Results and Discussion A Hybrid Expert System-Neural Network (“Expert Network”) for Capsule Formulation Support 1Gunjan Kalra, 2Mintong Guo, 1Yun Peng, 2Larry L. Augsburger University of Maryland, Department of Computer Science and Electrical Engineer, Baltimore County; 2 University of Maryland, School of Pharmacy, Baltimore Conclusion Training Data Set Introduction GUI: interface C functions 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. 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.   Prolog Engine BCS II N CAPEX Y 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. 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 Acknowledgement This work is being supported by Capsugel. We also gratefully acknowledge Pfizer Central Research for the gift of piroxicam. Input Package Dose/Sol >250? Permeability > 0.0004? Dose Low < 50mg CU Module Eval_HalfDose? Using the given equation to calculate required PS to achieve required tolerance Use New PS Ask Mixing Style from user ? Interactive Physical Blending Liquid Addition CAPEX Diluent MINsol for OM User Input: tapped & bulk density of OM Computer Carr Index Dose Volume > 1000 User Input: Bulk density of dose Compute Carr’ Index Compute Capsule Size Diluent needed? N Y High 100-1000mg Mod 50-100mg V. high >1000mg PS < 10 um Old PS New PS Diluent MPS for OM Choose Disintegrant Choose Lubricant Choose Glidant BCS -IV BCS -I or III BCS -II OM Module DF Module Choose Diluent SSM Flowability Adhere to metal? Lubricant Granulate Fair Bad Good V. Good Low or Medium Large or V. Large Test for DF (Plug Formation) Diluent F-PS Dose Volume >250-1000mL >1000mL Particle Size >150μm Ask User for new particle size >50- 150μm >10-50μm Diluent F-Insol Diluent M-InSol Diluent M-PS Drug Poorly Soluble 250-1000ml Insoluble >1000ml 4% Sodium starch glycolate Croscarmellose 8% Sodium starch glycolate Wettable? Wetting Agent Sodium Lauryl Sulfate Prediction Engine User: Acceptable? N Y Parameter Adjustment Final formulation Predicted dissolution rate for the current formulation Reformulate compute Materials and Method MES Prediction Control ANN result Microcrystalline cellulose (Avicel PH102 (FMC), Emcocel 90M (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 ~ 200N to achieve the specific target weight. The plug height was adjusted at 14mm. 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.   Results and Discussion An expert system (MES) in the decision module (based on a decision tree modeled after the Capsugel Expert System1 [CAPEX]) was developed to provide decision rules for 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).