these design strategies in the phase II setting are essential for accelerating the clinical validation of biomarker guided-therapy.

NCI-RTOG translational program strategic guidelines for the early-stage development of radiosensitizers

The addition of chemotherapeutic agents to ionizing radiation has improved survival in many malignancies. Cure rates may be further improved by adding novel targeted agents to current radiotherapy or radiochemotherapy regimens. Despite promising laboratory data, progress in the clinical development of new drugs with radiation has been limited. To define and address the problems involved, a collaborative effort between individuals within the translational research program of the Radiation Oncology Therapy Group and the National Cancer Institute was established. We discerned challenges to drug development with radiation including: 1) the limited relevance of preclinical work, 2) the pharmaceutical industry's diminished interest, and 3) the important individual skills and institutional commitments required to ensure a successful program. The differences between early-phase trial designs with and without radiation are noted as substantial. The traditional endpoints for early-phase clinical trials-acute toxicity and maximum-tolerated dose-are of limited value when combining targeted agents with radiation. Furthermore, response rate is not a useful surrogate marker of activity in radiation combination trials.Consequently, a risk-stratified model for drug-dose escalation with radiation is proposed, based upon the known and estimated adverse effects. The guidelines discuss new clinical trial designs, such as the time-to-event continual reassessment method design for phase I trials, randomized phase II "screening" trials, and the use of surrogate endpoints, such as pathological response. It is hoped that by providing a clear pathway, this article will accelerate the rate of drug development with radiation.

Clinical trial design in head and neck cancer: what has the oncologist learned?

Chemotherapy has assumed an important role in multidisciplinary management of patients with head and neck cancer. Much recent progress is attributable to successful design and careful implementation of clinical trials. In addition to showing the efficacy of chemotherapy, trials also instruct about how to improve experimental design so that we can make the most of what is learned. In this Personal View, several important studies in head and neck cancer are reviewed, with focus on issues raised by their design, potential solutions to these difficulties, and challenges that future investigations of this disease will face.

Design of clinical trials for biomarker research in oncology

The developmental pathway from discovery to clinical practice for biomarkers and biomarker-directed therapies is complex. While several issues need careful consideration, two critical issues that surround the validation of biomarkers are the choice of clinical trial design (which is based on the strength of the preliminary evidence and marker prevalence) and the biomarker assay related issues surrounding the marker assessment methods such as the reliability and reproducibility of the assay. This review focuses on trial designs for marker validation, both in the setting of early phase trials for initial validation, as well as in the context of larger definitive trials. Designs for biomarker validation are broadly classified as retrospective (i.e., using data from previously well-conducted, randomized, controlled trials) or prospective (enrichment, allcomers or adaptive). We believe that the systematic evaluation and implementation of these design strategies are essential to accelerate the clinical validation of biomarker-guided therapy, thereby taking us a step closer to the goal of personalized medicine.