Top-Down Synthesis of a Lamivudine-Zidovudine Nano Co-Crystal
- Witika, Bwalya A, Smith, Vincent J, Walker, Roderick B
- Authors: Witika, Bwalya A , Smith, Vincent J , Walker, Roderick B
- Date: 2021
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/183172 , vital:43918 , xlink:href="https://doi.org/10.3390/cryst11010033"
- Description: Lamivudine (3TC) and zidovudine (AZT) are antiretroviral agents used to manage HIV/AIDS infection. A wet media milling top-down approach was used to develop and produce nano co-crystals of 3TC and AZT. Micro co-crystals were prepared by solvent evaporation and subsequently milled in the presence of two surfactants, viz., sodium lauryl sulfate (SLS) and α-tocopheryl polyethylene glycol succinate 1000 (TPGS 1000). Optimisation was undertaken using design of experiments (DoE) and response surface methodology (RSM) to establish and identify parameters that may affect the manufacturing of nano co-crystals. The impact of SLS and TPGS 1000 concentration, milling time, and number of units of milling medium on the manufacturing of nano co-crystals, was investigated. The critical quality attributes (CQA) monitored were particle size (PS), Zeta potential (ZP), and polydispersity index (PDI). Powder X-ray diffraction, Fourier transform infrared spectroscopy, differential scanning calorimetry, transmission electron microscopy, energy dispersive X-ray spectroscopy scanning electron microscopy, and cytotoxicity assays were used for additional characterization of the optimised nano co-crystal. The mean PS, PDI, and ZP of the optimised top-down nanocrystal were 271.0 ± 92.0 nm, 0.467 ± 0.073, and −41.9 ± 3.94 mV, respectively. In conclusion, a simple, inexpensive, rapid, and precise method of nano co-crystal manufacturing was developed, validated, and optimised using DoE and RSM, and the final product exhibited the target CQA.
- Full Text:
- Date Issued: 2021
- Authors: Witika, Bwalya A , Smith, Vincent J , Walker, Roderick B
- Date: 2021
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/183172 , vital:43918 , xlink:href="https://doi.org/10.3390/cryst11010033"
- Description: Lamivudine (3TC) and zidovudine (AZT) are antiretroviral agents used to manage HIV/AIDS infection. A wet media milling top-down approach was used to develop and produce nano co-crystals of 3TC and AZT. Micro co-crystals were prepared by solvent evaporation and subsequently milled in the presence of two surfactants, viz., sodium lauryl sulfate (SLS) and α-tocopheryl polyethylene glycol succinate 1000 (TPGS 1000). Optimisation was undertaken using design of experiments (DoE) and response surface methodology (RSM) to establish and identify parameters that may affect the manufacturing of nano co-crystals. The impact of SLS and TPGS 1000 concentration, milling time, and number of units of milling medium on the manufacturing of nano co-crystals, was investigated. The critical quality attributes (CQA) monitored were particle size (PS), Zeta potential (ZP), and polydispersity index (PDI). Powder X-ray diffraction, Fourier transform infrared spectroscopy, differential scanning calorimetry, transmission electron microscopy, energy dispersive X-ray spectroscopy scanning electron microscopy, and cytotoxicity assays were used for additional characterization of the optimised nano co-crystal. The mean PS, PDI, and ZP of the optimised top-down nanocrystal were 271.0 ± 92.0 nm, 0.467 ± 0.073, and −41.9 ± 3.94 mV, respectively. In conclusion, a simple, inexpensive, rapid, and precise method of nano co-crystal manufacturing was developed, validated, and optimised using DoE and RSM, and the final product exhibited the target CQA.
- Full Text:
- Date Issued: 2021
An artificial neural network approach to predict the effects of formulation and process variables on prednisone release from a multipartite system
- Manda, Arthur, Walker, Roderick B, Khamanga, Sandile M
- Authors: Manda, Arthur , Walker, Roderick B , Khamanga, Sandile M
- Date: 2019
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/183237 , vital:43933 , xlink:href="https://doi.org/10.3390/pharmaceutics11030109"
- Description: The impact of formulation and process variables on the in-vitro release of prednisone from a multiple-unit pellet system was investigated. Box-Behnken Response Surface Methodology (RSM) was used to generate multivariate experiments. The extrusion-spheronization method was used to produce pellets and dissolution studies were performed using United States Pharmacopoeia (USP) Apparatus 2 as described in USP XXIV. Analysis of dissolution test samples was performed using a reversed-phase high-performance liquid chromatography (RP-HPLC) method. Four formulation and process variables viz., microcrystalline cellulose concentration, sodium starch glycolate concentration, spheronization time and extrusion speed were investigated and drug release, aspect ratio and yield were monitored for the trained artificial neural networks (ANN). To achieve accurate prediction, data generated from experimentation were used to train a multi-layer perceptron (MLP) using back propagation (BP) and the Broyden-Fletcher-Goldfarb-Shanno (BFGS) 57 training algorithm until a satisfactory value of root mean square error (RMSE) was observed. The study revealed that the in-vitro release profile of prednisone was significantly impacted by microcrystalline cellulose concentration and sodium starch glycolate concentration. Increasing microcrystalline cellulose concentration retarded dissolution rate whereas increasing sodium starch glycolate concentration improved dissolution rate. Spheronization time and extrusion speed had minimal impact on prednisone release but had a significant impact on extrudate and pellet quality. This work demonstrated that RSM can be successfully used concurrently with ANN for dosage form manufacture to permit the exploration of experimental regions that are omitted when using RSM alone.
- Full Text:
- Date Issued: 2019
- Authors: Manda, Arthur , Walker, Roderick B , Khamanga, Sandile M
- Date: 2019
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/183237 , vital:43933 , xlink:href="https://doi.org/10.3390/pharmaceutics11030109"
- Description: The impact of formulation and process variables on the in-vitro release of prednisone from a multiple-unit pellet system was investigated. Box-Behnken Response Surface Methodology (RSM) was used to generate multivariate experiments. The extrusion-spheronization method was used to produce pellets and dissolution studies were performed using United States Pharmacopoeia (USP) Apparatus 2 as described in USP XXIV. Analysis of dissolution test samples was performed using a reversed-phase high-performance liquid chromatography (RP-HPLC) method. Four formulation and process variables viz., microcrystalline cellulose concentration, sodium starch glycolate concentration, spheronization time and extrusion speed were investigated and drug release, aspect ratio and yield were monitored for the trained artificial neural networks (ANN). To achieve accurate prediction, data generated from experimentation were used to train a multi-layer perceptron (MLP) using back propagation (BP) and the Broyden-Fletcher-Goldfarb-Shanno (BFGS) 57 training algorithm until a satisfactory value of root mean square error (RMSE) was observed. The study revealed that the in-vitro release profile of prednisone was significantly impacted by microcrystalline cellulose concentration and sodium starch glycolate concentration. Increasing microcrystalline cellulose concentration retarded dissolution rate whereas increasing sodium starch glycolate concentration improved dissolution rate. Spheronization time and extrusion speed had minimal impact on prednisone release but had a significant impact on extrudate and pellet quality. This work demonstrated that RSM can be successfully used concurrently with ANN for dosage form manufacture to permit the exploration of experimental regions that are omitted when using RSM alone.
- Full Text:
- Date Issued: 2019
- «
- ‹
- 1
- ›
- »