SITUACIÓN DE AUMENTO EN LA DEMANDA QUE HAGA INSUFICIENTES LAS

An initial clear understanding of the formulation and/or process is important. The following techniques can assist in summarizing current process knowledge. 1. Flow Diagram

A process flow diagram (Fig. 3) can often provide a focal point of early program planning activities. This diagram outlines the sequence of process steps and specific equipment to be used during development for a typical granulated prod- uct. Flow diagram complexity will depend on the particular product and process. The flow diagram provides a convenient basis on which to develop a detailed list of variables and responses.

2. Variables and Responses

For process using existing technology, many of the potential variables and re- sponses may have already been identified in previous product-development studies or in the pharmaceutical literature. Once properly identified, the list of variables and responses for the process is not likely to change appreciably. Typi- cal variables and responses that could be expected in a granulated solid dosage form are listed inTable 1.

In addition, the relative importance of variables and responses already identified will likely shift during development activities.

3. Cause-and-Effect Diagram

An efficient representation of complex relationships between many process and formulation variables (causes), and a single response (effect) can be shown by using a cause-and-effect diagram [1].Figure 4is a simple example.

A central arrow in Figure 4 points to a particular single effect. Branches off the central arrow lead to boxes representing specific process steps. Next, principle factors of each process step that can cause or influence the effect are drawn as subbranches of each branch, until a complete cause-and-effect diagram is developed. This should be as detailed a summary as possible. An example of a more complex cause-and-effect diagram is illustrated inFigure 5. A separate summary for each critical product characteristic (e.g., weight variation, dissolu- tion, friability) should be made.

4. Influence Matrix

Once the variables and responses have been identified, it is useful to summarize their relationships in an influence matrix format, as shown in Figure 6. Based on the available knowledge, each process variable is evaluated for its potential

effects on each of the process responses or product characteristics. The strength of the relationship between variables and responses can be indicated by some appropriate notation, such as strong (S), moderate (M), weak (W), or none (N), together with special classifications such as unknown (?).

Construction of the influence matrix assists in identifying those variables with the greatest influence on key process or product characteristics. These vari- ables are potentially the most critical for maintaining process control and should be included in the earliest experiments. Some may continue to be investigated during development and scale-up.

VIII. EXPERIMENTAL DESIGN AND ANALYSIS

Many different experimental designs and analysis methods can be used in devel- opment activities (Fig. 7). Indeed, the possibilities could fill several books. For- tunately, in any given situation, it is not necessary to search for that single design or analysis method that absolutely must be used; there are usually many possibilities. In general, designs that are usable offer different levels of effi- ciency, complexity, and effectiveness in achieving experimental objectives.

A. Types of Design

It is not possible to list specific designs that will always be appropriate for general occasions. Any attempt to do so would be sure to be ineffective, and the uniqueness of individual experimental situation carefully, including

Specific objectives Available resources

Availability of previous theoretical results Relevant variables and responses

Qualifications and experience of research team members Cost of experimentation

It should also be determined which design is appropriate. A statistician who is experienced in development applications can assist in suggesting and evaluating candidate designs. In some cases, the statistician should be a full-time member of the research team.

B. Data Analysis

The appropriate analysis of the experimental results will depend on the experi- mental objectives, the design used, and the characteristics of the data collected during the experiment. In many cases, a simple examination of a tabular or

graphical presentation of the data will be sufficient. In other cases, a formal statistical analysis may be required in order to draw any conclusions at all. It depends on the particular experimental situation. No rules of thumb are avail- able. In general, the simplest analysis consistent with experimental objectives and conditions is the most appropriate.

C. Experiment Documentation

Documentation is essential to program planning and coordination, in addition to the obvious use for the summary of activities and results. Written communica- tion becomes important for larger complex programs, especially when con- ducted under severe constraints on time and resources. Documentation can con- sist of some or all of the following items:

1. Objectives; an exact statement of quantifiable results expected from the experiment

2. Experimental design; a detailed list of the experimental conditions to be studied and the order of investigation

3. Proposed/alternate test methods

a. A list of test methods consistent with the type of experiment be- ing performed

b. A detailed description of the steps necessary to obtain a valid measurement

c. Documentation supporting the accuracy, precision, sensitivity, and so on of the test methods

4. Equipment procedures; documentation of safety precautions and step- by-step methods for equipment setup, operation, and cleanup 5. Sampling plans; the type, number, location, and purpose of samples

to be taken during the experiment; in addition, the type and number of all measurements to be performed on each sample

6. Protocol; a formal written experimental plan that presents the afore- mentioned experimental documentation in a manner suitable for re- view

7. Data records

a. Experiment log; details of events in the experiment noting process adjustments and any unusual occurrences

b. In-process measurements; records of the magnitude of critical process parameters during the experimental sequence

Sample measurements; recorded values of particular measure- ments on each sample

8. Report; documentation of experiment implementation, exceptions/ modifications to the protocol, results, and conclusion