Ameliorating the antitumor activity of lenalidomide using PLGA nanoparticles for the treatment of multiple myeloma

Abstract Lenalidomide (LND) is an anti-cancer drug and an effective derivative of thalidomide used for multiple myeloma therapy. Because of its poor solubility in water, LND is known to cause low oral bioavailability (below 33%), and as a direct consequence of this, the dosing frequency is extended thus increasing risk of toxicity. To improve its bioavailability and sustained release, the present study aims to formulate polymeric nanoparticles (NPs) for LND using [Poly (lactic-co-glycolic acid)] (PLGA) as a polymer. The polymeric NPs were evaluated for particle size, SEM, XRD, drug content, entrapment efficiency (EE), in vitro release studies and in vivo bioavailability studies in rats. The formulated NPs possessed a size of 179±0.9 nm and a zeta potential of -24.4 ± 0.2 mV. The drug loading and EE of the optimized formulation was 32 ± 0.37 % and 78 ± 0.92% respectively. After oral administration of LND PLGA-NPs, the relative bioavailability was enhanced about 3.67-fold compared to LND. This study demonstrates the novel drug delivery for LND with PLGA-NPs as effective drug delivery system for sustained delivery of LND

Ameliorating the antitumor activity of lenalidomide using PLGA nanoparticles for the treatment of multiple myeloma

Abstract Lenalidomide (LND) is an anti-cancer drug and an effective derivative of thalidomide used for multiple myeloma therapy. Because of its poor solubility in water, LND is known to cause low oral bioavailability (below 33%), and as a direct consequence of this, the dosing frequency is extended thus increasing risk of toxicity. To improve its bioavailability and sustained release, the present study aims to formulate polymeric nanoparticles (NPs) for LND using [Poly (lactic-co-glycolic acid)] (PLGA) as a polymer. The polymeric NPs were evaluated for particle size, SEM, XRD, drug content, entrapment efficiency (EE), in vitro release studies and in vivo bioavailability studies in rats. The formulated NPs possessed a size of 179±0.9 nm and a zeta potential of -24.4 ± 0.2 mV. The drug loading and EE of the optimized formulation was 32 ± 0.37 % and 78 ± 0.92% respectively. After oral administration of LND PLGA-NPs, the relative bioavailability was enhanced about 3.67-fold compared to LND. This study demonstrates the novel drug delivery for LND with PLGA-NPs as effective drug delivery system for sustained delivery of LND

Sensitive determination of amlodipine besylate using bare/unmodified and DNA-modified screen-printed electrodes in tablets and biological fluids

The screen-printed technique is widely used as an efficient tool for electrochemical analysis in environment, clinical and agri-food areas. Significantly, it has the ability to transfer electrochemical laboratory experiments into the field. In the present work, we report a highly sensitive, simple, low-cost protocol for determination of amlodipine (AML) using bare/unmodified and DNA-modified screen-printed electrodes (SPEs). The immobilization of DNA molecules onto SPE offers promising robust and chemically stable molecular wires, which provides a unique opportunity for charge transfer processes. Consequently, the electroanalytical sensing of AML was explored at bare/unmodified and DNA-modified SPEs in a linear range between 0.066–1.0 μM and 0.066–2.0 μM with the detection limit (3σ) found to be 20.70 nM and 14.94 nM, whilst corresponding sensitivities of: 0.43 A L mol−1 and 4.23 A L mol−1 respectively. Although, the superior electrochemical signature of bare SPEs is evident, the immobilization of DNA onto SPEs enhances the sensitivity 10-times more than the bare SPEs. Furthermore, the optimized electroanalytical protocol using the unmodified SPEs, which requires no pre-treatment and electrode modification step, was then further applied to the determination of AML in real samples

Categorizing Customer feedback using Machine Learning

Customer complaints/feedback are a treasure trove of information that often companies overlook. As regular users of a company’s product, customers are able to provide insightful feedback. Companies can leverage customer feedback to improve or create additional features for their existing products or even develop new product lines to serve their customer base better. Though customer complaints/feedback is insightful, it may not always be actionable. Given most of the customer complaints are in text format, combing through them manually to extract actionable insights is simply not feasible due to the amount of time it may take, and the costs associated with man hours. However, automatically processing the documents to retrieve useful information can be done using machine learning algorithms. By using machine learning efficiently businesses can benefit from automating the information retrieval process from huge corpuses of text data. In this research project, Customer feedback data collected by Customer financial protection bureau about various financial institutions is used for analysis and to train four different machine learning models. The goal of these models is, given a row of data (feedback in this case) the model should predict which product is the feedback about. I used Random Forest classifier, K-Nearest Neighbors, Decision Tree classifier, Gradient boosting classifier. I evaluated each of these models based on how they scored on Precision, Accuracy, Recall and F-1 Score. On comparing the model’s, Gradient Boosting classifier was the best performing model