Transparent computational intelligence models for pharmaceutical tableting process

Purpose Pharmaceutical industry is tightly regulated owing to health concerns. Over the years, the use of computational intelligence (CI) tools has increased in pharmaceutical research and development, manufacturing, and quality control. Quality characteristics of tablets like tensile strength are important indicators of expected tablet performance. Predictive, yet transparent, CI models which can be analysed for insights into the formulation and development process. Methods This work uses data from a galenical tableting study and computational intelligence methods like decision trees, random forests, fuzzy systems, artificial neural networks, and symbolic regression to establish models for the outcome of tensile strength. Data was divided in training and test fold according to ten fold cross validation scheme and RMSE was used as an evaluation metric. Tree based ensembles and symbolic regression methods are presented as transparent models with extracted rules and mathematical formula, respectively, explaining the CI models in greater detail. Results CI models for tensile strength of tablets based on the formulation design and process parameters have been established. Best models exhibit normalized RMSE of 7 %. Rules from fuzzy systems and random forests are shown to increase transparency of CI models. A mathematical formula generated by symbolic regression is presented as a transparent model. Conclusions CI models explain the variation of tensile strength according to formulation and manufacturing process characteristics. CI models can be further analyzed to extract actionable knowledge making the artificial learning process more transparent and acceptable for use in pharmaceutical quality and safety domains

Computational intelligence models to predict porosity of tablets using minimum features

The effects of different formulations and manufacturing process conditions on the physical properties of a solid dosage form are of importance to the pharmaceutical industry. It is vital to have in-depth understanding of the material properties and governing parameters of its processes in response to different formulations. Understanding the mentioned aspects will allow tighter control of the process, leading to implementation of quality-by-design (QbD) practices. Computational intelligence (CI) offers an opportunity to create empirical models that can be used to describe the system and predict future outcomes in silico. CI models can help explore the behavior of input parameters, unlocking deeper understanding of the system. This research endeavor presents CI models to predict the porosity of tablets created by roll-compacted binary mixtures, which were milled and compacted under systematically varying conditions. CI models were created using tree-based methods, artificial neural networks (ANNs), and symbolic regression trained on an experimental data set and screened using root-mean-square error (RMSE) scores. The experimental data were composed of proportion of microcrystalline cellulose (MCC) (in percentage), granule size fraction (in micrometers), and die compaction force (in kilonewtons) as inputs and porosity as an output. The resulting models show impressive generalization ability, with ANNs (normalized root-mean-square error [NRMSE] =1%) and symbolic regression (NRMSE =4%) as the best-performing methods, also exhibiting reliable predictive behavior when presented with a challenging external validation data set (best achieved symbolic regression: NRMSE =3%). Symbolic regression demonstrates the transition from the black box modeling paradigm to more transparent predictive models. Predictive performance and feature selection behavior of CI models hints at the most important variables within this factor space.This work was supported by the IPROCOM Marie Curie Initial Training Network, funded through the People Programme (Marie Curie Actions) of the European Union’s Seventh Framework Programme FP7/2007–2013/under REA grant agreement number 31655

Application of machine-learning algorithms for better understanding of tableting properties of lactose co-processed with lipid excipients

Co-processing (CP) provides superior properties to excipients and has become a reliable option to facilitated formulation and manufacturing of variety of solid dosage forms. Development of directly compressible formulations with high doses of poorly flowing/compressible active pharmaceutical ingredients, such as paracetamol, remains a great challenge for the pharmaceutical industry due to the lack of understanding of the interplay between the formulation properties, process of compaction, and stages of tablets’ detachment and ejection. The aim of this study was to analyze the influence of the compression load, excipients’ co-processing and the addition of paracetamol on the obtained tablets’ tensile strength and the specific parameters of the tableting process, such as (net) compression work, elastic recovery, detachment, and ejection work, as well as the ejection force. Two types of neural networks were used to analyze the data: classification (Kohonen network) and regression networks (multilayer perceptron and radial basis function), to build prediction models and identify the variables that are predominantly affecting the tableting process and the obtained tablets’ tensile strength. It has been demonstrated that sophisticated data-mining methods are necessary to interpret complex phenomena regarding the effect of co-processing on tableting properties of directly compressible excipients

Pregled primene algoritama mašinskog učenja u farmaceutskoj tehnologiji

Machine learning algorithms, and artificial intelligence in general, have a wide range of applications in the field of pharmaceutical technology. Starting from the formulation development, through a great potential for integration within the Quality by design framework, these data science tools provide a better understanding of the pharmaceutical formulations and respective processing. Machine learning algorithms can be especially helpful with the analysis of the large volume of data generated by the Process analytical technologies. This paper provides a brief explanation of the artificial neural networks, as one of the most frequently used machine learning algorithms. The process of the network training and testing is described and accompanied with illustrative examples of machine learning tools applied in the context of pharmaceutical formulation development and related technologies, as well as an overview of the future trends. Recently published studies on more sophisticated methods, such as deep neural networks and light gradient boosting machine algorithm, have been described. The interested reader is also referred to several official documents (guidelines) that pave the way for a more structured representation of the machine learning models in their prospective submissions to the regulatory bodies.Algoritmi mašinskog učenja, kao i veštačka inteligencija u širem smislu, su veoma značajni i primenjuju se u razne svrhe u okviru farmaceutske tehnologije. Počevši od razvoja formulacija, preko izuzetnog potencijala za integraciju u koncept dizajna kvaliteta (engl. Quality by design), algoritmi mašinskog učenja omogućavaju bolje razumevanje uticaja kako formulacionih faktora tako i odgovarajućih procesnih parametara. Algoritmi mašinskog učenja mogu biti od naročitog značaja i za analizu velikog obima podataka koji se generišu korišćenjem procesnih analitičkih tehnologija. U ovom radu su ukratko predstavljene veštačke neuronske mreže, kao jedan od najčešće korišćenih algoritama mašinskog učenja. Prikazani su procesi treninga i testiranja mreža, kao i ilustrativni primeri algoritama primenjenih za različite potrebe razvoja i/ili optimizacije farmaceutskih formulacija i postupaka njihove izrade. Takođe, dat je i pregled budućih trendova u ovoj oblasti, kao i novijih studija o sofisticiranim metodama, poput dubokih neuronskih mreža, i light gradient boosting algoritma. Zainteresovani čitaoci se takođe upućuju na nekoliko zvaničnih dokumenata (vodiča), po uzoru na koje mogu da se očekuju i preporuke za strukturiranu prezentaciju modela mašinskog učenja koji će se podnositi regulatornim telima u okviru dokumentacije koja se priprema za potrebe registracije novih lekova

Effect of roll compaction on granule size distribution of microcrystalline cellulose-mannitol mixtures : computational intelligence modeling and parametric analysis

Dry granulation using roll compaction is a typical unit operation for producing solid dosage forms in the pharmaceutical industry. Dry granulation is commonly used if the powder mixture is sensitive to heat and moisture and has poor flow properties. The output of roll compaction is compacted ribbons that exhibit different properties based on the adjusted process parameters. These ribbons are then milled into granules and finally compressed into tablets. The properties of the ribbons directly affect the granule size distribution (GSD) and the quality of final products; thus, it is imperative to study the effect of roll compaction process parameters on GSD. The understanding of how the roll compactor process parameters and material properties interact with each other will allow accurate control of the process, leading to the implementation of quality by design practices. Computational intelligence (CI) methods have a great potential for being used within the scope of quality by design approach. The main objective of this study was to show how the computational intelligence techniques can be useful to predict the GSD by using different process conditions of roll compaction and material properties. Different techniques such as multiple linear regression, artificial neural networks, random forest, Cubist and k-nearest neighbors algorithm assisted by sevenfold cross-validation were used to present generalized models for the prediction of GSD based on roll compaction process setting and material properties. The normalized root-mean-squared error and the coefficient of determination (R2) were used for model assessment. The best fit was obtained by Cubist model (normalized root-mean-squared error =3.22%, R2=0.95). Based on the results, it was confirmed that the material properties (true density) followed by compaction force have the most significant effect on GSD

Application of Machine-Learning Algorithms for Better Understanding the Properties of Liquisolid Systems Prepared with Three Mesoporous Silica Based Carriers

The processing of liquisolid systems (LSS), which are considered a promising approach to improving the oral bioavailability of poorly soluble drugs, has proven challenging due to the relatively high amount of liquid phase incorporated within them. The objective of this study was to apply machine-learning tools to better understand the effects of formulation factors and/or tableting process parameters on the flowability and compaction properties of LSS with silica-based mesoporous excipients as carriers. In addition, the results of the flowability testing and dynamic compaction analysis of liquisolid admixtures were used to build data sets and develop predictive multivariate models. In the regression analysis, six different algorithms were used to model the relationship between tensile strength (TS), the target variable, and eight other input variables. The AdaBoost algorithm provided the best-fit model for predicting TS (coefficient of determination = 0.94), with ejection stress (ES), compaction pressure, and carrier type being the parameters that influenced its performance the most. The same algorithm was best for classification (precision = 0.90), depending on the type of carrier used, with detachment stress, ES, and TS as variables affecting the performance of the model. Furthermore, the formulations with Neusilin® US2 were able to maintain good flowability and satisfactory values of TS despite having a higher liquid load compared to the other two carriers

Harnessing Artificial Intelligence for the Next Generation of 3D Printed Medicines

Artificial intelligence (AI) is redefining how we exist in the world. In almost every sector of society, AI is performing tasks with super-human speed and intellect; from the prediction of stock market trends to driverless vehicles, diagnosis of disease, and robotic surgery. Despite this growing success, the pharmaceutical field is yet to truly harness AI. Development and manufacture of medicines remains largely in a ‘one size fits all’ paradigm, in which mass-produced, identical formulations are expected to meet individual patient needs. Recently, 3D printing (3DP) has illuminated a path for on-demand production of fully customisable medicines. Due to its flexibility, pharmaceutical 3DP presents innumerable options during formulation development that generally require expert navigation. Leveraging AI within pharmaceutical 3DP removes the need for human expertise, as optimal process parameters can be accurately predicted by machine learning. AI can also be incorporated into a pharmaceutical 3DP ‘Internet of Things’, moving the personalised production of medicines into an intelligent, streamlined, and autonomous pipeline. Supportive infrastructure, such as The Cloud and blockchain, will also play a vital role. Crucially, these technologies will expedite the use of pharmaceutical 3DP in clinical settings and drive the global movement towards personalised medicine and Industry 4.0

La utilización de la investigación de operaciones como soporte a la toma de decisiones en el sector salud: Un estado del arte

The contributions of Operations Research (OR) in the healthcare field have been extensively studied in the scientific literature since the 1960s, covering decision support tools with operational, tactical, and strategic approaches. The aim of this article is to analyze the historical development of the application of OR models in healthcare. The application trends for optimization, planning, and decision- making models are studied through a descriptive literature review and a bibliometric analysis of scientific papers published between 1952 and 2016. An upward trend in the usage of operational models is observed with the predominance of resource optimization approaches and strategic decision-making for public health.Los aportes de la Investigación de Operaciones (IO) en el campo de la salud han sido ampliamente estudiados en la literatura científica desde la década de 1960, abarcando herramientas para el soporte a la decisión en enfoques operacionales, tácticos y estratégicos. El objetivo de este artículo es analizar el avance y el desarrollo histórico del uso de modelos operativos en el campo de la salud. A través de una revisión bibliográfica descriptiva y un análisis bibliométrico de artículos científicos publicados durante el periodo 1952-2016, se estudia el comportamiento de las tendencias en la aplicación de modelos operativos para la optimización, la planificación y la toma de decisiones en el sector salud. Se evidencia una tendencia creciente en el uso de modelos de IO durante el periodo estudiado, predominando las aplicaciones orientadas a la optimización de recursos y decisiones estratégicas de salud pública

Roller Compaction of Pharmaceutical Ingredients : on the Undrestanding of the Compaction and the Use of Knowledge based Applications in the Formulation of Tablets

Roller compaction is a dry granulation process which is still not well understood since a large number of processing variables and inherent material attributes make the description of the mechanisms involved in the process extremely complicated. Neither the intermediary compact (i.e., the ribbon) nor the final product (i.e., the granule) is normally used as dosage form. Thus, the granule is usually processed for the production of tablets. When the substance needs to be dry-granulated prior to the tablet production, not only the initial composition of the formulation, but also the processing conditions (device parameters, intermediary steps, etc.), affect the workability of the granule and, consequently, the characteristics of the produced tablets. Thus, the development of a formulation and the optimization of the production process are necessary to meet the mandatory tablet requirements that warrant the pharmaco-therapeutic success of the drug. Expert Systems (ES) are tools that result from combining experience and computing. They can simplify the task of development of new formulations in an accurate, efficient and rapid way by processing, extracting and putting to anyone’s disposal data contained in their data base. This work pretends to join the effort of others on the characterisation of the compaction. The experiments described here supply key hints for the understanding of the process and the variability in the product attributes due to composition of the formulation and processing conditions. Finally, the information and the predictive models created during the experiments have been implemented in the development of an ES in order to demonstrate the benefits of these applications in formulation research. For that purpose, this work reports experiments that analyze the compact process visually and use a critical attribute of the ribbon, the solid fraction, to evidence the interactions between the powder and the rollers, and to describe the effect of the lubrication and other compaction conditions over the dynamics and the densification of the material between the rollers. Moreover, the suitability of a number of techniques for the characterisation of the ribbon solid fraction is analyzed and compared. Furthermore, after close examination of the ribbon quality and also of the granule and the tablets produced from different pharmaceutical substances it has been shown that these product characteristics are strongly affected by the initial composition and by the parameters and the conditions of the process. Moreover, predictive models based on computer general mathematic regressions and artificial neural networks, have been developed using gathered experimental data. The models identify satisfactorily the underlying relationships between the independent variables (composition and process parameters) and the product characteristics, and deliver prognoses of the properties of the intermediary (ribbons and granules) and the final products (tablets). Finally the models are integrated into an ES that has shown to be a user-friendly application that manages the information and retrieves the models to generate a report that includes predictions of the product attributes and helpful information to be born in mind during the production. ESs are extremely useful tools, especially for the pharmaceutical industry, as they ease the formulation development, reducing effort and expenditures, and increasing the consistency, the quality and the efficiency of the formulation tasks. In addition they ensure the permanency and the accessibility of the knowledge and assist the work of both experienced and novice formulators