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A Hybrid Expert System-Neural Network (“Expert Network”) for Capsule Formulation Support 1Gunjan 2Mintong 1Yun 2Larry Kalra, Guo, Peng, L. Augsburger University of Maryland, Department of Computer Science and Electrical Engineer, Baltimore County; 2 University of Maryland, School of Pharmacy, Baltimore Introduction GUI: interface 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. C functions Prolog Engine BCS II N CAPEX Y CU: calculate PS to meet contentuniformity limit OM: if PS is small, add diluent and use blend style Conclusion Training Data Set INDEPENDENT VARIABLES %Lactose Wetting Filler Particle Disintegrant Lubricant in Lactose/ Disintegrant Agent Run No. Size(um) Type Level (%) SSF/MS MCC Blend Level(%) SLS (%) 1 100 Explotab 1 0 50 5.0 0.5 7 100 Explotab 0.5 0 100 5.0 1 8 100 Explotab 0.5 0 0 5.0 1 33 60 Ac-Di-Sol 0.3 0 100 4.0 0.2 34 60 Ac-Di-Sol 0.6 50 0 8.0 0.6 37 60 Explotab 0.6 100 100 12.0 0.2 These are representative of a total of 62 runs that were used in training. 13 100 Explotab 1.5 0 100 5.0 0 14 100 Explotab 1.5 0 0 5.0 0 Final Formulation: calculate capsule size, % excipients, and final formulation 18.0 18.0 63.76 40.25 77.02 60.82 79.49 65.48 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. Acknowledgement Input Package 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 Materials and Method 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 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. 3.31 1.61 DF: choose excipients types Reformulate 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. RESPONSE Surface lubricant % dissolved % dissolved % dissolved (m^2)/g BlendTime in 10 min in 30 min in 45 min (min.) 10 30 45 2.46 10.0 66.09 79.86 84.08 1.61 2.0 68.04 81.85 84.03 3.31 2.0 53.52 78.81 87.79 2.77 3.0 67.50 96.19 99.60 2.77 3.0 48.85 85.28 96.95 2.77 3.0 73.33 95.32 101.39 N BCS -IV SSM Y Dose compute ANN result Low < 50mg Mod 50-100mg High 100-1000mg V. high >1000mg 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). CU Module Eval_HalfDose? Using the given equation to calculate required PS to achieve required tolerance Y N CAPEX Fair Bad DF Module Y Dose d 100 %CV 100 N 6D N 3 Choose Glidant N Large or V. Large Low or Medium Granulate Y Compute Carr’ Index 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 250-1000ml 4% Sodium starch glycolate Croscarmellose CAPEX N Wetting Agent Sodium Lauryl Sulfate Choose Disintegrant Y Interactive Physical Blending Adhere to metal? 1/ 2 Use New PS Old PS Good V. Good Flowability Insoluble >1000ml 8% Sodium starch glycolate Croscarmellose Drug Diluent needed? Dose Volume > 1000 Choose Diluent >150μm Ask User for new particle size Particle Size >1050μm >50150μm Diluent M-PS Computer Carr Index Dose Volume >1000mL Ask User for new particle size Diluent MPS for OM Diluent MINsol for OM User Input: tapped & bulk density of OM >250-1000mL Diluent F-PS Particle Size >1050μm Diluent F-Insol Test for DF (Plug Formation) >150μm >50150μm Diluent M-InSol