Mapping the dose-effect relationship of orbifan from sparse data with an artificial neural network

ArticleinJournal of Pharmaceutical Sciences 94(11):2475-86 · November 2005with4 Reads
DOI: 10.1002/jps.20384 · Source: PubMed
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.
    • ". ANNs techniques have been also used in the fields of robotics, pattern identification, psychology , physics, computer science, biology and many others [37- 40]. In addition, ANNs have been applied to the modeling of several systems in a wide range of applications such as animal science [41], cancer imaging extraction and classification [42] [43], pharmacodynamic and pharmacokinetic modeling [44] [45] and mapping of dose–effect relationships on pharmacological response [46], to predict secondary structures [47] and transmembrane segments [48], simulation of C13 nuclear magnetic resonance spectra [49], prediction of drug resistance of HIV-1 protease ligands [50], prediction of toxicity of chemicals to aquatic species [51], and as well as predicting physicochemical properties from the perspective of pharmaceutical research [52]. "
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