Design, Methodology, and Analysis Considerations

Nonclinical research provides very useful information and plays a considerable role in the successful development of a new drug. It is also required by current regulatory statutes. Nonetheless, no matter how meticulously, rigorously, and comprehensively nonclinical testing is conducted, no animal model is a perfect model of the drug s actions and effects in humans. Therefore, in addition to an appreciation of the usefulness of nonclinical data, it is valuable to have an appreciation of their limitations and of statistical considerations of particular pertinence to toxicological data. 

Gad (2006) discussed several characteristics of toxicological data, including  

These observations underline the fact that assessments of data from nonclinical toxicological experimentation should be undertaken with full knowledge of the involved uncertainties, weaknesses, and difficulties (Gad, 2006). 

The topic of randomization in clinical trials is addressed in the following chapter (see Section 5.6). Briefly, the randomization process involves randomly assigning subjects to one or other of the treatment groups to make the treatment groups as similar as possible in every regard except the treatment that they receive. As will be seen in later chapters, this allows any difference in response between the treatment groups to be ascribed to the treatment they received. 

Randomization is also important in nonclinical studies. Treatments should be assigned at random whether the experimental units are humans, animals, or test tubes. As Machin and Campbell (2005) noted, Medical investigators often appreciate the effect that biological variation has in patients, but overlook or underestimate its presence in the laboratory.  

---

Presenting the design, methodology, and data to be collected in a study protocol. This study protocol specifies the manner of data collection and addresses all methodological considerations necessary to ensure the collection of optimum quality data for subsequent statistical analysis. 

This book, which focuses on biological considerations in clinical trials, is written very much in this spirit. While study design, experimental methodology, and statistical analysis are central characters in our discussions of new drug development, their importance lies in their role in the development of drugs that influence a patient s biology for the better. 

The design strategies employed to improve combinatorial chemistry have evolved considerably since the early days of peptide and peptidomimetic libraries. The main concern early was on the availability of suitable synthetic methods that could be applied to the synthesis of libraries of small molecules however, this early obstacle has been intensively addressed and at this point can be considered overcome (for examples of new methodology developed for library production see Ref 21). With the ability in hand to prepare many different types of molecules in a variety of formats, the current challenge is to decide what compounds to make. As a consequence, much attention is now focused on the definition and analysis of chemical diversity. 

For example, in the analgesic example cited above (25), a comparison was made between an analysis using the last observation carried forward (LOCF) method and the proposed mixed effects maximum likelihood method. Although this was a retrospective analysis, similar contrasts could be included in the trial s simulation to ascertain the most appropriate analytical methodology to include in the study design (protocol). Other analysis factors for consideration include appropriate correction of variability, where such sources may include differences between sites or regional differences. 

These provisions presume that the culture and the management commitment include upstream design and engineering considerations and knowledge of hazard identification and analysis and risk assessment methodologies. 

---