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Computational Toxicology


Computational Toxicology

Risk Assessment for Chemicals
Wiley Series on Technologies for the Pharmaceutical Industry 1. Aufl.

von: Sean Ekins

159,99 €

Verlag: Wiley
Format: EPUB
Veröffentl.: 15.01.2018
ISBN/EAN: 9781119282587
Sprache: englisch
Anzahl Seiten: 432

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Beschreibungen

<p>A key resource for toxicologists across a broad spectrum of fields, this book offers a comprehensive analysis of molecular modelling approaches and strategies applied to risk assessment for pharmaceutical and environmental chemicals.</p> <ul> <li>Provides a perspective of what is currently achievable with computational toxicology and a view to future developments</li> <li>Helps readers overcome questions of data sources, curation, treatment, and how to model / interpret critical endpoints that support 21st century hazard assessment</li> <li>Assembles cutting-edge concepts and leading authors into a unique and powerful single-source reference</li> <li>Includes in-depth looks at QSAR models, physicochemical drug properties, structure-based drug targeting, chemical mixture assessments, and environmental modeling</li> <li>Features coverage about consumer product safety assessment and chemical defense along with chapters on open source toxicology and big data</li> </ul>
<p>List of Contributors xvii</p> <p>Preface xxi</p> <p>Acknowledgments xxiii</p> <p><b>Part I Computational Methods 1</b></p> <p><b>1 AccessibleMachine Learning Approaches for Toxicology 3<br /></b><i>Sean Ekins, Alex M. Clark, Alexander L. Perryman, Joel S. Freundlich, Alexandru Korotcov, and Valery</i> <i>Tkachenko</i></p> <p>1.1 Introduction 3</p> <p>1.2 Bayesian Models 5</p> <p>1.2.1 CDD Models 7</p> <p>1.3 Deep LearningModels 13</p> <p>1.4 Comparison of Different Machine LearningMethods 16</p> <p>1.4.1 Classic Machine LearningMethods 17</p> <p>1.4.1.1 Bernoulli Naive Bayes 17</p> <p>1.4.1.2 Linear Logistic Regression with Regularization 18</p> <p>1.4.1.3 AdaBoost Decision Tree 18</p> <p>1.4.1.4 Random Forest 18</p> <p>1.4.1.5 Support Vector Machine 19</p> <p>1.4.2 Deep Neural Networks 19</p> <p>1.4.3 Comparing Models 20</p> <p>1.5 FutureWork 21</p> <p>Acknowledgments 21</p> <p>References 21</p> <p><b>2 Quantum Mechanics Approaches in Computational Toxicology 31<br /></b><i>Jakub Kostal</i></p> <p>2.1 Translating Computational Chemistry to Predictive Toxicology 31</p> <p>2.2 Levels of Theory in Quantum Mechanical Calculations 33</p> <p>2.3 Representing Molecular Orbitals 38</p> <p>2.4 Hybrid Quantum and Molecular Mechanical Calculations 39</p> <p>2.5 Representing System Dynamics 40</p> <p>2.6 Developing QM Descriptors 42</p> <p>2.6.1 Global Electronic Parameters 42</p> <p>2.6.1.1 Electrostatic Potential, Dipole, and Polarizability 43</p> <p>2.6.1.2 Global Electronic Parameters Derived from Frontier Molecular Orbitals (FMOs) 45</p> <p>2.6.2 Local (Atom-Based) Electronic Parameters 47</p> <p>2.6.2.1 Parameters Derived from Frontier Molecular Orbitals (FMOs) 48</p> <p>2.6.2.2 Partial Atomic Charges 51</p> <p>2.6.2.3 Hydrogen-Bonding Interactions 51</p> <p>2.6.2.4 Bond Enthalpies 53</p> <p>2.6.3 Modeling Chemical Reactions 53</p> <p>2.6.4 QM/MM Calculations of Covalent Host-Guest Interactions 56</p> <p>2.6.5 Medium Effects and Hydration Models 59</p> <p>2.7 Rational Design of Safer Chemicals 61</p> <p>References 64</p> <p><b>Part II Applying Computers to Toxicology Assessment: Pharmaceutical, Industrial and Clinical 69</b></p> <p><b>3 Computational Approaches for Predicting hERG Activity 71<br /></b><i>Vinicius M. Alves, Rodolpho C. Braga, and Carolina Horta Andrade</i></p> <p>3.1 Introduction 71</p> <p>3.2 Computational Approaches 73</p> <p>3.3 Ligand-Based Approaches 73</p> <p>3.4 Structure-Based Approaches 77</p> <p>3.5 Applications to Predict hERG Blockage 77</p> <p>3.5.1 Pred-hERGWeb App 79</p> <p>3.6 Other Computational Approaches Related to hERG Liability 82</p> <p>3.7 Final Remarks 83</p> <p>References 83</p> <p><b>4 Computational Toxicology for Traditional Chinese Medicine 93<br /></b><i>Ni Ai and Xiaohui Fan</i></p> <p>4.1 Background, Current Status, and Challenges 93</p> <p>4.2 Case Study: Large-Scale Prediction on Involvement of Organic Anion Transporter 1 in Traditional Chinese Medicine-Drug Interactions 99</p> <p>4.2.1 Introduction to OAT1 and TCM 99</p> <p>4.2.2 Construction of TCM Compound Database 101</p> <p>4.2.3 OAT1 Inhibitor Pharmacophore Development 101</p> <p>4.2.4 External Test Set Evaluation 102</p> <p>4.2.5 Database Searching 102</p> <p>4.2.6 Results: OAT1 Inhibitor Pharmacophore 103</p> <p>4.2.7 Results: OAT1 Inhibitor Pharmacophore Evaluation 104</p> <p>4.2.8 Results: TCM Compound Database Searching Using OAT1 Inhibitor Pharmacophore 104</p> <p>4.2.9 Discussion 110</p> <p>4.3 Conclusion 114</p> <p>Acknowledgment 114</p> <p>References 114</p> <p><b>5 PharmacophoreModels for Toxicology Prediction 121<br /></b><i>Daniela Schuster</i></p> <p>5.1 Introduction 121</p> <p>5.2 Antitarget Screening 125</p> <p>5.3 Prediction of Liver Toxicity 125</p> <p>5.4 Prediction of Cardiovascular Toxicity 127</p> <p>5.5 Prediction of Central Nervous System (CNS) Toxicity 128</p> <p>5.6 Prediction of Endocrine Disruption 130</p> <p>5.7 Prediction of ADME 135</p> <p>5.8 General Remarks on the Limits and Future Perspectives for Employing Pharmacophore Models in Toxicological Studies 136</p> <p>References 137</p> <p><b>6 Transporters in Hepatotoxicity 145<br /></b><i>Eleni Kotsampasakou, Sankalp Jain, Daniela Digles, and Gerhard F. Ecker</i></p> <p>6.1 Introduction 145</p> <p>6.2 Basolateral Transporters 146</p> <p>6.3 Canalicular Transporters 148</p> <p>6.4 Data Sources for Transporters in Hepatotoxicity 148</p> <p>6.5 In Silico Transporters Models 150</p> <p>6.6 Ligand-Based Approaches 150</p> <p>6.7 OATP1B1 and OATP1B3 150</p> <p>6.8 NTCP 154</p> <p>6.9 OCT1 154</p> <p>6.10 OCT2 154</p> <p>6.11 MRP1, MRP3, and MRP4 155</p> <p>6.12 BSEP 155</p> <p>6.13 MRP2 156</p> <p>6.14 MDR1/P-gp 156</p> <p>6.15 MDR3 157</p> <p>6.16 BCRP 157</p> <p>6.17 MATE1 158</p> <p>6.18 ASBT 159</p> <p>6.19 Structure-Based Approaches 159</p> <p>6.20 Complex Models Incorporating Transporter Information 160</p> <p>6.21 In Vitro Models 160</p> <p>6.22 Multiscale Models 161</p> <p>6.23 Outlook 162</p> <p>Acknowledgments 164</p> <p>References 164</p> <p><b>7 Cheminformatics in a Clinical Setting 175<br /></b><i>Matthew D. Krasowski and Sean Ekins</i></p> <p>7.1 Introduction 175</p> <p>7.2 Similarity Analysis Applied to Drug of Abuse/Toxicology Immunoassays 177</p> <p>7.3 Similarity Analysis Applied toTherapeutic Drug Monitoring Immunoassays 187</p> <p>7.4 Similarity Analysis Applied to Steroid Hormone Immunoassays 191</p> <p>7.5 Cheminformatics Applied to "Designer Drugs" 195</p> <p>7.6 Relevance to Antibody-Ligand Interactions 202</p> <p>7.7 Conclusions and Future Directions 203</p> <p>Acknowledgment 204</p> <p>References 204</p> <p><b>Part III Applying Computers to Toxicology Assessment: Environmental and Regulatory Perspectives 211</b></p> <p><b>8 Computational Tools for ADMET Profiling 213<br /></b><i>Denis Fourches, Antony J.Williams, Grace Patlewicz, Imran Shah, Chris Grulke, JohnWambaugh, Ann</i> <i>Richard, and Alexander Tropsha</i></p> <p>8.1 Introduction 213</p> <p>8.2 Cheminformatics Approaches for ADMET Profiling 214</p> <p>8.2.1 Chemical Data Curation Prior to ADMET Modeling 215</p> <p>8.2.2 QSAR Modelability Index 217</p> <p>8.2.3 Predictive QSAR Model DevelopmentWorkflow 218</p> <p>8.2.4 Hybrid QSAR Modeling 220</p> <p>8.2.4.1 Simple Consensus 223</p> <p>8.2.4.2 Mixed Chemical and Biological Features 223</p> <p>8.2.4.3 Two-Step HierarchicalWorkflow 224</p> <p>8.2.5 Chemical Biological Read-Across 226</p> <p>8.2.6 Public Chemotype Approach to Data-Mining 229</p> <p>8.3 Unsolved Challenges in Structure Based Profiling 230</p> <p>8.3.1 Biological Data Curation 231</p> <p>8.3.2 Identification and Treatment of Activity and Toxicity Cliffs 233</p> <p>8.3.3 In Vitro to In Vivo Continuum in the Context of AOP 233</p> <p>8.4 Perspectives 234</p> <p>8.4.1 Profilers on the Go with Mobile Devices 235</p> <p>8.4.2 Structure–Exposure–Activity Relationships 236</p> <p>8.5 Conclusions 237</p> <p>Acknowledgments 237</p> <p>Disclaimer 237</p> <p>References 238</p> <p><b>9 Computational Toxicology and Reach 245<br /></b><i>Emilio Enfenati, Anna Lombardo, and Alessandra Roncaglioni</i></p> <p>9.1 A Theoretical and Historical Introduction to the Evolution Toward Predictive Models 245</p> <p>9.2 Reach and the Other Legislations 247</p> <p>9.3 Annex XI of Reach for QSARModels 248</p> <p>9.3.1 The First Condition of Annex XI and QMRF 249</p> <p>9.3.2 The Second Condition and the Applicability Domain 251</p> <p>9.3.3 TheThird Condition of Annex XI, and the Use of the QSAR Models 252</p> <p>9.3.4 Adequate and Reliable Documentation of the Applied Method 254</p> <p>9.4 The ECHA Guidelines and the Use of QSAR Models within ECHA 255</p> <p>9.4.1 Example of Bioconcentration Factor (BCF) 255</p> <p>9.4.2 Example of Mutagenicity (Reverse-Mutation Assay) Prediction 260</p> <p>9.5 Conclusions 266</p> <p>References 266</p> <p><b>10 Computational Approaches to Predicting Dermal Absorption of Complex Topical Mixtures 269<br /></b><i>Jim E. Riviere and Jason Chittenden</i></p> <p>10.1 Introduction 269</p> <p>10.2 Principles of Dermal Absorption 270</p> <p>10.3 Dermal Mixtures 274</p> <p>10.4 Model Systems 275</p> <p>10.5 Local Skin Versus Systemic Endpoints 277</p> <p>10.6 QSAR Approaches to Model Dermal Absorption 278</p> <p>10.7 PharmacokineticModels 281</p> <p>10.8 Conclusions 284</p> <p>References 285</p> <p><b>Part IV New Technologies for Toxicology, Future Perspectives 291</b></p> <p><b>11 Big Data in Computational Toxicology: Challenges and Opportunities 293<br /></b><i>Linlin Zhao and Hao Zhu</i></p> <p>11.1 Big Data Scenario of Computational Toxicology 293</p> <p>11.2 Fast-Growing Chemical Toxicity Data 295</p> <p>11.3 The Use of Big Data Approaches in Modern Computational Toxicology 299</p> <p>11.3.1 Profiling the Toxicants with Massive Biological Data 299</p> <p>11.3.2 Read-Across Study to Fill Data Gap 301</p> <p>11.3.3 Unstructured Data Curation 302</p> <p>11.4 Challenges of Big Data Research in Computational Toxicology and Relevant Forecasts 303</p> <p>References 304</p> <p><b>12 HLA-Mediated Adverse Drug Reactions: Challenges and Opportunities for Predictive Molecular</b> <b>Modeling 313<br /></b><i>George van Den Driessche and Denis Fourches</i></p> <p>12.1 Introduction 313</p> <p>12.2 Human Leukocyte Antigens 314</p> <p>12.2.1 HLA Proteins 314</p> <p>12.2.2 ADR–HLA Associations 316</p> <p>12.2.3 HLA-Drug-Peptide Proposed T-Cell Signaling Mechanisms 321</p> <p>12.3 Structure-Based Molecular Docking to Study HLA-Mediated ADRs 322</p> <p>12.3.1 Structure-Based Docking 324</p> <p>12.3.2 Case Study: Abacavir with B*57:01 326</p> <p>12.3.3 Limitations 332</p> <p>12.4 Perspectives 334</p> <p>References 335</p> <p><b>13 Open Science Data Repository for Toxicology 341<br /></b><i>Valery Tkachenko, Richard Zakharov, and Sean Ekins</i></p> <p>13.1 Introduction 341</p> <p>13.2 Open Science Data Repository 342</p> <p>13.3 Benefits of OSDR 344</p> <p>13.3.1 Chemically and Semantically Enabled Scientific Data Repository 344</p> <p>13.3.2 Chemical Validation and Standardization Platform 346</p> <p>13.3.3 Format Adapters 347</p> <p>13.3.4 Open Platform for Data Acquisition, Curation, and Dissemination 350</p> <p>13.3.5 Dataledger 350</p> <p>13.4 Technical Details 351</p> <p>13.5 FutureWork 353</p> <p>13.5.1 Implementation of Ontology-Based Properties 356</p> <p>13.5.2 Implementation of an Advanced Search System 357</p> <p>13.5.3 Implementation of a Scientist Profile, Advanced Security, Data Sharing Capabilities and Notifications Framework 357</p> <p>References 358</p> <p><b>14 Developing Next Generation Tools for Computational Toxicology 363<br /></b><i>Alex M. Clark, Kimberley M. Zorn, Mary A. Lingerfelt, and Sean Ekins</i></p> <p>14.1 Introduction 363</p> <p>14.2 Developing Apps for Chemistry 364</p> <p>14.3 Green Chemistry 364</p> <p>14.3.1 Green Solvents and Lab Solvents 367</p> <p>14.3.2 Green Lab Notebook 370</p> <p>14.4 Polypharma and Assay Central 374</p> <p>14.4.1 Future Efforts with Assay Central 380</p> <p>14.5 Conclusion 382</p> <p>Acknowledgments 383</p> <p>References 383</p> <p>Index 389</p>
<p> <strong>Sean Ekins, MSc, PhD, DSc</strong> has over 20 years of pharmaceutical and toxicology experience. He is the founder or co-founder of two companies and Adjunct Professor at three universities. He has been awarded 16 NIH grants as Principal Investigator. He has authored or co authored over 285 peer-reviewed papers and book chapters and edited five books with Wiley. His research is focused on collaborations to facilitate rare and neglected disease drug discovery.
<p><em>Computational Toxicology: Risk Assessment for Chemicals</em> offers a comprehensive analysis of applied approaches and strategies. The book begins with an introduction to toxicology and relevant technologies and then addresses the most advanced currently available molecular-modelling software and its role in toxicity testing. <p>The chapters feature in-depth looks at QSAR models, physicochemical drug properties, structure-based drug targeting, chemical mixture assessments, and environmental modelling. Additional coverage includes consumer product safety assessment and chemical defense, along with chapters on open source toxicology and big data. <p>An important resource for researchers and regulators involved in toxicology and risk assessment: <ul> <li>Provides a perspective of what is currently achievable with computational toxicology and a view to future developments</li> <li>Helps readers overcome questions of data sources, curation, treatment, and how to model / interpret critical endpoints that support 21st century hazard assessment</li> <li>Assembles cutting-edge concepts and leading authors into a unique and powerful single-source reference</li> </ul> <br>

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