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Translating Molecules into Medicines: Cross-Functional Integration at the Drug Discovery-Development Interface Softcover reprint of the original 1st ed. 2017 [Pehme köide]

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  • Formaat: Paperback / softback, 461 pages, kõrgus x laius: 235x155 mm, kaal: 7314 g, 81 Illustrations, color; 20 Illustrations, black and white; XXIV, 461 p. 101 illus., 81 illus. in color., 1 Paperback / softback
  • Sari: AAPS Advances in the Pharmaceutical Sciences Series 25
  • Ilmumisaeg: 20-Jul-2018
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 3319843036
  • ISBN-13: 9783319843032
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  • Formaat: Paperback / softback, 461 pages, kõrgus x laius: 235x155 mm, kaal: 7314 g, 81 Illustrations, color; 20 Illustrations, black and white; XXIV, 461 p. 101 illus., 81 illus. in color., 1 Paperback / softback
  • Sari: AAPS Advances in the Pharmaceutical Sciences Series 25
  • Ilmumisaeg: 20-Jul-2018
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 3319843036
  • ISBN-13: 9783319843032
Teised raamatud teemal:
Tackling translational medicine with a focus on the drug discovery development-interface, this book integrates approaches and tactics from multiple disciplines, rather than just the pharmaceutical aspect of the field. The authors of each chapter address the paradox between the molecular understanding of diseases, drug discovery, and drug development. Laying out the detailed trends from various fields, different chapters are dedicated to target engagement, toxicological safety assessments, and the compelling relationship of optimizing early clinical studies with design strategies. The book also highlights the importance of balancing the three pillars: sufficient efficacy, acceptable safety and appropriate pharmacokinetics, all of which are crucial to successful efforts in discovery and development. With discussions regarding the combined approaches of molecular research, personalized medicine, pre-clinical and clinical development, as well as targeted therapies—this compendium is a flexible fit, perfect for professionals in the pharmaceutical industry and related academic fields.
Part I Discovery, Development and Commercialization of Drug Candidates: Overview and Issues
1 Pharmaceutical Industry Performance
3(24)
David C. Swinney
1.1 Introduction
3(6)
1.1.1 Definitions
5(2)
1.1.2 Unmet Need
7(1)
1.1.3 NMEs and the Degree of Innovation
8(1)
1.2 Drug Discovery and Development Overview
9(4)
1.2.1 Learn and Confirm Cycle
9(2)
1.2.2 Process to Identify Safe and Effective Medicines
11(2)
1.3 How Medicines Work
13(3)
1.4 Drug Discovery Strategies: How Medicines Are Discovered
16(4)
1.5 Mechanistic Paradox and Precision Medicine
20(2)
1.6 Opportunities
22(1)
References
23(4)
2 New Product Planning and the Drug Discovery-Development Interface
27(14)
Robin Reagan
2.1 Overview and Introduction
27(2)
2.2 Understanding the Disease State
29(1)
2.3 Customer Needs
30(3)
2.4 Does Science Matter?
33(1)
2.5 The SWOT Team or How to Look Critically at Your Program
33(1)
2.6 Those Pesky Competitors
34(1)
2.7 How to Have an R&D and Marketing Marriage Made in Heaven
35(2)
2.8 Should R&D and Marketing Collaborate Early or Late? Yes!
37(1)
2.9 R&D and Marketing Are Allies, Not Enemies
37(1)
References
38(3)
Part II Druggable Targets, Discovery Technologies and Generation of Lead Molecules
3 Target Engagement Measures in Preclinical Drug Discovery: Theory, Methods, and Case Studies
41(40)
Timothy B. Durham
Michael R. Wiley
3.1 Introduction
41(1)
3.2 Basic Concepts
42(5)
3.3 Target Engagement in Vivo
47(5)
3.4 Application to In Vivo Experimental Design
52(23)
3.4.1 Compound Delivery via Pump as a Means to Facilitate Target Validation
57(2)
3.4.2 Designing an Osmotic Pump Study
59(2)
3.4.3 Approaches to Measuring Target Engagement In Vivo
61(2)
3.4.4 The Relationship of TE to Pharmacodynamics
63(3)
3.4.5 Case Studies in Using TE
66(9)
3.5 Conclusion
75(1)
References
76(5)
4 In Silico ADME Techniques Used in Early-Phase Drug Discovery
81(38)
Matthew L. Danielson
Bingjie Hu
Jie Shen
Prashant V. Desai
4.1 Structure-Based In Silico Models
82(4)
4.1.1 Molecular Docking
83(2)
4.1.2 Molecular Dynamics
85(1)
4.2 Ligand-Based In Silico Models and Tools
86(21)
4.2.1 Quantitative Structure-Property Relationship (QSPR) Models
86(8)
4.2.2 ADME QSPR Models Used at Eli Lilly and Company
94(1)
4.2.3 Prospective Validation of ADME QSPR Models at Eli Lilly and Company
95(2)
4.2.4 Trends Between Calculated Physicochemical Properties and ADME Parameters
97(3)
4.2.5 Pharmacophore Modeling
100(3)
4.2.6 Site of Metabolism Prediction
103(1)
4.2.7 SPR/STR Knowledge Extraction Using Matched Molecular Pair Analysis
104(3)
4.3 Integrated and Iterative Use of Models in Early Drug Discovery
107(2)
4.4 Summary
109(1)
References
110(9)
5 Discover Toxicology: An Early Safety Assessment Approach
119(46)
Thomas K. Baker
Steven K. Engle
Bartley W. Halstead
Brianna M. Paisley
George H. Searfoss
Jeffrey A. Willy
5.1 Introduction
119(1)
5.2 Toxicology Target Evaluation and Assessment
120(3)
5.3 Off-Target Assessment
123(4)
5.3.1 In Silico Safety Pharmacology
123(1)
5.3.2 Enzyme Safety Pharmacology
124(2)
5.3.3 Summary
126(1)
5.4 In Silico Preclinical Predictive Modeling
127(11)
5.4.1 Physical and Chemical Properties
127(2)
5.4.2 Structural Risk Assessment
129(1)
5.4.3 Similarity Analyses
130(2)
5.4.4 Substructural Analysis: Identification of Toxicophores
132(1)
5.4.5 In Silico Models for In Vitro Tox Endpoints
133(2)
5.4.6 In Vivo Tox Prediction
135(2)
5.4.7 Summary
137(1)
5.5 Cellular Systems: General Screening and Models of Key Target Organs
138(12)
5.5.1 General Screening
139(1)
5.5.2 Focused Cell Screens
140(1)
5.5.3 Liver Injury Cell Models
141(1)
5.5.4 Gastrointestinal Injury Cell Models
142(1)
5.5.5 Heart Injury Cell Models
143(2)
5.5.6 Skeletal Muscle Injury Cell Models
145(1)
5.5.7 Injection Site Irritation
145(1)
5.5.8 Hematopoietic System and Hematopoiesis
146(2)
5.5.9 iPSC-Derived Cell Models
148(2)
5.5.10 Microphysiological Culture Systems
150(1)
5.6 In Vivo Biomarker Screens
150(3)
5.7 Technologies
153(2)
5.7.1 Multiplex and High-Content Approaches
153(2)
5.8 Organizational Framework for Early Safety Assessment Activities
155(1)
5.9 Summary
156(1)
References
157(8)
Part III Optimizing Lead Molecules into Drug Candidates
6 Integrated Lead Optimization: Translational Models as We Advance Toward the Clinic
165(66)
Bianca M. Liederer
Xingrong Liu
Simon Wong
Daniel R. Mudra
6.1 Introduction
166(2)
6.2 Integrated Approaches to Assess and Predict Human Clearance
168(23)
6.2.1 Allometric Scaling
169(6)
6.2.2 Mechanistic Scaling
175(8)
6.2.3 Mechanistic Prediction of Human Clearance
183(7)
6.2.4 Summary
190(1)
6.3 Integrated Approaches to Assess Drug-Drug Interactions
191(17)
6.3.1 Induction
191(2)
6.3.2 Reversible (Direct) Inhibition
193(1)
6.3.3 Time-Dependent Inhibition
193(2)
6.3.4 Strategies for Mitigating DDI-Related Liabilities
195(2)
6.3.5 In Vitro Assessment of DDI Potential
197(5)
6.3.6 Assessing Clinical DDI Risk
202(6)
6.3.7 Summary
208(1)
6.4 Integrated Approaches to Assess Brain Penetration
208(11)
6.4.1 Pharmacokinetics of Brain Drug Delivery
209(3)
6.4.2 Drug Transporters at the BBB
212(3)
6.4.3 Integrated Approaches in Assessment of Brain Drug Delivery
215(3)
6.4.4 Summary
218(1)
References
219(12)
7 Developability Assessment of Clinical Candidates
231(36)
Shobha N. Bhattachar
Jeffrey S. Tan
David M. Bender
7.1 Introduction
232(1)
7.2 Components of Developability Assessment
233(13)
7.2.1 Synthetic Complexity of Drug Substance
233(3)
7.2.2 Physicochemical Properties
236(3)
7.2.3 Solid Form Criteria for Developability
239(3)
7.2.4 Solid Form Selection for Absorption Enhancement
242(3)
7.2.5 Integrated Developability Risk Assessment and Feedback to Discovery Teams
245(1)
7.2.6 Clinical and Commercial Formulations
245(1)
7.3 Drug Product Performance
246(3)
7.3.1 Product Performance Criteria in the Context of PK-PD
246(1)
7.3.2 Solubility and In Vitro Dissolution
247(2)
7.4 Absorption Modeling
249(6)
7.4.1 Basic Principles and Commonly Used Tools
249(3)
7.4.2 Absorption Parameters from Modeling
252(3)
7.5 Toxicology Formulation
255(1)
7.6 Developability Summary
256(2)
7.6.1 Drug Substance and Drug Product Parameters
256(1)
7.6.2 Patient-Centered Design Parameters
257(1)
7.6.3 Business Parameters
257(1)
7.7 Case Studies/Illustrative Hypothetical Scenarios
258(4)
7.7.1 mTOR Inhibitors Rapamune® (Sirolimus) and Afinitor® (Everolimus)
258(1)
7.7.2 BEZ-235 (PI3K/mTOR Inhibitor)
259(1)
7.7.3 BRAF Inhibitors (Vemurafenib: Zelboraf)
260(2)
7.8 Conclusion
262(1)
References
262(5)
8 Lead Optimization, Preclinical Toxicology
267(30)
Marcus H. Andrews
Vincent L. Reynolds
8.1 Overview
268(7)
8.2 The LO Toxicology Workflow
275(18)
8.2.1 Early-Stage LO Toxicology Activities
275(3)
8.2.2 Mid-Stage LO Toxicology Activities
278(9)
8.2.3 Late-Stage LO Toxicology Activities
287(4)
8.2.4 Additional LO Toxicology Activities
291(2)
8.3 Communications with the Development Team
293(1)
References
294(3)
Part IV Early Clinical Development of Drug Candidates
9 Design of Clinical Studies in Early Development
297(20)
Margaret S. Landis
9.1 Introduction
297(1)
9.2 Key Cross Pharmaceutical Industry Initiatives
298(8)
9.2.1 US Food and Drug Administration (FDA) Critical Path Initiative
298(2)
9.2.2 The Pharmacological Audit Trail (PhAT)
300(2)
9.2.3 National Institutes of Health (NIH) Bench-To-Bedside (B2B) Initiative
302(1)
9.2.4 Translational Medicine Paradigms
302(2)
9.2.5 The Biopharmaceutics Risk Assessment Roadmap (BioRAM)
304(1)
9.2.6 Other Related Pharmaceutical Industry Initiatives
305(1)
9.3 Early Development Themes to Address Better Clinical Outcomes
306(6)
9.3.1 Biomarker and Diagnostics Identification and Co-Development
306(2)
9.3.2 Early Focus on Predictive Model Development as a Key Success Factor
308(1)
9.3.3 Application of Early Adaptive Clinical Design Strategies
309(2)
9.3.4 Use of Fully Integrated Information Technology (IT) and Knowledge Management (KM) Systems as a Key Success Factor
311(1)
9.4 Summary
312(1)
References
312(5)
10 Design of Clinical Formulations in Early Development
317(24)
Catherine M. Ambler
Avinash G. Thombre
Madhushree Gokhale
John S. Morrison
10.1 Introduction to Early Clinical Studies
317(3)
10.2 First-In-Human (FIH) Phase I Clinical Formulation Design
320(3)
10.2.1 Simplified Manufactured Dosage Forms
321(1)
10.2.2 Traditional Manufactured Solid Dosage Forms
321(1)
10.2.3 Extemporaneously Prepared Dosage Forms
322(1)
10.3 Understanding Modified Release Formulations in Early Clinical Studies
323(6)
10.3.1 General Considerations
323(2)
10.3.2 Ideal Drug Candidate for Modified Release Formulations
325(1)
10.3.3 Strategic Considerations in Modified Release Deployment
325(4)
10.3.4 Translation of Modified Release Options from Early to Later Development
329(1)
10.4 First-In-Human Case Studies
329(3)
10.4.1 Study A: Powder in Capsule
330(1)
10.4.2 Study B: Enabled Formulation
330(1)
10.4.3 Study C: Particle Size Evaluation
331(1)
10.4.4 Study D: EP-Osmotic Capsule (Adapted from Ref. 7)
331(1)
10.4.5 Study E: Modified Release (Adapted from Ref. 7)
331(1)
10.5 Formulation Design Following First-In-Human Studies
332(5)
10.5.1 General Considerations
332(4)
10.5.2 Other Specific Considerations
336(1)
10.6 Summary
337(1)
References
338(3)
11 Translational Research: Preclinical to Healthy Volunteer to Patient
341(32)
Brinda Tammara
Sangeeta Raje
William McKeand
Joan M. Korth-Bradley
11.1 Introduction to Clinical Pharmacology Studies
341(6)
11.1.1 Preliminary Clinical Development Plan and First-In-Human Study Design
342(3)
11.1.2 Research Goals and Study Design of Phase 1 Clinical Pharmacology Studies
345(2)
11.1.3 Data Analysis and Interpretation
347(1)
11.2 Drug-Drug Interactions
347(1)
11.3 Pharmacokinetics in the Patient
348(1)
11.4 Biomarkers in Clinical Development
349(4)
11.4.1 Biomarkers of Bone Health
349(2)
11.4.2 Biomarkers in Inflammatory Diseases
351(2)
11.5 Case Study: Development of Bazedoxifene for the Prevention and/or Treatment of Postmenopausal Osteoporosis
353(5)
11.5.1 In Vivo Pharmacology: Effects of Bazedoxifene on Bone Repair in Monkeys and Rats
354(1)
11.5.2 Preclinical Pharmacokinetics
354(1)
11.5.3 Translation to Clinical Evidence of Efficacy
355(2)
11.5.4 Translation of Preclinical to Clinical Evidence of Safety
357(1)
11.5.5 Summary
358(1)
11.6 Case Study: Development of Bapineuzumab for the Treatment of Alzheimer's Disease
358(8)
11.6.1 In Vivo Pharmacology
359(1)
11.6.2 Preclinical/Clinical Comparison of Pharmacokinetics
360(1)
11.6.3 Preclinical/Clinical Evidence of Efficacy
361(2)
11.6.4 Evidence of Safety
363(2)
11.6.5 Biomarkers
365(1)
11.6.6 Exposure-Response Analyses
365(1)
11.6.7 Summary
366(1)
11.7 Conclusions
366(1)
References
367(6)
12 Regulatory Aspects at the Drug Discovery Development Interface
373(18)
Lynn Gold
Ken Phelps
12.1 Introduction
374(1)
12.2 US FDA Regulatory Expectations and Guidelines for a Phase 1 FIH Clinical Study
374(2)
12.3 The Drug Development Plan
376(1)
12.4 Development Target Product Profile
377(3)
12.5 The Investigational New Drug (IND) Application
380(7)
12.5.1 Pre-Investigational New Drug (PIND) Submission Meeting
380(1)
12.5.2 IND Introduction
381(1)
12.5.3 IND Preparation
381(4)
12.5.4 IND Submission
385(1)
12.5.5 IND Maintenance
386(1)
12.6 Summary
387(4)
Part V Evolution of the Drug Discovery/Development Paradigm
13 Alternate Routes of Administration
391(30)
Neil Mathias
Srini Sridharan
13.1 Opportunities for Non-oral Routes of Administration (RoAs)
392(5)
13.1.1 Challenges to Oral Delivery
393(1)
13.1.2 The Needs of the Patient, Caregiver, and Payer in Drug Product Design
394(1)
13.1.3 Non-Oral Product Opportunities
394(3)
13.2 Selecting Alternate Routes of Administration
397(15)
13.2.1 Non-injectable Routes of Administration (RoAs)
398(9)
13.2.2 Injectable Drug Delivery
407(5)
13.3 Self-Administration of Therapy by Patients
412(4)
13.3.1 Prefilled Syringes (PFSs)
412(1)
13.3.2 Pens
413(1)
13.3.3 Auto-Injectors
414(1)
13.3.4 Patch Pumps
414(1)
13.3.5 Needle-Free Injectors
415(1)
13.4 Vision for the Future
416(1)
References
416(5)
14 Outlook for the Future
421(28)
John S. Morrison
Michael J. Hageman
14.1 Introduction
422(1)
14.2 Causes of Poor Productivity in the Pharmaceutical Industry
423(3)
14.2.1 Poor Drug Candidate Development "Effectiveness"
425(1)
14.2.2 Poor Drug Candidate Development "Efficiency"
425(1)
14.3 Key Challenges to Improving Productivity
426(3)
14.3.1 Scientific Knowledge Gaps
426(1)
14.3.2 Decision-Making in a Resource-Constrained and Uncertain Environment
427(1)
14.3.3 Incompatible Stakeholder Interests
428(1)
14.4 How the Productivity Problem Has typically Been Addressed
429(4)
14.4.1 Consolidations and Adopting "Best Practices"
429(1)
14.4.2 Partnerships and Collaborations
430(2)
14.4.3 The Results of Productivity Improvement Efforts
432(1)
14.5 Pathway to a More Successful Future
433(6)
14.5.1 Bridging Knowledge Gaps
434(1)
14.5.2 Improving Drug Candidate Selection
435(1)
14.5.3 Better Risk Assessment and Management
436(1)
14.5.4 Aligning Stakeholder Interests
437(2)
14.6 Summary
439(1)
References
440(9)
Index 449
Shobha N. Bhattachar is a Director in Small Molecule Design and Development at Eli Lilly and Company. She has a Bachelors degree in Pharmacy from Bangalore University, India, and a Masters degree in Pharmaceutical chemistry from the University of Kansas. Prior to joining Lilly, Bhattachar worked for eight years at Pfizer in Ann Arbor MI, in the Research Formulations group. She has authored/co-authored 15 publications covering wide ranging topics in the field of Pharmaceutical Sciences. Her current interests include the discovery-development interface, clinical candidate selection and developability assessment, clinical formulation development and biopharmaceutical aspects of drug product design. John S. Morrison earned his doctorate in physical organic chemistry from the University of Western Ontario studying the photochemical mechanisms responsible for the light-struck reaction in beer.  After graduation John joined the Preformulation and Analytical Method Development group of Apotex Inc evaluating drug substances and novel drug product formulations.  He then transitioned to the Discovery Pharmaceutics group of Bristol-Myers Squibb characterizing early drug candidates and establishing delivery strategies for a variety of dosing routes and molecular modalities.  His current role bridges the discovery/development divide, collaboratively supporting multi-disciplinary discovery teams as well as determining and mitigating developability risks for potential clinical drug candidates.  John has also led several AAPS committees and co-organizes the ACS Drug Design and Delivery webinar series.

David M. Bender is  a Senior Research Scientist in Small Molecule Design and Development at Eli Lilly and Co.  He received a B.S in chemistry from Miami University and an M.S. in organic chemistry from Colorado State University where he conducted research into the design and synthesis of novel antibacterial agents.  He joined Eli Lilly and Co. in 1998 as a synthetic organic chemist in Discovery Chemistry Research. In 2009, he joined the Product Research and Development organization, where he has focused on formulation development, small molecule developability, absorption modeling and alternate drug delivery. Mr. Bender is currently a group leader in the Product Design and Developability group, and is responsible for overseeing the design and manufacture of clinical drug product for small molecules entering Lillys development pipeline. 

Daniel R. Mudra is Director of ADME at Eli Lilly and Company.  He earned his B.S. from The University of Dayton studying pharmacokinetic/pharmacodynamic effects of drug-impregnated implants and an M.S. from Loyola University Chicago investigating the biochemical regulation of the p38 kinase pathway.  He earned his doctorate in Pharmaceutical Chemistry from The University of Kansas developing in situ and computational models of absorption studying the effects of excipients on permeability, metabolism and transport.  Dr. Mudra began his industrial research career at XenoTech, LLC and has published on models of P450 induction and inhibition, drug absorption and PK.  Since joining Lilly, he has contributed to projects from target identification to clinical development across a variety of therapeutic areas.  He leads in the incorporation of mechanistic and physiologically based PK modeling including the use of molecular attributes to predict human clearance pathways.