Clinical benefit in Phase-I trials of novel molecularly targeted agents: does dose matter?

REDOMA: Bayesian random-effects dose-optimization meta-analysis using spike-and-slab priors

The rise of cutting-edge precision cancer treatments has led to a growing significance of the optimal biological dose (OBD) in modern oncology trials. These trials now prioritize the consideration of both toxicity and efficacy simultaneously when determining the most desirable dosage for treatment. Traditional approaches in early-phase oncology trials have conventionally relied on the assumption of a monotone relationship between treatment efficacy and dosage. However, this assumption may not hold valid for novel oncology therapies. In reality, the dose-efficacy curve of such treatments may reach a plateau at a specific dose, posing challenges for conventional methods in accurately identifying the OBD. Furthermore, achieving reliable identification of the OBD is typically not possible based on a single small-sample trial. With data from multiple phase I and phase I/II trials, we propose a novel Bayesian random-effects dose-optimization meta-analysis (REDOMA) approach to identify the OBD by synthesizing toxicity and efficacy data from each trial. The REDOMA method can address trials with heterogeneous characteristics. We adopt a curve-free approach based on a Gamma process prior to model the average dose-toxicity relationship. In addition, we utilize a Bayesian model selection framework that uses the spike-and-slab prior as an automatic variable selection technique to eliminate monotonic constraints on the dose-efficacy curve. The good performance of the REDOMA method is confirmed by extensive simulation studies.

The patient perspective on dose optimization for anticancer treatments: A new era of cancer drug dosing-Challenging the "more is better" dogma

The Patient-Centered Dosing Initiative, a patient-led effort advocating for a paradigm shift in determining cancer drug dosing strategies, pioneers a departure from traditional oncology drug dosing practices. Historically, oncology drug dosing relies on identifying the maximum tolerated dose through phase 1 dose escalation methodology, favoring higher dosing for greater efficacy, often leading to higher toxicity. However, this approach is not universally applicable, especially for newer treatments like targeted therapies and immunotherapies. Patient-Centered Dosing Initiative challenges this "more is better" ethos, particularly as metastatic breast cancer patients themselves, as they not only seek longevity but also a high quality of life since most metastatic breast cancer patients stay on treatment for the rest of their lives. Surveying 1221 metastatic breast cancer patients and 119 oncologists revealed an evident need for flexible dosing strategies, advocating personalized care discussions based on patient attributes. The survey results also demonstrated an openness toward flexible dosing and a willingness from both patients and clinicians to discuss dosing as part of their care. Patient-centered dosing emphasizes dialogue between clinicians and patients, delving into treatment efficacy-toxicity trade-offs. Similarly, clinical trial advocacy for multiple dosing regimens encourages adaptive strategies, moving away from strict adherence to maximum tolerated dose, supported by recent research in optimizing drug dosages. Recognizing the efficacy-effectiveness gap between clinical trials and real-world practice, Patient-Centered Dosing Initiative underscores the necessity for patient-centered dosing strategies. A focus on individual patient attributes aligns with initiatives like Project Optimus and Project Renewal, aiming to optimize drug dosages for improved treatment outcomes at both the pre- and post-approval phases. Patient-Centered Dosing Initiative's efforts extend to patient education, providing tools to initiate dosage-related conversations with physicians. In addition, it emphasizes physician-patient dialogues and post-marketing studies as essential in determining optimal dosing and refining drug regimens. A dose-finding paradigm prioritizing drug safety, tolerability, and efficacy benefits all stakeholders, reducing emergency care needs and missed treatments for patients, aligning with oncologists' and patients' shared goals. Importantly, it represents a win-win scenario across healthcare sectors. In summary, the Patient-Centered Dosing Initiative drives transformative changes in cancer drug dosing, emphasizing patient well-being and personalized care, aiming to enhance treatment outcomes and optimize oncology drug delivery.

Optimal biological dose selection in dose-finding trials with model-assisted designs based on efficacy and toxicity: a simulation study

With the emergence of molecular targeted agents and immunotherapies in anti-cancer treatment, a concept of optimal biological dose (OBD), accounting for efficacy and toxicity in the framework of dose-finding, has been widely introduced into phase I oncology clinical trials. Various model-assisted designs with dose-escalation rules based jointly on toxicity and efficacy are now available to establish the OBD, where the OBD is generally selected at the end of the trial using all toxicity and efficacy data obtained from the entire cohort. Several measures to select the OBD and multiple methods to estimate the efficacy probability have been developed for the OBD selection, leading to many options in practice; however, their comparative performance is still uncertain, and practitioners need to take special care of which approaches would be the best for their applications. Therefore, we conducted a comprehensive simulation study to demonstrate the operating characteristics of the OBD selection approaches. The simulation study revealed key features of utility functions measuring the toxicity-efficacy trade-off and suggested that the measure used to select the OBD could vary depending on the choice of the dose-escalation procedure. Modelling the efficacy probability might lead to limited gains in OBD selection.

Patients' selection and trial matching in early-phase oncology clinical trials

BACKGROUND

Early-phase clinical trials (EPCT) represent an important part of innovations in medical oncology and a valuable therapeutic option for patients with metastatic cancers, particularly in the era of precision medicine. Nevertheless, adult patients' participation in oncology clinical trials is low, ranging from 2% to 8% worldwide, with unequal access, and up to 40% risk of early discontinuation in EPCT, mostly due to cancer-related complications.

DESIGN

We review the tools and initiatives to increase patients' orientation and access to early phase cancer clinical trials, and to limit early discontinuation.

RESULTS

New approaches to optimize the early-phase clinical trial referring process in oncology include automatic trial matching, tools to facilitate the estimation of patients' prognostic and/or to better predict patients' eligibility to clinical trials. Classical and innovative approaches should be associated to double patient recruitment, improve clinical trial enrollment experience and reduce early discontinuation rates.

CONCLUSIONS

Whereas EPCT are essential for patients to access the latest medical innovations in oncology, offering the appropriate trial when it is relevant for patients should increase by organizational and technological innovations. The oncologic community will need to closely monitor their performance, portability and simplicity for implementation in daily clinical practice.

BOIN-ETC: A Bayesian optimal interval design considering efficacy and toxicity to identify the optimal dose combinations

One of the primary objectives of a dose-finding trial for novel anti-cancer agent combination therapies, such as molecular targeted agents and immune-oncology therapies, is to identify optimal dose combinations that are tolerable and therapeutically beneficial for subjects in subsequent clinical trials. The goal differs from that of a dose-finding trial for traditional cytotoxic agents, in which the goal is to determine the maximum tolerated dose combinations. This paper proposes the new design, named 'BOIN-ETC' design, to identify optimal dose combinations based on both efficacy and toxicity outcomes using the waterfall approach. The BOIN-ETC design is model-assisted, so it is expected to be robust, and straightforward to implement in actual oncology dose-finding trials. These characteristics are quite valuable from a practical perspective. Simulation studies show that the BOIN-ETC design has advantages compared with the other approaches in the percentage of correct optimal dose combination selection and the average number of patients allocated to the optimal dose combinations across various realistic settings.

TITE-gBOIN-ET: Time-to-event generalized Bayesian optimal interval design to accelerate dose-finding accounting for ordinal graded efficacy and toxicity outcomes

One of the primary objectives of an oncology dose-finding trial for novel therapies, such as molecular-targeted agents and immune-oncology therapies, is to identify an optimal dose (OD) that is tolerable and therapeutically beneficial for subjects in subsequent clinical trials. These new therapeutic agents appear more likely to induce multiple low or moderate-grade toxicities than dose-limiting toxicities. Besides, for efficacy, evaluating the overall response and long-term stable disease in solid tumors and considering the difference between complete remission and partial remission in lymphoma are preferable. It is also essential to accelerate early-stage trials to shorten the entire period of drug development. However, it is often challenging to make real-time adaptive decisions due to late-onset outcomes, fast accrual rates, and differences in outcome evaluation periods for efficacy and toxicity. To solve the issues, we propose a time-to-event generalized Bayesian optimal interval design to accelerate dose finding, accounting for efficacy and toxicity grades. The new design named "TITE-gBOIN-ET" design is model-assisted and straightforward to implement in actual oncology dose-finding trials. Simulation studies show that the TITE-gBOIN-ET design significantly shortens the trial duration compared with the designs without sequential enrollment while having comparable or higher performance in the percentage of correct OD selection and the average number of patients allocated to the ODs across various realistic settings.

Modified isotonic regression based phase I/II clinical trial design identifying optimal biological dose

Conventional phase I/II clinical trial designs often use complicated parametric models to characterize the dose-response relationships and conduct the trials. However, the parametric models are hard to justify in practice, and the misspecification of parametric models can lead to substantially undesirable performances in phase I/II trials. Moreover, it is difficult for the physicians conducting phase I/II trials to clinically interpret the parameters of these complicated models, and such significant learning costs impede the translation of novel statistical designs into practical trial implementation. To solve these issues, we propose a transparent and efficient phase I/II clinical trial design, referred to as the modified isotonic regression-based design (mISO), to identify the optimal biological doses for molecularly targeted agents and immunotherapy. The mISO design makes no parametric model assumptions on the dose-response relationship and yields desirable performances under any clinically meaningful dose-response curves. The concise, clinically interpretable dose-response models and dose-finding algorithm make the proposed designs highly translational from the statistical community to the clinical community. We further extend the mISO design and develop the mISO-B design to handle the delayed outcomes. Our comprehensive simulation studies show that the mISO and mISO-B designs are highly efficient in optimal biological dose selection and patients allocation and outperform many existing phase I/II clinical trial designs. We also provide a trial example to illustrate the practical implementation of the proposed designs. The software for simulation and trial implementation are available for free download.

Improving Dose-Optimization Processes Used in Oncology Drug Development to Minimize Toxicity and Maximize Benefit to Patients

This review highlights strategies to integrate dose optimization into premarketing drug development and discusses the underlying statistical principles. Poor dose optimization can have negative consequences for patients, most commonly because of toxicity, including poor quality of life, reduced effectiveness because of inability of patients to stay on current therapy or receive subsequent therapy because of toxicities, and difficulty in developing combination regimens. We reviewed US Food and Drug Administration initial approvals (2019-2021) of small molecules and antibody-drug conjugates for oncologic indications to determine the proportion with a recommended dosage at the maximum tolerated dose or the maximal administered dose, to characterize the use of randomized evaluations of multiple dosages in dose selection, to describe the frequency of dose modifications at the recommended dosage, and to identify case examples that highlight key principles for premarket dose optimization during drug development. Herein, we highlight major principles for dose optimization and review examples of recent US Food and Drug Administration approvals that illustrate how investigation of dose- and exposure-response relationships and use of randomized dose trials can support dose optimization. Although there has been some progress, dose optimization through randomized dose evaluation in oncology trials is not routinely conducted. Dose optimization is essential to ensure that patients receive therapies which maximize efficacy while minimizing toxicity.

gBOIN-ET: The generalized Bayesian optimal interval design for optimal dose-finding accounting for ordinal graded efficacy and toxicity in early clinical trials

One of the primary objectives of an oncology dose-finding trial for novel therapies, such as molecular targeted agents and immune-oncology therapies, is to identify an optimal dose (OD) that is tolerable and therapeutically beneficial for subjects in subsequent clinical trials. These new therapeutic agents appear more likely to induce multiple low- or moderate-grade toxicities than dose-limiting toxicities. Besides, efficacy should be evaluated as an overall response and stable disease in solid tumors and the difference between complete remission and partial remission in lymphoma. This paper proposes the generalized Bayesian optimal interval design for dose-finding accounting for efficacy and toxicity grades. The new design, named "gBOIN-ET" design, is model-assisted, simple, and straightforward to implement in actual oncology dose-finding trials than model-based approaches. These characteristics are quite valuable in practice. A simulation study shows that the gBOIN-ET design has advantages compared with the other model-assisted designs in the percentage of correct OD selection and the average number of patients allocated to the ODs across various realistic settings.

Contemporary dose-escalation methods for early phase studies in the immunotherapeutics era

Phase 1 dose-escalation trials are crucial to drug development by providing a framework to assess the toxicity of novel agents in a stepwise and monitored fashion. Despite widely adopted, rule-based dose-escalation methods (such as 3 + 3) are limited in finding the maximum tolerated dose (MTD) and tend to treat a significant number of patients at subtherapeutic doses. Newer methods of dose escalation, such as model-based and model-assisted designs, have emerged and are more accurate in finding MTD. However, these designs have not yet been broadly embraced by investigators. In this review, we summarise the advantages and disadvantages of contemporary dose-escalation methods, with emphasis on model-assisted designs, including time-to-event designs and hybrid methods involving optimal biological dose (OBD). The methods reviewed include mTPI, keyboard, BOIN, and their variations. In addition, the challenges of drug development (and dose-escalation) in the era of immunotherapeutics are discussed, where many of these agents typically have a wide therapeutic window. Fictional examples of how the dose-escalation method chosen can alter the outcomes of a phase 1 study are described, including the number of patients enrolled, the trial's timeframe, and the dose level chosen as MTD. Finally, the recent trends in dose-escalation methods applied in phase 1 trials in the immunotherapeutics era are reviewed. Among 856 phase I trials from 2014 to 2019, a trend towards the increased use of model-based and model-assisted designs over time (OR = 1.24) was detected. However, only 8% of the studies used non-rule-based dose-escalation methods. Increasing familiarity with such dose-escalation methods will likely facilitate their uptake in clinical trials.

Therapeutic Drug Monitoring of Targeted Anticancer Protein Kinase Inhibitors in Routine Clinical Use: A Critical Review

BACKGROUND

Therapeutic response to oral targeted anticancer protein kinase inhibitors (PKIs) varies widely between patients, with insufficient efficacy of some of them and unacceptable adverse reactions of others. There are several possible causes for this heterogeneity, such as pharmacokinetic (PK) variability affecting blood concentrations, fluctuating medication adherence, and constitutional or acquired drug resistance of cancer cells. The appropriate management of oncology patients with PKI treatments thus requires concerted efforts to optimize the utilization of these drug agents, which have probably not yet revealed their full potential.

METHODS

An extensive literature review was performed on MEDLINE on the PK, pharmacodynamics, and therapeutic drug monitoring (TDM) of PKIs (up to April 2019).

RESULTS

This review provides the criteria for determining PKIs suitable candidates for TDM (eg, availability of analytical methods, observational PK studies, PK-pharmacodynamics relationship analysis, and randomized controlled studies). It reviews the major characteristics and limitations of PKIs, the expected benefits of TDM for cancer patients receiving them, and the prerequisites for the appropriate utilization of TDM. Finally, it discusses various important practical aspects and pitfalls of TDM for supporting better implementation in the field of cancer treatment.

CONCLUSIONS

Adaptation of PKIs dosage regimens at the individual patient level, through a rational TDM approach, could prevent oncology patients from being exposed to ineffective or unnecessarily toxic drug concentrations in the era of personalized medicine.

The Use of Patient-Reported Outcome Measures in Phase I Oncology Clinical Trials

OBJECTIVE

To investigate patient-reported outcome (PRO) usage in phase I oncology clinical trials, including types of PRO measures and changes over time.

METHODS

We analyzed ClinicalTrials.gov records of phase I oncology clinical trials completed by December 2019.

RESULTS

Of all eligible trials, 2.3% (129/5,515) reported ≥1 PRO, totaling 181 instances of PRO usage. PRO usage increased over time, from 0.6% (trials initiated before 2000) to 3.4% (trials starting between 2015 and 2019). The most common PRO measures were unspecified (29%), tumor-specific (24%), and generic cancer (19%).

CONCLUSION

Although uncommon in phase I oncology clinical trials, PRO usage is increasing over time. PRO measures were often unspecified on ClinicalTrials.gov, suggesting that more precise reporting and standardization are needed.

Targeted drug delivery strategies for precision medicines

Progress in the field of precision medicine has changed the landscape of cancer therapy. Precision medicine is propelled by technologies that enable molecular profiling, genomic analysis, and optimized drug design to tailor treatments for individual patients. Although precision medicines have resulted in some clinical successes, the use of many potential therapeutics has been hindered by pharmacological issues, including toxicities and drug resistance. Drug delivery materials and approaches have now advanced to a point where they can enable the modulation of a drug's pharmacological parameters without compromising the desired effect on molecular targets. Specifically, they can modulate a drug's pharmacokinetics, stability, absorption, and exposure to tumours and healthy tissues, and facilitate the administration of synergistic drug combinations. This Review highlights recent progress in precision therapeutics and drug delivery, and identifies opportunities for strategies to improve the therapeutic index of cancer drugs, and consequently, clinical outcomes.

First-In-Human Phase 1 Clinical Trials - A Single-Center Experience In The Era Of Modern Oncotherapeutics

In the era of precision medicine the treatment options for cancer patients and subsequent outcomes are expected to improve. We present a review of patients enrolled in first-in-human Phase1 trials at University of Alabama at Birmingham. Between 1/2015-6/2017, 162 cancer patients (whole cohort, WC) were enrolled on phase1 studies receiving either targeted therapy (TT) or immuno-therapy (IOT). We assessed 90 day mortality (90DM) and time to treatment failure (TTF) to determine the predictors. Of the WC (122 (TT), 40 (IOT)), 90 (56%) received ≥ 2 prior therapies and 38 (24%) ⩾ 5 prior therapies. Overall, Grade 3 or 4 events were observed in 33% (WC) vs 31% (TT) vs 38% (IOT). The 90DM was 9.3% (WC) vs 7.4% (TT) vs 15% (IOT). The median TTF was 4.2 months vs 4.5 m vs 3.6 m. The number of lines of prior therapy and performance status were identified as outcome predictors. Our data reflects the new trend in precision oncology where majority received non-cytotoxic therapeutic interventions. The observation that number of lines of prior therapy and performance status predictive of PFS and 90DM emphasizes the need to consider phase1 trials earlier, preferably upon progression following definitive therapy.

TITE-BOIN-ET: Time-to-event Bayesian optimal interval design to accelerate dose-finding based on both efficacy and toxicity outcomes

One of the primary purposes of an oncology dose-finding trial is to identify an optimal dose (OD) that is both tolerable and has an indication of therapeutic benefit for subjects in subsequent clinical trials. In addition, it is quite important to accelerate early stage trials to shorten the entire period of drug development. However, it is often challenging to make adaptive decisions of dose escalation and de-escalation in a timely manner because of the fast accrual rate, the difference of outcome evaluation periods for efficacy and toxicity and the late-onset outcomes. To solve these issues, we propose the time-to-event Bayesian optimal interval design to accelerate dose-finding based on cumulative and pending data of both efficacy and toxicity. The new design, named "TITE-BOIN-ET" design, is nonparametric and a model-assisted design. Thus, it is robust, much simpler, and easier to implement in actual oncology dose-finding trials compared with the model-based approaches. These characteristics are quite useful from a practical point of view. A simulation study shows that the TITE-BOIN-ET design has advantages compared with the model-based approaches in both the percentage of correct OD selection and the average number of patients allocated to the ODs across a variety of realistic settings. In addition, the TITE-BOIN-ET design significantly shortens the trial duration compared with the designs without sequential enrollment and therefore has the potential to accelerate early stage dose-finding trials.

Molecular Pharmacodynamics-Guided Scheduling of Biologically Effective Doses: A Drug Development Paradigm Applied to MET Tyrosine Kinase Inhibitors

The development of molecularly targeted agents has benefited from use of pharmacodynamic markers to identify "biologically effective doses" (BED) below MTDs, yet this knowledge remains underutilized in selecting dosage regimens and in comparing the effectiveness of targeted agents within a class. We sought to establish preclinical proof-of-concept for such pharmacodynamics-based BED regimens and effectiveness comparisons using MET kinase small-molecule inhibitors. Utilizing pharmacodynamic biomarker measurements of MET signaling (tumor pY1234/1235MET/total MET ratio) in a phase 0-like preclinical setting, we developed optimal dosage regimens for several MET kinase inhibitors and compared their antitumor efficacy in a MET-amplified gastric cancer xenograft model (SNU-5). Reductions in tumor pY1234/1235MET/total MET of 95%-99% were achievable with tolerable doses of EMD1214063/MSC2156119J (tepotinib), XL184 (cabozantinib), and XL880/GSK1363089 (foretinib), but not ARQ197 (tivantinib), which did not alter the pharmacodynamic biomarker. Duration of kinase suppression and rate of kinase recovery were specific to each agent, emphasizing the importance of developing customized dosage regimens to achieve continuous suppression of the pharmacodynamic biomarker at the required level (here, ≥90% MET kinase suppression). The customized dosage regimen of each inhibitor yielded substantial and sustained tumor regression; the equivalent effectiveness of customized dosage regimens that achieve the same level of continuous molecular target control represents preclinical proof-of-concept and illustrates the importance of proper scheduling of targeted agent BEDs. Pharmacodynamics-guided biologically effective dosage regimens (PD-BEDR) potentially offer a superior alternative to pharmacokinetic guidance (e.g., drug concentrations in surrogate tissues) for developing and making head-to-head comparisons of targeted agents. Mol Cancer Ther; 17(3); 698-709. ©2018 AACR.

Adaptive designs for identifying optimal biological dose for molecularly targeted agents

Background Traditionally, the purpose of a dose-finding design in cancer is to find the maximum tolerated dose based solely on toxicity. However, for molecularly targeted agents, little toxicity may arise within the therapeutic dose range and the dose-response curves may not be monotonic. This challenges the principle that more is better, which is widely accepted for conventional chemotherapy. Methods We propose three adaptive dose-finding designs for trials evaluating molecularly targeted agents, for which the dose-response curves are unimodal or plateaued. The goal of these designs is to find the optimal biological dose, which is defined as the lowest dose with the highest rate of efficacy while safe. The first proposed design is parametric and assumes a logistic dose-efficacy curve for dose finding, the second design is nonparametric and uses the isotonic regression to identify the optimal biological dose, and the third design has the spirit of a 'semiparametric' approach by assuming a logistic model only locally around the current dose. Results We conducted extensive simulation studies to investigate the operating characteristics of the proposed designs. Simulation studies show that the nonparametric and semiparametric designs have good operating characteristics for finding the optimal biological dose. Limitations The proposed designs assume a binary endpoint. Extension of the proposed designs to ordinal and time-to-event endpoints is worth further investigation. Conclusion Among the three proposed designs, the nonparametric and semiparametric designs yield consistently good operating characteristics and thus are recommended for practical use. The software to implement these two designs is available for free download at http://odin.mdacc.tmc.edu/~yyuan/ .

Potential utility of a longitudinal relative dose intensity of molecularly targeted agents in phase 1 dose-finding trials

Phase 1 trials of molecularly targeted agents (MTA) often do not use toxicity data beyond the first cycle of treatment to determine a recommended phase 2 dose (RP2D). We investigated the potential utility of longitudinal relative dose intensity (RDI) that may be a better new way of determining a more accurate RP2D as a lower dose that is presumably more tolerable over the long term without compromising efficacy. All consecutive patients who were initially treated using a single MTA at the conventional RP2D or at one level lower dose (OLLD) of that RP2D in 9 phase 1 trials sponsored by the National Cancer Institute were included. The associations between longitudinal RDI, time to first progression, and response rate were analyzed. The RDI of the conventional RP2D group were maintained a rate of ≥70% throughout 10 cycles, and were higher than those of the OLLD group, although in both groups the RDI gradually decreased with additional treatment cycles. The RP2D group was similar to the OLLD group with respect to time to first progression and response rate. In both groups, however, the decreasing RDI over time was significantly associated with shorter time to first disease progression; therefore, the longitudinal RDI, which takes into account lower grade toxicity occurrences, may be useful in determining a more desirable dose to use in phase 2 and 3 studies.

Early Phase Clinical Trial Designs - State of Play and Adapting for the Future

The process of anti-cancer drug development is complex, with high attrition rates. Factors that may optimise this process include well-constructed and relevant pre-clinical testing and use of biomarkers for patient selection. However, the design of early phase clinical trials will probably play a vital role in both the robust clinical investigation of new targeted therapies and in streamlining drug development. In this overview, we assess current concepts in phase I clinical trials, highlighting issues and opportunities to improve their meaningfulness. The particular challenge of how to design combination trials is addressed, with focus on the potential of new adaptive and model-based designs.

Early phase clinical trials of anticancer agents in children and adolescents - an ITCC perspective

In the past decade, the landscape of drug development in oncology has evolved dramatically; however, this paradigm shift remains to be adopted in early phase clinical trial designs for studies of molecularly targeted agents and immunotherapeutic agents in paediatric malignancies. In drug development, prioritization of drugs on the basis of knowledge of tumour biology, molecular 'drivers' of disease and a drug's mechanism of action, and therapeutic unmet needs are key elements; these aspects are relevant to early phase paediatric trials, in which molecular profiling is strongly encouraged. Herein, we describe the strategy of the Innovative Therapies for Children with Cancer (ITCC) Consortium, which advocates for the adoption of trial designs that enable uninterrupted patient recruitment, the extrapolation from studies in adults when possible, and the inclusion of expansion cohorts. If a drug has neither serious dose-related toxicities nor a narrow therapeutic index, then studies should generally be started at the adult recommended phase II dose corrected for body surface area, and act as dose-confirmation studies. The use of adaptive trial designs will enable drugs with promising activity to progress rapidly to randomized studies and, therefore, will substantially accelerate drug development for children and adolescents with cancer.

Bayesian Phase I/II Biomarker-based Dose Finding for Precision Medicine with Molecularly Targeted Agents

The optimal dose for treating patients with a molecularly targeted agent may differ according to the patient's individual characteristics, such as biomarker status. In this article, we propose a Bayesian phase I/II dose-finding design to find the optimal dose that is personalized for each patient according to his/her biomarker status. To overcome the curse of dimensionality caused by the relatively large number of biomarkers and their interactions with the dose, we employ canonical partial least squares (CPLS) to extract a small number of components from the covariate matrix containing the dose, biomarkers, and dose-by-biomarker interactions. Using these components as the covariates, we model the ordinal toxicity and efficacy using the latent-variable approach. Our model accounts for important features of molecularly targeted agents. We quantify the desirability of the dose using a utility function and propose a two-stage dose-finding algorithm to find the personalized optimal dose according to each patient's individual biomarker profile. Simulation studies show that our proposed design has good operating characteristics, with a high probability of identifying the personalized optimal dose.

Challenges and Innovations in Phase I Dose-Finding Designs for Molecularly Targeted Agents and Cancer Immunotherapies

Phase I oncology trials are designed to identify a safe dose with an acceptable toxicity profile. In traditional phase I dose-finding design, the dose is typically determined based on the probability of severe toxicity observed during the first treatment cycle. The recent development of molecularly targeted agents and cancer immunotherapies call for new innovations in phase I designs, because of prolonged treatment cycles often involved. Various phase I designs using toxicity and efficacy endpoints from multiple treatment cycles have been developed for these new treatment agents. Here, we will review the novel endpoints and designs for the phase I oncology clinical trials.

Phase I cancer clinical trials

An efficient phase I trial is a crucial step in developing a new drug in a safe and timely manner. The main objective of a phase I trial is to determine the maximum tolerated dose in order to recommend the dose for a phase II trial. There are many designs that are implemented in phase I trials. Rule-based designs such as the traditional 3 + 3 method and rolling six design are easy to implement and assess for safety using a conservative approach. Model-based designs such as the continual reassessment method and the time-to-event continual reassessment method use mathematical models to increase the precision of dose estimation. The advantages and shortcomings of these designs, along with other designs, are reviewed.

Effective implementation of novel MET pharmacodynamic assays in translational studies

MET tyrosine kinase (TK) dysregulation is significantly implicated in many types of cancer. Despite over 20 years of drug development to target MET in cancers, a pure anti-MET therapeutic has not yet received market approval. The failure of two recently concluded phase III trials point to a major weakness in biomarker strategies to identify patients who will benefit most from MET therapies. The capability to interrogate oncogenic mutations in MET via circulating tumor DNA (ctDNA) provides an important advancement in identification and stratification of patients for MET therapy. However, a wide range in type and frequency of these mutations suggest there is a need to carefully link these mutations to MET dysregulation, at least in proof-of-concept studies. In this review, we elaborate how we can utilize recently developed and validated pharmacodynamic biomarkers of MET not only to show target engagement, but more importantly to quantitatively measure MET dysregulation in tumor tissues. The MET assay endpoints provide evidence of both canonical and non-canonical MET signaling, can be used as "effect markers" to define biologically effective doses (BEDs) for molecularly targeted drugs, confirm mechanism-of-action in testing combination of drugs, and establish whether a diagnostic test is reporting MET dysregulation. We have established standard operating procedures for tumor biopsy collections to control pre-analytical variables that have produced valid results in proof-of-concept studies. The reagents and procedures are made available to the research community for potential implementation on multiple platforms such as ELISA, quantitative immunofluorescence assay (qIFA), and immuno-MRM assays.

A study of motivations and expectations of patients seen in phase 1 oncology clinics

BACKGROUND

To better inform clinical practice, this study was aimed at capturing patients' motivations for enrolling in phase 1 trials and at quantifying their expectations of the benefits, risks, and commitment associated with clinical trials and the impact of the initial consultation on their expectations.

METHODS

This was a single-center, prospective, quantitative study of newly referred adult patients considering their first phase 1 oncology trial. Participants completed questionnaires before they were seen and an abbreviated follow-up version after their consultation.

RESULTS

Questionnaires were completed by 396 (99%) and 301 (76%) before and after the clinic, respectively. Participants ranked the possibility of tumor shrinkage (84%) as the most important motivation for considering a phase 1 trial; this was followed by no alternative treatments (56%), their physician's recommendation (44%), and the fact that the research might benefit others (38%). When they were asked about the potential personal benefit, 43% predicted tumor shrinkage initially. After the consultation, this increased to 47%. Fourteen percent of patients expected a cure. When asked about risks, 71% of the participants expected moderate side effects. When asked about expectations of time commitments, a majority of patients did not anticipate weekly visits, although this was understood by 93% of patients after the consultation. Overall, patients were keen to consider trials and when asked before and after the consultation 72% and 84% were willing to enroll in studies, respectively.

CONCLUSIONS

This study reports that more than 80% of patients enroll in early-phase clinical oncology trials motivated by the potential of a clinical benefit, with approximately half expecting tumor shrinkage and approximately a tenth anticipating a cure. The typical phase 1 response rate is 4% to 20%, and this discrepancy exemplifies the challenges faced by patients and healthcare professionals during their interactions for phase 1 studies. Cancer 2016;122:3501-3508. © 2016 American Cancer Society.

Phase I Cancer Trials and Palliative Care: Antagonism, Irrelevance, or Synergy?

This article synthesizes the presentations and conclusions of an international symposium on Phase 1 oncology trials, palliative care, and ethics held in 2014. The purpose of the symposium was to discuss the intersection of three independent trends that unfolded in the past decade. First, large-scale reviews of hundreds of Phase I trials have indicated there is a relatively low risk of serious harm and some prospect of clinical benefit that can be meaningful to patients. Second, changes in the design and analysis of Phase I trials, the introduction of "targeted" investigational agents that are generally less toxic, and an increase in Phase I trials that combine two or more agents in a novel way have changed the conduct of these trials and decreased fears and apprehensions about participation. Third, the field of palliative care in cancer has expanded greatly, offering symptom management to late-stage cancer patients, and demonstrated that it is not mutually exclusive with disease-targeted therapies or clinical research. Opportunities for collaboration and further research at the intersection of Phase 1 oncology trials and palliative care are highlighted.

Trends in the characteristics, dose-limiting toxicities and efficacy of phase I oncology trials: The Cancer Research UK experience

INTRODUCTION

Phase I oncology trials have evolved over the years, and these changes could have implications for future studies and patients.

METHODS

Adult trials sponsored by Cancer Research UK Centre for Drug Development between 1995 and 2013 were analysed. Forty-nine trials were divided into two groups based on the starting date for recruitment: 1995-2003 (24 trials, n = 603) and 2004-2013 (25 trials, n = 750) for comparative purposes.

RESULTS

From 1995-2003 to 2004-2013, there was a shift towards studying non-cytotoxic agents that are administered orally. In later trials, patients tended to have better performance status, were older, had greater disease burden, and were more likely to have received prior treatment. In 2004-2013, wider variety of dose escalation designs were used, and studies were more likely to be multicentre, target/disease specific, conducted in first-/any-line setting and to require tumour biopsy. The overall incidence of dose-limiting toxicities (DLTs) was unchanged (10.9%; risk of death 0.4%), but DLTs such as neuropathy, stomatitis and thrombocytopaenia were less frequent in the more recent trials, while elevated liver enzymes were more frequent. Non-classical DLTs emerged in the later trials, including hypertension, hypophosphataemia, cardiac and ophthalmic toxicities. Disease control rate (DCR) increased from 27.9% (1995-2003) to 36.0% (2004-2013; P = 0.0033) due to higher rates of disease stabilisation.

CONCLUSION

Changes in trial designs, therapeutic agents, patient characteristics and DLTs were observed. Although the nature of DLTs changed, the incidence was similar in the two time periods and DCR improved, suggesting that the benefit-risk balance for patients participating in early-phase trials remains acceptable.

Theory and practice of clinical pharmacodynamics in oncology drug development

The clinical development of molecularly targeted cancer therapies is enhanced by proof of mechanism of action as well as proof of concept, which relate molecular pharmacodynamics to efficacy via changes in cancer cell biology and physiology resulting from drug action on its intended target. Here, we present an introduction to the field of clinical pharmacodynamics, its medical and laboratory aspects, and its practical incorporation into clinical trials. We also describe key success factors that are useful for judging the quality of clinical pharmacodynamic studies, including biopsy quality and suitability, specimen handling, assay fitness-for-purpose, and reagent quality control. This introduction provides not only context for the following articles in this issue, but also an appreciation of the role of well-conducted clinical pharmacodynamic studies in oncology drug development.

An explorative study to assess the association between health-related quality of life and the recommended phase II dose in a phase I trial: idarubicin-loaded beads for chemoembolisation of hepatocellular carcinoma

OBJECTIVES

The objective of this study was to explore the association between health-related quality of life (HRQoL) and the recommended phase 2 dose in a phase I clinical trial according to the Time to HRQoL deterioration approach (TTD).

SETTING

This is a phase I dose-escalation trial of transarterial chemoembolisation (TACE) with idarubicin-loaded beads performed in cirrhotic patients with hepatocellular carcinoma. Patients had to complete the EORTC QLQ-C30 HRQoL questionnaire at baseline and at days 15, 30 and 60 after TACE.

PARTICIPANTS

Patients aged ≥18 years with HCC unsuitable for curative treatments were evaluated for the study (N=21).

PRIMARY AND SECONDARY OUTCOME MEASUREMENTS

The primary objective was to determine the maximum tolerated dose (MTD) of idarubicin loaded after a single TACE session. MTD was defined as the dose level closest to that causing dose-limiting toxicity in 20% of patients. HRQoL was the secondary end point.

RESULTS

Between March 2010 and March 2011, 9, 6 and 6 patients were included at idarubicin dose levels of 5, 10 and 15 mg, respectively. Calculated MTD of idarubicin was 10 mg. At the 10 mg idarubicin dose, patients presented a longer TTD than at 5 mg, for global health status (HR=0.91 (95% CI 0.18 to 4.72)), physical functioning (HR=0.38 (0.04 to 3.22)), fatigue (HR=0.67 (0.18 to 2.56)) and pain (HR=0.47 (0.05 to 4.24)).

CONCLUSIONS

These HRQoL results were consistent with the estimated MTD, with a median TTD for global health status of 41 days (21 to NA) at 5 mg, 23 days (20 to NA) at 10 mg and 25 days (17 to NA) at 15 mg. These results show the importance of studying HRQoL in phase I trials.

TRIAL REGISTRATION NUMBER

NCT01040559; Post-results.

Recent innovations in the USA National Cancer Institute-sponsored investigator initiated Phase I and II anticancer drug development

Exciting recent advancements in deep-sequencing technology have enabled a rapid and cost-effective molecular characterization of patient-derived tumor samples. Incorporating these innovative diagnostic technologies into early clinical trials could significantly propel implementation of precision medicine by identifying genetic markers predictive of sensitivity to agents. It may also markedly accelerate drug development and subsequent regulatory approval of novel agents. Particularly noteworthy, a high-response rate in a Phase II trial involving a biomarker-enriched patient cohort could result in a regulatory treatment approval in rare histologies, which otherwise would not be a candidate for a large randomized clinical trial. Furthermore, even if a trial does not meet its statistical endpoint, tumors from a few responders should be molecularly characterized as part of the new biomarker-mining processes. In order to accommodate patient screening and accelerate the accrual process, institutions conducting early clinical trials need to be a part of a multi-institution clinical trials network. Future clinical trial design will incorporate new biomarkers discovered by a 'phenotype-to-genotype' effort with an appropriate statistical design. To help advance such changes, the National Cancer Institute has recently reformed the existing early phase clinical trials network. A new clinical trial network, the Experimental Therapeutics Clinical Trials Network (ET-CTN), was begun and, in addition to its pre-existing infrastructure, an up-to-date clinical trial registration system, clinical trial monitoring system including electronic database and a central Institutional Review Board were formed. Ultimately, these reforms support identifying the most appropriate therapy for each tumor type by incorporating state-of-the-art molecular diagnostic tools into early clinical trials.

TEAMS: Toxicity- and Efficacy-based Dose Insertion Design with Adaptive Model Selection for Phase I/II Dose-Escalation Trials in Oncology

In many oncology clinical trials it is necessary to insert new candidate doses when the prespecified doses are poorly elicited. Formal statistical designs with dose insertion are lacking. We propose a dose insertion design for phase I/II clinical trials in oncology based on both efficacy and toxicity outcomes. We also implement Bayesian model selection during the course of the trial so that better models can be adaptively chosen to achieve more accurate inference. The new design, TEAMS, achieves great operating characteristics in extensive simulation studies due to its ability to adaptively insert new doses as well as perform model selection. As a result, appropriate doses are inserted when necessary and desirable doses are selected with higher probabilities at the end of the trial.

New treatment modalities for brain tumors in dogs and cats

Despite advancements in standard therapies, intracranial tumors remain a significant source of morbidity and mortality in veterinary and human medicine. Several newer approaches are gaining more widespread acceptance or are currently being prepared for translation from experimental to routine therapeutic use. Clinical trials in dogs with spontaneous brain tumors have contributed to the development and human translation of several novel therapeutic brain tumor approaches.

Predictive value of phase I trials for safety in later trials and final approved dose: analysis of 61 approved cancer drugs

Phase I trials use a small number of patients to define a maximum tolerated dose (MTD) and the safety of new agents. We compared data from phase I and registration trials to determine whether early trials predicted later safety and final dose. We searched the U.S. Food and Drug Administration (FDA) website for drugs approved in nonpediatric cancers (January 1990-October 2012). The recommended phase II dose (R2PD) and toxicities from phase I were compared with doses and safety in later trials. In 62 of 85 (73%) matched trials, the dose from the later trial was within 20% of the RP2D. In a multivariable analysis, phase I trials of targeted agents were less predictive of the final approved dose (OR, 0.2 for adopting ± 20% of the RP2D for targeted vs. other classes; P = 0.025). Of the 530 clinically relevant toxicities in later trials, 70% (n = 374) were described in phase I. A significant relationship (P = 0.0032) between increasing the number of patients in phase I (up to 60) and the ability to describe future clinically relevant toxicities was observed. Among 28,505 patients in later trials, the death rate that was related to drug was 1.41%. In conclusion, dosing based on phase I trials was associated with a low toxicity-related death rate in later trials. The ability to predict relevant toxicities correlates with the number of patients on the initial phase I trial. The final dose approved was within 20% of the RP2D in 73% of assessed trials.

Capillary isoelectric-focusing immunoassays to study dynamic oncoprotein phosphorylation and drug response to targeted therapies in non-small cell lung cancer

Developing proteomic biomarkers is valuable for evaluating therapeutic effects of drugs and generating better treatment strategies. However, conventional protein analysis is often challenging due to inadequate sample size of clinical specimens, lack of assay reproducibility, accuracy, and sensitivity. A novel capillary isoelectricfocusing (IEF) immunoassay system (NanoPro) was used to study the dynamic phosphorylation status of signaling molecules in non-small cell lung cancer (NSCLC) cells treated with EGFR tyrosine kinase and MEK inhibitors. NanoPro showed the same dynamic ERK phosphorylation as Western blotting with good assay reproducibility using 1,000 times less protein. The IEF separation in NanoPro system enables multiple protein phosphorylation isoforms to be resolved and detected simultaneously. With NanoPro, we identified a specific on-target mitogen-activated protein/extracellular signal-regulated kinase (MEK) response pattern to MEK inhibitor PD325901, which was not detectable by Western blot analysis. We also revealed a MEK2 signal that may be associated with NSCLC cell sensitivity to the EGF receptor inhibitor erlotinib, and distinguished erlotinib-sensitive cells from intrinsic as well as acquired resistant cells to erlotinib. Moreover, NanoPro could differentiate human ERK1 isoforms from the mouse isoforms based on their isoelectric point differences and showed that erlotinib effectively inhibited ERK phosphorylation in targeted human xenograft cancer cells but not in surrounding mouse stromal cells. With 8 μg of tumor aspirates, we precisely quantified the response of 18 signaling molecules to erlotinib and MEK1 inhibitor treatments in an NSCLC patient. NanoPro's higher sensitivity, better resolution of protein phosphorylation status, and reduced tissue requirement warrant NanoPro's investigation for future drug development and evaluation of drug effects of targeted therapies.

Appraising iniparib, the PARP inhibitor that never was--what must we learn?

Several drugs targeting poly(ADP-ribose) polymerase (PARP) enzymes are under development. Responses have been observed in patients with germline mutations in BRCA1 and BRCA2, with further data supporting antitumour activity of PARP inhibitors in sporadic ovarian cancer. Strategies to identify other predictive biomarkers remain under investigation. Iniparib was purported to be a PARP inhibitor that showed promising results in randomized phase II trials in patients with triple-negative breast cancer. Negative results from a phase III study in this disease setting, however, tempered enthusiasm for this agent. Recently, data from in vitro experiments suggest that iniparib is not only structurally distinct from other described PARP inhibitors, but is also a poor inhibitor of PARP activity. In this context, the negative iniparib phase III data might have erroneously promulgated the notion that PARP inhibition is not an effective therapeutic strategy. Here, we scrutinize the development of iniparib from preclinical studies to registration trials, and identify and discuss the pitfalls in the development of anticancer drugs to prevent future late-stage trial failures.

Clinical benefit for patients with non-small cell lung cancer enrolled in phase I trials

AIM

To analyze the clinical features and outcomes of advanced non-small cell lung cancer (NSCLC) patients treated in phase I trials.

PATIENTS AND METHODS

The clinical characteristics, efficacy and toxicity data of 70 pretreated NSCLC patients enrolled in 17 phase I trials between January 2005 and June 2010 were analyzed at our institution.

RESULTS

The histological types were: adenocarcinoma (79%), squamous cell carcinoma (13%), and others. Patients received a median number of 3 prior lines of treatment before inclusion. 1 complete response (CR), 11 (16%) partial responses (PRs), and 29 (41%) stable diseases (SDs) were observed (according to Response Evaluation Criteria in Solid Tumors (RECIST)). The median overall survival (OS) time was 18 months and the median progression-free survival (PFS) time was 4.1 months. The median PFS of these patients within their prior therapy line before phase I inclusion was 4.3 months. A performance status score of 0 and the number of prior lines of treatment were significant for OS and PFS in multivariate analysis, respectively. Grade 3/4 toxicities were observed in 20 (27%) patients, and there was 1 treatment-related death.

CONCLUSION

Patients in good general condition and with limited pretreatment derived an improved benefit, suggesting that phase I studies may be a valid option for pretreated NSCLC patients.

Meta-analysis of the relationship between dose and benefit in phase I targeted agent trials

BACKGROUND

To date, the primary objective of phase I trials has been to safely select the maximum tolerated dose (MTD) of a drug or drug combination for utilization in subsequent trials. Although conventional cytotoxic chemotherapy is generally more effective at the MTD than molecularly targeted agents (MTAs), recent single-institution data suggest that molecularly targeted agent may not require an MTD for efficacy. We analyzed patient outcome results in MTA phase I trials at multiple institutions throughout North America sponsored by the National Cancer Institute's Cancer Therapy Evaluation Program.

METHODS

We retrospectively collected and analyzed data on patients treated on monotherapy phase I trials investigating novel MTAs with a defined MTD from 2000 to 2009. Logistic regression analysis was used to test whether there was an increase in the probability of a response as dose increased. A Cox proportional hazards model was used to determine if overall survival increased with increasing dose. All statistical tests were two-sided.

RESULTS

We analyzed 1908 patients treated on 55 eligible clinical trials. The probability of both overall response (complete response plus partial response) and overall survival increased with increasing dose (odds ratio for increased response = 1.56, P = .10; hazard ratio for death = 0.37, P = .008) when controlling for study as a covariate.

CONCLUSIONS

Patients treated in the context of phase I trials with MTAs continue to derive reasonable clinical benefit. Contrary to other single institution data, our data suggest clinical benefit in terms of increasing response and overall survival with increasing dose.