Pub Date : 2024-07-09DOI: 10.3389/fddsv.2024.1424402
Janki Darlami, Shweta Sharma
Quantitative structure activity relationship (QSAR) is a widely used tool in rational drug design that establishes relationships between the physicochemical and topological descriptors of ligands and their biological activities. Obtained QSAR models help identify descriptors that play pivotal roles in the biological activity of ligands. This not only helps the prediction of new compounds with desirable biological activities but also helps with the design of new compounds with better activities and low toxicities. QSAR commonly uses lipophilicity (logP), hydrophobicity (logD), water solubility (logS), the acid–base dissociation constant (pKa), the dipole moment, the highest occupied molecular orbital (HOMO), the lowest unoccupied molecular orbital (LUMO), molecular weight (MW), molar volume (MV), molar refractivity (MR), and the kappa index as physicochemical parameters. Some commonly used topological indices in QSAR are the Wiener index, Platt index, Hosoya index, Zagreb indices, Balaban index, and E-state index. This review presents a brief description of the significance of the most extensively used physicochemical and topological parameters in drug design.
定量结构活性关系（QSAR）是合理药物设计中广泛使用的一种工具，它建立了配体的物理化学和拓扑描述指标与其生物活性之间的关系。获得的 QSAR 模型有助于确定在配体生物活性中起关键作用的描述符。这不仅有助于预测具有理想生物活性的新化合物，还有助于设计具有更好活性和低毒性的新化合物。QSAR 通常使用亲油性（logP）、疏水性（logD）、水溶性（logS）、酸碱解离常数（pKa）、偶极矩、最高占位分子轨道（HOMO）、最低未占位分子轨道（LUMO）、分子量（MW）、摩尔体积（MV）、摩尔折射率（MR）和卡帕指数作为理化参数。QSAR 中常用的拓扑指数包括维纳指数、普拉特指数、细谷指数、萨格勒布指数、巴拉班指数和 E 态指数。本综述简要介绍了药物设计中最广泛使用的物理化学和拓扑参数的意义。
{"title":"The role of physicochemical and topological parameters in drug design","authors":"Janki Darlami, Shweta Sharma","doi":"10.3389/fddsv.2024.1424402","DOIUrl":"https://doi.org/10.3389/fddsv.2024.1424402","url":null,"abstract":"Quantitative structure activity relationship (QSAR) is a widely used tool in rational drug design that establishes relationships between the physicochemical and topological descriptors of ligands and their biological activities. Obtained QSAR models help identify descriptors that play pivotal roles in the biological activity of ligands. This not only helps the prediction of new compounds with desirable biological activities but also helps with the design of new compounds with better activities and low toxicities. QSAR commonly uses lipophilicity (logP), hydrophobicity (logD), water solubility (logS), the acid–base dissociation constant (pKa), the dipole moment, the highest occupied molecular orbital (HOMO), the lowest unoccupied molecular orbital (LUMO), molecular weight (MW), molar volume (MV), molar refractivity (MR), and the kappa index as physicochemical parameters. Some commonly used topological indices in QSAR are the Wiener index, Platt index, Hosoya index, Zagreb indices, Balaban index, and E-state index. This review presents a brief description of the significance of the most extensively used physicochemical and topological parameters in drug design.","PeriodicalId":507971,"journal":{"name":"Frontiers in Drug Discovery","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141666468","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Permeability-glycoprotein (P-gp) belongs to the ABS transporter protein family, with a high expression rate in cancerous cells. The substrate/inhibitors of the protein are structurally diverse, with no lucid mechanism of inhibition. There are two schools of thought on the inhibition mechanism: (i) P-gp inhibitors bind to the huge hydrophobic cavity between two Trans-Membrane Domains (TMDs), supported by ample literary proof and (ii) P-gp inhibitors bind to the vicinity of Nucleotide-Binding Sites (NBSs). Structural biologists have presented several experimental and theoretical structures of P-gp with bound nucleotides and inhibitors to explain the same. However, the available experimental P-gp structures are insufficient to address the catalytic transition path of mammalian P-gp in detail, thus the dynamics and mechanism by which drugs are effluxed is still unknown. Targeted Molecular Dynamics (targeted MD) could be used to minutely analyse and explore the catalytic transition inward open (IO) to outward open (OO) and relaxation path (OO to IO). Finally, analysis of targeted MD trajectory may help to explore different conformational states of Pg-p (reaction coordinate of catalytic transition/relaxation), efflux of compounds aided by the dynamics of Nucleotide Binding Domains/NBDs (ATP coupled process) and TMDs (peristalsis-like movement pushes the bound molecule). This review presents an understanding of the catalytic transition and dynamics of protein which provides insights at the efflux of chemotherapeutic drug using in cancer treatment.
{"title":"A review on dynamics of permeability-glycoprotein in efflux of chemotherapeutic drugs","authors":"Priyanka Rani, Pranabesh Mandal, Bikash Kumar Rajak, Durg Vijay Singh","doi":"10.3389/fddsv.2024.1363364","DOIUrl":"https://doi.org/10.3389/fddsv.2024.1363364","url":null,"abstract":"Permeability-glycoprotein (P-gp) belongs to the ABS transporter protein family, with a high expression rate in cancerous cells. The substrate/inhibitors of the protein are structurally diverse, with no lucid mechanism of inhibition. There are two schools of thought on the inhibition mechanism: (i) P-gp inhibitors bind to the huge hydrophobic cavity between two Trans-Membrane Domains (TMDs), supported by ample literary proof and (ii) P-gp inhibitors bind to the vicinity of Nucleotide-Binding Sites (NBSs). Structural biologists have presented several experimental and theoretical structures of P-gp with bound nucleotides and inhibitors to explain the same. However, the available experimental P-gp structures are insufficient to address the catalytic transition path of mammalian P-gp in detail, thus the dynamics and mechanism by which drugs are effluxed is still unknown. Targeted Molecular Dynamics (targeted MD) could be used to minutely analyse and explore the catalytic transition inward open (IO) to outward open (OO) and relaxation path (OO to IO). Finally, analysis of targeted MD trajectory may help to explore different conformational states of Pg-p (reaction coordinate of catalytic transition/relaxation), efflux of compounds aided by the dynamics of Nucleotide Binding Domains/NBDs (ATP coupled process) and TMDs (peristalsis-like movement pushes the bound molecule). This review presents an understanding of the catalytic transition and dynamics of protein which provides insights at the efflux of chemotherapeutic drug using in cancer treatment.","PeriodicalId":507971,"journal":{"name":"Frontiers in Drug Discovery","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140716581","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-08DOI: 10.3389/fddsv.2024.1355044
Thomas Hartung
Animals like mice and rats have long been used in medical research to help understand disease and test potential new treatments before human trials. However, while animal studies have contributed to important advances, too much reliance on animal models can also mislead drug development. This article explains for a general audience how animal research is used to develop new medicines, its benefits and limitations, and how more accurate and humane techniques—alternatives to animal testing—could improve this process.
{"title":"The (misleading) role of animal models in drug development","authors":"Thomas Hartung","doi":"10.3389/fddsv.2024.1355044","DOIUrl":"https://doi.org/10.3389/fddsv.2024.1355044","url":null,"abstract":"Animals like mice and rats have long been used in medical research to help understand disease and test potential new treatments before human trials. However, while animal studies have contributed to important advances, too much reliance on animal models can also mislead drug development. This article explains for a general audience how animal research is used to develop new medicines, its benefits and limitations, and how more accurate and humane techniques—alternatives to animal testing—could improve this process.","PeriodicalId":507971,"journal":{"name":"Frontiers in Drug Discovery","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140731693","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-05DOI: 10.3389/fddsv.2024.1362456
Utkarsha Naithani, Vandana Guleria
In the drug discovery and development, the identification of leadcompoundsplaysa crucial role in the quest for novel therapeutic agents. Leadcompounds are the initial molecules that show promising pharmacological activity againsta specific target and serve as the foundation for drug development. Integrativecomputational approaches have emerged as powerful tools in expediting this complex andresource-intensive process. They enable the efficient screening of vast chemical librariesand the rational design of potential drug candidates, significantly accelerating the drugdiscoverypipeline. This review paper explores the multi-layered landscape of integrative computationalmethodologies employed in lead compound discovery and evaluation. These approaches include various techniques, including molecular modelling, cheminformatics, structure-based drug design (SBDD), high-throughput screening, molecular dynamics simulations, ADMET (absorption, distribution, metabolism, excretion, and toxicity) prediction, anddrug-target interaction analysis. By revealing the critical role ofintegrative computational methods, this review highlights their potential to transformdrug discovery into a more efficient, cost-effective, and target-focused endeavour, ultimately paving the way for the development of innovative therapeutic agents to addressa multitude of medical challenges.
{"title":"Integrative computational approaches for discovery and evaluation of lead compound for drug design","authors":"Utkarsha Naithani, Vandana Guleria","doi":"10.3389/fddsv.2024.1362456","DOIUrl":"https://doi.org/10.3389/fddsv.2024.1362456","url":null,"abstract":"In the drug discovery and development, the identification of leadcompoundsplaysa crucial role in the quest for novel therapeutic agents. Leadcompounds are the initial molecules that show promising pharmacological activity againsta specific target and serve as the foundation for drug development. Integrativecomputational approaches have emerged as powerful tools in expediting this complex andresource-intensive process. They enable the efficient screening of vast chemical librariesand the rational design of potential drug candidates, significantly accelerating the drugdiscoverypipeline. This review paper explores the multi-layered landscape of integrative computationalmethodologies employed in lead compound discovery and evaluation. These approaches include various techniques, including molecular modelling, cheminformatics, structure-based drug design (SBDD), high-throughput screening, molecular dynamics simulations, ADMET (absorption, distribution, metabolism, excretion, and toxicity) prediction, anddrug-target interaction analysis. By revealing the critical role ofintegrative computational methods, this review highlights their potential to transformdrug discovery into a more efficient, cost-effective, and target-focused endeavour, ultimately paving the way for the development of innovative therapeutic agents to addressa multitude of medical challenges.","PeriodicalId":507971,"journal":{"name":"Frontiers in Drug Discovery","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140737900","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-04DOI: 10.3389/fddsv.2024.1360732
J. F. Morales, M. E. Ruiz, Robert E. Stratford, Alan Talevi
Purpose: Optimizing brain bioavailability is highly relevant for the development of drugs targeting the central nervous system. Several pharmacokinetic parameters have been used for measuring drug bioavailability in the brain. The most biorelevant among them is possibly the unbound brain-to-plasma partition coefficient, Kpuu,brain,ss, which relates unbound brain and plasma drug concentrations under steady-state conditions. In this study, we developed new in silico models to predict Kpuu,brain,ss.Methods: A manually curated 157-compound dataset was compiled from literature and split into training and test sets using a clustering approach. Additional models were trained with a refined dataset generated by removing known P-gp and/or Breast Cancer Resistance Protein substrates from the original dataset. Different supervised machine learning algorithms have been tested, including Support Vector Machine, Gradient Boosting Machine, k-nearest neighbors, classificatory Partial Least Squares, Random Forest, Extreme Gradient Boosting, Deep Learning and Linear Discriminant Analysis. Good practices of predictive Quantitative Structure-Activity Relationships modeling were followed for the development of the models.Results: The best performance in the complete dataset was achieved by extreme gradient boosting, with an accuracy in the test set of 85.1%. A similar estimation of accuracy was observed in a prospective validation experiment, using a small sample of compounds and comparing predicted unbound brain bioavailability with observed experimental data.Conclusion: New in silico models were developed to predict the Kpuu,brain,ss of drug candidates. The dataset used in this study is publicly disclosed, so that the models may be reproduced, refined, or expanded, as a useful tool to assist drug discovery processes.
{"title":"Application of machine learning to predict unbound drug bioavailability in the brain","authors":"J. F. Morales, M. E. Ruiz, Robert E. Stratford, Alan Talevi","doi":"10.3389/fddsv.2024.1360732","DOIUrl":"https://doi.org/10.3389/fddsv.2024.1360732","url":null,"abstract":"Purpose: Optimizing brain bioavailability is highly relevant for the development of drugs targeting the central nervous system. Several pharmacokinetic parameters have been used for measuring drug bioavailability in the brain. The most biorelevant among them is possibly the unbound brain-to-plasma partition coefficient, Kpuu,brain,ss, which relates unbound brain and plasma drug concentrations under steady-state conditions. In this study, we developed new in silico models to predict Kpuu,brain,ss.Methods: A manually curated 157-compound dataset was compiled from literature and split into training and test sets using a clustering approach. Additional models were trained with a refined dataset generated by removing known P-gp and/or Breast Cancer Resistance Protein substrates from the original dataset. Different supervised machine learning algorithms have been tested, including Support Vector Machine, Gradient Boosting Machine, k-nearest neighbors, classificatory Partial Least Squares, Random Forest, Extreme Gradient Boosting, Deep Learning and Linear Discriminant Analysis. Good practices of predictive Quantitative Structure-Activity Relationships modeling were followed for the development of the models.Results: The best performance in the complete dataset was achieved by extreme gradient boosting, with an accuracy in the test set of 85.1%. A similar estimation of accuracy was observed in a prospective validation experiment, using a small sample of compounds and comparing predicted unbound brain bioavailability with observed experimental data.Conclusion: New in silico models were developed to predict the Kpuu,brain,ss of drug candidates. The dataset used in this study is publicly disclosed, so that the models may be reproduced, refined, or expanded, as a useful tool to assist drug discovery processes.","PeriodicalId":507971,"journal":{"name":"Frontiers in Drug Discovery","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140744492","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-04DOI: 10.3389/fddsv.2024.1347798
Catharine E. Krebs, Kathrin Herrmann
Laws and policies are in place around the world to promote the replacement and reduction of nonhuman animals in science. These principles are rooted not just in ethical considerations for animals, but also in scientific considerations regarding the limitations of using nonhuman animals to model human biology, health, and disease. New nonanimal research approaches that use human biology, cells, and data to mimic complex human physiological states and therapeutic responses have become increasingly effective and accessible, replacing the use of animals in several applications, and becoming a crucial tool for biomedical research and drug development. Despite many advantages, acceptance of these new nonanimal methods has been slow, and barriers to their broader uptake remain. One such barrier is animal methods bias, the preference for animal-based methods where they are not necessary or where animal-free methods are suitable. This bias can impact research assessments and can discourage researchers from using novel nonanimal approaches. This article provides an introductory overview of animal methods bias for the general public, reviewing evidence, exploring consequences, and discussing ongoing mitigation efforts aimed at reducing barriers in the shift away from animal use in biomedical research and testing.
{"title":"Confronting the bias towards animal experimentation (animal methods bias)","authors":"Catharine E. Krebs, Kathrin Herrmann","doi":"10.3389/fddsv.2024.1347798","DOIUrl":"https://doi.org/10.3389/fddsv.2024.1347798","url":null,"abstract":"Laws and policies are in place around the world to promote the replacement and reduction of nonhuman animals in science. These principles are rooted not just in ethical considerations for animals, but also in scientific considerations regarding the limitations of using nonhuman animals to model human biology, health, and disease. New nonanimal research approaches that use human biology, cells, and data to mimic complex human physiological states and therapeutic responses have become increasingly effective and accessible, replacing the use of animals in several applications, and becoming a crucial tool for biomedical research and drug development. Despite many advantages, acceptance of these new nonanimal methods has been slow, and barriers to their broader uptake remain. One such barrier is animal methods bias, the preference for animal-based methods where they are not necessary or where animal-free methods are suitable. This bias can impact research assessments and can discourage researchers from using novel nonanimal approaches. This article provides an introductory overview of animal methods bias for the general public, reviewing evidence, exploring consequences, and discussing ongoing mitigation efforts aimed at reducing barriers in the shift away from animal use in biomedical research and testing.","PeriodicalId":507971,"journal":{"name":"Frontiers in Drug Discovery","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140743563","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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