Optimal Biological Dose Research Articles

IMPRINTER: An open label, multicenter, dose escalation/expansion, phase 1 study of imu-201 (PD1-Vaxx), a B-cell immunotherapy as monotherapy or in combination with atezolizumab, in adults with non-small cell lung cancer (IMU.201.101).

e21134 Background: Therapies with monoclonal antibodies targeting PD-1 and its ligands are associated with remarkable outcomes and have revolutionized cancer treatment (Honey 2017). However, patients treated with PD-1/PD-L1 blockade may develop “a primary or secondary resistance” to therapy (Sharma, Hu-Lieskovan et al. 2017). Contrary to monoclonal antibodies, chimeric B-cell cancer vaccines have the advantage of producing polyclonal B-cell antibodies that can potentially induce memory B- and T-cell responses, while reducing immune evasion and suppression. The hypothesis is that a polyclonal induced B-cell antibody response will be more effective or as effective with improved safety over current monoclonal antibody therapy. IMU-201 (PD1-Vaxx) is being developed using an active immunization approach to treat cancers that overexpress PD-L1 by inducing the production of anti-PD-1 antibodies with a peptide epitope designed to stimulate polyclonal antibodies against PD-1 (Kaumaya et al. 2020). Methods: The IMPRINTER study is an ongoing open-label dose escalation study of IMU-201 as monotherapy (Phase 1) or in combination with atezolizumab (Phase 1b) for patients with PD-L1 expressing non-small cell lung cancer (NSCLC). All patients enrolled in Phase 1 of the study must have previously received an immune checkpoint inhibitor and experienced disease progression. The primary objective is to evaluate the safety and tolerability of IMU-201 and identify the optimal biological dose (OBD). The secondary objective is to evaluate the efficacy of IMU-201 as monotherapy and in combination with atezolizumab. Humoral and cellular immunogenicity data will be evaluated, including IMU-201 and PD-1 specific antibodies (IgG, IgM), vaccine-specific cytokine levels, and regulatory and effector T and B cells. IMU-201 is administered by intramuscular (IM) injection on Day 1, Day 15, and Day 29. Dose-limiting toxicity (DLT) assessment is completed after 29 days on treatment. Tumor progression is evaluated according to RECIST 1.1 at Day 43 then every 42 days until progression or withdrawal. Results: In Phase 1, four patients were enrolled into each of the three cohorts at 10 μg/dose, 50 μg/dose and 100 μg/dose IMU-201 with no DLTs observed. In the 10 μg/dose cohort, one patient achieved CR and one patient SD; in the 50 μg/dose cohort, four patients achieved SD; and in the 100 μg/dose cohort, one patient achieved PR and two patients achieved SD. Within the 100 μg/dose cohort, one patient experienced an immune related pneumonitis after two IMU-201 administrations and discontinued from study treatment. Conclusions: IMU-201 had no observed DLT and demonstrated preliminary signs of efficacy. The study will therefore move into Phase 1b with IMU-201 being assessed in combination with atezolizumab. Clinical trial information: NCT04432207.

Phase I study of intratumoral administration of CF33-HNIS-antiPDL1 in patients with metastatic triple negative breast cancer.

e13070 Background: Despite recent FDA approval of immune checkpoint inhibitor pembrolizumab and drug-antibody conjugate in the treatment of metastatic triple negative breast cancer (mTNBC), the overall survival benefit of these therapies remains modest. Oncolytic virotherapy (OV) utilizes genetically modified viruses to infect and kill cancer cells while sparing healthy cells. CF33-hNIS-anti-PD-L1 (CHECKvacc) is a novel chimeric orthopoxvirus with robust anti-cancer activity in TNBC xenografts. Cells infected with CHECKvacc were shown to express functional human sodium-iodide symporter (hNIS) and anti-PD-L1 proteins. hNIS gene transfer allows tracking of virus by 99mTc single-photon emission computed tomography (SPECT). Our preliminary animal studies demonstrated that tumor cells infected with CHECKvacc successfully secrete functional hNIS and anti-PD-L1. CHECKvacc is safe and well-tolerated, and detects and effectively kills TNBC at doses several magnitudes lower than other OVs in xenograft models. Methods: This study is a first-in-human (FIH) phase I, single center, single arm clinical trial evaluating the safety and tolerability of CHECKvacc intratumoral injection in patients with mTNBC. Key eligibility criteria include patients with unresectable or metastatic TNBC; progressed on at least 2 prior chemotherapies including immune checkpoint inhibitor-containing regimen; ECOG 0-1; RECIST 1.1 measurable disease; and at least one tumor amenable to repeated intratumoral injections. Eligible patients receive CHECKvacc intratumorally at one of 8 assigned dose levels (1 × 105 PFU, 3 × 105 PFU, 1 × 106 PFU, 3× 106 PFU, 1 × 107 PFU, 3 × 107 PFU, 1 × 108 PFU, 3 x 108 PFU) on Days 1 and 15 of each 28-day cycle for a total of 3 cycles of treatment. Primary objectives are to evaluate the safety and tolerability of CHECKvacc (DLTs and other toxicities by CTCAE v5.0). Secondary objectives are to determine optimal biological dose (OBD), recommended phase II dose (RP2D), and response rate by RECIST1.1. Results: The first 3 subjects of dose level 1 will be enrolled sequentially for safety monitoring. Once the initial 3 subjects are treated sequentially, the study will follow the Phase I Queue 3+3 (IQ 3+3) design which expands a dose level up to 8 subjects after a single DLT has been observed. Enrollment to the final RP2D may be expanded to include up to 12 patients for efficacy assessment. The estimated targeted accrual is 33 patients (minimum) to 78 patients (maximum). Correlative aims include assessing viral kinetics, viral plaque assay, 99mTc SPECT imaging for virus tracking, peripheral blood and tumor tissue for antiviral immune activation, and tumor microenvironment changes in association with response to therapy. Conclusions: This FIH trial of CF33-hNIS-antiPD-L1 intratumoral injection in patients with mTNBC is currently underway. Clinical trial information: NCT05081492.

Model-based dose selection to inform translational clinical oncology development of WNT974, a first-in-class Porcupine inhibitor.

WNT974 is a potent, selective, and orally bioavailable first‐in‐class inhibitor of Porcupine, a membrane‐bound O‐acyltransferase required for Wnt secretion, currently under clinical development in oncology. A phase I clinical trial is being conducted in patients with advanced solid tumors. During the dose‐escalation part, various dosing regimens, including once or twice daily continuous and intermittent dosing at a dose range of 5–45 mg WNT974 were studied, however, the protocol‐defined maximum tolerated dose (MTD) was not established based on dose‐limiting toxicity. To assist in the selection of the recommended dose for expansion (RDE), a model‐based approach was utilized. It integrated population pharmacokinetic (PK) modeling and exposure–response analyses of a target‐inhibition biomarker, skin AXIN2 mRNA expression, and the occurrence of the adverse event, dysgeusia. The target exposure range of WNT974 that would provide a balance between target inhibition and tolerability was estimated based on exposure–response analyses. The dose that was predicted to yield an exposure within the target exposure range was selected as RDE. This model‐based approach integrated PK, biomarker, and safety data to determine the RDE and represented an alternative as opposed to the conventional MTD approach for selecting an optimal biological dose. The strategy can be broadly applied to select doses in early oncology trials and inform translational clinical oncology drug development.

Anti-CSF-1R emactuzumab in combination with anti-PD-L1 atezolizumab in advanced solid tumor patients naïve or experienced for immune checkpoint blockade

BackgroundThis phase 1b study (NCT02323191) evaluated the safety, antitumor activity, pharmacokinetics, and pharmacodynamics of colony-stimulating factor-1 receptor-blocking monoclonal antibody (mAb) emactuzumab in combination with the programmed cell death-1 ligand (PD-L1)-blocking...

Finite Element-Based Personalized Simulation of Duodenal Hydrogel Spacer: Spacer Location Dependent Duodenal Sparing and a Decision Support System for Spacer-Enabled Pancreatic Cancer Radiation Therapy.

PurposePancreatic cancer is the fourth leading cause of cancer-related death, with a very low 5-year overall survival rate (OS). Radiation therapy (RT) together with dose escalation significantly increases the OS at 2 and 3 years. However, dose escalation is very limited due to the proximity of the duodenum. Hydrogel spacers are an effective way to reduce duodenal toxicity, but the complexity of the anatomy and the procedure makes the success and effectiveness of the spacer procedure highly uncertain. To provide a preoperative simulation of hydrogel spacers, we presented a patient-specific spacer simulator algorithm and used it to create a decision support system (DSS) to provide a preoperative optimal spacer location to maximize the spacer benefits.Materials and MethodsOur study was divided into three phases. In the validation phase, we evaluated the patient-specific spacer simulator algorithm (FEMOSSA) for the duodenal spacer using the dice similarity coefficient (DSC), overlap volume histogram (OVH), and radial nearest neighbor distance (RNND). For the simulation phase, we simulated four virtual spacer scenarios based on the location of the spacer in para-duodenal space. Next, stereotactic body radiation therapy (SBRT) plans were designed and dosimetrically analyzed. Finally, in the prediction phase, using the result of the simulation phase, we created a Bayesian DSS to predict the optimal spacer location and biological effective dose (BED).ResultsA realistic simulation of the spacer was achieved, reflected in a statistically significant increase in average target and duodenal DSC for the simulated spacer. Moreover, the small difference in average mean and 5th-percentile RNNDs (0.5 and 2.1 mm) and OVH thresholds (average of less than 0.75 mm) showed that the simulation attained similar separation as the real spacer. We found a spacer-location-independent decrease in duodenal V20Gy, a highly spacer-location-dependent change in V33Gy, and a strong correlation between L1cc and V33Gy. Finally, the Bayesian DSS predicted the change in BED with a root mean squared error of 3.6 Gys.ConclusionsA duodenal spacer simulator platform was developed and used to systematically study the dosimetric effect of spacer location. Further, L1cc is an informative anatomical feedback to guide the DSS to indicate the spacer efficacy, optimum location, and expected improvement.

CFO: Calibration-free odds design for phase I/II clinical trials.

Recent revolution in oncology treatment has witnessed emergence and fast development of the targeted therapy and immunotherapy. In contrast to traditional cytotoxic agents, these types of treatment tend to be more tolerable and thus efficacy is of more concern. As a result, seamless phase I/II trials have gained enormous popularity, which aim to identify the optimal biological dose (OBD) rather than the maximum tolerated dose (MTD). To enhance the accuracy and robustness for identification of OBD, we develop a calibration-free odds (CFO) design. For toxicity monitoring, the CFO design casts the current dose in competition with its two neighboring doses to obtain an admissible set. For efficacy monitoring, CFO selects the dose that has the largest posterior probability to achieve the highest efficacy under the Bayesian paradigm. In contrast to most of the existing designs, the prominent merit of CFO is that its main dose-finding component is model-free and calibration-free, which can greatly ease the burden on artificial input of design parameters and thus enhance the robustness and objectivity of the design. Extensive simulation studies demonstrate that the CFO design strikes a good balance between efficiency and safety for MTD identification under phase I trials, and yields comparable or sometimes slightly better performance for OBD identification than the competing methods under phase I/II trials.

Abstract P1-18-22: AKT inhibition in combination with endocrine therapy and a CDK4/6 inhibitor (CDK4/6i) in patients with hormone receptor positive (HR+)/HER2 negative metastatic breast cancer (MBC) and prior CDK4/6i exposure: A translational investigation

Abstract Background: The cyclin-dependent kinase 4/6 inhibitors, with endocrine therapy (ET), have become the standard of care for patients with HR+/HER2- MBC. Prior insight from tumor biopsies and preclinical analyses suggest that AKT1 activation can provoke CDK4/6i resistance, highlighting a potential therapeutic role for AKT inhibition (AKTi) in this setting. However, combinatorial inhibition can be associated with significant toxicity and identification of the optimal biological dose is often challenging. In this translational co-clinical study, we evaluated escalating doses of AKTi combination with CDK 4/6i in parallel patient-derived pre-clinical models as well as a phase 1b clinical trial. Methods: In an open-label phase Ib dose-escalation clinical trial (TAKTIC, NCT03959891), we evaluated the safety, tolerability and efficacy of escalating doses of the AKT1 inhibitor ipatasertib (ipat) in combination with palbociclib (palbo) and fulvestrant (fulv) for patients with HR+/HER2- MBC. Inclusion criteria include unresectable or metastatic disease, at least 1 prior therapy for MBC including any CDK4/6i, and up to 2 prior lines of chemotherapy for MBC (no limit on prior endocrine therapy). In addition, response to escalating doses of ipat and palbo (with fulv) were explored in vitro via an ATP-based viability assay in tumor cell lines derived from circulating tumor cells (CTC) isolated from patients with endocrine-refractory HR+ MBC. Results: In the dose-escalation portion of the phase 1b clinical trial, 23 patients received the triplet combination of ipat, palbo, and fulv (median number of prior lines = 4.3, range 1-7; 100% with prior CDK4/6i): 3 pts received ipat at 200mg + 125mg palbo, 15 pts received 300mg + 125mg palbo, and 5 pts received 400mg + 100mg palbo, all with fulv (500 mg). Among the 23 patients, 20 patients (86.9%) had disease control (4 partial response and 16 stable disease) as the best response, per RECIST. Grade 3/4 toxicities included neutropenia (n=20), lymphopenia (n=3), diarrhea (n=3), thrombocytopenia (n=2), transaminitis (n=2), and rash (n=2). Two DLTs were observed in the 300mg ipat + 125mg palbo cohort (grade 4 neutropenia ≥ 7 days), but none at 400mg + 100mg palbo. The combination of ipat and palbo demonstrated an additive effect in vitro, with increased sensitivity to lower doses of palbo in the presence of ipat. Based on the totality of data, 400mg ipat + 100mg palbo + fulv 500 mg was selected as the recommended phase II dose (RP2D) in the post-CDK4/6i setting. Conclusions: The triplet combination of endocrine therapy with AKTi and lower dose CDK4/6i appears to be well tolerated in heavily pre-treated pts, with preliminary evidence of clinical activity. Further study is needed to evaluate biomarkers associated with higher AKTi benefit in order to guide rational development of combination therapy for patients with HR+/HER2- MBC in the post-CDK4/6i setting. Overall, this translational study demonstrates how insight into the molecular mechanisms of CDK4/6i resistance and combinatorial modeling can be leveraged to develop actionable therapeutic regimens for patients with MBC. Citation Format: Seth A. Wander, Douglas S. Micalizzi, Taronish Dubash, Dejan Juric, Laura M. Spring, Neelima Vidula, Jennifer Keenan, Maureen Beeler, Elene Viscosi, Dante Che, Elizabeth L. Fisher, Rachel A. Hepp, Beverly Moy, Steven J. Isakoff, Leif W. Ellisen, Jeffrey G. Supko, Shyamala Maheswaran, Daniel A. Haber, Aditya Bardia. AKT inhibition in combination with endocrine therapy and a CDK4/6 inhibitor (CDK4/6i) in patients with hormone receptor positive (HR+)/HER2 negative metastatic breast cancer (MBC) and prior CDK4/6i exposure: A translational investigation [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P1-18-22.

TITE-BOIN12: A Bayesian phase I/II trial design to find the optimal biological dose with late-onset toxicity and efficacy.

In the era of immunotherapies and targeted therapies, the focus of early phase clinical trials has shifted from finding the maximum tolerated dose to identifying the optimal biological dose (OBD), which maximizes the toxicity-efficacy trade-off. One major impediment to using adaptive designs to find OBD is that efficacy or/and toxicity are often late-onset, hampering the designs' real-time decision rules for treating new patients. To address this issue, we propose the model-assisted TITE-BOIN12 design to find OBD with late-onset toxicity and efficacy. As an extension of the BOIN12 design, the TITE-BOIN12 design also uses utility to quantify the toxicity-efficacy trade-off. We consider two approaches, Bayesian data augmentation and an approximated likelihood method, to enable real-time decision making when some patients' toxicity and efficacy outcomes are pending. Extensive simulations show that, compared to some existing designs, TITE-BOIN12 significantly shortens the trial duration while having comparable or higher accuracy to identify OBD and a lower risk of overdosing patients. To facilitate the use of the TITE-BOIN12 design, we develop a user-friendly software freely available at http://www.trialdesign.org.

Novel bayesian adaptive early phase designs to accelerate the development of CAR T-cell therapy

Chimeric antigen receptor (CAR) T-cell therapy has revolutionized cancer treatment, particularly for hematopoietic malignancies. CAR T-cell therapy is a living drug with fundamentally different characteristics from those of other therapies. For example, CAR T-cell therapy efficacy may not increase with dose, and dose-limiting toxicity is rarely observed in the therapeutic dose range. Consequently, the conventional trial design paradigm is not suitable for the development of CAR T-cell therapy. Here, we review and introduce the phase I-II trial design paradigm to optimize the dose of CAR T-cell therapy on the basis of both toxicity and efficacy. We describe several novel Bayesian model-assisted designs, including BOIN12 and U-BOIN, which are simple to implement and have excellent operating characteristics for identifying the optimal biological dose for CAR T-cell therapy. Examples and software are provided to facilitate the use of these novel designs to accelerate the development of CAR T-cell therapy.

Pharmacodynamics and molecular correlates of response to glofitamab in relapsed/refractory non-Hodgkin lymphoma

AbstractGlofitamab, a novel CD20xCD3, T-cell–engaging bispecific antibody, exhibited single-agent activity in Study NP30179, a first-in-human, phase 1 trial in relapsed/refractory B-cell non-Hodgkin lymphoma. Preclinical studies showed that glofitamab leads to T-cell activation, proliferation, and tumor cell killing upon binding to CD20 on malignant cells. Here, we provide evidence of glofitamab's clinical activity, including pharmacodynamic profile, mode of action, and factors associated with clinical response, by evaluating biomarkers in patient samples from the dose-escalation part of this trial. Patients enrolled in Study NP30179 received single-dose obinutuzumab pretreatment (1000 mg) 7 days before IV glofitamab (5 µg-25 mg). Glofitamab treatment lasted ≤12 cycles once every 2 or 3 weeks. Blood samples were collected at predefined time points per the clinical protocol; T-cell populations were evaluated centrally by flow cytometry, and cytokine profiles were analyzed. Immunohistochemical and genomic biomarker analyses were performed on tumor biopsy samples. Pharmacodynamic modulation was observed with glofitamab treatment, including dose-dependent induction of cytokines, and T-cell margination, proliferation, and activation in peripheral blood. Gene expression analysis of pretreatment tumor biopsy samples indicated that tumor cell intrinsic factors such as TP53 signaling are associated with resistance to glofitamab, but they may also be interlinked with a diminished effector T-cell profile in resistant tumors and thus represent a poor prognostic factor per se. This integrative biomarker data analysis provides clinical evidence regarding glofitamab's mode of action, supports optimal biological dose selection, and will further guide clinical development. This trial was registered at www.clinicaltrials.gov as #NCT03075696.

BOIN Suite: A Software Platform to Design and Implement Novel Early-Phase Clinical Trials.

Using novel Bayesian adaptive designs has great potential to improve the efficiency of early-phase clinical trials. A major barrier for clinical researchers to adopt novel designs is the lack of easy-to-use software. Our purpose is to develop a user-friendly software platform to implement novel clinical trial designs that address various challenges in early-phase dose-finding trials. We used R Shiny to develop a web-based software platform to facilitate the use of recent novel adaptive designs. We developed a web-based software suite, called Bayesian optimal interval (BOIN) suite, which includes R Shiny applications to handle various clinical settings, including single-agent phase I trials with and without prior information, trials with late-onset toxicity, trials to find the optimal biological dose based on risk-benefit trade-off, and drug combination trials to find a single maximum tolerated dose (MTD) or the MTD contour. The applications are built using the same software architecture to ensure the best and a uniform user experience, and they are developed using a proven software development standard operating procedure to ensure accuracy, robustness, and reproducibility. The suite is freely available with internet access and a web browser without the need of installing any other software. The BOIN suite allows clinical researchers to design various types of early-phase clinical trials under a unified framework. This work is extremely important because it not only advances the clinical research and drug development by facilitating the use of novel trial designs with optimal performance but also enhances collaborations between biostatisticians and clinicians by disseminating novel statistical methodology to broader scientific communities through user-friendly software. The BOIN suite establishes a KISS principle: keep it simple, but smart.

A Bayesian Phase I/II Design for Cancer Clinical Trials Combining an Immunotherapeutic Agent with a Chemotherapeutic Agent

Abstract Immunotherapy is an innovative treatment approach that harnesses a patient’s immune system to treat cancer. It has provided an alternative and complementary treatment modality to conventional chemotherapy. Combining immunotherapy with cytotoxic chemotherapy agent has become the leading trend and the most active research field in oncology. To accommodate this growing trend, we propose a Bayesian phase I/II dose-finding design to identify the optimal biological dose combination (OBDC), defined as the dose combination with the highest desirability in the risk-benefit trade-off. We propose new statistical models to describe the relationship between the doses and treatment outcomes, including immune response, toxicity and progression-free survival (PFS). During the trial, based on accrued data, we continuously update model estimates and adaptively assign patients to dose combinations with high desirability. The simulation study shows that our design has desirable operating characteristics.

Missing data handling technique in joint modeling context

The maximum tolerated dose (MTD) is commonly practiced for dose selection in oncology. Higher doses work as the best effective treatment. Conventionally, the doses are selected in phase 2 from a phase. Perhaps, the quality of life gets compromised due to the high dose of chemotherapeutic regimes in the early phase of the trial. Alternative chemotherapy administration is Metronomic Chemotherapy (MC). Here very minimal doses are administered to avoid high toxicity. This work is about to handle missing data of circulating endothelial cells (CEC) and supports the optimal biological dose (OBD) for MC. It is performed with mimic data. A data simulation strategy is adopted. Simulation work is performed with R software. It helps to identify the suitable technique to handle missing data. The results conclude that the MC is efficacious by improving the Progression Free Survival (PFS) and Oveall Survival (OS) through controlled toxicity. Now the illustrated example can be extended to explore the impact of CEC for OS. This is a preliminary attempt towards MC to address some critical issues.

First in Human, Phase I/II Trial of the Bruton's Tyrosine Kinase Inhibitor (BTKi) M7583 in Patients with B Cell Malignancies: Study Design and Initial Outcomes

First in Human, Phase I/II Trial of the Bruton's Tyrosine Kinase Inhibitor (BTKi) M7583 in Patients with B Cell Malignancies: Study Design and Initial Outcomes

BOIN: a novel Bayesian design platform to accelerate early phase brain tumor clinical trials.

Despite decades of extensive research, the progress in developing effective treatments for primary brain tumors lags behind that of other cancers, largely due to the unique challenges of brain tumors (eg, the blood-brain barrier and high heterogeneity) that limit the delivery and efficacy of many therapeutic agents. One way to address this issue is to employ novel trial designs to better optimize the treatment regimen (eg, dose and schedule) in early phase trials to improve the success rate of subsequent phase III trials. The objective of this article is to introduce Bayesian optimal interval (BOIN) designs as a novel platform to design various types of early phase brain tumor trials, including single-agent and combination regimen trials, trials with late-onset toxicities, and trials aiming to find the optimal biological dose (OBD) based on both toxicity and efficacy. Unlike many novel Bayesian adaptive designs, which are difficult to understand and complicated to implement by clinical investigators, the BOIN designs are self-explanatory and user friendly, yet yield more robust and powerful operating characteristics than conventional designs. We illustrate the BOIN designs using a phase I clinical trial of brain tumor and provide software (freely available at www.trialdesign.org) to facilitate the application of the BOIN design.

UTPI: A utility-based toxicity probability interval design for phase I/II dose-finding trials.

Unlike chemotherapy, the maximum tolerated dose (MTD) of molecularly targeted agents and immunotherapy may not pose significant clinical benefit over the lower doses. By simultaneously considering both toxicity and efficacy endpoints, phase I/II trials can identify a more clinically meaningful dose for subsequent phase II trials than traditional toxicity-based phase I trials in terms of risk-benefit tradeoff. To strengthen and simplify the current practice of phase I/II trials, we propose a utility-based toxicity probability interval (uTPI) design for finding the optimal biological dose, based on a numerical utility that provides a clinically meaningful, one-dimensional summary representation of the patient's bivariate toxicity and efficacy outcome. The uTPI design does not rely on any parametric specification of the dose-response relationship, and it directly models the dose desirability through a quasi binomial likelihood. Toxicity probability intervals are used to screen out overly toxic dose levels, and then the dose escalation/de-escalation decisions are made adaptively by comparing the posterior desirability distributions of the adjacent levels of the current dose. The uTPI design is flexible in accommodating various dose desirability formulations, while only requiring minimum design parameters. It has a clear decision structure such that a dose-assignment decision table can be calculated before the trial starts and can be used throughout the trial, which simplifies the practical implementation of the design. Extensive simulation studies demonstrate that the proposed uTPI design yields desirable as well as robust performance under various scenarios.

Optimal biological dose: a systematic review in cancer phase I clinical trials

BackgroundClassical phase 1 dose-finding designs based on a single toxicity endpoint to assess the maximum tolerated dose were initially developed in the context of cytotoxic drugs. With the emergence of molecular targeted agents and immunotherapies, the concept of optimal biological dose (OBD) was subsequently introduced to account for efficacy in addition to toxicity. The objective was therefore to provide an overview of published phase 1 cancer clinical trials relying on the concept of OBD.MethodsWe performed a systematic review through a computerized search of the MEDLINE database to identify early phase cancer clinical trials that relied on OBD. Relevant publications were selected based on a two-step process by two independent readers. Relevant information (phase, type of therapeutic agents, objectives, endpoints and dose-finding design) were collected.ResultsWe retrieved 37 articles. OBD was clearly mentioned as a trial objective (primary or secondary) for 22 articles and was traditionally defined as the smallest dose maximizing an efficacy criterion such as biological target: biological response, immune cells count for immunotherapies, or biological cell count for targeted therapies. Most trials considered a binary toxicity endpoint defined in terms of the proportion of patients who experienced a dose-limiting toxicity. Only two articles relied on an adaptive dose escalation design.ConclusionsIn practice, OBD should be a primary objective for the assessment of the recommended phase 2 dose (RP2D) for a targeted therapy or immunotherapy phase I cancer trial. Dose escalation designs have to be adapted accordingly to account for both efficacy and toxicity.

Phase 1 Study of Axatilimab (SNDX-6352), a CSF-1R Humanized Antibody, for Chronic Graft-Versus-Host Disease after 2 or More Lines of Systemic Treatment

Phase 1 Study of Axatilimab (SNDX-6352), a CSF-1R Humanized Antibody, for Chronic Graft-Versus-Host Disease after 2 or More Lines of Systemic Treatment

BOIN12: Bayesian Optimal Interval Phase I/II Trial Design for Utility-Based Dose Finding in Immunotherapy and Targeted Therapies

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. 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. 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. 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.

TSNP: A two-stage nonparametric phase I/II clinical trial design for immunotherapy.

We develop a transparent and efficient two-stage nonparametric (TSNP) phase I/II clinical trial design to identify the optimal biological dose (OBD) of immunotherapy. We propose a nonparametric approach to derive the closed-form estimates of the joint toxicity-efficacy response probabilities under the monotonic increasing constraint for the toxicity outcomes. These estimates are then used to measure the immunotherapy's toxicity-efficacy profiles at each dose and guide the dose finding. The first stage of the design aims to explore the toxicity profile. The second stage aims to find the OBD, which can achieve the optimal therapeutic effect by considering both the toxicity and efficacy outcomes through a utility function. The closed-form estimates and concise dose-finding algorithm make the TSNP design appealing in practice. The simulation results show that the TSNP design yields superior operating characteristics than the existing Bayesian parametric designs. User-friendly computational software is freely available to facilitate the application of the proposed design to real trials. We provide comprehensive illustrations and examples about implementing the proposed design with associated software.