Dose-Finding Designs for HIV Studies

We present a class of simple designs that can be used in early dose-finding studies in HIV. Such designs, in contrast with Phase I designs in cancer, have a lot of the Phase II flavor about them. Information on efficacy is obtained during the trial and is as important as that relating to toxicity. The designs proposed here sequentially incorporate the information obtained on viral reduction. Initial doses are given from some fixed range of dose regimens. The doses are ordered in terms of their toxic potential. At any dose, a patient can have one of three outcomes: inability to take the treatment (toxicity), ability to take the treatment but insufficient reduction in viral load (viral failure), and ability to take the treatment as well as a sufficient reduction of viral load (success). A clear goal for some class of designs would be the identification of the dose leading to the greatest percentage of successes. Under certain assumptions, which we identify and discuss, we can obtain efficient designs for this task. Under weaker, sometimes more realistic assumptions, we can still obtain designs that have good operating characteristics in identifying a level, if such a level exists, having some given or greater success rate. In the absence of such a level, the designs will come to an early closure, indicating the ineffectiveness of the new treatment.     

CAR-T Cell Therapy for Breast Cancer: From Basic Research to Clinical Application

Breast cancer rises as the most commonly diagnosed cancer in 2020. Among women, breast cancer ranks first in both cancer incidence rate and mortality. Treatment resistance developed from the current clinical therapies limits the efficacy of therapeutic outcomes, thus new treatment approaches are urgently needed. Chimeric antigen receptor (CAR) T cell therapy is a type of immunotherapy developed from adoptive T cell transfer, which typically uses patients'' own immune cells to combat cancer. CAR-T cells are armed with specific antibodies to recognize antigens in self-tumor cells thus eliciting cytotoxic effects. In recent years, CAR-T cell therapy has achieved remarkable successes in treating hematologic malignancies; however, the therapeutic effects in solid tumors are not up to expectations including breast cancer. This review aims to discuss the development of CAR-T cell therapy in breast cancer from preclinical studies to ongoing clinical trials. Specifically, we summarize tumor-associated antigens in breast cancer, ongoing clinical trials, obstacles interfering with the therapeutic effects of CAR-T cell therapy, and discuss potential strategies to improve treatment efficacy. Overall, we hope our review provides a landscape view of recent progress for CAR-T cell therapy in breast cancer and ignites interest for further research directions.     

Enhanced fructose levels in field grown potato tubers expressing a thermostable glucose isomerase

A thermostable glucose isomerase was expressed in Solanum tuberosum Desirée using the tuber-specific granular-bound starch synthase promoter. The fructose content was substantially increased in microtubers, greenhouse grown tubers as well as tubers produced in field trials as compared with the controls. Furthermore, the tuber yield of field grown potatoes was enhanced by 30% in the transgenic lines (from 1.04 kg/plant in the wild type to 1.36 kg/plant in the transgenic lines).     

人心脏型脂肪酸结合蛋白检测试剂盒的临床评价

目的评价人心脏型脂肪酸结合蛋白(H-FABP)检测试剂盒(胶体金法)在急性心肌梗死(AMI)诊断中的价值。方法采用平行、盲法、对照的对比试验设计,比较其试验产品和对比产品对诊断AMI的敏感性、特异性、准确性。结果共测定240份临床血液标本。试验产品和对比产品的阳性符合率为100%,阴性符合率为96.15%,总符合率为97.92%。对比产品和试验产品结果不一致的5例标本以临床诊断结果为标准进行验证后,试验产品与临床诊断结果的阳性符合率为100%,总符合率为100%。采用Kappa检验考核两种产品测定结果的一致性,Kappa指数为0.958。经McNamara's test分析,两产品之间无差异,χ2=3.20,P>0.05。结论试验产品显示出较好的诊断价值,可以作为AMI早期诊断标志物,试验产品与对比产品等效。     

槐小卷蛾性信息素活性成份的鉴定和林间试验(英文)

通过气相色谱（GC）、气相色谱──质谱（GC─MS）分析和微量化学反应,槐小卷蛾性信息素的一个活性成分被鉴定为（反,反）－8,10－十二碳双烯乙酸酯（E8,E10－12∶AC）。林间试验结果表明合成的E8,E10－12∶Ac对槐小卷蛾雄蛾有强烈引诱作用。     

The future of psychopharmacology: a critical appraisal of ongoing phase 2/3 trials,and of some current trends aiming to de-risk trial programmes of novel agents

Despite considerable progress in pharmacotherapy over the past seven decades, many mental disorders remain insufficiently treated. This situation is in part due to the limited knowledge of the pathophysiology of these disorders and the lack of biological markers to stratify and individualize patient selection, but also to a still restricted number of mechanisms of action being targeted in monotherapy or combination/augmentation treatment, as well as to a variety of challenges threatening the successful development and testing of new drugs. In this paper, we first provide an overview of the most promising drugs with innovative mechanisms of action that are undergoing phase 2 or 3 testing for schizophrenia, bipolar disorder, major depressive disorder, anxiety and trauma-related disorders, substance use disorders, and dementia. Promising repurposing of established medications for new psychiatric indications, as well as variations in the modulation of dopamine, noradrenaline and serotonin receptor functioning, are also considered. We then critically discuss the clinical trial parameters that need to be considered in depth when developing and testing new pharmacological agents for the treatment of mental disorders. Hurdles and perils threatening success of new drug development and testing include inadequacy and imprecision of inclusion/exclusion criteria and ratings, sub-optimally suited clinical trial participants, multiple factors contributing to a large/increasing placebo effect, and problems with statistical analyses. This information should be considered in order to de-risk trial programmes of novel agents or known agents for novel psychiatric indications, increasing their chances of success.     

A matching procedure for sequential experiments that iteratively learns which covariates improve power

We propose a dynamic allocation procedure that increases power and efficiency when measuring an average treatment effect in sequential randomized trials exploiting some subjects' previous assessed responses. Subjects arrive sequentially and are either randomized or paired to a previously randomized subject and administered the alternate treatment. The pairing is made via a dynamic matching criterion that iteratively learns which specific covariates are important to the response. We develop estimators for the average treatment effect as well as an exact test. We illustrate our method's increase in efficiency and power over other allocation procedures in both simulated scenarios and a clinical trial dataset. An R package “SeqExpMatch ” for use by practitioners is available on CRAN .     

Clustered restricted mean survival time regression

For multicenter randomized trials or multilevel observational studies, the Cox regression model has long been the primary approach to study the effects of covariates on time-to-event outcomes. A critical assumption of the Cox model is the proportionality of the hazard functions for modeled covariates, violations of which can result in ambiguous interpretations of the hazard ratio estimates. To address this issue, the restricted mean survival time (RMST), defined as the mean survival time up to a fixed time in a target population, has been recommended as a model-free target parameter. In this article, we generalize the RMST regression model to clustered data by directly modeling the RMST as a continuous function of restriction times with covariates while properly accounting for within-cluster correlations to achieve valid inference. The proposed method estimates regression coefficients via weighted generalized estimating equations, coupled with a cluster-robust sandwich variance estimator to achieve asymptotically valid inference with a sufficient number of clusters. In small-sample scenarios where a limited number of clusters are available, however, the proposed sandwich variance estimator can exhibit negative bias in capturing the variability of regression coefficient estimates. To overcome this limitation, we further propose and examine bias-corrected sandwich variance estimators to reduce the negative bias of the cluster-robust sandwich variance estimator. We study the finite-sample operating characteristics of proposed methods through simulations and reanalyze two multicenter randomized trials.     

Inference in response-adaptive clinical trials when the enrolled population varies over time

A common assumption of data analysis in clinical trials is that the patient population, as well as treatment effects, do not vary during the course of the study. However, when trials enroll patients over several years, this hypothesis may be violated. Ignoring variations of the outcome distributions over time, under the control and experimental treatments, can lead to biased treatment effect estimates and poor control of false positive results. We propose and compare two procedures that account for possible variations of the outcome distributions over time, to correct treatment effect estimates, and to control type-I error rates. The first procedure models trends of patient outcomes with splines. The second leverages conditional inference principles, which have been introduced to analyze randomized trials when patient prognostic profiles are unbalanced across arms. These two procedures are applicable in response-adaptive clinical trials. We illustrate the consequences of trends in the outcome distributions in response-adaptive designs and in platform trials, and investigate the proposed methods in the analysis of a glioblastoma study.