A Hybrid Expert System-Neural Network (“Expert Network”) for Capsule Formulation Support 1 Gunjan Kalra, 2 Mintong Guo, 1 Yun Peng, 2 Larry L. Augsburger University of Maryland, Department of Computer Science and Electrical Engineer, Baltimore County; 2 University of Maryland, School of Pharmacy, Baltimore Introduction The objective was to construct a prototype intelligent hybrid Prototype Expert Network (PEN) for capsule formulation, which may yield formulations meeting specific running and drug delivery performance design criteria for BCS II drugs. To that end, a rule-based expert system (MES) was developed to specifically address BCS Class II drugs and integrated with a neural network (NN). These two components, which comprise the decision module and the prediction module, respectively, are connected together by two information exchange paths to form a loop. The system is believed to have the power to design a suitable capsule formulation to meet both requirements of quality control and dissolution. GUI: interface C functions Prolog Engine BCS II N Conclusion Training Data Set Preliminary results indicate that the PEN is a working system. Good predictive power of the NN module requires sufficient training samples and a cross validation process. Further research will be directed toward: • Validation and refinement of PEN • Automation of the parameter adjustment as a process of optimization. • Generalization of PEN to other drugs in BCS Class II. CAPEX Y CU: calculate PS to meet content-uniformity limit OM: if PS is small, add diluent and use blend style DF: choose excipients types Final Formulation: calculate capsule size, % excipients, and final formulation Prediction Engine N N User: Acceptable? Parameter Adjustment Y Y Final formulation Permeability > 0. 0004? BCS -I or III Predicted dissolution rate for the current formulation Reformulate Microcrystalline cellulose (Avicel PH 102 (FMC), Emcocel 90 M (Penwest)), anhydrous lactose (direct tableting grade, Quest International), piroxicam (donated from Pfizer), magnesium stearate, Explotab (Penwest), Ac-Di-Sol ( FMC) and sodium lauryl sulfate have been used in the study. An instrumented Zanasi LZ-64 was used for the encapsulation process, and the compression force was maintained at 100 ~ 200 N to achieve the specific target weight. The plug height was adjusted at 14 mm. The dissolution testing was conducted on a Vankel 5000 dissolution station, and followed the USP procedure. The percentage dissolved in 10, 30 and 45 minutes were recorded as the measurements for the dissolution rate. Sixty-three batches have been generated to train and validate the system. A rule-based expert system was developed in Prolog by followed the decision procedures in the flow chat, and integrated with a neural network (NN). These two components, which comprise the decision module and the prediction module, respectively, are connected together by two information exchange paths to form a loop. MES Prediction Control This work is being supported by Capsugel. We also gratefully acknowledge Pfizer Central Research for the gift of piroxicam. Dose/Sol >250? BCS -II CAPEX Materials and Method Acknowledgement Input Package compute N BCS -IV SSM Y Dose ANN result Low < 50 mg Results and Discussion An expert system (MES) in the decision module (based on a decision tree modeled after the Capsugel Expert System 1 [CAPEX]) was developed to provide decision rules formulation recommendation. The NN in the prediction module (using backpropagation learning) was developed to provide predictive capability for the expected outcomes of the recommended formulation. The NN was trained with experiment data to capture the causal associations between the formulation and the outcome. The training was conducted with two experimental datasets using piroxicam as a model drug. The datasets represent two response surface designs for the capsule formulation which were developed to reflect the mapping from such variables as filler type/ratio, lubrication systems, drug particle size/specific surface area, disintegrants and surfactants to dissolution of the model compound. The capsules were filled using dosator-type automatic filling machines. 1 S. Lai, F. Podcek, J. M. Newton, and R. Daumesnil. An expert system to aid the development of capsule formulations. Pharm. Tech. Eur. , 8: 60 -65 (1996). Mod 50 -100 mg High 100 -1000 mg CU Module V. high >1000 mg Eval_Half. Dose? Using the given equation to calculate required PS to achieve required tolerance Y N CAPEX Fair Bad DF Module Y Dose Choose Glidant N Use New PS Old PS Large or V. Large Compute Carr’ Index N Lubricant CAPEX Choose Lubricant New PS PS < 10 um N Wettable ? Drug User Input: Bulk density of dose Y OM Module Ask Mixing Style from user ? Compute Capsule Size Liquid Addition Dose Volume Poorly Soluble 250 -1000 ml 4% Sodium starch glycolate Croscarmellose CAPEX N Wetting Agent Sodium Lauryl Sulfate Choose Disintegrant Y Interactive Physical Blending Adhere to metal? Low or Medium Granulate Y Good V. Good Flowability Insoluble >1000 ml 8% Sodium starch glycolate Croscarmellose Drug Diluent needed? Dose Volume > 1000 Diluent MINsol for OM User Input: tapped & bulk density of OM >250 -1000 m. L Ask User for new particle size Diluent MPS for OM Choose Diluent >150μm Ask User for new particle size Particle Size >50150μm Diluent M-PS Computer Carr Index Dose Volume >1000 m. L >1050μm Diluent F-PS >1050μm Diluent F-Insol Test for DF (Plug Formation) Particle Size >150μm >50150μm Diluent M-In. Sol
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