automated knowledge refinement for rule-based formulation expert system

3
At pH 2, the low MW terpolymer beads released 10% of their loaded insulin after 100 minutes, compared with a 5% loss from the intermediate and high MW beads. This indicated that at this pH there was not a burst release of insulin from the polymer beads. The remaining insulin was released at pH 7.4 and the release rate was a function of polymer MW. An increase in MW resulted in a reduction in the rate of insulin re- lease from the polymer beads. At this pH, most of the insulin was released from the low MW beads after two hours, followed by the interme- diate beads after four hours and the high MW beads after eight hours. These release profiles indicate that the number of pseudo crosslinks produced by entanglement increased as the polymer MW increased (Fig. 3). As MW increased, there was a concomitant decrease in the free space available for solute and water diffusion, giving rise to a diffusion- controlled system. In addition to network for- mation, the authors suggested that the high MW polymer bead might be exhibiting a glassy- to-rubbery phase transition upon water pen- etration, which results in a swelling controlled system. Thus, a combination of diffusion and swelling controlled release might account for the observed release of insulin from the high MW polymers. For the intermediate polymers, insulin release was a combination of dissolu- tion, swelling and diffusion mechanisms. The increased rate of release for the low MW poly- mers was accounted for by disintegration. At acidic pH and body temperature, the poly- mer beads were insoluble and therefore no (or insignificant) quantities of drug would be re- leased into the stomach. As such, these polym- eric beads may be very useful vehicles for the delivery of peptides and proteins to the gas- trointestinal tract. The rapid release of insulin from the low MW beads at pH 7.4 indicates that these could be used to target the small intestine and the intermediate and high MW beads could be employed in targeting the large intes- tine. However, colonic drug delivery would re- quire the development of a system that would inhibit drug release until the colon was reached. The widespread use of such a delivery system will ultimately depend on finding suitable selec- tive enhancers for intestinal absorption. PROFILE Automated knowledge refinement for rule-based formulation expert system Susan M. Craw and Robin A. Boswell The School of Computer and Mathematical Sciences The Robert Gordon University Aberdeen, UK AB25 1HG e-mail: [email protected] Raymond C. Rowe AstraZeneca Alderley Park Macclesfield UK SK10 2NA e-mail: [email protected] In all cases of product formulation — whether it be for tablets, capsules, parenterals or any one of the numerous other dosage forms — the process is generically the same. It begins with the presentation of some form of product speci- fication and ends with the generation of one or more formulations that fulfil the requirements. Often, the specification has potentially conflict- ing requirements and it is left to the highly skilled and experienced formulator to balance these and produce the optimum compromise formulation. Expert systems, of which several exist in pharmaceutical product formulation 1 , are therefore an attractive option for companies who wish to retain human expertise. It is com- mon for the expertise to be represented as ex- plicit knowledge, encapsulated as rules of the form, IF [conditions] THEN [conclusion], where the knowledge in the conclusion can be de- duced if facts satisfying the conditions are already known to be true. Such systems solve problems as follows: they are presented with facts describing the situation relevant to the problem to be solved and systematically fire those rules, whose conditions are satisfied until some final conclusion is deduced. The devel- oper’s goal is to build a system that consistently produces correct answers to the problems it is set, in the domain for which it has been con- structed; such as in tablet formulation. An important research topic in this area is the development of knowledge refinement tools, which automatically monitor the prob- lem-solving performance of a system and sug- gest repairs to existing knowledge or new knowledge that should be acquired with the goal of improving the accuracy of the answers provided by the system. Refinement tools react to evidence that a system is faulty (typically through the use of examples when the expert disagrees with the solution provided by the system) by exploring possible causes in the knowledge and suggesting repairs to cor- rect this behaviour. This Profile describes one such refinement, KRUST, and shows how it can be applied to a tablet formulation expert system. KRUST KRUST is an automatic refinement tool devel- oped at the Robert Gordon University (Aberdeen, UK) 2,3 . Figure 1 shows the process followed by KRUST. PSTT Vol. 2, No. 9 September 1999 monitor profile 383 Figure 1. The knowledge refinement cycle as implemented in KRUST. Pharmaceutical Science & Technology Today Faulty knowledge base Wrongly solved example Testing examples Blame allocation Faults Refinement generation Repairs Consistency filter Consistent repairs Refined knowledge bases Best refined knowledge base Refinement implementation Selection E 1 E 2 E 3 E 4 E n 1461-5347/99/$ – see front matter ©1999 Elsevier Science Ltd. All rights reserved. PII: S1461-5347(99)00182-0

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Page 1: Automated knowledge refinement for rule-based formulation expert system

At pH 2, the low MW terpolymer beads released

10% of their loaded insulin after 100 minutes,

compared with a 5% loss from the intermediate

and high MW beads. This indicated that at this

pH there was not a burst release of insulin from

the polymer beads. The remaining insulin was

released at pH 7.4 and the release rate was a

function of polymer MW. An increase in MW

resulted in a reduction in the rate of insulin re-

lease from the polymer beads. At this pH, most

of the insulin was released from the low MW

beads after two hours, followed by the interme-

diate beads after four hours and the high MW

beads after eight hours.

These release profiles indicate that the number

of pseudo crosslinks produced by entanglement

increased as the polymer MW increased (Fig. 3).

As MW increased, there was a concomitant

decrease in the free space available for solute

and water diffusion, giving rise to a diffusion-

controlled system. In addition to network for-

mation, the authors suggested that the high

MW polymer bead might be exhibiting a glassy-

to-rubbery phase transition upon water pen-

etration, which results in a swelling controlled

system. Thus, a combination of diffusion and

swelling controlled release might account for

the observed release of insulin from the high

MW polymers. For the intermediate polymers,

insulin release was a combination of dissolu-

tion, swelling and diffusion mechanisms. The

increased rate of release for the low MW poly-

mers was accounted for by disintegration.

At acidic pH and body temperature, the poly-

mer beads were insoluble and therefore no (or

insignificant) quantities of drug would be re-

leased into the stomach. As such, these polym-

eric beads may be very useful vehicles for the

delivery of peptides and proteins to the gas-

trointestinal tract. The rapid release of insulin

from the low MW beads at pH 7.4 indicates that

these could be used to target the small intestine

and the intermediate and high MW beads

could be employed in targeting the large intes-

tine. However, colonic drug delivery would re-

quire the development of a system that would

inhibit drug release until the colon was reached.

The widespread use of such a delivery system

will ultimately depend on finding suitable selec-

tive enhancers for intestinal absorption.

PROFILEAutomated knowledgerefinement for rule-basedformulation expert system

Susan M. Craw and Robin A. BoswellThe School of Computer and Mathematical SciencesThe Robert Gordon UniversityAberdeen, UK AB25 1HGe-mail: [email protected]

Raymond C. RoweAstraZenecaAlderley ParkMacclesfieldUK SK10 2NAe-mail: [email protected]

In all cases of product formulation — whether it

be for tablets, capsules, parenterals or any one

of the numerous other dosage forms — the

process is generically the same. It begins with

the presentation of some form of product speci-

fication and ends with the generation of one or

more formulations that fulfil the requirements.

Often, the specification has potentially conflict-

ing requirements and it is left to the highly

skilled and experienced formulator to balance

these and produce the optimum compromise

formulation.

Expert systems, of which several exist in

pharmaceutical product formulation1, are

therefore an attractive option for companies

who wish to retain human expertise. It is com-

mon for the expertise to be represented as ex-

plicit knowledge, encapsulated as rules of the

form, IF [conditions] THEN [conclusion], where

the knowledge in the conclusion can be de-

duced if facts satisfying the conditions are

already known to be true. Such systems solve

problems as follows: they are presented with

facts describing the situation relevant to the

problem to be solved and systematically fire

those rules, whose conditions are satisfied until

some final conclusion is deduced. The devel-

oper’s goal is to build a system that consistently

produces correct answers to the problems it is

set, in the domain for which it has been con-

structed; such as in tablet formulation.

An important research topic in this area is

the development of knowledge refinement

tools, which automatically monitor the prob-

lem-solving performance of a system and sug-

gest repairs to existing knowledge or new

knowledge that should be acquired with the

goal of improving the accuracy of the answers

provided by the system. Refinement tools react

to evidence that a system is faulty (typically

through the use of examples when the expert

disagrees with the solution provided

by the system) by exploring possible causes in

the knowledge and suggesting repairs to cor-

rect this behaviour. This Profile describes one

such refinement, KRUST, and shows how it

can be applied to a tablet formulation expert

system.

KRUSTKRUST is an automatic refinement tool devel-

oped at the Robert Gordon University (Aberdeen,

UK)2,3. Figure 1 shows the process followed by

KRUST.

PSTT Vol. 2, No. 9 September 1999 monitorprofile

383

Figure 1. The knowledge refinement cycle as implemented in KRUST.

Pharmaceutical Science & Technology Today

Faulty knowledge base

Wrongly solvedexample

Testingexamples

Blame allocation

FaultsRefinement generation

RepairsConsistency filter

Consistent repairs

Refined knowledge bases

Best refinedknowledge base

Refinement implementation

Selection

E1E2E3E4 • • •En

1461-5347/99/$ – see front matter ©1999 Elsevier Science Ltd. All rights reserved. PII: S1461-5347(99)00182-0

Page 2: Automated knowledge refinement for rule-based formulation expert system

Blame allocation explores the rules that were

applied for a particular wrongly solved example.

In addition, it also investigates rules that were

not applied for reasons such as failure to satisfy

the conditions of the rule, but these rules con-

tribute to the deduction of the correct solution.

The outcome of blame allocation is to identify

error-causing rules that should be prevented

from being applied, or, conversely, target rules

that did not participate in the solution but may

be usefully applied.

Refinement generation investigates changes

to the knowledge in error-causing rules that

would prevent the rule being applied for the

particular wrongly solved example. For instance,

it may add further constraints to the conditions

of the rule by strengthening an existing re-

quirement or adding a new condition. It also

proposes changes to target rules that allow

them to be applied to the wrongly solved exam-

ple. For instance, it may weaken, or even re-

move, the condition that is currently not satis-

fied and so prevent application of the rule.

Other refinements that may be proposed in-

clude the removal of a rule, or learning a new

rule from a set of related examples that are

currently wrongly solved.

Repair implementation actually implements

the proposed refinements as changes in copies

of the knowledge-based system. In addition to

these three main steps, a refinement tool must

manage the set of proposed refinements and

refined knowledge bases and then select which of

the refined knowledge bases is the one that pro-

vides the most satisfactory repair. In practice,

there are many potential repairs and KRUST ap-

plies selection methods at several stages of the

Blame–Generate–Implement refinement cycle.

ApplicationThe applications used were three versions of a tab-

let formulation expert system implemented in the

Product Formulation Expert System Shell (PFES;

Logica UK Ltd, London, UK). The system and the

shell have been fully described elsewhere1. Three

versions of the expert system were used.

• TFS-1A – an initial version corresponding to

an early stage in development. This con-

tained bugs and produced faulty tablet

formulations.

• TFS-1B – a manually debugged version of

TFS-1A, which produced correct formu-

lations during its period of use.

• TFS-2 – a manually updated version of TFS-

1B resulting from a paradigm shift in the

policy for tablet formulation.

Two series of experiments were performed. The

first involved TFS-1A as the system to be refined

and TFS-1B was the oracle producing the current

answers. It should be noted that the oracle is nor-

mally a human expert, whose feedback is the in-

put to the refinement process. Here, a later version

of the expert system was used to correct an earlier

version of the system. The second experiment

saw TFS-1B as the system to be refined and TFS-2

as the oracle producing the correct answers. In

the first series of experiments, KRUST refined

TFS-1A, which contained three types of fault.

monitor profile PSTT Vol. 2, No. 9 September 1999

384

Figure 2. The sequence of events in the identification and repair of the wrong filler fault in the original version of the tablet formation expert system. The rules are expressed in LISP format.

Pharmaceutical Science & Technology Today

Problem

Fault

DatabaseMAX-LEVEL of CALCIUM PHOSPHATE = ?

Rule: remove excessive fillersIf: <FILLER> is on FILLER-MENU and

CONCENTRATION of <FILLER> is GREATER-THAN MAX-LEVEL of <FILLER>

Then: remove <FILLER> from FILLER-MENU

Filler MenuCALCIUM PHOSPHATECALCIUM CARBONATE

DatabaseMAX-LEVEL of CALCIUMPHOSPHATE…?

KRUST seeks to prevent calcium phosphate from being selected for the filler menu by supplying a missing value for the maximum value for calcium phosphate in TFS-1B.

TFS-1A says TFS-1B says:Filler = Calcium phosphate Filler = Calcium carbonate

Repair

Rule: get insoluble fillerIf: REQD-FILLER SOLUBILITY has

value INSOLUBLE and <FILLER> is on FILLER-MENU and <FILLER> is insoluble

Then: refine FILLER to be <FILLER>

Rule: remove excessive fillersIf: <FILLER> is on FILLER-MENU and

CONCENTRATION of <FILLER> is GREATER-THAN MAX-LEVEL of <FILLER>

Then: remove <FILLER> from FILLER-MENU

Because its maximum level is missing from the database, calcium phosphate is NOT removed from the menu as it should be, and TFS-1A chooses it in preference to calcium carbonate.

Page 3: Automated knowledge refinement for rule-based formulation expert system

• Wrong filler examples were caused by

a missing database entry in TFS-1A – this

was accidentally omitted in the original

knowledge acquisition. The knowledge

was repaired by learning the required data-

base entry, the maximum level of the filler

to be used, and adding it to the knowl-

edge. The sequence of events is illustrated in

Fig. 2.

• Wrong binder quantity examples were

caused by TFS-1A using the incorrect rule:

IF [Binder present in formulation] THEN [Setlevel to be 0.04].The rule was corrected by learning that the

level should be 0.02 and not 0.04.

• Multiple specification fault examples con-

tained many discrepancies between the

expert and TFS formulations. KRUST discov-

ered that all of these discrepancies origi-

nated from an error in the calculation of the

target tablet weight. The rule containing the

incorrect formula for target tablet weight

was identified and the rule was corrected by

learning a new formula.

The repairs that KRUST suggested for the

‘wrong filler’ and ‘wrong binder level faults’

coincided exactly with the manual updates

in TFS-1B. For the third type of fault, KRUST

learned a new formula that was competitive

with the original formula and the formula for

high values of dose in a new TFS-1B rule4.

In the experiments to refine TFS-1B, at the

outset KRUST was given some additional knowl-

edge concerning the paradigm shift: it was told

that the excipients were grouped into classes

and that those in certain classes were to be pre-

ferred to those in subsequent classes. KRUST

was able to refine TFS-1B by adding one or

more conditions to the selection rules5.

ConclusionsThe main finding is that KRUST was successful

in repairing real faults in a real expert system

from real evidence. There was experience of ap-

plying KRUST to corrupted versions of bench-

mark knowledge bases and simple theories, but

this was the first experience of historical ver-

sions of an industrial expert system.

Originally, the tablet formulation expert had

described the faults in the bug-containing TFS-

1A as: ‘one is easy to find’, ‘another will test

your system’, and ‘I don’t expect you to find the

third fault!’ KRUST successfully debugged TFS-

1A automatically from wrongly solved exam-

ples. All three faults were found by KRUST, two

were fixed automatically and a competitive for-

mula was chosen to repair the remaining fault.

More importantly, KRUST successfully refined

TFS-1B, thereby achieving the maintenance de-

manded by a change in formulation policy. KRUST

induced new conditions and rules by incorpo-

rating knowledge about the newly introduced

categories for excipients. The tablet formulation

expert agreed with the new knowledge added

to the knowledge base, and approved the for-

mulations produced by the refined knowledge

base.

This experience confirmed the applicability of

automated knowledge refinement for mainte-

nance tasks, as well as the more traditional de-

bugging roles. It also highlighted the differ-

ences when refining a design system in solution

synthesis applications; it had previously only

been used to refine systems where the task had

been to select from a list of possible hypotheses.

Clearly, KRUST can be directly applied to other

formulation expert systems with similar prob-

lem solving approaches to the one used here.

References1 Rowe, R.C. and Roberts, R.J. (1998) Intelligent

Software for Product Formulation,Taylor and Francis,

London, UK

2 Craw, S. and Sleeman, D. (1995) Expert Systems

with Applications 8, 343–349

3 Craw, S. (1996) Int. J. Human – Computer Studies

44, 245–256

4 Boswell, R., Craw, S. and Rowe, R.C. (1997) in

Proceedings of the European Knowledge Acquisition Workshop

(EKAW97) (Plaza, E. and Benjamins, R., eds),

pp. 49–64, Springer, Saint Feliu de Guixols, Spain

5 Craw, S., Boswell, R. and Rowe, R.C. (1997)

Proc. 9th IEEE International Tools with Artificial

Intelligence (TAI’97), pp. 446–453, IEEE Press,

Newport Beach, CA, USA

PSTT Vol. 2, No. 9 September 1999 monitorprofile

385

About the Profiles section of Monitor …

We welcome contributions for the Profiles series, which gives a commentary onpromising lines of research and new technologies in drug development. Articles shouldprovide an accurate summary of the essential facts together with an expert commentaryto provide a perspective.

Brief outlines of proposed articles should be directed to the Monitor Editor (see contactdetails on page 381). Articles for publication in Monitor are subject to peer review andoccasionally may be rejected or, as is more often the case, authors may be asked torevise their contribution. The Monitor Editor also reserves the right to edit articles after acceptance.