Application of Neural Computing in Pharmaceutical Product Development: Computer Aided Formulation Design

Hussain, Ajaz; Shivanand, Padmaja; Johnson, Robert D.

doi:10.3109/03639049409038390

Cited by 79 publications

(48 citation statements)

References 17 publications

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“…The amount of drug was determined using a previously validated HPLC method. 42 The method included a mobile phase composed of methanol:acetonitrile:1% o-phosphoric acid 80:18:2 v/v/v with a pH of 6.2 using Chromosil C 18 analytical column (250 mm ×4.6 mm, 5 µm). A sample of 20 µL was injected at ambient temperature for 6 minutes run time with a flow rate of 1 mL min -1 and effluents were identified at 240 nm with a UV detector (RF-551; Shimadzu, Kyoto, Japan).…”

Section: Simulated In Vitro Inhalationmentioning

confidence: 99%

“…16,17 In addition, ANNs can be used for historical data, and models generated can be updated with new experiments. 18 ANNs combined with genetic algorithm also enable special operations such as "what-if" predictions and optimizations. 19 Carvedilol (CAR) is a nonselective β-blocking agent that displays α 1 -adrenergic antagonism, resulting in a blood pressure reducing action through vasodilatation.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Preparation, optimization, and in vitro simulated inhalation delivery of carvedilol nanoparticles loaded on a coarse carrier intended for pulmonary administration

Abdelbary¹,

Al-mahallawi²,

Abdelrahim³

et al. 2015

IJN

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Carvedilol (CAR) is a potent antihypertensive drug but has poor oral bioavailability (24%). A nanosuspension suitable for pulmonary delivery to enhance bioavailability and bypass first-pass metabolism of CAR could be advantageous. Accordingly, the aim of this work was to prepare CAR nanosuspensions and to use artificial neural networks associated with genetic algorithm to model and optimize the formulations. The optimized nanosuspension was lyophilized to obtain dry powder suitable for inhalation. However, respirable particles must have a diameter of 1–5 µm in order to deposit in the lungs. Hence, mannitol was used during lyophilization for cryoprotection and to act as a coarse carrier for nanoparticles in order to deliver them into their desired destination. The bottom-up technique was adopted for nanosuspension formulation using Pluronic stabilizers (F127, F68, and P123) combined with sodium deoxycholate at 1:1 weight ratio, at three levels with two drug loads and two aqueous to organic phase volume ratios. The drug crystallinity was studied using differential scanning calorimetry and powder X-ray diffractometry. The in vitro emitted doses of CAR were evaluated using a dry powder inhaler sampling apparatus and the aerodynamic characteristics were evaluated using an Andersen MKII cascade impactor. The artificial neural networks results showed that Pluronic F127 was the optimum stabilizer based on the desired particle size, polydispersity index, and zeta potential. Results of differential scanning calorimetry combined with powder X-ray diffractometry showed that CAR crystallinity was observed in the lyophilized nanosuspension. The aerodynamic characteristics of the optimized lyophilized nanosuspension demonstrated significantly higher percentage of total emitted dose (89.70%) and smaller mass median aerodynamic diameter (2.80 µm) compared with coarse drug powder (73.60% and 4.20 µm, respectively). In summary, the above strategy confirmed the applicability of formulating CAR in the form of nanoparticles loaded on a coarse carrier suitable for inhalation delivery.

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Section: Simulated In Vitro Inhalationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Preparation, optimization, and in vitro simulated inhalation delivery of carvedilol nanoparticles loaded on a coarse carrier intended for pulmonary administration

Abdelbary¹,

Al-mahallawi²,

Abdelrahim³

et al. 2015

IJN

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show abstract

“…et al, 1997;Polański, 2003;Taskinen & Yliruusi, 2003), pharmacoeconomics and epidemiology (Polak & Mendyk, 2004;Kolarzyk et al, 2006), in vitro in vivo correlation (Dowell et al, 1999) and pharmaceutical technology (Behzadia et al 2009;Hussain et al, 1991;Bourquin et al, 1998aBourquin et al, , 1998bBourquin et al, , 1998cChen et al, 1999;Gašperlin et al, 2000;Kandimalla et al, 1999;Mendyk & Jachowicz, 2005Rocksloh et al, 1999;Takahara et al, 1997;Takayama et al, 2003;Türkoğlu et al, 1995).…”

Section: Artificial Neural Network (Ann) Foundationsmentioning

confidence: 99%

Decision Support Systems for Pharmaceutical Formulation Development Based on Artificial Neural Networks

Mendyk¹,

Jachowicz²

2010

Decision Support Systems

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“…Several authors have used ANNs to predict the eOE ect of process and formulation variables, such as lubricant type, compression pressure and the duration of lubricant mixing on the crushing strength and disintegration time of tablets (Turkoglu et al 1995 ;Bourquin et al 1998a, b, c ;Rocksloh et al 1999). ANNs have also proved bene® cial in the optimisation of drug release rate from controlled-release formulations, which are particularly complex in terms of the number of interacting factors which in¯uence drug release (Hussain et al 1994 ;Bozic et al 1997 ;Wu et al 2000).…”

Section: Arti® Cial Neural Networkmentioning

confidence: 99%

“…The most common form of training used for formulation systems is supervised back propagation training, which adjusts the weights of the connections between neurons by back propagation of errors to bring the predicted output closer to the experimental output. Once trained, the ANN can predict the outcome of a set of formulation or process variables on product performance and vice-versa (Hussain et al 1994 ;Rowe 1996b ;Takayama et al 1999 ;Agatonovic-Krustin & Beresford 2000).…”

Section: Arti® Cial Neural Networkmentioning

confidence: 99%

Predictive and correlative techniques for the design, optimisation and manufacture of solid dosage forms

Hardy

Cook

2003

Journal of Pharmacy and Pharmacology

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There is much interest in predicting the properties of pharmaceutical dosage forms from the properties of the raw materials they contain. Achieving this with reasonable accuracy would aid the faster development and manufacture of dosage forms. A variety of approaches to prediction or correlation of properties are reviewed. These approaches have variable accuracy, with no single technique yet able to provide an accurate prediction of the overall properties of the dosage form. However, there have been some successes in predicting trends within a formulation series based on the physicochemical and mechanical properties of raw materials, predicting process scale-up through mechanical characterisation of materials and predicting product characteristics by process monitoring. Advances in information technology have increased predictive capability and accuracy by facilitating the analysis of complex multivariate data, mapping formulation characteristics and capturing past knowledge and experience.

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Application of Neural Computing in Pharmaceutical Product Development: Computer Aided Formulation Design

Cited by 79 publications

References 17 publications

Preparation, optimization, and in vitro simulated inhalation delivery of carvedilol nanoparticles loaded on a coarse carrier intended for pulmonary administration

Preparation, optimization, and in vitro simulated inhalation delivery of carvedilol nanoparticles loaded on a coarse carrier intended for pulmonary administration

Decision Support Systems for Pharmaceutical Formulation Development Based on Artificial Neural Networks

Predictive and correlative techniques for the design, optimisation and manufacture of solid dosage forms

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