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The MCP-Mod Procedure for Dose-Response Testing and Estimation

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Contents:

1st Edition

Handbook of Adaptive Designs in Pharmaceutical and Clinical Development

Clinical Pharmacology—The Quarterback of Drug Development

CHALLENGES FOR DOSE SELECTION DURING DRUG DEVELOPMENT

Dosing information on the drug label is based on discussion and agreement between the pharmaceutical manufacturer and the drug regulatory agency. A drug label is a high level summary of results obtained from many scientific experiments.

Scientists with biological, chemical, medical, or statistical background working in the pharmaceutical industry designed and executed these experiments to obtain information to help understand the dosing information. This book introduces the drug development process, the design and analysis of clinical trials. Many of the discussions are based on applications of statistical methods in the design and analysis of dose response studies. The potential readers include pharmacokienticists, clinical scientists, clinical pharmacologists, pharmacists, project managers, pharmaceutical scientists, clinicians, programmers, data managers, regulatory specialists, and study report writers.

This book is also a good reference for professionals working in a drug regulatory environment, for example, the FDA. In addition, statistical and medical professionals in academia may find this book helpful in understanding the drug development process and practical concerns in selecting doses for a new drug. Naitee Ting received his Ph. Ting is currently an Associate Director of Biostatistics in Pfizer Global Research and Development, supporting clinical development of new drugs.

He has over 18 years of experiences in designing and analyzing late phase clinical trials.

1st Edition

During his tenure at Pfizer, Dr. Ting has published more than 20 statistical papers in peer-reviewed journals and book chapters. Our survey highlighted lack of resources for evaluating trial designs as a barrier. The example in Box 1 shows that software templates can speed up design evaluation. Sufficient software training and support during grant development would be very valuable. Experiences from pharmaceutical companies show that model-based designs are readily accepted by health authorities and ethics boards. A model-based phase I trial design must be described and justified in a clinical trial authorisation application like any other design choice.

We encourage regulators to make their position clear to clinicians and statisticians. Funders drive the academic clinical research agenda by setting strategic health priorities and commissioning research projects. They influence the direction and quality of research, as researchers aim to deliver what funders demand.

Funders can play a pivotal role in encouraging better statistical methods in the design and analysis of dose-finding studies by setting strategic objectives, implementing rigorous statistical peer review, and integrating statistical expertise into their processes. We encourage funding bodies and ethics committees to question the use of algorithm-based designs, conduct statistical reviews of all phase I trial applications, and embrace model-based studies.

Ignorance of the benefits of model-based designs and disadvantages of algorithm-based designs is blocking wider implementation of more efficient phase I trial designs. Educating funding bodies, ethics committees, and regulatory agencies via tailored training sessions will enable more scientific appraisal of phase I trial designs. This will provide a greater return on investment: studies will produce more reliable results, increasing the likelihood of successful drug development.

We can extend these principles to publications. Journal editors and reviewers should question study designs and how they affect the reliability of dose recommendations for future studies.

By encouraging earlier clinical and statistical discussion, highlighting available training resources and practical examples, and calling for education for funders and other review committees, we hope to help overcome the barriers to model-based designs identified here. Implementing model-based designs will generate more accurate dose recommendations for later-stage testing and increase the efficiency and likelihood of successful drug development.

Stat Med 17 10 : — J Natl Cancer Inst 3 : pii: dju Braun TM The current design of oncology phase I clinical trials: progressing from algorithms to statistical models. Chin Clin Oncol 3 1 : 2. Calvert AH, Plummer R The development of phase I cancer trial methodologies: the use of pharmacokinetic and pharmacodynamic end points sets the scene for phase 0 cancer clinical trials. Clin Cancer Res 14 12 : — Carter SK The phase I study.

Fundamentals of Cancer Chemotherapy. Biometrics 56 4 : — Chevret S Bayesian adaptive clinical trials: a dream for statisticians only? Stat Med 31 11—12 : — Committee for Medicinal Products for Human Use Guideline on strategies to identify and mitigate risks for first-in-human clinical trials with investigational medicinal products. Cancer 23 : — Trials 16 : Dimairo M, Julious SA, Todd S, Nicholl JP, Boote J b Cross-sector surveys assessing perceptions of key stakeholders towards barriers, concerns and facilitators to the appropriate use of adaptive designs in confirmatory trials.

Ann Oncol 26 2 : — Garrett-Mayer E The continual reassessment method for dose-finding studies: a tutorial. Clin Trials 3 1 : 57— Hemmings R Philosophy and methodology of dose-finding — a regulatory perspective. In: Chevret S ed. Statistical Methods for Dose-Finding Experiments.

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Wiley: Chichester, UK. J Clin Oncol 33 19 : — Clin Cancer Res 18 19 : — J Clin Oncol 32 23 : — Clin Trials 5 5 : — Iasonos A, Zohar S, O'Quigley J Two-stage continual reassessment method that determines dose escalation based on individual toxicity grades. Clin Trials 7 : Ishizuka N, Ohashi Y The continual reassessment method and its applications: a Bayesian methodology for phase I cancer clinical trials. Stat Med 20 17—18 : — Jaki T Uptake of novel statistical methods for early-phase clinical studies in the UK public sector. Clin Trials 10 2 : — Cancer Chemother Pharmacol 71 5 : — J Thorac Oncol 7 11 : — Stat Med 13 18 : — J Natl Cancer Inst 10 : — Br J Cancer 95 3 : — Stat Med 26 11 : — Ther Innov Regul Sci 48 4 : — Drug Inf J 41 6 : — Stat Med 27 13 : — O'Quigley J Another look at two phase I clinical trial designs.

Stat Med 18 20 : — Stat Med 30 17 : — Biometrics 46 1 : 33— J Biopharm Stat 19 3 : — Eur J Cancer 42 10 : — Ann Oncol 26 9 : — Stat Med 36 2 : — Potter DM Adaptive dose finding for phase I clinical trials of drugs used for chemotherapy of cancer.

Handbook of Adaptive Designs in Pharmaceutical and Clinical Development

We consider a dose-finding trial in phase IIB of drug development. For choosing an appropriate design for this trial the specification of two points is critical: an appropriate model for describing the dose-effect relationship and the specification of the aims of the trial objectives , which will be the focus in the present paper.

For many practical situations it is essential to have a robust trial objective that has little risk of changing during the complete trial due to external information. An important and realistic objective of a dose-finding trial is to obtain precise information about the interesting part of the dose-effect curve.

We reflect this goal in a statistical optimality criterion and derive efficient designs using optimal design theory. In particular we determine non-adaptive Bayesian optimal designs, i. Compared with a traditional balanced design for this trial it is shown that the optimal design is substantially more efficient. This implies either a gain in information or essential savings in sample size. Further, we investigate an adaptive Bayesian optimal design that uses two different optimal designs before and after an interim analysis, and we compare the adaptive with the non-adaptive Bayesian optimal design.

The basic concept is illustrated using a modification of a recent AstraZeneca trial. Sequential design for binary dose—response experiments.

Clinical Pharmacology—The Quarterback of Drug Development

Xiaoli Yu. In dose—response studies, experiments are often carried out according to optimal designs for the purpose of accurately determining a specific effective dose ED level. If the interest is in the dose—response relationship over a range of ED levels, the existing optimal designs is misaligned.

In this paper, we propose a two-stage sequential ED-design for this purpose. We use a small number of trials to provide a tentative estimation of the parameters.

CHALLENGES FOR DOSE SELECTION DURING DRUG DEVELOPMENT

Finally, two sample size were investigated assuming either 60 per arm or 50 10 per arm patients. BMC Musculoskelet Disord ; The association between various QST measures and clinical pain has been well-documented, both in connection to acute and chronic pain perception, sensitivity in forecasting clinical deterioration, as well as prediction of postoperative pain outcomes in a variety of surgical procedures. The aim of Phase II trials is to find out if the new drug is effective in patients through hypothesis testing and at what dose the balance between efficacy and safety is optimal. In its current implementation, however, the MCP step is limited to the exploration of several mean dose response functions measured at end of trial and, hence, ignores longitudinal information. However, it is well recognized that estimation of sample size in clinical trials involve knowledge of treatment effect and variability, which are usually unknown at the planning stage of the trial. Block stratification is used to minimize the bias and unbalanced allocation, especially if the trial is not placebo controlled.

The dose levels of the subsequent trials are then selected sequentially, based on the latest model information, to maximize the efficiency of the ED estimation over several ED levels. Simulations indicate that the proposed design compares favorably with existing designs under various scenarios. Bayesian two-stage dose finding for cytostatic agents via model adaptation. Jiajing Xu Guosheng Yin.

In phase I clinical trials with cytostatic agents, the typical objective is to identify the optimal biological dose, which should be tolerable as well as achieving the highest effectiveness. Towards this goal, we consider binary toxicity and efficacy end points simultaneously and develop a two-stage Bayesian adaptive design.

Stage 1 searches for the maximum tolerated dose by using a beta—binomial model in conjunction with a probit model, for which decision making is based on the model that fits the toxicity data better. Stage 2 identifies the optimal biological dose while still controlling the level of toxicity. We enumerate all the possibilities that each of the admissible doses may deliver the highest effectiveness so that the dose—efficacy curve is allowed to be increasing, decreasing or concave.