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Prediction of drug release profiles using an intelligent learning system: an experimental study in transdermal iontophoresis

Lim CP, Quek SS, Peh KK

J Pharm Biomed Anal, 2003 Feb 05;31(1):159-68.
PMID: 12560060

This paper investigates the use of a neural-network-based intelligent learning system for the prediction of drug release profiles. An experimental study in transdermal iontophoresis (TI) is employed to evaluate the applicability of a particular neural network (NN) model, i.e. the Gaussian mixture model (GMM), in modeling and predicting drug release profiles. A number of tests are systematically designed using the face-centered central composite design (CCD) approach to examine the effects of various process variables simultaneously during the iontophoresis process. The GMM is then applied to model and predict the drug release profiles based on the data samples collected from the experiments. The GMM results are compared with those from multiple regression models. In addition, the bootstrap method is used to assess the reliability of the network predictions by estimating confidence intervals associated with the results. The results demonstrate that the combination of the face-centered CCD and GMM can be employed as a useful intelligent tool for the prediction of time-series profiles in pharmaceutical and biomedical experiments.
Use of artificial neural networks to predict drug dissolution profiles and evaluation of network performance using similarity factor

Peh KK, Lim CP, Quek SS, Khoh KH

Pharm Res, 2000 Nov;17(11):1384-8.
PMID: 11205731

PURPOSE: To use artificial neural networks for predicting dissolution profiles of matrix-controlled release theophylline pellet preparation, and to evaluate the network performance by comparing the predicted dissolution profiles with those obtained from physical experiments using similarity factor.
METHODS: The Multi-Layered Perceptron (MLP) neural network was used to predict the dissolution profiles of theophylline pellets containing different ratios of microcrystalline cellulose (MCC) and glyceryl monostearate (GMS). The concepts of leave-one-out as well as a time-point by time-point estimation basis were used to predict the rate of drug release for each matrix ratio. All the data were used for training, except for one set which was selected to compare with the predicted output. The closeness between the predicted and the reference dissolution profiles was investigated using similarity factor (f2).
RESULTS: The f2 values were all above 60, indicating that the predicted dissolution profiles were closely similar to the dissolution profiles obtained from physical experiments.
CONCLUSION: The MLP network could be used as a model for predicting the dissolution profiles of matrix-controlled release theophylline pellet preparation in product development.
Fulltext A ten-year review of ruptured sinus of valsalva: clinico-pathological and echo-Doppler features

Shah RP, Ding ZP, Ng AS, Quek SS

Singapore Med J, 2001 Oct;42(10):473-6.
PMID: 11874151

Rupture of the sinus of valsalva (RSOV) is an uncommon condition with a variety of manifestations ranging from an asymptomatic murmur to cardiogenic shock. This retrospective 10-year review (1985-1995) of 18 patients from a single institution revealed that 6 (33%) were female and 12 (67%) were male with a mean age of 37.6 +/- 13.4 years and that 72% were Chinese by ethnic descent with the remaining 28% being Malay. Eight patients (44.4%) presented with an asymptomatic murmur, 4 (22.2%) with acute chest pain, 4 (22.2%) with mild heart failure, 2 (11.1%) with severe heart failure, and 2 (11.1%) with cardiogenic shock. Rupture of the right aneurysmal coronary cusp (RCC) made up 15 (83.3%) while those of the non-coronary cusp (NCC) made up the remaining. Most of the RCC ruptures were directed into the right ventricle and all of the NCC ruptures were into the right atrium. Ventricular septal defects (VSDs) were found in 9 (50%) of the patients, (although detected by echocardiography in only one third of those patients), aortic regurgitation in 6 (33.3%) and aortic valve vegetations in 2 (11.1%). Echocardiography was found to be accurate in diagnosing RSOVs with 100% diagnostic accuracy after 1990 with four misdiagnoses before 1990. Of these four patients, two were misdiagnosed as having VSDs, one as having a coronary arteriovenous fistula and one as having a patent ductus arteriosus. The anatomical structure of the "windsock" was seen in 64% of the patients who were correctly diagnosed. The pattern of colour flow and spectral Doppler was seen in all patients and helped to localise the site of rupture and the direction of flow. In summary, echocardiography is a simple and accurate way of diagnosing and defining RSOVs and is the imaging modality of choice.

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