A drug–drug and drug–excipient interactions and compatibilities study was conducted for two fixed-dose combination (FDC) products containing olmesartan medoxomil (OLM)/hydrochlorothiazide (HCT) 20/12.5 mg and OLM/HCT 40/12.5 mg during their development including storage. The study consisted of the evaluation of samples retrieved during all stages of a real manufacturing process. Powder X-ray diffraction (PXRD), differential scanning calorimetry (DSC), thermogravimetry (TGA), Fourier transform infrared spectroscopy (FT-IR), and contact angle techniques were applied to the samples to determine interactions and incompatibilities. Dissolution tests and long-term stability studies were conducted to evaluate dosage form performance. Results showed weak solid–state interactions able to obtain a eutectic mixture of OLM and HCT while microcrystalline cellulose (MC) impacted the thermal stability of both drugs. Reliable dissolution and long-term stability tests confirmed that the interactions observed were not considered incompatibilities because they were not influenced by the performance of the final products.

Este proyecto de investigación busca contribuir con el desarrollo de una metodología pionera para la caracterización de tabletas farmacéuticas durante el proceso de recubrimiento por el método de aspersión aleatoria, utilizando diferentes técnicas. Se procura apoyar el desarrollo de una opción viable para sistemas de mayor complejidad, alto volumen de producción y gran procesamiento de información. Los resultados parciales obtenidos, permiten identificar patrones de comportamiento que pueden ser utilizados para facilitar el desarrollo de dispositivos y metodologías automatizables, aplicables a procesos similares, que puedan impactar en los procesos de recubrimiento como factores clave de la calidad y de la gestión productiva.

Se propone una aplicación novedosa de la fórmula SCTV (Spot Color Tone Value) proveniente de la industria de reproducción gráfica, para el control de la calidad del proceso de recubrimiento de tabletas farmacéuticas. Se analizan dos procedimientos: uno mediante la obtención de los espectros de reflectancia, de los cuales se pueden derivar los atributos cromáticos, y otro a partir de la obtención de las coordenadas de color de una imagen fotográfica calibrada. Nuestro objetivo es reducir la necesidad de intervención, extracción y destrucción de tabletas durante el control de calidad, además de facilitar el control y evaluación del proceso de forma remota, generando respuestas en tiempo real para la toma de decisiones.

Melanoma is one of the most aggressive skin cancers, however, its early detection can significantly increase probabilities to cure it. Unfortunately, it is one of the most difficult skin cancers to detect, its detection relies mainly on the dermatologist’s expertise and experience with Melanoma. This research deals with targeting most of the common Melanoma stains or spots that could potentially evolve to Melanoma skin cancer. Region-based Convolutional Neural Networks were used as the model to detect and segment images of the skin area of interest. The neural network model is focused on providing instance segmentation rather than only a boxbounding object detection. The Mask R-CNN model was implemented to provide a solution for small trained datasets scenarios. Two pipelines were implemented, the first one was with only the Region-Based Convolutional Neural Network and the other one was a combined pipeline with a first stage using Mask R-CNN and then getting the result to use as feedback in a second stage implementing Grabcut, which is another segmentation method based on graphic cuts. Results demonstrated through Dice Similarity Coefficient and Jaccard Index that Mask R-CNN alone performed better in proper segmentation than Mask R-CNN + Grabcut model. In both models’ results, variation was very small when the training dataset size changed between 160, 100, and 50 images. In both of the pipelines, the models were capable of running the segmentation correctly, which illustrates that focalization of the zone is possible with very small datasets and the potential use of automatic segmentation to assist in Melanoma detection.