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Wang N, Zhang Y, Wang W, Ye Z, Chen H, Hu G, Ouyang D. How can machine learning and multiscale modeling benefit ocular drug development? Adv Drug Deliv Rev 2023; 196:114772. [PMID: 36906232 DOI: 10.1016/j.addr.2023.114772] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Revised: 02/06/2023] [Accepted: 03/05/2023] [Indexed: 03/12/2023]
Abstract
The eyes possess sophisticated physiological structures, diverse disease targets, limited drug delivery space, distinctive barriers, and complicated biomechanical processes, requiring a more in-depth understanding of the interactions between drug delivery systems and biological systems for ocular formulation development. However, the tiny size of the eyes makes sampling difficult and invasive studies costly and ethically constrained. Developing ocular formulations following conventional trial-and-error formulation and manufacturing process screening procedures is inefficient. Along with the popularity of computational pharmaceutics, non-invasive in silico modeling & simulation offer new opportunities for the paradigm shift of ocular formulation development. The current work first systematically reviews the theoretical underpinnings, advanced applications, and unique advantages of data-driven machine learning and multiscale simulation approaches represented by molecular simulation, mathematical modeling, and pharmacokinetic (PK)/pharmacodynamic (PD) modeling for ocular drug development. Following this, a new computer-driven framework for rational pharmaceutical formulation design is proposed, inspired by the potential of in silico explorations in understanding drug delivery details and facilitating drug formulation design. Lastly, to promote the paradigm shift, integrated in silico methodologies were highlighted, and discussions on data challenges, model practicality, personalized modeling, regulatory science, interdisciplinary collaboration, and talent training were conducted in detail with a view to achieving more efficient objective-oriented pharmaceutical formulation design.
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Affiliation(s)
- Nannan Wang
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences (ICMS), University of Macau, Macau, China
| | - Yunsen Zhang
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences (ICMS), University of Macau, Macau, China
| | - Wei Wang
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences (ICMS), University of Macau, Macau, China
| | - Zhuyifan Ye
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences (ICMS), University of Macau, Macau, China
| | - Hongyu Chen
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences (ICMS), University of Macau, Macau, China; Faculty of Science and Technology (FST), University of Macau, Macau, China
| | - Guanghui Hu
- Faculty of Science and Technology (FST), University of Macau, Macau, China
| | - Defang Ouyang
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences (ICMS), University of Macau, Macau, China; Department of Public Health and Medicinal Administration, Faculty of Health Sciences (FHS), University of Macau, Macau, China.
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Huang K, Zhong P, Xu B. Discrimination on Potential Adulteration of Extra Virgin Olive Oils Consumed in China by Differential Scanning Calorimeter Combined with Dimensionality Reduction Classification Techniques. Food Chem 2022; 405:134996. [DOI: 10.1016/j.foodchem.2022.134996] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2022] [Revised: 11/13/2022] [Accepted: 11/15/2022] [Indexed: 11/21/2022]
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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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Mak KK, Balijepalli MK, Pichika MR. Success stories of AI in drug discovery - where do things stand? Expert Opin Drug Discov 2021; 17:79-92. [PMID: 34553659 DOI: 10.1080/17460441.2022.1985108] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
INTRODUCTION Artificial intelligence (AI) in drug discovery and development (DDD) has gained more traction in the past few years. Many scientific reviews have already been made available in this area. Thus, in this review, the authors have focused on the success stories of AI-driven drug candidates and the scientometric analysis of the literature in this field. AREA COVERED The authors explore the literature to compile the success stories of AI-driven drug candidates that are currently being assessed in clinical trials or have investigational new drug (IND) status. The authors also provide the reader with their expert perspectives for future developments and their opinions on the field. EXPERT OPINION Partnerships between AI companies and the pharma industry are booming. The early signs of the impact of AI on DDD are encouraging, and the pharma industry is hoping for breakthroughs. AI can be a promising technology to unveil the greatest successes, but it has yet to be proven as AI is still at the embryonic stage.
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Affiliation(s)
- Kit-Kay Mak
- School of Postgraduate Studies and Research, International Medical University, Bukit Jalil, Malaysia.,Department of Pharmaceutical Chemistry, School of Pharmacy, International Medical University, Bukit Jalil, Malaysia.,Centre for Bioactive Molecules and Drug Delivery, Institute for Research, Development, and Innovation (Irdi), International Medical University, Bukit Jalil, Malaysia
| | | | - Mallikarjuna Rao Pichika
- Department of Pharmaceutical Chemistry, School of Pharmacy, International Medical University, Bukit Jalil, Malaysia.,Centre for Bioactive Molecules and Drug Delivery, Institute for Research, Development, and Innovation (Irdi), International Medical University, Bukit Jalil, Malaysia
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Wang W, Ye Z, Gao H, Ouyang D. Computational pharmaceutics - A new paradigm of drug delivery. J Control Release 2021; 338:119-136. [PMID: 34418520 DOI: 10.1016/j.jconrel.2021.08.030] [Citation(s) in RCA: 56] [Impact Index Per Article: 18.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2021] [Revised: 08/17/2021] [Accepted: 08/17/2021] [Indexed: 01/18/2023]
Abstract
In recent decades pharmaceutics and drug delivery have become increasingly critical in the pharmaceutical industry due to longer time, higher cost, and less productivity of new molecular entities (NMEs). However, current formulation development still relies on traditional trial-and-error experiments, which are time-consuming, costly, and unpredictable. With the exponential growth of computing capability and algorithms, in recent ten years, a new discipline named "computational pharmaceutics" integrates with big data, artificial intelligence, and multi-scale modeling techniques into pharmaceutics, which offered great potential to shift the paradigm of drug delivery. Computational pharmaceutics can provide multi-scale lenses to pharmaceutical scientists, revealing physical, chemical, mathematical, and data-driven details ranging across pre-formulation studies, formulation screening, in vivo prediction in the human body, and precision medicine in the clinic. The present paper provides a comprehensive and detailed review in all areas of computational pharmaceutics and "Pharma 4.0", including artificial intelligence and machine learning algorithms, molecular modeling, mathematical modeling, process simulation, and physiologically based pharmacokinetic (PBPK) modeling. We not only summarized the theories and progress of these technologies but also discussed the regulatory requirements, current challenges, and future perspectives in the area, such as talent training and a culture change in the future pharmaceutical industry.
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Affiliation(s)
- Wei Wang
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences (ICMS), University of Macau, Macau, China
| | - Zhuyifan Ye
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences (ICMS), University of Macau, Macau, China
| | - Hanlu Gao
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences (ICMS), University of Macau, Macau, China
| | - Defang Ouyang
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences (ICMS), University of Macau, Macau, China.
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