Quality Management Model for Phase I Clinical Drug Trials:A Structural Equation Model

This study aimed to construct a quality management model for phase I clinical drug trials.A cross-sectional survey was conducted and data were collected from 604 respondents at 69 institutions in China engaged in phase I clinical drug trials.Exploratory and confirmatory factor analyses were used to develop the survey tool.Structural equation modeling was used to construct a quality management model for phase I clinical drug trials.The results showed that the final survey tool had good reliability and validity(Cronbach’sα=0.938,root mean square error of approximation=0.074,comparative fit index=0.962,and Tucker—Lewis index=0.955).The model included five dimensions:government regulation,industry management,medical institution management,research team management,and contract research organization(CRO)management.In total,22 measurement items were obtained.The structural equation model indicated government regulation,industry management,medical institution management,and CRO management significantly affected the quality of phase I clinical drug trials(β=0.195,β=0.331,β=0.279,andβ=−0.267,respectively;P<0.05).Research team management had no effect on the quality of trials(β=0.041,P=0.610).In conclusion,the model is valuable for identifying factors influencing phase I clinical drug trials and guiding quality management practices.

Stepwise Method Based on Confidence Bound and Information Incorporation for Identifying the Maximum Tolerable Dose

The primary goal of a phase I clinical trial is to find the maximum tolerable dose of a treatment. In this paper, we propose a new stepwise method based on confidence bound and information incorporation to determine the maximum tolerable dose among given dose levels. On the one hand, in order to avoid severe even fatal toxicity to occur and reduce the experimental subjects, the new method is executed from the lowest dose level, and then goes on in a stepwise fashion. On the other hand, in order to improve the accuracy of the recommendation, the final recommendation of the maximum tolerable dose is accomplished through the information incorporation of an additional experimental cohort at the same dose level. Furthermore, empirical simulation results show that the new method has some real advantages in comparison with the modified continual reassessment method.

Using Intelligent Screening Service Platform(ISSP)to Improve the Screening Process of Clinical Trial Subjects during COVID-19 Pandemic:An Experimental Study

Background:During the COVID-19 pandemic,clinical trial recruitment could not be carried out due to travel restrictions,transmission risks and other factors,resulting in the stagnation of many ongoing or upcoming clinical trials.Objective:An intelligent screening tool was developed using artificial intelligence technology to rapidly prescreen potential patients for phase I solid tumor drug clinical trials.Methods:A total of 429 screening process records were collected from 27 phase I solid tumor drug clinical trials at the First Affiliated Hospital of Bengbu Medical College from April 2018 to May 2021.Features of the experimental data were analyzed,and the collinearity(principal component analysis)and strong correlation(χ^(2)test)among features were eliminated.XGBoost,random forest,and naive Bayes were used to determine the weight importance of the features.Finally,prescreening models were constructed using a classification machine learning algorithm,and the optimal model was selected.Results:Among the 429 screening records,33 were generated by repeated subject participation in different clinical trials,and of the remaining 396 screening records,246(62.12%)were screened successfully.The gold standard for subject screening success was the final judgment made by the principal investigator(PI)based on the clinical trial protocol.A Venn diagram was used to identify the important feature intersections of the machine learning algorithms.After intersecting the top 15 characteristic variables of the different feature screening models,9 common variables were obtained:age,sex,distance from residence to the central institution,tumor histology,tumor stage,tumorectomy,interval from diagnosis/postoperative to screening,chemotherapy,and Eastern Cooperative Oncology Group(ECOG)score.To select the optimal subset,the 9 important feature variables were expanded to 12 and 15 feature subsets,and the performance of different feature subsets under different machine learning models was validated.The results showed that optimal performance,accuracy and practicability were achieved using XGBoost with the 12-feature subset.The final model could accurately predict the screening success rates in both internal(AUC=0.895)and external(AUC=0.796)validation and has been transformed into a convenient tool to facilitate its application in clinical settings.Subjects with a probability exceeding or equal to the threshold in the final model had a greater probability of being successfully screened.Conclusion:Based on the optimal model,we created an online prediction calculator and visualization app,the Intelligent Screening Service Platform(ISSP),which can rapidly screen patients for phase I solid tumor drug clinical trials.The IsSP can effectively solve the problems of space and time intervals.On the mobile terminal,matching between clinical trial projects and patients can be achieved,and the rapid screening of clinical trial subjects can be completed to obtain more clinical trial subjects.As an auxiliary tool,the ISSP optimizes the screening process of clinical trials and provides more convenient services for clinical investigators and patients.

Safety and tolerability of isradipine in Phase I trial in Chinese population

Hypertension is one of the well-established risk factor for cardiovascular diseases. Calcium channel blockers（CCBs）, chemicals that could block voltage-gated calcium channels（VGCCs） in cardiac muscle and blood vessels, has been widely used for the treatment of hypertension. Isradipine, a second-generation CCB with high affinity for voltage-operated calcium channels, has not been marked in China. The purpose of this study was to investigate the efficacy, safety and tolerability of isradipine in a phase I clinical trial including 31 healthy Chinese subjects. All subjects received different doses of isradipine at 2.5, 5.0 and 10.0 mg in single-dose study. When the test is completed, subjects treated with 5.0 mg isradipine stayed at the research center for multiple-dose study（5.0 mg isradipine twice daily for 9 d）. Systolic blood pressure（SBP） and diastolic blood pressure（DBP） were measured pre-dose and post-dose（1, 2, 4, 6, 8, 12, 24, 36 and 48 h after isradipine treatment）. Electrocardiography（ECG） and peripheral edema were monitored pre-dose and 4, 8, 24 and 48 h after isradipine treatment. SBP and DBP in single-dose study decreased after isradipine treatment. SBP reached the lowest values 8 h after dosing with a decrease of（7.0±9.7） mmHg（5.4%, P = 0.111） in 2.5 mg group,（7.0±6.9） mmHg（6.0%, P = 0.008） in 5.0 mg group, and（14.0±10.5） mmHg（12.7%, P = 0.005） for 10.0 mg group respectively. Similarly, DBP also reached the lowest values 8 h after dosing with a decrease of（10.0±7.9） mmHg（12.8%, P = 0.004） in 2.5 mg group,（6.0±7.0） mmHg（8.6%, P = 0.003） in 5.0 mg group, and（11.0±4.1） mmHg（15.1%, P = 0.000） in 10.0 mg group respectively. No significant changes of SBP and DBP were observed in multiple-dose study. We detected mild adverse events（AEs）, such as increased transaminase and headache that resolved rapidly and spontaneously without intervention. No serious or potentially life-threatening AE was detected. Our results indicate that isradipin has a good safety and tolerability in Chinese healthy subjects. Long-term study with larger sample size is needed to confirm our conclusion.

A system for determining maximum tolerated dose in clinical trial

Toxicity study,especially in determining the maximum tolerated dose(MTD)in phase I clinical trial,is an important step in developing new life-saving drugs.In practice,toxicity levels may be categorised as binary grades,multiple grades,or in a more generalised case,continuous grades.In this study,we propose an overall MTD framework that includes all the aforementioned cases for a single toxicity outcome(response).The mechanism of determining MTD involves a function that is predetermined by user.Analytic properties of such a system are investigated and simu-lation studies are performed for various scenarios.The concept of the continual reassessment method(CRM)is also implied in the framework and Bayesian analysis,including Markov chain Monte Carlo(MCMC)methods are used in estimating the model parameters.