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Identifying the right dose is one of the most important decisions in drug development. Adaptive designs are promoted to conduct dose-finding clinical trials as they are more efficient and ethical compared with static designs. However, current techniques in response-adaptive designs for dose allocation are complex and need significant computational effort, which is a major impediment for implementation in practice. This study proposes a Bayesian nonparametric framework for estimating the dose-response curve, which uses a piecewise linear approximation to the curve by consecutively connecting the expected mean response at each dose. Our extensive numerical results reveal that a first-order Bayesian nonparametric model with a known correlation structure in prior for the expected mean response performs competitively when compared with the standard approach and other more complex models in terms of several relevant metrics and enjoys computational efficiency. Furthermore, structural properties for the optimal learning problem, which seeks to minimize the variance of the target dose, are established under this simple model. Summary of Contribution: In this work, we propose a methodology to derive efficient patient allocation rules in response-adaptive dose-finding clinical trials, where computational issues are the main concern. We show that our methodologies are competitive with the state-of-the-art methodology in terms of solution quality, are significantly more computationally efficient, and are more robust in terms of the shape of the dose-response curve, among other parameter changes. This research fits in “the intersection of computing and operations research” as it adapts operations research techniques to produce computationally attractive solutions to patient allocation problems in dose-finding clinical trials.

International audience; Dose-finding clinical trials in oncology aim to determine the maximum tolerated dose (MTD) of a new drug, generally defined by the proportion of patients with short-term dose-limiting toxicities (DLTs). Model-based approaches for such phase I oncology trials have been widely designed and are mostly restricted to the DLTs occurring during the first cycle of treatment, although patients continue to receive treatment for multiple cycles. We aim to estimate the probability of DLTs over sequences of treatment cycles via a Bayesian cumulative modeling approach, where the probability of DLT is modeled taking into account the cumulative effect of the administered drug and the DLT cycle of occurrence. We propose a design, called DICE (Dose-fInding CumulativE), for dose escalation and de-escalation according to previously observed toxicities, which aims at finding the MTD sequence (MTS). We performed an extensive simulation study comparing this approach to the time-to-event continual reassessment method (TITE-CRM) and a benchmark. In general, our approach achieved a better or comparable percentage of correct MTS selection. Moreover, we investigated the DICE prediction ability.

PURPOSE Novel Bayesian adaptive designs provide an effective way to improve clinical trial efficiency. These designs are superior to conventional methods, but implementing them can be challenging. The aim of this article was to describe what we learned while applying a novel Bayesian phase I-II design in a recent trial. METHODS The primary goal of the trial was to optimize radiation therapy (RT) dose among three levels (low, standard, and high), given either with placebo (P) or an investigational agent (A), for treating locally advanced, radiation-naive pancreatic cancer, deemed appropriate for RT rather than surgery. Up to 48 patients were randomly assigned fairly between RT plus P and RT plus A, with RT dose-finding done within each arm using the late-onset efficacy-toxicity design on the basis of two coprimary end points, tumor response and dose-limiting toxicity, both evaluated at up to 90 days. The random assignment was blinded, but within each arm, unblinded RT doses were chosen adaptively using software developed within the institution. RESULTS Implementing the design involved double-blind balance-restricted random assignment, real-time assessment of patient outcomes to evaluate the efficacy-toxicity trade-off for each RT dose in each arm to optimize each patient's RT dose adaptively, and transition from a single-center trial to a multicenter trial. We present lessons learned and illustrative documentation. CONCLUSION Implementing novel Bayesian adaptive trial designs requires close collaborations between physicians, pharmacists, statisticians, data managers, and sponsors. The process is difficult but manageable and essential for efficient trial conduct. Close collaboration during trial conduct is a key component of any trial that includes real-time adaptive decision rules.

Traditionally, dose-finding process for oncology compound is carried out in phase I dose escalation study and is driven by safety in order to find maximum tolerated dose (MTD). However, with the recent paradigm shift from cytotoxic drugs to new generation of targeted therapies and immuno-oncology therapies, it may be difficult or unnecessary to identify the MTD because of the possible non-monotonic dose–response curves, and efficacy data should be incorporated into the dose-finding process. In this article, we have proposed efficacy-driven dose-finding designs with a safety-driven warm-up phase. Both local investigation and adaptive randomization using the framework of double-sided isotonic regression are investigated. Simulation studies are used to compare the proposed design to the original double-sided isotonic design. The results show that a safety-driven warm-up phase at the beginning can significantly improve the performance of double-sided isotonic regression, and both local investigation and adaptive randomization have good operating characteristics for finding the best dose/dose range under different tested scenarios.

Background Patient‐reported adverse events (AEs) may be a useful adjunct to clinician‐assessed AEs for assessing tolerability in early phase, dose‐finding oncology trials (DFOTs). We reviewed DFOTs on ClinicalTrials.gov to describe trends in patient‐reported outcome (PRO) use. Methods DFOTs commencing 01 January 2007 – 20 January 2020 with ‘PROs’ or ‘quality of life’ as an outcome were extracted and inclusion criteria confirmed. Study and PRO characteristics were extracted. Completed trials that reported PRO outcomes and published manuscripts on ClinicalTrials.gov were identified, and PRO reporting details were extracted. Results 5.3% (548/10 372) DFOTs included PROs as an outcome. 231 (42.2%) were eligible: adult (224, 97%), solid tumour (175, 75.8%), and seamless phase 1/2 (108, 46.8%). PRO endpoints were identified in more trials (2.3 increase/year, 95% CI: 1.6–2.9) from an increasing variety of countries (0.7/year) (95% CI: 0.4–0.9) over time. PROs were typically secondary endpoints (207, 89.6%). 15/77 (19.5%) completed trials reported results on the ClinicalTrials.gov results database, and of those eight included their PRO results. Eighteen trials had published manuscripts available on ClinicalTrials.gov. Three (16.7%) used PROs to confirm the maximum tolerated dose. No trials identified who completed the PROs or how PROs were collected. Conclusions PRO use in DFOT has increased but remains limited. Future work should explore the role of PROs in DFOT and determine what guidelines are needed to standardise PRO use., PRO use in DFOT has increased but remains limited. Future work should explore the role of PROs in DFOT and determine what guidelines are needed to standardise PRO use.

The goal of a phase I dose-finding trial is to determine the dose level of a new drug with acceptable toxicity. The optimal dose level is determined by sequentially allocating patients to increasin...

We develop three approaches to phase I dose finding designs for engineered T cells in oncology. Our goal is to address a very particular difficulty in this clinical setting: an inability to fully administer the dose allocated to some patients. Current designs can be biased as a result of this incomplete information being ignored or discarded from the analysis. The performance of the three proposed solutions is largely similar, and all offer an advantage over the currently used design. One of the three methods is supported by theoretical study, and we provide some new results on this approach.

Supplemental Digital Content is available in the text., Background: This phase 2 observer-blind, randomized, multicenter, dose-ranging study evaluated immunogenicity and safety of different formulations of an AS03-adjuvanted H5N1 influenza vaccine in children 6–35 months of age. Methods: One hundred eighty-five children randomized into 5 groups [1.9 µg hemagglutinin (HA)/AS03B, 0.9 µg HA/AS03C, 1.9 µg HA/AS03C, 3.75 µg HA/AS03C or 3.75 µg HA/AS03D] were to receive 2 doses administered 21 days apart (primary vaccination). AS03 was classified by amount of DL-α-tocopherol, with AS03B the highest amount. One year later, all subjects were to receive unadjuvanted 3.75 µg HA as antigen challenge. Immunogenicity was assessed 21 days after primary vaccination (day 42) and 7 days after antigen challenge (day 392). Immunogenicity-fever index, based on hemagglutination inhibition and microneutralization antibody titers at day 42 and fever 7 days after each vaccination, was used to guide the selection of an acceptable formulation. Results: After primary vaccination, formulations elicited strong homologous immune responses with all subjects’ hemagglutination inhibition titers ≥1:40 post-vaccination. Immunogenicity-fever index based on hemagglutination inhibition and microneutralization assays showed that 1.9 µg HA/AS03B ranked the highest. Antibody levels persisted >4 times above baseline 12 months after primary vaccination with all formulations (day 385). Antibodies increased >4-fold after antigen challenge (day 392/day 385) with 1.9 µg HA/AS03B, 0.9 µg HA/AS03C and 1.9 µg HA/AS03C formulations. Overall per subject, the incidence of fever ranged from 28.6% (3.75 µg HA/AS03D) to 60.5% (1.9 µg HA/AS03B). Conclusions: All formulations were highly immunogenic and demonstrated acceptable safety profiles, with the 1.9 µg HA/AS03B providing the most favorable balance of immunogenicity versus reactogenicity for use in children 6–35 months of age.

Background: Relevant improvement in first-line treatment of metastatic pancreatic cancer (mPC) was provided by FOLFIRINOX and by gemcitabine (gem) plus nab-paclitaxel (Nab-p) regimens. Regardless of the first-line treatment survival benefit, most patients survive less than 1 year. Aim: The objectives of this multicenter phase I/II study were to evaluate as first-line chemotherapy (CT) two modified regimens of FOLFIRINOX, replacing either oxaliplatin (Oxa) or irinotecan with Nab-p, in patients with mPC. Methods: The primary objectives of phase 1 were the definition of the dose limit binations, while for phase II they were the characterization of safety and activity of Nab-FOLFIRI and Nab-FOLFOX in mPC. Results: Sixty-three patients received Nab-FOLFIRI or Nab-FOLFOX in phase I. We defined MTD at 120 mg/m2 for Nab-p with FOLFIRI and 160 mg/m2 with FOLFOX. In phase II, we randomized 42 patients for each arm with the following results: (1) overall response rate (ORR) was 31% for both schedules, (2) a clinical benefit rate (CBR) of 69% and 71%, (3) 1-year survival was 41% and 50%, (4) progression free survival (PFS) was 6 months and 5.6 months, (5) median overall survival (OS) was 10.2 and 10.4 months for Nab-FOLFIRI and Nab-FOLFOX, respectively. (6) Neutropenia was the most common grade ≥3 adverse event in our regimens, significantly lower than that reported for the FOLFIRINOX triplet. Conclusion: Nab-FOLFIRI and Nab-FOLFOX might be hopeful first-line CT options for mPC patients, with promising activity and a good safety profile.

BACKGROUND: The purpose of the present study was to identify the ideal polidocanol (POL) concentration for mechanochemical ablation (MOCA) of the great saphenous vein (GSV) using the ClariVein system (Merit Medical, South Jordan, Utah).METHODS: We performed a multicenter, randomized, controlled, single-blind trial with a follow-up period of 6 months. Patients with symptomatic primary truncal GSV incompetence were randomized to MOCA + 2% POL liquid (2% group) or MOCA + 3% POL liquid (3% group). The primary outcome was technical success (TS), defined as an open part of the treated vein segment of ≤10 cm in length. The secondary outcomes were alternative TS, defined as ≥85% occlusion of the treated vein segment, postoperative pain, venous clinical severity scores, Aberdeen varicose vein questionnaire scores, and short-form 36-item health survey questionnaire scores, and complications.RESULTS: From 2012 to 2018, 364 patients (375 limbs) were included, of which, 189 limbs were randomly allocated to the 2% group and 186 to the 3% group. The TS rate at 6 months was 69.8% in the 2% group vs 78.0% in the 3% group (P = .027). A higher overall TS rate was seen in GSVs of ≤5.9 mm compared with GSVs >5.9 mm (84.3% vs 59.5%, respectively; P < .001). The alternative TS rate at 6 months was 61.4% in the 2% group and 67.7% in the 3% group (P = .028). The venous clinical severity scores, Aberdeen varicose vein questionnaire scores, and most short-form 36-item health survey questionnaire domains had improved in both groups (P < .002). Postprocedural pain was low. Two pulmonary embolisms and two deep vein thromboses were seen. Superficial venous thrombosis had occurred more often in the 3% group (18 vs 8 in the 2% group; P = .033).CONCLUSIONS: The results from the present study showed a higher success rate for MOCA with 3% POL liquid than for MOCA with 2% POL liquid at 6 months of follow-up. However, the difference in quality of life was not significant. Long-term follow-up studies are required to investigate whether these results will be sustained in the future.

Models of transient synovitis that can be controlled with antiinflammatory and analgesic drugs have been used to study pain amelioration. To this end, we aimed to determine the dose of intraarticularly administered E. coli LPS that induced signs of synovitis without systemic signs in clinically healthy male castrated sheep (n = 14). In phase 1, a single dose of LPS (0.5, 1.0, 1.5, or 2.0 ng in a total volume of 0.5 mL) was administered into the right stifle joint. In phase 2, a dose of LPS (1.0 or 2.0 μg) in 0.3 mL was administered to 4 naïve sheep. In phase 3, 4 sheep from phase 1 were inoculated after a 60 d washout period with either 0.5 or 1.0 μg of LPS. During the first 48 h after LPS administration, the following were performed: assessment of clinical parameters; scoring for lameness, pain on limb flexion, and local swelling; and ultrasonography of the joints were performed. The doses tested during phase 1 produced subtle signs. During phase 2, mild to moderate lameness with no evidence of systemic signs occurred at both doses. In phase 3, clinical responses were similar between the 0.5- and 1-μg doses. Signs of swelling were not observed at any time. Therefore, we consider the 0.5-μg to be the most appropriate for this model, because it was the lowest dose tested capable of causing lameness without signs of systemic inflammation in all animals.

Intravenous (IV) atezolizumab is approved for non–small cell lung and other cancers. Subcutaneous (SC) atezolizumab coformulated with recombinant human hyaluronidase, a permeation enhancer for SC dispersion and absorption, is being developed to improve treatment options, reduce burden, and increase efficiency for patients and practitioners. IMscin001 (NCT03735121), a 2‐part, open‐label, global, multicenter, phase 1b/3 study, is evaluating the pharmacokinetics (PK), safety, and efficacy of SC atezolizumab. The part 1 (phase 1b) objective was determination of an SC atezolizumab dose yielding a serum trough concentration (Ctrough) comparable with IV. Patients enrolled in 3 cohorts received SC atezolizumab 1800 mg (thigh) once (cohort 1), 1200 mg (thigh) every 2 weeks for 3 cycles (cohort 2), or 1800 mg (abdomen) every 3 weeks cycle 1, then cycles 2 and 3 (thigh) every 3 weeks (cohort 3). In subsequent cycles, IV atezolizumab 1200 mg every 3 weeks was administered until loss of clinical benefit. SC atezolizumab 1800 mg every 3 weeks and 1200 mg every 2 weeks provided similar Ctrough and area under the curve values in cycle 1 to the corresponding IV atezolizumab reference, was well tolerated, and exhibited a safety profile consistent with the established IV formulation. Exposure following SC injection in the abdomen was lower (20%, 28%, and 27% for Ctrough, maximum concentration, and area under the concentration‐time curve from time 0 to day 21, respectively) than in the thigh. Part 1 SC and IV PK data were analyzed using a population PK modeling approach, followed by simulations. Part 2 (phase 3) will now be initiated to demonstrate that SC atezolizumab PK exposure is not lower than that of IV.

Despite numerous publications emphasizing the value of dose finding, drug development in oncology is dominated by the mindset that higher dose provides higher efficacy. Examples of dose finding implemented by biopharmaceutical firms can change this mindset. The purpose of this article is to outline a pragmatic dose selection strategy for immuno-oncology (IO) and other targeted monoclonal antibodies (mAbs). The approach was implemented for pembrolizumab. Selecting a recommended phase II dose (RP2D) with a novel mechanism of action is often challenging due to uncertain relationships between pharmacodynamics measurements and clinical end points. Additionally, phase I efficacy and safety data are generally inadequate for RP2D selection for IO mAbs. Here, the RP2D was estimated based on phase I (clinical study KN001 A and A2) pharmacokinetics data as the dose required for target saturation, which represents a surrogate for maximal pharmacological effect for antagonist mAbs. Due to limitations associated with collecting and analyzing tumor biopsies, characterizing intratumoral target engagement (TE) is challenging. To overcome this gap, a physiologically-based pharmacokinetic model was implemented to predict intratumoral TE. As tumors are spatially heterogeneous, TE was predicted in well-vascularized and poorly vascularized tumor regions. Additionally, impact of differences in target expression, for example, due to interindividual variability and cancer type, was simulated. Simulations showed that 200 mg every 3 weeks can achieve ≥ 90% TE in clinically relevant scenarios, resulting in the recommendation of 200 mg every 3 weeks as the RP2D. Randomized dose comparison studies (KN001 B2 and D) showing similar efficacy over a fivefold dose/exposure range confirmed the RP2D as the pivotal dose.

Background Vitamin D supplementation may prevent falls in older persons, but evidence is inconsistent, possibly because of dosage differences. Objective To compare the effects of 4 doses of vitamin D3 supplements on falls. Design 2-stage Bayesian, response-adaptive, randomized trial. (ClinicalTrials.gov: NCT02166333). Setting 2 community-based research units. Participants 688 participants, aged 70 years and older, with elevated fall risk and a serum 25-hydroxyvitamin D [25-(OH)D] level of 25 to 72.5 nmol/L. Intervention 200 (control), 1000, 2000, or 4000 IU of vitamin D3 per day. During the dose-finding stage, participants were randomly assigned to 1 of the 4 vitamin D3 doses, and the best noncontrol dose for preventing falls was determined. After dose finding, participants previously assigned to receive noncontrol doses received the best dose, and new enrollees were randomly assigned to receive 200 IU/d or the best dose. Measurements Time to first fall or death over 2 years (primary outcome). Results During the dose-finding stage, the primary outcome rates were higher for the 2000- and 4000-IU/d doses than for the 1000-IU/d dose, which was selected as the best dose (posterior probability of being best, 0.90). In the confirmatory stage, event rates were not significantly different between participants with experience receiving the best dose (events and observation time limited to the period they were receiving 1000 IU/d; n = 308) and those randomly assigned to receive 200 IU/d (n = 339) (hazard ratio [HR], 0.94 [95% CI, 0.76 to 1.15]; P = 0.54). Analysis of falls with adverse outcomes suggested greater risk in the experience-with-best-dose group versus the 200-IU/d group (serious fall: HR, 1.87 [CI, 1.03 to 3.41]; fall with hospitalization: HR, 2.48 [CI, 1.13 to 5.46]). Limitations The control group received 200 IU of vitamin D3 per day, not a placebo. Dose finding ended before the prespecified thresholds for dose suspension and dose selection were reached. Conclusion In older persons with elevated fall risk and low serum 25-(OH)D levels, vitamin D3 supplementation at doses of 1000 IU/d or higher did not prevent falls compared with 200 IU/d. Several analyses raised safety concerns about vitamin D3 doses of 1000 IU/d or higher. Primary funding source National Institute on Aging.

We revisit our earlier extension of the CRM and introduce a new version of the dose escalation procedure. Its performance is assessed by simulation, with respect to a template of five operating mea...

Background/aims This article describes the proposed design of a phase I study evaluating the safety of ceramide nanoliposome and vinblastine among an initial set of 19 possible dose combinations in patients with relapsed/refractory acute myeloid leukemia and patients with untreated acute myeloid leukemia who are not candidates for intensive induction chemotherapy. Methods Extensive collaboration between statisticians and clinical investigators revealed the need to incorporate several adaptive features into the design, including the flexibility of adding or eliminating certain dose combinations based on safety criteria applied to multiple dose pairs. During the design stage, additional dose levels of vinblastine were added, increasing the dimension of the drug combination space and thus the complexity of the problem. Increased complexity made application of existing drug combination dose-finding methods unsuitable in their current form. Results Our solution to these challenges was to adapt a method based on isotonic regression to meet the research objectives of the study. Application of this adapted method is described herein, and a simulation study of the design’s operating characteristics is conducted. Conclusion The aim of this article is to bring to light examples of novel design applications as a means of augmenting the implementation of innovative designs in the future and to demonstrate the flexibility of adaptive designs in satisfying changing design conditions.

Background A wide lower face and a square jaw are considered esthetic problems, particularly in Asia. Objective To investigate the optimal dose of a novel botulinum toxin (prabotulinum toxin A) for treating masseteric hypertrophy. Methods Ninety subjects with masseteric hypertrophy were randomly divided into 5 groups and treated with placebo (A, normal saline) or prabotulinum toxin A (B: 24, C: 48, D: 72, and E: 96 units). Photography, ultrasonography, and 3-dimensional imaging were performed before and after injection at baseline and at 4, 8, 12, and 16 weeks after treatment. The participants also rated their satisfaction. Results Masseter thickness significantly reduced in all groups at 12 weeks, compared with that in the placebo group. A dose-dependent reduction in masseter thickness was observed at the resting and maximal clenching positions. Sonography and 3-dimensional imaging revealed a gradual reduction in masseter thickness and volume, respectively, during the first 12 weeks. Despite being slightly effective, a dose of 24 units might be insufficient for resolving square face problems. Patients in Group E reported discomfort during jaw movement. Conclusion Prabotulinum toxin A could effectively improve lower face contour without major complications, with an optimal dose of 48 to 72 units, followed by reinjection after 12 weeks.

Background/Aims: Combinations of treatments that have already received regulatory approval can offer additional benefit over Each of the treatments individually. However, trials of these combinations are lower priority than those that develop novel therapies, which can restrict funding, timelines and patient availability. This article develops a novel trial design to facilitate the evaluation of New combination therapies. This trial design combines elements of phase II and phase III trials to reduce the burden of evaluating combination therapies, while also maintaining a feasible sample size. This design was developed for a randomised trial that compares the properties of three combination doses of ketamine and dexmedetomidine, given intranasally, to ketamine delivered intravenously for children undergoing a closed reduction for a fracture or dislocation. Methods: This trial design uses response-adaptive randomisation to evaluate different dose combinations and increase the information collected for successful novel drug combinations. The design then uses Bayesian dose-response modelling to undertake a comparative effectiveness analysis for the most successful dose combination against a relevant comparator. We used simulation methods determine the thresholds for adapting the trial and making conclusions. We also used simulations to evaluate the probability of selecting the dose combination with the highest true effectiveness the operating characteristics of the design and its Bayesian predictive power. Results: With 410 participants, five interim updates of the randomisation ratio and a probability of effectiveness of 0.93, 0.88 and 0.83 for the three dose combinations, we have an 83% chance of randomising the largest number of patients to the drug with the highest probability of effectiveness. Based on this adaptive randomisation procedure, the comparative effectiveness analysis has a type I error of less than 5% and a 93% chance of correcting concluding non-inferiority, when the probability of effectiveness for the optimal combination therapy is 0.9. In this case, the trial has a greater than 77% chance of meeting its dual aims of dose-finding and comparative effectiveness. Finally, the Bayesian predictive power of the trial is over 90%. Conclusions: By simultaneously determining the optimal dose and collecting data on the relative effectiveness of an intervention, we can minimise administrative burden and recruitment time for a trial. This will minimise the time required to get effective, safe combination therapies to patients quickly. The proposed trial has high potential to meet the dual study objectives within a feasible overall sample size.

Dose-finding trials aim to determine a safe dose to be tested in larger trials for efficacy. In oncology, designs generally assume conventional monotonic increasing dose-toxicity relationships, mostly with binary outcomes (e.g., dose-limiting toxicity or not), measured in the first cycle of therapy or for a fixed number of cycles. However, with new anti-cancer agents such as molecularly targeted therapies and immunotherapies, late-onset toxicities have become more frequent. Designs with prolonged observation windows and censored endpoints analyzed using survival models, appear particularly suited to these settings. Moreover, in this setting, the observation of the late-onset toxicity endpoint could be precluded by trial discontinuation due to death, progression, patient withdrawal, or physician discretion, defining a competing event to toxicity. We propose extensions of the Continual Reassessment Method (CRM) dose-finding design using survival working models for right-censored endpoints and for handling treatment discontinuation in the toxicity observation window, namely the Survival-CRM (Surv-CRM) and the informative survival-CRM (iSurv-CRM). We also developed a benchmark approach for its evaluation. In a simulation study, we compared the performance of the Surv-CRM and iSurv-CRM, to those of the Time-to-event (TITE)-CRM and the nonparametric benchmark. The performance of the proposed methods was consistent with the complexity of scenarios as assessed by the nonparametric benchmark. Without treatment discontinuations, the Surv-CRM provides proportions of correct dose selection close to those of the TITE-CRM with fewer observed toxicities and patients assigned to overtoxic dose levels. In the presence of treatment discontinuation, the iSurv-CRM outperforms the TITE-CRM in identifying the correct dose level.

PURPOSE For immunotherapy, such as checkpoint inhibitors and chimeric antigen receptor T-cell therapy, where the efficacy does not necessarily increase with the dose, the maximum tolerated dose may not be the optimal dose for treating patients. For these novel therapies, the objective of dose-finding trials is to identify the optimal biologic dose (OBD) that optimizes patients’ risk-benefit trade-off. METHODS We propose a simple and flexible Bayesian optimal interval phase I/II (BOIN12) trial design to find the OBD that optimizes the risk-benefit trade-off. The BOIN12 design makes the decision of dose escalation and de-escalation by simultaneously taking account of efficacy and toxicity and adaptively allocates patients to the dose that optimizes the toxicity-efficacy trade-off. We performed simulation studies to evaluate the performance of the BOIN12 design. RESULTS Compared with existing phase I/II dose-finding designs, the BOIN12 design is simpler to implement, has higher accuracy to identify the OBD, and allocates more patients to the OBD. One of the most appealing features of the BOIN12 design is that its adaptation rule can be pretabulated and included in the protocol. During the trial conduct, clinicians can simply look up the decision table to allocate patients to a dose without complicated computation. CONCLUSION The BOIN12 design is simple to implement and yields desirable operating characteristics. It overcomes the computational and implementation complexity that plagues existing Bayesian phase I/II dose-finding designs and provides a useful design to optimize the dose of immunotherapy and targeted therapy. User-friendly software is freely available to facilitate the application of the BOIN12 design.

With the recent advancement in immuno-oncology, next-stage early oncology development is focusing on identifying the best combinations of established immunotherapies with new agents to either overc...

The attrition rate of anticancer drugs during the clinical development remains very high. Interspecies extrapolation of anticancer drug pharmacodynamics (PD) could help to bridge the gap between preclinical and clinical settings and to improve drug development. Indeed, when combined with a physiologically-based-pharmacokinetics (PBPK) approach, PD interspecies extrapolation could be a powerful tool for predicting drug behavior in clinical trials. The present study aimed to explore this field for anticipating the clinical efficacy of a new Bcl-2 inhibitor, S 55746, for which dose ranging studies in xenografted mice and clinical data from a phase 1 trial involving cancer patients were available. Different strategies based on empirical or more mechanistic assumptions (based on PBPK-PD modelling) were developped and compared: the Rocchetti approach (ROC); the Orthogonal Rocchetti approach (oROC), a variant of ROC based on an orthogonal regression; the Consistent across species approach, bringing out an efficacy parameter assumed to be consistent across species; and the Scaling species-specific parameters approach, assuming the concentration-efficacy link is the same in mice as in humans, after allometric scaling. Empirical approaches (ROC and oROC) gave similar predictive performances and seemed to overestimate the active S 55746 dose compared to mechanistic approaches, while strategies elaborated from semi-mechanistic concepts and PBPK-PD modelling did not seem to be invalidated by clinical efficacy data. Also, empirical methods only predict a single dose level for the subsequent clinical studies, whereas mechanism-based strategies are more informative about the dose response relationship, highlighting the potential interest of such approaches in drug development.

Purpose Camrelizumab inhibits PD-1 in non-clinical models and showed typical non-clinical pharmacokinetic (PK) and safety profiles for an IgG4 monoclonal antibody. We report results from the First-in-Human Phase 1 trial of camrelizumab in Australian population.

Abstract. Low concentrations of serum vitamin K accompany high concentrations of undercarboxylated osteocalcin (ucOC) and osteoporotic fractures. Although vitamin K2 (MK-4) is approved as a therapeutic agent for the treatment of osteoporosis in some countries, the dose-response is unknown. The objective of this study was to assess the improvement in carboxylation of osteocalcin (OC) in response to escalating doses of MK-4 supplementation. A nine-week, open-labeled, prospective cohort study was conducted in 29 postmenopausal women who suffered hip or vertebral compression fractures. Participants took low-dose MK-4 (0.5 mg) for 3 weeks (until the second visit), then medium-dose MK-4 (5 mg) for 3 weeks (until the third visit), then high-dose MK-4 (45 mg) for 3 weeks. The mean ± SD age of the participants was 69 ± 9 years. MK-4 dose (p

A dose-escalation study was conducted to find the effective dose of Lactococcus lactis subsp. cremoris FC for improving defecation in healthy subjects. Twenty-seven subjects were recruited and consecutively ingested a placebo and two dose levels of L. cremoris FC (dose level 1, 1 × 107 cfu; dose level 2, 2 × 107 cfu) capsules daily for two weeks. Frequency of defecation (times/week) was significantly increased by dose level 2, and stool volume (units/week) was significantly increased by dose level 1. This dose-escalation study elucidated that intake of at least 1 × 107 cfu L. cremoris FC improves defecation.

Simulation studies have shown that novel designs such as the continual reassessment method and the Bayesian optimal interval (BOIN) design outperform the 3 + 3 design by recommending the maximum tolerated dose (MTD) more often, using less patients, and allotting more patients to the MTD. However, it is not clear whether these novel designs would have yielded different results in the context of real-world dose-finding trials. This is a commonly mentioned reason for the continuous use of 3 + 3 designs for oncology trials, with investigators considering simulation studies not sufficiently convincing to warrant the additional design complexity of novel designs. METHODS: We randomly sampled 60 published dose-finding trials to obtain 22 that used the 3 + 3 design, identified an MTD, published toxicity data, and had more than two dose levels. We compared the published MTD with the estimated MTD using the continual reassessment method and BOIN using target toxicity rates of 25% and 30% and toxicity data from the trial. Moreover, we compared patient allocation and sample size assuming that these novel designs had been implemented. RESULTS: Model-based designs chose dose levels higher than the published MTD in about 40% of the trials, with estimated and observed toxicity rates closer to the target toxicity rates of 25% and 30%. They also assigned less patients to suboptimal doses and permitted faster dose escalation. CONCLUSION: This study using published dose-finding trials shows that novel designs would recommend different MTDs and confirms the advantages of these designs compared with the 3 + 3 design, which were demonstrated by simulation studies.

PURPOSE We propose a novel early completion method for phase I dose-finding trials using model-assisted designs. The trials can halt when a maximum tolerated dose (MTD) is estimated with sufficient accuracy. Early completion can reduce the average number of patients treated relative to the planned number, thereby allowing the trial to proceed to enrolling an expansion cohort for efficacy and enabling the trial to reach the next phase faster. METHODS Early completion is conducted on the basis of a dose-retainment probability using dose-assignment decisions. We evaluated early the completion for two actual trials. In addition, we performed a computer simulation to confirm the percentage of correctly selected MTDs, the early completion percentage, and the average number of patients treated. RESULTS In the evaluation of the two actual trials, we confirmed that the trials completed early. In the simulation results, we confirmed that the percentages of correct MTD selection were maintained relative to the original model-assisted designs. The early completion percentages ranged from 50% to 90%, and the number of patients treated reduced from 20%-60% relative to the planned number of patients. CONCLUSION We conclude that the early completion method can be applied unproblematically to the model-assisted design of phase I dose-finding trials.

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Background/aims: Dose feasibility is a challenge that may arise in the development of adoptive T cell therapies for cancer. In early-phase clinical trials, dose is quantified either by a fixed or per unit body weight number of cells infused. It may not be feasible, however, to administer a patient’s assigned dose due to an insufficient number of cells harvested or functional heterogeneity of the product. The study objective becomes to identify the maximum tolerated dose with high feasibility of being administered. This article describes a new dose-finding method that adaptively accounts for safety and feasibility endpoints in guiding dose allocation. Methods: We propose an adaptive dose-finding method that integrates accumulating feasibility and safety data to select doses for participant cohorts in early-phase trials examining adoptive cell immunotherapy. We sequentially model the probability of dose-limiting toxicity and the probability of feasibility using independent beta-binomial models. The probability model for toxicity borrows information across all dose levels using isotonic regression, allowing participants infused at a lower dose than his or her planned dose to contribute safety data to the dose-finding algorithm. We applied the proposed methodology in a single simulated trial and evaluated its operating characteristics through extensive simulation studies. Results: In simulations conducted for a phase I study of adoptive immunotherapy for newly diagnosed glioblastoma, the proposed method demonstrates the ability to identify accurately the feasible maximum tolerated doses and to treat participants at and around these doses. Over 10 hypothesized scenarios studied, the percentage of correctly selecting the true feasible and maximum tolerated dose ranged from 50% to 90% with sample sizes averaging between 21 and 24 participants. A comparison to the only known existing method accounting for safety and feasibility yields competitive performance. Conclusion: We have developed a new practical adaptive dose-finding method to assess feasibility in early-phase adoptive cell therapy trials. A design that incorporates feasibility, as a function of the quantity and quality of the product manufactured, in addition to safety will have an impact on the recommended phase II doses in studies that evaluate patient outcomes.

Oncology dose-finding clinical trials determine the maximum tolerated dose (MTD) based on toxicity outcomes captured by clinicians. With the availability of more rigorous instruments for measuring toxicity directly from patients, there is a growing interest to incorporate patient-reported outcomes (PRO) in clinical trials to inform patient tolerability. This is particularly important for dose-finding trials to ensure the identification of a well-tolerated dose. In this paper, we propose three extensions of the continual reassessment method (CRM), termed PRO-CRMs, that incorporate both clinician and patient outcomes. The first method is a marginal modeling approach whereby clinician and patient toxicity outcomes are modeled separately. The other two methods impose a constraint using a joint outcome defined based on both clinician and patient toxicities and model them either jointly or marginally. Simulation studies show that while all three PRO-CRMs select well-tolerated doses based on clinician's and patient's perspectives, the methods using a joint outcome perform better and have similar performance. We also show that the proposed PRO-CRMs are consistent under robust model assumptions.

Applications in early-phase cancer trials have motivated the development of many statistical designs since the late 1980s, including dose-finding methods, futility screening, treatment selection, and early stopping rules. These methods are often proposed to address the conventional cytotoxic therapeutics for neoplastic diseases and cancer. Recent advances in precision medicine have motivated novel trial designs, most notably the idea of master protocol (eg, platform trial, basket trial, umbrella trial, N-of-1 trial), for the evaluation of molecularly targeted cancer therapies. In this article, we review the concepts and methodology of early-phase cancer trial designs with a focus on dose finding and treatment screening and put these methods in the context of platform trials of molecularly targeted cancer therapies. Because most cancer trial designs have been developed for cytotoxic agents, we will discuss how these time-tested design principles hold relevance for targeted cancer therapies, and we will delineate how a master protocol may serve as an efficient platform for safety and efficacy evaluations of novel targeted therapies.

Pediatric drug development (PDD) became an industry goal when the Food and Drug Administration (FDA) granted patent extensions. This was later expanded to obligations for pediatric studies and to the European Medicines Agency's (EMA's) strict pediatric investigation plans (PIPs). Industry now sponsors many often international studies in young patients that are difficult or impossible to recruit. PDD's intellectual foundations characterize children as "therapeutic orphans," allegedly discriminated in drug treatment and development. While toxicities occured in newborns, demanding separate efficacy and safety (ES) studies in all age groups is wasteful and reflects hidden conflicts of interest. The American Academy of Pediatrics (AAP) successfully procured pediatric research funds; the FDA dislikes pediatric off-label use and envisions labels as instructions for physicians. Pediatricians have continuously improved child health care by careful use of available drugs. Instead of physiologically defining children vis-à-vis drug treatment, the FDA defines children as ≤16 years old, offering convincing pretense for the need for mostly senseless "pediatric" studies in young adults, adolescents, and children. Although these studies may help advance pediatric academic careers, they do not improve pediatric health care. The EMA defines children as18 years old and demands even more senseless and potentially harmful "pediatric" studies. Young patients need pharmacokinetic/pharmacodynamic and dose finding, but not separate ES, studies. Institutional review boards and ethics committees should suspend or reject questionable FDA/EMA-demanded "pediatric" studies. Industry and science need repositioning towards "PDD"; US/EU pediatric laws need revision. We hope this will not take decades.

Introduction 5-Methoxy-N,N-Dimethyltryptamine (5-MeO-DMT) is a tryptamine with ultra-rapid onset and short duration of psychedelic effects. Prospective studies for other tryptamines have suggested beneficial effects on mental health outcomes. Objectives In preparation for a study in patients with depression, the present study GH001-HV-101 aimed to assess the impact of four different dose levels of a novel vaporized 5-MeO-DMT formulation (GH001) administered via inhalation as single doses, and in an individualized dose escalation regimen on the safety, tolerability, and the dose-related psychoactive effects in healthy volunteers (n=22). Further, we aimed to assess the impact on cognitive functioning, mood, and well-being. Methods The psychedelic experience was assessed with a novel Peak Experience Scale (PES), the Mystical Experience Questionnaire (MEQ), the Ego Dissolution Inventory (EDI), the Challenging Experience Questionnaire (CEQ), and the 5-Dimensional Altered States of Consciousness Questionnaire (5D-ASC). Results 5-MeO-DMT produced dose-related increments in the intensity of the psychedelic experience ratings on all questionnaires, except the CEQ. Prominent effects were observed following single doses of 6, 12, and 18 mg on PES and MEQ ratings, while maximal effects on PES, MEQ, EDI, and 5D-ASC ratings were observed following individualized dose escalation of 5-MeO-DMT. Measures of cognition, mood, and well-being were not affected. Vital signs at 1 and 3 h after administration were not affected and adverse events were generally mild and resolved spontaneously. Conclusions Individualized dose escalation of 5-MeO-DMT may be preferable over single dose administration for clinical applications that aim to enhance the short-term psychoactive effects to elicit a strong therapeutic response. Disclosure This study was funded by GH Research PLC, Dublin, Ireland.

Background Contemporary Phase I oncology trials often include efficacy expansion in various tumor indications post dose finding. Preliminary anti-tumor activity from efficacy expansion can aid Go/No-Go decision for Phase 2 or Phase 3 initiation. Tumor cohorts in efficacy expansion are commonly analyzed independently in practice, which are often underpowered due to small sample size. Pooled analysis is also sometimes conducted, but it ignores the heterogeneity of the anti-tumor activity across cohorts. Methods We propose an optimal one-stage design and analysis strategy for the efficacy expansion to assess whether the treatment is effective. Allowing heterogeneous anti-tumor effects across tumor cohorts, inactive cohorts are pruned, and the potentially active cohorts are pooled together to gain study power. For a prospective design with a target power, the total sample size across all cohorts is minimized; or for an ad hoc analysis with pre-specified sample size for each cohort, the pruning criteria are optimized to achieve maximum power. The global type I error is controlled after proper multiplicity adjustment, and a penalty adjusted significance level is used for the pooled test. Results Simulation studies show that the proposed optimal design has desirable operating characteristics in increasing the overall power and detecting more true positive tumor cohorts. Conclusion The proposed optimal design and analysis strategy provides a practical approach to design and analyze heterogeneous efficacy expansion cohorts in a basket setting with global type I and type II error being controlled.

An IQ consortium working group (WG) conducted a survey across multiple biopharmaceutical companies to gain information about the level of blinding commonly utilized for early clinical development trials. The main objectives were: (1) to understand blinding practices between healthy volunteer (HV) and early explorative patient trials in all therapeutic areas except oncology where early clinical trials are commonly open-label; (2) to understand the rationale for blinding/unblinding practices; (3) to understand the groups and personnel involved in unblinding; and (4) strategic considerations around blinding/unblinding options in early clinical development trials-risk of bias vs. potential for acceleration. A survey containing 31 main questions with additional sub-clarifying questions was conducted. Sixteen large and mid-size pharmaceutical companies responded. Responses were aligned across functions within each participating company. Additional information was gathered at an American Association of Pharmaceutical Scientists (AAPS) webinar with polling options to roughly 550 registered attendees to evaluate the reason for the unblinding decisions. The results revealed divergence across companies in the blinding approaches most commonly applied but with some study types, there were clearly favored options. Based on these results, the WG developed strategic considerations for first-in-human HV trials and nonpivotal explorative trials in patients. This paper should facilitate discussions among various clinical development functions, such as Clinical Pharmacology, Statistics, Clinical, Bioanalytics, and Regulatory Functions. Such discussions on study design and operations are warranted to allow implementation of more flexible blinding approaches to accelerate data driven decisions in drug development and allow earlier access of patients to needful medicines.

Pulmonary arterial hypertension is an unmet clinical need. Imatinib, a tyrosine kinase inhibitor, 200 to 400 mg daily reduces pulmonary artery pressure and increases functional capacity in this patient group, but is generally poorly tolerated at the higher dose. We have designed an open-label, single-arm clinical study to investigate whether there is a tolerated dose of imatinib that can be better targeted to patients who will benefit. The study consists of two parts. Part 1 seeks to identify the best tolerated dose of Imatinib in the range from 100 and up to 400 mg using a Bayesian Continuous Reassessment Method. Part 2 will measure efficacy after 24 weeks treatment with the best tolerated dose using a Simon's two-stage design. The primary efficacy endpoint is a binary variable. For patients with a baseline pulmonary vascular resistance (PVR) >1000 dynes �� s �� cm-5, success is defined by an absolute reduction in PVR of ���300 dynes �� s �� cm-5 at 24 weeks. For patients with a baseline PVR ���1000 dynes �� s �� cm-5, success is a 30% reduction in PVR at 24 weeks. PVR will also be evaluated as a continuous variable by genotype as an exploratory analysis. Evaluating the response to that dose by genotype may inform a prospective biomarker-driven study.

Purpose The early identification of maximum tolerated dose (MTD) in phase I trial leads to faster progression to a phase II trial or an expansion cohort to confirm efficacy. Methods We propose a novel adaptive design for identifying MTD early to accelerate dose-finding trials. The early identification of MTD is determined adaptively by dose-retainment probability using a trial data via Bayesian analysis. We applied the early identification design to an actual trial. A simulation study evaluates the performance of the early identification design. Results In the actual study, we confirmed the MTD could be early identified and the study period was shortened. In the simulation study, the percentage of the correct MTD selection in the early identification Keyboard and early identification Bayesian optimal interval (BOIN) designs was almost same from the non-early identification version. The early identification Keyboard and BOIN designs reduced the study duration by about 50% from the model-assisted designs. In addition, the early identification Keyboard and BOIN designs reduced the study duration by about 20% from time-to-event model-assisted designs. Conclusion We proposed the early identification of MTD maintaining the accuracy to be able to short the study period.

A Bayesian biomarker-based phase I/II design (BIPSE) is presented for immunotherapy trials with a progression-free survival endpoint. The objective is to identify the subgroup-specific optimal dose, defined as the dose with the best risk-benefit tradeoff in each biomarker subgroup. We jointly model the immune response, toxicity outcome, and progression-free survival with information borrowing across subgroups. A plateau model is used to describe the marginal distribution of the immune response. Conditional on the immune response, we model toxicity using probit regression and model progression-free survival using the mixture cure rate model. During the trial, based on the accumulating data, we continuously update model estimates and adaptively randomize patients to doses with high desirability within each subgroup. Simulation studies show that the BIPSE design has desirable operating characteristics in selecting the subgroup-specific optimal doses and allocating patients to those optimal doses, and outperforms conventional designs.

Traditional dose-finding designs are substantially inefficient for targeted agents and cancer immunotherapies by failing to incorporate efficacy signals, mild and moderate adverse events, and late, cumulative toxicities. However, the lack of user-friendly software is a barrier to the practical use of the novel phase I designs, despite their demonstrated superiority of traditional 3+3 designs. To overcome these barriers, we present an R package, phase1RMD, which provides a comprehensive implementation of novel designs with repeated toxicity measures and early efficacy. A novel phase I repeated measures design that used a continuous toxicity score from multiple treatment cycles was implemented. Furthermore, in studies where preliminary efficacy is evaluated, an adaptive, multi-stage design to identify the most efficacious dose with acceptable toxicity was demonstrated. Functions are provided to recommend the next dose based on the data collected in a phase I trial, as well as to assess trial characteristics given design parameters via simulations. The repeated measure designs accurately estimated both the magnitude and direction of toxicity trends in late treatment cycles, and allocated more patients at therapeutic doses. The R package for implementing these designs is available from the Comprehensive R Archive Network. To our best knowledge, this is the first software that implement novel phase I dose-finding designs that simultaneously accounts for the multiple-grade toxicity events over multiple treatment cycles and a continuous early efficacy outcome. With the software published on CRAN, we will pursue the implementation of these designs in phase I trials in real-life settings.

Afatinib is a target anticancer drug of the second-generation EGFR TKI type, showing an advantage in treatment effect compared to conventional chemotherapy. However, patients on EGFR-TKI drugs also usually progress after 9 to 13 months according to secondary resistance. HAD-B1 is composed of drugs that are effective against lung cancer. This study is an exploratory study to evaluate the efficacy and safety between dosage groups by conducting a clinical trial in subjects requiring afatinib drug treatment in non-small cell lung cancer with EGFR mutation positive to determine the optimal dosage for HAD-B1 administration. At the final visit compared to before administration, each change in the disease control rate was measured according to the HAD-B1 doses of the test group 1 (972 mg), the test group 2 (1944 mg), and the control group. The efficacy and safety of HAD-B1 were compared and evaluated through sub-evaluation variables. As a result of the study, there was no statistically significant difference in the disease control rate at 12 weeks after dosing, but complete and partial remission were evaluated as 1 patient each in the test group 1, and none in the other groups. There was no statistically significant difference between groups in the sub-evaluation variable. In addition, there was no problem of safety from taking the test drug. However, the initially planned number of subjects was 66, but the number of enrolled subjects was only 14, which may limit the results of this study.

Background The overwhelming majority of dose-escalation clinical trials use methods that seek a maximum tolerable dose, including rule-based methods like the 3+3, and model-based methods like CRM and EWOC. These methods assume that the incidences of efficacy and toxicity always increase as dose is increased. This assumption is widely accepted with cytotoxic therapies. In recent decades, however, the search for novel cancer treatments has broadened, increasingly focusing on inhibitors and antibodies. The rationale that higher doses are always associated with superior efficacy is less clear for these types of therapies. Methods We extracted dose-level efficacy and toxicity outcomes from 115 manuscripts reporting dose-finding clinical trials in cancer between 2008 and 2014. We analysed the outcomes from each manuscript using flexible non-linear regression models to investigate the evidence supporting the monotonic efficacy and toxicity assumptions. Results We found that the monotonic toxicity assumption was well-supported across most treatment classes and disease areas. In contrast, we found very little evidence supporting the monotonic efficacy assumption. Conclusions Our conclusion is that dose-escalation trials routinely use methods whose assumptions are violated by the outcomes observed. As a consequence, dose-finding trials risk recommending unjustifiably high doses that may be harmful to patients. We recommend that trialists consider experimental designs that allow toxicity and efficacy outcomes to jointly determine the doses given to patients and recommended for further study.

Aim: To determine the safety of Coenzyme Q10 (CoQ10) in breast cancer patients receiving doxorubicin treatment. Methods: Phase I randomized, placebo-controlled, cross-over, dose-finding pharmacokinetic study among women with stage I-III breast cancer receiving 4 cycles of doxorubicin plus cyclophosphamide. The study was designed to test the maximum tolerated dose of CoQ10 using up to 1200 mg/day. Eligible patients were randomized to Arm A (CoQ10 after Cycle 3, followed by placebo after Cycle 4) or Arm B (placebo after cycle 3, followed by CoQ10 after cycle 4). CoQ10 concentrations and total antioxidant capacity (TAC) were measured before and after chemotherapy cycles. Non-compartmental pharmacokinetic parameters of doxorubicin and its active metabolites were measured with and without CoQ10. Paired t-tests assessed intra-patient differences in pharmacokinetic parameters, serum CoQ10 concentrations, TAC and adverse events. Results: Six patients received 300 mg/day of CoQ10 [Arm A (n=3), Arm B (n=3)]. One patient received 600 mg/day of CoQ10 but was discontinued due to non-adherence. Serum CoQ10 concentrations were increased in patients receiving 300 mg/day (mean±SD change: CoQ10, 1.6±0.9 ug/mL; placebo, -0.01±0.3 ug/mL; P=0.01). There were no clinically significant pharmacokinetic interactions between 300 mg/day CoQ10 and doxorubicin and no differences in TAC or adverse events during treatment and nontreatment periods. The trial was closed early due to slow accrual. Conclusions: 300 mg/day of CoQ10 with doxorubicin did not change doxorubicin pharmacokinetics and was not associated with treatment-related adverse events. Future studies should evaluate the long-term effects of CoQ10 at 300 mg/day and safety studies should examine higher doses.

BACKGROUND: Oral administration of midazolam syrup is one of the most favorable methods of premedication, the optimal dose of midazolam and midazolam with S-ketamine for preschool children has not been determined. This prospective, double-blind, randomized, sequential dose-finding study was designed to estimate the 90% effective doses of oral midazolam with and without intranasal S-ketamine in a grade III child medical center. METHODS: Eighty successive children were recruited and randomly allocated to midazolam group and midazolam with S-ketamine group. The initial oral doses of midazolam were 0.25 mg/kg in both groups, and the dose of midazolam for the next child was based on the response of the preceding child as the biased coin up-and-down designed. The primary outcome was parental separation anxiety score = 1 throughout the period of transferring from premedication center to the operation room 30 min after premedication. Secondary outcomes were the preoperative and post-operative observations. Finally, the 90% effective dose and 95% confidence intervals were estimated by isotonic regression. RESULTS: The 90% effective dose of oral midazolam or oral midazolam with intranasal S-ketamine was 0.461 mg/kg (95% confidence interval: 0.425–0.488) and 0.253 mg/kg (95% confidence interval: 0.242–0.278), respectively. Oral midazolam with intranasal S-ketamine was quicker onset (8.9±3.8 vs. 19.7±7.4 min, P

A Bayesian phase I-II design is presented that optimizes the dose of a new agent within predefined prognostic subgroups. The design is motivated by a trial to evaluate targeted agents for treating metastatic clear cell renal carcinoma, where a prognostic risk score defined by clinical variables and biomarkers is well established. Two clinical outcomes are used for dose-finding, time-to-toxicity during a prespecified follow-up period, and efficacy characterized by ordinal disease status evaluated at the end of follow-up. A joint probability model is constructed for these outcomes as functions of dose and subgroup. The model performs adaptive clustering of adjacent subgroups having similar dose-outcome distributions to facilitate borrowing information across subgroups. To quantify toxicity-efficacy risk-benefit trade-offs that may differ between subgroups, the objective function is based on outcome utilities elicited separately for each subgroup. In the context of the renal cancer trial, a design is constructed and a simulation study is presented to evaluate the design's reliability, safety, and robustness, and to compare it to designs that either ignore subgroups or run a separate trial within each subgroup.

Moxifloxacin is a widely used fluoroquinolone for the treatment of complicated intra‐abdominal infections. We applied physiologically‐based pharmacokinetic (PBPK) and population pharmacokinetic (popPK) modeling to support dose selection in pediatric patients. We scaled an existing adult PBPK model to children based on prior physiological knowledge. The resulting model proposed an age‐dependent dosing regimen that was tested in a phase I study. Refined doses were then tested in a phase III study. A popPK analysis of all clinical pediatric data confirmed the PBPK predictions, including the proposed dosing schedule in children, and supported pharmacokinetics‐related safety/efficacy questions. The pediatric PBPK model adequately predicted the doses necessary to achieve antimicrobial efficacy while maintaining safety in the phase I and III pediatric studies. Altogether, this study retroactively demonstrated the robustness and utility of modeling to support dose finding and confirmation in pediatric drug development for moxifloxacin.