Empirical search for factors affecting mean particle size of PLGA microspheres containing macromolecular drugs

Establishment and Analysis of an Artificial Neural Network Model for Early Detection of Polycystic Ovary Syndrome Using Machine Learning Techniques

Background

To identify novel gene combinations and to develop an early diagnostic model for Polycystic Ovary Syndrome (PCOS) through the integration of artificial neural networks (ANN) and random forest (RF) methods.

Methods

We retrieved and processed gene expression datasets for PCOS from the Gene Expression Omnibus (GEO) database. Differential expression analysis of genes (DEGs) within the training set was performed using the "limma" R package. Enrichment analyses on DEGs using gene ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG), and immune cell infiltration. The identification of critical genes from DEGs was then performed using random forests, followed by the developing of new diagnostic models for PCOS using artificial neural networks.

Results

We identified 130 up-regulated genes and 132 down-regulated genes in PCOS compared to normal samples. Gene Ontology analysis revealed significant enrichment in myofibrils and highlighted crucial biological functions related to myofilament sliding, myofibril, and actin-binding. Compared with normal tissues, the types of immune cells expressed in PCOS samples are different. A random forest algorithm identified 10 significant genes proposed as potential PCOS-specific biomarkers. Using these genes, an artificial neural network diagnostic model accurately distinguished PCOS from normal samples. The diagnostic model underwent validation using the independent validation set, and the resulting area under the receiver operating characteristic curve (AUC) values was consistent with the anticipated outcomes.

Conclusion

Utilizing unique gene combinations, this research created a diagnostic model by merging random forest techniques with artificial neural networks. The AUC indicated a notably superior performance of the diagnostic model.

Atomistic Descriptors for Machine Learning Models of Solubility Parameters for Small Molecules and Polymers

Descriptors derived from atomic structure and quantum chemical calculations for small molecules representing polymer repeat elements were evaluated for machine learning models to predict the Hildebrand solubility parameters of the corresponding polymers. Since reliable cohesive energy density data and solubility parameters for polymers are difficult to obtain, the experimental heat of vaporization ΔHvap of a set of small molecules was used as a proxy property to evaluate the descriptors. Using the atomistic descriptors, the multilinear regression model showed good accuracy in predicting ΔHvap of the small-molecule set, with a mean absolute error of 2.63 kJ/mol for training and 3.61 kJ/mol for cross-validation. Kernel ridge regression showed similar performance for the small-molecule training set but slightly worse accuracy for the prediction of ΔHvap of molecules representing repeating polymer elements. The Hildebrand solubility parameters of the polymers derived from the atomistic descriptors of the repeating polymer elements showed good correlation with values from the CROW polymer database.

Preparation of long-acting microspheres loaded with octreotide for the treatment of portal hypertensive

The purpose of this study was to optimize the preparation method of injectable Octreotide microspheres. To explore the correlation between the solvent system and the general properties of microspheres to reduce burst release and enable them to be used for portal hypertension. Octreotide microspheres were prepared by modified double emulsion solution evaporation method after optimizing preparation conditions. The results showed that Octreotide microspheres had a particle size of 57.48 ± 15.24 μm, and the initial release was significantly reduced. In vitro release and in vivo pharmacokinetic data indicated that Octreotide was released stably within 1200 h. The effects on portal vein pressure, liver tissue morphology and other related indexes were observed after administration. As obvious results, injection of Octreotide microspheres could significantly reduce portal vein pressure and reduce the portal vein lumen area in experimental cirrhotic portal hypertensive rats. The optimized Octreotide PLGA microsphere preparation has been proved to have a good effect on PHT in vivo after detecting aminotransferase (AST) and alanine aminotransferase (ALT) activity, liver tissue hydroxyproline (Hyp) content, serum and liver tissue malondialdehyde (MDA) levels, plasma prostacyclin (PGI₂) levels, and liver tissue tumor necrosis factor (TNFα) content. In addition, serum and liver tissue superoxide dismutase (SOD) activity and liver tissue glutathione (GSH) content, plasma thromboxane (TXA₂), serum nitric oxide (NO), liver tissue nitric oxide synthase (NOS), and plasma and liver tissue endothelin (ET) were significantly increased.

Potential of Raman spectroscopy in facilitating pharmaceutical formulations development - An AI perspective

Drug development is time-consuming and inherently possesses a high failure rate. Pharmaceutical formulation development is the bridge that links a new chemical entity (NCE) to pre-clinical and clinical trials, and has a high impact on the efficacy and safety of the final drug product. Further, the time required for this process is escalating as formulation techniques are becoming more complicated due to the rising demands for drug products with better efficacy and patient compliance, as well as the inherent difficulties of addressing the unfavorable properties of NCEs such as low water solubility. The advent of artificial intelligence (AI) provides possibilities to accelerate the drug development process. In this review, we first examine applications of AI methods in different types of pharmaceutical formulations and formulation techniques. Moreover, as availability of data is the engine for the advancement of AI, we then suggest a potential way (i.e. applying Raman spectroscopy) for faster high-quality data gathering from formulations. Raman techniques have the capability of analyzing the composition and distribution of components and the physicochemical properties thereof within formulations, which are prominent factors governing drug dissolution profiles and subsequently bioavailability. Thus, useful information can be obtained bridging formulation development to the final product quality.

Robust Data-Driven Soft Sensors for Online Monitoring of Volatile Fatty Acids in Anaerobic Digestion Processes

The concentration of volatile fatty acids (VFAs) is one of the most important measurements for evaluating the performance of anaerobic digestion (AD) processes. In real-time applications, VFAs can be measured by dedicated sensors, which are still currently expensive and very sensitive to harsh environmental conditions. Moreover, sensors usually have a delay that is undesirable for real-time monitoring. Due to these problems, data-driven soft sensors are very attractive alternatives. This study proposes different data-driven methods for estimating reliable VFA values. We evaluated random forest (RF), artificial neural network (ANN), extreme learning machine (ELM), support vector machine (SVM) and genetic programming (GP) based on synthetic data obtained from the international water association (IWA) Benchmark Simulation Model No. 2 (BSM2). The organic load to the AD in BSM2 was modified to simulate the behavior of an anaerobic co-digestion process. The prediction and generalization performances of the different models were also compared. This comparison showed that the GP soft sensor is more precise than the other soft sensors. In addition, the model robustness was assessed to determine the performance of each model under different process states. It is also shown that, in addition to their robustness, GP soft sensors are easy to implement and provide useful insights into the process by providing explicit equations.

Quantitative Assessment of the Physiological Parameters Influencing QT Interval Response to Medication: Application of Computational Intelligence Tools

Human heart electrophysiology is complex biological phenomenon, which is indirectly assessed by the measured ECG signal. ECG trace is further analyzed to derive interpretable surrogates including QT interval, QRS complex, PR interval, and T wave morphology. QT interval and its modification are the most commonly used surrogates of the drug triggered arrhythmia, but it is known that the QT interval itself is determined by other nondrug related parameters, physiological and pathological. In the current study, we used the computational intelligence algorithms to analyze correlations between various simulated physiological parameters and QT interval. Terfenadine given concomitantly with 8 enzymatic inhibitors was used as an example. The equation developed with the use of genetic programming technique leads to general reasoning about the changes in the prolonged QT. For small changes of the QT interval, the drug-related IKr and ICa currents inhibition potentials have major impact. The physiological parameters such as body surface area, potassium, sodium, and calcium ions concentrations are negligible. The influence of the physiological variables increases gradually with the more pronounced changes in QT. As the significant QT prolongation is associated with the drugs triggered arrhythmia risk, analysis of the role of physiological parameters influencing ECG seems to be advisable.

3D printed orodispersible films with Aripiprazole

Three dimensional printing technology is gaining in importance because of its increasing availability and wide applications. One of the three dimensional printing techniques is Fused Deposition Modelling (FDM) which works on the basis of hot melt extrusion-well known in the pharmaceutical technology. Combination of fused deposition modelling with preparation of the orodispersible film with poorly water soluble substance such as aripiprazole seems to be extra advantageous in terms of dissolution rate. 3D printed as well as casted films were compared in terms of physicochemical and mechanical properties. Moreover, drug-free films were prepared to evaluate the impact of the extrusion process and aripiprazole presence on the film properties. X-ray diffractometry and thermal analyses confirmed transition of aripiprazole into amorphous state during film preparation using 3D printing technique. Amorphization of the aripiprazole and porous structure of printed film led to increased dissolution rate in comparison to casted films, which, however have slightly better mechanical properties due to their continuous structure. It can be concluded that fused deposition modelling is suitable technique and polyvinyl alcohol is applicable polymer for orodispersible films preparation.