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Savekar PL, Nadaf SJ, Killedar SG, Kumbar VM, Hoskeri JH, Bhagwat DA, Gurav SS. Citric acid cross-linked pomegranate peel extract-loaded pH-responsive β-cyclodextrin/carboxymethyl tapioca starch hydrogel film for diabetic wound healing. Int J Biol Macromol 2024; 274:133366. [PMID: 38914385 DOI: 10.1016/j.ijbiomac.2024.133366] [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: 09/28/2023] [Revised: 06/11/2024] [Accepted: 06/21/2024] [Indexed: 06/26/2024]
Abstract
Pomegranate peel extract (PPE) hydrogel films filled with citric acid (CA) and β-cyclodextrin-carboxymethyl tapioca starch (CMS) were designed mainly to prevent wound infections and speed up the healing process. FTIR and NMR studies corroborated the carboxymethylation of neat tapioca starch (NS). CMS exhibited superior swelling behavior than NS. The amount of CA and β-CD controlled the physicochemical parameters of developed PPE/CA/β-CD/CMS films. Optimized film (OF) exhibited acceptable swellability, wound fluid absorptivity, water vapor transmission rate, water contact angle, and mechanical properties. Biodegradable, biocompatible, and antibacterial films exhibited pH dependence in the release of ellagic acid for up to 24 h. In mice model, PPE/CA/β-CD/CMS hydrogel film treatment showed promising wound healing effects, including increased collagen deposition, reduced inflammation, activation of the Wingless-related integration site (wnt) pathway leading to cell division, proliferation, and migration to the wound site. The expression of the WNT3A gene did not show any significant differences among all the studied groups. Developed PPE-loaded CA/β-CD/CMS film promoted wound healing by epithelialization, granulation tissue thickness, collagen deposition, and angiogenesis, hence could be recommended as a biodegradable and antibacterial hydrogel platform to improve the cell proliferation during the healing of diabetic wounds.
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Affiliation(s)
- Pranav L Savekar
- Shivraj College of Pharmacy, Gadhinglaj 416502, Maharashtra, India
| | - Sameer J Nadaf
- Bharati Vidyapeeth College of Pharmacy, Palus 416310, Maharashtra, India.
| | - Suresh G Killedar
- Anandi Pharmacy College, Kalambe Tarf Kale 416205, Maharashtra, India
| | - Vijay M Kumbar
- Dr. Prabhakar Kore Basic Science Research Centre, KLE Academy of Higher Education (KLE University), Nehru Nagar, Belagavi 590 010, Karnataka, India
| | - Joy H Hoskeri
- Department of Bioinformatics and Biotechnology, Karnataka State Akkamahadevi Women's University, Vijayapura, Karnataka, India
| | | | - Shailendra S Gurav
- Department of Pharmacognosy, Goa College of Pharmacy, Goa University, Goa 403001, India.
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2
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Hang NT, Long NT, Duy ND, Chien NN, Van Phuong N. Towards safer and efficient formulations: Machine learning approaches to predict drug-excipient compatibility. Int J Pharm 2024; 653:123884. [PMID: 38341049 DOI: 10.1016/j.ijpharm.2024.123884] [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: 09/06/2023] [Revised: 01/28/2024] [Accepted: 02/03/2024] [Indexed: 02/12/2024]
Abstract
Predicting drug-excipient compatibility is a critical aspect of pharmaceutical formulation design. In this study, we introduced an innovative approach that leverages machine learning techniques to improve the accuracy of drug-excipient compatibility predictions. Mol2vec and 2D molecular descriptors combined with the stacking technique were used to improve the performance of the model. This approach achieved a significant advancement in the predictive capacity as demonstrated by the accuracy, precision, recall, AUC, and MCC of 0.98, 0.87, 0.88, 0.93 and 0.86, respectively. Using the DE-INTERACT model as the benchmark, our stacking model could remarkably detect drug-excipient incompatibility in 10/12 tested cases, while DE-INTERACT managed to recognize only 3 out of 12 incompatibility cases in the validation experiments. To ensure user accessibility, the trained model was deployed to a user-friendly web platform (URL: https://decompatibility.streamlit.app/). This interactive interface accommodated inputs through various types, including names, PubChem CID, or SMILES strings. It promptly generated compatibility predictions alongside corresponding probability scores. However, the continual refinement of model performance is crucial before applying this model in practice.
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Affiliation(s)
- Nguyen Thu Hang
- Department of Pharmacognosy, Hanoi University of Pharmacy, Hanoi, Viet Nam
| | - Nguyen Thanh Long
- Department of Pharmacognosy, Hanoi University of Pharmacy, Hanoi, Viet Nam
| | - Nguyen Dang Duy
- Department of Pharmacognosy, Hanoi University of Pharmacy, Hanoi, Viet Nam
| | - Nguyen Ngoc Chien
- National Institute of Pharmaceutical Technology, Hanoi University of Pharmacy, Hanoi, Viet Nam
| | - Nguyen Van Phuong
- Department of Pharmacognosy, Hanoi University of Pharmacy, Hanoi, Viet Nam.
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3
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Bose A, Valdivia-Berroeta GA, Gonnella NC. Predicting Autoxidation of Sulfides in Drug-like Molecules Using Quantum Mechanical/Density Functional Theory Methods. J Chem Inf Model 2024; 64:128-137. [PMID: 38127785 DOI: 10.1021/acs.jcim.3c01158] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2023]
Abstract
Autoxidation of drugs and drug-like molecules is a major concern in the development of safe and effective therapeutics. Because active pharmaceutical ingredients (APIs) that contain sulfur atoms can form sulfoxides under oxidative stress, predicting oxidative susceptibilities within an organic molecule can have a major impact in accelerating the compound's stability assessment. For investigation of a sulfur atom's oxidative stability, density functional theory (DFT) methods were applied to accurately predict S-O estimated bond dissociation enthalpies (BDEs) of sulfoxides. Our process employed B3LYP/6-31+G(d) for geometry optimization and frequency calculation, and we employed B3P86/6-311++G(2df,2p) to obtain electronic energies from single-point energy calculations. A total of 84 drug-like molecules containing 50 different sulfide scaffolds were used to develop a risk scale. Our results showed that when S-O BDE is less than 69 kcal/mol, the sulfur atom has low oxidative susceptibility. High oxidation risk occurs when the S-O BDE is greater than 75 kcal/mol. The risk scale was successful in predicting the relative propensities of sulfide oxidation among the small organic molecules and commercial drugs examined.
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Affiliation(s)
- Arnab Bose
- Material and Analytical Sciences, Boehringer Ingelheim Pharmaceuticals, Inc., Ridgefield, Connecticut 06877, United States
| | - Gabriel A Valdivia-Berroeta
- Material and Analytical Sciences, Boehringer Ingelheim Pharmaceuticals, Inc., Ridgefield, Connecticut 06877, United States
| | - Nina C Gonnella
- Material and Analytical Sciences, Boehringer Ingelheim Pharmaceuticals, Inc., Ridgefield, Connecticut 06877, United States
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4
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Kumar A, Gupta GD, Raikwar S. Artificial Intelligence Technologies used for the Assessment of Pharmaceutical Excipients. Curr Pharm Des 2024; 30:407-409. [PMID: 38288798 DOI: 10.2174/0113816128285827240119095013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Accepted: 01/05/2024] [Indexed: 05/16/2024]
Affiliation(s)
- Ashutosh Kumar
- Department of Pharmaceutics, ISF College of Pharmacy, Moga, Punjab 142001, India
| | - Ghanshyam Das Gupta
- Department of Pharmaceutics, ISF College of Pharmacy, Moga, Punjab 142001, India
| | - Sarjana Raikwar
- Department of Pharmaceutics, ISF College of Pharmacy, Moga, Punjab 142001, India
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5
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Vora LK, Gholap AD, Jetha K, Thakur RRS, Solanki HK, Chavda VP. Artificial Intelligence in Pharmaceutical Technology and Drug Delivery Design. Pharmaceutics 2023; 15:1916. [PMID: 37514102 PMCID: PMC10385763 DOI: 10.3390/pharmaceutics15071916] [Citation(s) in RCA: 50] [Impact Index Per Article: 50.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2023] [Revised: 06/28/2023] [Accepted: 07/04/2023] [Indexed: 07/30/2023] Open
Abstract
Artificial intelligence (AI) has emerged as a powerful tool that harnesses anthropomorphic knowledge and provides expedited solutions to complex challenges. Remarkable advancements in AI technology and machine learning present a transformative opportunity in the drug discovery, formulation, and testing of pharmaceutical dosage forms. By utilizing AI algorithms that analyze extensive biological data, including genomics and proteomics, researchers can identify disease-associated targets and predict their interactions with potential drug candidates. This enables a more efficient and targeted approach to drug discovery, thereby increasing the likelihood of successful drug approvals. Furthermore, AI can contribute to reducing development costs by optimizing research and development processes. Machine learning algorithms assist in experimental design and can predict the pharmacokinetics and toxicity of drug candidates. This capability enables the prioritization and optimization of lead compounds, reducing the need for extensive and costly animal testing. Personalized medicine approaches can be facilitated through AI algorithms that analyze real-world patient data, leading to more effective treatment outcomes and improved patient adherence. This comprehensive review explores the wide-ranging applications of AI in drug discovery, drug delivery dosage form designs, process optimization, testing, and pharmacokinetics/pharmacodynamics (PK/PD) studies. This review provides an overview of various AI-based approaches utilized in pharmaceutical technology, highlighting their benefits and drawbacks. Nevertheless, the continued investment in and exploration of AI in the pharmaceutical industry offer exciting prospects for enhancing drug development processes and patient care.
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Affiliation(s)
- Lalitkumar K Vora
- School of Pharmacy, Queen's University Belfast, 97 Lisburn Road, Belfast BT9 7BL, UK
| | - Amol D Gholap
- Department of Pharmaceutics, St. John Institute of Pharmacy and Research, Palghar 401404, Maharashtra, India
| | - Keshava Jetha
- Department of Pharmaceutics and Pharmaceutical Technology, L. M. College of Pharmacy, Ahmedabad 380009, Gujarat, India
- Ph.D. Section, Gujarat Technological University, Ahmedabad 382424, Gujarat, India
| | | | - Hetvi K Solanki
- Pharmacy Section, L. M. College of Pharmacy, Ahmedabad 380009, Gujarat, India
| | - Vivek P Chavda
- Department of Pharmaceutics and Pharmaceutical Technology, L. M. College of Pharmacy, Ahmedabad 380009, Gujarat, India
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6
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Chen L, Wang Y. Interdisciplinary advances reshape the delivery tools for effective NASH treatment. Mol Metab 2023; 73:101730. [PMID: 37142161 DOI: 10.1016/j.molmet.2023.101730] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Revised: 04/10/2023] [Accepted: 04/20/2023] [Indexed: 05/06/2023] Open
Abstract
BACKGROUND Nonalcoholic steatohepatitis (NASH), a severe systemic and inflammatory subtype of nonalcoholic fatty liver disease, eventually develops into cirrhosis and hepatocellular carcinoma with few options for effective treatment. Currently potent small molecules identified in preclinical studies are confronted with adverse effects and long-term ineffectiveness in clinical trials. Nevertheless, highly specific delivery tools designed from interdisciplinary concepts may address the significant challenges by either effectively increasing the concentrations of drugs in target cell types, or selectively manipulating the gene expression in liver to resolve NASH. SCOPE OF REVIEW We focus on dissecting the detailed principles of the latest interdisciplinary advances and concepts that direct the design of future delivery tools to enhance the efficacy. Recent advances have indicated that cell and organelle-specific vehicles, non-coding RNA research (e.g. saRNA, hybrid miRNA) improve the specificity, while small extracellular vesicles and coacervates increase the cellular uptake of therapeutics. Moreover, strategies based on interdisciplinary advances drastically elevate drug loading capacity and delivery efficiency and ameliorate NASH and other liver diseases. MAJOR CONCLUSIONS The latest concepts and advances in chemistry, biochemistry and machine learning technology provide the framework and strategies for the design of more effective tools to treat NASH, other pivotal liver diseases and metabolic disorders.
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Affiliation(s)
- Linshan Chen
- School of Kinesiology, Shanghai University of Sport, Shanghai 200438, China
| | - Yibing Wang
- School of Kinesiology, Shanghai University of Sport, Shanghai 200438, China; Shanghai Frontiers Science Research Base of Exercise and Metabolic Health.
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7
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Tang J, Yang Z, Zhang Y, Huang R, Yu C, Yu C. Preparation of PEGylated nedaplatin liposomes with sustained release behavior for enhancing the antitumor efficacy of non-small cell lung cancer. Int J Pharm 2023; 635:122708. [PMID: 36764415 DOI: 10.1016/j.ijpharm.2023.122708] [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: 10/21/2022] [Revised: 01/15/2023] [Accepted: 02/04/2023] [Indexed: 02/11/2023]
Abstract
Nedaplatin (NDP) plays an important role in the chemotherapies of non-small cell lung cancer (NSCLC). However, dose-limiting toxicities such as myelosuppression and drug resistance restrict its clinical application. Herein, we intended to overcome these defects by developing a PEGylated liposomal formulation encapsulated NDP (NDP-LPs). For the first time, we found the incompatibility between NDP and natural phospholipids such as egg phosphatidylcholine (EPC) using the high-performance liquid chromatography (HPLC) method. The orthogonal experimental design was applied to optimize the conditions for preparing NDP-LPs, with encapsulation efficiency (EE) as the evaluation indicator. The physicochemical properties of optimized NDP-LPs were further characterized, including particle size, zeta potential, EE, drug release profiles, and so on. Results showed that a significantly sustained-release profile of NDP-LPs was observed and the releasing time of NDP could reach as long as 8 days. At the cellular level, NDP encapsulated in the PEGylated liposomes enhanced its cellular uptake and possessed potent cytotoxic activity. After intravenous injection, NDP-LPs could accumulate at tumor sites and effectivelyinhibit tumor growth of mice without obvious adverse effects. In conclusion, our results demonstrated that PEGylated liposomes could serve as a promising carrier to enhance the therapeutic effects of NDP.
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Affiliation(s)
- Jinsong Tang
- Research Center of Pharmaceutical Preparations and Nanomedicine, College of Pharmacy, Chongqing Medical University, Chongqing, 400016, China
| | - Zhangyou Yang
- Pharmaceutical Engineering Research Center, College of Pharmacy, Chongqing Medical University, Chongqing 400016, China
| | - Yuan Zhang
- Department of Pharmacy, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400010, China
| | - Ruixue Huang
- Research Center of Pharmaceutical Preparations and Nanomedicine, College of Pharmacy, Chongqing Medical University, Chongqing, 400016, China
| | - Chaoqun Yu
- Research Center of Pharmaceutical Preparations and Nanomedicine, College of Pharmacy, Chongqing Medical University, Chongqing, 400016, China.
| | - Chao Yu
- Research Center of Pharmaceutical Preparations and Nanomedicine, College of Pharmacy, Chongqing Medical University, Chongqing, 400016, China; Pharmaceutical Engineering Research Center, College of Pharmacy, Chongqing Medical University, Chongqing 400016, China.
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8
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Influence of Short Uptake Moisture on Drug-Excipient Binary Mixture: DSC and FTIR Contribution. CHEMISTRY AFRICA 2023. [DOI: 10.1007/s42250-022-00519-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/08/2023]
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9
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Jiang J, Lu A, Ma X, Ouyang D, Williams RO. The applications of machine learning to predict the forming of chemically stable amorphous solid dispersions prepared by hot-melt extrusion. Int J Pharm X 2023; 5:100164. [PMID: 36798832 PMCID: PMC9925947 DOI: 10.1016/j.ijpx.2023.100164] [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: 01/16/2023] [Accepted: 01/17/2023] [Indexed: 01/24/2023] Open
Abstract
Amorphous solid dispersion (ASD) is one of the most important strategies to improve the solubility and dissolution rate of poorly water-soluble drugs. As a widely used technique to prepare ASDs, hot-melt extrusion (HME) provides various benefits, including a solvent-free process, continuous manufacturing, and efficient mixing compared to solvent-based methods, such as spray drying. Energy input, consisting of thermal and specific mechanical energy, should be carefully controlled during the HME process to prevent chemical degradation and residual crystallinity. However, a conventional ASD development process uses a trial-and-error approach, which is laborious and time-consuming. In this study, we have successfully built multiple machine learning (ML) models to predict the amorphization of crystalline drug formulations and the chemical stability of subsequent ASDs prepared by the HME process. We utilized 760 formulations containing 49 active pharmaceutical ingredients (APIs) and multiple types of excipients. By evaluating the built ML models, we found that ECFP-LightGBM was the best model to predict amorphization with an accuracy of 92.8%. Furthermore, ECFP-XGBoost was the best in estimating chemical stability with an accuracy of 96.0%. In addition, the feature importance analyses based on SHapley Additive exPlanations (SHAP) and information gain (IG) revealed that several processing parameters and material attributes (i.e., drug loading, polymer ratio, drug's Extended-connectivity fingerprints (ECFP) fingerprints, and polymer's properties) are critical for achieving accurate predictions for the selected models. Moreover, important API's substructures related to amorphization and chemical stability were determined, and the results are largely consistent with the literature. In conclusion, we established the ML models to predict formation of chemically stable ASDs and identify the critical attributes during HME processing. Importantly, the developed ML methodology has the potential to facilitate the product development of ASDs manufactured by HME with a much reduced human workload.
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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
| | - Anqi Lu
- 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, NY 10282, USA
| | - Defang Ouyang
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences (ICMS), University of Macau, 999078, Macau
| | - Robert O. Williams
- Division of Molecular Pharmaceutics and Drug Delivery, College of Pharmacy, The University of Texas at Austin, Austin, TX 78712, USA,Corresponding author.
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10
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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: 11] [Impact Index Per Article: 5.5] [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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Accelerating pre-formulation investigations in early drug product life cycles using predictive methodologies and computational algorithms. Ther Deliv 2021; 12:789-797. [PMID: 34792419 DOI: 10.4155/tde-2021-0043] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
Precisely developed computational methodologies can allow the drug product lifecycle process to be time-efficient, cost-effective and reliable through a thorough fundamental understanding at the molecular level. Computational methodologies include computational simulations, virtual screening, mathematical modeling and predictive tools. In light of current trends and increased expectations of product discovery in early pharmaceutical development, we have discussed different case studies. These case studies clearly demonstrate the successful application of predictive tools alone or in combination with analytical techniques to predict the physicochemical properties of drug substances and drug products, thereby shortening research and development timelines. The overall goal of this report is to summarize unique predictive methodologies, which can assist pharmaceutical scientists in achieving time-sensitive research goals and avoiding associated risks that can potentially affect the drug product quality.
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12
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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: 60] [Impact Index Per Article: 20.0] [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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