An Editorial Comment
Catching Up with Expert Systems Metin Çelik
T The use of expert systems (ESs), computer programs that either recommend or make decisions based on knowledge gathered from experts, has increased dramatically in the past two decades. Their use by the phamaceutical industry, however, lags behind that of numerous other fields. This article is a brief description of the design and implementation of ES programs and addresses the challenges the pharmaceutical industry faces in adopting their use.
Metin Çelik, PhD
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he pharmaceutical industry has entered the twenty-first century, a new era that will be far more scientific, technologic, and sophisticated than anyone would have imagined just a quarter of a century ago. However, the continued success in all areas of pharmaceutical science will depend entirely on how fast pharmaceutical scientists will adapt to rapidly changing technology. Almost 10 years ago, a survey by Shangraw and Demarest revealed a very interesting fact about solid-dosage formulation design and development: Tradition was still a very important reason for preferring to use a particular excipient (1). It is not difficult to predict that, in this century, trial and error formulation development and traditional excipient selection will be a part of history. Pharmaceutical formulators will enjoy the availability of the harmonized and fingerprinted (in terms of functionality testing) excipients, and formulations will be developed using databases (preformulation and compaction data banks, etc.) (2). The awareness of and the use of artificial intelligence–based expert systems (rule-based systems, fuzzy logic, genetic algorithm, artificial neural networks [ANNs], simulations, etc.) in the areas of preformulation, formulation and process development, regulatory affairs, new drug delivery system development, project management, and all other areas of pharmaceutical science will increase dramatically. To shorten the adaptation period of pharmaceutical scientists to rapidly changing technological advances, I recently formed two new focus groups, namely, the Expert Systems Focus Group and the Excipients Focus Group. They have been approved by the American Association of Pharmaceutical Scientists (AAPS). They will act in conjunction with the Pharmaceutical Technology Section of AAPS and are open to all members of AAPS and other pharmaceutical associations. I would like to give a brief overview of expert systems (ESs) and then address some challenges facing ES developments in terms of their verification and validation (V&V) processes, in part because of FDA’s interest in the V&V of all types of software. In a future article, each of the following issues will be discussed in depth. ESs, also known as knowledge-based systems, basically are computer programs that either recommend or make decisions based on knowledge gathered from experts in the field. Functional areas of ESs include, but are not limited to, control, design, diagnosis, instruction, interpretation, monitoring, planwww.phar maportal.com
ning, prediction, prescriptions, selection, and simulation. ESs are being used in many disciplines such as agriculture, business, chemistry, communications, computers, education, electronics, engineering, environment, geology, image, information, law, manufacturing, mathematics, medicine, meteorology, military, science, space, and transformations. The literature reported less than 50 ESs in use in 1985; this number increased to more than 12,000 in about seven years. However, although problems in the pharmaceutical industry are not necessarily more complicated than some of the problems encountered in the abovelisted fields, the number of ESs used in pharmaceutical science is still negligibly low. One of the main reasons for the insignificant use of ESs in our field is that pharmaceutical scientists prefer to use wellestablished concepts. We let somebody else try a new concept first, and if it works, we will join the crowd. In a way, this means making a choice between being a leader or a follower. It is a safe approach to use an established system, but it does not provide us with the immediate benefits of being on the technological edge. On the other hand, it is always risky to try a new concept, even though the outcome may prove to be rewarding for both the person(s) and the company. When compared with human experts, ESs have the following advantages: An ES’s knowledge is permanent and can be easily transferrable. The decision process is fast and consistent, therefore predictable, and it is easily documented. Despite these advantages, ESs are not intended to take the place of formulation scientists. They must be considered as vital tools to be used by formulators for the rapid, cost-effective, and scientifically sound development of a dosage form as well as useful for training inexperienced scientists. To build an ES, the full participation of a domain expert, knowledge engineer, and user is essential. A domain expert possesses the knowledge and skill to solve a specific problem in a manner superior to the others. This expert’s highly specialized knowledge is stored in the knowledge-base component of an artificial intelligence (AI)-based program by the knowledge engineer. The user also can help define the interface specifications. There are three essential components of an ES: the knowledge base, which contains the domain knowledge; the working memory, which contains the facts about the current problem discovered during the problem-solving session; and the inference engine, which matches the facts in the working memory to domain knowledge in the knowledge base and draws a conclusion. Of course, an ES may have additional components such as an explanation facility, depending on the type of application. The explanation component of an ES provides answers to the hows and whys of the problem-solving process. This feature is very useful in many instances; for example, because a formula ingredient selection and/or process selection has to be justified as part of the new FDA requrements, the user must know step by step how and why such a decision or recommendation was made by the ES. This also helps the user gain the problem-solving skills of the domain experts via the ES.
Phases of an ES development process Feasibility study. A project team assesses whether an ES can or
The differences between the artifical intelligence and conventional programming tools provide flexibility and special capabilities to an expert system, but these differences also make the use of traditional verification and validation of an expert system difficult. should be developed for a specific problem or project. The team evaluates the motivation for the development of the ES in terms of improved productivity, quality, and image as well as cost reduction. The team also must consider the problem and the people-related feasibility issues very carefully. Some of the important questions that must be answered positively are: ● Are the problem-solving steps definable? ● Is the problem stable and its complexity reasonable? ● Is the management supportive of the project, receptive to change, not skeptical, and does it have reasonable expectations? If all the answers to these questions are in the affirmative, then the project team should continue to evaluate the other problem — the deployment-related issues concerning the development of the ES for that particular problem or project. If and when a decision is made in favor of the development of the ES, then the project team defines the features and specifications of each component of the expert system and develops flow charts for each specific problem. Acquisition of the knowledge. Rules are determined for each specific problem or critical step involved in, for example, the development of film coating formulation and process. Domain experts play an extremely important role in this phase. Design of the ES. The knowledge engineer determines which software to use to transform the acquired knowledge into a coded program for the development of the ES. Some of the artificial intelligence tools (knowledge representation techniques) used alone or in combinations in the development of an ES include decision trees, object–attribute–value triplets, rules (if– then–else–because statements) with forward and/or backward chaining, fuzzy logic, genetic algorithm, case-based reasoning, and ANNs. A successful ES usually is developed by combining more than one AI technique. Testing the modules and development of the prototype. Case studies with known results are used to test the ability of the rules, databases, and programming to perform properly.
Implementation, testing, and troubleshooting of the final program. Case studies as well as untested materials and parameters Pharmaceutical Technology
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If two domain experts have conflicting views over a problemsolving process, who will decide which one is correct? are used to verify the proper operation of the program and to troubleshoot any additional problems identified. Training of users. A user acceptance questionnaire is used during the implementation of the program. Maintenance and upgrade of the program. Depending on the availability of the new knowledge and/or data in the field of a particular ES, an upgrade may be needed to ensure that the ES will evolve continuously to overcome new challenges concerning that specific project or problems.
Problems associated with the V&V of an ES Verification of an ES determines whether the system is developed according to its specifications. Validation of an ES determines whether the system meets the purpose for which it was intended. Very critical differences exist between an ES and conventional systems in terms of V&V of an ES. An ES is both a piece of software and a domain model, and there may not be a unique, correct answer to a problem given to an ES. An ES can adapt itself by modifying its behavior in relation to changes in its internal representation of the environment. An ES should be considered correct when it is complete, consistent, and satisfies the requirements that express expert knowledge about how the system should behave. If a system has hundreds of rules, however, it may require thousands of distinct decision paths, and this makes the aspect of correctness hard to establish. This is not, of course, a problem in a conventional programming technique. These differences between the AI and conventional programming tools provide flexibility and special capabilities to an ES, but these differences also make the use of traditional V&V of an ES difficult. This is one of the problems slowing the development and acceptance of ESs. Experts do not agree on how to accomplish the V&V of ESs. One of the impediments to a successful V&V effort for ESs is the nature of ESs themselves. They are often used for working with incomplete and uncertain information or ill-structured situations. Because the ES specifications often do not provide precise criteria against which to test, there is a problem in verifying and validating them according to the definitions. This is unavoidable. If there are precise enough specifications for a system, there would not be any need to use an AI tool to develop the system, and the conventional programming language would be sufficient for the development of a piece of software for that system. In reality, the first part of V&V, i.e., verification of an ES, is not so difficult to establish because it is possible, and also highly recommended, to build small modules (sub-ESs) for each prob124
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lem within a system. This is a significant help to the verification process of the whole system. This is true even if the ES is developed by combining more than one system. The main problem is the second part of V&V, i.e., validation. ESs will make a recommendation based on the domain knowledge. If the domain knowledge is junk, then the recommendation of the ES naturally will be junk. How can someone validate the correctness of knowledge provided by a domain expert, or if two domain experts have conflicting views over a problem-solving process, who will decide which is correct? As if the above problems are not enough, FDA’s requirements for the submission of the software code adds additional burden to the software validation of an ES. This is a serious obstacle because only a few AI tool providers and ES developers will be willing to share the code. Because some of the AI tools may cost more than $100,000, who can blame the software providers if they do not wish to share the code?
Summary I have tried to address briefly some of the issues concerning ESs and their development. The above-mentioned issues as well as other issues will be discussed in depth in a future article. One must admit the fact that it is a highly complicated process to develop an ES to the full satisfaction of the user, domain expert, company, FDA, etc. However, none of these obstacles should discourage pharmaceutical scientists. On the contrary, despite all of these problems, the overwhelming advantages of ESs must encourage pharmaceutical scientists to learn more about them. In the same way that we cannot do much without computers today, we will not be able to do much without ESs in the future. Sooner or later, all of us will be happily using them. Those who use them sooner will enjoy being the pioneers in their fields. They also will have the personal satisfaction of contributing to pharmaceutical science by catching up with the rest of the world in the application of such useful tools. I hope, now that it is established, the Expert System Focus Group will contribute to the implementation of ESs in pharmaceutical science by helping those who wish to be involved in the early stages of this venture.
References 1. R.F. Shangraw and D.A. Demarest, “A Survey of Current Industrial Practices in the Formulation and Manufacture of Tablets and Capsules,” Pharm. Technol. 17 (1), 32–44 (1993). 2. M. Çelik, “The Past, Present, and Future of Tableting Technology,” Drug Dev. Ind. Pharm. 22 (1), 1–10 (1996). PT Metin Çelik, PhD, is president of Pharmaceutical Technologies International, Inc., PO Box 186, Belle Mead, NJ 08502, tel. 908.874.7231, e-mail
[email protected]. He worked at SandozSwitzerland and Sandoz-Turkey before he joined Smith, Kline, & French Laboratories to establish the first compaction simulator system in the Western hemisphere. He has been a consultant to FDA and is past chairman of the AAPS Process Development Focus Group. His recent areas of interest include the development of pharma-ceutical expert systems, excipient databases, and compaction simulators; and the theory and practice of pharmaceutical com-paction. He is a member of the Editorial Advisory Board of Pharmaceutical Technology. www.phar maportal.com
