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.

A basket trial design based on constrained hierarchical Bayesian model for latent subgroups.

It is well known a basket trial consisting of multiple cancer types has the potential of borrowing strength across the baskets defined by the cancer types, leading to an efficient design in terms of sample size and trial duration. The treatment effects in those baskets are often heterogeneous and categorized by the cancer types being sensitive or insensitive to the treatment. Hence, the assumption of exchangeability in many existing basket trials may be violated, and there is a need to design trials without this assumption. In this paper, we simplify the constrained hierarchical Bayesian model for latent subgroups (CHBM-LS) for two classifiers to deal with the potential heterogeneity of treatment effects due to the single classifier of the cancer type. Different baskets are aggregated into subgroups using a latent subgroup modeling approach. The treatment effects are similar and exchangeable to facilitate information borrowing within each latent subgroup. Applying the simplified CHBM-LS approach to the real basket trials where baskets defined by only cancer types shows better performance than other available approaches. Further simulation study also demonstrates this CHBM-LS approach outperforms other approaches with higher statistical power and better-controlled type I error rates under various scenarios.

A generalized Bayesian optimal interval design for dose optimization in immunotherapy.

For novel immuno-oncology therapies, the primary purpose of a dose-finding trial is to identify an optimal dose (OD), defined as the tolerable dose having adequate efficacy and immune response under the unpredictable dose-outcome (toxicity, efficacy, and immune response) relationships. In addition, the multiple low or moderate-grade toxicities rather than dose-limiting toxicities (DLTs) and multiple levels of efficacy should be evaluated differently in dose-finding to determine true OD for developing novel immuno-oncology therapies. We proposed a generalized Bayesian optimal interval design for immunotherapy, simultaneously considering efficacy and toxicity grades and immune response outcomes. The proposed design, named gBOIN-ETI design, is model-assisted and easy to implement to develop immunotherapy efficiently. The operating characteristics of the gBOIN-ETI are compared with other dose-finding trial designs in oncology by simulation across various realistic settings. Our simulations show that the gBOIN-ETI design could outperform the other available approaches in terms of both the percentage of correct OD selection and the average number of patients allocated to the OD across various realistic trial settings.

Incorporating historical information to improve dose optimization design with toxicity and efficacy endpoints: iBOIN-ET.

In modern oncology drug development, adaptive designs have been proposed to identify the recommended phase 2 dose. The conventional dose finding designs focus on the identification of maximum tolerated dose (MTD). However, designs ignoring efficacy could put patients under risk by pushing to the MTD. Especially in immuno-oncology and cell therapy, the complex dose-toxicity and dose-efficacy relationships make such MTD driven designs more questionable. Additionally, it is not uncommon to have data available from other studies that target on similar mechanism of action and patient population. Due to the high variability from phase I trial, it is beneficial to borrow historical study information into the design when available. This will help to increase the model efficiency and accuracy and provide dose specific recommendation rules to avoid toxic dose level and increase the chance of patient allocation at potential efficacious dose levels. In this paper, we propose iBOIN-ET design that uses prior distribution extracted from historical studies to minimize the probability of decision error. The proposed design utilizes the concept of skeleton from both toxicity and efficacy data, coupled with prior effective sample size to control the amount of historical information to be incorporated. Extensive simulation studies across a variety of realistic settings are reported including a comparison of iBOIN-ET design to other model based and assisted approaches. The proposed novel design demonstrates the superior performances in percentage of selecting the correct optimal dose (OD), average number of patients allocated to the correct OD, and overdosing control during dose escalation process.

An adaptive biomarker basket design in phase II oncology trials.

The phase II basket trial in oncology is a novel design that enables the simultaneous assessment of treatment effects of one anti-cancer targeted agent in multiple cancer types. Biomarkers could potentially associate with the clinical outcomes and re-define clinically meaningful treatment effects. It is therefore natural to develop a biomarker-based basket design to allow the prospective enrichment of the trials with the adaptive selection of the biomarker-positive (BM+) subjects who are most sensitive to the experimental treatment. We propose a two-stage phase II adaptive biomarker basket (ABB) design based on a potential predictive biomarker measured on a continuous scale. At Stage 1, the design incorporates a biomarker cutoff estimation procedure via a hierarchical Bayesian model with biomarker as a covariate (HBMbc). At Stage 2, the design enrolls only BM+ subjects, defined as those with the biomarker values exceeding the biomarker cutoff within each cancer type, and subsequently assesses the early efficacy and/or futility stopping through the pre-defined interim analyses. At the end of the trial, the response rate of all BM+ subjects for each cancer type can guide drug development, while the data from all subjects can be used to further model the relationship between the biomarker value and the clinical outcome for potential future research. The extensive simulation studies show that the ABB design could produce a good estimate of the biomarker cutoff to select BM+ subjects with high accuracy and could outperform the existing phase II basket biomarker cutoff design under various scenarios.

A Bayesian optimal interval design for dose optimization with a randomization scheme based on pharmacokinetics outcomes in oncology.

The primary objective of an oncology dose-finding trial for novel therapies, such as molecularly targeted agents and immune-oncology therapies, is to identify the optimal dose (OD) that is tolerable and therapeutically beneficial for subjects in subsequent clinical trials. Pharmacokinetic (PK) information is considered an appropriate indicator for evaluating the level of drug intervention in humans from a pharmacological perspective. Several novel anticancer agents have been shown to have significant exposure-efficacy relationships, and some PK information has been considered an important predictor of efficacy. This paper proposes a Bayesian optimal interval design for dose optimization with a randomization scheme based on PK outcomes in oncology. A simulation study shows that the proposed design has advantages compared to the other designs in the percentage of correct OD selection and the average number of patients allocated to OD in 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.

Constrained hierarchical Bayesian model for latent subgroups in basket trials with two classifiers.

The basket trial in oncology is a novel clinical trial design that enables the simultaneous assessment of one treatment in multiple cancer types. In addition to the usual basket classifier of the cancer types, many recent basket trials further contain other classifiers like biomarkers that potentially affect the clinical outcomes. In other words, the treatment effects in those baskets are often categorized by not only the cancer types but also the levels of other classifiers. Therefore, the assumption of exchangeability is often violated when some baskets are more sensitive to the targeted treatment, whereas others are less. In this article, we propose a constrained hierarchical Bayesian model for latent subgroups (CHBM-LS) to deal with potential heterogeneity of treatment effects due to both the cancer type (first classifier) and another classifier (second classifier) in basket trials. Different baskets defined by multiple cancer types and multiple levels of the second classifier are aggregated into subgroups using a latent subgroup modeling approach. Within each latent subgroup, the treatment effects are similar and approximately exchangeable to borrow information. The CHBM-LS approach evaluates the treatment effect for each basket while allowing adaptive information borrowing across the baskets by identifying latent subgroups. The simulation study shows that the CHBM-LS approach outperforms other approaches with higher statistical power and better-controlled type I error rates under various scenarios with heterogeneous treatment effects across baskets.

TITE-gBOIN: Time-to-event Bayesian optimal interval design to accelerate dose-finding accounting for toxicity grades.

The new therapeutic agents, such as molecular targeted agents and immuno-oncology therapies, appear more likely to induce multiple toxicities at different grades than dose-limiting toxicities defined in traditional dose-finding trials. In addition, it is often challenging to make adaptive decisions on dose escalation and de-escalation on time because of the fast accrual rate and/or the late-onset toxicity outcomes, causing the potential suspension of the enrollment and the delay of the trials. To address these issues, we propose a time-to-event Bayesian optimal interval design to accelerate the dose-finding process utilizing toxicity grades based on both cumulative and pending toxicity outcomes. The proposed design, named "TITE-gBOIN" design, is a nonparametric and model-assisted design and has the virtues of robustness, simplicity and straightforward to implement in actual oncology dose-finding trials. A simulation study shows that the TITE-gBOIN design has a higher probability of selecting the MTDs correctly and allocating more patients to the MTDs across various realistic settings while reducing the trial duration significantly, therefore can accelerate early-stage dose-finding trials.

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.

[Introduction of Oncology Dose-Finding Trial Designs].

The primary objective of oncology dose-finding trials is to estimate the maximum tolerated dose(MTD)and determine the optimal dose(OD)for subsequent clinical trials by evaluating pharmacokinetics and pharmacodynamics of new drugs, treatment effects, and predictive markers. Oncology dose-finding trial designs can be categorized into 3 types based on their statistical bases and implementation approaches: algorithm-based, model-based, and model-assisted designs. In this paper, we introduce the characteristics of various oncology dose-finding trial designs according to the categories. First, oncology dose-finding trial designs solely based on toxicity for MTD determination are discussed, followed by oncology dose-finding trial designs based on efficacy and toxicity for identifying OD. Sequential enrollment, combination therapy, toxicity grade, and historical data are also briefly introduced.

A phase 1 study of oral ASP5878, a selective small-molecule inhibitor of fibroblast growth factor receptors 1-4, as a single dose and multiple doses in patients with solid malignancies.

ASP5878 is a selective small-molecule inhibitor of fibroblast growth factor receptors (FGFRs). This study investigated safety, tolerability, and antitumor effect of single and multiple oral doses of ASP5878 in patients with solid tumors. This phase 1, open label, first-in-human study comprised dose-escalation and dose-expansion parts. Primary objectives of the dose-escalation part were to identify the dose-limiting toxicity (DLT), maximum tolerated dose, and recommended dose of ASP5878 for the dose-expansion part. Nine dose cohorts of ASP5878 were evaluated (0.5─2 mg once daily; 2─40 mg twice daily [BID]). A single dose of ASP5878 was followed by a 2-day pharmacokinetic collection, and then either 28-day cycles of daily dosing (ASP5878 ≤ 10 mg BID) or 5-day dosing/2-day interruption (ASP5878 ≥ 20 mg BID). The primary objective of the dose-expansion part was to determine the safety of ASP5878 (16 mg BID) administered in 28-day cycles of 5-day dosing/2-day interruption in patients with urothelial carcinoma, hepatocellular carcinoma, or squamous cell lung carcinoma with FGFR genetic alterations. Safety was assessed by monitoring adverse events (AEs). Thirty-five patients were enrolled and 31 discontinued in the dose-escalation part; 51 patients were enrolled and 51 discontinued in the dose-expansion part. In the dose-escalation part, 66.7% of patients in the 20 mg BID 5-day dosing/2-day interruption group reported DLTs of hyperphosphatemia. The recommended dose for the dose-expansion part was 16 mg BID. Common AEs included retinal detachment, diarrhea, and increased alanine aminotransferase. One death occurred that was not related to ASP5878. ASP5878 was well tolerated with manageable toxicities including hyperphosphatemia.

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.

ASP8273 tolerability and antitumor activity in tyrosine kinase inhibitor-naïve Japanese patients with EGFR mutation-positive non-small-cell lung cancer.

Epidermal growth factor receptor (EGFR) activating mutations occur in approximately 50% of East Asian patients with non-small-cell lung cancer (NSCLC) and confer sensitivity to tyrosine kinase inhibitors (TKIs). ASP8273 is an irreversible EGFR-TKI, given orally, that inhibits EGFR activating mutations and has shown clinical activity in patients with EGFR mutation-positive NSCLC. Epidermal growth factor receptor-TKI-naïve Japanese adult patients (≥20 years) with NSCLC harboring EGFR mutations were enrolled in this open-label, single-arm, phase II study (ClinicalTrials.gov identifier NCT02500927). Patients received ASP8273 300 mg once daily until discontinuation criteria were met. The primary end-point was to determine the safety of ASP8273 300 mg; the secondary end-point was antitumor activity defined by RECIST version 1.1. Thirty-one patients (12 men and 19 women; median age, 64 years [range, 31-82 years]) with EGFR mutation-positive NSCLC were enrolled; as of 23 February 2016, 25 patients (81%) were still on study. Of the 31 patients, 27 (87%) had an exon 19 deletion (n = 13, 42%) or an L858R (n = 14, 45%) EGFR activating mutation, and two (7%) had an L861Q mutation. Five patients (16%) had other EGFR activating mutations, two had an activating mutation and the T790M resistance mutation. The most commonly reported treatment-emergent adverse event was diarrhea (n = 24, 77%). All patients had at least one post-baseline scan; one patient (3%) achieved a confirmed complete response, 13 (42%) had a confirmed partial response, and 15 (48%) had confirmed stable disease (disease control rate, 94% [n = 29/31]) per investigator assessment. Once-daily ASP8273 at 300 mg was generally well tolerated and showed antitumor activity in TKI-naïve Japanese patients with EGFR mutation-positive NSCLC.

Clinical activity of ASP8273 in Asian patients with non-small-cell lung cancer with EGFR activating and T790M mutations.

Epidermal growth factor receptor (EGFR)-activating mutations confer sensitivity to tyrosine kinase inhibitor (TKI) treatment for non-small-cell lung cancer (NSCLC). ASP8273 is a highly specific, irreversible, once-daily, oral, EGFR TKI that inhibits both activating and resistance mutations. This ASP8273 dose-escalation/dose-expansion study (NCT02192697) was undertaken in two phases. In phase I, Japanese patients (aged ≥20 years) with NSCLC previously treated with ≥1 EGFR TKI received escalating ASP8273 doses (25-600 mg) to assess safety/tolerability and to determine the maximum tolerated dose (MTD) and/or the recommended phase II dose (RP2D) by the Bayesian Continual Reassessment Method. In phase II, adult patients with T790M-positive NSCLC in Japan, Korea, and Taiwan received ASP8273 at RP2D to further assess safety/tolerability and determine antitumor activity, which was evaluated according to Simon's two-stage design (threshold response = 30%, expected response = 50%, α = 0.05, ß = 0.1). Overall, 121 (n = 45 [33W/12M] phase I, n = 76 [48W/28M]) phase 2) patients received ≥1 dose of ASP8273. In phase I, RP2D and MTD were established as 300 and 400 mg, respectively. As 27 of the 63 patients treated with ASP8273 300 mg achieved a clinical response, ASP8273 was determined to have antitumor activity. The overall response rate at week 24 in all patients was 42% (n = 32/76; 95% confidence interval, 30.9-54.0). Median duration of progression-free survival was 8.1 months (95% confidence interval, 5.6, upper bound not reached). The most commonly reported treatment-related adverse event in phase II was diarrhea (57%, n = 43/76). ASP8273 300 mg was generally well tolerated and showed antitumor activity in Asian patients with both EGFR-activating and T790M mutations.

Bayesian dose-finding phase I trial design incorporating historical data from a preceding trial.

We consider the problem of incorporating historical data from a preceding trial to design and conduct a subsequent dose-finding trial in a possibly different population of patients. In oncology, for example, after a phase I dose-finding trial is completed in Caucasian patients, investigators often conduct a further phase I trial to determine the maximum tolerated dose in Asian patients. This may be due to concerns about possible differences in treatment tolerability between populations. In this study, we propose to adaptively incorporate historical data into prior distributions assumed in a new dose-finding trial. Our proposed approach aims to appropriately borrow strength from a previous trial to improve the maximum tolerated dose determination in another patient population. We define a "historical-to-current (H-C)" parameter representing the degree of borrowing based on a retrospective analysis of previous trial data. In simulation studies, we examine the operating characteristics of the proposed method in comparison with 3 alternative approaches and assess how the H-C parameter functions across a variety of realistic settings.

Bayesian dose-finding phase I trial design incorporating pharmacokinetic assessment in the field of oncology.

The main purpose of a phase I dose-finding study in the field of oncology is to evaluate toxicity and pharmacokinetic (PK) data and to estimate the optimal dose (OD) for subsequent clinical trials. From a pharmacological perspective, PK information is considered as an appropriate indicator for evaluating the degree of drug intervention in humans. Dose proportionality is typically assessed to investigate the PK properties of a drug. If we rely solely on the dose-exposure relationship, then when this relationship is not proportional, eg, if there is saturation of drug elimination or absorption, the performance of OD selection may be affected. This may be because any exposure ratio between two dose levels differs from the dose ratio between the dose levels. In addition, large inter-individual variability in exposure affects the occurrence of toxicity. Therefore, incorporating PK assessment in a phase I dose-finding trial may enable us to more precisely estimate OD. In most oncology dose-finding clinical trials, however, the result of PK analysis is not explicitly incorporated in the dose-finding determination analysis. In this study, we propose a Bayesian approach to incorporating PK assessment into OD estimation in a dose-finding trial. Our proposed approach incorporates into the statistical model an adjustment based on the latest area under the time-concentration curve assessment. The simulation study shows that compared with standard methods, the proposed method may be better able to select the correct ODs across a variety of realistic settings.

BOIN-ET: Bayesian optimal interval design for dose finding based on both efficacy and toxicity outcomes.

One of the main purposes of a phase I dose-finding trial in oncology is to identify an optimal dose (OD) that is both tolerable and has an indication of therapeutic benefit for subjects in subsequent phase II and III trials. Many phase I dose-finding methods based solely on toxicity considerations have been proposed under the assumption that both toxicity and efficacy monotonically increase with the dose level. Such an assumption may not be necessarily the case, however, when evaluating the OD for molecular targeted, cytostatic, and biological agents, as well as immune-oncology therapy. To address this issue, we extend the Bayesian optimal interval (BOIN) design, which is nonparametric and thus does not require the assumption used in model-based designs, in order to identify an OD based on both efficacy and toxicity outcomes. The new design is named "BOIN-ET." A simulation study is presented that includes a comparison of this proposed method to the model-based approaches in terms of both efficacy and toxicity responses. The simulation shows that BOIN-ET has advantages in both the percentages of correct ODs selected and the average number of patients allocated to the ODs across a variety of realistic settings.

A simulation study of methods for selecting subgroup-specific doses in phase 1 trials.

Patient heterogeneity may complicate dose-finding in phase 1 clinical trials if the dose-toxicity curves differ between subgroups. Conducting separate trials within subgroups may lead to infeasibly small sample sizes in subgroups having low prevalence. Alternatively,it is not obvious how to conduct a single trial while accounting for heterogeneity. To address this problem,we consider a generalization of the continual reassessment method on the basis of a hierarchical Bayesian dose-toxicity model that borrows strength between subgroups under the assumption that the subgroups are exchangeable. We evaluate a design using this model that includes subgroup-specific dose selection and safety rules. A simulation study is presented that includes comparison of this method to 3 alternative approaches,on the basis of nonhierarchical models,that make different types of assumptions about within-subgroup dose-toxicity curves. The simulations show that the hierarchical model-based method is recommended in settings where the dose-toxicity curves are exchangeable between subgroups. We present practical guidelines for application and provide computer programs for trial simulation and conduct.

Potentiation of Acetylcholine-Mediated Facilitation of Inhibitory Synaptic Transmission by an Azaindolizione Derivative, ZSET1446 (ST101), in the Rat Hippocampus.

The integrity of the hippocampal network depends on the coordination of excitatory and inhibitory signaling, which are under dynamic control by various regulatory influences such as the cholinergic systems. ZSET1446 (ST101; spiro[imidazo[1,2-a]pyridine-3,2-indan]-2(3H)-one) is a newly synthesized azaindolizinone derivative that significantly improves learning deficits in various types of Alzheimer disease (AD) models in rats. We examined the effect of ZSET1446 on the nicotinic acetylcholine (ACh) receptor (nAChR)-mediated regulation of synaptic transmission in hippocampal slices of rats. ZSET1446 significantly potentiated the facilitatory effect of nicotine and ACh on the frequency of spontaneous postsynaptic currents (sPSCs) recorded in CA1 pyramidal neurons with a maximum effect at 100 pM (tested range, 10 pM-1000 pM). The basal sPSC frequency without ACh was not affected. Such potentiation by ZSET1446 was observed in both the pharmacologic isolations of inhibitory and excitatory sPSCs and markedly reduced by blockade of either α7 or α4ß2 nAChRs. ZSET1446 did not affect ACh-activated inward currents or depolarization of interneurons in the stratum radiatum and the lacunosum moleculare. These results indicate that ZSET1446 potentiates the nicotine-mediated enhancement of synaptic transmission in the hippocampal neurons without affecting nAChRs themselves, providing a novel possible mechanism of procognitive action that might improve learning deficits in clinical therapy.