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The field of machine learning (ML) is advancing to a larger extent and finding its applications across numerous fields. ML has the potential to optimize the development process of microneedle patch by predicting the drug release pattern prior to its fabrication and production. The early predictions could not only assist the in-vitro and in-vivo experimentation of drug release but also conserve materials, reduce cost, and save time. In this work, we have used a dataset gleaned from the literature to train and evaluate different ML models, such as stacking regressor, artificial neural network (ANN) model, and voting regressor model. In this study, models were developed to improve prediction accuracy of the in-vitro drug release amount from the hydrogel-type microneedle patch and the in-vitro drug permeation amount through the micropores created by solid microneedles on the skin. We compared the performance of these models using various metrics, including R-squared score (R 2 score), root mean squared error (RMSE), and mean absolute error (MAE). Voting regressor model performed better with drug permeation percentage as an outcome feature having RMSE value of 3.24. In comparison, stacking regressor have a RMSE value of 16.54, and ANN model has shown a RMSE value of 14. The value of permeation amount calculated from the predicted percentage is found to be more accurate with RMSE of 654.94 than direct amount prediction, having a RMSE of 669.69. All our models have performed far better than the previously developed model before this research, which had a RMSE of 4447.23. We then optimized voting regressor model's hyperparameter and cross validated its performance. Furthermore, it was deployed in a webapp using Flask framework, showing a way to develop an application to allow other users to easily predict drug permeation amount from the microneedle patch at a particular time period. This project demonstrates the potential of ML to facilitate the development of microneedle patch and other drug delivery systems., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Drug-drug cocrystalllization is a novel mechanism for effective pharmacological combination therapy. In this work, we have demonstrated the preparation of a drug-drug cocrystal of a hypertension drug (Telmisartan; TEL) with a hyperuricemia drug (Febuxostat; FEB) in 1:1 molar ratio using a solvent evaporation method for the first time. Generally, a multi-component system may yield either a eutectic, salt, and/or a cocrystal. This study adopted a methodical orthogonal framework to analyze the final solid form. A single crystal X-ray structural investigation revealed the formation of a heterosynthon with carboxylic and benzimidazole groups of FEB and TEL, respectively, in the triclinic P-1 space group. ΔpKa of the heterosynthon is ∼1.5, hence, based on the empirical rules, a salt-cocrystal continuum is hypothesized. Further, attenuated total reflectance Fourier transform infrared (ATR-FTIR), and Raman spectroscopy were employed to corroborate the hydrogen bond formation in the heterosynthon (-N---H-O-), which confirmed the propensity for cocrystal formation. An accelerated stability study and an in vitro biorelevant dissolution study of the cocrystal were performed, which demonstrated that it is physiochemically stable, but it resulted in a slower dissolution rate when compared with plain drugs., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 American Pharmacists Association. Published by Elsevier Inc. All rights reserved.)

Particle size distribution (PSD), spatial location and particle cluster size of ingredients, polymorphism, compositional distribution of a pharmaceutical product are few of the most important attributes in establishing the drug release-controlling microstructural and solid state properties that would be used to (re)design or reproduce similar products. There are numerous solid-state techniques available for PSD analysis. Laser diffraction (LD) is mostly used to study PSD of raw materials. However, a constraint of LD is the interference between the active pharmaceutical ingredients (API) and excipients, where it is very challenging to measure API size in a tablet. X-ray powder diffraction (XRPD) is widely employed in establishing the polymorphism of API and excipients. This research examined a commercial osmotic tablet in terms of extracting solid state properties of API and functional excipient by Raman Imaging. Establishing repeatability, reproducibility, and sample representativeness when the samples are non-uniform and inhomogeneous necessitates multiple measurements. In such scenarios, when employing imaging-based techniques, it can be time-consuming and tedious. Advanced statistical methodologies are used to overcome these disadvantages and expedite the characterization process. Overall, this study demonstrates that Raman imaging can be employed as a non-invasive and effective offline method for assessing the solid-state characteristics of API and functional excipients in complex dosage forms like osmotic tablets., Competing Interests: Conflict of interest There is no conflict of interest and there was no financial assistance for this work., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Particle size/shape characterization of active pharmaceutical ingredient (API) is integral to successful product development. It is more of a correlative property than a decision-making measure. Though microscopy is the only technique that provides a direct measure of particle properties, it is neglected for reasons like non-repeatability and non-reproducibility which is often attributed to a) fundamental error, b) segregation error, c) human error, d) sample randomness, e) sample representativeness etc. Using the "Sucrose" as model sample, we propose "analytics continuum" approach that integrates optical microscope PSD measurements complimented by NIR spectroscopy-based trending analysis as a prescreening tool to demonstrate sample randomness and representativeness. Furthermore, plethora of statistical tests are utilized to infer population statistics. Subsequently, an attribute-based control chart and bootstrap-based confidence interval was developed to monitor product performance. A flowchart to serve as an elementary guideline is developed, which is then extended to handle more complex situations involving API crystallized from two different solvent systems. The results show that the developed methodology can be utilized as a quantitative procedure to assess the suitability of API/excipients from different batches or from alternate vendors and can significantly help in understanding the differences between material even on a minor scale., (Copyright © 2020 American Pharmacists Association®. Published by Elsevier Inc. All rights reserved.)