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Kumar V, Banerjee A, Roy K. Breaking the Barriers: Machine-Learning-Based c-RASAR Approach for Accurate Blood-Brain Barrier Permeability Prediction. J Chem Inf Model 2024; 64:4298-4309. [PMID: 38700741 DOI: 10.1021/acs.jcim.4c00433] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/28/2024]
Abstract
The intricate nature of the blood-brain barrier (BBB) poses a significant challenge in predicting drug permeability, which is crucial for assessing central nervous system (CNS) drug efficacy and safety. This research utilizes an innovative approach, the classification read-across structure-activity relationship (c-RASAR) framework, that leverages machine learning (ML) to enhance the accuracy of BBB permeability predictions. The c-RASAR framework seamlessly integrates principles from both read-across and QSAR methodologies, underscoring the need to consider similarity-related aspects during the development of the c-RASAR model. It is crucial to note that the primary goal of this research is not to introduce yet another model for predicting BBB permeability but rather to showcase the refinement in predicting the BBB permeability of organic compounds through the introduction of a c-RASAR approach. This groundbreaking methodology aims to elevate the accuracy of assessing neuropharmacological implications and streamline the process of drug development. In this study, an ML-based c-RASAR linear discriminant analysis (LDA) model was developed using a dataset of 7807 compounds, encompassing both BBB-permeable and -nonpermeable substances sourced from the B3DB database (freely accessible from https://github.com/theochem/B3DB), for predicting BBB permeability in lead discovery for CNS drugs. The model's predictive capability was then validated using three external sets: one containing 276,518 natural products (NPs) from the LOTUS database (accessible from https://lotus.naturalproducts.net/download) for data gap filling, another comprising 13,002 drug-like/drug compounds from the DrugBank database (available from https://go.drugbank.com/), and a third set of 56 FDA-approved drugs to assess the model's reliability. Further diversifying the predictive arsenal, various other ML-based c-RASAR models were also developed for comparison purposes. The proposed c-RASAR framework emerged as a powerful tool for predicting BBB permeability. This research not only advances the understanding of molecular determinants influencing CNS drug permeability but also provides a versatile computational platform for the rapid assessment of diverse compounds, facilitating informed decision-making in drug development and design.
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Affiliation(s)
- Vinay Kumar
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700032, India
| | - Arkaprava Banerjee
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700032, India
| | - Kunal Roy
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700032, India
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2
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Dehnbostel FO, Dixit VA, Preissner R, Banerjee P. Non-animal models for blood-brain barrier permeability evaluation of drug-like compounds. Sci Rep 2024; 14:8908. [PMID: 38632344 PMCID: PMC11024088 DOI: 10.1038/s41598-024-59734-9] [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] [Received: 07/21/2023] [Accepted: 04/15/2024] [Indexed: 04/19/2024] Open
Abstract
Diseases related to the central nervous system (CNS) are major health concerns and have serious social and economic impacts. Developing new drugs for CNS-related disorders presents a major challenge as it actively involves delivering drugs into the CNS. Therefore, it is imperative to develop in silico methodologies to reliably identify potential lead compounds that can penetrate the blood-brain barrier (BBB) and help to thoroughly understand the role of different physicochemical properties fundamental to the BBB permeation of molecules. In this study, we have analysed the chemical space of the CNS drugs and compared it to the non-CNS-approved drugs. Additionally, we have collected a feature selection dataset from Muehlbacher et al. (J Comput Aided Mol Des 25(12):1095-1106, 2011. 10.1007/s10822-011-9478-1) and an in-house dataset. This information was utilised to design a molecular fingerprint that was used to train machine learning (ML) models. The best-performing models reported in this study achieved accuracies of 0.997 and 0.98, sensitivities of 1.0 and 0.992, specificities of 0.971 and 0.962, MCCs of 0.984 and 0.958, and ROC-AUCs of 0.997 and 0.999 on an imbalanced and a balanced dataset, respectively. They demonstrated overall good accuracies and sensitivities in the blind validation dataset. The reported models can be applied for fast and early screening drug-like molecules with BBB potential. Furthermore, the bbbPythoN package can be used by the research community to both produce the BBB-specific molecular fingerprints and employ the models mentioned earlier for BBB-permeability prediction.
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Affiliation(s)
- Frederic O Dehnbostel
- Institute for Physiology, Charité - University Medicine Berlin, 10115, Berlin, Germany
| | - Vaibhav A Dixit
- Department of Medicinal Chemistry, Department of Pharmaceuticals, National Institute of Pharmaceutical Education and Research, Guwahati, (NIPER Gu-Wahati), Ministry of Chemicals and Fertilizers, Government of India, Sila Katamur (Halugurisuk), Kamrup, P.O.: Changsari, Guwahati, Assam, 781101, India
| | - Robert Preissner
- Institute for Physiology, Charité - University Medicine Berlin, 10115, Berlin, Germany
| | - Priyanka Banerjee
- Institute for Physiology, Charité - University Medicine Berlin, 10115, Berlin, Germany.
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3
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Qiu Y, Cheng F. Artificial intelligence for drug discovery and development in Alzheimer's disease. Curr Opin Struct Biol 2024; 85:102776. [PMID: 38335558 DOI: 10.1016/j.sbi.2024.102776] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Revised: 12/29/2023] [Accepted: 01/15/2024] [Indexed: 02/12/2024]
Abstract
The complex molecular mechanism and pathophysiology of Alzheimer's disease (AD) limits the development of effective therapeutics or prevention strategies. Artificial Intelligence (AI)-guided drug discovery combined with genetics/multi-omics (genomics, epigenomics, transcriptomics, proteomics, and metabolomics) analysis contributes to the understanding of the pathophysiology and precision medicine of the disease, including AD and AD-related dementia. In this review, we summarize the AI-driven methodologies for AD-agnostic drug discovery and development, including de novo drug design, virtual screening, and prediction of drug-target interactions, all of which have shown potentials. In particular, AI-based drug repurposing emerges as a compelling strategy to identify new indications for existing drugs for AD. We provide several emerging AD targets from human genetics and multi-omics findings and highlight recent AI-based technologies and their applications in drug discovery using AD as a prototypical example. In closing, we discuss future challenges and directions in AI-based drug discovery for AD and other neurodegenerative diseases.
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Affiliation(s)
- Yunguang Qiu
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA. https://twitter.com/YunguangQiu
| | - Feixiong Cheng
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA; Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH 44195, USA; Cleveland Clinic Genome Center, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA.
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4
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Dichiara M, Cosentino G, Giordano G, Pasquinucci L, Marrazzo A, Costanzo G, Amata E. Designing drugs optimized for both blood-brain barrier permeation and intra-cerebral partition. Expert Opin Drug Discov 2024; 19:317-329. [PMID: 38145409 DOI: 10.1080/17460441.2023.2294118] [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: 10/08/2023] [Accepted: 12/07/2023] [Indexed: 12/26/2023]
Abstract
INTRODUCTION With the increasing incidence and prevalence of neurological disorders globally, there is a paramount need for new pharmacotherapies. BBB effectively protects the brain but raises a profound challenge to drug permeation, with less than 2% of most drugs reaching the CNS. AREAS COVERED This article reviews aspects of the most recent design strategies, providing insights into ideas and concepts in CNS drug discovery. An overview of the products available on the market is given and why clinical trials are continuously failing is discussed. EXPERT OPINION Among the available CNS drugs, small molecules account for most successful CNS therapeutics due to their ability to penetrate the BBB through passive or carrier-mediated mechanisms. The development of new CNS drugs is very difficult. To date, there is a lack of effective drugs for alleviating or even reversing the progression of brain diseases. Particularly, the use of artificial intelligence strategies, together with more appropriate animal models, may enable the design of molecules with appropriate permeation, to elicit a biological response from the neurotherapeutic target.
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Affiliation(s)
- Maria Dichiara
- Dipartimento di Scienze del Farmaco e della Salute, Università degli Studi di Catania, Catania, Italy
| | - Giuseppe Cosentino
- Dipartimento di Scienze del Farmaco e della Salute, Università degli Studi di Catania, Catania, Italy
| | - Giorgia Giordano
- Dipartimento di Scienze del Farmaco e della Salute, Università degli Studi di Catania, Catania, Italy
| | - Lorella Pasquinucci
- Dipartimento di Scienze del Farmaco e della Salute, Università degli Studi di Catania, Catania, Italy
| | - Agostino Marrazzo
- Dipartimento di Scienze del Farmaco e della Salute, Università degli Studi di Catania, Catania, Italy
| | - Giuliana Costanzo
- Dipartimento di Scienze del Farmaco e della Salute, Università degli Studi di Catania, Catania, Italy
| | - Emanuele Amata
- Dipartimento di Scienze del Farmaco e della Salute, Università degli Studi di Catania, Catania, Italy
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5
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Dong H, Lin J, Tao Y, Jia Y, Sun L, Li WJ, Sun H. AI-enhanced biomedical micro/nanorobots in microfluidics. LAB ON A CHIP 2024; 24:1419-1440. [PMID: 38174821 DOI: 10.1039/d3lc00909b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2024]
Abstract
Human beings encompass sophisticated microcirculation and microenvironments, incorporating a broad spectrum of microfluidic systems that adopt fundamental roles in orchestrating physiological mechanisms. In vitro recapitulation of human microenvironments based on lab-on-a-chip technology represents a critical paradigm to better understand the intricate mechanisms. Moreover, the advent of micro/nanorobotics provides brand new perspectives and dynamic tools for elucidating the complex process in microfluidics. Currently, artificial intelligence (AI) has endowed micro/nanorobots (MNRs) with unprecedented benefits, such as material synthesis, optimal design, fabrication, and swarm behavior. Using advanced AI algorithms, the motion control, environment perception, and swarm intelligence of MNRs in microfluidics are significantly enhanced. This emerging interdisciplinary research trend holds great potential to propel biomedical research to the forefront and make valuable contributions to human health. Herein, we initially introduce the AI algorithms integral to the development of MNRs. We briefly revisit the components, designs, and fabrication techniques adopted by robots in microfluidics with an emphasis on the application of AI. Then, we review the latest research pertinent to AI-enhanced MNRs, focusing on their motion control, sensing abilities, and intricate collective behavior in microfluidics. Furthermore, we spotlight biomedical domains that are already witnessing or will undergo game-changing evolution based on AI-enhanced MNRs. Finally, we identify the current challenges that hinder the practical use of the pioneering interdisciplinary technology.
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Affiliation(s)
- Hui Dong
- School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou, China.
- School of Mechatronics Engineering, Harbin Institute of Technology, Harbin, China
- State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin, China
| | - Jiawen Lin
- School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou, China.
| | - Yihui Tao
- Department of Automation Control and System Engineering, University of Sheffield, Sheffield, UK
| | - Yuan Jia
- Sino-German College of Intelligent Manufacturing, Shenzhen Technology University, Shenzhen, China
| | - Lining Sun
- School of Mechatronics Engineering, Harbin Institute of Technology, Harbin, China
- State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin, China
| | - Wen Jung Li
- Department of Mechanical Engineering, City University of Hong Kong, Hong Kong, China
| | - Hao Sun
- School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou, China.
- School of Mechatronics Engineering, Harbin Institute of Technology, Harbin, China
- Research Center of Aerospace Mechanism and Control, Harbin Institute of Technology, Harbin, China
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6
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Abbasi EY, Deng Z, Ali Q, Khan A, Shaikh A, Reshan MSA, Sulaiman A, Alshahrani H. A machine learning and deep learning-based integrated multi-omics technique for leukemia prediction. Heliyon 2024; 10:e25369. [PMID: 38352790 PMCID: PMC10862685 DOI: 10.1016/j.heliyon.2024.e25369] [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: 08/25/2023] [Revised: 12/13/2023] [Accepted: 01/25/2024] [Indexed: 02/16/2024] Open
Abstract
In recent years, scientific data on cancer has expanded, providing potential for a better understanding of malignancies and improved tailored care. Advances in Artificial Intelligence (AI) processing power and algorithmic development position Machine Learning (ML) and Deep Learning (DL) as crucial players in predicting Leukemia, a blood cancer, using integrated multi-omics technology. However, realizing these goals demands novel approaches to harness this data deluge. This study introduces a novel Leukemia diagnosis approach, analyzing multi-omics data for accuracy using ML and DL algorithms. ML techniques, including Random Forest (RF), Naive Bayes (NB), Decision Tree (DT), Logistic Regression (LR), Gradient Boosting (GB), and DL methods such as Recurrent Neural Networks (RNN) and Feedforward Neural Networks (FNN) are compared. GB achieved 97 % accuracy in ML, while RNN outperformed by achieving 98 % accuracy in DL. This approach filters unclassified data effectively, demonstrating the significance of DL for leukemia prediction. The testing validation was based on 17 different features such as patient age, sex, mutation type, treatment methods, chromosomes, and others. Our study compares ML and DL techniques and chooses the best technique that gives optimum results. The study emphasizes the implications of high-throughput technology in healthcare, offering improved patient care.
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Affiliation(s)
- Erum Yousef Abbasi
- State Key Laboratory of Wireless Network Positioning and Communication Engineering Integration Research, School of Electronics Engineering, Beijing University of Posts and Telecommunications, Beijing, China
| | - Zhongliang Deng
- State Key Laboratory of Wireless Network Positioning and Communication Engineering Integration Research, School of Electronics Engineering, Beijing University of Posts and Telecommunications, Beijing, China
| | - Qasim Ali
- Department of Software Engineering, Mehran University of Engineering and Technology, Jamshoro, Pakistan
| | - Adil Khan
- State Key Laboratory of Wireless Network Positioning and Communication Engineering Integration Research, School of Electronics Engineering, Beijing University of Posts and Telecommunications, Beijing, China
| | - Asadullah Shaikh
- Department of Information Systems, College of Computer Science and Information Systems, Najran University, Najran, 61441, Saudi Arabia
| | - Mana Saleh Al Reshan
- Department of Information Systems, College of Computer Science and Information Systems, Najran University, Najran, 61441, Saudi Arabia
- Scientific and Engineering Research Centre, Najran University, Najran, 61441, Saudi Arabia
| | - Adel Sulaiman
- Department of Computer Science, College of Computer Science and Information Systems, Najran University, Najran, 61441, Saudi Arabia
| | - Hani Alshahrani
- Department of Computer Science, College of Computer Science and Information Systems, Najran University, Najran, 61441, Saudi Arabia
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7
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Komura H, Watanabe R, Mizuguchi K. The Trends and Future Prospective of In Silico Models from the Viewpoint of ADME Evaluation in Drug Discovery. Pharmaceutics 2023; 15:2619. [PMID: 38004597 PMCID: PMC10675155 DOI: 10.3390/pharmaceutics15112619] [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: 10/09/2023] [Revised: 11/05/2023] [Accepted: 11/07/2023] [Indexed: 11/26/2023] Open
Abstract
Drug discovery and development are aimed at identifying new chemical molecular entities (NCEs) with desirable pharmacokinetic profiles for high therapeutic efficacy. The plasma concentrations of NCEs are a biomarker of their efficacy and are governed by pharmacokinetic processes such as absorption, distribution, metabolism, and excretion (ADME). Poor ADME properties of NCEs are a major cause of attrition in drug development. ADME screening is used to identify and optimize lead compounds in the drug discovery process. Computational models predicting ADME properties have been developed with evolving model-building technologies from a simplified relationship between ADME endpoints and physicochemical properties to machine learning, including support vector machines, random forests, and convolution neural networks. Recently, in the field of in silico ADME research, there has been a shift toward evaluating the in vivo parameters or plasma concentrations of NCEs instead of using predictive results to guide chemical structure design. Another research hotspot is the establishment of a computational prediction platform to strengthen academic drug discovery. Bioinformatics projects have produced a series of in silico ADME models using free software and open-access databases. In this review, we introduce prediction models for various ADME parameters and discuss the currently available academic drug discovery platforms.
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Affiliation(s)
- Hiroshi Komura
- University Research Administration Center, Osaka Metropolitan University, 1-2-7 Asahimachi, Abeno-ku, Osaka 545-0051, Osaka, Japan
| | - Reiko Watanabe
- Institute for Protein Research, Osaka University, 3-2 Yamadaoka, Suita 565-0871, Osaka, Japan; (R.W.); (K.M.)
- Artificial Intelligence Centre for Health and Biomedical Research, National Institutes of Biomedical Innovation, Health, and Nutrition (NIBIOHN), 3-17 Senrioka-shinmachi, Settu 566-0002, Osaka, Japan
| | - Kenji Mizuguchi
- Institute for Protein Research, Osaka University, 3-2 Yamadaoka, Suita 565-0871, Osaka, Japan; (R.W.); (K.M.)
- Artificial Intelligence Centre for Health and Biomedical Research, National Institutes of Biomedical Innovation, Health, and Nutrition (NIBIOHN), 3-17 Senrioka-shinmachi, Settu 566-0002, Osaka, Japan
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8
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Shen R, Zhao W, Li X, Liu J, Yang A, Kou X. Emodin derivatives as promising multi-aspect intervention agents for amyloid aggregation: molecular docking/dynamics simulation, bioactivities evaluation, and cytoprotection. Mol Divers 2023:10.1007/s11030-023-10733-4. [PMID: 37737959 DOI: 10.1007/s11030-023-10733-4] [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: 05/09/2023] [Accepted: 09/15/2023] [Indexed: 09/23/2023]
Abstract
Alzheimer's disease (AD) is a progressive neurodegenerative disease with complex pathogenesis. Despite the pathogenesis is unknown, the misfolding and accumulation of β-amyloid (Aβ) peptide play the important role in the occurrence and development of AD. Hence, multi-aspect intervention of the misfolded Aβ peptides aggregation is a promising therapy for AD. In previous work, we obtained the emodin derivatives (a-d) with multifunctional anti-AD activities, including metal ions chelation, cholinesterase inhibition, and hydroxyl/superoxide anion radical elimination. In this work, we predicted the interaction of emodin derivatives (a-d) with Aβ by combining molecular docking simulation and molecular dynamics simulation, and evaluated the ability to intervene with the self-, Cu2+- and AChE-induced Aβ aggregation via in vitro methods. The results indicated that a-d could act as the potent multi-aspect intervention agents for Aβ aggregation. In addition, a-d could effectively eliminate peroxyl radical, had virtually no neurotoxicity, and protect cells from oxidative and Aβ-induced damage. The prediction results of ADMET properties showed that a-d had suitable pharmacokinetic characteristics. It suggested that a-d could act as the promising multi-targeted directed ligands (MTDLs) for AD. These results may provide meaningful information for the development of the potential MTDLs for AD which are modified from natural-origin scaffolds.
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Affiliation(s)
- Rui Shen
- School of Chinese Materia Medica, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, People's Republic of China
| | - Wenshuang Zhao
- School of Chinese Materia Medica, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, People's Republic of China
| | - Xiangyu Li
- School of Chinese Materia Medica, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, People's Republic of China
| | - Juanjuan Liu
- School of Chinese Materia Medica, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, People's Republic of China
| | - Aihong Yang
- School of Chinese Materia Medica, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, People's Republic of China.
| | - Xiaodi Kou
- School of Chinese Materia Medica, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, People's Republic of China.
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9
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Tomitaka A, Vashist A, Kolishetti N, Nair M. Machine learning assisted-nanomedicine using magnetic nanoparticles for central nervous system diseases. NANOSCALE ADVANCES 2023; 5:4354-4367. [PMID: 37638161 PMCID: PMC10448356 DOI: 10.1039/d3na00180f] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Accepted: 07/24/2023] [Indexed: 08/29/2023]
Abstract
Magnetic nanoparticles possess unique properties distinct from other types of nanoparticles developed for biomedical applications. Their unique magnetic properties and multifunctionalities are especially beneficial for central nervous system (CNS) disease therapy and diagnostics, as well as targeted and personalized applications using image-guided therapy and theranostics. This review discusses the recent development of magnetic nanoparticles for CNS applications, including Alzheimer's disease, Parkinson's disease, epilepsy, multiple sclerosis, and drug addiction. Machine learning (ML) methods are increasingly applied towards the processing, optimization and development of nanomaterials. By using data-driven approach, ML has the potential to bridge the gap between basic research and clinical research. We review ML approaches used within the various stages of nanomedicine development, from nanoparticle synthesis and characterization to performance prediction and disease diagnosis.
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Affiliation(s)
- Asahi Tomitaka
- Department of Computer and Information Sciences, College of Natural and Applied Science, University of Houston-Victoria Texas 77901 USA
| | - Arti Vashist
- Department of Immunology and Nano-Medicine, Herbert Wertheim College of Medicine, Florida International University Miami Florida 33199 USA
- Institute of NeuroImmune Pharmacology, Centre for Personalized Nanomedicine, Herbert Wertheim College of Medicine, Florida International University Miami Florida 33199 USA
| | - Nagesh Kolishetti
- Department of Immunology and Nano-Medicine, Herbert Wertheim College of Medicine, Florida International University Miami Florida 33199 USA
- Institute of NeuroImmune Pharmacology, Centre for Personalized Nanomedicine, Herbert Wertheim College of Medicine, Florida International University Miami Florida 33199 USA
| | - Madhavan Nair
- Department of Immunology and Nano-Medicine, Herbert Wertheim College of Medicine, Florida International University Miami Florida 33199 USA
- Institute of NeuroImmune Pharmacology, Centre for Personalized Nanomedicine, Herbert Wertheim College of Medicine, Florida International University Miami Florida 33199 USA
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10
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Mazumdar B, Deva Sarma PK, Mahanta HJ, Sastry GN. Machine learning based dynamic consensus model for predicting blood-brain barrier permeability. Comput Biol Med 2023; 160:106984. [PMID: 37137267 DOI: 10.1016/j.compbiomed.2023.106984] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Revised: 03/27/2023] [Accepted: 04/27/2023] [Indexed: 05/05/2023]
Abstract
The blood-brain barrier (BBB) is an important defence mechanism that restricts disease-causing pathogens and toxins to enter the brain from the bloodstream. In recent years, many in silico methods were proposed for predicting BBB permeability, however, the reliability of these models is questionable due to the smaller and class-imbalance dataset which subsequently leads to a very high false positive rate. In this study, machine learning and deep learning-based predictive models were built using XGboost, Random Forest, Extra-tree classifiers and deep neural network. A dataset of 8153 compounds comprising both the BBB permeable and BBB non-permeable was curated and subjected to calculations of molecular descriptors and fingerprints for generating the features for machine learning and deep learning models. Three balancing techniques were then applied to the dataset to address the class-imbalance issue. A comprehensive comparison among the models showed that the deep neural network model generated on the balanced MACCS fingerprint dataset outperformed with an accuracy of 97.8% and a ROC-AUC score of 0.98 among all the models. Additionally, a dynamic consensus model was prepared with the machine learning models and validated with a benchmark dataset for predicting BBB permeability with higher confidence scores.
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Affiliation(s)
- Bitopan Mazumdar
- Department of Computer Science, Assam University, Silchar, 788011, Assam, India; Advanced Computation and Data Sciences Division, CSIR- North East Institute of Science and Technology, Jorhat, 785006, Assam, India
| | | | - Hridoy Jyoti Mahanta
- Advanced Computation and Data Sciences Division, CSIR- North East Institute of Science and Technology, Jorhat, 785006, Assam, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, Uttar Pradesh, India.
| | - G Narahari Sastry
- Advanced Computation and Data Sciences Division, CSIR- North East Institute of Science and Technology, Jorhat, 785006, Assam, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, Uttar Pradesh, India
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11
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Jabin A, Uddin MF, Al Azad S, Rahman A, Tabassum F, Sarker P, Morshed AKMH, Rahman S, Raisa FF, Sakib MR, Olive AH, Islam T, Tahsin R, Ahmed SZ, Biswas P, Habiba MU, Siddiquy M, Jafary M. Target-specificity of different amyrin subunits in impeding HCV influx mechanism inside the human cells considering the quantum tunnel profiles and molecular strings of the CD81 receptor: a combined in silico and in vivo study. In Silico Pharmacol 2023; 11:8. [PMID: 36999133 PMCID: PMC10052254 DOI: 10.1007/s40203-023-00144-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Accepted: 02/11/2023] [Indexed: 03/31/2023] Open
Abstract
HCV is a hepatotropic RNA virus recognized for its frequent virulence and fatality worldwide. Despite many vaccine development programs underway, researchers are on a quest for natural bioactive compounds due to their multivalent efficiencies against viral infections, considering which the current research aimed to figure out the target-specificity and therapeutic potentiality of α, β, and δ subunits of amyrin, as novel bioactive components against the HCV influx mechanism. Initially, the novelty of amyrin subunits was conducted from 203 pharmacophores, comparing their in-silico pharmacokinetic and pharmacodynamic profiles. Besides, the best active site of CD81 was determined following the quantum tunneling algorithm. The molecular dynamic simulation was conducted (100 ns) following the molecular docking steps to reveal the parameters- RMSD (Å); Cα; RMSF (Å); MolSA (Å2); Rg (nm); PSA (Å); SASA (Å2), and the MM-GBSA dG binding scores. Besides, molecular strings of CD81, along with the co-expressed genes, were classified, as responsible for encoding CD81-mediated protein clusters during HCV infection, resulting in the potentiality of amyrins as targeted prophylactics in HCV infection. Finally, in vivo profiling of the oxidative stress marker, liver-specific enzymes, and antioxidant markers was conducted in the DMN-induced mice model, where β-amyrin scored the most significant values in all aspects.
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Affiliation(s)
- Anika Jabin
- grid.443020.10000 0001 2295 3329Department of Biochemistry and Microbiology, North South University, Dhaka, 1229 Bangladesh
| | - Mohammad Fahim Uddin
- grid.413273.00000 0001 0574 8737College of Material Science and Engineering, Zhejiang Sci-Tech University, Hangzhou, 310018 Zhejiang People’s Republic of China
| | - Salauddin Al Azad
- grid.258151.a0000 0001 0708 1323Key Laboratory of Industrial Biotechnology, Ministry of Education, School of Biotechnology, Jiangnan University, Wuxi, 214122 Jiangsu Province People’s Republic of China
| | - Ashfaque Rahman
- grid.443020.10000 0001 2295 3329Department of Biochemistry and Microbiology, North South University, Dhaka, 1229 Bangladesh
| | - Fawzia Tabassum
- grid.412506.40000 0001 0689 2212Department of Genetic Engineering and Biotechnology, Shahjalal University of Science and Technology, Sylhet, 3114 Bangladesh
| | - Pritthy Sarker
- grid.443020.10000 0001 2295 3329Department of Biochemistry and Microbiology, North South University, Dhaka, 1229 Bangladesh
| | - A K M Helal Morshed
- grid.207374.50000 0001 2189 3846Pathology and Pathophysiology Major, Academy of Medical Science, Zhengzhou University, Zhengzhou City, 450001 Henan Province People’s Republic of China
| | - Samiur Rahman
- grid.443020.10000 0001 2295 3329Department of Biochemistry and Microbiology, North South University, Dhaka, 1229 Bangladesh
| | - Fatima Fairuz Raisa
- grid.52681.380000 0001 0746 8691Department of Electrical and Electronic Engineering, Brac University, Dhaka, 1212 Bangladesh
| | - Musfiqur Rahman Sakib
- grid.449329.10000 0004 4683 9733Department of Pharmacy, Bangabandhu Sheikh Mujibur Rahman Science and Technology University, Gopalganj, 8100 Bangladesh
| | - Abeer Hasan Olive
- grid.442996.40000 0004 0451 6987Department of Pharmacy, East West University, Dhaka, 1212 Bangladesh
| | - Tabassum Islam
- grid.442996.40000 0004 0451 6987Department of Computer Science and Engineering, East West University, Dhaka, 1212 Bangladesh
| | - Ramisha Tahsin
- grid.443020.10000 0001 2295 3329Department of Pharmaceutical Sciences, North South University, Dhaka, 1229 Bangladesh
| | - Shahlaa Zernaz Ahmed
- grid.443020.10000 0001 2295 3329Department of Biochemistry and Microbiology, North South University, Dhaka, 1229 Bangladesh
| | - Partha Biswas
- Department of Genetic Engineering and Biotechnology, Jashore University of Science and Technology, Jashore, 7408 Bangladesh
| | - Mst. Umme Habiba
- Data Science Research Unit, RPG Interface Lab, Jashore, 7400 Bangladesh
| | - Mahbuba Siddiquy
- grid.258151.a0000 0001 0708 1323State Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi, 214122 Jiangsu Province People’s Republic of China
| | - Maryam Jafary
- grid.411705.60000 0001 0166 0922Division of Food Safety and Hygiene, Department of Environmental Health Engineering, School of Public Health, Tehran University of Medical Sciences, Tehran, 1416634793 Iran
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12
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Kumar M, Nguyen TPN, Kaur J, Singh TG, Soni D, Singh R, Kumar P. Opportunities and challenges in application of artificial intelligence in pharmacology. Pharmacol Rep 2023; 75:3-18. [PMID: 36624355 PMCID: PMC9838466 DOI: 10.1007/s43440-022-00445-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Revised: 12/23/2022] [Accepted: 12/25/2022] [Indexed: 01/11/2023]
Abstract
Artificial intelligence (AI) is a machine science that can mimic human behaviour like intelligent analysis of data. AI functions with specialized algorithms and integrates with deep and machine learning. Living in the digital world can generate a huge amount of medical data every day. Therefore, we need an automated and reliable evaluation tool that can make decisions more accurately and faster. Machine learning has the potential to learn, understand and analyse the data used in healthcare systems. In the last few years, AI is known to be employed in various fields in pharmaceutical science especially in pharmacological research. It helps in the analysis of preclinical (laboratory animals) and clinical (in human) trial data. AI also plays important role in various processes such as drug discovery/manufacturing, diagnosis of big data for disease identification, personalized treatment, clinical trial research, radiotherapy, surgical robotics, smart electronic health records, and epidemic outbreak prediction. Moreover, AI has been used in the evaluation of biomarkers and diseases. In this review, we explain various models and general processes of machine learning and their role in pharmacological science. Therefore, AI with deep learning and machine learning could be relevant in pharmacological research.
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Affiliation(s)
- Mandeep Kumar
- Department of Pharmacy, Unit of Pharmacology and Toxicology, University of Genoa, Genoa, Italy
| | - T P Nhung Nguyen
- Department of Pharmacy, Unit of Pharmacology and Toxicology, University of Genoa, Genoa, Italy
- Department of Pharmacy, Da Nang University of Medical Technology and Pharmacy, Da Nang, Vietnam
| | - Jasleen Kaur
- Department of Pharmacology and Toxicology, National Institute of Pharmaceutical Education and Research (NIPER), Lucknow, Uttar Pradesh, 226002, India
| | | | - Divya Soni
- Department of Pharmacology, Central University of Punjab, Ghudda, Bathinda, Punjab, 151401, India
| | - Randhir Singh
- Department of Pharmacology, Central University of Punjab, Ghudda, Bathinda, Punjab, 151401, India
| | - Puneet Kumar
- Department of Pharmacology, Central University of Punjab, Ghudda, Bathinda, Punjab, 151401, India.
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13
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Recent Studies of Artificial Intelligence on In Silico Drug Distribution Prediction. Int J Mol Sci 2023; 24:ijms24031815. [PMID: 36768139 PMCID: PMC9915725 DOI: 10.3390/ijms24031815] [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: 12/14/2022] [Revised: 01/11/2023] [Accepted: 01/13/2023] [Indexed: 01/19/2023] Open
Abstract
Drug distribution is an important process in pharmacokinetics because it has the potential to influence both the amount of medicine reaching the active sites and the effectiveness as well as safety of the drug. The main causes of 90% of drug failures in clinical development are lack of efficacy and uncontrolled toxicity. In recent years, several advances and promising developments in drug distribution property prediction have been achieved, especially in silico, which helped to drastically reduce the time and expense of screening undesired drug candidates. In this study, we provide comprehensive knowledge of drug distribution background, influencing factors, and artificial intelligence-based distribution property prediction models from 2019 to the present. Additionally, we gathered and analyzed public databases and datasets commonly utilized by the scientific community for distribution prediction. The distribution property prediction performance of five large ADMET prediction tools is mentioned as a benchmark for future research. On this basis, we also offer future challenges in drug distribution prediction and research directions. We hope that this review will provide researchers with helpful insight into distribution prediction, thus facilitating the development of innovative approaches for drug discovery.
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14
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Shen R, Chen Y, Li X, Wang X, Yang A, Kou X. Carrier-free Chinese herbal small molecules self-assembly with 3D-porous crystal framework as a synergistic anti-AD agent. Int J Pharm 2023; 630:122458. [PMID: 36462740 DOI: 10.1016/j.ijpharm.2022.122458] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2022] [Revised: 11/25/2022] [Accepted: 11/26/2022] [Indexed: 12/02/2022]
Abstract
Alzheimer's disease (AD) is a devastating neurodegenerative disease caused by multiple factors. Single-target drugs have limited efficacy for AD treatment. Therefore, multi-target intervention strategy has great potential. Traditional Chinese medicine (TCM) is mostly used in the form of compound prescription, which has the polypharmacology behavior. Rhizoma Coptidis and Radix et Rhizoma Rhei are frequently used as the couplet medicines of TCM for AD therapy. In this study, the novel carrier-free nanoassembly with 3D-porous frame crystal structure has formulated from supramolecular self-assembly of berberine (BER) and rhein (RHE), the main active components of Rhizoma Coptidis and Radix et Rhizoma Rhei, respectively. Combining with the spectral data and single crystal structure, the self-assembly process was clarified as dominated by electrostatic interaction and π-π stacking. In vitro release property, cholinesterase (ChE) inhibition, β-amyloid (Aβ) aggregation regulation, radical elimination, metal ions chelation and cytotoxicity assay indicated that the obtained BER-RHE assembly had the Fickian diffusion-controlled sustained release ability, synergistic biological activities and virtually no neurotoxicity. In addition, in vivo reactive oxygen species (ROS) level evaluation showed that the assembly could reduce the accumulation of intracellular ROS in Caenorhabditis elegans (C. elegans). Meanwhile, BER-RHE assembly could also be used as a novel potential carrier for drug delivery due to its superior 3D-porous frame. This green and facile strategy for carrier-free nanoassembly microscopic construction via supramolecular self-assembly might provide inspiration for the development of multi-target therapy for AD and the design of the novel drug delivery system.
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Affiliation(s)
- Rui Shen
- School of Chinese Materia Medica, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
| | - Yuhong Chen
- School of Chinese Materia Medica, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
| | - Xiangyu Li
- School of Chinese Materia Medica, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
| | - Xi Wang
- School of Chinese Materia Medica, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
| | - Aihong Yang
- School of Chinese Materia Medica, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China.
| | - Xiaodi Kou
- School of Chinese Materia Medica, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China.
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15
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Blood brain barrier-on-a-chip to model neurological diseases. J Drug Deliv Sci Technol 2023. [DOI: 10.1016/j.jddst.2023.104174] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
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16
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Hasanzadeh A, Hamblin MR, Kiani J, Noori H, Hardie JM, Karimi M, Shafiee H. Could artificial intelligence revolutionize the development of nanovectors for gene therapy and mRNA vaccines? NANO TODAY 2022; 47:101665. [PMID: 37034382 PMCID: PMC10081506 DOI: 10.1016/j.nantod.2022.101665] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Gene therapy enables the introduction of nucleic acids like DNA and RNA into host cells, and is expected to revolutionize the treatment of a wide range of diseases. This growth has been further accelerated by the discovery of CRISPR/Cas technology, which allows accurate genomic editing in a broad range of cells and organisms in vitro and in vivo. Despite many advances in gene delivery and the development of various viral and non-viral gene delivery vectors, the lack of highly efficient non-viral systems with low cellular toxicity remains a challenge. The application of cutting-edge technologies such as artificial intelligence (AI) has great potential to find new paradigms to solve this issue. Herein, we review AI and its major subfields including machine learning (ML), neural networks (NNs), expert systems, deep learning (DL), computer vision and robotics. We discuss the potential of AI-based models and algorithms in the design of targeted gene delivery vehicles capable of crossing extracellular and intracellular barriers by viral mimicry strategies. We finally discuss the role of AI in improving the function of CRISPR/Cas systems, developing novel nanobots, and mRNA vaccine carriers.
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Affiliation(s)
- Akbar Hasanzadeh
- Cellular and Molecular Research Center, Iran University of Medical Sciences, Tehran 1449614535, Iran
- Department of Medical Nanotechnology, Faculty of Advanced Technologies in Medicine, Iran University of Medical Sciences, Tehran 1449614535, Iran
| | - Michael R Hamblin
- Laser Research Centre, Faculty of Health Science, University of Johannesburg, Doornfontein 2028, South Africa
- Radiation Biology Research Center, Iran University of Medical Sciences, Tehran, Iran
| | - Jafar Kiani
- Oncopathology Research Center, Iran University of Medical Sciences, Tehran 1449614535, Iran
- Department of Molecular Medicine, Faculty of Advanced Technologies in Medicine, Iran University of Medical Sciences, Tehran, Iran
| | - Hamid Noori
- Cellular and Molecular Research Center, Iran University of Medical Sciences, Tehran 1449614535, Iran
- Department of Medical Nanotechnology, Faculty of Advanced Technologies in Medicine, Iran University of Medical Sciences, Tehran 1449614535, Iran
| | - Joseph M. Hardie
- Division of Engineering in Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, 02139 USA
| | - Mahdi Karimi
- Cellular and Molecular Research Center, Iran University of Medical Sciences, Tehran 1449614535, Iran
- Department of Medical Nanotechnology, Faculty of Advanced Technologies in Medicine, Iran University of Medical Sciences, Tehran 1449614535, Iran
- Oncopathology Research Center, Iran University of Medical Sciences, Tehran 1449614535, Iran
- Research Center for Science and Technology in Medicine, Tehran University of Medical Sciences, Tehran 141556559, Iran
- Applied Biotechnology Research Centre, Tehran Medical Science, Islamic Azad University, Tehran 1584743311, Iran
| | - Hadi Shafiee
- Division of Engineering in Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, 02139 USA
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17
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Faramarzi S, Kim MT, Volpe DA, Cross KP, Chakravarti S, Stavitskaya L. Development of QSAR models to predict blood-brain barrier permeability. Front Pharmacol 2022; 13:1040838. [PMID: 36339562 PMCID: PMC9633177 DOI: 10.3389/fphar.2022.1040838] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Accepted: 10/10/2022] [Indexed: 07/29/2023] Open
Abstract
Assessing drug permeability across the blood-brain barrier (BBB) is important when evaluating the abuse potential of new pharmaceuticals as well as developing novel therapeutics that target central nervous system disorders. One of the gold-standard in vivo methods for determining BBB permeability is rodent log BB; however, like most in vivo methods, it is time-consuming and expensive. In the present study, two statistical-based quantitative structure-activity relationship (QSAR) models were developed to predict BBB permeability of drugs based on their chemical structure. The in vivo BBB permeability data were harvested for 921 compounds from publicly available literature, non-proprietary drug approval packages, and University of Washington's Drug Interaction Database. The cross-validation performance statistics for the BBB models ranged from 82 to 85% in sensitivity and 80-83% in negative predictivity. Additionally, the performance of newly developed models was assessed using an external validation set comprised of 83 chemicals. Overall, performance of individual models ranged from 70 to 75% in sensitivity, 70-72% in negative predictivity, and 78-86% in coverage. The predictive performance was further improved to 93% in coverage by combining predictions across the two software programs. These new models can be rapidly deployed to predict blood brain barrier permeability of pharmaceutical candidates and reduce the use of experimental animals.
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Affiliation(s)
- Sadegh Faramarzi
- US Food and Drug Administration, Center for Drug Evaluation and Research, Silver Spring, MD, United States
| | - Marlene T. Kim
- US Food and Drug Administration, Center for Drug Evaluation and Research, Silver Spring, MD, United States
| | - Donna A. Volpe
- US Food and Drug Administration, Center for Drug Evaluation and Research, Silver Spring, MD, United States
| | | | | | - Lidiya Stavitskaya
- US Food and Drug Administration, Center for Drug Evaluation and Research, Silver Spring, MD, United States
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18
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Kou X, Hu C, Shi X, Li X, Yang A, Shen R. A multifunctional metal regulator as the potential theranostic agent: Design, synthesis, anti-AD activities and metallic ion sensing properties. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2022; 274:121110. [PMID: 35276472 DOI: 10.1016/j.saa.2022.121110] [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: 01/29/2022] [Revised: 03/01/2022] [Accepted: 03/03/2022] [Indexed: 06/14/2023]
Abstract
Although there is no cure for Alzheimer's disease (AD) due to its complex pathogenesis, early detection and treatment can help delay the development of the disease. So, it is necessary to develop multifunctional metal regulators that can integrate the therapeutics and diagnostics effect against AD. In this work, N-(anthracene-9-ylmethylene)benzohydrazide (probe 1), a fluorescent probe with imine and carbonyl as chelating sites was designed and synthesized. Results showed that 1 had good activities related to AD, such as regulation of metal homeostasis, inhibition of β-amyloid (Aβ) aggregation and scavenging of reactive oxygen species. The selectivity experiment showed that probe 1 had a good recognition effect on Cu2+. Fluorescence imaging assay also indicated that probe 1 had a good fluorescence imaging effect on Cu2+ in living cells. Furthermore, probe 1 had showed no cytotoxicity and good BBB permeability. These results indicated that probe 1 had potential diagnostic and therapeutic capabilities, and can be used as the multifunctional theranostic agent for AD.
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Affiliation(s)
- Xiaodi Kou
- School of Chinese Materia Medica, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, PR China
| | - Chengting Hu
- School of Chinese Materia Medica, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, PR China
| | - Xuli Shi
- School of Chinese Materia Medica, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, PR China
| | - Xingying Li
- School of Chinese Materia Medica, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, PR China
| | - Aihong Yang
- School of Chinese Materia Medica, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, PR China.
| | - Rui Shen
- School of Chinese Materia Medica, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, PR China.
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19
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Ding Y, Jiang X, Kim Y. Relational graph convolutional networks for predicting blood-brain barrier penetration of drug molecules. Bioinformatics 2022; 38:2826-2831. [PMID: 35561199 PMCID: PMC9113341 DOI: 10.1093/bioinformatics/btac211] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Revised: 03/28/2022] [Accepted: 04/05/2022] [Indexed: 02/03/2023] Open
Abstract
MOTIVATION Evaluating the blood-brain barrier (BBB) permeability of drug molecules is a critical step in brain drug development. Traditional methods for the evaluation require complicated in vitro or in vivo testing. Alternatively, in silico predictions based on machine learning have proved to be a cost-efficient way to complement the in vitro and in vivo methods. However, the performance of the established models has been limited by their incapability of dealing with the interactions between drugs and proteins, which play an important role in the mechanism behind the BBB penetrating behaviors. To address this limitation, we employed the relational graph convolutional network (RGCN) to handle the drug-protein interactions as well as the properties of each individual drug. RESULTS The RGCN model achieved an overall accuracy of 0.872, an area under the receiver operating characteristic (AUROC) of 0.919 and an area under the precision-recall curve (AUPRC) of 0.838 for the testing dataset with the drug-protein interactions and the Mordred descriptors as the input. Introducing drug-drug similarity to connect structurally similar drugs in the data graph further improved the testing results, giving an overall accuracy of 0.876, an AUROC of 0.926 and an AUPRC of 0.865. In particular, the RGCN model was found to greatly outperform the LightGBM base model when evaluated with the drugs whose BBB penetration was dependent on drug-protein interactions. Our model is expected to provide high-confidence predictions of BBB permeability for drug prioritization in the experimental screening of BBB-penetrating drugs. AVAILABILITY AND IMPLEMENTATION The data and the codes are freely available at https://github.com/dingyan20/BBB-Penetration-Prediction. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Yan Ding
- Center for Secure Artificial Intelligence for Healthcare, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Xiaoqian Jiang
- Center for Secure Artificial Intelligence for Healthcare, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Yejin Kim
- To whom correspondence should be addressed.
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20
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Kumar R, Sharma A, Alexiou A, Bilgrami AL, Kamal MA, Ashraf GM. DeePred-BBB: A Blood Brain Barrier Permeability Prediction Model With Improved Accuracy. Front Neurosci 2022; 16:858126. [PMID: 35592264 PMCID: PMC9112838 DOI: 10.3389/fnins.2022.858126] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Accepted: 03/14/2022] [Indexed: 11/13/2022] Open
Abstract
The blood-brain barrier (BBB) is a selective and semipermeable boundary that maintains homeostasis inside the central nervous system (CNS). The BBB permeability of compounds is an important consideration during CNS-acting drug development and is difficult to formulate in a succinct manner. Clinical experiments are the most accurate method of measuring BBB permeability. However, they are time taking and labor-intensive. Therefore, numerous efforts have been made to predict the BBB permeability of compounds using computational methods. However, the accuracy of BBB permeability prediction models has always been an issue. To improve the accuracy of the BBB permeability prediction, we applied deep learning and machine learning algorithms to a dataset of 3,605 diverse compounds. Each compound was encoded with 1,917 features containing 1,444 physicochemical (1D and 2D) properties, 166 molecular access system fingerprints (MACCS), and 307 substructure fingerprints. The prediction performance metrics of the developed models were compared and analyzed. The prediction accuracy of the deep neural network (DNN), one-dimensional convolutional neural network, and convolutional neural network by transfer learning was found to be 98.07, 97.44, and 97.61%, respectively. The best performing DNN-based model was selected for the development of the “DeePred-BBB” model, which can predict the BBB permeability of compounds using their simplified molecular input line entry system (SMILES) notations. It could be useful in the screening of compounds based on their BBB permeability at the preliminary stages of drug development. The DeePred-BBB is made available at https://github.com/12rajnish/DeePred-BBB.
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Affiliation(s)
- Rajnish Kumar
- Amity Institute of Biotechnology, Amity University Uttar Pradesh, Lucknow, India
| | - Anju Sharma
- Department of Applied Science, Indian Institute of Information Technology Allahabad, Prayagraj, India
| | - Athanasios Alexiou
- Department of Science and Engineering, Novel Global Community Educational Foundation, Hebersham, NSW, Australia
- AFNP Med Austria, Vienna, Austria
| | - Anwar L. Bilgrami
- Department of Entomology, Rutgers, The State University of New Jersey, New Brunswick, NJ, United States
- Deanship of Scientific Research, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Mohammad Amjad Kamal
- Institutes for Systems Genetics, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Chengdu, China
- King Fahd Medical Research Center, King Abdulaziz University, Jeddah, Saudi Arabia
- Department of Pharmacy, Faculty of Allied Health Sciences, Daffodil International University, Dhaka, Bangladesh
- Enzymoics, Hebersham, NSW, Australia
- Novel Global Community Educational Foundation, Hebersham, NSW, Australia
| | - Ghulam Md Ashraf
- Pre-Clinical Research Unit, King Fahd Medical Research Center, King Abdulaziz University, Jeddah, Saudi Arabia
- Department of Medical Laboratory Sciences, Faculty of Applied Medical Sciences, King Abdulaziz University, Jeddah, Saudi Arabia
- *Correspondence: Ghulam Md Ashraf, ,
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21
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Parakkal S, Datta R, Das D. DeepBBBP: High accuracy Blood-Brain-Barrier Permeability Prediction with a Mixed Deep Learning Model. Mol Inform 2022; 41:e2100315. [PMID: 35393777 DOI: 10.1002/minf.202100315] [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: 11/24/2021] [Accepted: 04/07/2022] [Indexed: 11/05/2022]
Abstract
Blood-brain-barrier permeability (BBBP) is an important property that is used to establish the drug-likeness of a molecule, as it establishes whether the molecule can cross the BBB when desired. It also eliminates those molecules which are not supposed to cross the barrier, as doing so would lead to toxicity. BBBP can be measured in vivo, in vitro or in silico. With the advent and subsequent rise of in silico methods for virtual drug screening, quite a bit of work has been done to predict this feature using statistical machine learning (ML) and deep learning (DL) based methods. In this work a mixed DL-based model, consisting of a Multi-layer Perceptron (MLP) and Convolutional Neural Network layers, has been paired with Mol2vec. Mol2vec is a convenient and unsupervised machine learning technique which produces high-dimensional vector representations of molecules and its molecular substructures. These succinct vector representations are utilized as inputs to the mixed DL model that is used for BBBP predictions. Several well-known benchmarks incorporating BBBP data have been used for supervised training and prediction by our mixed DL model which demonstrates superior results when compared to existing ML and DL techniques used for predicting BBBP.
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22
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de Oliveira ECL, da Costa KS, Taube PS, Lima AH, Junior CDSDS. Biological Membrane-Penetrating Peptides: Computational Prediction and Applications. Front Cell Infect Microbiol 2022; 12:838259. [PMID: 35402305 PMCID: PMC8992797 DOI: 10.3389/fcimb.2022.838259] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Accepted: 02/21/2022] [Indexed: 12/14/2022] Open
Abstract
Peptides comprise a versatile class of biomolecules that present a unique chemical space with diverse physicochemical and structural properties. Some classes of peptides are able to naturally cross the biological membranes, such as cell membrane and blood-brain barrier (BBB). Cell-penetrating peptides (CPPs) and blood-brain barrier-penetrating peptides (B3PPs) have been explored by the biotechnological and pharmaceutical industries to develop new therapeutic molecules and carrier systems. The computational prediction of peptides’ penetration into biological membranes has been emerged as an interesting strategy due to their high throughput and low-cost screening of large chemical libraries. Structure- and sequence-based information of peptides, as well as atomistic biophysical models, have been explored in computer-assisted discovery strategies to classify and identify new structures with pharmacokinetic properties related to the translocation through biomembranes. Computational strategies to predict the permeability into biomembranes include cheminformatic filters, molecular dynamics simulations, artificial intelligence algorithms, and statistical models, and the choice of the most adequate method depends on the purposes of the computational investigation. Here, we exhibit and discuss some principles and applications of these computational methods widely used to predict the permeability of peptides into biomembranes, exhibiting some of their pharmaceutical and biotechnological applications.
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Affiliation(s)
- Ewerton Cristhian Lima de Oliveira
- Institute of Technology, Federal University of Pará, Belém, Brazil
- *Correspondence: Kauê Santana da Costa, ; Ewerton Cristhian Lima de Oliveira,
| | - Kauê Santana da Costa
- Laboratory of Computational Simulation, Institute of Biodiversity, Federal University of Western Pará, Santarém, Brazil
- *Correspondence: Kauê Santana da Costa, ; Ewerton Cristhian Lima de Oliveira,
| | - Paulo Sérgio Taube
- Laboratory of Computational Simulation, Institute of Biodiversity, Federal University of Western Pará, Santarém, Brazil
| | - Anderson H. Lima
- Laboratório de Planejamento e Desenvolvimento de Fármacos, Instituto de Ciências Exatas e Naturais, Universidade Federal do Pará, Belém, Brazil
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23
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Machine learning & deep learning in data-driven decision making of drug discovery and challenges in high-quality data acquisition in the pharmaceutical industry. Future Med Chem 2021; 14:245-270. [PMID: 34939433 DOI: 10.4155/fmc-2021-0243] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023] Open
Abstract
Predicting novel small molecule bioactivities for the target deconvolution, hit-to-lead optimization in drug discovery research, requires molecular representation. Previous reports have demonstrated that machine learning (ML) and deep learning (DL) have substantial implications in virtual screening, peptide synthesis, drug ADMET screening and biomarker discovery. These strategies can increase the positive outcomes in the drug discovery process without false-positive rates and can be achieved in a cost-effective way with a minimum duration of time by high-quality data acquisition. This review substantially discusses the recent updates in AI tools as cheminformatics application in medicinal chemistry for the data-driven decision making of drug discovery and challenges in high-quality data acquisition in the pharmaceutical industry while improving small-molecule bioactivities and properties.
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24
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Liu Q, Wan J, Wang G. A survey on computational methods in discovering protein inhibitors of SARS-CoV-2. Brief Bioinform 2021; 23:6384382. [PMID: 34623382 PMCID: PMC8524468 DOI: 10.1093/bib/bbab416] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 08/25/2021] [Accepted: 09/12/2021] [Indexed: 12/13/2022] Open
Abstract
The outbreak of acute respiratory disease in 2019, namely Coronavirus Disease-2019 (COVID-19), has become an unprecedented healthcare crisis. To mitigate the pandemic, there are a lot of collective and multidisciplinary efforts in facilitating the rapid discovery of protein inhibitors or drugs against COVID-19. Although many computational methods to predict protein inhibitors have been developed [
1–
5], few systematic reviews on these methods have been published. Here, we provide a comprehensive overview of the existing methods to discover potential inhibitors of COVID-19 virus, so-called severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). First, we briefly categorize and describe computational approaches by the basic algorithms involved in. Then we review the related biological datasets used in such predictions. Furthermore, we emphatically discuss current knowledge on SARS-CoV-2 inhibitors with the latest findings and development of computational methods in uncovering protein inhibitors against COVID-19.
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Affiliation(s)
- Qiaoming Liu
- Key Laboratory of Tree Genetics and Breeding, Northeast Forestry University, Harbin, Heilongjiang 150001, China
| | - Jun Wan
- Department of BioHealth Informatics, School of Informatics and Computing, Indiana University-Purdue University Indianapolis, Indianapolis, IN, USA.,Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Guohua Wang
- Key Laboratory of Tree Genetics and Breeding, Northeast Forestry University, Harbin, Heilongjiang 150001, China.,Information and Computer Engineering College, Northeast Forestry University, Harbin, Heilongjiang 150001, China
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Lynch MJ, Gobbo OL. Advances in Non-Animal Testing Approaches towards Accelerated Clinical Translation of Novel Nanotheranostic Therapeutics for Central Nervous System Disorders. NANOMATERIALS (BASEL, SWITZERLAND) 2021; 11:2632. [PMID: 34685073 PMCID: PMC8538557 DOI: 10.3390/nano11102632] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Revised: 09/21/2021] [Accepted: 10/01/2021] [Indexed: 12/11/2022]
Abstract
Nanotheranostics constitute a novel drug delivery system approach to improving systemic, brain-targeted delivery of diagnostic imaging agents and pharmacological moieties in one rational carrier platform. While there have been notable successes in this field, currently, the clinical translation of such delivery systems for the treatment of neurological disorders has been limited by the inadequacy of correlating in vitro and in vivo data on blood-brain barrier (BBB) permeation and biocompatibility of nanomaterials. This review aims to identify the most contemporary non-invasive approaches for BBB crossing using nanotheranostics as a novel drug delivery strategy and current non-animal-based models for assessing the safety and efficiency of such formulations. This review will also address current and future directions of select in vitro models for reducing the cumbersome and laborious mandate for testing exclusively in animals. It is hoped these non-animal-based modelling approaches will facilitate researchers in optimising promising multifunctional nanocarriers with a view to accelerating clinical testing and authorisation applications. By rational design and appropriate selection of characterised and validated models, ranging from monolayer cell cultures to organ-on-chip microfluidics, promising nanotheranostic particles with modular and rational design can be screened in high-throughput models with robust predictive power. Thus, this article serves to highlight abbreviated research and development possibilities with clinical translational relevance for developing novel nanomaterial-based neuropharmaceuticals for therapy in CNS disorders. By generating predictive data for prospective nanomedicines using validated in vitro models for supporting clinical applications in lieu of requiring extensive use of in vivo animal models that have notable limitations, it is hoped that there will be a burgeoning in the nanotherapy of CNS disorders by virtue of accelerated lead identification through screening, optimisation through rational design for brain-targeted delivery across the BBB and clinical testing and approval using fewer animals. Additionally, by using models with tissue of human origin, reproducible therapeutically relevant nanomedicine delivery and individualised therapy can be realised.
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Affiliation(s)
- Mark J. Lynch
- School of Pharmacy and Pharmaceutical Sciences, Panoz Building, Trinity College Dublin, D02 PN40 Dublin, Ireland
| | - Oliviero L. Gobbo
- School of Pharmacy and Pharmaceutical Sciences, Panoz Building, Trinity College Dublin, D02 PN40 Dublin, Ireland
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26
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Pereira T, Abbasi M, Oliveira JL, Ribeiro B, Arrais J. Optimizing blood-brain barrier permeation through deep reinforcement learning for de novo drug design. Bioinformatics 2021; 37:i84-i92. [PMID: 34252946 PMCID: PMC8336597 DOI: 10.1093/bioinformatics/btab301] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022] Open
Abstract
MOTIVATION The process of placing new drugs into the market is time-consuming, expensive and complex. The application of computational methods for designing molecules with bespoke properties can contribute to saving resources throughout this process. However, the fundamental properties to be optimized are often not considered or conflicting with each other. In this work, we propose a novel approach to consider both the biological property and the bioavailability of compounds through a deep reinforcement learning framework for the targeted generation of compounds. We aim to obtain a promising set of selective compounds for the adenosine A2A receptor and, simultaneously, that have the necessary properties in terms of solubility and permeability across the blood-brain barrier to reach the site of action. The cornerstone of the framework is based on a recurrent neural network architecture, the Generator. It seeks to learn the building rules of valid molecules to sample new compounds further. Also, two Predictors are trained to estimate the properties of interest of the new molecules. Finally, the fine-tuning of the Generator was performed with reinforcement learning, integrated with multi-objective optimization and exploratory techniques to ensure that the Generator is adequately biased. RESULTS The biased Generator can generate an interesting set of molecules, with approximately 85% having the two fundamental properties biased as desired. Thus, this approach has transformed a general molecule generator into a model focused on optimizing specific objectives. Furthermore, the molecules' synthesizability and drug-likeness demonstrate the potential applicability of the de novo drug design in medicinal chemistry. AVAILABILITY AND IMPLEMENTATION All code is publicly available in the https://github.com/larngroup/De-Novo-Drug-Design. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Tiago Pereira
- CSUC/DEI, University of Coimbra, Coimbra 3030-290, Portugal.,IEETA/DETI, University of Aveiro, Aveiro 3810-193, Portugal
| | - Maryam Abbasi
- CSUC/DEI, University of Coimbra, Coimbra 3030-290, Portugal
| | | | | | - Joel Arrais
- CSUC/DEI, University of Coimbra, Coimbra 3030-290, Portugal
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27
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Urbina F, Zorn KM, Brunner D, Ekins S. Comparing the Pfizer Central Nervous System Multiparameter Optimization Calculator and a BBB Machine Learning Model. ACS Chem Neurosci 2021; 12:2247-2253. [PMID: 34028255 DOI: 10.1021/acschemneuro.1c00265] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
The ability to calculate whether small molecules will cross the blood-brain barrier (BBB) is an important task for companies working in neuroscience drug discovery. For a decade, scientists have relied on relatively simplistic rules such as Pfizer's central nervous system multiparameter optimization models (CNS-MPO) for guidance during the drug selection process. In parallel, there has been a continued development of more sophisticated machine learning models that utilize different molecular descriptors and algorithms; however, these models represent a "black box" and are generally less interpretable. In both cases, these methods predict the ability of small molecules to cross the BBB using the molecular structure information on its own without in vitro or in vivo data. We describe here the implementation of two versions of Pfizer's algorithm (Pf-MPO.v1 and Pf-MPO.v2) and compare it with a Bayesian machine learning model of BBB penetration trained on a data set of 2296 active and inactive compounds using extended connectivity fingerprint descriptors. The predictive ability of these approaches was compared with 40 known CNS active drugs initially used by Pfizer as their positive set for validation of the Pf-MPO.v1 score. 37/40 (92.5%) compounds were predicted as active by the Bayesian model, while only 30/40 (75%) received a desirable Pf-MPO.v1 score ≥4 and 33/40 (82.5%) received a desirable Pf-MPO.v2 score ≥4, suggesting the Bayesian model is more accurate than MPO algorithms. This also indicates machine learning models are more flexible and have better predictive power for BBB penetration than simple rule sets that require multiple, accurate descriptor calculations. Our machine learning model statistics are comparable to recent published studies. We describe the implications of these findings and how machine learning may have a role alongside more interpretable methods.
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Affiliation(s)
- Fabio Urbina
- Department of Cell Biology and Physiology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599-7545, United States
- Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina 27606, United States
| | - Kimberley M. Zorn
- Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina 27606, United States
| | - Daniela Brunner
- PsychoGenics, 215 College Road, Paramus, New Jersey 07652, United States
| | - Sean Ekins
- Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina 27606, United States
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Singh AV, Chandrasekar V, Janapareddy P, Mathews DE, Laux P, Luch A, Yang Y, Garcia-Canibano B, Balakrishnan S, Abinahed J, Al Ansari A, Dakua SP. Emerging Application of Nanorobotics and Artificial Intelligence To Cross the BBB: Advances in Design, Controlled Maneuvering, and Targeting of the Barriers. ACS Chem Neurosci 2021; 12:1835-1853. [PMID: 34008957 DOI: 10.1021/acschemneuro.1c00087] [Citation(s) in RCA: 46] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023] Open
Abstract
The blood-brain barrier (BBB) is a prime focus for clinicians to maintain the homeostatic function in health and deliver the theranostics in brain cancer and number of neurological diseases. The structural hierarchy and in situ biochemical signaling of BBB neurovascular unit have been primary targets to recapitulate into the in vitro modules. The microengineered perfusion systems and development in 3D cellular and organoid culture have given a major thrust to BBB research for neuropharmacology. In this review, we focus on revisiting the nanoparticles based bimolecular engineering to enable them to maneuver, control, target, and deliver the theranostic payloads across cellular BBB as nanorobots or nanobots. Subsequently we provide a brief outline of specific case studies addressing the payload delivery in brain tumor and neurological disorders (e.g., Alzheimer's disease, Parkinson's disease, multiple sclerosis, etc.). In addition, we also address the opportunities and challenges across the nanorobots' development and design. Finally, we address how computationally powered machine learning (ML) tools and artificial intelligence (AI) can be partnered with robotics to predict and design the next generation nanorobots to interact and deliver across the BBB without causing damage, toxicity, or malfunctions. The content of this review could be references to multidisciplinary science to clinicians, roboticists, chemists, and bioengineers involved in cutting-edge pharmaceutical design and BBB research.
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Affiliation(s)
- Ajay Vikram Singh
- Department of Chemical and Product Safety, German Federal Institute for Risk Assessment (BfR), Max-Dohrn-Straße 8-10, 10589 Berlin, Germany
| | | | - Poonam Janapareddy
- Department of Surgery, Hamad Medical Corporation (HMC), 3050 Doha, Qatar
| | - Divya Elsa Mathews
- Department of Surgery, Hamad Medical Corporation (HMC), 3050 Doha, Qatar
| | - Peter Laux
- Department of Chemical and Product Safety, German Federal Institute for Risk Assessment (BfR), Max-Dohrn-Straße 8-10, 10589 Berlin, Germany
| | - Andreas Luch
- Department of Chemical and Product Safety, German Federal Institute for Risk Assessment (BfR), Max-Dohrn-Straße 8-10, 10589 Berlin, Germany
| | - Yin Yang
- College of Science and Engineering, Hamad Bin Khalifa University (HBKU), 24404 Doha, Qatar
| | | | | | - Julien Abinahed
- Department of Surgery, Hamad Medical Corporation (HMC), 3050 Doha, Qatar
| | - Abdulla Al Ansari
- Department of Surgery, Hamad Medical Corporation (HMC), 3050 Doha, Qatar
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Radchenko EV, Dyabina AS, Palyulin VA. Towards Deep Neural Network Models for the Prediction of the Blood-Brain Barrier Permeability for Diverse Organic Compounds. Molecules 2020; 25:molecules25245901. [PMID: 33322142 PMCID: PMC7763607 DOI: 10.3390/molecules25245901] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Revised: 12/06/2020] [Accepted: 12/10/2020] [Indexed: 11/24/2022] Open
Abstract
Permeation through the blood–brain barrier (BBB) is among the most important processes controlling the pharmacokinetic properties of drugs and other bioactive compounds. Using the fragmental (substructural) descriptors representing the occurrence number of various substructures, as well as the artificial neural network approach and the double cross-validation procedure, we have developed a predictive in silico LogBB model based on an extensive and verified dataset (529 compounds), which is applicable to diverse drugs and drug-like compounds. The model has good predictivity parameters (Q2=0.815, RMSEcv=0.318) that are similar to or better than those of the most reliable models available in the literature. Larger datasets, and perhaps more sophisticated network architectures, are required to realize the full potential of deep neural networks. The analysis of fragment contributions reveals patterns of influence consistent with the known concepts of structural characteristics that affect the BBB permeability of organic compounds. The external validation of the model confirms good agreement between the predicted and experimental LogBB values for most of the compounds. The model enables the evaluation and optimization of the BBB permeability of potential neuroactive agents and other drug compounds.
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