Mapping the dose-effect relationship of orbofiban from sparse data with an artificial neural network.
ABSTRACT A neural network (NN) pharmacodynamic model was developed that correlates the inhibition of ex vivo platelet aggregation by orbofiban, an oral glycoprotein IIb/IIIa antagonist, with the administered dose and patient characteristics. Data were obtained from a Phase-II dose-finding study of orbofiban in patients presenting with acute coronary syndromes. A back-propagation NN was designed to predict drug effect measured at predose and 4 and 6 h on treatment days 1, 28, and 84 (nine responses/patient). The training set consisted of patients for whom complete response profiles were reported (n = 67), and remaining patients were included in the validation data set (n = 47). The concentration-effect relationship was described additionally using a population direct-effect inhibitory sigmoidal model, and a comparison of the predictive performances of both models was performed. The final NN reasonably described orbofiban pharmacodynamics from sparse data sets without specifying a structural model or drug concentrations. Despite considerable inter-patient variability in response-time profiles, the population model revealed a strong correlation between drug concentration and effect and exhibited greater precision than the NN model. Although the population model showed greater precision, these results suggest that NNs may be useful for individualizing pharmacotherapy when drug concentrations are relatively unpredictable or unavailable.
- SourceAvailable from: Adam E Gaweda[show abstract] [hide abstract]
ABSTRACT: This work presents a pharmacodynamic population analysis in chronic renal failure patients using Artificial Neural Networks (ANNs). In pursuit of an effective and cost-efficient strategy for drug delivery in patients with renal failure, two different types of ANN are applied to perform drug dose-effect modeling and their performance compared. Applied in a clinical environment, such models will allow for prediction of patient response to the drug at the effect site and, subsequently, for adjusting the dosing regimen.Neural Networks 01/2003; 16(5-6):841-5. · 1.93 Impact Factor
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ABSTRACT: The external administration of recombinant human erythropoietin is the chosen treatment for those patients with secondary anemia due to chronic renal failure undergoing periodic hemodialysis. The goal is to carry out an individualised prediction of the erythropoietin dosage to be administered. It is justified because of the high cost of this medication, its secondary effects and the phenomenon of potential resistance which some individuals suffer. One hundred and ten patients were included in this study and several factors were collected in order to develop the neural models. Since the results obtained were excellent, an easy-to-use decision-aid computer application was implemented.Computers in Biology and Medicine 08/2003; 33(4):361-73. · 1.16 Impact Factor
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ABSTRACT: To develop a predictive population pharmacokinetic/ pharmacodynamic (PK/PD) model for repaglinide (REP), an oral hypoglycemic agent, using artificial neural networks (ANNs). REP, glucose concentrations, and demographic data from a dose ranging Phase 2 trial were divided into a training set (70%) and a test set (30%). NeuroShell Predictor was used to create predictive PK and PK/PD models using population covariates: evaluate the relative significance of different covariates; and simulate the effect of covariates on the PK/PD of REP. Predictive performance was evaluated by calculating root mean square error and mean error for the training and test sets. These values were compared to naive averaging (NA) and randomly generated numbers (RN). Covariates found to have an influence on PK of REP include dose, gender. race, age, and weight. Covariates affecting the glucose response included dose, gender, and weight. These differences are not expected to be clinically significant. We came to the following three conclusions: 1) ANNs are more precise than NA and RN for both PK and PD; 2) the bias was acceptable for ANNs as compared with NA and RN; and 3) neural networks offer a quick and simple method for predicting, for identifying significant covariates, and for generating hypotheses.Pharmaceutical Research 02/2002; 19(1):87-91. · 4.74 Impact Factor