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Computational Pharmaceutical Solid State Chemistry


Computational Pharmaceutical Solid State Chemistry


1. Aufl.

von: Yuriy A. Abramov

132,99 €

Verlag: Wiley
Format: PDF
Veröffentl.: 20.05.2016
ISBN/EAN: 9781119229193
Sprache: englisch
Anzahl Seiten: 448

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Beschreibungen

This book is the first to combine computational material science and modeling of molecular solid states for pharmaceutical industry applications.<br /><br />•    Provides descriptive and applied state-of-the-art computational approaches and workflows  to guide pharmaceutical solid state chemistry experiments and to support/troubleshoot API solid state selection<br />•    Includes real industrial case examples related to application of modeling methods in problem solving<br />•    Useful as a supplementary reference/text for undergraduate, graduate and postgraduate students in computational chemistry, pharmaceutical and biotech sciences, and materials science
<p>List of Contributors xiii</p> <p>Preface xvii</p> <p>Editor’s biography xix</p> <p><b>1 Computational Pharmaceutical Solid‐State Chemistry: An Introduction 1<br /></b><i>Yuriy A. Abramov</i></p> <p>1.1 Introduction 1</p> <p>1.2 Pharmaceutical Solid‐State Landscape 2</p> <p>1.2.1 Some Definitions 2</p> <p>1.2.2 Impact of Solid‐State Form on API and Product Properties 4</p> <p>1.2.3 Challenges of Pharmaceutical Industry Related to Solid Form Selection 6</p> <p>1.3 Pharmaceutical Computational Solid‐State Chemistry 8</p> <p>1.4 Conclusions 9</p> <p>Acknowledgment 10</p> <p>References 10</p> <p><b>2 Navigating the Solid Form Landscape with Structural Informatics 15<br /></b><i>Peter T. A. Galek, Elna Pidcock, Peter A. Wood, Neil Feeder, and Frank H. Allen</i></p> <p>2.1 Introduction 15</p> <p>2.2 The CSD System 17</p> <p>2.3 Hydrogen‐Bond Propensity: Theory and Applications to Polymorphism 18</p> <p>2.3.1 Methodology 18</p> <p>2.3.2 Case Study 1: Ritonavir 19</p> <p>2.4 Hydrogen‐Bond Landscapes: Developing the Propensity Approach 21</p> <p>2.4.1 Methodology 21</p> <p>2.4.2 Case Study 2: Metastable versus Stable Form of Piroxicam 22</p> <p>2.4.3 Case Study 3: Exploring the Likely Hydrogen‐Bond Landscape of Axitinib (Inlyta®) 25</p> <p>2.5 Informatics‐Based Cocrystal Screening 25</p> <p>2.5.1 Methodology 25</p> <p>2.5.2 Case Study 4: Paracetamol 26</p> <p>2.5.3 Case Study 5: AMG 517 – Sorbic Acid Cocrystal 29</p> <p>2.6 Conclusions and Outlook 32</p> <p>References 33</p> <p><b>3 Theoretical Hydrogen‐Bonding Analysis for Assessment of Physical Stability of Pharmaceutical Solid Forms 37<br /></b><i>Yuriy A. Abramov</i></p> <p>3.1 Introduction 37</p> <p>3.2 Experimental Scales of H‐Bonding Basicity and Acidity 39</p> <p>3.2.1 In Solution Phase 39</p> <p>3.2.2 In Solid‐State Phase 40</p> <p>3.3 Theoretical Study of H‐Bonding Strength in Solution and in Solid State 40</p> <p>3.3.1 Supermolecular Approach 41</p> <p>3.3.2 Descriptor‐Based Approaches 41</p> <p>3.3.3 Solid‐State H‐bonding Strength 42</p> <p>3.4 Application to Solid Form Selection 47</p> <p>3.4.1 Examples of Theoretical H‐Bonding Analysis to Support Solid Form Selection 48</p> <p>3.4.2 Consideration of Limitations of Hydrogen‐Bonding Propensity Approach 50</p> <p>3.5 Conclusion 52</p> <p>Acknowledgment 53</p> <p>References 53</p> <p><b>4 Improving Force Field Parameters for Small‐Molecule Conformation Generation 57<br /></b><i>Dmitry Lupyan, Yuriy A. Abramov, and Woody Sherman</i></p> <p>4.1 Introduction 57</p> <p>4.2 Methods 62</p> <p>4.3 Results and Discussion 66</p> <p>4.3.1 Close S⋯O Interactions 66</p> <p>4.3.2 Halogen X⋯O Interactions 75</p> <p>4.3.3 Generalization of the Approach to Other Interactions 77</p> <p>4.3.4 An Improved OPLS Force Field (OPLS2) 80</p> <p>4.4 Conclusion 81</p> <p>References 82</p> <p><b>5 Advances in Crystal Structure Prediction and Applications to Pharmaceutical Materials 87<br /></b><i>Graeme M. Day</i></p> <p>5.1 Introduction 87</p> <p>5.1.1 Motivation 88</p> <p>5.2 Crystal Structure Prediction Methodologies 89</p> <p>5.2.1 Molecular Geometry 89</p> <p>5.2.2 Crystal Structure Searching 99</p> <p>5.2.3 Structure Ranking 102</p> <p>5.3 Applications of Crystal Structure Prediction 105</p> <p>5.3.1 Crystal Structure Determination 106</p> <p>5.3.2 Solid Form Screening 108</p> <p>5.4 Summary 110</p> <p>References 110</p> <p><b>6 Integrating Computational Materials Science Tools in Form and Formulation Design 117<br /></b><i>Joseph F. Krzyzaniak, Paul A. Meenan, Cheryl L. Doherty, Klimentina Pencheva, Suman Luthra, and Aurora Cruz‐Cabeza</i></p> <p>6.1 Introduction 117</p> <p>6.2 From Molecule to Crystal Structure 119</p> <p>6.2.1 Single Crystal Structure 120</p> <p>6.2.2 Structural Analysis 120</p> <p>6.2.3 Molecular Packing and HB Geometry Analyses 122</p> <p>6.2.4 Full Interaction Maps 123</p> <p>6.2.5 Crystal Structure Prediction 124</p> <p>6.3 From Crystals to Particles 131</p> <p>6.4 From Particles to Dosage Forms 134</p> <p>6.4.1 Structural Investigation of Crystal Surfaces and Structure Dehydration 137</p> <p>6.4.2 Structural Investigations of Crystal Surfaces and Chemical Stability 139</p> <p>6.5 Conclusion 141</p> <p>Acknowledgments 142</p> <p>References 142</p> <p><b>7 Current Computational Approaches at Astrazeneca for Solid‐State and Property Predictions 145<br /></b><i>Sten O. Nilsson Lill, Staffan Schantz, Viktor Broo, and Anders Broo</i></p> <p>7.1 Introduction 145</p> <p>7.2 Polymorphism 146</p> <p>7.3 Conformer Search 157</p> <p>7.4 Molecular Perturbations to Achieve Solubility for GPR119 Ligands 158</p> <p>7.5 Solid‐State Nuclear Magnetic Resonance and Azd8329 Case Study 163</p> <p>7.6 CCDC Tools 168</p> <p>7.7 Tautomerism 169</p> <p>7.8 Conclusions 170</p> <p>Acknowledgments 170</p> <p>References 170</p> <p><b>8 Synthonic Engineering: From Molecular and Crystallographic Structure to the Rational Design of Pharmaceutical Solid Dosage Forms 175</b></p> <p><i>Kevin J. Roberts, Robert B. Hammond, Vasuki Ramachandran, and Robert Docherty</i></p> <p>8.1 Introduction 175</p> <p>8.2 The Crystal 177</p> <p>8.2.1 Crystallography 177</p> <p>8.2.2 Crystal Chemistry and Crystal Packing of Drug Molecules 179</p> <p>8.2.3 Deconstructing the Supra‐Molecular Interactions in Bulk – Intrinsic Synthons 181</p> <p>8.3 Morphology and Surface Structure 185</p> <p>8.3.1 Nucleation and the Crystal Growth Process 185</p> <p>8.3.2 Particle Morphology and Surface Structure 186</p> <p>8.3.3 Crystal Morphology Prediction 188</p> <p>8.3.4 Deconstructing the Supra‐Molecular Interactions at Surfaces – Extrinsic Synthons 190</p> <p>8.3.5 Grid Searching – Probing Inter‐molecular Interactions at Surfaces and Environments 190</p> <p>8.4 The Crystallisation Perspective 191</p> <p>8.4.1 Nucleation, Surface Energies and Directed Polymorphism 191</p> <p>8.4.2 The Impact of Solvent on Morphology 194</p> <p>8.4.3 The Impact of Impurities on Morphology 196</p> <p>8.5 The Drug Product Perspective 197</p> <p>8.5.1 Excipient Compatibility 197</p> <p>8.5.2 Inhaled Drug Delivery Design 199</p> <p>8.5.3 Mechanical Properties 201</p> <p>8.5.4 Dissolution 203</p> <p>8.6 Summary and Future Outlook: Synthonic Engineering Particle Passport and the Future of the Drug Product Design 205</p> <p>Acknowledgements 207</p> <p>References 207</p> <p><b>9 New Developments in Prediction of Solid‐State Solubility and Cocrystallization Using COSMO‐RS Theory 211<br /></b><i>Christoph Loschen and Andreas Klamt</i></p> <p>9.1 Introduction 211</p> <p>9.2 COSMO‐RS 212</p> <p>9.3 Prediction of Drug Solubility Using COSMO‐RS 215</p> <p>9.4 Solubility Prediction with Multiple Reference Solvents 218</p> <p>9.5 Melting Point and Fusion Enthalpy QSPR Models 221</p> <p>9.6 Cocrystal Screening 225</p> <p>9.7 Solvate Formation 229</p> <p>9.8 Summary 231</p> <p>References 231</p> <p><b>10 Modeling and Prediction of Solid Solubility by Ge Models 235<br /></b><i>Larissa P. Cunico, Anjan K. Tula, Roberta Ceriani, and Rafiqul Gani</i></p> <p>10.1 Introduction 235</p> <p>10.2 Framework 236</p> <p>10.2.1 Thermodynamic Basis 238</p> <p>10.2.2 The Necessary Property‐Related Information for Solid Solubility Prediction and the Developed Databases 238</p> <p>10.2.3 SLE Thermodynamic Consistency Tests 241</p> <p>10.2.4 SolventPro 252</p> <p>10.3 Conclusion 259</p> <p>References 260</p> <p><b>11 Molecular Simulation Methods to Compute Intrinsic Aqueous Solubility of Crystalline Drug‐Like Molecules 263<br /></b><i>David S. Palmer and Maxim V. Fedorov</i></p> <p>11.1 Introduction 263</p> <p>11.2 Definitions of Solubility 264</p> <p>11.3 Solubility and Thermodynamics 264</p> <p>11.3.1 Solubility and Free Energy of Solution 264</p> <p>11.3.2 Computation of Solubility from the Thermodynamic Cycle of Solid to Supercooled Liquid to Aqueous Solution 265</p> <p>11.3.3 Computation of Solubility from the Thermodynamic Cycle of Solid to Gas Phase to Aqueous Solution 267</p> <p>11.4 Calculation of Δ<i>G</i><sub>hyd </sub>269</p> <p>11.4.1 Implicit Continuum Solvent Models 270</p> <p>11.4.2 Explicit Solvent Models: Atomistic Simulations 270</p> <p>11.4.3 Explicit Solvent Models: Molecular Theories of Liquids 271</p> <p>11.5 Calculation of Δ<i>G</i><sub>sub</sub> 275</p> <p>11.5.1 Crystal Polymorphism 275</p> <p>11.5.2 Crystal Structure Prediction 275</p> <p>11.5.3 Calculation of Δ<i>G</i><sub>sub</sub> 276</p> <p>11.5.4 Calculation of Δ<i>H</i><sub>sub</sub> 276</p> <p>11.5.5 Calculation of Δ<i>S</i><sub>sub</sub> 277</p> <p>11.5.6 Other Methods to Compute Δ<i>G</i><sub>sub</sub> 278</p> <p>11.6 Experimental Data 279</p> <p>11.7 Conclusion and Future Outlook 280</p> <p>Acknowledgments 280</p> <p>References 280</p> <p><b>12 Calculation of NMR Tensors: Application to Small‐Molecule Pharmaceutical Solids 287<br /></b><i>Luis Mafra, Sergio Santos, Mariana Sardo, and Heather Frericks Schmidt</i></p> <p>12.1 SSNMR Spectroscopy: A Short Introduction 287</p> <p>12.2 The Chemical Shielding Tensors: Fundamentals 288</p> <p>12.3 Computational Approaches to the Calculation of Chemical Shift Tensors in Solids 290</p> <p>12.3.1 Cluster Approach 290</p> <p>12.3.2 Periodic Approach 291</p> <p>12.3.3 Pitfalls and Practical Considerations 292</p> <p>12.4 NICS 294</p> <p>12.5 Case Studies Combining Experimental and Computational NMR Methods 294</p> <p>12.5.1 NMR Assignment of Polymorphs Aided by Computing NMR Parameters 295</p> <p>12.5.2 Calculated vs Experimental Chemical Shift Tensors Using Different NMR Methods 302</p> <p>12.5.3 Studying Crystal Packing Interactions 312</p> <p>12.5.4 Employing Chemical Shifts for Crystal Structure Elucidation/Determination 315</p> <p>12.6 Summary 325</p> <p>References 326</p> <p><b>13 Molecular Dynamics Simulations of Amorphous Systems 331<br /></b><i>Bradley D. Anderson and Tian‐Xiang Xiang</i></p> <p>13.1 Introduction 331</p> <p>13.2 MD Simulation Methodology 332</p> <p>13.3 Polymer Properties—MD Simulation Versus Experiment 334</p> <p>13.3.1 Glass Transition Temperature (<i>T</i><sub>g</sub>) 334</p> <p>13.3.2 Amorphous Structure and Dynamics 337</p> <p>13.4 Hydrogen Bonding Patterns, Water Uptake, and Distribution in Amorphous Solids 342</p> <p>13.4.1 Poly(D,L)lactide 343</p> <p>13.4.2 Polyvinylpyrrolidone 345</p> <p>13.4.3 Hydroxypropylmethylcellulose Acetate Succinate (HPMCAS) 347</p> <p>13.4.4 Amorphous Indomethacin 350</p> <p>13.5 Amorphous Drug–Polymer Blends 354</p> <p>13.5.1 Molecular Interactions Probed by MD Simulation 354</p> <p>13.5.2 Solubility and Miscibility Prediction 357</p> <p>13.5.3 Molecular Mobility and Small‐Molecule Diffusion in Amorphous Dispersions 361</p> <p>13.5.4 Plasticization by Water Clusters 365</p> <p>13.6 Summary 367</p> <p>References 368</p> <p><b>14 Numerical Simulations of Unit Operations in Pharmaceutical Solid Dose Manufacturing 375<br /></b><i>Ekneet Kaur Sahni, Shivangi Naik, and Bodhisattwa Chaudhuri</i></p> <p>14.1 Introduction 375</p> <p>14.2 Numerical Method 376</p> <p>14.2.1 Contact Drying in an Agitated Filter Dryer 376</p> <p>14.2.2 Coating in a Conventional Pan Coater 378</p> <p>14.2.3 Modeling of milling in a Wiley Mill 379</p> <p>14.3 Experimental Method for Milling 380</p> <p>14.4 Results and Discussion 380</p> <p>14.4.1 Simulation of Contact Drying 380</p> <p>14.4.2 Simulation of Tablet Coating 384</p> <p>14.4.3 Simulation of Size Fragmentation (Milling) 387</p> <p>14.5 Summary and Conclusions 391</p> <p>References 392</p> <p>Index 395</p>
<b>Yuriy A. Abramov, PhD, </b>is a Senior Principal Scientist with over 14 years of experience in computational sciences in drug discovery and development with Pfizer, Inc., in Groton, CT, USA. He holds a PhD in Physical Chemistry from the D. Mendeleev University of Chemical Technology of Russia and Karpov Institute of Physical Chemistry in Moscow.
<b>The first book combining computational material science and modeling of molecular solid states for pharmaceutical industry applications.</b><br /><br />Approximately 70% of the drug products marketed worldwide are formulated in oral solid-dosage forms. It is well recognized that active pharmaceutical ingredients (APIs) can exist in a number of solid forms (amorphous, crystalline polymorphs, solvates or hydrates), which may undergo interconversion under specific environmental and storage conditions. Variations of pharmaceutical solid forms can result in alternations of physicochemical properties of the drug products, which may affect drug performance, safety, and processing. Therefore, solid form selection of APIs can be a critical issue in the pharmaceutical industry, and it is strongly regulated by Food and Drug Administration according to guidelines outlined in an International Conference on Harmonisation, as well as by other regulatory agencies around the world. <br /><br /><i>Computational Pharmaceutical Solid State Chemistry</i> undertakes the issue of solid form selection using computational approaches. These approaches are used to guide solid form experiments that optimize the physical and chemical properties of APIs related to their stability, bioavailability and formulatability. <br /><br />Chapters cover:<br /><br />•    Computational approaches allowing physical stability analysis of pharmaceutical solids<br />•    Synthonic engineering of solid dosage form with a special focus on surface energy and morphology prediction<br />•    Solubility prediction of crystalline drug-like compounds<br />•    Prediction of NMR tensors and NMR crystallography<br />•    Molecular dynamics simulation of amorphous pharmaceutical systems<br />•    Numerical simulations of unit operations in pharmaceutical solid dose manufacturing <br /><br />The book is addressed to a wide audience, including experts within the field and those without much experience in molecular modelling.   With real case industrial examples related to the application of modeling methods in problem solving, it is an ideal reference for students and researches alike.

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