Puzzle out Machine Learning Model-Explaining Disintegration Process in ODTs.

Tablets are the most common dosage form of pharmaceutical products. While tablets represent the majority of marketed pharmaceutical products, there remain a significant number of patients who find it difficult to swallow conventional tablets. Such difficulties lead to reduced patient compliance. Orally disintegrating tablets (ODT), sometimes called oral dispersible tablets, are the dosage form of choice for patients with swallowing difficulties. ODTs are defined as a solid dosage form for rapid disintegration prior to swallowing. The disintegration time, therefore, is one of the most important and optimizable critical quality attributes (CQAs) for ODTs. Current strategies to optimize ODT disintegration times are based on a conventional trial-and-error method whereby a small number of samples are used as proxies for the compliance of whole batches. We present an alternative machine learning approach to optimize the disintegration time based on a wide variety of machine learning (ML) models through the H2O AutoML platform. ML models are presented with inputs from a database originally presented by Han et al., which was enhanced and curated to include chemical descriptors representing active pharmaceutical ingredient (API) characteristics. A deep learning model with a 10-fold cross-validation NRMSE of 8.1% and an R2 of 0.84 was obtained. The critical parameters influencing the disintegration of the directly compressed ODTs were ascertained using the SHAP method to explain ML model predictions. A reusable, open-source tool, the ODT calculator, is now available at Heroku platform.

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.

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.

OPTIMIZATION OF FUROSEMIDE LIQUISOLID TABLETS PREPARATION PROCESS LEADING TO THEIR MASS AND SIZE REDUCTION.

The great number of drug substances currently used in solid oral dosage forms is characterized by poor water solubility. Therefore, various methods of dissolution rate enhancement are an important topic of research interest in modem drug technology. The purpose of this study was to enhance the furosemide dissolution rate from liquisolid tablets while maintaining an acceptable size and mass. Two types of dibasic calcium phosphate (Fujicalin®/Emcompress®) and microcrystalline cellulose (Vivapur® 102/Vivapur® 12) were used as carriers and magnesium aluminometasilicate (Neusilin® US2) was used as a coating material. The flowable liquid retention potential for those excipients was tested by measuring the angle of slide. To evaluate the impact of used excipients on tablet properties fourteen tablet formulations were prepared. It was found that LS2 tablets containing spherically granulated dibasic calcium phosphate and magnesium aluminometasilicate exhibit the best dissolution profile and mechanical properties while tablets composed only with Neusilin® US2 was characterized by the smallest size and mass with preserved good mechanical properties and furosemide dissolution.

Nanoneurotoxicity to nanoneuroprotection using biological and computational approaches.

Nanoparticles (NPs) that are ∼100 nm in diameter can potentially cause toxicity in the central nervous system (CNS). Although NPs exhibit positive aspects, these molecules primarily exert negative or harmful effects. Thus, the beneficial and harmful effects should be compared. The prevalence of neurodegenerative diseases, such as Alzheimer disease, Parkinson disease, and some brain tumors, has increased. However, the major cause of these diseases remains unknown. NPs have been considered as one of the major potential causes of these diseases, penetrating the human body via different pathways. This review summarizes various pathways for NP-induced neurotoxicity, suggesting the development of strategies for nanoneuroprotection using in silico and biological methods. Studies of oxidative stress associated with gene expression analyses provide efficient information for understanding neuroinflammation and neurodegeneration associated with NPs. The brain is a sensitive and fragile organ, and evolution has developed mechanisms to protect it from injury; however, this protection also hinders the methods used for therapeutic purposes. Thus, brain and CNS-related diseases that are the cause of disability and disorder are the most difficult to treat. There are many obstacles to drug delivery in the CNS, such as the blood brain barrier and blood tumor barrier. Considering these barriers, we have reviewed the strategies available to map NPs using biological techniques. The surface adsorption energy of NPs is the basic force driving NP gathering, protein corona formation, and many other interactions of NPs within biological systems. These interactions can be described using an approach named the biological surface adsorption index. A quantitative structural activity relationship study helps to understand different protein-protein or protein-ligand interactions. Moreover, equilibrium between cerebrovascular permeability is required when a drug is transferred via the circulatory system for the therapy of neurodegenerative diseases. Various drug delivery approaches, such as chemical drug delivery and carrier-mediated drug delivery, have been established to avoid different barriers inhibiting CNS penetration by therapeutic substances. Developing an improved understanding of drug receptors and the sites of drug action, together with advances in medicinal chemistry, will make it possible to design drugs with greatly enhanced activity and selectivity; this may result in a significant increase in the therapeutic index.

Gene Locater: Genetic linkage analysis software using three-point testcross.

UNLABELLED: Locating genes on a chromosome is important for understanding the gene function and its linkage and recombination. Knowledge of gene positions on chromosomes is necessary for annotation. The study is essential for disease genetics and genomics, among other aspects. Currently available software's for calculating recombination frequency is mostly limited to the range and flexibility of this type of analysis. GENE LOCATER is a fully customizable program for calculating recombination frequency, written in JAVA. Through an easy-to-use interface, GENE LOCATOR allows users a high degree of flexibility in calculating genetic linkage and displaying linkage group. Among other features, this software enables user to identify linkage groups with output visualized graphically. The program calculates interference and coefficient of coincidence with elevated accuracy in sample datasets. AVAILABILITY: The database is available for free at http://www.moperandib.com.

Homology modeling of an antifungal metabolite plipastatin synthase from the Bacillus subtilis 168.

Lipopeptides have a widespread role in different pathways of Bacillus subtilis; they can act as antagonists, spreader and immunostimulators. Plipastatin, an antifungal antibiotic, is one of the most important lipopeptide nonribosomly produced by Bacillus subtilis. Plipastatin has strong fungitoxic activity and involve in inhibition of phospholipase A2 and biofilm formation. For better understanding of the molecule and pathway by which lipopeptide plipastatin is synthesized, we present a computationally predicted structure of plipastatin using homology modeling. Primary and secondary structure analysis suggested that ppsD is a hydrophilic protein containing a significant proportion of alpha helices, and subcellular localization predictions suggested it is a cytoplasmic protein. The tertiary structure of protein (plipastatin synthase subunit D) was predicted by homology modeling. The results suggest a flexible structure which is also an important characteristic of active enzymes enabling them to bind various cofactors and substrates for proper functioning. Validation of 3D structure was done using Ramachandran plot ProsA-web and QMEAN score.This predicted information will help in better understanding of mechanisms underlying plipastatin synthase subunit D synthesis. Plipastatin can be used as an inhibitor of various fungal diseases in plants.