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Li P, Tang Y, Zeng Q, Mo C, Ali N, Bai B, Ji S, Zhang Y, Luo J, Liang H, Wu R. Diagnostic performance of machine learning in systemic infection following percutaneous nephrolithotomy and identification of associated risk factors. Heliyon 2024; 10:e30956. [PMID: 38818205 PMCID: PMC11137387 DOI: 10.1016/j.heliyon.2024.e30956] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Revised: 05/05/2024] [Accepted: 05/08/2024] [Indexed: 06/01/2024] Open
Abstract
Objective This study aims to investigate the predictive performance of machine learning in predicting the occurrence of systemic inflammatory response syndrome (SIRS) and urosepsis after percutaneous nephrolithotomy (PCNL). Methods A retrospective analysis was conducted on patients who underwent PCNL treatment between January 2016 and July 2022. Machine learning techniques were employed to establish and select the best predictive model for postoperative systemic infection. The feasibility of using relevant risk factors as predictive markers was explored through interpretability with Machine Learning. Results A total of 1067 PCNL patients were included in this study, with 111 (10.4 %) patients developing SIRS and 49 (4.5 %) patients developing urosepsis. In the validation set, the risk model based on the GBM protocol demonstrated a predictive power of 0.871 for SIRS and 0.854 for urosepsis. Preoperative and postoperative platelet changes were identified as the most significant predictors. Both thrombocytopenia and thrombocytosis were found to be risk factors for SIRS or urosepsis after PCNL. Furthermore, it was observed that when the change in platelet count before and after PCNL surgery exceeded 30*109/L (whether an increase or decrease), the risk of developing SIRS or urosepsis significantly increased. Conclusion Machine learning can be effectively utilized for predicting the occurrence of SIRS or urosepsis after PCNL. The changes in platelet count before and after PCNL surgery serve as important predictors.
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Affiliation(s)
- Pengju Li
- Department of Urology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, PR China
| | - Yiming Tang
- Department of Urology, The Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, PR China
| | - Qinsong Zeng
- Department of Urology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, PR China
| | - Chengqiang Mo
- Department of Urology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, PR China
| | - Nur Ali
- Department of Urology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, PR China
| | - Baohua Bai
- Department of Urology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, PR China
| | - Song Ji
- Department of Urology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, PR China
| | - Yubing Zhang
- Department of Urology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, PR China
| | - Junhang Luo
- Department of Urology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, PR China
- Institute of Precision Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, PR China
| | - Hui Liang
- Department of Urology, Affiliated Longhua People's Hospital, Southern Medical University, Shenzhen, PR China
| | - Rongpei Wu
- Department of Urology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, PR China
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Ur Rahman A, Nasir F, Ali Khattak M, Hidayatullah T, Pervez S, Rabqa Zainab S, Tahir Ali A, Gohar S, E Maryam G, Almalki WH. Comparative pharmacokinetic evaluation of glimepiride orodispersable and conventional tablets in rabbits. Drug Dev Ind Pharm 2024; 50:173-180. [PMID: 38265062 DOI: 10.1080/03639045.2024.2307421] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Accepted: 12/20/2023] [Indexed: 01/25/2024]
Abstract
OBJECTIVES Glimepiride Orodispersable Tablets (ODT) were prepared with the goal to have rapid onset of action and higher bioavailability with ease administration to individuals with swallowing difficulty to ameliorate patient compliance. SIGNIFICANCE Glimepiride is a contemporary hypoglycemic medication that belongs to the family of sulfonylurea derivatives. It is used in type 2 diabetes mellitus. Compliance adherence remains one of the limitations with the conventional drug delivery system especially in pediatric, geriatric, psychiatric, and traveling patients, for such population ODT provides a good alternate dosage form compared with Commercial Tablets. METHOD The Comparative in vivo pharmacokinetic parameters of the prepared ODT and conventional tablets (CT) were evaluated using an animal model. The plasma concentration of Glimepiride after oral administration of a single dose was determined at predetermined time intervals with HPLC. The pharmacokinetic parameters were calculated using PK Solutions 2.0 from Summit PK® software. RESULTS The Cmax obtained with ODT (22.08 µg/ml) was significantly (p = 0.006) high, a lower tmax of 3.0 hr was achieved with the orodispersable formulation of the drug. The ODT showed 104.34% relative bioavailability as compared to CT and left shift of tmax as well. CONCLUSION As per findings of the in vivo investigation, the Glimepiride ODT would be beneficial in terms of patient compliance, quick onset of action, and increased bioavailability.
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Affiliation(s)
- Altaf Ur Rahman
- Department of Pharmacy, University of Peshawar, Peshawar, Pakistan
| | - Fazli Nasir
- Department of Pharmacy, University of Peshawar, Peshawar, Pakistan
| | - Muzna Ali Khattak
- Department of Pharmacy, University of Peshawar, Peshawar, Pakistan
- Department of Pharmacy, CECOS University Peshawar, Peshawar, Pakistan
| | | | - Sadia Pervez
- Department of Pharmacy, University of Peshawar, Peshawar, Pakistan
| | | | - Arbab Tahir Ali
- Department of Pharmacy, University of Peshawar, Peshawar, Pakistan
| | - Shazma Gohar
- Department of Pharmacy, University of Peshawar, Peshawar, Pakistan
| | - Gul E Maryam
- Department of Pharmacy, Qurtuba University Peshawar, Peshawar, Pakistan
| | - Waleed H Almalki
- Department of Pharmacology and Toxicology, College of Pharmacy, Umm Al-Qura University, Makkah
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Sahu A, Rathee S, Saraf S, Jain SK. A Review on the Recent Advancements and Artificial Intelligence in Tablet Technology. Curr Drug Targets 2024; 25:416-430. [PMID: 38213164 DOI: 10.2174/0113894501281290231221053939] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 12/01/2023] [Accepted: 12/06/2023] [Indexed: 01/13/2024]
Abstract
BACKGROUND Tablet formulation could be revolutionized by the integration of modern technology and established pharmaceutical sciences. The pharmaceutical sector can develop tablet formulations that are not only more efficient and stable but also patient-friendly by utilizing artificial intelligence (AI), machine learning (ML), and materials science. OBJECTIVES The primary objective of this review is to explore the advancements in tablet technology, focusing on the integration of modern technologies like artificial intelligence (AI), machine learning (ML), and materials science to enhance the efficiency, cost-effectiveness, and quality of tablet formulation processes. METHODS This review delves into the utilization of AI and ML techniques within pharmaceutical research and development. The review also discusses various ML methodologies employed, including artificial neural networks, an ensemble of regression trees, support vector machines, and multivariate data analysis techniques. RESULTS Recent studies showcased in this review demonstrate the feasibility and effectiveness of ML approaches in pharmaceutical research. The application of AI and ML in pharmaceutical research has shown promising results, offering a potential avenue for significant improvements in the product development process. CONCLUSION The integration of nanotechnology, AI, ML, and materials science with traditional pharmaceutical sciences presents a remarkable opportunity for enhancing tablet formulation processes. This review collectively underscores the transformative role that AI and ML can play in advancing pharmaceutical research and development, ultimately leading to more efficient, reliable and patient-centric tablet formulations.
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Affiliation(s)
- Amit Sahu
- Pharmaceutics Research Laboratory, Department of Pharmaceutical Sciences, Dr. Harisingh Gour Vishwavidyalaya (A Central University), Sagar, Madhya Pradesh, 470003, India
| | - Sunny Rathee
- Pharmaceutics Research Laboratory, Department of Pharmaceutical Sciences, Dr. Harisingh Gour Vishwavidyalaya (A Central University), Sagar, Madhya Pradesh, 470003, India
| | - Shivani Saraf
- Pharmaceutics Research Laboratory, Department of Pharmaceutical Sciences, Dr. Harisingh Gour Vishwavidyalaya (A Central University), Sagar, Madhya Pradesh, 470003, India
| | - Sanjay K Jain
- Pharmaceutics Research Laboratory, Department of Pharmaceutical Sciences, Dr. Harisingh Gour Vishwavidyalaya (A Central University), Sagar, Madhya Pradesh, 470003, India
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Jiang J, Ma X, Ouyang D, Williams RO. Emerging Artificial Intelligence (AI) Technologies Used in the Development of Solid Dosage Forms. Pharmaceutics 2022; 14:2257. [PMID: 36365076 PMCID: PMC9694557 DOI: 10.3390/pharmaceutics14112257] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Revised: 10/11/2022] [Accepted: 10/17/2022] [Indexed: 07/30/2023] Open
Abstract
Artificial Intelligence (AI)-based formulation development is a promising approach for facilitating the drug product development process. AI is a versatile tool that contains multiple algorithms that can be applied in various circumstances. Solid dosage forms, represented by tablets, capsules, powder, granules, etc., are among the most widely used administration methods. During the product development process, multiple factors including critical material attributes (CMAs) and processing parameters can affect product properties, such as dissolution rates, physical and chemical stabilities, particle size distribution, and the aerosol performance of the dry powder. However, the conventional trial-and-error approach for product development is inefficient, laborious, and time-consuming. AI has been recently recognized as an emerging and cutting-edge tool for pharmaceutical formulation development which has gained much attention. This review provides the following insights: (1) a general introduction of AI in the pharmaceutical sciences and principal guidance from the regulatory agencies, (2) approaches to generating a database for solid dosage formulations, (3) insight on data preparation and processing, (4) a brief introduction to and comparisons of AI algorithms, and (5) information on applications and case studies of AI as applied to solid dosage forms. In addition, the powerful technique known as deep learning-based image analytics will be discussed along with its pharmaceutical applications. By applying emerging AI technology, scientists and researchers can better understand and predict the properties of drug formulations to facilitate more efficient drug product development processes.
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Affiliation(s)
- Junhuang Jiang
- Division of Molecular Pharmaceutics and Drug Delivery, College of Pharmacy, The University of Texas at Austin, Austin, TX 78712, USA
| | - Xiangyu Ma
- Global Investment Research, Goldman Sachs, New York, NY 10282, USA
| | - Defang Ouyang
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences (ICMS), University of Macau, Macau 999078, China
| | - Robert O. Williams
- Division of Molecular Pharmaceutics and Drug Delivery, College of Pharmacy, The University of Texas at Austin, Austin, TX 78712, USA
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Muthudoss P, Tewari I, Chi RLR, Young KJ, Ann EYC, Hui DNS, Khai OY, Allada R, Rao M, Shahane S, Das S, Babla I, Mhetre S, Paudel A. Machine Learning-Enabled NIR Spectroscopy in Assessing Powder Blend Uniformity: Clear-Up Disparities and Biases Induced by Physical Artefacts. AAPS PharmSciTech 2022; 23:277. [PMID: 36229571 DOI: 10.1208/s12249-022-02403-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Accepted: 08/22/2022] [Indexed: 11/30/2022] Open
Abstract
NIR spectroscopy is a non-destructive characterization tool for the blend uniformity (BU) assessment. However, NIR spectra of powder blends often contain overlapping physical and chemical information of the samples. Deconvoluting the information related to chemical properties from that associated with the physical effects is one of the major objectives of this work. We achieve this aim in two ways. Firstly, we identified various sources of variability that might affect the BU results. Secondly, we leverage the machine learning-based sophisticated data analytics processes. To accomplish the aforementioned objectives, calibration samples of amlodipine as an active pharmaceutical ingredient (API) with the concentrations ranging between 67 and 133% w/w (dose ~ 3.6% w/w), in powder blends containing excipients, were prepared using a gravimetric approach and assessed using NIR spectroscopic analysis, followed by HPLC measurements. The bias in NIR results was investigated by employing data quality metrics (DQM) and bias-variance decomposition (BVD). To overcome the bias, the clustered regression (non-parametric and linear) was applied. We assessed the model's performance by employing the hold-out and k-fold internal cross-validation (CV). NIR-based blend homogeneity with low mean absolute error and an interval estimates of 0.674 (mean) ± 0.218 (standard deviation) w/w was established. Additionally, bootstrapping-based CV was leveraged as part of the NIR method lifecycle management that demonstrated the mean absolute error (MAE) of BU ± 3.5% w/w and BU ± 1.5% w/w for model generalizability and model transferability, respectively. A workflow integrating machine learning to NIR spectral analysis was established and implemented. Impact of various data learning approaches on NIR spectral data.
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Affiliation(s)
- Prakash Muthudoss
- Oncogen Pharma (Malaysia), Sdn Bhd, 3, Jalan Jururancang U1/21, Hicom-glenmarie Industrial Park, 40150, Shah Alam, Selangor, Malaysia.,A2Z4.0 Research and Analytics Private Limited, Old No:810, New No:62, CTH Road, Behind Lenskart, Thirumullaivoil, Chennai, Tamilnadu, India
| | - Ishan Tewari
- The Machine Learning Company, Beed, Maharashtra, India.,Institute of Technology, Nirma University, Ahmedabad, Gujarat, India
| | - Rayce Lim Rui Chi
- Oncogen Pharma (Malaysia), Sdn Bhd, 3, Jalan Jururancang U1/21, Hicom-glenmarie Industrial Park, 40150, Shah Alam, Selangor, Malaysia
| | - Kwok Jia Young
- Oncogen Pharma (Malaysia), Sdn Bhd, 3, Jalan Jururancang U1/21, Hicom-glenmarie Industrial Park, 40150, Shah Alam, Selangor, Malaysia
| | - Eddy Yii Chung Ann
- Oncogen Pharma (Malaysia), Sdn Bhd, 3, Jalan Jururancang U1/21, Hicom-glenmarie Industrial Park, 40150, Shah Alam, Selangor, Malaysia
| | - Doreen Ng Sean Hui
- Oncogen Pharma (Malaysia), Sdn Bhd, 3, Jalan Jururancang U1/21, Hicom-glenmarie Industrial Park, 40150, Shah Alam, Selangor, Malaysia
| | - Ooi Yee Khai
- Perkin Elmer Sdn Bhd, L2, 2-01, Wisma Academy, Jalan 19/1, Seksyen 19, 46300, Petaling Jaya, Selangor, Malaysia
| | - Ravikiran Allada
- Novugen Pharma (Malaysia), Sdn Bhd, 3, Jalan Jururancang U1/21, Hicom-glenmarie Industrial Park, 40150, Shah Alam, Selangor, Malaysia
| | - Manohar Rao
- PerkinElmer (India) Private Limited, Vayudooth Chambers, 12th floor, Trinity Circle, Mahatma Gandhi Rd, Bengaluru, Karnataka, 560001, India
| | | | - Samir Das
- Oncogen Pharma (Malaysia), Sdn Bhd, 3, Jalan Jururancang U1/21, Hicom-glenmarie Industrial Park, 40150, Shah Alam, Selangor, Malaysia
| | - Irfan Babla
- Oncogen Pharma (Malaysia), Sdn Bhd, 3, Jalan Jururancang U1/21, Hicom-glenmarie Industrial Park, 40150, Shah Alam, Selangor, Malaysia
| | - Sandeep Mhetre
- Oncogen Pharma (Malaysia), Sdn Bhd, 3, Jalan Jururancang U1/21, Hicom-glenmarie Industrial Park, 40150, Shah Alam, Selangor, Malaysia
| | - Amrit Paudel
- Research Center Pharmaceutical Engineering GmbH (RCPE), Inffeldgasse 13, 8010, Graz, Austria. .,Institute of Process and Particle Engineering, Graz University of Technology, Inffeldgasse 13/3, 8010, Graz, Austria.
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