This article aims to give a brief review of current drug development procedures and issues which were to be discussed in the biostatistics seminar which was held on 19, December 2010 in Kobe. A few typical global development plans for clinical development of a new drug are presented. These plans use the bridging concept and multiregional trials. It is necessary to take several factors into account when we plan global development of a new drug. Points to be discussed are given below: 1) definition of regions, 2) how to look at similarity of response to drugs among different ethnic groups, and how to measure the similarity of trial results? 3) definition of the treatment main effect, 4) design of a multiregional trial and how to allocate sample sizes to regions, 5) how to evaluate the effect of covariates and ethnic factors, 6) statistical persuasiveness of the trial results, 7) evaluation of consistency of the quality of a trial among regions, 8)cross-cultural reliability of rating scales, 9) evaluation of safety, 10) relationships between exploration and confirmation.
The Japanese ‘drug lag,’ where drugs approved in US and EU are not approved in Japan, has been aggravated for many years. The bridging strategy has been accepted to utilize foreign clinical trial data in a new drug application since ICH E5 guideline was published in 1998, but the ‘drug lag’ was not improved. Instead of the bridging strategy, multinational clinical trials including Japan have been encouraged to promote efficient and rapid development of new drugs in Japan for recent several years. Although a global drug development strategy including Japan was changed, it is a fundamental issue that foreign clinical data have to be accepted as full or partial support for approval of a new drug application in Japan. For assuring that foreign clinical data can be extrapolated in other regions, it needs to consider what ethnic factors might affect on efficacy and safety of experimental drugs, how the influence from each ethnic factor should be evaluated, how many patients should be enrolled in each region participated in a global clinical trial, and so on. In this article, these issues are discussed from the point of view of a statistician working in a Japanese pharmaceutical company.
Multi-Regional Clinical Trials (MRCT) have increased for simultaneous new drug development in many therapeutic areas. Primary endpoint in oncology MRCT is usually survival time and two points to be considered should be taken into account in this case. One is different accrual periods among regions and the other is different responses in control treatment among regions. In this paper, we show how they affect actual power to overall result and assurance probabilities by simulation. We also deal with the difference of the approval institutions between Japan and the United States and discuss the issue derived from the difference.
In the condition of multiregional clinical trial, we considered the comparison with the frequency of adverse event between rigions. Several parameters, RRR (ratio of risk ratio), ORR (ratio of odds ratio) and PRRR (ratio of proportional reporting ratio), were defined to compare the frequency of adverse event. The influence of report bias was considered these parameters using modeled. report bias for drug adverse event between regions The RRR gave most robust estimator for several types report bias of adverse event between regions. The characteristics of RRR were examined by simulation using binomial random number on the several study conditions.
PK-PD model is one of the most effective tools for understanding relationship between drug concentration profile and pharmacological. effect. However PK-PD analysis needs different techniques from PK and statistical model analyses. In this article, I introduce several points to consider for conducting PK-PD analysis effectively.
In Model-Based Drug Development (MBDD), modeling and simulation is repeatedly performed aiming to develop drugs efficiently in a quantitative manner. Through planning stages of clinical trials, the design is planned, evaluated and decided by the results of the clinical trial simulation updated continuously based on the latest information. For executing a clinical trial simulation, a mathematical model is designed and constructed for various purposes on the basis of three models (i.e., Disease model, Drug model, and Trial model). It is revised accordingly based on the latest information. The development policy of a drug is determined by triol designs which are planned and evaluated based on the results of the clinical trial simulations. It is necessary for decision makers to consider the uncertainty of the simulation’s results in their decision processes. In this paper, we explain the outline of three models and introduce statistical theories and simulation methods showing examples as a basic theory of pharmacometrics for executing MBDD. Moreover, the decision making process considering uncertain factors is outlined.
In the recent business and regulatory climate in drug development, the principle of Model-Based Drug Development (MBDD) is growing in importance as a driving force in development. MBDD is a strategy which is able to produce the essential clue to quantitative decision-making and the design of clinical trials by Monte-Carlo simulation. Throughout the process of MBDD, a nonlinear mixed effect model plays a very important role to describe the pharmacokinetics/pharmacodynamics (PK-PD) of an investigational drug and the disease progression. In order to be familiar with he technical aspect of MBDD, it is quite important to understand some real examples of models, which are already reported. In this article, four examples are introduced; 1) Apokyne PK-PD model, which impacted the decision of labeling in FDA’s review process, 2) HAE-1 PK-PD model, developed according to formerly developed drug and used for the design of Phase 2 study, 3) Rivoglitazone model integrating PK, PD and AE, and 4) QT-drug concentration model. Since the models in MBDD are complicated, pharmacometricians have to then conduct MBDD in close collaboration with pharmacometricians and other clinical development staff, especially with biostatisticians who also are experts in the quantitative data analysis.
Model-Based drug development (MBDD) is not so widely used Japan relative to U. S. A and EU. This is because 1) not so many well educated pharmacometricians with enough skills exist in Japan, 2) necessary databases for MBDD are not well organized in Japan. These two deficiencies lead to the paucity of disease progression model for Japanese patients. Pharmacometricians including pahramacokineticists, biostatisticians, and clinical pharmacologists have to collaborate with each other to solve these problems and contribute to the public health of Japan by ensuring development of new effective and safe drug compounds.