A Bayesian latent-subgroup platform design for dose optimization.

The US Food and Drug Administration launched Project Optimus to reform the dose optimization and dose selection paradigm in oncology drug development, calling for the paradigm shift from finding the maximum tolerated dose to the identification of optimal biological dose (OBD). Motivated by a real-world drug development program, we propose a master-protocol-based platform trial design to simultaneously identify OBDs of a new drug, combined with standards of care or other novel agents, in multiple indications. We propose a Bayesian latent subgroup model to accommodate the treatment heterogeneity across indications, and employ Bayesian hierarchical models to borrow information within subgroups. At each interim analysis, we update the subgroup membership and dose-toxicity and -efficacy estimates, as well as the estimate of the utility for risk-benefit tradeoff, based on the observed data across treatment arms to inform the arm-specific decision of dose escalation and de-escalation and identify the OBD for each arm of a combination partner and an indication. The simulation study shows that the proposed design has desirable operating characteristics, providing a highly flexible and efficient way for dose optimization. The design has great potential to shorten the drug development timeline, save costs by reducing overlapping infrastructure, and speed up regulatory approval.

Issues and Solutions in Psychiatric Clinical Trial with Case Studies.

The coronavirus disease-2019 pandemic resulted in a major increase in depression and anxiety disorders worldwide, which increased the demand for mental health services. However, clinical interventions for treating mental disorders are currently insufficient to meet this growing demand. There is an urgent need to conduct scientific and standardized clinical research that are consistent with the features of mental disorders in order to deliver more effective and safer therapies in the clinic. Our study aimed to expose the challenges, complexities of study design, ethical issues, sample selection, and efficacy evaluation in clinical research for mental disorders. The reliance on subjective symptom presentation and rating scales for diagnosing mental diseases was discovered, emphasizing the lack of clear biological standards, which hampers the construction of rigorous research criteria. We underlined the possibility of psychotherapy in efficacy evaluation alongside medication treatment, proposing for a multidisciplinary approach comprising psychiatrists, neuroscientists, and statisticians. To comprehend mental disorders progression, we recommend the development of artificial intelligence integrated evaluation tools, the use of precise biomarkers, and the strengthening of longitudinal designs. In addition, we advocate for international collaboration to diversity samples and increase the dependability of findings, with the goal of improving clinical research quality in mental disorders through sample representativeness, accurate medical history gathering, and adherence to ethical principles.

Biological signatures of the International Prognostic Index in diffuse large B-cell lymphoma.

ABSTRACT: Diffuse large B-cell lymphoma (DLBCL) is a highly aggressive subtype of lymphoma with clinical and biological heterogeneity. The International Prognostic Index (IPI) shows great prognostic capability in the era of rituximab, but the biological signatures of IPI remain to be discovered. In this study, we analyzed the clinical data in a large cohort of 2592 patients with newly diagnosed DLBCL. Among them, 1233 underwent DNA sequencing for oncogenic mutations, and 487 patients underwent RNA sequencing for lymphoma microenvironment (LME) alterations. Based on IPI scores, patients were categorized into 4 distinct groups, with 5-year overall survival of 41.6%, 55.3%, 71.7%, and 89.7%, respectively. MCD-like subtype was associated with age of >60 years, multiple extranodal involvement, elevated serum lactate dehydrogenase (LDH), and IPI scores ranging from 2 to 5, whereas ST2-like subtype showed an opposite trend. Patients with EZB-like MYC+ and TP53Mut subtypes exhibited poor clinical outcome independent of the IPI; integrating TP53Mut into IPI could better distinguish patients with dismal survival. The EZB-like MYC-, BN2-like, N1-like, and MCD-like subtypes had inferior prognosis in patients with IPI scores of ≥2, indicating necessity for enhanced treatment. Regarding LME categories, the germinal center-like LME was more prevalent in patients with normal LDH and IPI scores of 0 to 1. The mesenchymal LME served as an independent protective factor, whereas the germinal center-like, inflammatory, and depleted LME categories correlated with inferior prognosis for IPI scores of 2 to 5. In summary, our work explored the biological signatures of IPI, thus providing useful rationale for future optimization of the IPI-based treatment strategies with multi-omics information in DLBCL.

Genetic subtype-guided immunochemotherapy in diffuse large B cell lymphoma: The randomized GUIDANCE-01 trial.

We report the results of GUIDANCE-01 (NCT04025593), a randomized, phase II trial of R-CHOP alone or combined with targeted agents (R-CHOP-X) guided by genetic subtyping of newly diagnosed, intermediate-risk, or high-risk diffuse large B cell lymphoma (DLBCL). A total of 128 patients were randomized 1:1 to receive R-CHOP-X or R-CHOP. The study achieved the primary endpoint, showing significantly higher complete response rate with R-CHOP-X than R-CHOP (88% vs. 66%, p = 0.003), with overall response rate of 92% vs. 73% (p = 0.005). Two-year progression-free survival rates were 88% vs. 63% (p < 0.001), and 2-year overall survival rates were 94% vs. 77% (p = 0.001). Meanwhile, post hoc RNA-sequencing validated our simplified genetic subtyping algorithm and previously established lymphoma microenvironment subtypes. Our findings highlight the efficacy and safety of R-CHOP-X, a mechanism-based tailored therapy, which dually targeted genetic and microenvironmental alterations in patients with newly diagnosed DLBCL.

Positron emission tomography-adapted therapy in low-risk diffuse large B-cell lymphoma: results of a randomized, phase III, non-inferiority trial.

BACKGROUND: The current standard of care for non-bulky diffuse large B-cell lymphoma (DLBCL) patients with an International Prognostic Index (IPI) of 0 is four cycles of rituximab plus cyclophosphamide, doxorubicin, vincristine and prednisone (R-CHOP) but whether the same efficacy can be achieved with reduced chemotherapy regimen of four cycles for non-bulky DLBCL patients with an IPI of 1 remains unclear. This study compared four cycles versus six cycles of chemotherapy in non-bulky low-risk DLBCL patients with negative interim positron emission tomography with computed tomography (PET-CT, Deauville 1-3), irrespective of age and other IPI risk factors (IPI 0-1). METHODS: This was an open-label, randomized, phase III, non-inferiority trial. Patients aged 14-75 years with newly diagnosed low-risk DLBCL, according to IPI, achieving PET-CT confirmed complete response (CR) after four cycles of R-CHOP were randomized (1:1) between four cycles of rituximab (4R-CHOP+4R arm) or two cycles of R-CHOP plus two cycles of rituximab (6R-CHOP+2R arm). The primary endpoint was 2-year progression-free survival (PFS), conducted in the intention-to-treat population. Safety was assessed in patients with at least one cycle of assigned treatment. The non-inferiority margin was -8%. RESULTS: A total of 287 patients were included in the intention-to-treat analysis, the median follow-up was 47.3 months, and the 2-year PFS rate was 95% (95% confidence interval [CI], 92% to 99%) and 94% (95% CI, 91% to 98%) for the 4R-CHOP+4R and 6R-CHOP+2R arm. The absolute difference in 2-year PFS between the two arms was 1% (95% CI, -5% to 7%), supporting the non-inferiority of 4R-CHOP+4R. Grade 3-4 neutropenia was lower in the last four cycles of rituximab alone in the 4R-CHOP+4R arm (16.7% versus 76.9%), with decreased risk of febrile neutropenia (0.0% versus 8.4%) and infection (2.1% versus 14.0%). CONCLUSIONS: For newly diagnosed low-risk DLBCL patients, interim PET-CT after four cycles of R-CHOP was effective in identifying patients with Deauville 1-3 who would have a good response and Deauville 4-5 patients who might have high-risk biological features or develop resistance. Reducing the standard six cycles to four cycles of chemotherapy had comparable clinical efficacy and fewer adverse events in low-risk, non-bulky DLBCL with interim PET-CT confirmed CR.

A dose-finding design for phase I clinical trials based on Bayesian stochastic approximation.

BACKGROUND: Current dose-finding designs for phase I clinical trials can correctly select the MTD in a range of 30-80% depending on various conditions based on a sample of 30 subjects. However, there is still an unmet need for efficiency and cost saving. METHODS: We propose a novel dose-finding design based on Bayesian stochastic approximation. The design features utilization of dose level information through local adaptive modelling and free assumption of toxicity probabilities and hyper-parameters. It allows a flexible target toxicity rate and varying cohort size. And we extend it to accommodate historical information via prior effective sample size. We compare the proposed design to some commonly used methods in terms of accuracy and safety by simulation. RESULTS: On average, our design can improve the percentage of correct selection to about 60% when the MTD resides at a early or middle position in the search domain and perform comparably to other competitive methods otherwise. A free online software package is provided to facilitate the application, where a simple decision tree for the design can be pre-printed beforehand. CONCLUSION: The paper proposes a novel dose-finding design for phase I clinical trials. Applying the design to future cancer trials can greatly improve the efficiency, consequently save cost and shorten the development period.

A Bayesian phase I/II platform design for co-developing drug combination therapies for multiple indications.

There is a growing trend to combine a new targeted or immunotherapy agent with the cancer-specific standard of care to treat different types of cancers. We propose a master-protocol-based, Bayesian phase I/II platform design to co-develop combination (BPCC) therapies in multiple indications. Under the BPCC design, only a single master protocol is needed, and the combined drug is evaluated in different indications in a concurrent or staggered fashion. For each indication, we jointly model dose-toxicity and -efficacy relationships and employ Bayesian hierarchical models to borrow information across them for more efficient indication-specific decision-making. To account for the characteristic of targeted or immunotherapy agents that their efficacy may not monotonically increase with the dose, and often plateau at high doses, we use the utility to quantify the risk-benefit tradeoff of the treatment. At each interim, we update the toxicity and efficacy model, as well as the estimate of the utility, based on the observed data across indications to inform the indication-specific decision of dose escalation and de-escalation and identify the optimal biological dose for each indication. Simulation study shows that the BPCC design has desirable operating characteristics, and that it provides an efficient approach to accelerate the development of combination therapies.

An adaptive gBOIN design with shrinkage boundaries for phase I dose-finding trials.

BACKGROUND: With the emergence of molecularly targeted agents and immunotherapies, the landscape of phase I trials in oncology has been changed. Though these new therapeutic agents are very likely induce multiple low- or moderate-grade toxicities instead of DLT, most of the existing phase I trial designs account for the binary toxicity outcomes. Motivated by a pediatric phase I trial of solid tumor with a continuous outcome, we propose an adaptive generalized Bayesian optimal interval design with shrinkage boundaries, gBOINS, which can account for continuous, toxicity grades endpoints and regard the conventional binary endpoint as a special case. RESULT: The proposed gBOINS design enjoys convergence properties, e.g., the induced interval shrinks to the toxicity target and the recommended dose converges to the true maximum tolerated dose with increased sample size. CONCLUSION: The proposed gBOINS design is transparent and simple to implement. We show that the gBOINS design has the desirable finite property of coherence and large-sample property of consistency. Numerical studies show that the proposed gBOINS design yields good performance and is comparable with or superior to the competing design.

A two-stage Bayesian adaptive design for minimum effective dose (MinED)-based dosing-finding trials.

Conventional phase I designs for finding a phase II recommended dose (P2RD) based on toxicity alone is problematic because the maximum tolerated dose (MTD) is not necessarily the optimal dose. Instead, recently attention has been given to find the minimum effective dose (MinED) - defined as the lowest effective dose. Traditional paradigms for the MinED studies are conducted as dose-ranging or dose-response trials which involve several doses and randomize patients among doses to find the MinED. An alternative approach for the MinED study is the so-called MinED-based dose-finding study, in which instead of conducting hypothesis testings and without power analysis, this kind of trial conduct dose escalation/de-escalation to target a pre-set MinED target. In this study, we propose a new Bayesian two-stage adaptive design schema based on framework of the interval-based phase I method. The proposed method is model-free without curve pre-specifications, which is suitable for various dose-efficacy relationships. The proposed method shows desirable theoretical finite property of semi-coherence and large sample property of consistency. A random scenario generative algorithm for the MinED has also been proposed for extensive simulation studies, which demonstrated desirable performances of the proposed method. An R package "MinEDfind" and a Shiny app have been developed for implementing the method.

A Bayesian adaptive design for biosimilar trials with time-to-event endpoint.

A biosimilar drug is a biological product that is highly similar to and at the same time has no clinically meaningful difference from licensed product in terms of safety, purity, and potency. Biosimilar study design is essential to demonstrate the equivalence between biosimilar drug and reference product. However, existing designs and assessment methods are primarily based on binary and continuous endpoints. We propose a Bayesian adaptive design for biosimilarity trials with time-to-event endpoint. The features of the proposed design are twofold. First, we employ the calibrated power prior to precisely borrow relevant information from historical data for the reference drug. Second, we propose a two-stage procedure using the Bayesian biosimilarity index (BBI) to allow early stop and improve the efficiency. Extensive simulations are conducted to demonstrate the operating characteristics of the proposed method in contrast with some naive method. Sensitivity analysis and extension with respect to the assumptions are presented.

A Bayesian adaptive phase I/II platform trial design for pediatric immunotherapy trials.

Immunotherapy is the most promising new cancer treatment for various pediatric tumors and has resulted in an unprecedented surge in the number of novel immunotherapeutic treatments that need to be evaluated in clinical trials. Most phase I/II trial designs have been developed for evaluating only one candidate treatment at a time, and are thus not optimal for this task. To address these issues, we propose a Bayesian phase I/II platform trial design, which accounts for the unique features of immunotherapy, thereby allowing investigators to continuously screen a large number of immunotherapeutic treatments in an efficient and seamless manner. The elicited numerical utility is adopted to account for the risk-benefit trade-off and to quantify the desirability of the dose. During the trial, inefficacious or overly toxic treatments are adaptively dropped from the trial and the promising treatments are graduated from the trial to the next stage of development. Once an experimental treatment is dropped or graduated, the next available new treatment can be immediately added and tested. Extensive simulation studies have demonstrated the desirable operating characteristics of the proposed design.

Predicting events in clinical trials using two time-to-event outcomes.

In clinical trials with time-to-event outcomes, it is of interest to predict when a prespecified number of events can be reached. Interim analysis is conducted to estimate the underlying survival function. When another correlated time-to-event endpoint is available, both outcome variables can be used to improve estimation efficiency. In this paper, we propose to use the convolution of two time-to-event variables to estimate the survival function of interest. Propositions and examples are provided based on exponential models that accommodate possible change points. We further propose a new estimation equation about the expected time that exploits the relationship of two endpoints. Simulations and the analysis of real data show that the proposed methods with bivariate information yield significant improvement in prediction over that of the univariate method.