Information technology system to promote drug production command supervision

[1] Socal, M., Sharfstein, J., & Greene, J. (2021). The Pandemic and the Supply Chain: Gaps in Pharmaceutical Production and Distribution. American Journal of Public Health, 111(4), 635-639. Search in Google Scholar

[2] Nilay Yücenur, G., Çataltepe, S., & Sakin, İ. (2020). An integrated approach by FMEA & fuzzy prioritization method at pharmaceutical industry quality control. Cumhuriyet Science Journal, 41(1), 106-121. Search in Google Scholar

[3] Djamgoz, M. B. A. (2022). Comments on: antiepileptic drugs and prostate cancer risk in the Finnish randomized study of screening for prostate cancer. International Journal of Cancer, 150(7), 1212-1213. Search in Google Scholar

[4] Morrow, R. L., Mintzes, B., Souverein, P. C., et al. (2022). Influence of drug safety advisories on drug utilisation: an international interrupted time series and meta-analysis. BMJ Quality & Safety, 31(3). Search in Google Scholar

[5] Su, Q., Ganesh, S., Moreno, M., et al. (2019). A perspective on Quality-by-Control (QbC) in pharmaceutical continuous manufacturing. Computers & Chemical Engineering, 125, 216-231. Search in Google Scholar

[6] Demelenne, A., Servais, A. C., Crommen, J., et al. (2021). Analytical techniques currently used in the pharmaceutical industry for the quality control of RNA-based therapeutics and ongoing developments. Journal of Chromatography A, 1651, 462283. Search in Google Scholar

[7] Wu, X., Liu, Q., Chen, D., et al. (2020). Identification of quality control markers in Suhuang antitussive capsule based on HPLC-PDA fingerprint and anti-inflammatory screening. Journal of Pharmaceutical and Biomedical Analysis, 180, 113053. Search in Google Scholar

[8] Jerome, R. N., Pulley, J. M., Roden, D. M., et al. (2018). Using Human ‘Experiments of Nature’ to Predict Drug Safety Issues: An Example with PCSK9 Inhibitors. Drug Safety. Search in Google Scholar

[9] Coito, T., Martins, M., Firme, B., et al. (2022). Assessing the impact of automation in pharmaceutical quality control labs using a digital twin. Journal of Manufacturing Systems, 62, 62. Search in Google Scholar

[10] Abdo, R. W., Saadi, N., Hijazi, N. I., et al. (2020). Quality control and testing evaluation of pharmaceutical aerosols. Drug Delivery Systems, 579-614. Search in Google Scholar

[11] Smirnov, V. A., Goryachkin, V. V., Shestakov, V. N., et al. (2021). Development of Retraining Systems for the Implementation of the Pharmaceutical Quality System at Pharmaceutical Production Enterprises of the EAEU Countries. Drug Development & Registration, 10(1), 130-135. Search in Google Scholar

[12] Misra, S., & Maravelias, C. T. (2022). Overview of Scheduling Methods for Pharmaceutical Production. Springer Optimization and Its Applications. Search in Google Scholar

[13] Burgalassi, S., Ceccanti, S., Vecchiani, S., et al. (2021). Objectionable microorganisms in pharmaceutical production: Validation of a decision tree. European Journal of Pharmaceutical Sciences, 166. Search in Google Scholar

[14] Steven, G. (2020). The Role of Food and Drug Administration Regulation of In Vitro Diagnostic Devices—Applications to Genetics Testing. Clinical Chemistry, 5. Search in Google Scholar

[15] Wang, L., Tong, W., Antonucci, V., et al. (2022). Highly sensitive LC-MS method for stereochemical quality control of a pharmaceutical drug substance intermediate. Chirality: The Pharmacological, Biological, and Chemical Consequences of Molecular Asymmetry, 6. Search in Google Scholar

[16] Gan, Y., Meng, B., Chen, Y., & Sun, F. (2022). An intelligent measurement method of the resonant frequency of ultrasonic scalpel transducers based on PSO-BP neural network. Measurement, 190, 110680-. Search in Google Scholar