ABCL-396: Incidence, Onset, and Management of Edema and Effusion in Patients Treated with Loncastuximab Tesirine for R/R DLBCL in the LOTIS Clinical Trial Program

Abstract Background and Aims : Observation of impactful clinical outcomes in a clinical trial setting for ADPKD is challenging due to the life-long progressive nature of ADPKD and longer-term associated outcomes of interest in this population (e.g., renal function decline, cardiovascular events, and mortality). Since 2004, the tolvaptan (TOL) clinical trial program enrolled subjects in multiple clinical studies with the opportunity to enroll in subsequent clinical trials for treatment and outcomes evaluation. Method : Data from 6 ADPKD studies (protocols 156-04-250, 156-04-251, 156-06-260, 156-09-284, 156-09-290, 156-08-271) were pooled and evaluated over time for overall treatment duration, treatment time, and treatment gaps. Treatment duration for the individual clinical trials ranged from 1 week to up to 3 years. Results : Overall, 1,437 subjects received TOL in these ADPKD clinical trials. For these subjects, the mean overall treatment duration was 4.1 years (3.8 years on treatment) with a maximum of 9.7 years (9.0 years on treatment). In this cohort, 513 subjects (35.7%) received TOL treatment for more than 5 years. Mean treatment compliance was 94.1%. Overall, 723 subjects (50.3%) received TOL treatment in ≥2 trials, with a median treatment gap duration between trials of 0.1 years (maximum, 5.6 years). At least 7 years of follow-up data are available for estimated glomerular filtration rate in 241 subjects (mean at baseline, 78.6 mL/min/1.73m2) and for total kidney volume in 130 subjects (mean at baseline, 1,816.9 mL). Conclusion : This analysis provides longitudinal follow-up over an extended timeframe in a large number of subjects treated with TOL, with the greatest number of subjects being enrolled in clinical trials enriched for rapidly progressing ADPKD. Treatment compliance over years was reasonably good despite treatment gaps.

Download Full-text

Certolizumab pegol in the treatment of Crohn’s disease: evidence from the PRECiSE clinical trial program

Clinical Investigation ◽

10.4155/cli.10.33 ◽

2011 ◽

Vol 1 (3) ◽

pp. 459-465

Author(s):

Ian C Lawrance

Keyword(s):

Clinical Trial ◽

Crohn’S Disease ◽

Crohn's Disease ◽

Certolizumab Pegol ◽

Clinical Trial Program

Download Full-text

Pegvaliase for the treatment of phenylketonuria: Results of a long-term phase 3 clinical trial program (PRISM)

Molecular Genetics and Metabolism ◽

10.1016/j.ymgme.2018.03.006 ◽

2018 ◽

Vol 124 (1) ◽

pp. 27-38 ◽

Cited By ~ 45

Author(s):

Janet Thomas ◽

Harvey Levy ◽

Stephen Amato ◽

Jerry Vockley ◽

Roberto Zori ◽

...

Keyword(s):

Clinical Trial ◽

Phase 3 ◽

Clinical Trial Program

Download Full-text

Adaptive Learning of Drug Quality and Optimization of Patient Recruitment for Clinical Trials with Dropouts

Manufacturing & Service Operations Management ◽

10.1287/msom.2020.0936 ◽

2021 ◽

Author(s):

Zhili Tian ◽

Weidong Han ◽

Warren B. Powell

Keyword(s):

Clinical Trial ◽

Clinical Trials ◽

Dynamic Programming ◽

Value Function ◽

Standard Treatment ◽

Treatment Effectiveness ◽

Interim Analyses ◽

Clinical Trial Program ◽

Patient Enrollment ◽

The Value Function

Problem definition: Clinical trials are crucial to new drug development. This study investigates optimal patient enrollment in clinical trials with interim analyses, which are analyses of treatment responses from patients at intermediate points. Our model considers uncertainties in patient enrollment and drug treatment effectiveness. We consider the benefits of completing a trial early and the cost of accelerating a trial by maximizing the net present value of drug cumulative profit. Academic/practical relevance: Clinical trials frequently account for the largest cost in drug development, and patient enrollment is an important problem in trial management. Our study develops a dynamic program, accurately capturing the dynamics of the problem, to optimize patient enrollment while learning the treatment effectiveness of an investigated drug. Methodology: The model explicitly captures both the physical state (enrolled patients) and belief states about the effectiveness of the investigated drug and a standard treatment drug. Using Bayesian updates and dynamic programming, we establish monotonicity of the value function in state variables and characterize an optimal enrollment policy. We also introduce, for the first time, the use of backward approximate dynamic programming (ADP) for this problem class. We illustrate the findings using a clinical trial program from a leading firm. Our study performs sensitivity analyses of the input parameters on the optimal enrollment policy. Results: The value function is monotonic in cumulative patient enrollment and the average responses of treatment for the investigated drug and standard treatment drug. The optimal enrollment policy is nondecreasing in the average response from patients using the investigated drug and is nonincreasing in cumulative patient enrollment in periods between two successive interim analyses. The forward ADP algorithm (or backward ADP algorithm) exploiting the monotonicity of the value function reduced the run time from 1.5 months using the exact method to a day (or 20 minutes) within 4% of the exact method. Through an application to a leading firm’s clinical trial program, the study demonstrates that the firm can have a sizable gain of drug profit following the optimal policy that our model provides. Managerial implications: We developed a new model for improving the management of clinical trials. Our study provides insights of an optimal policy and insights into the sensitivity of value function to the dropout rate and prior probability distribution. A firm can have a sizable gain in the drug’s profit by managing its trials using the optimal policies and the properties of value function. We illustrated that firms can use the ADP algorithms to develop their patient enrollment strategies.

Download Full-text