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Zhu Y, Peng J, Xu C, Lan Z. Unsupervised Machine Learning in the Analysis of Nonadiabatic Molecular Dynamics Simulation. J Phys Chem Lett 2024; 15:9601-9619. [PMID: 39270134 DOI: 10.1021/acs.jpclett.4c01751] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/15/2024]
Abstract
The all-atomic full-dimensional-level simulations of nonadiabatic molecular dynamics (NAMD) in large realistic systems has received high research interest in recent years. However, such NAMD simulations normally generate an enormous amount of time-dependent high-dimensional data, leading to a significant challenge in result analyses. Based on unsupervised machine learning (ML) methods, considerable efforts were devoted to developing novel and easy-to-use analysis tools for the identification of photoinduced reaction channels and the comprehensive understanding of complicated molecular motions in NAMD simulations. Here, we tried to survey recent advances in this field, particularly to focus on how to use unsupervised ML methods to analyze the trajectory-based NAMD simulation results. Our purpose is to offer a comprehensive discussion on several essential components of this analysis protocol, including the selection of ML methods, the construction of molecular descriptors, the establishment of analytical frameworks, their advantages and limitations, and persistent challenges.
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Affiliation(s)
- Yifei Zhu
- MOE Key Laboratory of Environmental Theoretical Chemistry, SCNU Environmental Research Institute, Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety, School of Environment, South China Normal University, Guangzhou 510006, P. R. China
| | - Jiawei Peng
- MOE Key Laboratory of Environmental Theoretical Chemistry, SCNU Environmental Research Institute, Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety, School of Environment, South China Normal University, Guangzhou 510006, P. R. China
| | - Chao Xu
- MOE Key Laboratory of Environmental Theoretical Chemistry, SCNU Environmental Research Institute, Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety, School of Environment, South China Normal University, Guangzhou 510006, P. R. China
| | - Zhenggang Lan
- MOE Key Laboratory of Environmental Theoretical Chemistry, SCNU Environmental Research Institute, Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety, School of Environment, South China Normal University, Guangzhou 510006, P. R. China
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2
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Zhao J, Hermans E, Sepassi K, Tistaert C, Bergström CAS, Ahmad M, Larsson P. Effect of Data Quality and Data Quantity on the Estimation of Intrinsic Solubility: Analysis Based on a Single-Source Data Set. Mol Pharm 2024. [PMID: 39267585 DOI: 10.1021/acs.molpharmaceut.4c00685] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/17/2024]
Abstract
Aqueous solubility is one of the most important physicochemical properties of drug molecules and a major driving force for oral drug absorption. To date, the performance of in silico models for the estimation of solubility for novel chemical space is limited. To investigate possible reasons and remedies for this, the Johnson and Johnson in-house aqueous solubility data with over 40,000 compounds was leveraged. All data were generated through the same high-throughput assay, providing a unique opportunity to explore the relationship between data quality, quantity, and model estimations. Six intrinsic solubility data sets with different sizes and noise levels were generated by making use of three different approaches: (i) inclusion or exclusion of amorphous solid residue, (ii) measured or experimental log D to identify the intrinsic solubility, and (iii) adopting or omitting a quality check process in the data processing workflow. A random forest regressor was trained on the data sets with three different sets of descriptors calculated from RDKit, ADMET predictor, or Mordred, and the performances were evaluated with nested cross-validation as well as ten refined test sets. The models confirm, as expected, that with the same data set size, high-quality data leads to better model performance; however, also, models trained with larger data sets containing analytical variability can give equally accurate estimations compared to models trained with small, clean, and diverse data sets. However, noise introduced by including the presence of amorphous solid postsolubility measurement in the training data set cannot be overcome by increasing data size, as they are introducing a biased systematic positive error in the data set, confirming the importance of critical data review. Finally, two top-performing models were tested on the first test set from the second solubility challenge, achieving RMSE values of 0.74 and 0.72 and log S ± 0.5 of 46 and 48%, respectively. These results demonstrated improved performance compared to those reported in the findings of the competition, highlighting that a single-source curated data set can enhance the prediction of intrinsic solubility.
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Affiliation(s)
- Jiaxi Zhao
- Department of Pharmacy, Uppsala University, 751 23 Uppsala, Sweden
| | - Eline Hermans
- Pharmaceutical & Material Sciences, Janssen Pharmaceutica NV, B-2340 Beerse, Belgium
| | - Kia Sepassi
- Discovery Pharmaceutics, Janssen Research & Development, LLC, La Jolla, California 92121, United States
| | - Christophe Tistaert
- Pharmaceutical & Material Sciences, Janssen Pharmaceutica NV, B-2340 Beerse, Belgium
| | | | - Mazen Ahmad
- In Silico Discovery, Janssen Pharmaceutica NV, B-2340 Beerse, Belgium
| | - Per Larsson
- Department of Pharmacy, Uppsala University, 751 23 Uppsala, Sweden
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3
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Alamoudi JA. Recent advancements toward the incremsent of drug solubility using environmentally-friendly supercritical CO 2: a machine learning perspective. Front Med (Lausanne) 2024; 11:1467289. [PMID: 39286644 PMCID: PMC11402729 DOI: 10.3389/fmed.2024.1467289] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2024] [Accepted: 08/20/2024] [Indexed: 09/19/2024] Open
Abstract
Inadequate bioavailability of therapeutic drugs, which is often the consequence of their unacceptable solubility and dissolution rates, is an indisputable operational challenge of pharmaceutical companies due to its detrimental effect on the therapeutic efficacy. Over the recent decades, application of supercritical fluids (SCFs) (mainly SCCO2) has attracted the attentions of many scientists as promising alternative of toxic and environmentally-hazardous organic solvents due to possessing positive advantages like low flammability, availability, high performance, eco-friendliness and safety/simplicity of operation. Nowadays, application of different machine learning (ML) as a versatile, robust and accurate approach for the prediction of different momentous parameters like solubility and bioavailability has been of great attentions due to the non-affordability and time-wasting nature of experimental investigations. The prominent goal of this article is to review the role of different ML-based tools for the prediction of solubility/bioavailability of drugs using SCCO2. Moreover, the importance of solubility factor in the pharmaceutical industry and different possible techniques for increasing the amount of this parameter in poorly-soluble drugs are comprehensively discussed. At the end, the efficiency of SCCO2 for improving the manufacturing process of drug nanocrystals is aimed to be discussed.
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Affiliation(s)
- Jawaher Abdullah Alamoudi
- Department of Pharmaceutical Sciences, College of Pharmacy, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia
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4
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Bannigan P, Hickman RJ, Aspuru-Guzik A, Allen C. The Dawn of a New Pharmaceutical Epoch: Can AI and Robotics Reshape Drug Formulation? Adv Healthc Mater 2024:e2401312. [PMID: 39155417 DOI: 10.1002/adhm.202401312] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2024] [Revised: 07/21/2024] [Indexed: 08/20/2024]
Abstract
Over the last four decades, pharmaceutical companies' expenditures on research and development have increased 51-fold. During this same time, clinical success rates for new drugs have remained unchanged at about 10 percent, predominantly due to lack of efficacy and/or safety concerns. This persistent problem underscores the need to innovate across the entire drug development process, particularly in drug formulation, which is often deprioritized and under-resourced.
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Affiliation(s)
- Pauric Bannigan
- Intrepid Labs Inc., MaRS Centre, West Tower, 661 University Avenue Suite 1300, Toronto, ON, M5G 0B7, Canada
| | - Riley J Hickman
- Intrepid Labs Inc., MaRS Centre, West Tower, 661 University Avenue Suite 1300, Toronto, ON, M5G 0B7, Canada
| | - Alán Aspuru-Guzik
- Intrepid Labs Inc., MaRS Centre, West Tower, 661 University Avenue Suite 1300, Toronto, ON, M5G 0B7, Canada
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, ON, M5S 3E5, Canada
- Acceleration Consortium, University of Toronto, Toronto, ON, M5S 3H6, Canada
- Department of Chemistry, University of Toronto, Toronto, ON, M5S 3H6, Canada
| | - Christine Allen
- Intrepid Labs Inc., MaRS Centre, West Tower, 661 University Avenue Suite 1300, Toronto, ON, M5G 0B7, Canada
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, ON, M5S 3E5, Canada
- Acceleration Consortium, University of Toronto, Toronto, ON, M5S 3H6, Canada
- Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto, ON, M5S 3M2, Canada
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5
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Mi K, Chou WC, Chen Q, Yuan L, Kamineni VN, Kuchimanchi Y, He C, Monteiro-Riviere NA, Riviere JE, Lin Z. Predicting tissue distribution and tumor delivery of nanoparticles in mice using machine learning models. J Control Release 2024; 374:219-229. [PMID: 39146980 DOI: 10.1016/j.jconrel.2024.08.015] [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: 06/14/2024] [Revised: 07/24/2024] [Accepted: 08/11/2024] [Indexed: 08/17/2024]
Abstract
Nanoparticles (NPs) can be designed for targeted delivery in cancer nanomedicine, but the challenge is a low delivery efficiency (DE) to the tumor site. Understanding the impact of NPs' physicochemical properties on target tissue distribution and tumor DE can help improve the design of nanomedicines. Multiple machine learning and artificial intelligence models, including linear regression, support vector machine, random forest, gradient boosting, and deep neural networks (DNN), were trained and validated to predict tissue distribution and tumor delivery based on NPs' physicochemical properties and tumor therapeutic strategies with the dataset from Nano-Tumor Database. Compared to other machine learning models, the DNN model had superior predictions of DE to tumors and major tissues. The determination coefficients (R2) for the test datasets were 0.41, 0.42, 0.45, 0.79, 0.87, and 0.83 for DE in tumor, heart, liver, spleen, lung, and kidney, respectively. All the R2 and root mean squared error (RMSE) results of the test datasets were similar to the 5-fold cross validation results. Feature importance analysis showed that the core material of NPs played an important role in output predictions among all physicochemical properties. Furthermore, multiple NP formulations with greater DE to the tumor were determined by the DNN model. To facilitate model applications, the final model was converted to a web dashboard. This model could serve as a high-throughput pre-screening tool to support the design of new and efficient nanomedicines with greater tumor DE and serve as an alternative tool to reduce, refine, and partially replace animal experimentation in cancer nanomedicine research.
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Affiliation(s)
- Kun Mi
- Department of Environmental and Global Health, College of Public Health and Health Professions, University of Florida, Gainesville, FL 32608, USA; Center for Environmental and Human Toxicology, University of Florida, Gainesville, FL 32610, USA
| | - Wei-Chun Chou
- Department of Environmental and Global Health, College of Public Health and Health Professions, University of Florida, Gainesville, FL 32608, USA; Center for Environmental and Human Toxicology, University of Florida, Gainesville, FL 32610, USA; Department of Environmental Sciences, College of Natural & Agricultural Sciences, University of California, Riverside, CA 92521, USA
| | - Qiran Chen
- Department of Environmental and Global Health, College of Public Health and Health Professions, University of Florida, Gainesville, FL 32608, USA; Center for Environmental and Human Toxicology, University of Florida, Gainesville, FL 32610, USA
| | - Long Yuan
- Department of Environmental and Global Health, College of Public Health and Health Professions, University of Florida, Gainesville, FL 32608, USA; Center for Environmental and Human Toxicology, University of Florida, Gainesville, FL 32610, USA
| | - Venkata N Kamineni
- Department of Environmental and Global Health, College of Public Health and Health Professions, University of Florida, Gainesville, FL 32608, USA; Center for Environmental and Human Toxicology, University of Florida, Gainesville, FL 32610, USA
| | - Yashas Kuchimanchi
- Department of Environmental and Global Health, College of Public Health and Health Professions, University of Florida, Gainesville, FL 32608, USA; Center for Environmental and Human Toxicology, University of Florida, Gainesville, FL 32610, USA
| | - Chunla He
- Department of Environmental and Global Health, College of Public Health and Health Professions, University of Florida, Gainesville, FL 32608, USA; Department of Biostatistics, College of Public Health and Health Professions, University of Florida, Gainesville, FL 32608, USA
| | - Nancy A Monteiro-Riviere
- Nanotechnology Innovation Center of Kansas State, Kansas State University, Manhattan, KS 66506, USA; Center for Chemical Toxicology Research and Pharmacokinetics, North Carolina State University, Raleigh, NC 27606, USA
| | - Jim E Riviere
- Center for Chemical Toxicology Research and Pharmacokinetics, North Carolina State University, Raleigh, NC 27606, USA; 1Data Consortium, Kansas State University, Olathe, KS 66061, USA
| | - Zhoumeng Lin
- Department of Environmental and Global Health, College of Public Health and Health Professions, University of Florida, Gainesville, FL 32608, USA; Center for Environmental and Human Toxicology, University of Florida, Gainesville, FL 32610, USA.
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6
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Suriyaamporn P, Pamornpathomkul B, Patrojanasophon P, Ngawhirunpat T, Rojanarata T, Opanasopit P. The Artificial Intelligence-Powered New Era in Pharmaceutical Research and Development: A Review. AAPS PharmSciTech 2024; 25:188. [PMID: 39147952 DOI: 10.1208/s12249-024-02901-y] [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: 04/28/2024] [Accepted: 07/22/2024] [Indexed: 08/17/2024] Open
Abstract
Currently, artificial intelligence (AI), machine learning (ML), and deep learning (DL) are gaining increased interest in many fields, particularly in pharmaceutical research and development, where they assist in decision-making in complex situations. Numerous research studies and advancements have demonstrated how these computational technologies are used in various pharmaceutical research and development aspects, including drug discovery, personalized medicine, drug formulation, optimization, predictions, drug interactions, pharmacokinetics/ pharmacodynamics, quality control/quality assurance, and manufacturing processes. Using advanced modeling techniques, these computational technologies can enhance efficiency and accuracy, handle complex data, and facilitate novel discoveries within minutes. Furthermore, these technologies offer several advantages over conventional statistics. They allow for pattern recognition from complex datasets, and the models, typically developed from data-driven algorithms, can predict a given outcome (model output) from a set of features (model inputs). Additionally, this review discusses emerging trends and provides perspectives on the application of AI with quality by design (QbD) and the future role of AI in this field. Ethical and regulatory considerations associated with integrating AI into pharmaceutical technology were also examined. This review aims to offer insights to researchers, professionals, and others on the current state of AI applications in pharmaceutical research and development and their potential role in the future of research and the era of pharmaceutical Industry 4.0 and 5.0.
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Affiliation(s)
- Phuvamin Suriyaamporn
- Pharmaceutical Development of Green Innovations Group (PDGIG), Department of Industrial Pharmacy, Faculty of Pharmacy, Silpakorn University, Nakhon Pathom, Thailand
| | - Boonnada Pamornpathomkul
- Pharmaceutical Development of Green Innovations Group (PDGIG), Department of Industrial Pharmacy, Faculty of Pharmacy, Silpakorn University, Nakhon Pathom, Thailand
| | - Prasopchai Patrojanasophon
- Pharmaceutical Development of Green Innovations Group (PDGIG), Department of Industrial Pharmacy, Faculty of Pharmacy, Silpakorn University, Nakhon Pathom, Thailand
| | - Tanasait Ngawhirunpat
- Pharmaceutical Development of Green Innovations Group (PDGIG), Department of Industrial Pharmacy, Faculty of Pharmacy, Silpakorn University, Nakhon Pathom, Thailand
| | - Theerasak Rojanarata
- Pharmaceutical Development of Green Innovations Group (PDGIG), Department of Industrial Pharmacy, Faculty of Pharmacy, Silpakorn University, Nakhon Pathom, Thailand
| | - Praneet Opanasopit
- Pharmaceutical Development of Green Innovations Group (PDGIG), Department of Industrial Pharmacy, Faculty of Pharmacy, Silpakorn University, Nakhon Pathom, Thailand.
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7
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Snow O, Kazemi A, Bhanshali F, Nasiri A, Rozek A, Ester M. Identifying Synergistic Components of Botanical Fungicide Formulations Using Interpretable Graph Neural Networks. J Chem Inf Model 2024; 64:5786-5795. [PMID: 39031079 DOI: 10.1021/acs.jcim.4c00128] [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: 07/22/2024]
Abstract
Botanical formulations are promising candidates for developing new biopesticides that can protect crops from pests and diseases while reducing harm to the environment. These biopesticides can be combined with permeation enhancer compounds to boost their efficacy against pests and fungal diseases. However, finding synergistic combinations of these compounds is challenging due to the large and complex chemical space. In this paper, we propose a novel deep learning method that can predict the synergy of botanical products and permeation enhancers based on in vitro assay data. Our method uses a weighted combination of component feature vectors to represent the input mixtures, which enables the model to handle a variable number of components and to interpret the contribution of each component to the synergy. We also employ an ensemble of interpretation methods to provide insights into the underlying mechanisms of synergy. We validate our method by testing the predicted synergistic combinations in wet-lab experiments and show that our method can discover novel and effective biopesticides that would otherwise be difficult to find. Our method is generalizable and applicable to other domains, where predicting mixtures of chemical compounds is important.
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Affiliation(s)
- Oliver Snow
- Terramera, Vancouver, British Columbia V5Y 1K3, Canada
| | - Amirreza Kazemi
- Simon Fraser University, Burnaby, British Columbia V5A 1S6, Canada
| | | | - Alyas Nasiri
- Simon Fraser University, Burnaby, British Columbia V5A 1S6, Canada
| | - Annett Rozek
- Terramera, Vancouver, British Columbia V5Y 1K3, Canada
| | - Martin Ester
- Simon Fraser University, Burnaby, British Columbia V5A 1S6, Canada
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8
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Chen Z, Li N, Zhang P, Li Y, Li X. CardioDPi: An explainable deep-learning model for identifying cardiotoxic chemicals targeting hERG, Cav1.2, and Nav1.5 channels. JOURNAL OF HAZARDOUS MATERIALS 2024; 474:134724. [PMID: 38805819 DOI: 10.1016/j.jhazmat.2024.134724] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/18/2024] [Revised: 05/08/2024] [Accepted: 05/22/2024] [Indexed: 05/30/2024]
Abstract
The cardiotoxic effects of various pollutants have been a growing concern in environmental and material science. These effects encompass arrhythmias, myocardial injury, cardiac insufficiency, and pericardial inflammation. Compounds such as organic solvents and air pollutants disrupt the potassium, sodium, and calcium ion channels cardiac cell membranes, leading to the dysregulation of cardiac function. However, current cardiotoxicity models have disadvantages of incomplete data, ion channels, interpretability issues, and inability of toxic structure visualization. Herein, an interpretable deep-learning model known as CardioDPi was developed, which is capable of discriminating cardiotoxicity induced by the human Ether-à-go-go-related gene (hERG) channel, sodium channel (Na_v1.5), and calcium channel (Ca_v1.5) blockade. External validation yielded promising area under the ROC curve (AUC) values of 0.89, 0.89, and 0.94 for the hERG, Na_v1.5, and Ca_v1.5 channels, respectively. The CardioDPi can be freely accessed on the web server CardioDPipredictor (http://cardiodpi.sapredictor.cn/). Furthermore, the structural characteristics of cardiotoxic compounds were analyzed and structural alerts (SAs) can be extracted using the user-friendly CardioDPi-SAdetector web service (http://cardiosa.sapredictor.cn/). CardioDPi is a valuable tool for identifying cardiotoxic chemicals that are environmental and health risks. Moreover, the SA system provides essential insights for mode-of-action studies concerning cardiotoxic compounds.
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Affiliation(s)
- Zhaoyang Chen
- Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Jinan 250014, China
| | - Na Li
- Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Jinan 250014, China
| | - Pei Zhang
- Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Jinan 250014, China
| | - Yan Li
- Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Jinan 250014, China
| | - Xiao Li
- Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Jinan 250014, China.
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9
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Gholap AD, Uddin MJ, Faiyazuddin M, Omri A, Gowri S, Khalid M. Advances in artificial intelligence for drug delivery and development: A comprehensive review. Comput Biol Med 2024; 178:108702. [PMID: 38878397 DOI: 10.1016/j.compbiomed.2024.108702] [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: 01/03/2024] [Revised: 05/12/2024] [Accepted: 06/01/2024] [Indexed: 07/24/2024]
Abstract
Artificial intelligence (AI) has emerged as a powerful tool to revolutionize the healthcare sector, including drug delivery and development. This review explores the current and future applications of AI in the pharmaceutical industry, focusing on drug delivery and development. It covers various aspects such as smart drug delivery networks, sensors, drug repurposing, statistical modeling, and simulation of biotechnological and biological systems. The integration of AI with nanotechnologies and nanomedicines is also examined. AI offers significant advancements in drug discovery by efficiently identifying compounds, validating drug targets, streamlining drug structures, and prioritizing response templates. Techniques like data mining, multitask learning, and high-throughput screening contribute to better drug discovery and development innovations. The review discusses AI applications in drug formulation and delivery, clinical trials, drug safety, and pharmacovigilance. It addresses regulatory considerations and challenges associated with AI in pharmaceuticals, including privacy, data security, and interpretability of AI models. The review concludes with future perspectives, highlighting emerging trends, addressing limitations and biases in AI models, and emphasizing the importance of collaboration and knowledge sharing. It provides a comprehensive overview of AI's potential to transform the pharmaceutical industry and improve patient care while identifying further research and development areas.
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Affiliation(s)
- Amol D Gholap
- Department of Pharmaceutics, St. John Institute of Pharmacy and Research, Palghar, Maharashtra, 401404, India.
| | - Md Jasim Uddin
- Department of Pharmaceutical Technology, Faculty of Pharmacy, Universiti Malaya, 50603, Kuala Lumpur, Malaysia.
| | - Md Faiyazuddin
- School of Pharmacy, Al-Karim University, Katihar, Bihar, 854106, India; Centre for Global Health Research, Saveetha Institute of Medical and Technical Sciences, Tamil Nadu, India.
| | - Abdelwahab Omri
- Department of Chemistry and Biochemistry, The Novel Drug and Vaccine Delivery Systems Facility, Laurentian University, Sudbury, ON, P3E 2C6, Canada.
| | - S Gowri
- PG & Research, Department of Physics, Cauvery College for Women, Tiruchirapalli, Tamil Nadu, 620018, India
| | - Mohammad Khalid
- James Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK; Sunway Centre for Electrochemical Energy and Sustainable Technology (SCEEST), School of Engineering and Technology, Sunway University, No. 5, Jalan Universiti, Bandar Sunway, 47500 Selangor Darul Ehsan, Malaysia; University Centre for Research and Development, Chandigarh University, Mohali, Punjab, 140413, India.
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10
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Sheth TS, Acharya F. Optimization and evaluation of modified release solid dosage forms using artificial neural network. Sci Rep 2024; 14:16358. [PMID: 39014107 PMCID: PMC11252257 DOI: 10.1038/s41598-024-67274-5] [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: 01/25/2024] [Accepted: 07/09/2024] [Indexed: 07/18/2024] Open
Abstract
This study aims to optimize and evaluate drug release kinetics of Modified-Release (MR) solid dosage form of Quetiapine Fumarate MR tablets by using the Artificial Neural Networks (ANNs). In training the neural network, the drug contents of Quetiapine Fumarate MR tablet such as Sodium Citrate, Eudragit® L100 55, Eudragit® L30 D55, Lactose Monohydrate, Dicalcium Phosphate (DCP), and Glyceryl Behenate were used as variable input data and Drug Substance Quetiapine Fumarate, Triethyl Citrate, and Magnesium Stearate were used as constant input data for the formulation of the tablet. The in-vitro dissolution profiles of Quetiapine Fumarate MR tablets at ten different time points were used as a target data. Several layers together build the neural network by connecting the input data with the output data via weights, these weights show importance of input nodes. The training process optimises the weights of the drug product excipients to achieve the desired drug release through the simulation process in MATLAB software. The percentage drug release of predicted formulation matched with the manufactured formulation using the similarity factor (f2), which evaluates network efficiency. The ANNs have enormous potential for rapidly optimizing pharmaceutical formulations with desirable performance characteristics.
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Affiliation(s)
- Tulsi Sagar Sheth
- Department of Applied Sciences and Humanities, Parul Institute of Engineering and Technology, Parul University, Vadodara, Gujarat, 391760, India
- Parul Institute of Applied Sciences, Parul University, Vadodara, Gujarat, 391760, India
| | - Falguni Acharya
- Department of Applied Sciences and Humanities, Parul Institute of Engineering and Technology, Parul University, Vadodara, Gujarat, 391760, India.
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11
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Abbas MKG, Rassam A, Karamshahi F, Abunora R, Abouseada M. The Role of AI in Drug Discovery. Chembiochem 2024; 25:e202300816. [PMID: 38735845 DOI: 10.1002/cbic.202300816] [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: 12/03/2023] [Revised: 05/09/2024] [Accepted: 05/10/2024] [Indexed: 05/14/2024]
Abstract
The emergence of Artificial Intelligence (AI) in drug discovery marks a pivotal shift in pharmaceutical research, blending sophisticated computational techniques with conventional scientific exploration to break through enduring obstacles. This review paper elucidates the multifaceted applications of AI across various stages of drug development, highlighting significant advancements and methodologies. It delves into AI's instrumental role in drug design, polypharmacology, chemical synthesis, drug repurposing, and the prediction of drug properties such as toxicity, bioactivity, and physicochemical characteristics. Despite AI's promising advancements, the paper also addresses the challenges and limitations encountered in the field, including data quality, generalizability, computational demands, and ethical considerations. By offering a comprehensive overview of AI's role in drug discovery, this paper underscores the technology's potential to significantly enhance drug development, while also acknowledging the hurdles that must be overcome to fully realize its benefits.
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Affiliation(s)
- M K G Abbas
- Center for Advanced Materials, Qatar University, P.O. Box, 2713, Doha, Qatar
| | - Abrar Rassam
- Secondary Education, Educational Sciences, Qatar University, P.O. Box, 2713, Doha, Qatar
| | - Fatima Karamshahi
- Department of Chemistry and Earth Sciences, Qatar University, P.O. Box, 2713, Doha, Qatar
| | - Rehab Abunora
- Faculty of Medicine, General Medicine and Surgery, Helwan University, Cairo, Egypt
| | - Maha Abouseada
- Department of Chemistry and Earth Sciences, Qatar University, P.O. Box, 2713, Doha, Qatar
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12
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Branco F, Cunha J, Mendes M, Vitorino C, Sousa JJ. Peptide-Hitchhiking for the Development of Nanosystems in Glioblastoma. ACS NANO 2024; 18:16359-16394. [PMID: 38861272 PMCID: PMC11223498 DOI: 10.1021/acsnano.4c01790] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/05/2024] [Revised: 05/15/2024] [Accepted: 05/23/2024] [Indexed: 06/12/2024]
Abstract
Glioblastoma (GBM) remains the epitome of aggressiveness and lethality in the spectrum of brain tumors, primarily due to the blood-brain barrier (BBB) that hinders effective treatment delivery, tumor heterogeneity, and the presence of treatment-resistant stem cells that contribute to tumor recurrence. Nanoparticles (NPs) have been used to overcome these obstacles by attaching targeting ligands to enhance therapeutic efficacy. Among these ligands, peptides stand out due to their ease of synthesis and high selectivity. This article aims to review single and multiligand strategies critically. In addition, it highlights other strategies that integrate the effects of external stimuli, biomimetic approaches, and chemical approaches as nanocatalytic medicine, revealing their significant potential in treating GBM with peptide-functionalized NPs. Alternative routes of parenteral administration, specifically nose-to-brain delivery and local treatment within the resected tumor cavity, are also discussed. Finally, an overview of the significant obstacles and potential strategies to overcome them are discussed to provide a perspective on this promising field of GBM therapy.
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Affiliation(s)
- Francisco Branco
- Faculty
of Pharmacy, University of Coimbra, Pólo das Ciências
da Saúde, Azinhaga de Santa Comba, 3000-548 Coimbra, Portugal
| | - Joana Cunha
- Faculty
of Pharmacy, University of Coimbra, Pólo das Ciências
da Saúde, Azinhaga de Santa Comba, 3000-548 Coimbra, Portugal
| | - Maria Mendes
- Faculty
of Pharmacy, University of Coimbra, Pólo das Ciências
da Saúde, Azinhaga de Santa Comba, 3000-548 Coimbra, Portugal
- Coimbra
Chemistry Centre, Institute of Molecular Sciences − IMS, Faculty
of Sciences and Technology, University of
Coimbra, 3004-535 Coimbra, Portugal
| | - Carla Vitorino
- Faculty
of Pharmacy, University of Coimbra, Pólo das Ciências
da Saúde, Azinhaga de Santa Comba, 3000-548 Coimbra, Portugal
- Coimbra
Chemistry Centre, Institute of Molecular Sciences − IMS, Faculty
of Sciences and Technology, University of
Coimbra, 3004-535 Coimbra, Portugal
| | - João J. Sousa
- Faculty
of Pharmacy, University of Coimbra, Pólo das Ciências
da Saúde, Azinhaga de Santa Comba, 3000-548 Coimbra, Portugal
- Coimbra
Chemistry Centre, Institute of Molecular Sciences − IMS, Faculty
of Sciences and Technology, University of
Coimbra, 3004-535 Coimbra, Portugal
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13
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Pätzmann N, O'Dwyer PJ, Beránek J, Kuentz M, Griffin BT. Predictive computational models for assessing the impact of co-milling on drug dissolution. Eur J Pharm Sci 2024; 198:106780. [PMID: 38697312 DOI: 10.1016/j.ejps.2024.106780] [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: 02/10/2024] [Revised: 04/12/2024] [Accepted: 04/27/2024] [Indexed: 05/04/2024]
Abstract
Co-milling is an effective technique for improving dissolution rate limited absorption characteristics of poorly water-soluble drugs. However, there is a scarcity of models available to forecast the magnitude of dissolution rate improvement caused by co-milling. Therefore, this study endeavoured to quantitatively predict the increase in dissolution by co-milling based on drug properties. Using a biorelevant dissolution setup, a series of 29 structurally diverse and crystalline drugs were screened in co-milled and physically blended mixtures with Polyvinylpyrrolidone K25. Co-Milling Dissolution Ratios after 15 min (COMDR15 min) and 60 min (COMDR60 min) drug release were predicted by variable selection in the framework of a partial least squares (PLS) regression. The model forecasts the COMDR15 min (R2 = 0.82 and Q2 = 0.77) and COMDR60 min (R2 = 0.87 and Q2 = 0.84) with small differences in root mean square errors of training and test sets by selecting four drug properties. Based on three of these selected variables, applicable multiple linear regression equations were developed with a high predictive power of R2 = 0.83 (COMDR15 min) and R2 = 0.84 (COMDR60 min). The most influential predictor variable was the median drug particle size before milling, followed by the calculated drug logD6.5 value, the calculated molecular descriptor Kappa 3 and the apparent solubility of drugs after 24 h dissolution. The study demonstrates the feasibility of forecasting the dissolution rate improvements of poorly water-solube drugs through co-milling. These models can be applied as computational tools to guide formulation in early stage development.
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Affiliation(s)
- Nicolas Pätzmann
- School of Pharmacy, University College Cork, Cork, Ireland; Department Preformulation and Biopharmacy, Zentiva, k.s., Prague, Czechia
| | | | - Josef Beránek
- Department Preformulation and Biopharmacy, Zentiva, k.s., Prague, Czechia
| | - Martin Kuentz
- Institute of Pharma Technology, University of Applied Sciences and Arts Northwestern Switzerland, Muttenz, Switzerland
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14
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Zhang Y, Watson S, Ramaswamy Y, Singh G. Intravitreal therapeutic nanoparticles for age-related macular degeneration: Design principles, progress and opportunities. Adv Colloid Interface Sci 2024; 329:103200. [PMID: 38788306 DOI: 10.1016/j.cis.2024.103200] [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: 10/24/2023] [Revised: 05/11/2024] [Accepted: 05/19/2024] [Indexed: 05/26/2024]
Abstract
Age-related macular degeneration (AMD) is a leading cause of vision loss in the elderly. The current standard treatment for AMD involves frequent intravitreal administrations of therapeutic agents. While effective, this approach presents challenges, including patient discomfort, inconvenience, and the risk of adverse complications. Nanoparticle-based intravitreal drug delivery platforms offer a promising solution to overcome these limitations. These platforms are engineered to target the retina specifically and control drug release, which enhances drug retention, improves drug concentration and bioavailability at the retinal site, and reduces the frequency of injections. This review aims to uncover the design principles guiding the development of highly effective nanoparticle-based intravitreal drug delivery platforms for AMD treatment. By gaining a deeper understanding of the physiology of ocular barriers and the physicochemical properties of nanoparticles, we establish a basis for designing intravitreal nanoparticles to optimize drug delivery and drug retention in the retina. Furthermore, we review recent nanoparticle-based intravitreal therapeutic strategies to highlight their potential in improving AMD treatment efficiency. Lastly, we address the challenges and opportunities in this field, providing insights into the future of nanoparticle-based drug delivery to improve therapeutic outcomes for AMD patients.
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Affiliation(s)
- Yuhang Zhang
- The School of Biomedical Engineering, Faculty of IT and Engineering, Sydney Nano Institute, The University of Sydney, Camperdown, NSW 2008, Australia
| | - Stephanie Watson
- Faculty of Medicine and Health, Clinical Ophthalmology and Eye Health, Save Sight Institute, The University of Sydney, Camperdown, NSW 2008, Australia
| | - Yogambha Ramaswamy
- The School of Biomedical Engineering, Faculty of IT and Engineering, Sydney Nano Institute, The University of Sydney, Camperdown, NSW 2008, Australia
| | - Gurvinder Singh
- The School of Biomedical Engineering, Faculty of IT and Engineering, Sydney Nano Institute, The University of Sydney, Camperdown, NSW 2008, Australia.
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15
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Bao Z, Yung F, Hickman RJ, Aspuru-Guzik A, Bannigan P, Allen C. Data-driven development of an oral lipid-based nanoparticle formulation of a hydrophobic drug. Drug Deliv Transl Res 2024; 14:1872-1887. [PMID: 38158474 DOI: 10.1007/s13346-023-01491-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/28/2023] [Indexed: 01/03/2024]
Abstract
Due to its cost-effectiveness, convenience, and high patient adherence, oral drug administration normally remains the preferred approach. Yet, the effective delivery of hydrophobic drugs via the oral route is often hindered by their limited water solubility and first-pass metabolism. To mitigate these challenges, advanced delivery systems such as solid lipid nanoparticles (SLNs) and nanostructured lipid carriers (NLCs) have been developed to encapsulate hydrophobic drugs and enhance their bioavailability. However, traditional design methodologies for these complex formulations often present intricate challenges because they are restricted to a relatively narrow design space. Here, we present a data-driven approach for the accelerated design of SLNs/NLCs encapsulating a model hydrophobic drug, cannabidiol, that combines experimental automation and machine learning. A small subset of formulations, comprising 10% of all formulations in the design space, was prepared in-house, leveraging miniaturized experimental automation to improve throughput and decrease the quantity of drug and materials required. Machine learning models were then trained on the data generated from these formulations and used to predict properties of all SLNs/NLCs within this design space (i.e., 1215 formulations). Notably, formulations predicted to be high-performers via this approach were confirmed to significantly enhance the solubility of the drug by up to 3000-fold and prevented degradation of drug. Moreover, the high-performance formulations significantly enhanced the oral bioavailability of the drug compared to both its free form and an over-the-counter version. Furthermore, this bioavailability matched that of a formulation equivalent in composition to the FDA-approved product, Epidiolex®.
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Affiliation(s)
- Zeqing Bao
- Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto, ON, M5S 3M2, Canada
| | - Fion Yung
- Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto, ON, M5S 3M2, Canada
| | - Riley J Hickman
- Department of Chemistry, University of Toronto, Toronto, ON, M5S 3H6, Canada
- Department of Computer Science, University of Toronto, Toronto, ON, M5S 2E4, Canada
- Vector Institute for Artificial Intelligence, Toronto, ON, M5S 1M1, Canada
| | - Alán Aspuru-Guzik
- Department of Chemistry, University of Toronto, Toronto, ON, M5S 3H6, Canada
- Department of Computer Science, University of Toronto, Toronto, ON, M5S 2E4, Canada
- Vector Institute for Artificial Intelligence, Toronto, ON, M5S 1M1, Canada
- Lebovic Fellow, Canadian Institute for Advanced Research (CIFAR), Toronto, ON, M5S 1M1, Canada
- Department of Chemical Engineering & Applied Chemistry, University of Toronto, Toronto, ON, M5S 3E5, Canada
- Department of Materials Science & Engineering, University of Toronto, Toronto, ON, M5S 3E4, Canada
- CIFAR Artificial Intelligence Research Chair, Vector Institute, Toronto, ON, M5S 1M1, Canada
- Acceleration Consortium, Toronto, ON, M5S 3H6, Canada
| | - Pauric Bannigan
- Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto, ON, M5S 3M2, Canada.
| | - Christine Allen
- Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto, ON, M5S 3M2, Canada.
- Department of Chemical Engineering & Applied Chemistry, University of Toronto, Toronto, ON, M5S 3E5, Canada.
- Acceleration Consortium, Toronto, ON, M5S 3H6, Canada.
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16
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Xie X, Xiao YF, Yang H, Peng X, Li JJ, Zhou YY, Fan CQ, Meng RP, Huang BB, Liao XP, Chen YY, Zhong TT, Lin H, Koulaouzidis A, Yang SM. A new artificial intelligence system for both stomach and small-bowel capsule endoscopy. Gastrointest Endosc 2024:S0016-5107(24)03259-0. [PMID: 38851456 DOI: 10.1016/j.gie.2024.06.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/11/2024] [Revised: 05/31/2024] [Accepted: 06/02/2024] [Indexed: 06/10/2024]
Abstract
BACKGROUND AND AIMS Despite the benefits of artificial intelligence in small-bowel (SB) capsule endoscopy (CE) image reading, information on its application in the stomach and SB CE is lacking. METHODS In this multicenter, retrospective diagnostic study, gastric imaging data were added to the deep learning-based SmartScan (SS), which has been described previously. A total of 1069 magnetically controlled GI CE examinations (comprising 2,672,542 gastric images) were used in the training phase for recognizing gastric pathologies, producing a new artificial intelligence algorithm named SS Plus. A total of 342 fully automated, magnetically controlled CE examinations were included in the validation phase. The performance of both senior and junior endoscopists with both the SS Plus-assisted reading (SSP-AR) and conventional reading (CR) modes was assessed. RESULTS SS Plus was designed to recognize 5 types of gastric lesions and 17 types of SB lesions. SS Plus reduced the number of CE images required for review to 873.90 (median, 1000; interquartile range [IQR], 814.50-1000) versus 44,322.73 (median, 42,393; IQR, 31,722.75-54,971.25) for CR. Furthermore, with SSP-AR, endoscopists took 9.54 minutes (median, 8.51; IQR, 6.05-13.13) to complete the CE video reading. In the 342 CE videos, SS Plus identified 411 gastric and 422 SB lesions, whereas 400 gastric and 368 intestinal lesions were detected with CR. Moreover, junior endoscopists remarkably improved their CE image reading ability with SSP-AR. CONCLUSIONS Our study shows that the newly upgraded deep learning-based algorithm SS Plus can detect GI lesions and help improve the diagnostic performance of junior endoscopists in interpreting CE videos.
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Affiliation(s)
- Xia Xie
- Department of Gastroenterology, The Second Affiliated Hospital, The Third Military Medical University, Chongqing, China
| | - Yu-Feng Xiao
- Department of Gastroenterology, The Second Affiliated Hospital, The Third Military Medical University, Chongqing, China
| | - Huan Yang
- Department of Gastroenterology, The Second Affiliated Hospital, The Third Military Medical University, Chongqing, China
| | - Xue Peng
- Department of Gastroenterology, The Second Affiliated Hospital, The Third Military Medical University, Chongqing, China
| | - Jian-Jun Li
- Department of Gastroenterology, The Second Affiliated Hospital, The Third Military Medical University, Chongqing, China
| | - Yuan-Yuan Zhou
- Department of Gastroenterology, The Second Affiliated Hospital, The Third Military Medical University, Chongqing, China
| | - Chao-Qiang Fan
- Department of Gastroenterology, The Second Affiliated Hospital, The Third Military Medical University, Chongqing, China
| | - Rui-Ping Meng
- Department of Gastroenterology, The Second Affiliated Hospital, The Third Military Medical University, Chongqing, China
| | - Bao-Bao Huang
- Department of Gastroenterology, The Second Affiliated Hospital, The Third Military Medical University, Chongqing, China
| | - Xi-Ping Liao
- Department of Gastroenterology, The Second Affiliated Hospital, The Third Military Medical University, Chongqing, China
| | - Yu-Yang Chen
- Department of Gastroenterology, The Second Affiliated Hospital, The Third Military Medical University, Chongqing, China
| | - Ting-Ting Zhong
- Department of Gastroenterology, The Second Affiliated Hospital, The Third Military Medical University, Chongqing, China
| | - Hui Lin
- Department of Gastroenterology, The Second Affiliated Hospital, The Third Military Medical University, Chongqing, China; Department of Epidemiology, the Third Military Medical University, Chongqing, China.
| | - Anastasios Koulaouzidis
- Department of Clinical Research University of Southern Denmark, Odense, Denmark; Centre for Clinical Implementation of Capsule Endoscopy, Store Adenomer Tidlige Cancere Centre, Svendborg, Denmark.
| | - Shi-Ming Yang
- Department of Gastroenterology, The Second Affiliated Hospital, The Third Military Medical University, Chongqing, China.
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17
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Anchordoquy T, Artzi N, Balyasnikova IV, Barenholz Y, La-Beck NM, Brenner JS, Chan WCW, Decuzzi P, Exner AA, Gabizon A, Godin B, Lai SK, Lammers T, Mitchell MJ, Moghimi SM, Muzykantov VR, Peer D, Nguyen J, Popovtzer R, Ricco M, Serkova NJ, Singh R, Schroeder A, Schwendeman AA, Straehla JP, Teesalu T, Tilden S, Simberg D. Mechanisms and Barriers in Nanomedicine: Progress in the Field and Future Directions. ACS NANO 2024; 18:13983-13999. [PMID: 38767983 PMCID: PMC11214758 DOI: 10.1021/acsnano.4c00182] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
In recent years, steady progress has been made in synthesizing and characterizing engineered nanoparticles, resulting in several approved drugs and multiple promising candidates in clinical trials. Regulatory agencies such as the Food and Drug Administration and the European Medicines Agency released important guidance documents facilitating nanoparticle-based drug product development, particularly in the context of liposomes and lipid-based carriers. Even with the progress achieved, it is clear that many barriers must still be overcome to accelerate translation into the clinic. At the recent conference workshop "Mechanisms and Barriers in Nanomedicine" in May 2023 in Colorado, U.S.A., leading experts discussed the formulation, physiological, immunological, regulatory, clinical, and educational barriers. This position paper invites open, unrestricted, nonproprietary discussion among senior faculty, young investigators, and students to trigger ideas and concepts to move the field forward.
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Affiliation(s)
- Thomas Anchordoquy
- Department of Pharmaceutical Sciences, The Skaggs School of Pharmacy and Pharmaceutical Sciences, the University of Colorado Anschutz Medical Campus, Aurora, Colorado 80045, United States
| | - Natalie Artzi
- Brigham and Woman's Hospital, Department of Medicine, Division of Engineering in Medicine, Harvard Medical School, Boston, Massachusetts 02215, United States
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, Massachusetts 02215, United States
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, Massachusetts 02215, United States
| | - Irina V Balyasnikova
- Department of Neurological Surgery, Feinberg School of Medicine, Northwestern University; Northwestern Medicine Malnati Brain Tumor Institute of the Lurie Comprehensive Cancer Center, Feinberg School of Medicine, Northwestern University, Chicago, Illinois 60611, United States
| | - Yechezkel Barenholz
- Membrane and Liposome Research Lab, IMRIC, Hebrew University Hadassah Medical School, Jerusalem 9112102, Israel
| | - Ninh M La-Beck
- Department of Immunotherapeutics and Biotechnology, Jerry H. Hodge School of Pharmacy, Texas Tech University Health Sciences Center, Abilene, Texas 79601, United States
| | - Jacob S Brenner
- Departments of Medicine and Pharmacology, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
| | - Warren C W Chan
- Institute of Biomedical Engineering, University of Toronto, Rosebrugh Building, 164 College Street, Toronto, Ontario M5S 3G9, Canada
- Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada
| | - Paolo Decuzzi
- Laboratory of Nanotechnology for Precision Medicine, Italian Institute of Technology, 16163 Genova, Italy
| | - Agata A Exner
- Departments of Radiology and Biomedical Engineering, Case Western Reserve University School of Medicine, Cleveland, Ohio 44106, United States
| | - Alberto Gabizon
- The Helmsley Cancer Center, Shaare Zedek Medical Center and The Hebrew University of Jerusalem-Faculty of Medicine, Jerusalem, 9103102, Israel
| | - Biana Godin
- Department of Nanomedicine, Houston Methodist Research Institute, Houston, Texas 77030, United States
- Department of Obstetrics and Gynecology, Houston Methodist Hospital, Houston, Texas 77030, United States
- Department of Obstetrics and Gynecology, Weill Cornell Medicine College (WCMC), New York, New York 10065, United States
- Department of Biomedical Engineering, Texas A&M, College Station, Texas 7784,3 United States
| | - Samuel K Lai
- Division of Pharmacoengineering and Molecular Pharmaceutics, Eshelman School of Pharmacy, University of North Carolina-Chapel Hill, Chapel Hill, North Carolina 27599, United States
| | - Twan Lammers
- Department of Nanomedicine and Theranostics, Institute for Experimental Molecular Imaging, Center for Biohybrid Medical Systems, University Hospital RWTH Aachen, 52074 Aachen, Germany
| | - Michael J Mitchell
- Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
- Penn Institute for RNA Innovation, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
| | - S Moein Moghimi
- School of Pharmacy, Newcastle University, Newcastle upon Tyne NE1 7RU, U.K
- Translational and Clinical Research Institute, Faculty of Health and Medical Sciences, Newcastle University, Newcastle upon Tyne NE2 4HH, U.K
- Colorado Center for Nanomedicine and Nanosafety, University of Colorado Anschutz Medical Center, Aurora, Colorado 80045, United States
| | - Vladimir R Muzykantov
- Department of Systems Pharmacology and Translational Therapeutics, The Perelman School of Medicine, The University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
| | - Dan Peer
- Laboratory of Precision Nanomedicine, Shmunis School of Biomedicine and Cancer Research, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv, 69978, Israel
- Department of Materials Sciences and Engineering, Iby and Aladar Fleischman Faculty of Engineering, Tel Aviv University, Tel Aviv, 69978, Israel
- Center for Nanoscience and Nanotechnology, Tel Aviv University, Tel Aviv, 69978, Israel
- Cancer Biology Research Center, Tel Aviv University, Tel Aviv, 69978, Israel
| | - Juliane Nguyen
- Division of Pharmacoengineering and Molecular Pharmaceutics, Eshelman School of Pharmacy, University of North Carolina-Chapel Hill, Chapel Hill, North Carolina 27599, United States
| | - Rachela Popovtzer
- Faculty of Engineering and the Institute of Nanotechnology & Advanced Materials, Bar-Ilan University, 5290002 Ramat Gan, Israel
| | - Madison Ricco
- Department of Pharmaceutical Sciences, The Skaggs School of Pharmacy and Pharmaceutical Sciences, the University of Colorado Anschutz Medical Campus, Aurora, Colorado 80045, United States
| | - Natalie J Serkova
- Department of Radiology, University of Colorado Cancer Center, Anschutz Medical Campus, Aurora, Colorado 80045, United States
| | - Ravi Singh
- Department of Cancer Biology, Wake Forest University School of Medicine, Winston-Salem, North Carolina 27101, United States
- Atrium Health Wake Forest Baptist Comprehensive Cancer Center, Winston-Salem, North Carolina 27101, United States
| | - Avi Schroeder
- Department of Chemical Engineering, Technion, Israel Institute of Technology, Haifa 32000, Israel
| | - Anna A Schwendeman
- Department of Pharmaceutical Sciences, College of Pharmacy, University of Michigan, Ann Arbor, Michigan 48108; Biointerfaces Institute, University of Michigan, Ann Arbor, Michigan 48108, United States
| | - Joelle P Straehla
- Dana-Farber/Boston Children's Cancer and Blood Disorders Center, Boston, Massachusetts 02115 United States
- Koch Institute for Integrative Cancer Research at MIT, Cambridge Massachusetts 02139 United States
| | - Tambet Teesalu
- Laboratory of Precision and Nanomedicine, Institute of Biomedicine and Translational Medicine, University of Tartu, 50411 Tartu, Estonia
| | - Scott Tilden
- Department of Pharmaceutical Sciences, The Skaggs School of Pharmacy and Pharmaceutical Sciences, the University of Colorado Anschutz Medical Campus, Aurora, Colorado 80045, United States
| | - Dmitri Simberg
- Department of Pharmaceutical Sciences, The Skaggs School of Pharmacy and Pharmaceutical Sciences, the University of Colorado Anschutz Medical Campus, Aurora, Colorado 80045, United States
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18
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Zhou Y, Wang Z, Huang Z, Li W, Chen Y, Yu X, Tang Y, Liu G. In silico prediction of ocular toxicity of compounds using explainable machine learning and deep learning approaches. J Appl Toxicol 2024; 44:892-907. [PMID: 38329145 DOI: 10.1002/jat.4586] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Revised: 01/16/2024] [Accepted: 01/16/2024] [Indexed: 02/09/2024]
Abstract
The accurate identification of chemicals with ocular toxicity is of paramount importance in health hazard assessment. In contemporary chemical toxicology, there is a growing emphasis on refining, reducing, and replacing animal testing in safety evaluations. Therefore, the development of robust computational tools is crucial for regulatory applications. The performance of predictive models is heavily reliant on the quality and quantity of data. In this investigation, we amalgamated the most extensive dataset (4901 compounds) sourced from governmental GHS-compliant databases and literature to develop binary classification models of chemical ocular toxicity. We employed 12 molecular representations in conjunction with six machine learning algorithms and two deep learning algorithms to create a series of binary classification models. The findings indicated that the deep learning method GCN outperformed the machine learning models in cross-validation, achieving an impressive AUC of 0.915. However, the top-performing machine learning model (RF-Descriptor) demonstrated excellent performance with an AUC of 0.869 on the test set and was therefore selected as the best model. To enhance model interpretability, we conducted the SHAP method and attention weights analysis. The two approaches offered visual depictions of the relevance of key descriptors and substructures in predicting ocular toxicity of chemicals. Thus, we successfully struck a delicate balance between data quality and model interpretability, rendering our model valuable for predicting and comprehending potential ocular-toxic compounds in the early stages of drug discovery.
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Affiliation(s)
- Yiqing Zhou
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Ze Wang
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Zejun Huang
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Weihua Li
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Yuanting Chen
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Xinxin Yu
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Yun Tang
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Guixia Liu
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
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19
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Li X, Sun Y, Zhou Z, Li J, Liu S, Chen L, Shi Y, Wang M, Zhu Z, Wang G, Lu Q. Deep Learning-Driven Exploration of Pyrroloquinoline Quinone Neuroprotective Activity in Alzheimer's Disease. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2308970. [PMID: 38454653 PMCID: PMC11095145 DOI: 10.1002/advs.202308970] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Revised: 02/15/2024] [Indexed: 03/09/2024]
Abstract
Alzheimer's disease (AD) is a pressing concern in neurodegenerative research. To address the challenges in AD drug development, especially those targeting Aβ, this study uses deep learning and a pharmacological approach to elucidate the potential of pyrroloquinoline quinone (PQQ) as a neuroprotective agent for AD. Using deep learning for a comprehensive molecular dataset, blood-brain barrier (BBB) permeability is predicted and the anti-inflammatory and antioxidative properties of compounds are evaluated. PQQ, identified in the Mediterranean-DASH intervention for a diet that delays neurodegeneration, shows notable BBB permeability and low toxicity. In vivo tests conducted on an Aβ₁₋₄₂-induced AD mouse model verify the effectiveness of PQQ in reducing cognitive deficits. PQQ modulates genes vital for synapse and anti-neuronal death, reduces reactive oxygen species production, and influences the SIRT1 and CREB pathways, suggesting key molecular mechanisms underlying its neuroprotective effects. This study can serve as a basis for future studies on integrating deep learning with pharmacological research and drug discovery.
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Affiliation(s)
- Xinuo Li
- Jiangsu Provincial Key Laboratory of Drug Metabolism and PharmacokineticsState Key Laboratory of Natural MedicinesChina Pharmaceutical UniversityNanjing211166China
| | - Yuan Sun
- Jiangsu Provincial Key Laboratory of Drug Metabolism and PharmacokineticsState Key Laboratory of Natural MedicinesChina Pharmaceutical UniversityNanjing211166China
| | - Zheng Zhou
- Department of Computer ScienceRWTH Aachen University52074AachenGermany
| | - Jinran Li
- Jiangsu Provincial Key Laboratory of Drug Metabolism and PharmacokineticsState Key Laboratory of Natural MedicinesChina Pharmaceutical UniversityNanjing211166China
| | - Sai Liu
- Jiangsu Provincial Key Laboratory of Drug Metabolism and PharmacokineticsState Key Laboratory of Natural MedicinesChina Pharmaceutical UniversityNanjing211166China
| | - Long Chen
- Jiangsu Provincial Key Laboratory of Drug Metabolism and PharmacokineticsState Key Laboratory of Natural MedicinesChina Pharmaceutical UniversityNanjing211166China
| | - Yiting Shi
- Jiangsu Provincial Key Laboratory of Drug Metabolism and PharmacokineticsState Key Laboratory of Natural MedicinesChina Pharmaceutical UniversityNanjing211166China
| | - Min Wang
- Affiliated Brain Hospital of Nanjing Medical UniversityNanjing210029China
| | - Zheying Zhu
- School of PharmacyThe University of NottinghamNottinghamNG7 2RDUK
| | - Guangji Wang
- Jiangsu Provincial Key Laboratory of Drug Metabolism and PharmacokineticsState Key Laboratory of Natural MedicinesChina Pharmaceutical UniversityNanjing211166China
| | - Qiulun Lu
- Jiangsu Provincial Key Laboratory of Drug Metabolism and PharmacokineticsState Key Laboratory of Natural MedicinesChina Pharmaceutical UniversityNanjing211166China
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20
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Wan K, He J, Shi X. Construction of High Accuracy Machine Learning Interatomic Potential for Surface/Interface of Nanomaterials-A Review. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2305758. [PMID: 37640376 DOI: 10.1002/adma.202305758] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Revised: 08/24/2023] [Indexed: 08/31/2023]
Abstract
The inherent discontinuity and unique dimensional attributes of nanomaterial surfaces and interfaces bestow them with various exceptional properties. These properties, however, also introduce difficulties for both experimental and computational studies. The advent of machine learning interatomic potential (MLIP) addresses some of the limitations associated with empirical force fields, presenting a valuable avenue for accurate simulations of these surfaces/interfaces of nanomaterials. Central to this approach is the idea of capturing the relationship between system configuration and potential energy, leveraging the proficiency of machine learning (ML) to precisely approximate high-dimensional functions. This review offers an in-depth examination of MLIP principles and their execution and elaborates on their applications in the realm of nanomaterial surface and interface systems. The prevailing challenges faced by this potent methodology are also discussed.
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Affiliation(s)
- Kaiwei Wan
- Laboratory of Theoretical and Computational Nanoscience, National Center for Nanoscience and Technology, Chinese Academy of Sciences, Beijing, 100190, China
- University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing, 100049, China
| | - Jianxin He
- Laboratory of Theoretical and Computational Nanoscience, National Center for Nanoscience and Technology, Chinese Academy of Sciences, Beijing, 100190, China
- University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing, 100049, China
| | - Xinghua Shi
- Laboratory of Theoretical and Computational Nanoscience, National Center for Nanoscience and Technology, Chinese Academy of Sciences, Beijing, 100190, China
- University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing, 100049, China
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21
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Du Y, Song J, Lu L, Yeung E, Givand J, Procopio A, Su Y, Hu G. Design of a Reciprocal Injection Device for Stability Studies of Parenteral Biological Drug Products. J Pharm Sci 2024; 113:1330-1338. [PMID: 38113997 DOI: 10.1016/j.xphs.2023.12.014] [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: 10/01/2023] [Revised: 12/14/2023] [Accepted: 12/14/2023] [Indexed: 12/21/2023]
Abstract
Formulation screening, essential for assessing the impact of physical, chemical, and mechanical stresses on protein stability, plays a critical role in biologics drug product development. This research introduces a Reciprocal Injection Device (RID) designed to accelerate formulation screening by probing protein stability under intensified stress conditions within prefilled syringes. This versatile device is designed to accommodate a broad spectrum of injection parameters and diverse syringe dimensions. A commercial drug product was employed as a model monoclonal antibody formulation. Our findings effectively highlight the efficacy of the RID in assessing concentration-dependent protein stability. This device exhibits significant potential to amplify the influences of interfacial interactions, such as those with buffer salts, excipients, air, metals, and silicone oils, commonly found in combination drug products, and to evaluate the protein stability under varied stresses.
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Affiliation(s)
- Yong Du
- Analytical Research and Development, Merck & Co., Inc., Rahway, NJ 07065, United States
| | - Jing Song
- Analytical Research and Development, Merck & Co., Inc., Rahway, NJ 07065, United States
| | - Lynn Lu
- Pharmaceutical Sciences and Clinical Supply, Merck & Co., Inc., Rahway, NJ 07065, United States
| | - Edward Yeung
- Pharmaceutical Sciences and Clinical Supply, Merck & Co., Inc., Rahway, NJ 07065, United States
| | - Jeffrey Givand
- Pharmaceutical Sciences and Clinical Supply, Merck & Co., Inc., Rahway, NJ 07065, United States
| | - Adam Procopio
- Pharmaceutical Sciences and Clinical Supply, Merck & Co., Inc., Rahway, NJ 07065, United States
| | - Yongchao Su
- Pharmaceutical Sciences and Clinical Supply, Merck & Co., Inc., Rahway, NJ 07065, United States.
| | - Guangli Hu
- Pharmaceutical Sciences and Clinical Supply, Merck & Co., Inc., Rahway, NJ 07065, United States.
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22
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Zhou L, Ni C, Liao R, Tang X, Yi T, Ran M, Huang M, Liao R, Zhou X, Qin D, Wang L, Huang F, Xie X, Wan Y, Luo J, Wang Y, Wu J. Activating SRC/MAPK signaling via 5-HT1A receptor contributes to the effect of vilazodone on improving thrombocytopenia. eLife 2024; 13:RP94765. [PMID: 38573820 PMCID: PMC10994662 DOI: 10.7554/elife.94765] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/06/2024] Open
Abstract
Thrombocytopenia caused by long-term radiotherapy and chemotherapy exists in cancer treatment. Previous research demonstrates that 5-Hydroxtrayptamine (5-HT) and its receptors induce the formation of megakaryocytes (MKs) and platelets. However, the relationships between 5-HT1A receptor (5-HTR1A) and MKs is unclear so far. We screened and investigated the mechanism of vilazodone as a 5-HTR1A partial agonist in promoting MK differentiation and evaluated its therapeutic effect in thrombocytopenia. We employed a drug screening model based on machine learning (ML) to screen the megakaryocytopoiesis activity of Vilazodone (VLZ). The effects of VLZ on megakaryocytopoiesis were verified in HEL and Meg-01 cells. Tg (itga2b: eGFP) zebrafish was performed to analyze the alterations in thrombopoiesis. Moreover, we established a thrombocytopenia mice model to investigate how VLZ administration accelerates platelet recovery and function. We carried out network pharmacology, Western blot, and immunofluorescence to demonstrate the potential targets and pathway of VLZ. VLZ has been predicted to have a potential biological action. Meanwhile, VLZ administration promotes MK differentiation and thrombopoiesis in cells and zebrafish models. Progressive experiments showed that VLZ has a potential therapeutic effect on radiation-induced thrombocytopenia in vivo. The network pharmacology and associated mechanism study indicated that SRC and MAPK signaling are both involved in the processes of megakaryopoiesis facilitated by VLZ. Furthermore, the expression of 5-HTR1A during megakaryocyte differentiation is closely related to the activation of SRC and MAPK. Our findings demonstrated that the expression of 5-HTR1A on MK, VLZ could bind to the 5-HTR1A receptor and further regulate the SRC/MAPK signaling pathway to facilitate megakaryocyte differentiation and platelet production, which provides new insights into the alternative therapeutic options for thrombocytopenia.
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Affiliation(s)
- Ling Zhou
- Sichuan Key Medical Laboratory of New Drug Discovery and Druggability, Luzhou Key Laboratory of Activity Screening and Druggability Evaluation for Chinese Materia Medica, School of Pharmacy, Southwest Medical UniversityLuZhouChina
| | - Chengyang Ni
- Sichuan Key Medical Laboratory of New Drug Discovery and Druggability, Luzhou Key Laboratory of Activity Screening and Druggability Evaluation for Chinese Materia Medica, School of Pharmacy, Southwest Medical UniversityLuZhouChina
| | - Ruixue Liao
- Sichuan Key Medical Laboratory of New Drug Discovery and Druggability, Luzhou Key Laboratory of Activity Screening and Druggability Evaluation for Chinese Materia Medica, School of Pharmacy, Southwest Medical UniversityLuZhouChina
| | - Xiaoqin Tang
- Sichuan Key Medical Laboratory of New Drug Discovery and Druggability, Luzhou Key Laboratory of Activity Screening and Druggability Evaluation for Chinese Materia Medica, School of Pharmacy, Southwest Medical UniversityLuZhouChina
| | - Taian Yi
- School of Pharmacy, Chengdu University of Traditional Chinese MedicineChengduChina
| | - Mei Ran
- Sichuan Key Medical Laboratory of New Drug Discovery and Druggability, Luzhou Key Laboratory of Activity Screening and Druggability Evaluation for Chinese Materia Medica, School of Pharmacy, Southwest Medical UniversityLuZhouChina
- School of Basic Medical Sciences, Southwest Medical UniversityLuzhouChina
| | - Miao Huang
- School of Pharmacy, Chengdu University of Traditional Chinese MedicineChengduChina
| | - Rui Liao
- Sichuan Key Medical Laboratory of New Drug Discovery and Druggability, Luzhou Key Laboratory of Activity Screening and Druggability Evaluation for Chinese Materia Medica, School of Pharmacy, Southwest Medical UniversityLuZhouChina
| | - Xiaogang Zhou
- Sichuan Key Medical Laboratory of New Drug Discovery and Druggability, Luzhou Key Laboratory of Activity Screening and Druggability Evaluation for Chinese Materia Medica, School of Pharmacy, Southwest Medical UniversityLuZhouChina
| | - Dalian Qin
- Sichuan Key Medical Laboratory of New Drug Discovery and Druggability, Luzhou Key Laboratory of Activity Screening and Druggability Evaluation for Chinese Materia Medica, School of Pharmacy, Southwest Medical UniversityLuZhouChina
| | - Long Wang
- Sichuan Key Medical Laboratory of New Drug Discovery and Druggability, Luzhou Key Laboratory of Activity Screening and Druggability Evaluation for Chinese Materia Medica, School of Pharmacy, Southwest Medical UniversityLuZhouChina
| | - Feihong Huang
- Sichuan Key Medical Laboratory of New Drug Discovery and Druggability, Luzhou Key Laboratory of Activity Screening and Druggability Evaluation for Chinese Materia Medica, School of Pharmacy, Southwest Medical UniversityLuZhouChina
| | - Xiang Xie
- School of Basic Medical Sciences, Public Center of Experimental Technology, Model Animal and Human Disease Research of Luzhou Key Laboratory, Southwest Medical UniversityLuzhouChina
| | - Ying Wan
- School of Basic Medical Sciences, Southwest Medical UniversityLuzhouChina
| | - Jiesi Luo
- School of Basic Medical Sciences, Southwest Medical UniversityLuzhouChina
| | - Yiwei Wang
- School of Basic Medical Sciences, Southwest Medical UniversityLuzhouChina
| | - Jianming Wu
- Sichuan Key Medical Laboratory of New Drug Discovery and Druggability, Luzhou Key Laboratory of Activity Screening and Druggability Evaluation for Chinese Materia Medica, School of Pharmacy, Southwest Medical UniversityLuZhouChina
- School of Basic Medical Sciences, Southwest Medical UniversityLuzhouChina
- Education Ministry Key Laboratory of Medical Electrophysiology, Southwest Medical UniversityLuzhouChina
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23
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Agu PC, Obulose CN. Piquing artificial intelligence towards drug discovery: Tools, techniques, and applications. Drug Dev Res 2024; 85:e22159. [PMID: 38375772 DOI: 10.1002/ddr.22159] [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: 11/08/2023] [Revised: 01/12/2024] [Accepted: 01/29/2024] [Indexed: 02/21/2024]
Abstract
The purpose of this study was to discuss how artificial intelligence (AI) methods have affected the field of drug development. It looks at how AI models and data resources are reshaping the drug development process by offering more affordable and expedient options to conventional approaches. The paper opens with an overview of well-known information sources for drug development. The discussion then moves on to molecular representation techniques that make it possible to convert data into representations that computers can understand. The paper also gives a general overview of the algorithms used in the creation of drug discovery models based on AI. In particular, the paper looks at how AI algorithms might be used to forecast drug toxicity, drug bioactivity, and drug physicochemical properties. De novo drug design, binding affinity prediction, and other AI-based models for drug-target interaction were covered in deeper detail. Modern applications of AI in nanomedicine design and pharmacological synergism/antagonism prediction were also covered. The potential advantages of AI in drug development are highlighted as the evaluation comes to a close. It underlines how AI may greatly speed up and improve the efficiency of drug discovery, resulting in the creation of new and better medicines. To fully realize the promise of AI in drug discovery, the review acknowledges the difficulties that come with its uses in this field and advocates for more study and development.
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Affiliation(s)
- Peter Chinedu Agu
- Department of Biochemistry, College of Science, Evangel University, Akaeze, Ebonyi State, Nigeria
| | - Chidiebere Nwiboko Obulose
- Department of Computer Sciences, Our Savior Institute of Science, Agriculture, and Technology (OSISATECH Polytechnic), Enugu, Nigeria
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24
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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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25
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Liu Z, Lu T, Qian R, Wang Z, Qi R, Zhang Z. Exploiting Nanotechnology for Drug Delivery: Advancing the Anti-Cancer Effects of Autophagy-Modulating Compounds in Traditional Chinese Medicine. Int J Nanomedicine 2024; 19:2507-2528. [PMID: 38495752 PMCID: PMC10944250 DOI: 10.2147/ijn.s455407] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2023] [Accepted: 03/06/2024] [Indexed: 03/19/2024] Open
Abstract
Background Cancer continues to be a prominent issue in the field of medicine, as demonstrated by recent studies emphasizing the significant role of autophagy in the development of cancer. Traditional Chinese Medicine (TCM) provides a variety of anti-tumor agents capable of regulating autophagy. However, the clinical application of autophagy-modulating compounds derived from TCM is impeded by their restricted water solubility and bioavailability. To overcome this challenge, the utilization of nanotechnology has been suggested as a potential solution. Nonetheless, the current body of literature on nanoparticles delivering TCM-derived autophagy-modulating anti-tumor compounds for cancer treatment is limited, lacking comprehensive summaries and detailed descriptions. Methods Up to November 2023, a comprehensive research study was conducted to gather relevant data using a variety of databases, including PubMed, ScienceDirect, Springer Link, Web of Science, and CNKI. The keywords utilized in this investigation included "autophagy", "nanoparticles", "traditional Chinese medicine" and "anticancer". Results This review provides a comprehensive analysis of the potential of nanotechnology in overcoming delivery challenges and enhancing the anti-cancer properties of autophagy-modulating compounds in TCM. The evaluation is based on a synthesis of different classes of autophagy-modulating compounds in TCM, their mechanisms of action in cancer treatment, and their potential benefits as reported in various scholarly sources. The findings indicate that nanotechnology shows potential in enhancing the availability of autophagy-modulating agents in TCM, thereby opening up a plethora of potential therapeutic avenues. Conclusion Nanotechnology has the potential to enhance the anti-tumor efficacy of autophagy-modulating compounds in traditional TCM, through regulation of autophagy.
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Affiliation(s)
- Zixian Liu
- School of Medicine, Nanjing University of Chinese Medicine, Jiangsu, Nanjing, People’s Republic of China
| | - Tianming Lu
- School of Medicine, Nanjing University of Chinese Medicine, Jiangsu, Nanjing, People’s Republic of China
| | - Ruoning Qian
- School of Medicine, Nanjing University of Chinese Medicine, Jiangsu, Nanjing, People’s Republic of China
| | - Zian Wang
- School of Medicine, Nanjing University of Chinese Medicine, Jiangsu, Nanjing, People’s Republic of China
| | - Ruogu Qi
- School of Medicine, Nanjing University of Chinese Medicine, Jiangsu, Nanjing, People’s Republic of China
| | - Zhengguang Zhang
- School of Medicine, Nanjing University of Chinese Medicine, Jiangsu, Nanjing, People’s Republic of China
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26
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Duran T, Chaudhuri B. Where Might Artificial Intelligence Be Going in Pharmaceutical Development? Mol Pharm 2024; 21:993-995. [PMID: 38376360 DOI: 10.1021/acs.molpharmaceut.4c00112] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/21/2024]
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27
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He D, Liu Q, Mi Y, Meng Q, Xu L, Hou C, Wang J, Li N, Liu Y, Chai H, Yang Y, Liu J, Wang L, Hou Y. De Novo Generation and Identification of Novel Compounds with Drug Efficacy Based on Machine Learning. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2307245. [PMID: 38204214 PMCID: PMC10962488 DOI: 10.1002/advs.202307245] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Revised: 12/05/2023] [Indexed: 01/12/2024]
Abstract
One of the main challenges in small molecule drug discovery is finding novel chemical compounds with desirable activity. Traditional drug development typically begins with target selection, but the correlation between targets and disease remains to be further investigated, and drugs designed based on targets may not always have the desired drug efficacy. The emergence of machine learning provides a powerful tool to overcome the challenge. Herein, a machine learning-based strategy is developed for de novo generation of novel compounds with drug efficacy termed DTLS (Deep Transfer Learning-based Strategy) by using dataset of disease-direct-related activity as input. DTLS is applied in two kinds of disease: colorectal cancer (CRC) and Alzheimer's disease (AD). In each case, novel compound is discovered and identified in in vitro and in vivo disease models. Their mechanism of actionis further explored. The experimental results reveal that DTLS can not only realize the generation and identification of novel compounds with drug efficacy but also has the advantage of identifying compounds by focusing on protein targets to facilitate the mechanism study. This work highlights the significant impact of machine learning on the design of novel compounds with drug efficacy, which provides a powerful new approach to drug discovery.
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Affiliation(s)
- Dakuo He
- College of Information Science and EngineeringState Key Laboratory of Synthetical Automation for Process IndustriesNortheastern UniversityShenyang110819China
| | - Qing Liu
- College of Information Science and EngineeringState Key Laboratory of Synthetical Automation for Process IndustriesNortheastern UniversityShenyang110819China
| | - Yan Mi
- Key Laboratory of Bioresource Research and Development of Liaoning ProvinceCollege of Life and Health SciencesNational Frontiers Science Center for Industrial Intelligence and Systems OptimizationNortheastern UniversityShenyang110169China
- Key Laboratory of Data Analytics and Optimization for Smart IndustryMinistry of EducationNortheastern UniversityShenyang110169China
| | - Qingqi Meng
- Key Laboratory of Bioresource Research and Development of Liaoning ProvinceCollege of Life and Health SciencesNational Frontiers Science Center for Industrial Intelligence and Systems OptimizationNortheastern UniversityShenyang110169China
- Key Laboratory of Data Analytics and Optimization for Smart IndustryMinistry of EducationNortheastern UniversityShenyang110169China
| | - Libin Xu
- Key Laboratory of Bioresource Research and Development of Liaoning ProvinceCollege of Life and Health SciencesNational Frontiers Science Center for Industrial Intelligence and Systems OptimizationNortheastern UniversityShenyang110169China
- Key Laboratory of Data Analytics and Optimization for Smart IndustryMinistry of EducationNortheastern UniversityShenyang110169China
| | - Chunyu Hou
- College of Information Science and EngineeringState Key Laboratory of Synthetical Automation for Process IndustriesNortheastern UniversityShenyang110819China
| | - Jinpeng Wang
- College of Information Science and EngineeringState Key Laboratory of Synthetical Automation for Process IndustriesNortheastern UniversityShenyang110819China
| | - Ning Li
- School of Traditional Chinese Materia MedicaKey Laboratory for TCM Material Basis Study and Innovative Drug Development of Shenyang CityShenyang Pharmaceutical UniversityShenyang110016China
| | - Yang Liu
- Key Laboratory of Structure‐Based Drug Design & Discovery of Ministry of EducationShenyang Pharmaceutical UniversityShenyang110016China
| | - Huifang Chai
- School of PharmacyGuizhou University of Traditional Chinese MedicineGuiyang550025China
| | - Yanqiu Yang
- Key Laboratory of Bioresource Research and Development of Liaoning ProvinceCollege of Life and Health SciencesNational Frontiers Science Center for Industrial Intelligence and Systems OptimizationNortheastern UniversityShenyang110169China
- Key Laboratory of Data Analytics and Optimization for Smart IndustryMinistry of EducationNortheastern UniversityShenyang110169China
| | - Jingyu Liu
- Key Laboratory of Bioresource Research and Development of Liaoning ProvinceCollege of Life and Health SciencesNational Frontiers Science Center for Industrial Intelligence and Systems OptimizationNortheastern UniversityShenyang110169China
- Key Laboratory of Data Analytics and Optimization for Smart IndustryMinistry of EducationNortheastern UniversityShenyang110169China
| | - Lihui Wang
- Department of PharmacologyShenyang Pharmaceutical UniversityShenyang110016China
| | - Yue Hou
- Key Laboratory of Bioresource Research and Development of Liaoning ProvinceCollege of Life and Health SciencesNational Frontiers Science Center for Industrial Intelligence and Systems OptimizationNortheastern UniversityShenyang110169China
- Key Laboratory of Data Analytics and Optimization for Smart IndustryMinistry of EducationNortheastern UniversityShenyang110169China
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28
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Lu A, Williams RO, Maniruzzaman M. 3D printing of biologics-what has been accomplished to date? Drug Discov Today 2024; 29:103823. [PMID: 37949427 DOI: 10.1016/j.drudis.2023.103823] [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: 08/18/2023] [Revised: 10/27/2023] [Accepted: 11/06/2023] [Indexed: 11/12/2023]
Abstract
Three-dimensional (3D) printing is a promising approach for the stabilization and delivery of non-living biologics. This versatile tool builds complex structures and customized resolutions, and has significant potential in various industries, especially pharmaceutics and biopharmaceutics. Biologics have become increasingly prevalent in the field of medicine due to their diverse applications and benefits. Stability is the main attribute that must be achieved during the development of biologic formulations. 3D printing could help to stabilize biologics by entrapment, support binding, or crosslinking. Furthermore, gene fragments could be transited into cells during co-printing, when the pores on the membrane are enlarged. This review provides: (i) an introduction to 3D printing technologies and biologics, covering genetic elements, therapeutic proteins, antibodies, and bacteriophages; (ii) an overview of the applications of 3D printing of biologics, including regenerative medicine, gene therapy, and personalized treatments; (iii) information on how 3D printing could help to stabilize and deliver biologics; and (iv) discussion on regulations, challenges, and future directions, including microneedle vaccines, novel 3D printing technologies and artificial-intelligence-facilitated research and product development. Overall, the 3D printing of biologics holds great promise for enhancing human health by providing extended longevity and enhanced quality of life, making it an exciting area in the rapidly evolving field of biomedicine.
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Affiliation(s)
- Anqi Lu
- Division of Molecular Pharmaceutics and Drug Delivery, College of Pharmacy, The University of Texas at Austin, Austin, TX 78712, USA
| | - Robert O Williams
- Division of Molecular Pharmaceutics and Drug Delivery, College of Pharmacy, The University of Texas at Austin, Austin, TX 78712, USA
| | - Mohammed Maniruzzaman
- Division of Molecular Pharmaceutics and Drug Delivery, College of Pharmacy, The University of Texas at Austin, Austin, TX 78712, USA; Pharmaceutical Engineering and 3D Printing (PharmE3D) Lab, Department of Pharmaceutics and Drug Delivery, School of Pharmacy, The University of Mississippi, University, MS 38677, USA.
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29
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Ortiz-Perez A, Zhang M, Fitzpatrick LW, Izquierdo-Lozano C, Albertazzi L. Advanced optical imaging for the rational design of nanomedicines. Adv Drug Deliv Rev 2024; 204:115138. [PMID: 37980951 DOI: 10.1016/j.addr.2023.115138] [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: 06/09/2023] [Revised: 11/06/2023] [Accepted: 11/08/2023] [Indexed: 11/21/2023]
Abstract
Despite the enormous potential of nanomedicines to shape the future of medicine, their clinical translation remains suboptimal. Translational challenges are present in every step of the development pipeline, from a lack of understanding of patient heterogeneity to insufficient insights on nanoparticle properties and their impact on material-cell interactions. Here, we discuss how the adoption of advanced optical microscopy techniques, such as super-resolution optical microscopies, correlative techniques, and high-content modalities, could aid the rational design of nanocarriers, by characterizing the cell, the nanomaterial, and their interaction with unprecedented spatial and/or temporal detail. In this nanomedicine arena, we will discuss how the implementation of these techniques, with their versatility and specificity, can yield high volumes of multi-parametric data; and how machine learning can aid the rapid advances in microscopy: from image acquisition to data interpretation.
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Affiliation(s)
- Ana Ortiz-Perez
- Department of Biomedical Engineering, Institute of Complex Molecular Systems, Eindhoven University of Technology, Eindhoven, the Netherlands
| | - Miao Zhang
- Department of Biomedical Engineering, Institute of Complex Molecular Systems, Eindhoven University of Technology, Eindhoven, the Netherlands
| | - Laurence W Fitzpatrick
- Department of Biomedical Engineering, Institute of Complex Molecular Systems, Eindhoven University of Technology, Eindhoven, the Netherlands
| | - Cristina Izquierdo-Lozano
- Department of Biomedical Engineering, Institute of Complex Molecular Systems, Eindhoven University of Technology, Eindhoven, the Netherlands
| | - Lorenzo Albertazzi
- Department of Biomedical Engineering, Institute of Complex Molecular Systems, Eindhoven University of Technology, Eindhoven, the Netherlands.
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30
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Stielow M, Witczyńska A, Kubryń N, Fijałkowski Ł, Nowaczyk J, Nowaczyk A. The Bioavailability of Drugs-The Current State of Knowledge. Molecules 2023; 28:8038. [PMID: 38138529 PMCID: PMC10745386 DOI: 10.3390/molecules28248038] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Revised: 12/04/2023] [Accepted: 12/09/2023] [Indexed: 12/24/2023] Open
Abstract
Drug bioavailability is a crucial aspect of pharmacology, affecting the effectiveness of drug therapy. Understanding how drugs are absorbed, distributed, metabolized, and eliminated in patients' bodies is essential to ensure proper and safe treatment. This publication aims to highlight the relevance of drug bioavailability research and its importance in therapy. In addition to biochemical activity, bioavailability also plays a critical role in achieving the desired therapeutic effects. This may seem obvious, but it is worth noting that a drug can only produce the expected effect if the proper level of concentration can be achieved at the desired point in a patient's body. Given the differences between patients, drug dosages, and administration forms, understanding and controlling bioavailability has become a priority in pharmacology. This publication discusses the basic concepts of bioavailability and the factors affecting it. We also looked at various methods of assessing bioavailability, both in the laboratory and in the clinic. Notably, the introduction of new technologies and tools in this field is vital to achieve advances in drug bioavailability research. This publication also discusses cases of drugs with poorly described bioavailability, providing a deeper understanding of the complex challenges they pose to medical researchers and practitioners. Simultaneously, the article focuses on the perspectives and trends that may shape the future of research regarding bioavailability, which is crucial to the development of modern pharmacology and drug therapy. In this context, the publication offers an essential, meaningful contribution toward understanding and highlighting bioavailability's role in reliable patient treatment. The text also identifies areas that require further research and exploration.
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Affiliation(s)
| | - Adrianna Witczyńska
- Department of Organic Chemistry, Faculty of Pharmacy, Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University, 2 Jurasza Street, 85-089 Bydgoszcz, Poland; (A.W.); (N.K.); (Ł.F.)
| | - Natalia Kubryń
- Department of Organic Chemistry, Faculty of Pharmacy, Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University, 2 Jurasza Street, 85-089 Bydgoszcz, Poland; (A.W.); (N.K.); (Ł.F.)
| | - Łukasz Fijałkowski
- Department of Organic Chemistry, Faculty of Pharmacy, Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University, 2 Jurasza Street, 85-089 Bydgoszcz, Poland; (A.W.); (N.K.); (Ł.F.)
| | - Jacek Nowaczyk
- Department of Physical Chemistry and Physicochemistry of Polymers, Faculty of Chemistry, Nicolaus Copernicus University, 7 Gagarina Street, 87-100 Toruń, Poland;
| | - Alicja Nowaczyk
- Department of Organic Chemistry, Faculty of Pharmacy, Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University, 2 Jurasza Street, 85-089 Bydgoszcz, Poland; (A.W.); (N.K.); (Ł.F.)
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31
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Murray JD, Lange JJ, Bennett-Lenane H, Holm R, Kuentz M, O'Dwyer PJ, Griffin BT. Advancing algorithmic drug product development: Recommendations for machine learning approaches in drug formulation. Eur J Pharm Sci 2023; 191:106562. [PMID: 37562550 DOI: 10.1016/j.ejps.2023.106562] [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: 05/15/2023] [Revised: 07/09/2023] [Accepted: 08/07/2023] [Indexed: 08/12/2023]
Abstract
Artificial intelligence is a rapidly expanding area of research, with the disruptive potential to transform traditional approaches in the pharmaceutical industry, from drug discovery and development to clinical practice. Machine learning, a subfield of artificial intelligence, has fundamentally transformed in silico modelling and has the capacity to streamline clinical translation. This paper reviews data-driven modelling methodologies with a focus on drug formulation development. Despite recent advances, there is limited modelling guidance specific to drug product development and a trend towards suboptimal modelling practices, resulting in models that may not give reliable predictions in practice. There is an overwhelming focus on benchtop experimental outcomes obtained for a specific modelling aim, leaving the capabilities of data scraping or the use of combined modelling approaches yet to be fully explored. Moreover, the preference for high accuracy can lead to a reliance on black box methods over interpretable models. This further limits the widespread adoption of machine learning as black boxes yield models that cannot be easily understood for the purposes of enhancing product performance. In this review, recommendations for conducting machine learning research for drug product development to ensure trustworthiness, transparency, and reliability of the models produced are presented. Finally, possible future directions on how research in this area might develop are discussed to aim for models that provide useful and robust guidance to formulators.
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Affiliation(s)
- Jack D Murray
- School of Pharmacy, University College Cork, Cork, Ireland
| | - Justus J Lange
- School of Pharmacy, University College Cork, Cork, Ireland; Roche Pharmaceutical Research & Early Development, Pre-Clinical CMC, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd, Grenzacherstrasse 124, Basel, Switzerland
| | | | - René Holm
- Department of Physics, Chemistry and Pharmacy, University of Southern Denmark, Campusvej 55, Odense 5230, Denmark
| | - Martin Kuentz
- School of Life Sciences, University of Applied Sciences and Arts Northwestern Switzerland, Muttenz CH 4132, Switzerland
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32
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Ferdous J, Rahman ME, Sraboni FS, Dutta AK, Rahman MS, Ali MR, Sikdar B, Khan A, Hasan MF. Assessment of the hypoglycemic and anti-hemostasis effects of Paederia foetida (L.) in controlling diabetes and thrombophilia combining in vivo and computational analysis. Comput Biol Chem 2023; 107:107954. [PMID: 37738820 DOI: 10.1016/j.compbiolchem.2023.107954] [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: 07/31/2023] [Revised: 09/03/2023] [Accepted: 09/05/2023] [Indexed: 09/24/2023]
Abstract
Paederia foetida is valued for its folk medicinal properties. This research aimed to assess the acute toxicity, hypoglycemic and anti-hemostasis properties of the methanolic extract of P. foetida leaves (PFLE). Acute toxicity of PFLE was performed on a mice model. Hypoglycemic and anti-hemostasis properties of PFLE were investigated on normal and streptozotocin-induced mice models. Deep learning, molecular docking, density functional theory, and molecular simulation techniques were employed to understand the underlying mechanisms through in silico study. Oral administration of PFLE at a dosage of 300 µg/kg body weight (BW) showed no signs of toxicity. Treatment with PFLE (300 µg/kg/BW) for 14 days resulted in a hypoglycemic condition and a 30.47% increase in body weight. Additionally, PFLE mixed with blood exhibited a 44.6% anti-hemostasis effect. Deep learning predicted the inhibitory concentration (pIC50, nM) of Cleomiscosins against SGLT2 and FXa to be 7.478 and 6.017, respectively. Molecular docking analysis revealed strong binding interactions of Cleomiscosins with crucial residues of the target proteins, exhibiting binding energies of -8.2 kcal/mol and -7.1 kcal/mol, respectively. ADME/Tox predictions indicated favorable pharmacokinetic properties of Cleomiscosins, and DFT calculations of frontier molecular orbitals analyzed the stability and reactivity of these compounds. Molecular simulation dynamics, principal component analysis and MM-PBSA calculation demonstrated the stable, compact, and rigid nature of the protein-ligand complexes. The methanolic PFLE exhibited significant hypoglycemic and anti-hemostasis properties. Cleomiscosin may have inhibitory properties for the development of novel drugs to manage diabetes and thrombophilia in the near future.
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Affiliation(s)
- Jannatul Ferdous
- Department of Genetic Engineering and Biotechnology, University of Rajshahi, Rajshahi 6205, Bangladesh.
| | - Md Ekhtiar Rahman
- Department of Genetic Engineering and Biotechnology, University of Rajshahi, Rajshahi 6205, Bangladesh.
| | - Farzana Sayed Sraboni
- Department of Genetic Engineering and Biotechnology, University of Rajshahi, Rajshahi 6205, Bangladesh.
| | - Amit Kumar Dutta
- Department of Microbiology, University of Rajshahi, Rajshahi 6205, Bangladesh.
| | - Md Siddikur Rahman
- Department of Genetic Engineering and Biotechnology, University of Rajshahi, Rajshahi 6205, Bangladesh.
| | - Md Roushan Ali
- Department of Genetic Engineering and Biotechnology, University of Rajshahi, Rajshahi 6205, Bangladesh.
| | - Biswanath Sikdar
- Department of Microbiology, University of Rajshahi, Rajshahi 6205, Bangladesh.
| | - Alam Khan
- Department of Pharmacy, University of Rajshahi, Rajshahi 6205, Bangladesh
| | - Md Faruk Hasan
- Department of Microbiology, University of Rajshahi, Rajshahi 6205, Bangladesh.
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33
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Rantanen J, Rades T, Strachan C. Solid-state analysis for pharmaceuticals: Pathways to feasible and meaningful analysis. J Pharm Biomed Anal 2023; 236:115649. [PMID: 37657177 DOI: 10.1016/j.jpba.2023.115649] [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: 05/18/2023] [Revised: 08/12/2023] [Accepted: 08/13/2023] [Indexed: 09/03/2023]
Abstract
The solid state of matter is the preferred starting point for designing a pharmaceutical product. This is driven by both patient preferences and the relative ease of supplying a solid pharmaceutical product with desired quality and performance. Solid form diversity is increasingly prevalent as a crucial element in designing these products, which underpins the importance of solid-state analytical methods. This paper provides a critical analysis of challenges related to solid-state analytics, as well as considerations and suggestions for feasible and meaningful pharmaceutical analysis.
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Affiliation(s)
- Jukka Rantanen
- Department of Pharmacy, Faculty of Health and Medical Sciences, University of Copenhagen, Denmark.
| | - Thomas Rades
- Department of Pharmacy, Faculty of Health and Medical Sciences, University of Copenhagen, Denmark.
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34
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Sharma V, Giammona M, Zubarev D, Tek A, Nugyuen K, Sundberg L, Congiu D, La YH. Formulation Graphs for Mapping Structure-Composition of Battery Electrolytes to Device Performance. J Chem Inf Model 2023; 63:6998-7010. [PMID: 37948621 PMCID: PMC10685446 DOI: 10.1021/acs.jcim.3c01030] [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: 07/07/2023] [Revised: 10/21/2023] [Accepted: 10/24/2023] [Indexed: 11/12/2023]
Abstract
Advanced computational methods are being actively sought to address the challenges associated with the discovery and development of new combinatorial materials, such as formulations. A widely adopted approach involves domain-informed high-throughput screening of individual components that can be combined together to form a formulation. This manages to accelerate the discovery of new compounds for a target application but still leaves the process of identifying the right "formulation" from the shortlisted chemical space largely a laboratory experiment-driven process. We report a deep learning model, the Formulation Graph Convolution Network (F-GCN), that can map the structure-composition relationship of the formulation constituents to the property of liquid formulation as a whole. Multiple GCNs are assembled in parallel that featurize formulation constituents domain-intuitively on the fly. The resulting molecular descriptors are scaled based on the respective constituent's molar percentage in the formulation, followed by integration into a combined formulation descriptor that represents the complete formulation to an external learning architecture. The use case of the proposed formulation learning model is demonstrated for battery electrolytes by training and testing it on two exemplary data sets representing electrolyte formulations vs battery performance: one data set is sourced from the literature about Li/Cu half-cells, while the other is obtained by lab experiments related to lithium-iodide full-cell chemistry. The model is shown to predict performance metrics such as Coulombic efficiency (CE) and specific capacity of new electrolyte formulations with the lowest reported errors. The best-performing F-GCN model uses molecular descriptors derived from molecular graphs (GCNs) that are informed with HOMO-LUMO and electric moment properties of the molecules using a knowledge transfer technique.
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Affiliation(s)
- Vidushi Sharma
- IBM Almaden Research Center, 650 Harry Rd, San Jose, California 95120, United States
| | - Maxwell Giammona
- IBM Almaden Research Center, 650 Harry Rd, San Jose, California 95120, United States
| | - Dmitry Zubarev
- IBM Almaden Research Center, 650 Harry Rd, San Jose, California 95120, United States
| | - Andy Tek
- IBM Almaden Research Center, 650 Harry Rd, San Jose, California 95120, United States
| | - Khanh Nugyuen
- IBM Almaden Research Center, 650 Harry Rd, San Jose, California 95120, United States
| | - Linda Sundberg
- IBM Almaden Research Center, 650 Harry Rd, San Jose, California 95120, United States
| | - Daniele Congiu
- IBM Almaden Research Center, 650 Harry Rd, San Jose, California 95120, United States
| | - Young-Hye La
- IBM Almaden Research Center, 650 Harry Rd, San Jose, California 95120, United States
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35
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Dong J, Wu Z, Xu H, Ouyang D. FormulationAI: a novel web-based platform for drug formulation design driven by artificial intelligence. Brief Bioinform 2023; 25:bbad419. [PMID: 37991246 PMCID: PMC10783856 DOI: 10.1093/bib/bbad419] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2023] [Revised: 10/13/2023] [Accepted: 10/31/2023] [Indexed: 11/23/2023] Open
Abstract
Today, pharmaceutical industry faces great pressure to employ more efficient and systematic ways in drug discovery and development process. However, conventional formulation studies still strongly rely on personal experiences by trial-and-error experiments, resulting in a labor-consuming, tedious and costly pipeline. Thus, it is highly required to develop intelligent and efficient methods for formulation development to keep pace with the progress of the pharmaceutical industry. Here, we developed a comprehensive web-based platform (FormulationAI) for in silico formulation design. First, the most comprehensive datasets of six widely used drug formulation systems in the pharmaceutical industry were collected over 10 years, including cyclodextrin formulation, solid dispersion, phospholipid complex, nanocrystals, self-emulsifying and liposome systems. Then, intelligent prediction and evaluation of 16 important properties from the six systems were investigated and implemented by systematic study and comparison of different AI algorithms and molecular representations. Finally, an efficient prediction platform was established and validated, which enables the formulation design just by inputting basic information of drugs and excipients. FormulationAI is the first freely available comprehensive web-based platform, which provides a powerful solution to assist the formulation design in pharmaceutical industry. It is available at https://formulationai.computpharm.org/.
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Affiliation(s)
- Jie Dong
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, China
- Institute of Chinese Medical Sciences (ICMS), State Key Laboratory of Quality Research in Chinese Medicine, University of Macau, Macau, China
| | - Zheng Wu
- Institute of Chinese Medical Sciences (ICMS), State Key Laboratory of Quality Research in Chinese Medicine, University of Macau, Macau, China
| | - Huanle Xu
- Faculty of Science and Technology, University of Macau, Macau, China
| | - Defang Ouyang
- Institute of Chinese Medical Sciences (ICMS), State Key Laboratory of Quality Research in Chinese Medicine, University of Macau, Macau, China
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36
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Bao Z, Bufton J, Hickman RJ, Aspuru-Guzik A, Bannigan P, Allen C. Revolutionizing drug formulation development: The increasing impact of machine learning. Adv Drug Deliv Rev 2023; 202:115108. [PMID: 37774977 DOI: 10.1016/j.addr.2023.115108] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 09/24/2023] [Accepted: 09/25/2023] [Indexed: 10/01/2023]
Abstract
Over the past few years, the adoption of machine learning (ML) techniques has rapidly expanded across many fields of research including formulation science. At the same time, the use of lipid nanoparticles to enable the successful delivery of mRNA vaccines in the recent COVID-19 pandemic demonstrated the impact of formulation science. Yet, the design of advanced pharmaceutical formulations is non-trivial and primarily relies on costly and time-consuming wet-lab experimentation. In 2021, our group published a review article focused on the use of ML as a means to accelerate drug formulation development. Since then, the field has witnessed significant growth and progress, reflected by an increasing number of studies published in this area. This updated review summarizes the current state of ML directed drug formulation development, introduces advanced ML techniques that have been implemented in formulation design and shares the progress on making self-driving laboratories a reality. Furthermore, this review highlights several future applications of ML yet to be fully exploited to advance drug formulation research and development.
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Affiliation(s)
- Zeqing Bao
- Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto, ON M5S 3M2, Canada
| | - Jack Bufton
- Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto, ON M5S 3M2, Canada
| | - Riley J Hickman
- Department of Chemistry, University of Toronto, Toronto, ON M5S 3H6, Canada; Department of Computer Science, University of Toronto, Toronto, ON M5S 2E4, Canada; Vector Institute for Artificial Intelligence, Toronto, ON M5S 1M1, Canada
| | - Alán Aspuru-Guzik
- Department of Chemistry, University of Toronto, Toronto, ON M5S 3H6, Canada; Department of Computer Science, University of Toronto, Toronto, ON M5S 2E4, Canada; Vector Institute for Artificial Intelligence, Toronto, ON M5S 1M1, Canada; Lebovic Fellow, Canadian Institute for Advanced Research (CIFAR), Toronto, ON M5S 1M1, Canada; Department of Chemical Engineering & Applied Chemistry, University of Toronto, Toronto, ON M5S 3E5, Canada; Department of Materials Science & Engineering, University of Toronto, Toronto, ON M5S 3E4, Canada; CIFAR Artificial Intelligence Research Chair, Vector Institute, Toronto, ON M5S 1M1, Canada; Acceleration Consortium, Toronto, ON M5S 3H6, Canada
| | - Pauric Bannigan
- Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto, ON M5S 3M2, Canada.
| | - Christine Allen
- Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto, ON M5S 3M2, Canada; Department of Chemical Engineering & Applied Chemistry, University of Toronto, Toronto, ON M5S 3E5, Canada; Acceleration Consortium, Toronto, ON M5S 3H6, Canada.
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37
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Yuan Y, Han Y, Yap CW, Kochhar JS, Li H, Xiang X, Kang L. Prediction of drug permeation through microneedled skin by machine learning. Bioeng Transl Med 2023; 8:e10512. [PMID: 38023708 PMCID: PMC10658566 DOI: 10.1002/btm2.10512] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Revised: 02/22/2023] [Accepted: 03/08/2023] [Indexed: 04/07/2023] Open
Abstract
Stratum corneum is the outermost layer of the skin preventing external substances from entering human body. Microneedles (MNs) are sharp protrusions of a few hundred microns in length, which can penetrate the stratum corneum to facilitate drug permeation through skin. To determine the amount of drug delivered through skin, in vitro drug permeation testing is commonly used, but the testing is costly and time-consuming. To address this issue, machine learning methods were employed to predict drug permeation through the skin, circumventing the need of conducting skin permeation experiments. By comparing the experimental data and simulated results, it was found extreme gradient boosting (XGBoost) was the best among the four simulation methods. It was also found that drug loading, permeation time, and MN surface area were critical parameters in the models. In conclusion, machine learning is useful to predict drug permeation profiles for MN-facilitated transdermal drug delivery.
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Affiliation(s)
- Yunong Yuan
- School of Pharmacy, Faculty of Medicine and HealthUniversity of SydneyNew South Wales2006Australia
| | - Yiting Han
- Department of Clinical Pharmacy and Pharmacy Administration, School of PharmacyFudan UniversityShanghai201203China
- Harvard T.H. Chan School of Public Health677 Huntington AvenueBostonMassachusetts02115USA
| | - Chun Wei Yap
- National Healthcare Group1 Fusionopolis LinkSingapore138542Singapore
| | | | - Hairui Li
- MGI Tech21 Biopolis Road, NucleosSingapore138567Singapore
| | - Xiaoqiang Xiang
- Department of Clinical Pharmacy and Pharmacy Administration, School of PharmacyFudan UniversityShanghai201203China
| | - Lifeng Kang
- School of Pharmacy, Faculty of Medicine and HealthUniversity of SydneyNew South Wales2006Australia
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38
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Haddad S, Oktay L, Erol I, Şahin K, Durdagi S. Utilizing Heteroatom Types and Numbers from Extensive Ligand Libraries to Develop Novel hERG Blocker QSAR Models Using Machine Learning-Based Classifiers. ACS OMEGA 2023; 8:40864-40877. [PMID: 37929100 PMCID: PMC10620895 DOI: 10.1021/acsomega.3c06074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Accepted: 09/13/2023] [Indexed: 11/07/2023]
Abstract
The human ether-à-go-go-related gene (hERG) channel plays a crucial role in membrane repolarization. Any disruptions in its function can lead to severe cardiovascular disorders such as long QT syndrome (LQTS), which increases the risk of serious cardiovascular problems such as tachyarrhythmia and sudden cardiac death. Drug-induced LQTS is a significant concern and has resulted in drug withdrawals from the market in the past. The main objective of this study is to pinpoint crucial heteroatoms present in ligands that initiate interactions leading to the effective blocking of the hERG channel. To achieve this aim, ligand-based quantitative structure-activity relationships (QSAR) models were constructed using extensive ligand libraries, considering the heteroatom types and numbers, and their associated hERG channel blockage pIC50 values. Machine learning-assisted QSAR models were developed to analyze the key structural components influencing compound activity. Among the various methods, the KPLS method proved to be the most efficient, allowing the construction of models based on eight distinct fingerprints. The study delved into investigating the influence of heteroatoms on the activity of hERG blockers, revealing their significant role. Furthermore, by quantifying the effect of heteroatom types and numbers on ligand activity at the hERG channel, six compound pairs were selected for molecular docking. Subsequent molecular dynamics simulations and per residue MM/GBSA calculations were performed to comprehensively analyze the interactions of the selected pair compounds.
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Affiliation(s)
- Safa Haddad
- Computational
Biology and Molecular Simulations Laboratory, Department of Biophysics,
School of Medicine, Bahçeşehir
University, Istanbul 34353, Turkey
- Computational
Drug Design Center (HITMER), Bahçeşehir
University, Istanbul 34353, Turkey
| | - Lalehan Oktay
- Computational
Biology and Molecular Simulations Laboratory, Department of Biophysics,
School of Medicine, Bahçeşehir
University, Istanbul 34353, Turkey
- Computational
Drug Design Center (HITMER), Bahçeşehir
University, Istanbul 34353, Turkey
| | - Ismail Erol
- Computational
Biology and Molecular Simulations Laboratory, Department of Biophysics,
School of Medicine, Bahçeşehir
University, Istanbul 34353, Turkey
- Computational
Drug Design Center (HITMER), Bahçeşehir
University, Istanbul 34353, Turkey
| | - Kader Şahin
- Department
of Analytical Chemistry, School of Pharmacy, Bahçeşehir University, Istanbul 34734, Turkey
| | - Serdar Durdagi
- Computational
Biology and Molecular Simulations Laboratory, Department of Biophysics,
School of Medicine, Bahçeşehir
University, Istanbul 34353, Turkey
- Computational
Drug Design Center (HITMER), Bahçeşehir
University, Istanbul 34353, Turkey
- Molecular
Therapy Lab, Department of Pharmaceutical Chemistry, School of Pharmacy, Bahçeşehir University, Istanbul 34353, Turkey
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39
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Silva-Júnior EFD. "You've got the Body I've got the Brains" - Could the current AI-based tools replace the human ingenuity for designing new drug candidates? Bioorg Med Chem 2023; 94:117475. [PMID: 37741120 DOI: 10.1016/j.bmc.2023.117475] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Revised: 08/12/2023] [Accepted: 09/12/2023] [Indexed: 09/25/2023]
Abstract
The emergence of artificial intelligence (AI) tools has transformed the landscape of drug discovery, providing unprecedented speed, efficiency, and cost-effectiveness in the search for new therapeutics. From target identification to drug formulation and delivery, AI-driven algorithms have revolutionized various aspects of medicinal chemistry, significantly accelerating the drug design process. Despite the transformative power of AI, this perspective article emphasizes the limitations of AI tools in drug discovery, requiring inventive skills of medicinal chemists. However, the article highlighted that there is a need for a harmonious integration of AI-based tools and human expertise in drug discovery. Such a synergistic approach promises to lead to groundbreaking therapies that address unmet medical needs and benefit humankind. As the world evolves technologically, the question remains: When will AI tools effectively design and develop drugs? The answer may lie in the seamless collaboration between AI and human researchers, unlocking transformative therapies that combat diseases effectively.
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Affiliation(s)
- Edeildo Ferreira da Silva-Júnior
- Institute of Chemistry and Biotechnology, Federal University of Alagoas, Lourival Melo Mota Avenue, AC. Simões Campus, 57072-970 Alagoas, Maceió, Brazil
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40
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Malheiro V, Duarte J, Veiga F, Mascarenhas-Melo F. Exploiting Pharma 4.0 Technologies in the Non-Biological Complex Drugs Manufacturing: Innovations and Implications. Pharmaceutics 2023; 15:2545. [PMID: 38004525 PMCID: PMC10674941 DOI: 10.3390/pharmaceutics15112545] [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: 08/29/2023] [Revised: 10/15/2023] [Accepted: 10/23/2023] [Indexed: 11/26/2023] Open
Abstract
The pharmaceutical industry has entered an era of transformation with the emergence of Pharma 4.0, which leverages cutting-edge technologies in manufacturing processes. These hold tremendous potential for enhancing the overall efficiency, safety, and quality of non-biological complex drugs (NBCDs), a category of pharmaceutical products that pose unique challenges due to their intricate composition and complex manufacturing requirements. This review attempts to provide insight into the application of select Pharma 4.0 technologies, namely machine learning, in silico modeling, and 3D printing, in the manufacturing process of NBCDs. Specifically, it reviews the impact of these tools on NBCDs such as liposomes, polymeric micelles, glatiramer acetate, iron carbohydrate complexes, and nanocrystals. It also addresses regulatory challenges associated with the implementation of these technologies and presents potential future perspectives, highlighting the incorporation of digital twins in this field of research as it seems to be a very promising approach, namely for the optimization of NBCDs manufacturing processes.
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Affiliation(s)
- Vera Malheiro
- Drug Development and Technology Laboratory, Faculty of Pharmacy, University of Coimbra, Pólo das Ciências da Saúde, Azinhaga de Santa Comba, 3000-548 Coimbra, Portugal; (V.M.); (J.D.); (F.V.)
| | - Joana Duarte
- Drug Development and Technology Laboratory, Faculty of Pharmacy, University of Coimbra, Pólo das Ciências da Saúde, Azinhaga de Santa Comba, 3000-548 Coimbra, Portugal; (V.M.); (J.D.); (F.V.)
| | - Francisco Veiga
- Drug Development and Technology Laboratory, Faculty of Pharmacy, University of Coimbra, Pólo das Ciências da Saúde, Azinhaga de Santa Comba, 3000-548 Coimbra, Portugal; (V.M.); (J.D.); (F.V.)
- LAQV, REQUIMTE, Department of Pharmaceutical Technology, Faculty of Pharmacy, University of Coimbra, Pólo das Ciências da Saúde, Azinhaga de Santa Comba, 3000-548 Coimbra, Portugal
| | - Filipa Mascarenhas-Melo
- Drug Development and Technology Laboratory, Faculty of Pharmacy, University of Coimbra, Pólo das Ciências da Saúde, Azinhaga de Santa Comba, 3000-548 Coimbra, Portugal; (V.M.); (J.D.); (F.V.)
- LAQV, REQUIMTE, Department of Pharmaceutical Technology, Faculty of Pharmacy, University of Coimbra, Pólo das Ciências da Saúde, Azinhaga de Santa Comba, 3000-548 Coimbra, Portugal
- Higher School of Health, Polytechnic Institute of Guarda, Rua da Cadeia, 6300-307 Guarda, Portugal
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41
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Schrier J, Norquist AJ, Buonassisi T, Brgoch J. In Pursuit of the Exceptional: Research Directions for Machine Learning in Chemical and Materials Science. J Am Chem Soc 2023; 145:21699-21716. [PMID: 37754929 DOI: 10.1021/jacs.3c04783] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/28/2023]
Abstract
Exceptional molecules and materials with one or more extraordinary properties are both technologically valuable and fundamentally interesting, because they often involve new physical phenomena or new compositions that defy expectations. Historically, exceptionality has been achieved through serendipity, but recently, machine learning (ML) and automated experimentation have been widely proposed to accelerate target identification and synthesis planning. In this Perspective, we argue that the data-driven methods commonly used today are well-suited for optimization but not for the realization of new exceptional materials or molecules. Finding such outliers should be possible using ML, but only by shifting away from using traditional ML approaches that tweak the composition, crystal structure, or reaction pathway. We highlight case studies of high-Tc oxide superconductors and superhard materials to demonstrate the challenges of ML-guided discovery and discuss the limitations of automation for this task. We then provide six recommendations for the development of ML methods capable of exceptional materials discovery: (i) Avoid the tyranny of the middle and focus on extrema; (ii) When data are limited, qualitative predictions that provide direction are more valuable than interpolative accuracy; (iii) Sample what can be made and how to make it and defer optimization; (iv) Create room (and look) for the unexpected while pursuing your goal; (v) Try to fill-in-the-blanks of input and output space; (vi) Do not confuse human understanding with model interpretability. We conclude with a description of how these recommendations can be integrated into automated discovery workflows, which should enable the discovery of exceptional molecules and materials.
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Affiliation(s)
- Joshua Schrier
- Department of Chemistry, Fordham University, The Bronx, New York 10458, United States
| | - Alexander J Norquist
- Department of Chemistry, Haverford College, Haverford, Pennsylvania 19041, United States
| | - Tonio Buonassisi
- Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Jakoah Brgoch
- Department of Chemistry and Texas Center for Superconductivity, University of Houston, Houston, Texas 77204, United States
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42
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Zhou L, Yi W, Zhang Z, Shan X, Zhao Z, Sun X, Wang J, Wang H, Jiang H, Zheng M, Wang D, Li Y. STING agonist-boosted mRNA immunization via intelligent design of nanovaccines for enhancing cancer immunotherapy. Natl Sci Rev 2023; 10:nwad214. [PMID: 37693123 PMCID: PMC10484175 DOI: 10.1093/nsr/nwad214] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Revised: 07/03/2023] [Accepted: 07/24/2023] [Indexed: 09/12/2023] Open
Abstract
Messenger RNA (mRNA) vaccine is revolutionizing the methodology of immunization in cancer. However, mRNA immunization is drastically limited by multistage biological barriers including poor lymphatic transport, rapid clearance, catalytic hydrolysis, insufficient cellular entry and endosome entrapment. Herein, we design a mRNA nanovaccine based on intelligent design to overcome these obstacles. Highly efficient nanovaccines are carried out with machine learning techniques from datasets of various nanocarriers, ensuring successful delivery of mRNA antigen and cyclic guanosine monophosphate-adenosine monophosphate (cGAMP) to targets. It activates stimulator of interferon genes (STING), promotes mRNA-encoded antigen presentation and boosts antitumour immunity in vivo, thus inhibiting tumour growth and ensuring long-term survival of tumour-bearing mice. This work provides a feasible and safe strategy to facilitate STING agonist-synergized mRNA immunization, with great translational potential for enhancing cancer immunotherapy.
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Affiliation(s)
- Lei Zhou
- State Key Laboratory of Drug Research & Center of Pharmaceutics, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
- China State Institute of Pharmaceutical Industry, Shanghai 201203, China
| | - Wenzhe Yi
- State Key Laboratory of Drug Research & Center of Pharmaceutics, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
- Univerisity of Chinese Academy of Sciences, Beijing 100049, China
| | - Zehong Zhang
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
- Univerisity of Chinese Academy of Sciences, Beijing 100049, China
| | - Xiaoting Shan
- State Key Laboratory of Drug Research & Center of Pharmaceutics, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
- Univerisity of Chinese Academy of Sciences, Beijing 100049, China
| | - Zitong Zhao
- State Key Laboratory of Drug Research & Center of Pharmaceutics, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
| | - Xiangshi Sun
- State Key Laboratory of Drug Research & Center of Pharmaceutics, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
| | - Jue Wang
- State Key Laboratory of Drug Research & Center of Pharmaceutics, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
- Univerisity of Chinese Academy of Sciences, Beijing 100049, China
| | - Hao Wang
- China State Institute of Pharmaceutical Industry, Shanghai 201203, China
| | - Hualiang Jiang
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
- Univerisity of Chinese Academy of Sciences, Beijing 100049, China
| | - Mingyue Zheng
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
- Univerisity of Chinese Academy of Sciences, Beijing 100049, China
| | - Dangge Wang
- Precision Research Center for Refractory Diseases, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 201620, China
| | - Yaping Li
- State Key Laboratory of Drug Research & Center of Pharmaceutics, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
- Univerisity of Chinese Academy of Sciences, Beijing 100049, China
- Shandong Laboratory of Yantai Drug Discovery, Bohai Rim Advanced Research Institute for Drug Discovery, Yantai 264000, China
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43
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Ma L, Wu Q, Tam PKH. The Current Proceedings of PSC-Based Liver Fibrosis Therapy. Stem Cell Rev Rep 2023; 19:2155-2165. [PMID: 37490204 DOI: 10.1007/s12015-023-10592-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/12/2023] [Indexed: 07/26/2023]
Abstract
Liver fibrosis was initially considered to be an irreversible process which will eventually lead to the occurrence of liver cancer. So far there has been no effective therapeutic approach to treat liver fibrosis although scientists have put tremendous efforts into the underlying mechanisms of this disease. Therefore, in-depth research on novel and safe treatments of liver fibrosis is of great significance to human health. Pluripotent stem cells (PSCs) play important roles in the study of liver fibrosis due to their unique features in self-renewal ability, pluripotency, and paracrine function. This article mainly reviews the applications of PSCs in the study of liver fibrosis in recent years. We discuss the role of PSC-derived liver organoids in the study of liver fibrosis, and the latest research advances on the differentiation of PSCs into hepatocytes or macrophages. We also highlight the importance of exosomes of PSCs for the treatment of liver fibrosis.
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Affiliation(s)
- Li Ma
- The State Key Laboratory of Quality Research in Chinese Medicine, Macau University of Science and Technology, Taipa, China
| | - Qiang Wu
- The State Key Laboratory of Quality Research in Chinese Medicine, Macau University of Science and Technology, Taipa, China.
| | - Paul Kwong-Hang Tam
- Faculty of Medicine, Macau University of Science and Technology, Taipa, China.
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Madadian Bozorg N, Leclercq M, Lescot T, Bazin M, Gaudreault N, Dikpati A, Fortin MA, Droit A, Bertrand N. Design of experiment and machine learning inform on the 3D printing of hydrogels for biomedical applications. BIOMATERIALS ADVANCES 2023; 153:213533. [PMID: 37392520 DOI: 10.1016/j.bioadv.2023.213533] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 05/30/2023] [Accepted: 06/18/2023] [Indexed: 07/03/2023]
Abstract
In the biomedical field, 3D printing has the potential to deliver on some of the promises of personalized therapy, notably by enabling point-of-care fabrication of medical devices, dosage forms and bioimplants. To achieve this full potential, a better understanding of the 3D printing processes is necessary, and non-destructive characterization methods must be developed. This study proposes methodologies to optimize the 3D printing parameters for soft material extrusion. We hypothesize that combining image processing with design of experiment (DoE) analyses and machine learning could help obtaining useful information from a quality-by-design perspective. Herein, we investigated the impact of three critical process parameters (printing speed, printing pressure and infill percentage) on three critical quality attributes (gel weight, total surface area and heterogeneity) monitored with a non-destructive methodology. DoE and machine learning were combined to obtain information on the process. This work paves the way for a rational approach to optimize 3D printing parameters in the biomedical field.
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Affiliation(s)
- Neda Madadian Bozorg
- Faculté de Pharmacie, Centre de Recherche sur les Matériaux Avancés (CERMA), Université Laval, Quebec City, QC G1V 0A6, Canada; Centre de Recherche du CHU de Québec, Université Laval, Axe Endocrinologie et Néphrologie, Quebec City, QC G1V 4G2, Canada
| | - Mickael Leclercq
- Centre de Recherche du CHU de Québec, Université Laval, Axe Endocrinologie et Néphrologie, Quebec City, QC G1V 4G2, Canada
| | - Théophraste Lescot
- Faculté des Sciences et Génie, Département de Génie des Mines, de la Métallurgie et des Matériaux, Centre de Recherche sur les Matériaux Avancés (CERMA), Université Laval, Québec City G1V 0A6, Canada; Centre de Recherche du CHU de Québec, Université Laval, Axe Médecine Régénératrice, Quebec City, QC G1V 4G2, Canada
| | - Marc Bazin
- Centre de Recherche du CHU de Québec, Université Laval, Axe Neurosciences, Quebec City, QC G1V 4G2, Canada
| | - Nicolas Gaudreault
- Faculté de Pharmacie, Centre de Recherche sur les Matériaux Avancés (CERMA), Université Laval, Quebec City, QC G1V 0A6, Canada; Centre de Recherche du CHU de Québec, Université Laval, Axe Endocrinologie et Néphrologie, Quebec City, QC G1V 4G2, Canada
| | - Amrita Dikpati
- Faculté de Pharmacie, Centre de Recherche sur les Matériaux Avancés (CERMA), Université Laval, Quebec City, QC G1V 0A6, Canada; Centre de Recherche du CHU de Québec, Université Laval, Axe Endocrinologie et Néphrologie, Quebec City, QC G1V 4G2, Canada
| | - Marc-André Fortin
- Faculté des Sciences et Génie, Département de Génie des Mines, de la Métallurgie et des Matériaux, Centre de Recherche sur les Matériaux Avancés (CERMA), Université Laval, Québec City G1V 0A6, Canada; Centre de Recherche du CHU de Québec, Université Laval, Axe Médecine Régénératrice, Quebec City, QC G1V 4G2, Canada
| | - Arnaud Droit
- Centre de Recherche du CHU de Québec, Université Laval, Axe Endocrinologie et Néphrologie, Quebec City, QC G1V 4G2, Canada; Faculté de Médicine, Département de Médecine Moléculaire, Université Laval, Québec City G1V 0A6, Canada
| | - Nicolas Bertrand
- Faculté de Pharmacie, Centre de Recherche sur les Matériaux Avancés (CERMA), Université Laval, Quebec City, QC G1V 0A6, Canada; Centre de Recherche du CHU de Québec, Université Laval, Axe Endocrinologie et Néphrologie, Quebec City, QC G1V 4G2, Canada.
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45
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Vidhya KS, Sultana A, M NK, Rangareddy H. Artificial Intelligence's Impact on Drug Discovery and Development From Bench to Bedside. Cureus 2023; 15:e47486. [PMID: 37881323 PMCID: PMC10597591 DOI: 10.7759/cureus.47486] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/22/2023] [Indexed: 10/27/2023] Open
Abstract
Artificial intelligence (AI) techniques have the potential to revolutionize drug release modeling, optimize therapy for personalized medicine, and minimize side effects. By applying AI algorithms, researchers can predict drug release profiles, incorporate patient-specific factors, and optimize dosage regimens to achieve tailored and effective therapies. This AI-based approach has the potential to improve treatment outcomes, enhance patient satisfaction, and advance the field of pharmaceutical sciences. International collaborations and professional organizations play vital roles in establishing guidelines and best practices for data collection and sharing. Open data initiatives can enhance transparency and scientific progress, facilitating algorithm validation.
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Affiliation(s)
- K S Vidhya
- Bioinformatics, University of Visvesvaraya College of Engineering, Bangalore, IND
| | - Ayesha Sultana
- Pathology, St. George's University School of Medicine, St. George's, GRD
| | - Naveen Kumar M
- Pharmacology, Haveri Institute of Medical Sciences, Haveri, IND
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46
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Sarfraz M, Arafat M, Zaidi SHH, Eltaib L, Siddique MI, Kamal M, Ali A, Asdaq SMB, Khan A, Aaghaz S, Alshammari MS, Imran M. Resveratrol-Laden Nano-Systems in the Cancer Environment: Views and Reviews. Cancers (Basel) 2023; 15:4499. [PMID: 37760469 PMCID: PMC10526844 DOI: 10.3390/cancers15184499] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2023] [Revised: 08/31/2023] [Accepted: 09/07/2023] [Indexed: 09/29/2023] Open
Abstract
The genesis of cancer is a precisely organized process in which normal cells undergo genetic alterations that cause the cells to multiply abnormally, colonize, and metastasize to other organs such as the liver, lungs, colon, and brain. Potential drugs that could modify these carcinogenic pathways are the ones that will be used in clinical trials as anti-cancer drugs. Resveratrol (RES) is a polyphenolic natural antitoxin that has been utilized for the treatment of several diseases, owing to its ability to scavenge free radicals, control the expression and activity of antioxidant enzymes, and have effects on inflammation, cancer, aging, diabetes, and cardioprotection. Although RES has a variety of pharmacological uses and shows promising applications in natural medicine, its unpredictable pharmacokinetics compromise its therapeutic efficacy and prevent its use in clinical settings. RES has been encapsulated into various nanocarriers, such as liposomes, polymeric nanoparticles, lipidic nanocarriers, and inorganic nanoparticles, to address these issues. These nanocarriers can modulate drug release, increase bioavailability, and reach therapeutically relevant plasma concentrations. Studies on resveratrol-rich nano-formulations in various cancer types are compiled in the current article. Studies relating to enhanced drug stability, increased therapeutic potential in terms of pharmacokinetics and pharmacodynamics, and reduced toxicity to cells and tissues are the main topics of this research. To keep the readers informed about the current state of resveratrol nano-formulations from an industrial perspective, some recent and significant patent literature has also been provided. Here, the prospects for nano-formulations are briefly discussed, along with machine learning and pharmacometrics methods for resolving resveratrol's pharmacokinetic concerns.
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Affiliation(s)
- Muhammad Sarfraz
- College of Pharmacy, Al Ain University, Al Ain Campus, Al Ain P.O. Box 64141, United Arab Emirates
| | - Mosab Arafat
- College of Pharmacy, Al Ain University, Al Ain Campus, Al Ain P.O. Box 64141, United Arab Emirates
| | - Syeda Huma H. Zaidi
- Department of Chemistry, Faculty of Science, Northern Border University, Arar 91431, Saudi Arabia
| | - Lina Eltaib
- Department of Pharmaceutics, Faculty of Pharmacy, Northern Border University, Rafha 91911, Saudi Arabia
| | - Muhammad Irfan Siddique
- Department of Pharmaceutics, Faculty of Pharmacy, Northern Border University, Rafha 91911, Saudi Arabia
| | - Mehnaz Kamal
- Department of Pharmaceutical Chemistry, College of Pharmacy, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia;
| | - Abuzer Ali
- Department of Pharmacognosy, College of Pharmacy, Taif University, Taif 21944, Saudi Arabia
| | | | - Abida Khan
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Northern Border University, Rafha 91911, Saudi Arabia (M.I.)
| | - Shams Aaghaz
- Department of Pharmacy, School of Medical & Allied Sciences, Galgotias University, Greater Noida 203201, India
| | - Mohammed Sanad Alshammari
- Department of Computer Science, Faculty of Computing and Information Technology, Northern Border University, Rafha 91911, Saudi Arabia
| | - Mohd Imran
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Northern Border University, Rafha 91911, Saudi Arabia (M.I.)
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47
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Di Francesco V, Boso DP, Moore TL, Schrefler BA, Decuzzi P. Machine learning instructed microfluidic synthesis of curcumin-loaded liposomes. Biomed Microdevices 2023; 25:29. [PMID: 37542568 PMCID: PMC10404166 DOI: 10.1007/s10544-023-00671-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/27/2023] [Indexed: 08/07/2023]
Abstract
The association of machine learning (ML) tools with the synthesis of nanoparticles has the potential to streamline the development of more efficient and effective nanomedicines. The continuous-flow synthesis of nanoparticles via microfluidics represents an ideal playground for ML tools, where multiple engineering parameters - flow rates and mixing configurations, type and concentrations of the reagents - contribute in a non-trivial fashion to determine the resultant morphological and pharmacological attributes of nanomedicines. Here we present the application of ML models towards the microfluidic-based synthesis of liposomes loaded with a model hydrophobic therapeutic agent, curcumin. After generating over 200 different liposome configurations by systematically modulating flow rates, lipid concentrations, organic:water mixing volume ratios, support-vector machine models and feed-forward artificial neural networks were trained to predict, respectively, the liposome dispersity/stability and size. This work presents an initial step towards the application and cultivation of ML models to instruct the microfluidic formulation of nanoparticles.
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Affiliation(s)
- Valentina Di Francesco
- Laboratory of Nanotechnology for Precision Medicine, Istituto Italiano Di Tecnologia, Via Morego 30, Genova, 16163, Italy
| | - Daniela P Boso
- Department of Civil, Environmental and Architectural Engineering, University of Padova, Via Marzolo 9, Padova, 35131, Italy.
| | - Thomas L Moore
- Laboratory of Nanotechnology for Precision Medicine, Istituto Italiano Di Tecnologia, Via Morego 30, Genova, 16163, Italy
| | - Bernhard A Schrefler
- Department of Civil, Environmental and Architectural Engineering, University of Padova, Via Marzolo 9, Padova, 35131, Italy
- Institute for Advanced Studies, Technical University of Munich, Lichtenbergstraße 2 a, 85748, Garching, Germany
| | - Paolo Decuzzi
- Laboratory of Nanotechnology for Precision Medicine, Istituto Italiano Di Tecnologia, Via Morego 30, Genova, 16163, Italy
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48
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Staniszewska M, Romański M, Polak S, Garbacz G, Dobosz J, Myslitska D, Romanova S, Paszkowska J, Danielak D. A Rational Approach to Predicting Immediate Release Formulation Behavior in Multiple Gastric Motility Patterns: A Combination of a Biorelevant Apparatus, Design of Experiments, and Machine Learning. Pharmaceutics 2023; 15:2056. [PMID: 37631270 PMCID: PMC10458881 DOI: 10.3390/pharmaceutics15082056] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Revised: 07/24/2023] [Accepted: 07/28/2023] [Indexed: 08/27/2023] Open
Abstract
Gastric mechanical stress often impacts drug dissolution from solid oral dosage forms, but in vitro experiments cannot recreate the substantial variability of gastric motility in a reasonable time. This study, for the first time, combines a novel dissolution apparatus with the design of experiments (DoE) and machine learning (ML) to overcome this obstacle. The workflow involves the testing of soft gelatin capsules in a set of fasted-state biorelevant dissolution experiments created with DoE. The dissolution results are used by an ML algorithm to build the classification model of the capsule's opening in response to intragastric stress (IS) within the physiological space of timing and magnitude. Next, a random forest algorithm is used to model the further drug dissolution. The predictive power of the two ML models is verified with independent dissolution tests, and they outperform a polynomial-based DoE model. Moreover, the developed tool reasonably simulates over 50 dissolution profiles under varying IS conditions. Hence, we prove that our method can be utilized for the simulation of dissolution profiles related to the multiplicity of individual gastric motility patterns. In perspective, the developed workflow can improve virtual bioequivalence trials and the patient-centric development of immediate-release oral dosage forms.
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Affiliation(s)
- Marcela Staniszewska
- Physiolution Polska, 74 Piłsudskiego St., 50-020 Wrocław, Poland; (G.G.); (J.D.); (D.M.); (S.R.); (J.P.)
| | - Michał Romański
- Department of Physical Pharmacy and Pharmacokinetics, Poznan University of Medical Sciences, 3 Rokietnicka St., 60-806 Poznań, Poland; (M.R.); (D.D.)
| | - Sebastian Polak
- Faculty of Pharmacy, Medical College, Jagiellonian University, Medyczna 9 Street, 30-688 Kraków, Poland;
| | - Grzegorz Garbacz
- Physiolution Polska, 74 Piłsudskiego St., 50-020 Wrocław, Poland; (G.G.); (J.D.); (D.M.); (S.R.); (J.P.)
| | - Justyna Dobosz
- Physiolution Polska, 74 Piłsudskiego St., 50-020 Wrocław, Poland; (G.G.); (J.D.); (D.M.); (S.R.); (J.P.)
| | - Daria Myslitska
- Physiolution Polska, 74 Piłsudskiego St., 50-020 Wrocław, Poland; (G.G.); (J.D.); (D.M.); (S.R.); (J.P.)
| | - Svitlana Romanova
- Physiolution Polska, 74 Piłsudskiego St., 50-020 Wrocław, Poland; (G.G.); (J.D.); (D.M.); (S.R.); (J.P.)
| | - Jadwiga Paszkowska
- Physiolution Polska, 74 Piłsudskiego St., 50-020 Wrocław, Poland; (G.G.); (J.D.); (D.M.); (S.R.); (J.P.)
| | - Dorota Danielak
- Department of Physical Pharmacy and Pharmacokinetics, Poznan University of Medical Sciences, 3 Rokietnicka St., 60-806 Poznań, Poland; (M.R.); (D.D.)
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49
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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: 61] [Impact Index Per Article: 61.0] [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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50
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Correia AC, Moreira JN, Sousa Lobo JM, Silva AC. Design of experiment (DoE) as a quality by design (QbD) tool to optimise formulations of lipid nanoparticles for nose-to-brain drug delivery. Expert Opin Drug Deliv 2023; 20:1731-1748. [PMID: 37905547 DOI: 10.1080/17425247.2023.2274902] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Accepted: 10/20/2023] [Indexed: 11/02/2023]
Abstract
INTRODUCTION The nose-to-brain route has been widely investigated to improve drug targeting to the central nervous system (CNS), where lipid nanoparticles (solid lipid nanoparticles - SLN and nanostructured lipid carriers - NLC) seem promising, although they should meet specific criteria of particle size (PS) <200 nm, polydispersity index (PDI) <0.3, zeta potential (ZP) ~|20| mV and encapsulation efficiency (EE) >80%. To optimize SLN and NLC formulations, design of experiment (DoE) has been recommended as a quality by design (QbD) tool. AREAS COVERED This review presents recently published work on the optimization of SLN and NLC formulations for nose-to-brain drug delivery. The impact of different factors (or independent variables) on responses (or dependent variables) is critically analyzed. EXPERT OPINION Different DoEs have been used to optimize SLN and NLC formulations for nose-brain drug delivery, and the independent variables lipid and surfactant concentration and sonication time had the greatest impact on the dependent variables PS, EE, and PDI. Exploring different DoE approaches is important to gain a deeper understanding of the factors that affect successful optimization of SLN and NLC and to facilitate future work improving machine learning techniques.
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Affiliation(s)
- A C Correia
- Faculty of Pharmacy, University of Porto, UCIBIO, REQUIMTE, Porto, Portugal
- Associate Laboratory i4HB Institute for Health and Bioeconomy, Faculty of Pharmacy, University of Porto, Porto, Portugal
| | - J N Moreira
- CNC - Center for Neuroscience and Cell Biology, Center for Innovative Biomedicine and Biotechnology (CIBB), Faculty of Medicine (Pólo I), University of Coimbra, Coimbra, Portugal
- Faculty of Pharmacy, Univ Coimbra - University of Coimbra, CIBB, Pólo das Ciências da Saúde, Azinhaga de, Santa Comba, Coimbra, Portugal
| | - J M Sousa Lobo
- Faculty of Pharmacy, University of Porto, UCIBIO, REQUIMTE, Porto, Portugal
- Associate Laboratory i4HB Institute for Health and Bioeconomy, Faculty of Pharmacy, University of Porto, Porto, Portugal
| | - A C Silva
- Faculty of Pharmacy, University of Porto, UCIBIO, REQUIMTE, Porto, Portugal
- Associate Laboratory i4HB Institute for Health and Bioeconomy, Faculty of Pharmacy, University of Porto, Porto, Portugal
- FP-I3ID (Instituto de Investigação, Inovação e Desenvolvimento), FP-BHS (Biomedical and Health Sciences Research Unit), Faculty of Health Sciences, University Fernando Pessoa, Porto, Portugal
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