Comparative Study for Optimization of Pharmaceutical Self-Emulsifying Pre-concentrate by Design of Experiment and Artificial Neural Network

Parikh, Kinjal; Sawant, Krutika

doi:10.1208/s12249-018-1173-2

Cited by 16 publications

(3 citation statements)

References 35 publications

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“…In addition to these formulation techniques, researchers have applied AI methods to beads and pellets [67][68][69][70][71], microparticles and nanoparticles [72][73][74][75][76][77][78][79][80][81][82][83][84][85], microspheres and nanospheres [86][87][88], liposomes and nanoliposomes [89], colloidal systems [90], micelles [91,92], and liquid, solid dosage forms [93].…”

Section: Other Formulation Techniquesmentioning

confidence: 99%

Influence of artificial intelligence in modern pharmaceutical formulation and drug development

Ali,

Mohin,

Mondal

et al. 2024

Futur J Pharm Sci

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Background Artificial intelligence (AI) revolutionized the formulation and development of modern pharmaceuticals. With the help of AI, researchers can now optimize drug design, develop formulations, and streamline clinical trials in a much accurate and efficient way. Drug development might be greatly expedited and time-consuming procedure; however, with the help of AI this are significantly reduced. Main body of abstract The main advantages of AI in pharmaceutical formulation are its capacity to analyse vast amounts of data and spot patterns and connections that human researchers would miss. Various tools and technologies, such as ANN, fuzzy logic, neuro-fuzzy logic, and genetic algorithm are used for analysing the date, of which ANN is popular and mostly used. AI enables the discovery of novel pharmacological targets and the creation of more potent medications. AI may also be used to improve medication formulations by forecasting the solubility, stability, and bioavailability of drug candidates, increasing the likelihood that clinical trials will be successful. AI is also applied in designing clinical trials, reducing the time and cost of the process by identifying patient populations that are most likely to benefit from the treatment. Additionally, AI can monitor patients during clinical trials, detecting real-time adverse effects and adjusting dosages to improve patient outcomes. Conclusion AI is a potent pharmaceutical formulation and development tool, allowing researchers to analyse vast amounts of data, optimize drug formulations, and streamline clinical trials. As technology develops, experts anticipate that AI will increasingly show a crucial part in drug development, enabling faster, more efficient, and more effective treatments for various diseases.

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Section: Other Formulation Techniquesmentioning

confidence: 99%

Influence of artificial intelligence in modern pharmaceutical formulation and drug development

Ali,

Mohin,

Mondal

et al. 2024

Futur J Pharm Sci

View full text Add to dashboard Cite

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“…Parikh et al [37] used I-optimal mixing design and ANN to optimize and compare key parameters (oil dosage, surfactant dosage and cosurfactant dosage) influencing the size of microspheres in drug delivery system of selfemulsifying. Through comparative study, the contour map and three-dimensional map generated by artificial neural network show that the interaction between input and output data is better and the selected output parameters are more predictable.…”

Section: Application In Pharmaceutical Preparationsmentioning

confidence: 99%

Advances in Neural Networks for Pharmaceutical Applications

Yang,

Chen

2024

AJARR

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Artificial neural networks (ANNs) are rapidly changing the landscape of the pharmaceutical industry. Their unique capabilities, including collective computing, adaptive learning, and fault tolerance, make them ideal for tackling complex challenges in drug discovery, analysis, and personalized medicine. This article summarizes the latest research progress in ANNs for pharmacy, highlighting breakthroughs in areas like QSAR modeling for drug design, pharmacokinetic prediction, and optimization of pharmaceutical preparations. With their immense potential to accelerate drug development, improve drug efficacy, and personalize healthcare, ANNs are poised to revolutionize the future of pharmaceuticals.

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“…Also, it shows high first-pass metabolism [5]. Solubility of Ilo has been enhanced by microspheres, binary, ternary complexation with cyclodextrins, self-emulsifying preconcentrate, whereas bioavailability of Ilo has been improved by co-crystallization, sublingual film, and nanolipid carriers [5][6][7][8][9][10]. Among them, oral administration is mostly preferred as it advantageous compared with other routes of administration.…”

Section: Introductionmentioning

confidence: 99%

Formulation and Characterization of Sublingual Tablets of Iloperidone Prepared by Microenvironmental pH Regulated Approach

2020

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Purpose Iloperidone (Ilo) is an antipsychotic drug having low and pH-dependent solubility, hence less bioavailability. Thus, the aim of this study was to prepare and characterize binary (TAPOL B) and ternary complexes (TAPOL T) of drug with Kolliphor P-188 and tartaric acid to improve solubility. Further, sublingual tablets incorporating ternary complex were formulated to improve the dissolution. Methods Preliminary screening studies were conducted to select the acidifying agent and polymer based on saturation solubility studies. Binary and ternary complexes were prepared by the melting method. Complexes were characterized by DSC, powder Xray diffraction (PXRD), FTIR, microenvironmental studies, and in vitro dissolution studies. The sublingual tablets of ternary complexes were prepared by the direct compression method and evaluated for weight variation, disintegration time, and dissolution studies. Results Based on solubility studies, Kolliphor P-188 and tartaric acid were selected as polymer and acidifying agents. DSC studies and FTIR spectra showed the interaction of drugs with polymer and acidifying agents. PXRD spectra showed the crystalline nature of complexes. Microenvironmental studies carried out in pH 6.8 showed a drastic decrease in pH with ternary complex as compared with binary complex. Dissolution studies showed an increase in dissolution, TAPOL T > TAPOL B > drug. The sublingual tablets showed disintegration time < 30 s and more than 80% of a drug released within 10 min. Due to the presence of tartaric acid and polymer, solubility of the drug increases tremendously.Conclusion The present study demonstrates the increase in solubility and release of Iloperidone due to the microenvironmental pH effect from sublingual tablets. Hence, microenvironmental pH modification is a good approach to increase drug solubility and dissolution. Keywords Iloperidone . Tartaric acid . Kolliphor P-188 . Ternary complex . Microenvironmental pH . Sublingual tablets Highlights • Binary and ternary complexes of Ilo were formulated.• Kolliphor P-188 and tartaric acid were optimized as a polymer and an auxiliary agent. • Tartaric acid accelerated the dissolution efficiency of the drug due to pH change. • DC tablets showed ≥ 80% release within 5 min for 12 mg, maximum strength.

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Comparative Study for Optimization of Pharmaceutical Self-Emulsifying Pre-concentrate by Design of Experiment and Artificial Neural Network

Cited by 16 publications

References 35 publications

Influence of artificial intelligence in modern pharmaceutical formulation and drug development

Influence of artificial intelligence in modern pharmaceutical formulation and drug development

Advances in Neural Networks for Pharmaceutical Applications

Formulation and Characterization of Sublingual Tablets of Iloperidone Prepared by Microenvironmental pH Regulated Approach

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