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

Szlźk, Jakub; Pacławski, Adam; Lau, Raymond; Jachowicz, Renata; Kazemi, Pezhman; Mendyk, Aleksander

doi:10.1016/j.cmpb.2016.07.006

Cited by 21 publications

(9 citation statements)

References 22 publications

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“…After training and LOOCV, all models were used to predict

of polymer REs (16 molecules in Table 2 ), and the performance of all models was analyzed [ 28 ] using root-mean-square error (RMSE)

mean absolute error (MAE)

and the average of relative error (ARE)

where

and

are the reference and predicted values, respectively. In addition, the coefficient of determination (R 2 ) was used to describe the proportion of variability in a dataset that can be explained by the model [ 29 ].…”

Section: Methodsmentioning

confidence: 99%

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

et al. 2021

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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.

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“…After training and LOOCV, all models were used to predict

of polymer REs (16 molecules in Table 2 ), and the performance of all models was analyzed [ 28 ] using root-mean-square error (RMSE)

mean absolute error (MAE)

and the average of relative error (ARE)

where

and

Section: Methodsmentioning

confidence: 99%

“…where y i and ŷi are the reference and predicted values, respectively. In addition, the coefficient of determination (R 2 ) was used to describe the proportion of variability in a dataset that can be explained by the model [29].…”

Section: Machine Learning Modelsmentioning

confidence: 99%

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

et al. 2021

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“…Feature ranking obtained with the use of fscaret package of the R environment was employed to reduce the number of variables in the data set. The main advantages of the package are the vast number of available models for feature ranking creation, automation, and models verification based on the results obtained in earlier research, where the number of input variables was successfully reduced to 2% or 5% of the original vector [ 28 , 29 ]. The fscaret work cycle involves training models, scaling each one according to the global performance, namely, mean squared error (MSE) or root mean squared error (RMSE), and summarizing results into the feature ranking.…”

Section: Methodsmentioning

confidence: 99%

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

Polak

Wiśniowska

Mendyk

et al. 2018

Computational and Mathematical Methods in Medicine

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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.

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“…Briefly, variable ranking is performed in three steps: model training, variable ranking extraction, and variable ranking scaling according to the generalization error. The final variable ranking is obtained by multiplying the raw variable importance with the fraction of minimal error obtained from models by the model's actual error according to the equation below [39]:…”

Section: Feature Rankingmentioning

confidence: 99%

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

et al. 2020

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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.

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

Cited by 21 publications

References 22 publications

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

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

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

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

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