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Bassani D, Parrott NJ, Manevski N, Zhang JD. Another string to your bow: machine learning prediction of the pharmacokinetic properties of small molecules. Expert Opin Drug Discov 2024; 19:683-698. [PMID: 38727016 DOI: 10.1080/17460441.2024.2348157] [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: 10/23/2023] [Accepted: 04/23/2024] [Indexed: 05/22/2024]
Abstract
INTRODUCTION Prediction of pharmacokinetic (PK) properties is crucial for drug discovery and development. Machine-learning (ML) models, which use statistical pattern recognition to learn correlations between input features (such as chemical structures) and target variables (such as PK parameters), are being increasingly used for this purpose. To embed ML models for PK prediction into workflows and to guide future development, a solid understanding of their applicability, advantages, limitations, and synergies with other approaches is necessary. AREAS COVERED This narrative review discusses the design and application of ML models to predict PK parameters of small molecules, especially in light of established approaches including in vitro-in vivo extrapolation (IVIVE) and physiologically based pharmacokinetic (PBPK) models. The authors illustrate scenarios in which the three approaches are used and emphasize how they enhance and complement each other. In particular, they highlight achievements, the state of the art and potentials of applying machine learning for PK prediction through a comphrehensive literature review. EXPERT OPINION ML models, when carefully crafted, regularly updated, and appropriately used, empower users to prioritize molecules with favorable PK properties. Informed practitioners can leverage these models to improve the efficiency of drug discovery and development process.
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Affiliation(s)
- Davide Bassani
- Pharmaceutical Research & Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Basel, Switzerland
| | - Neil John Parrott
- Pharmaceutical Research & Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Basel, Switzerland
| | - Nenad Manevski
- Pharmaceutical Research & Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Basel, Switzerland
| | - Jitao David Zhang
- Pharmaceutical Research & Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Basel, Switzerland
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Buchanan D, Pham AM, Singh SK, Panda SS. Molecular Hybridization of Alkaloids Using 1,2,3-Triazole-Based Click Chemistry. Molecules 2023; 28:7593. [PMID: 38005315 PMCID: PMC10674395 DOI: 10.3390/molecules28227593] [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: 09/30/2023] [Revised: 10/30/2023] [Accepted: 11/09/2023] [Indexed: 11/26/2023] Open
Abstract
Alkaloids found in multiple species, known as 'driver species', are more likely to be included in early-stage drug development due to their high biodiversity compared to rare alkaloids. Many synthetic approaches have been employed to hybridize the natural alkaloids in drug development. Click chemistry is a highly efficient and versatile reaction targeting specific areas, making it a valuable tool for creating complex natural products and diverse molecular structures. It has been used to create hybrid alkaloids that address their limitations and serve as potential drugs that mimic natural products. In this review, we highlight the recent advancements made in modifying alkaloids using click chemistry and their potential medicinal applications. We discuss the significance, current trends, and prospects of click chemistry in natural product-based medicine. Furthermore, we have employed computational methods to evaluate the ADMET properties and drug-like qualities of hybrid molecules.
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Affiliation(s)
- Devan Buchanan
- Department of Chemistry and Biochemistry, Augusta University, Augusta, GA 30912, USA; (D.B.); (A.M.P.)
| | - Ashley M. Pham
- Department of Chemistry and Biochemistry, Augusta University, Augusta, GA 30912, USA; (D.B.); (A.M.P.)
| | - Sandeep K. Singh
- Jindal Global Business School, OP Jindal Global University, Sonipat 131001, India;
| | - Siva S. Panda
- Department of Chemistry and Biochemistry, Augusta University, Augusta, GA 30912, USA; (D.B.); (A.M.P.)
- Department Biochemistry and Molecular Biology, Augusta University Augusta, GA 30912, USA
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Yang Y, Ma M, Shen L, An J, Kim E, Liu H, Jin M, Wang S, Zhang J, Kim JS, Yin C. A Fluorescent Probe for Investigating the Role of Biothiols in Signaling Pathways Associated with Cerebral Ischemia-Reperfusion Injury. Angew Chem Int Ed Engl 2023; 62:e202310408. [PMID: 37584948 DOI: 10.1002/anie.202310408] [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: 07/20/2023] [Revised: 08/11/2023] [Accepted: 08/15/2023] [Indexed: 08/17/2023]
Abstract
Cerebral ischemia-reperfusion injury (CIRI) is intimately associated with the redox regulation of biothiol, a crucial antioxidant marker that precludes the onset of ROS. We designed a novel fluorescent probe, DCI-Ac-Py, showing various physicochemical properties, such as high selectivity, exceptional signal-to-noise ratio, near-infrared (NIR) optical window, and blood-brain barrier (BBB) penetrability, for detecting biothiols in the brain. The picolinate serves as a specific recognition group that is rapidly activated by biothiol and undergoes nucleophilic substitution with the adjacent acrylic ester to yield the desired NIR probe. Additionally, the probe's lipid solubility is improved through the inclusion of halogen atoms, which aids in penetrating the BBB. Using DCI-Ac-Py, we investigated changes of biothiols in vivo in the brains of mice during CIRI. We found that biothiol-mediated NF-kB classical (P65-related) and nonclassical (RelB-related) pathways contribute to abundant ROS production induced by CIRI and that biothiols are involved in redox regulation. These findings provide new insights into the study of CIRI and shed light on the physiological and pathological mechanisms of biothiols in the brain.
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Affiliation(s)
- Yutao Yang
- Key Laboratory of Medicinal Chemistry and Molecular Diagnosis of the Ministry of Education, Chemical Biology Key Laboratory of Hebei Province, College of Chemistry and Materials Science, Hebei University, Baoding, 071002, P. R. China
- Key Laboratory of Chemical Biology and Molecular Engineering of Ministry of Education, Institute of Molecular Science, Shanxi University, Taiyuan, 030006, China
| | - Ming Ma
- Key Laboratory of Medicinal Chemistry and Molecular Diagnosis of the Ministry of Education, Chemical Biology Key Laboratory of Hebei Province, College of Chemistry and Materials Science, Hebei University, Baoding, 071002, P. R. China
| | - Lei Shen
- Key Laboratory of Medicinal Chemistry and Molecular Diagnosis of the Ministry of Education, Chemical Biology Key Laboratory of Hebei Province, College of Chemistry and Materials Science, Hebei University, Baoding, 071002, P. R. China
| | - Jusung An
- Department of Chemistry, Korea University, Seoul, 02841, Korea
| | - Eunji Kim
- Department of Chemistry, Korea University, Seoul, 02841, Korea
| | - Hongmei Liu
- Key Laboratory of Medicinal Chemistry and Molecular Diagnosis of the Ministry of Education, Chemical Biology Key Laboratory of Hebei Province, College of Chemistry and Materials Science, Hebei University, Baoding, 071002, P. R. China
| | - Ming Jin
- Key Laboratory of Medicinal Chemistry and Molecular Diagnosis of the Ministry of Education, Chemical Biology Key Laboratory of Hebei Province, College of Chemistry and Materials Science, Hebei University, Baoding, 071002, P. R. China
| | - Shuxiang Wang
- Key Laboratory of Medicinal Chemistry and Molecular Diagnosis of the Ministry of Education, Chemical Biology Key Laboratory of Hebei Province, College of Chemistry and Materials Science, Hebei University, Baoding, 071002, P. R. China
| | - Jinchao Zhang
- Key Laboratory of Medicinal Chemistry and Molecular Diagnosis of the Ministry of Education, Chemical Biology Key Laboratory of Hebei Province, College of Chemistry and Materials Science, Hebei University, Baoding, 071002, P. R. China
| | - Jong Seung Kim
- Department of Chemistry, Korea University, Seoul, 02841, Korea
| | - Caixia Yin
- Key Laboratory of Chemical Biology and Molecular Engineering of Ministry of Education, Institute of Molecular Science, Shanxi University, Taiyuan, 030006, China
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Yang Y, Zhang Y, Ma M, Liu H, Ge K, Zhang C, Jin M, Liu D, Wang S, Yin C, Zhang J. Synergistic Modulation by Halogens and Pyridine Crossing the Blood-Brain Barrier for In Situ Visualization of Thiol Flux in the Epileptic Brain. Anal Chem 2022; 94:14443-14452. [PMID: 36197681 DOI: 10.1021/acs.analchem.2c03390] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Epilepsy is a nervous system disease, and seizures are closely related to oxidative stress. Thiols, as the main antioxidant in an organism, play a key role in regulating the redox balance and defending from oxidative stress. As a result of the complexity of the brain structure, there is still a lack of suitable in situ detection methods of thiols to reveal the relationship between epilepsy and thiol level fluctuations. Therefore, by combining picolinate as the new recognition site for thiols, parallel synthesis, and the fluorescence rapid screening method, DCI-Br-3 was developed as a rapid, highly sensitive, and selective probe to monitor thiols in vitro and in vivo. It is worth noting that DCI-Br-3 effectively crossed the blood-brain barrier (BBB) to reveal the negative relationship between the level of thiols and the occurrence of epilepsy and may further provide important information for the prevention and treatment of thiol-related neurological diseases.
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Affiliation(s)
- Yutao Yang
- Key Laboratory of Medicinal Chemistry and Molecular Diagnosis of the Ministry of Education, Chemical Biology Key Laboratory of Hebei Province, College of Chemistry & Environmental Science, Hebei University, Baoding 071002, China.,Key Laboratory of Chemical Biology and Molecular Engineering of Ministry of Education, Institute of Molecular Science, Shanxi University, Taiyuan 030006, China
| | - Yan Zhang
- Key Laboratory of Medicinal Chemistry and Molecular Diagnosis of the Ministry of Education, Chemical Biology Key Laboratory of Hebei Province, College of Chemistry & Environmental Science, Hebei University, Baoding 071002, China
| | - Ming Ma
- Key Laboratory of Medicinal Chemistry and Molecular Diagnosis of the Ministry of Education, Chemical Biology Key Laboratory of Hebei Province, College of Chemistry & Environmental Science, Hebei University, Baoding 071002, China
| | - Hongmei Liu
- Key Laboratory of Medicinal Chemistry and Molecular Diagnosis of the Ministry of Education, Chemical Biology Key Laboratory of Hebei Province, College of Chemistry & Environmental Science, Hebei University, Baoding 071002, China
| | - Kun Ge
- Key Laboratory of Medicinal Chemistry and Molecular Diagnosis of the Ministry of Education, Chemical Biology Key Laboratory of Hebei Province, College of Chemistry & Environmental Science, Hebei University, Baoding 071002, China
| | - Chunfang Zhang
- Key Laboratory of Medicinal Chemistry and Molecular Diagnosis of the Ministry of Education, Chemical Biology Key Laboratory of Hebei Province, College of Chemistry & Environmental Science, Hebei University, Baoding 071002, China
| | - Ming Jin
- Key Laboratory of Medicinal Chemistry and Molecular Diagnosis of the Ministry of Education, Chemical Biology Key Laboratory of Hebei Province, College of Chemistry & Environmental Science, Hebei University, Baoding 071002, China
| | - Dandan Liu
- Key Laboratory of Medicinal Chemistry and Molecular Diagnosis of the Ministry of Education, Chemical Biology Key Laboratory of Hebei Province, College of Chemistry & Environmental Science, Hebei University, Baoding 071002, China
| | - Shuxiang Wang
- Key Laboratory of Medicinal Chemistry and Molecular Diagnosis of the Ministry of Education, Chemical Biology Key Laboratory of Hebei Province, College of Chemistry & Environmental Science, Hebei University, Baoding 071002, China
| | - Caixia Yin
- Key Laboratory of Chemical Biology and Molecular Engineering of Ministry of Education, Institute of Molecular Science, Shanxi University, Taiyuan 030006, China
| | - Jinchao Zhang
- Key Laboratory of Medicinal Chemistry and Molecular Diagnosis of the Ministry of Education, Chemical Biology Key Laboratory of Hebei Province, College of Chemistry & Environmental Science, Hebei University, Baoding 071002, China
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Tang Q, Nie F, Zhao Q, Chen W. A merged molecular representation deep learning method for blood-brain barrier permeability prediction. Brief Bioinform 2022; 23:6674486. [PMID: 36002937 DOI: 10.1093/bib/bbac357] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2022] [Revised: 07/27/2022] [Accepted: 07/30/2022] [Indexed: 12/30/2022] Open
Abstract
The ability of a compound to permeate across the blood-brain barrier (BBB) is a significant factor for central nervous system drug development. Thus, for speeding up the drug discovery process, it is crucial to perform high-throughput screenings to predict the BBB permeability of the candidate compounds. Although experimental methods are capable of determining BBB permeability, they are still cost-ineffective and time-consuming. To complement the shortcomings of existing methods, we present a deep learning-based multi-model framework model, called Deep-B3, to predict the BBB permeability of candidate compounds. In Deep-B3, the samples are encoded in three kinds of features, namely molecular descriptors and fingerprints, molecular graph and simplified molecular input line entry system (SMILES) text notation. The pre-trained models were built to extract latent features from the molecular graph and SMILES. These features depicted the compounds in terms of tabular data, image and text, respectively. The validation results yielded from the independent dataset demonstrated that the performance of Deep-B3 is superior to that of the state-of-the-art models. Hence, Deep-B3 holds the potential to become a useful tool for drug development. A freely available online web-server for Deep-B3 was established at http://cbcb.cdutcm.edu.cn/deepb3/, and the source code and dataset of Deep-B3 are available at https://github.com/GreatChenLab/Deep-B3.
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Affiliation(s)
- Qiang Tang
- State Key Laboratory of Southwestern Chinese Medicine Resources, School of Basic Medical Science, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China
| | - Fulei Nie
- School of Public Health, North China University of Science and Technology, Tangshan 063210, China
| | - Qi Zhao
- School of Computer Science and Software Engineering, University of Science and Technology Liaoning, Anshan, 114051, China
| | - Wei Chen
- State Key Laboratory of Southwestern Chinese Medicine Resources, School of Basic Medical Science, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China.,School of Public Health, North China University of Science and Technology, Tangshan 063210, China
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Seo Y, Bang S, Son J, Kim D, Jeong Y, Kim P, Yang J, Eom JH, Choi N, Kim HN. Brain physiome: A concept bridging in vitro 3D brain models and in silico models for predicting drug toxicity in the brain. Bioact Mater 2022; 13:135-148. [PMID: 35224297 PMCID: PMC8843968 DOI: 10.1016/j.bioactmat.2021.11.009] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Revised: 11/01/2021] [Accepted: 11/06/2021] [Indexed: 12/12/2022] Open
Abstract
In the last few decades, adverse reactions to pharmaceuticals have been evaluated using 2D in vitro models and animal models. However, with increasing computational power, and as the key drivers of cellular behavior have been identified, in silico models have emerged. These models are time-efficient and cost-effective, but the prediction of adverse reactions to unknown drugs using these models requires relevant experimental input. Accordingly, the physiome concept has emerged to bridge experimental datasets with in silico models. The brain physiome describes the systemic interactions of its components, which are organized into a multilevel hierarchy. Because of the limitations in obtaining experimental data corresponding to each physiome component from 2D in vitro models and animal models, 3D in vitro brain models, including brain organoids and brain-on-a-chip, have been developed. In this review, we present the concept of the brain physiome and its hierarchical organization, including cell- and tissue-level organizations. We also summarize recently developed 3D in vitro brain models and link them with the elements of the brain physiome as a guideline for dataset collection. The connection between in vitro 3D brain models and in silico modeling will lead to the establishment of cost-effective and time-efficient in silico models for the prediction of the safety of unknown drugs.
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Affiliation(s)
- Yoojin Seo
- Brain Science Institute, Korea Institute of Science and Technology (KIST), Seoul, 02792, Republic of Korea
| | - Seokyoung Bang
- Brain Science Institute, Korea Institute of Science and Technology (KIST), Seoul, 02792, Republic of Korea
| | - Jeongtae Son
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea
| | - Dongsup Kim
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea
| | - Yong Jeong
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea
| | - Pilnam Kim
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea
| | - Jihun Yang
- Next&Bio Inc., Seoul, 02841, Republic of Korea
| | - Joon-Ho Eom
- Medical Device Research Division, National Institute of Food and Drug Safety Evaluation, Cheongju, 28159, Republic of Korea
| | - Nakwon Choi
- Brain Science Institute, Korea Institute of Science and Technology (KIST), Seoul, 02792, Republic of Korea
- Division of Bio-Medical Science & Technology, KIST School, Korea University of Science and Technology (UST), Seoul, 02792, Republic of Korea
- KU-KIST Graduate School of Converging Science and Technology, Korea University, Seoul, 02841, Republic of Korea
| | - Hong Nam Kim
- Brain Science Institute, Korea Institute of Science and Technology (KIST), Seoul, 02792, Republic of Korea
- Division of Bio-Medical Science & Technology, KIST School, Korea University of Science and Technology (UST), Seoul, 02792, Republic of Korea
- School of Mechanical Engineering, Yonsei University, Seoul, 03722, Republic of Korea
- Yonsei-KIST Convergence Research Institute, Yonsei University, Seoul, 03722, Republic of Korea
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7
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InflamNat: web-based database and predictor of anti-inflammatory natural products. J Cheminform 2022; 14:30. [PMID: 35659771 PMCID: PMC9167499 DOI: 10.1186/s13321-022-00608-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Accepted: 05/09/2022] [Indexed: 11/30/2022] Open
Abstract
Natural products (NPs) are a valuable source for anti-inflammatory drug discovery. However, they are limited by the unpredictability of the structures and functions. Therefore, computational and data-driven pre-evaluation could enable more efficient NP-inspired drug development. Since NPs possess structural features that differ from synthetic compounds, models trained with synthetic compounds may not perform well with NPs. There is also an urgent demand for well-curated databases and user-friendly predictive tools. We presented a comprehensive online web platform (InflamNat, http://www.inflamnat.com/ or http://39.104.56.4/) for anti-inflammatory natural product research. InflamNat is a database containing the physicochemical properties, cellular anti-inflammatory bioactivities, and molecular targets of 1351 NPs that tested on their anti-inflammatory activities. InflamNat provides two machine learning-based predictive tools specifically designed for NPs that (a) predict the anti-inflammatory activity of NPs, and (b) predict the compound-target relationship for compounds and targets collected in the database but lacking existing relationship data. A novel multi-tokenization transformer model (MTT) was proposed as the sequential encoder for both predictive tools to obtain a high-quality representation of sequential data. The experimental results showed that the proposed predictive tools achieved an AUC value of 0.842 and 0.872 in the prediction of anti-inflammatory activity and compound-target interactions, respectively.
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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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In Silico Investigation of Some Compounds from the N-Butanol Extract of Centaurea tougourensis Boiss. & Reut. CRYSTALS 2022. [DOI: 10.3390/cryst12030355] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Bioinformatics as a newly emerging discipline is considered nowadays a reference to characterize the physicochemical and pharmacological properties of the actual biocompounds contained in plants, which has helped the pharmaceutical industry a lot in the drug development process. In this study, a bioinformatics approach known as in silico was performed to predict, for the first time, the physicochemical properties, ADMET profile, pharmacological capacities, cytotoxicity, and nervous system macromolecular targets, as well as the gene expression profiles, of four compounds recently identified from Centaurea tougourensis via the gas chromatography–mass spectrometry (GC–MS) approach. Thus, four compounds were tested from the n-butanol (n-BuOH) extract of this plant, named, respectively, Acridin-9-amine, 1,2,3,4-tetrahydro-5,7-dimethyl- (compound 1), 3-[2,3-Dihydro-2,2-dimethylbenzofuran-7-yl]-5-methoxy-1,3,4-oxadiazol-2(3H)-one (compound 2), 9,9-Dimethoxybicyclo[3.3.1]nona-2,4-dione (compound 3), and 3-[3-Bromophenyl]-7-chloro-3,4-dihydro-10-hydroxy-1,9(2H,10H)-acridinedione (compound 4). The insilico investigation revealed that the four tested compounds could be a good candidate to regulate the expression of key genes and may also exert significant cytotoxic effects against several tumor celllines. In addition, these compounds could also be effective in the treatment of some diseases related to diabetes, skin pathologies, cardiovascular, and central nervous system disorders. The bioactive compounds of plant remain the best alternative in the context of the drug discovery and development process.
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Saldívar-González FI, Aldas-Bulos VD, Medina-Franco JL, Plisson F. Natural product drug discovery in the artificial intelligence era. Chem Sci 2022; 13:1526-1546. [PMID: 35282622 PMCID: PMC8827052 DOI: 10.1039/d1sc04471k] [Citation(s) in RCA: 50] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Accepted: 12/10/2021] [Indexed: 12/19/2022] Open
Abstract
Natural products (NPs) are primarily recognized as privileged structures to interact with protein drug targets. Their unique characteristics and structural diversity continue to marvel scientists for developing NP-inspired medicines, even though the pharmaceutical industry has largely given up. High-performance computer hardware, extensive storage, accessible software and affordable online education have democratized the use of artificial intelligence (AI) in many sectors and research areas. The last decades have introduced natural language processing and machine learning algorithms, two subfields of AI, to tackle NP drug discovery challenges and open up opportunities. In this article, we review and discuss the rational applications of AI approaches developed to assist in discovering bioactive NPs and capturing the molecular "patterns" of these privileged structures for combinatorial design or target selectivity.
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Affiliation(s)
- F I Saldívar-González
- DIFACQUIM Research Group, School of Chemistry, Department of Pharmacy, Universidad Nacional Autónoma de México Avenida Universidad 3000 04510 Mexico Mexico
| | - V D Aldas-Bulos
- Unidad de Genómica Avanzada, Laboratorio Nacional de Genómica para la Biodiversidad (Langebio), Centro de Investigación y de Estudios Avanzados del IPN Irapuato Guanajuato Mexico
| | - J L Medina-Franco
- DIFACQUIM Research Group, School of Chemistry, Department of Pharmacy, Universidad Nacional Autónoma de México Avenida Universidad 3000 04510 Mexico Mexico
| | - F Plisson
- CONACYT - Unidad de Genómica Avanzada, Laboratorio Nacional de Genómica para la Biodiversidad (Langebio), Centro de Investigación y de Estudios Avanzados del IPN Irapuato Guanajuato Mexico
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11
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Chen X, Liu C, Muok L, Zeng C, Li Y. Dynamic 3D On-Chip BBB Model Design, Development, and Applications in Neurological Diseases. Cells 2021; 10:3183. [PMID: 34831406 PMCID: PMC8622822 DOI: 10.3390/cells10113183] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2021] [Revised: 11/10/2021] [Accepted: 11/12/2021] [Indexed: 12/12/2022] Open
Abstract
The blood-brain barrier (BBB) is a vital structure for maintaining homeostasis between the blood and the brain in the central nervous system (CNS). Biomolecule exchange, ion balance, nutrition delivery, and toxic molecule prevention rely on the normal function of the BBB. The dysfunction and the dysregulation of the BBB leads to the progression of neurological disorders and neurodegeneration. Therefore, in vitro BBB models can facilitate the investigation for proper therapies. As the demand increases, it is urgent to develop a more efficient and more physiologically relevant BBB model. In this review, the development of the microfluidics platform for the applications in neuroscience is summarized. This article focuses on the characterizations of in vitro BBB models derived from human stem cells and discusses the development of various types of in vitro models. The microfluidics-based system and BBB-on-chip models should provide a better platform for high-throughput drug-screening and targeted delivery.
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Affiliation(s)
- Xingchi Chen
- Department of Chemical and Biomedical Engineering, FAMU-FSU College of Engineering, Florida State University, Tallahassee, FL 32310, USA; (X.C.); (C.L.); (L.M.)
- The High-Performance Materials Institute, Florida State University, Tallahassee, FL 32310, USA
| | - Chang Liu
- Department of Chemical and Biomedical Engineering, FAMU-FSU College of Engineering, Florida State University, Tallahassee, FL 32310, USA; (X.C.); (C.L.); (L.M.)
| | - Laureana Muok
- Department of Chemical and Biomedical Engineering, FAMU-FSU College of Engineering, Florida State University, Tallahassee, FL 32310, USA; (X.C.); (C.L.); (L.M.)
| | - Changchun Zeng
- The High-Performance Materials Institute, Florida State University, Tallahassee, FL 32310, USA
- Department of Industrial and Manufacturing Engineering, FAMU-FSU College of Engineering, Florida State University, Tallahassee, FL 32310, USA;
| | - Yan Li
- Department of Chemical and Biomedical Engineering, FAMU-FSU College of Engineering, Florida State University, Tallahassee, FL 32310, USA; (X.C.); (C.L.); (L.M.)
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12
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Omoboyowa DA, Balogun TA, Omomule OM, Saibu OA. Identification of Terpenoids From Abrus precatorius Against Parkinson's Disease Proteins Using In Silico Approach. Bioinform Biol Insights 2021; 15:11779322211050757. [PMID: 34707350 PMCID: PMC8544761 DOI: 10.1177/11779322211050757] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Accepted: 09/15/2021] [Indexed: 12/13/2022] Open
Abstract
Parkinson’s disease (PD) is the second major neuro-degenrative disorder that causes morbidity and mortality among older populations. Terpenoids were reported as potential neuro-protective agents. Therefore, this study seeks to unlock the inhibitory potential of terpenoids from Abrus precatorius seeds against proteins involve in PD pathogenesis. In this study, in silico molecular docking of 5 terpenoids derived from high-performance liquid chromatography (HPLC) analysis of A. precatorius seeds against α-synuclein, catechol-o-methyltransferase, and monoamine oxidase B which are markers of PD was performed using Autodock vina. The absorption, distribution, metabolism, excretion, and toxicity (ADME/Tox) of the hits were done using Swiss ADME predictor and molecular dynamic (MD) simulation of the hit-protein complex was performed using Desmond Schrodinger software. Five out of 6 compounds satisfied the ADME/Tox parameters and showed varying degrees of binding affinities with selected proteins. Drimenin-α-synuclein complex showed the lowest binding energy of −9.1 kcal/mol followed by interaction with key amino acid residues necessary for α-synuclein inhibition. The selection of this complex was justified by its stability in MD simulation conducted for 10 ns and exhibited stable interaction in terms of root mean square deviation (RMSD) and root mean square deviation error fluctuation (RMSF) values.
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Affiliation(s)
| | | | | | - Oluwatosin A Saibu
- Department of Environmental Toxicology, Universitat Duisburg-Essen, Duisburg, Germany
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13
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Zolkiffly SZI, Stanslas J, Abdul Hamid H, Mehat MZ. Ficus deltoidea: Potential inhibitor of pro-inflammatory mediators in lipopolysaccharide-induced activation of microglial cells. JOURNAL OF ETHNOPHARMACOLOGY 2021; 279:114309. [PMID: 34119609 DOI: 10.1016/j.jep.2021.114309] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Revised: 05/29/2021] [Accepted: 06/02/2021] [Indexed: 06/12/2023]
Abstract
ETHNOPHARMACOLOGICAL RELEVANCE Ficus deltoidea Jack (FD) is widely consumed in traditional medicine as a treatment for various diseases in Malaysia. Each part of the plant such as its leave, stem, fruit and root are used traditionally to treat different types of diseases. Vitexin and isovitexin are bioactive compounds abundantly found in the leaves of FD that possessed many pharmacological properties including neuroprotection. Nonetheless, its effects on key events in neuroinflammation are unknown. AIM OF THE STUDY To determine the inhibitory properties of FD aqueous extract on pro-inflammatory mediators involved in lipopolysaccharide (LPS)-induced microglial cells. METHODS Vitexin and isovitexin in the extract were quantified via high performance liquid chromatography (HPLC). The extract was evaluated for its cytotoxicity activity via 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay. Pre-treatment with the extract on LPS-induced microglial cells was done to determine its antioxidant and anti-neuroinflammatory properties by measuring the level of reactive oxygen species (ROS), nitric oxide (NO), tumour necrosis factor alpha (TNF-α), interleukin-1β (IL-1β) and interleukin-6 (IL-6) via 2'-7'-dichlorofluorescin diacetate (DCFDA) assay, Griess assay and Western blot respectively. RESULTS The extract at all tested concentrations (0.1 μg/mL, 1 μg/mL, 10 μg/mL, 100 μg/mL) were not cytotoxic as the percentage viability of microglial cells were all above ~80%. At the highest concentration (100 μg/mL), the extract significantly reduced the formation of ROS, NO, TNF-α, IL-1β and IL-6 in microglial cells induced by LPS. CONCLUSION The extract showed neuroprotective effects by attenuating the levels of pro-inflammatory and cytotoxic factors in LPS-induced microglial cells, possibly by mediating the nuclear factor-kappa B (NF-κB) signalling pathway.
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Affiliation(s)
- Siti Zaidathul Iman Zolkiffly
- Department of Human Anatomy, Faculty of Medicine & Health Sciences, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia
| | - Johnson Stanslas
- Department of Medicine, Faculty of Medicine & Health Sciences, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia
| | - Hafizah Abdul Hamid
- Department of Human Anatomy, Faculty of Medicine & Health Sciences, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia
| | - Muhammad Zulfadli Mehat
- Department of Human Anatomy, Faculty of Medicine & Health Sciences, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia.
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14
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Rácz A, Bajusz D, Miranda-Quintana RA, Héberger K. Machine learning models for classification tasks related to drug safety. Mol Divers 2021; 25:1409-1424. [PMID: 34110577 PMCID: PMC8342376 DOI: 10.1007/s11030-021-10239-x] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Accepted: 05/27/2021] [Indexed: 12/23/2022]
Abstract
In this review, we outline the current trends in the field of machine learning-driven classification studies related to ADME (absorption, distribution, metabolism and excretion) and toxicity endpoints from the past six years (2015-2021). The study focuses only on classification models with large datasets (i.e. more than a thousand compounds). A comprehensive literature search and meta-analysis was carried out for nine different targets: hERG-mediated cardiotoxicity, blood-brain barrier penetration, permeability glycoprotein (P-gp) substrate/inhibitor, cytochrome P450 enzyme family, acute oral toxicity, mutagenicity, carcinogenicity, respiratory toxicity and irritation/corrosion. The comparison of the best classification models was targeted to reveal the differences between machine learning algorithms and modeling types, endpoint-specific performances, dataset sizes and the different validation protocols. Based on the evaluation of the data, we can say that tree-based algorithms are (still) dominating the field, with consensus modeling being an increasing trend in drug safety predictions. Although one can already find classification models with great performances to hERG-mediated cardiotoxicity and the isoenzymes of the cytochrome P450 enzyme family, these targets are still central to ADMET-related research efforts.
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Affiliation(s)
- Anita Rácz
- Plasma Chemistry Research Group, Research Centre for Natural Sciences, Magyar tudósok krt. 2, Budapest, 1117, Hungary.
| | - Dávid Bajusz
- Medicinal Chemistry Research Group, Research Centre for Natural Sciences, Magyar tudósok krt. 2, Budapest, 1117, Hungary
| | | | - Károly Héberger
- Plasma Chemistry Research Group, Research Centre for Natural Sciences, Magyar tudósok krt. 2, Budapest, 1117, Hungary.
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15
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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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16
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Santana K, do Nascimento LD, Lima e Lima A, Damasceno V, Nahum C, Braga RC, Lameira J. Applications of Virtual Screening in Bioprospecting: Facts, Shifts, and Perspectives to Explore the Chemo-Structural Diversity of Natural Products. Front Chem 2021; 9:662688. [PMID: 33996755 PMCID: PMC8117418 DOI: 10.3389/fchem.2021.662688] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Accepted: 02/25/2021] [Indexed: 12/22/2022] Open
Abstract
Natural products are continually explored in the development of new bioactive compounds with industrial applications, attracting the attention of scientific research efforts due to their pharmacophore-like structures, pharmacokinetic properties, and unique chemical space. The systematic search for natural sources to obtain valuable molecules to develop products with commercial value and industrial purposes remains the most challenging task in bioprospecting. Virtual screening strategies have innovated the discovery of novel bioactive molecules assessing in silico large compound libraries, favoring the analysis of their chemical space, pharmacodynamics, and their pharmacokinetic properties, thus leading to the reduction of financial efforts, infrastructure, and time involved in the process of discovering new chemical entities. Herein, we discuss the computational approaches and methods developed to explore the chemo-structural diversity of natural products, focusing on the main paradigms involved in the discovery and screening of bioactive compounds from natural sources, placing particular emphasis on artificial intelligence, cheminformatics methods, and big data analyses.
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Affiliation(s)
- Kauê Santana
- Instituto de Biodiversidade, Universidade Federal do Oeste do Pará, Santarém, Brazil
| | | | - Anderson Lima e Lima
- Instituto de Ciências Exatas e Naturais, Universidade Federal do Pará, Belém, Brazil
| | - Vinícius Damasceno
- Instituto de Ciências Exatas e Naturais, Universidade Federal do Pará, Belém, Brazil
| | - Claudio Nahum
- Instituto de Ciências Exatas e Naturais, Universidade Federal do Pará, Belém, Brazil
| | | | - Jerônimo Lameira
- Instituto de Ciências Biológicas, Universidade Federal do Pará, Belém, Brazil
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17
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McComb M, Bies R, Ramanathan M. Machine learning in pharmacometrics: Opportunities and challenges. Br J Clin Pharmacol 2021; 88:1482-1499. [PMID: 33634893 DOI: 10.1111/bcp.14801] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2020] [Revised: 02/08/2021] [Accepted: 02/12/2021] [Indexed: 12/13/2022] Open
Abstract
The explosive growth in medical devices, imaging and diagnostics, computing, and communication and information technologies in drug development and healthcare has created an ever-expanding data landscape that the pharmacometrics (PMX) research community must now traverse. The tools of machine learning (ML) have emerged as a powerful computational approach in other data-rich disciplines but its effective utilization in the pharmaceutical sciences and PMX modelling is in its infancy. ML-based methods can complement PMX modelling by enabling the information in diverse sources of big data, e.g. population-based public databases and disease-specific clinical registries, to be harnessed because they are capable of efficiently identifying salient variables associated with outcomes and delineating their interdependencies. ML algorithms are computationally efficient, have strong predictive capabilities and can enable learning in the big data setting. ML algorithms can be viewed as providing a computational bridge from big data to complement PMX modelling. This review provides an overview of the strengths and weaknesses of ML approaches vis-à-vis population methods, assesses current research into ML applications in the pharmaceutical sciences and provides perspective for potential opportunities and strategies for the successful integration and utilization of ML in PMX.
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Affiliation(s)
- Mason McComb
- Department of Pharmaceutical Sciences, University at Buffalo, University at Buffalo, State University of New York, Buffalo, NY, USA
| | - Robert Bies
- Department of Pharmaceutical Sciences, University at Buffalo, University at Buffalo, State University of New York, Buffalo, NY, USA.,Institute for Computational Data Science, University at Buffalo, NY, USA
| | - Murali Ramanathan
- Department of Pharmaceutical Sciences, University at Buffalo, University at Buffalo, State University of New York, Buffalo, NY, USA.,Department of Neurology, University at Buffalo, State University of New York, Buffalo, NY, USA
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18
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Zhang R, Li X, Zhang X, Qin H, Xiao W. Machine learning approaches for elucidating the biological effects of natural products. Nat Prod Rep 2021; 38:346-361. [PMID: 32869826 DOI: 10.1039/d0np00043d] [Citation(s) in RCA: 42] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Covering: 2000 to 2020 Machine learning (ML) is an efficient tool for the prediction of bioactivity and the study of structure-activity relationships. Over the past decade, an emerging trend for combining these approaches with the study of natural products (NPs) has developed in order to manage the challenge of the discovery of bioactive NPs. In the present review, we will introduce the basic principles and protocols for using the ML approach to investigate the bioactivity of NPs, citing a series of practical examples regarding the study of anti-microbial, anti-cancer, and anti-inflammatory NPs, etc. ML algorithms manage a variety of classification and regression problems associated with bioactive NPs, from those that are linear to non-linear and from pure compounds to plant extracts. Inspired by cases reported in the literature and our own experience, a number of key points have been emphasized for reducing modeling errors, including dataset preparation and applicability domain analysis.
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Affiliation(s)
- Ruihan Zhang
- Key Laboratory of Medicinal Chemistry for Natural Resource, Ministry of Education, Yunnan Research & Development Center for Natural Products, School of Chemical Science and Technology, Yunnan University, 2 Rd Cuihubei, P. R. China.
| | - Xiaoli Li
- Key Laboratory of Medicinal Chemistry for Natural Resource, Ministry of Education, Yunnan Research & Development Center for Natural Products, School of Chemical Science and Technology, Yunnan University, 2 Rd Cuihubei, P. R. China.
| | - Xingjie Zhang
- Key Laboratory of Medicinal Chemistry for Natural Resource, Ministry of Education, Yunnan Research & Development Center for Natural Products, School of Chemical Science and Technology, Yunnan University, 2 Rd Cuihubei, P. R. China.
| | - Huayan Qin
- Key Laboratory of Medicinal Chemistry for Natural Resource, Ministry of Education, Yunnan Research & Development Center for Natural Products, School of Chemical Science and Technology, Yunnan University, 2 Rd Cuihubei, P. R. China.
| | - Weilie Xiao
- Key Laboratory of Medicinal Chemistry for Natural Resource, Ministry of Education, Yunnan Research & Development Center for Natural Products, School of Chemical Science and Technology, Yunnan University, 2 Rd Cuihubei, P. R. China.
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19
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Saxena D, Sharma A, Siddiqui MH, Kumar R. Blood Brain Barrier Permeability Prediction Using Machine Learning Techniques: An Update. Curr Pharm Biotechnol 2019; 20:1163-1171. [DOI: 10.2174/1389201020666190821145346] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2019] [Revised: 05/01/2019] [Accepted: 07/16/2019] [Indexed: 12/11/2022]
Abstract
Blood Brain Barrier (BBB) is the collection of vessels of blood with special properties of
permeability that allow a limited range of drug and compounds to pass through it. The BBB plays a vital
role in maintaining balance between intracellular and extracellular environment for brain. Brain Capillary
Endothelial Cells (BECs) act as vehicle for transport and the transport mechanisms across BBB
involve active and passive diffusion of compounds. Efficient prediction models of BBB permeability
can be vital at the preliminary stages of drug development. There have been persistent efforts in identifying
the prediction of BBB permeability of compounds employing multiple machine learning methods
in an attempt to minimize the attrition rate of drug candidates taking up preclinical and clinical trials.
However, there is an urgent need to review the progress of such machine learning derived prediction
models in the prediction of BBB permeability. In the current article, we have analyzed the recently developed
prediction model for BBB permeability using machine learning.
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Affiliation(s)
- Deeksha Saxena
- Amity Institute of Biotechnology, Amity University Uttar Pradesh, Lucknow-226028, Uttar Pradesh, India
| | - Anju Sharma
- Amity Institute of Biotechnology, Amity University Uttar Pradesh, Lucknow-226028, Uttar Pradesh, India
| | - Mohammed H. Siddiqui
- Department of Bioengineering, Integral University, Dasauli, P.O. Basha, Kursi Road, Lucknow, Uttar Pradesh, India
| | - Rajnish Kumar
- Amity Institute of Biotechnology, Amity University Uttar Pradesh, Lucknow-226028, Uttar Pradesh, India
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