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Gerussi A, Natalini A, Antonangeli F, Mancuso C, Agostinetto E, Barisani D, Di Rosa F, Andrade R, Invernizzi P. Immune-Mediated Drug-Induced Liver Injury: Immunogenetics and Experimental Models. Int J Mol Sci 2021; 22:4557. [PMID: 33925355 PMCID: PMC8123708 DOI: 10.3390/ijms22094557] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2021] [Revised: 04/22/2021] [Accepted: 04/23/2021] [Indexed: 02/06/2023] Open
Abstract
Drug-induced liver injury (DILI) is a challenging clinical event in medicine, particularly because of its ability to present with a variety of phenotypes including that of autoimmune hepatitis or other immune mediated liver injuries. Limited diagnostic and therapeutic tools are available, mostly because its pathogenesis has remained poorly understood for decades. The recent scientific and technological advancements in genomics and immunology are paving the way for a better understanding of the molecular aspects of DILI. This review provides an updated overview of the genetic predisposition and immunological mechanisms behind the pathogenesis of DILI and presents the state-of-the-art experimental models to study DILI at the pre-clinical level.
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Affiliation(s)
- Alessio Gerussi
- Centre for Autoimmune Liver Diseases, Division of Gastroenterology, Department of Medicine and Surgery, University of Milano-Bicocca, 20900 Monza, Italy; (C.M.); (D.B.); (P.I.)
- European Reference Network on Hepatological Diseases (ERN RARE-LIVER), San Gerardo Hospital, 20900 Monza, Italy
| | - Ambra Natalini
- Institute of Molecular Biology and Pathology (IBPM), National Research Council of Italy (CNR), 00185 Rome, Italy; (A.N.); (F.A.); (F.D.R.)
| | - Fabrizio Antonangeli
- Institute of Molecular Biology and Pathology (IBPM), National Research Council of Italy (CNR), 00185 Rome, Italy; (A.N.); (F.A.); (F.D.R.)
| | - Clara Mancuso
- Centre for Autoimmune Liver Diseases, Division of Gastroenterology, Department of Medicine and Surgery, University of Milano-Bicocca, 20900 Monza, Italy; (C.M.); (D.B.); (P.I.)
- European Reference Network on Hepatological Diseases (ERN RARE-LIVER), San Gerardo Hospital, 20900 Monza, Italy
| | - Elisa Agostinetto
- Academic Trials Promoting Team, Institut Jules Bordet, L’Universite’ Libre de Bruxelles (ULB), 1050 Brussels, Belgium;
- Medical Oncology and Hematology Unit, Humanitas Clinical and Research Center—IRCCS, Humanitas Cancer Center, Rozzano, 20089 Milan, Italy
- Department of Biomedical Sciences, Humanitas University, via Rita Levi Montalcini 4, Pieve Emanuele, 20090 Milan, Italy
| | - Donatella Barisani
- Centre for Autoimmune Liver Diseases, Division of Gastroenterology, Department of Medicine and Surgery, University of Milano-Bicocca, 20900 Monza, Italy; (C.M.); (D.B.); (P.I.)
- European Reference Network on Hepatological Diseases (ERN RARE-LIVER), San Gerardo Hospital, 20900 Monza, Italy
| | - Francesca Di Rosa
- Institute of Molecular Biology and Pathology (IBPM), National Research Council of Italy (CNR), 00185 Rome, Italy; (A.N.); (F.A.); (F.D.R.)
| | - Raul Andrade
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBERehd), UGC Aparato Digestivo, Instituto de Investigación Biomédica de Málaga-IBIMA, Hospital Universitario Virgen de la Victoria, Universidad de Málaga, 29016 Málaga, Spain;
| | - Pietro Invernizzi
- Centre for Autoimmune Liver Diseases, Division of Gastroenterology, Department of Medicine and Surgery, University of Milano-Bicocca, 20900 Monza, Italy; (C.M.); (D.B.); (P.I.)
- European Reference Network on Hepatological Diseases (ERN RARE-LIVER), San Gerardo Hospital, 20900 Monza, Italy
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2
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Lambrinidis G, Tsantili-Kakoulidou A. Multi-objective optimization methods in novel drug design. Expert Opin Drug Discov 2020; 16:647-658. [PMID: 33353441 DOI: 10.1080/17460441.2021.1867095] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Introduction: In multi-objective drug design, optimization gains importance, being upgraded to a discipline that attracts its own research. Current strategies are broadly classified into single - objective optimization (SOO) and multi-objective optimization (MOO).Areas covered: Starting with SOO and the ways used to incorporate multiple criteria into it, the present review focuses on MOO techniques, their comparison, advantages, and restrictions. Pareto analysis and the concept of dominance stand in the core of MOO. The Pareto front, Pareto ranking, and limitations of Pareto-based methods, due to high dimensions and data uncertainty, are outlined. Desirability functions and the weighted sum approaches are described as stand-alone techniques to transform the MOO problem to SOO or in combination with pareto analysis and evolutionary algorithms. Representative applications in different drug research areas are also discussed.Expert opinion: Despite their limitations, the use of combined MOO techniques, as well as being complementary to SOO or in conjunction with artificial intelligence, contributes dramatically to efficient drug design, assisting decisions and increasing success probabilities. For multi-target drug design, optimization is supported by network approaches, while applicability of MOO to other fields like drug technology or biological complexity opens new perspectives in the interrelated fields of medicinal chemistry and molecular biology.
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Affiliation(s)
- George Lambrinidis
- Division of Pharmaceutical Chemistry, Department of Pharmacy, National and Kapodistrian University of Athens, Panepistimiopolis, Zografou, Athens, Greece
| | - Anna Tsantili-Kakoulidou
- Division of Pharmaceutical Chemistry, Department of Pharmacy, National and Kapodistrian University of Athens, Panepistimiopolis, Zografou, Athens, Greece
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3
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Mervin LH, Johansson S, Semenova E, Giblin KA, Engkvist O. Uncertainty quantification in drug design. Drug Discov Today 2020; 26:474-489. [PMID: 33253918 DOI: 10.1016/j.drudis.2020.11.027] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2020] [Revised: 07/13/2020] [Accepted: 11/23/2020] [Indexed: 01/03/2023]
Abstract
Machine learning and artificial intelligence are increasingly being applied to the drug-design process as a result of the development of novel algorithms, growing access, the falling cost of computation and the development of novel technologies for generating chemically and biologically relevant data. There has been recent progress in fields such as molecular de novo generation, synthetic route prediction and, to some extent, property predictions. Despite this, most research in these fields has focused on improving the accuracy of the technologies, rather than on quantifying the uncertainty in the predictions. Uncertainty quantification will become a key component in autonomous decision making and will be crucial for integrating machine learning and chemistry automation to create an autonomous design-make-test-analyse cycle. This review covers the empirical, frequentist and Bayesian approaches to uncertainty quantification, and outlines how they can be used for drug design. We also outline the impact of uncertainty quantification on decision making.
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Affiliation(s)
- Lewis H Mervin
- Molecular AI, Discovery Sciences, R&D, AstraZeneca, Cambridge, UK.
| | - Simon Johansson
- Department of Computer Science and Engineering, Chalmers University of Technology, Gothenburg, Sweden; Molecular AI, Discovery Sciences, R&D, AstraZeneca, Gothenburg, Sweden
| | - Elizaveta Semenova
- Data Sciences and Quantitative Biology, Discovery Sciences, R&D, AstraZeneca, Cambridge, UK
| | - Kathryn A Giblin
- Medicinal Chemistry, Research and Early Development, Oncology R&D, AstraZeneca, Cambridge, UK
| | - Ola Engkvist
- Molecular AI, Discovery Sciences, R&D, AstraZeneca, Gothenburg, Sweden
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4
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Comparative study between deep learning and QSAR classifications for TNBC inhibitors and novel GPCR agonist discovery. Sci Rep 2020; 10:16771. [PMID: 33033310 PMCID: PMC7545175 DOI: 10.1038/s41598-020-73681-1] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2020] [Accepted: 09/18/2020] [Indexed: 12/30/2022] Open
Abstract
Machine learning is a well-known approach for virtual screening. Recently, deep learning, a machine learning algorithm in artificial neural networks, has been applied to the advancement of precision medicine and drug discovery. In this study, we performed comparative studies between deep neural networks (DNN) and other ligand-based virtual screening (LBVS) methods to demonstrate that DNN and random forest (RF) were superior in hit prediction efficiency. By using DNN, several triple-negative breast cancer (TNBC) inhibitors were identified as potent hits from a screening of an in-house database of 165,000 compounds. In broadening the application of this method, we harnessed the predictive properties of trained model in the discovery of G protein-coupled receptor (GPCR) agonist, by which computational structure-based design of molecules could be greatly hindered by lack of structural information. Notably, a potent (~ 500 nM) mu-opioid receptor (MOR) agonist was identified as a hit from a small-size training set of 63 compounds. Our results show that DNN could be an efficient module in hit prediction and provide experimental evidence that machine learning could identify potent hits in silico from a limited training set.
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5
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Cardoso MH, Orozco RQ, Rezende SB, Rodrigues G, Oshiro KGN, Cândido ES, Franco OL. Computer-Aided Design of Antimicrobial Peptides: Are We Generating Effective Drug Candidates? Front Microbiol 2020; 10:3097. [PMID: 32038544 PMCID: PMC6987251 DOI: 10.3389/fmicb.2019.03097] [Citation(s) in RCA: 108] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2019] [Accepted: 12/20/2019] [Indexed: 11/16/2022] Open
Abstract
Antimicrobial peptides (AMPs), especially antibacterial peptides, have been widely investigated as potential alternatives to antibiotic-based therapies. Indeed, naturally occurring and synthetic AMPs have shown promising results against a series of clinically relevant bacteria. Even so, this class of antimicrobials has continuously failed clinical trials at some point, highlighting the importance of AMP optimization. In this context, the computer-aided design of AMPs has put together crucial information on chemical parameters and bioactivities in AMP sequences, thus providing modes of prediction to evaluate the antibacterial potential of a candidate sequence before synthesis. Quantitative structure-activity relationship (QSAR) computational models, for instance, have greatly contributed to AMP sequence optimization aimed at improved biological activities. In addition to machine-learning methods, the de novo design, linguistic model, pattern insertion methods, and genetic algorithms, have shown the potential to boost the automated design of AMPs. However, how successful have these approaches been in generating effective antibacterial drug candidates? Bearing this in mind, this review will focus on the main computational strategies that have generated AMPs with promising activities against pathogenic bacteria, as well as anti-infective potential in different animal models, including sepsis and cutaneous infections. Moreover, we will point out recent studies on the computer-aided design of antibiofilm peptides. As expected from automated design strategies, diverse candidate sequences with different structural arrangements have been generated and deposited in databases. We will, therefore, also discuss the structural diversity that has been engendered.
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Affiliation(s)
- Marlon H Cardoso
- S-Inova Biotech, Programa de Pós-Graduação em Biotecnologia, Universidade Católica Dom Bosco, Campo Grande, Brazil.,Centro de Análises Proteômicas e Bioquímicas, Pós-Graduação em Ciências Genômicas e Biotecnologia, Universidade Católica de Brasília, Brasília, Brazil
| | - Raquel Q Orozco
- S-Inova Biotech, Programa de Pós-Graduação em Biotecnologia, Universidade Católica Dom Bosco, Campo Grande, Brazil.,Instituto de Ciências Biológicas, Departamento de Biologia, Programa de Pós-Graduação em Ciências Biológicas (Imunologia/Genética e Biotecnologia), Universidade Federal de Juiz de Fora, Juiz de Fora, Brazil
| | - Samilla B Rezende
- S-Inova Biotech, Programa de Pós-Graduação em Biotecnologia, Universidade Católica Dom Bosco, Campo Grande, Brazil
| | - Gisele Rodrigues
- Centro de Análises Proteômicas e Bioquímicas, Pós-Graduação em Ciências Genômicas e Biotecnologia, Universidade Católica de Brasília, Brasília, Brazil
| | - Karen G N Oshiro
- S-Inova Biotech, Programa de Pós-Graduação em Biotecnologia, Universidade Católica Dom Bosco, Campo Grande, Brazil.,Programa de Pós-Graduação em Patologia Molecular, Faculdade de Medicina, Universidade de Brasília, Brasília, Brazil
| | - Elizabete S Cândido
- S-Inova Biotech, Programa de Pós-Graduação em Biotecnologia, Universidade Católica Dom Bosco, Campo Grande, Brazil.,Centro de Análises Proteômicas e Bioquímicas, Pós-Graduação em Ciências Genômicas e Biotecnologia, Universidade Católica de Brasília, Brasília, Brazil
| | - Octávio L Franco
- S-Inova Biotech, Programa de Pós-Graduação em Biotecnologia, Universidade Católica Dom Bosco, Campo Grande, Brazil.,Centro de Análises Proteômicas e Bioquímicas, Pós-Graduação em Ciências Genômicas e Biotecnologia, Universidade Católica de Brasília, Brasília, Brazil.,Instituto de Ciências Biológicas, Departamento de Biologia, Programa de Pós-Graduação em Ciências Biológicas (Imunologia/Genética e Biotecnologia), Universidade Federal de Juiz de Fora, Juiz de Fora, Brazil.,Programa de Pós-Graduação em Patologia Molecular, Faculdade de Medicina, Universidade de Brasília, Brasília, Brazil
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6
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Khan K, Kar S, Sanderson H, Roy K, Leszczynski J. Ecotoxicological Modeling, Ranking and Prioritization of Pharmaceuticals Using QSTR and i‐QSTTR Approaches: Application of 2D and Fragment Based Descriptors. Mol Inform 2018; 38:e1800078. [DOI: 10.1002/minf.201800078] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2018] [Accepted: 11/01/2018] [Indexed: 12/22/2022]
Affiliation(s)
- Kabiruddin Khan
- Drug Theoretics and Cheminformatics Laboratory Department of Pharmaceutical Technology Jadavpur University Kolkata 700032 India
| | - Supratik Kar
- Interdisciplinary Center for Nanotoxicity Department of Chemistry, Physics and Atmospheric Sciences Jackson State University Jackson MS-39217 USA
| | - Hans Sanderson
- Department of Environmental Science, Section for Toxicology and Chemistry Aarhus University Frederiksborgvej 399 DK-4000 Roskilde Denmark
| | - Kunal Roy
- Drug Theoretics and Cheminformatics Laboratory Department of Pharmaceutical Technology Jadavpur University Kolkata 700032 India
| | - Jerzy Leszczynski
- Interdisciplinary Center for Nanotoxicity Department of Chemistry, Physics and Atmospheric Sciences Jackson State University Jackson MS-39217 USA
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7
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Olotu FA, Munsamy G, Soliman MES. Does Size Really Matter? Probing the Efficacy of Structural Reduction in the Optimization of Bioderived Compounds - A Computational "Proof-of-Concept". Comput Struct Biotechnol J 2018; 16:573-586. [PMID: 30546858 PMCID: PMC6280605 DOI: 10.1016/j.csbj.2018.11.005] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2018] [Revised: 11/14/2018] [Accepted: 11/18/2018] [Indexed: 02/07/2023] Open
Abstract
Over the years, numerous synthetic approaches have been utilized in drug design to improve the pharmacological properties of naturally derived compounds and most importantly, minimize toxic effects associated with their transition to drugs. The reduction of complex bioderived compounds to simpler bioactive fragments has been identified as a viable strategy to develop lead compounds with improved activities and minimal toxicities. Although this ‘reductive’ strategy has been widely exemplified, underlying biological events remain unresolved, hence the unanswered question remains how does the fragmentation of a natural compound improve its bioactivity and reduce toxicities? Herein, using a combinatorial approach, we initialize a computational “proof-of- concept” to expound the differential pharmacological and antagonistic activities of a natural compound, Anguinomycin D, and its synthetic fragment, SB640 towards Exportin Chromosome Region Maintenance 1 (CRM1). Interestingly, our findings revealed that in comparison with the parent compound, SB640 exhibited improved pharmacological attributes, while toxicities and off-target activities were relatively minimal. Moreover, we observed that the reduced size of SB640 allowed ‘deep access’ at the Nuclear Export Signals (NES) binding groove of CRM1, which favored optimal and proximal positioning towards crucial residues while the presence of the long polyketide tail in Anguinomycin D constrained its burial at the hydrophobic groove. Furthermore, with regards to their antagonistic functions, structural inactivation (rigidity) was more pronounced in CRM1 when bound by SB640 as compared to Anguinomycin D. These findings provide essential insights that portray synthetic fragmentation of natural compounds as a feasible approach towards the discovery of potential leads in disease treatment.
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Affiliation(s)
- Fisayo A Olotu
- Molecular Bio-computation and Drug Design Laboratory, School of Health Sciences, University of KwaZulu-Natal, Westville Campus, Durban 4001, South Africa
| | - Geraldene Munsamy
- Molecular Bio-computation and Drug Design Laboratory, School of Health Sciences, University of KwaZulu-Natal, Westville Campus, Durban 4001, South Africa
| | - Mahmoud E S Soliman
- Molecular Bio-computation and Drug Design Laboratory, School of Health Sciences, University of KwaZulu-Natal, Westville Campus, Durban 4001, South Africa
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8
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Lambrinidis G, Tsantili-Kakoulidou A. Challenges with multi-objective QSAR in drug discovery. Expert Opin Drug Discov 2018; 13:851-859. [DOI: 10.1080/17460441.2018.1496079] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
Affiliation(s)
- George Lambrinidis
- Division of Pharmaceutical Chemistry, Department of Pharmacy, National and Kapodistrian University of Athens, Zografou, Athens, Greece
| | - Anna Tsantili-Kakoulidou
- Division of Pharmaceutical Chemistry, Department of Pharmacy, National and Kapodistrian University of Athens, Zografou, Athens, Greece
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9
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Sosnina EA, Osolodkin DI, Radchenko EV, Sosnin S, Palyulin VA. Influence of Descriptor Implementation on Compound Ranking Based on Multiparameter Assessment. J Chem Inf Model 2018; 58:1083-1093. [PMID: 29689160 DOI: 10.1021/acs.jcim.7b00734] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Most of the common molecular descriptors have numerous different implementations. This can influence the results of compound prioritization based on the multiparameter assessment (MPA) approach that allows a medicinal chemist to simultaneously analyze and achieve the desired balance of the diverse and often conflicting molecular and pharmacological properties. In this study, we analyzed the feasibility of using different implementations of common descriptors (logP, logS, TPSA, logBB, hERG, nHBA) interchangeably in predesigned sets of requirements in the course of multiparameter compound optimization. The influence of methods of descriptor calculation, continuity or discreteness of their values, their applicability domains, as well as of the nature of desirability functions in an MPA profile were examined in terms of the stability of MPA compound ranking. It was shown that the interchangeable use of different methods of descriptor calculation is reliably acceptable only for continuously distributed parameters transformed by a smooth desirability function. If a descriptor in an MPA scheme is discretely distributed, only the implementation that was used for building the scoring profile may be used for assessment. An inconsistency of assessment due to different applicability domains of descriptors was also demonstrated.
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Affiliation(s)
- Ekaterina A Sosnina
- Department of Chemistry , Lomonosov Moscow State University , Moscow 119991 , Russia.,Center for Computational and Data-Intensive Science and Engineering , Skolkovo Institute of Science and Technology , Moscow 143026 , Russia.,Institute of Physiologically Active Compounds RAS , Chernogolovka 142432 , Russia
| | - Dmitry I Osolodkin
- Department of Chemistry , Lomonosov Moscow State University , Moscow 119991 , Russia.,Chumakov Institute of Poliomyelitis and Viral Encephalitides, Chumakov FSC R&D IBP RAS , Moscow 108819 , Russia.,Sechenov First Moscow State Medical University , Moscow 119991 , Russia
| | - Eugene V Radchenko
- Department of Chemistry , Lomonosov Moscow State University , Moscow 119991 , Russia.,Institute of Physiologically Active Compounds RAS , Chernogolovka 142432 , Russia
| | - Sergey Sosnin
- Center for Computational and Data-Intensive Science and Engineering , Skolkovo Institute of Science and Technology , Moscow 143026 , Russia.,Institute of Physiologically Active Compounds RAS , Chernogolovka 142432 , Russia
| | - Vladimir A Palyulin
- Department of Chemistry , Lomonosov Moscow State University , Moscow 119991 , Russia.,Institute of Physiologically Active Compounds RAS , Chernogolovka 142432 , Russia
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10
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Gupta N, Pandya P, Verma S. Computational Predictions for Multi-Target Drug Design. METHODS IN PHARMACOLOGY AND TOXICOLOGY 2018. [DOI: 10.1007/7653_2018_26] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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11
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Sánchez-Rodríguez A, Pérez-Castillo Y, Schürer SC, Nicolotti O, Mangiatordi GF, Borges F, Cordeiro MNDS, Tejera E, Medina-Franco JL, Cruz-Monteagudo M. From flamingo dance to (desirable) drug discovery: a nature-inspired approach. Drug Discov Today 2017. [PMID: 28624633 DOI: 10.1016/j.drudis.2017.05.008] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
The therapeutic effects of drugs are well known to result from their interaction with multiple intracellular targets. Accordingly, the pharma industry is currently moving from a reductionist approach based on a 'one-target fixation' to a holistic multitarget approach. However, many drug discovery practices are still procedural abstractions resulting from the attempt to understand and address the action of biologically active compounds while preventing adverse effects. Here, we discuss how drug discovery can benefit from the principles of evolutionary biology and report two real-life case studies. We do so by focusing on the desirability principle, and its many features and applications, such as machine learning-based multicriteria virtual screening.
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Affiliation(s)
- Aminael Sánchez-Rodríguez
- Departamento de Ciencias Naturales, Universidad Técnica Particular de Loja, Calle París S/N, EC1101608 Loja, Ecuador
| | | | - Stephan C Schürer
- Department of Molecular and Cellular Pharmacology, Miller School of Medicine and Center for Computational Science, University of Miami, Miami, FL 33136, USA
| | - Orazio Nicolotti
- Dipartimento di Farmacia - Scienze del Farmaco, Università di Bari Aldo Moro, Bari 072006, Italy
| | | | - Fernanda Borges
- CIQUP/Departamento de Química e Bioquímica, Faculdade de Ciências, Universidade do Porto, Porto 4169-007, Portugal
| | - M Natalia D S Cordeiro
- REQUIMTE/Departamento de Química e Bioquímica, Faculdade de Ciências, Universidade do Porto, Porto 4169-007, Portugal
| | - Eduardo Tejera
- Facultad de Medicina, Universidad de Las Américas, 170513 Quito, Ecuador
| | - José L Medina-Franco
- Universidad Nacional Autónoma de México, Departamento de Farmacia, Facultad de Química, Avenida Universidad 3000, Mexico City 04510, Mexico
| | - Maykel Cruz-Monteagudo
- Department of Molecular and Cellular Pharmacology, Miller School of Medicine and Center for Computational Science, University of Miami, Miami, FL 33136, USA; CIQUP/Departamento de Química e Bioquímica, Faculdade de Ciências, Universidade do Porto, Porto 4169-007, Portugal; REQUIMTE/Departamento de Química e Bioquímica, Faculdade de Ciências, Universidade do Porto, Porto 4169-007, Portugal; Department of General Education, West Coast University-Miami Campus, Doral, FL 33178, USA.
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12
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Minovski N, Novič M. Integrated in Silico Methods for the Design and Optimization of Novel Drug Candidates. Oncology 2017. [DOI: 10.4018/978-1-5225-0549-5.ch016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Although almost fully automated, the discovery of novel, effective, and safe drugs is still a long-term and highly expensive process. Consequently, the need for fleet, rational, and cost-efficient development of novel drugs is crucial, and nowadays the advanced in silico drug design methodologies seem to effectively meet these issues. The aim of this chapter is to provide a comprehensive overview of some of the current trends and advances in the in silico design of novel drug candidates with a special emphasis on 6-fluoroquinolone (6-FQ) antibacterials as potential novel Mycobacterium tuberculosis DNA gyrase inhibitors. In particular, the chapter covers some of the recent aspects of a wide range of in silico drug discovery approaches including multidimensional machine-learning methods, ligand-based and structure-based methodologies, as well as their proficient combination and integration into an intelligent virtual screening protocol for design and optimization of novel 6-FQ analogs.
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13
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Cummins DJ, Bell MA. Integrating Everything: The Molecule Selection Toolkit, a System for Compound Prioritization in Drug Discovery. J Med Chem 2016; 59:6999-7010. [DOI: 10.1021/acs.jmedchem.5b01338] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- David J. Cummins
- Eli Lilly and Company, 893 South
Delaware Street, Indianapolis, Indiana 46285, United States
| | - Michael A. Bell
- Eli Lilly and Company, 893 South
Delaware Street, Indianapolis, Indiana 46285, United States
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14
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Optimization Methodologies for the Production of Pharmaceutical Products. METHODS IN PHARMACOLOGY AND TOXICOLOGY 2016. [DOI: 10.1007/978-1-4939-2996-2_9] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
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15
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Multi-objective optimization methods in drug design. DRUG DISCOVERY TODAY. TECHNOLOGIES 2014; 10:e427-35. [PMID: 24050140 DOI: 10.1016/j.ddtec.2013.02.001] [Citation(s) in RCA: 95] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
Drug discovery is a challenging multi-objective problem where numerous pharmaceutically important objectives need to be adequately satisfied for a solution to be found. The problem is characterized by vast, complex solution spaces further perplexed by the presence of conflicting objectives. Multi-objective optimization methods, designed specifically to address such problems, have been introduced to the drug discovery field over a decade ago and have steadily gained in acceptance ever since. This paper reviews the latest multi-objective methods and applications reported in the literature, specifically in quantitative structure–activity modeling, docking, de novo design and library design. Further, the paper reports on related developments in drug discovery research and advances in the multi-objective optimization field.
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16
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González-Díaz H, Herrera-Ibatá DM, Duardo-Sánchez A, Munteanu CR, Orbegozo-Medina RA, Pazos A. ANN Multiscale Model of Anti-HIV Drugs Activity vs AIDS Prevalence in the US at County Level Based on Information Indices of Molecular Graphs and Social Networks. J Chem Inf Model 2014; 54:744-55. [DOI: 10.1021/ci400716y] [Citation(s) in RCA: 48] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Affiliation(s)
- Humberto González-Díaz
- Department
of Organic Chemistry II, Faculty of Science and Technology, University of the Basque Country UPV/EHU, 48940, Leioa, Vizcaya, Spain
- IKERBASQUE, Basque
Foundation for Science, 48011, Bilbao, Vizcaya, Spain
| | - Diana María Herrera-Ibatá
- Department of Information and Communication Technologies, University of A Coruña UDC, 15071, A Coruña, A Coruña, Spain
| | - Aliuska Duardo-Sánchez
- Department of Information and Communication Technologies, University of A Coruña UDC, 15071, A Coruña, A Coruña, Spain
| | - Cristian R. Munteanu
- Department of Information and Communication Technologies, University of A Coruña UDC, 15071, A Coruña, A Coruña, Spain
| | - Ricardo Alfredo Orbegozo-Medina
- Department
of Microbiology and Parasitology, University of Santiago de Compostela (USC), 15782, Santiago de Compostela, A Coruña, Spain
| | - Alejandro Pazos
- Department of Information and Communication Technologies, University of A Coruña UDC, 15071, A Coruña, A Coruña, Spain
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17
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Bickerton GR, Paolini GV, Besnard J, Muresan S, Hopkins AL. Quantifying the chemical beauty of drugs. Nat Chem 2012; 4:90-8. [PMID: 22270643 PMCID: PMC3524573 DOI: 10.1038/nchem.1243] [Citation(s) in RCA: 940] [Impact Index Per Article: 78.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2011] [Accepted: 12/02/2011] [Indexed: 01/15/2023]
Abstract
Drug-likeness is a key consideration when selecting compounds during the early stages of drug discovery. However, evaluation of drug-likeness in absolute terms does not reflect adequately the whole spectrum of compound quality. More worryingly, widely used rules may inadvertently foster undesirable molecular property inflation as they permit the encroachment of rule-compliant compounds towards their boundaries. We propose a measure of drug-likeness based on the concept of desirability called the quantitative estimate of drug-likeness (QED). The empirical rationale of QED reflects the underlying distribution of molecular properties. QED is intuitive, transparent, straightforward to implement in many practical settings and allows compounds to be ranked by their relative merit. We extended the utility of QED by applying it to the problem of molecular target druggability assessment by prioritizing a large set of published bioactive compounds. The measure may also capture the abstract notion of aesthetics in medicinal chemistry.
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Affiliation(s)
- G Richard Bickerton
- Division of Biological Chemistry and Drug Discovery, College of Life Sciences, University of Dundee, Dundee DD1 5EH, UK
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18
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Functional polymer thin films designed for antifouling materials and biosensors. CHEMICAL PAPERS 2012. [DOI: 10.2478/s11696-012-0147-1] [Citation(s) in RCA: 45] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
AbstractPolymer thin films offer a versatile and ubiquitous platform for a wide variety of real-world applications in biomedicine, nanotechnology, catalysis, photovoltaic devices, and energy conversion and storage. Depending on the chemical composition of the polymers and the associated microenvironment, the physicochemical properties (biocompatibility, stability, wettability, adhesion, morphology, surface free energy, and others) of polymer films can be tuned for a specific application through precisely controlled surface synthesis and the incorporation of desirable and responsive functional groups. In this short review, we first summarise the methods most commonly used for the fabrication of polymer thin films. Then we discuss how these polymer thin films can be used in a selection of biomedical applications in antifouling materials and biosensors. Some directions for the rational design of polymer thin films to achieve a specific function or application are also provided.
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19
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Cruz-Monteagudo M, Borges F, Cordeiro MNDS. Jointly Handling Potency and Toxicity of Antimicrobial Peptidomimetics by Simple Rules from Desirability Theory and Chemoinformatics. J Chem Inf Model 2011; 51:3060-77. [DOI: 10.1021/ci2002186] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Maykel Cruz-Monteagudo
- CIQ, Department of Chemistry and Biochemistry, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal
- Applied Chemistry Research Center - Faculty of Chemistry and Pharmacy, Molecular Simulation and Drug Design Group, Chemical Bioactive Center, Central University of Las Villas, Santa Clara, 54830, Cuba
| | - Fernanda Borges
- CIQ, Department of Chemistry and Biochemistry, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal
| | - M. Natália D. S. Cordeiro
- REQUIMTE, Department of Chemistry and Biochemistry, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal
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20
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Nicolotti O, Giangreco I, Introcaso A, Leonetti F, Stefanachi A, Carotti A. Strategies of multi-objective optimization in drug discovery and development. Expert Opin Drug Discov 2011; 6:871-84. [DOI: 10.1517/17460441.2011.588696] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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21
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Paolini GV, Lyons RA, Laflin P. How desirable are your IC50s? A way to enhance screening-based decision making. ACTA ACUST UNITED AC 2010; 15:1183-93. [PMID: 20980692 DOI: 10.1177/1087057110384402] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Dose-response curves, resulting in estimates of endpoints such as the IC(50), are fundamental to drug discovery. However, some estimates are more reliable than others. It is important to know just how reliable an estimate is if we want to base decisions on it or use it in further modeling. In this study, the authors propose a new measure of endpoint reliability, based on the concept of desirability first introduced by Harrington. The solution is not dependent on the application used to analyze the experimental data, provided a number of parameters to characterize the dose-response curve are available. The authors show how this score can be used as an objective and consistent measure to rank screening results, combine information from groups of experiments, and determine optimal levels of characterization of a compound's biological activity.
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22
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Cruz-Monteagudo M, PhamThe H, Cordeiro MNDS, Borges F. Prioritizing Hits with Appropriate Trade-Offs Between HIV-1 Reverse Transcriptase Inhibitory Efficacy and MT4 Blood Cells Toxicity Through Desirability-Based Multiobjective Optimization and Ranking. Mol Inform 2010; 29:303-21. [PMID: 27463058 DOI: 10.1002/minf.200900047] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2009] [Accepted: 02/19/2010] [Indexed: 11/07/2022]
Abstract
Nonnucleoside reverse transcriptase (RT) inhibitors (NNRTIs) constitute a promising therapeutic option for AIDS. However, the emergence of virus-NNRTIs resistance was found to be a major problem in the field. Toward that goal, a "knock-out" strategy stands out between the several options to circumvent the problem. However the high drug or drug-drug concentrations often used generate additional safety concerns. The need for approaches able to early integrate drug- or lead-likeness, toxicity and bioavailability criteria in the drug discovery phase is an emergent issue. Given that, we propose a combined strategy based on desirability-based multiobjective optimization (MOOP) and ranking for the prioritization of HIV-1 NNRTIs hits with appropriate trade-offs between inhibitory efficacy over the HIV-1 RT and toxic effects over MT4 blood cells. Through the MOOP process, the theoretical levels of the predictive variables required to reach a desirable RT inhibitor candidate with the best possible compromise between efficacy and safety were found. This information is used as a pattern to rank the library of compounds according to a similarity-based structural criterion, providing a ranking quality of 64 %/71 %/73 % in training/validation/test set. A comparative study between the sequential, parallel and multiobjective virtual screening revealed that the multiobjective approach can outperform the other approaches. These results suggest that the identification of NNRTIs hits with appropriate trade-offs between potency and safety, rather than fully optimized hits solely based on potency, can facilitate the hit to lead transition and increase the likelihood of the candidate to evolve into a successful antiretroviral drug.
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Affiliation(s)
- Maykel Cruz-Monteagudo
- Department of Chemistry, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal. , .,Department of Organic Chemistry, Faculty of Pharmacy, University of Porto, 4150-047 Porto, Portugal. , .,Applied Chemistry Research Center (CEQA), Faculty of Chemistry and Pharmacy, Central University of "Las Villas", Santa Clara, 54830, Cuba. , .,Molecular Simulation and Drug Design Group, Chemical Bioactive Center (CBQ), Central University of "Las Villas", Santa Clara, 54830, Cuba. ,
| | - Hai PhamThe
- Molecular Simulation and Drug Design Group, Chemical Bioactive Center (CBQ), Central University of "Las Villas", Santa Clara, 54830, Cuba
| | - M Natalia D S Cordeiro
- REQUIMTE, Department of Chemistry, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal.
| | - Fernanda Borges
- Department of Chemistry, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal
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23
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Cruz-Monteagudo M, Cordeiro MNDS, Teijeira M, González MP, Borges F. Multidimensional Drug Design: Simultaneous Analysis of Binding and Relative Efficacy Profiles of N6-substituted-4′-thioadenosines A3 Adenosine Receptor Agonists. Chem Biol Drug Des 2010; 75:607-18. [DOI: 10.1111/j.1747-0285.2010.00971.x] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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24
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Multi-target spectral moment QSAR versus ANN for antiparasitic drugs against different parasite species. Bioorg Med Chem 2010; 18:2225-2231. [PMID: 20185316 DOI: 10.1016/j.bmc.2010.01.068] [Citation(s) in RCA: 65] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2009] [Revised: 01/22/2010] [Accepted: 01/29/2010] [Indexed: 11/23/2022]
Abstract
There are many of pathogen parasite species with different susceptibility profile to antiparasitic drugs. Unfortunately, almost QSAR models predict the biological activity of drugs against only one parasite species. Consequently, predicting the probability with which a drug is active against different species with a single unify model is a goal of the major importance. In so doing, we use Markov Chains theory to calculate new multi-target spectral moments to fit a QSAR model that predict by the first time a mt-QSAR model for 500 drugs tested in the literature against 16 parasite species and other 207 drugs no tested in the literature using spectral moments. The data was processed by linear discriminant analysis (LDA) classifying drugs as active or non-active against the different tested parasite species. The model correctly classifies 311 out of 358 active compounds (86.9%) and 2328 out of 2577 non-active compounds (90.3%) in training series. Overall training performance was 89.9%. Validation of the model was carried out by means of external predicting series. In these series the model classified correctly 157 out 190, 82.6% of antiparasitic compounds and 1151 out of 1277 non-active compounds (90.1%). Overall predictability performance was 89.2%. In addition we developed four types of non Linear Artificial neural networks (ANN) and we compared with the mt-QSAR model. The improved ANN model had an overall training performance was 87%. The present work report the first attempts to calculate within a unify framework probabilities of antiparasitic action of drugs against different parasite species based on spectral moment analysis.
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25
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Pérez-Garrido A, Helguera AM, Rodríguez FG, Cordeiro MNDS. QSAR models to predict mutagenicity of acrylates, methacrylates and alpha,beta-unsaturated carbonyl compounds. Dent Mater 2010; 26:397-415. [PMID: 20122717 DOI: 10.1016/j.dental.2009.11.158] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2009] [Revised: 09/08/2009] [Accepted: 11/26/2009] [Indexed: 11/17/2022]
Abstract
OBJECTIVE The purpose of this study is to develop a quantitative structure-activity relationship (QSAR) model that can distinguish mutagenic from non-mutagenic species with alpha,beta-unsaturated carbonyl moiety using two endpoints for this activity - Ames test and mammalian cell gene mutation test - and also to gather information about the molecular features that most contribute to eliminate the mutagenic effects of these chemicals. METHODS Two data sets were used for modeling the two mutagenicity endpoints: (1) Ames test and (2) mammalian cells mutagenesis. The first one comprised 220 molecules, while the second one 48 substances, ranging from acrylates, methacrylates to alpha,beta-unsaturated carbonyl compounds. The QSAR models were developed by applying linear discriminant analysis (LDA) along with different sets of descriptors computed using the DRAGON software. RESULTS For both endpoints, there was a concordance of 89% in the prediction and 97% confidentiality by combining the three models for the Ames test mutagenicity. We have also identified several structural alerts to assist the design of new monomers. SIGNIFICANCE These individual models and especially their combination are attractive from the point of view of molecular modeling and could be used for the prediction and design of new monomers that do not pose a human health risk.
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Affiliation(s)
- Alfonso Pérez-Garrido
- Enviromental Engineering and Toxicology Dpt., Catholic University of San Antonio, Guadalupe, Murcia, Spain.
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26
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Multi-target spectral moment: QSAR for antiviral drugs vs. different viral species. Anal Chim Acta 2009; 651:159-64. [PMID: 19782806 DOI: 10.1016/j.aca.2009.08.022] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2009] [Revised: 08/05/2009] [Accepted: 08/18/2009] [Indexed: 11/23/2022]
Abstract
The antiviral QSAR models have an important limitation today. They predict the biological activity of drugs against only one viral species. This is determined by the fact that most of the current reported molecular descriptors encode only information about the molecular structure. As a result, predicting the probability with which a drug is active against different viral species with a single unifying model is a goal of major importance. In this work, we use Markov Chain theory to calculate new multi-target spectral moments to fit a QSAR model for drugs active against 40 viral species. The model is based on 500 drugs (including active and non-active compounds) tested as antiviral agents in the recent literature; not all drugs were predicted against all viruses, but only those with experimental values. The database also contains 207 well-known compounds (not as recent as the previous ones) reported in the Merck Index with other activities that do not include antiviral action against any virus species. We used Linear Discriminant Analysis (LDA) to classify all these drugs into two classes as active or non-active against the different viral species tested, whose data we processed. The model correctly classifies 5129 out of 5594 non-active compounds (91.69%) and 412 out of 422 active compounds (97.63%). Overall training predictability was 92.34%. The validation of the model was carried out by means of external predicting series, the model classifying, thus, 2568 out of 2779 non-active compounds and 224 out of 229 active compounds. Overall training predictability was 92.82%. The present work reports the first attempts to calculate within a unified framework the probabilities of antiviral drugs against different virus species based on a spectral moment analysis.
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27
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Viña D, Uriarte E, Orallo F, González-Díaz H. Alignment-free prediction of a drug-target complex network based on parameters of drug connectivity and protein sequence of receptors. Mol Pharm 2009; 6:825-35. [PMID: 19281186 DOI: 10.1021/mp800102c] [Citation(s) in RCA: 60] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
There are many drugs described with very different affinity to a large number of receptors. In this work, we selected drug-receptor pairs (DRPs) of affinity/nonaffinity drugs to similar/dissimilar receptors and we represented them as a large network, which may be used to identify drugs that can act on a receptor. Computational chemistry prediction of the biological activity based on quantitative structure-activity relationships (QSAR) substantially increases the potentialities of this kind of networks avoiding time- and resource-consuming experiments. Unfortunately, most QSAR models are unspecific or predict activity against only one receptor. To solve this problem, we developed here a multitarget QSAR (mt-QSAR) classification model. Overall model classification accuracy was 72.25% (1390/1924 compounds) in training, 72.28% (459/635) in cross-validation. Outputs of this mt-QSAR model were used as inputs to construct a network. The observed network has 1735 nodes (DRPs), 1754 edges or pairs of DRPs with similar drug-target affinity (sPDRPs), and low coverage density d = 0.12%. The predicted network has 1735 DRPs, 1857 sPDRPs, and also low coverage density d = 0.12%. After an edge-to-edge comparison (chi-square = 9420.3; p < 0.005), we have demonstrated that the predicted network is significantly similar to the one observed and both have a distribution closer to exponential than to normal.
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Affiliation(s)
- Dolores Viña
- Department of Organic Chemistry, University of Santiago de Compostela, 15782, Spain
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28
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Saiz-Urra L, Bustillo Pérez AJ, Cruz-Monteagudo M, Pinedo-Rivilla C, Aleu J, Hernández-Galán R, Collado IG. Global antifungal profile optimization of chlorophenyl derivatives against Botrytis cinerea and Colletotrichum gloeosporioides. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2009; 57:4838-4843. [PMID: 19489624 DOI: 10.1021/jf900375x] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/27/2023]
Abstract
Twenty-two aromatic derivatives bearing a chlorine atom and a different chain in the para or meta position were prepared and evaluated for their in vitro antifungal activity against the phytopathogenic fungi Botrytis cinerea and Colletotrichum gloeosporioides. The results showed that maximum inhibition of the growth of these fungi was exhibited for enantiomers S and R of 1-(4'-chlorophenyl)-2-phenylethanol (3 and 4). Furthermore, their antifungal activity showed a clear structure-activity relationship (SAR) trend confirming the importance of the benzyl hydroxyl group in the inhibitory mechanism of the compounds studied. Additionally, a multiobjective optimization study of the global antifungal profile of chlorophenyl derivatives was conducted in order to establish a rational strategy for the filtering of new fungicide candidates from combinatorial libraries. The MOOP-DESIRE methodology was used for this purpose providing reliable ranking models that can be used later.
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Affiliation(s)
- Liane Saiz-Urra
- Departamento de Química Orgánica, Facultad de Ciencias, Universidad de Cádiz, Puerto Real, Cádiz, Spain
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