Table of Contents: Good Research Practice in Non-Clinical Pharmacology and Biomedicine.

Good Research Practice in Non-Clinical Pharmacology and Biomedicine.

Bibliographic Details
Main Author:	Bespalov, Anton.
Other Authors:	Michel, Martin C., Steckler, Thomas.
Format:	eBook
Language:	English
Published:	Cham : Springer International Publishing AG, 2020.
Edition:	1st ed.
Series:	Handbook of Experimental Pharmacology Series
Subjects:	Electronic books.
Online Access:	Click to View

Table of Contents:

Intro
Preface
Contents
Quality in Non-GxP Research Environment
1 Why Do We Need a Quality Standard in Research?
2 Critical Points to Consider Before Implementing a Quality Standard in Research
2.1 GxP or Non-GxP Standard Implementation in Research?
2.1.1 Diverse Quality Mind-Set
2.2 Resource Constraints
3 Non-GxP Research Standard Basics
3.1 Data Integrity Principles: ALCOA+
3.2 Research Quality System Core Elements
3.2.1 Management and Governance
3.2.2 Secure Research Documentation and Data Management
3.2.3 Method and Assay Qualification
3.2.4 Material, Reagents and Samples Management
3.2.5 Facility, Equipment and Computerized System Management
3.2.6 Personnel and Training Records Management
3.2.7 Outsourcing/External Collaborations
3.3 Risk- and Principle-Based Quality System Assessment Approach
4 How Can the Community Move Forward?
4.1 Promoting Quality Culture
4.1.1 Raising Scientist Awareness, Training and Mentoring
4.1.2 Empowering of Associates
4.1.3 Incentives for Behaviours Which Support Research Quality
4.1.4 Promoting a Positive Error Culture
4.2 Creating a Recognized Quality Standard in Research: IMI Initiative - EQIPD
4.3 Funders Plan to Enhance Reproducibility and Transparency
5 Conclusion
References
Guidelines and Initiatives for Good Research Practice
1 Introduction
2 Guidelines and Resources Aimed at Improving Reproducibility and Robustness in Preclinical Data
2.1 Funders/Granting Agencies/Policy Makers
2.2 Publishers/Journal Groups
2.3 Summary of Overarching Themes
3 Gaps and Looking to the Future
References
Learning from Principles of Evidence-Based Medicine to Optimize Nonclinical Research Practices
1 Introduction.
2 Current Context of Nonclinical, Nonregulated Experimental Pharmacology Study Conduct: Purposes and Processes Across Sectors
2.1 Outcomes and Deliverables of Nonclinical Pharmacology Studies in Industry and Academia
2.2 Scientific Integrity: Responsible Conduct of Research and Awareness of Cognitive Bias
2.3 Initiating a Research Project and Documenting Prior Evidence
2.4 Existence and Use of Guidelines
2.5 Use of Experimental Bias Reduction Measures in Study Design and Execution
2.6 Biostatistics: Access and Use to Enable Appropriate Design of Nonclinical Pharmacology Studies
2.7 Data Integrity, Reporting, and Sharing
3 Overcoming Obstacles and Further Learning from Principles of Evidence-Based Medicine
3.1 Working Together to Improve Nonclinical Data Reliability
3.2 Enhancing Capabilities, from Training to Open Access to Data
4 Conclusion and Perspectives
References
General Principles of Preclinical Study Design
1 An Overview
2 General Scientific Methods for Designing In Vivo Experiments
2.1 Hypotheses and Effect Size
2.2 Groups, Experimental Unit and Sample Size
2.3 Measurements and Outcome Measures
2.4 Independent Variables and Analysis
3 Experimental Biases: Definitions and Methods to Reduce Them
4 Experimental Biases: Major Domains and General Principles
5 Existing Guidelines and How to Use Them
6 Exploratory and Confirmatory Research
References
Resolving the Tension Between Exploration and Confirmation in Preclinical Biomedical Research
1 Introduction
2 Discrimination Between Exploration and Confirmation
3 Exploration Must Lead to a High Rate of False Positives
4 The Garden of Forking Paths
5 Confirmation Must Weed Out the False Positives of Exploration
6 Exact Replication Does Not Equal Confirmation.
7 Design, Analysis, and Interpretation of Exploratory vs Confirmatory Studies
8 No Publication Without Confirmation?
9 Team Science and Preclinical Multicenter Trials
10 Resolving the Tension Between Exploration and Confirmation
References
Blinding and Randomization
1 Randomization and Blinding: Need for Disambiguation
2 Randomization
2.1 Varieties of Randomization
2.1.1 Simple Randomization
2.1.2 Block Randomization
2.1.3 Stratified Randomization
2.1.4 The Case of Within-Subject Study Designs
2.2 Tools to Conduct Randomization
2.3 Randomization: Exceptions and Special Cases
3 Blinding
3.1 Fit-for-Purpose Blinding
3.1.1 Assumed Blinding
3.1.2 Partial Blinding
3.1.3 Full Blinding
3.2 Implementation of Blinding
4 Concluding Recommendations
References
Out of Control? Managing Baseline Variability in Experimental Studies with Control Groups
1 What Are Control Groups?
2 Basic Considerations for Control Groups
2.1 Attribution of Animals to Control Groups
2.2 What Group Size for Control Groups?
2.3 Controls and Blinding
3 Primary Controls
3.1 Choosing Appropriate Control Treatments: Not All Negative Controls Are Equal
3.2 Vehicle Controls
3.3 Sham Controls
3.4 Non-neutral Control Groups
3.5 Controls for Mutant, Transgenic and Knockout Animals
4 Positive Controls
5 Secondary Controls
5.1 Can Baseline Values Be Used as Control?
5.2 Historical Control Values
6 When Are Control Groups Not Necessary?
7 Conclusion
References
Quality of Research Tools
1 Introduction
2 Drugs in the Twenty-First Century
2.1 Chemical Tools Versus Drugs
3 First Things First: Identity and Purity
3.1 The Case of Evans Blue
3.2 Identity and Purity of Research Reagents
4 Drug Specificity or Drug Selectivity?
5 Species Selectivity.
5.1 Animal Strain and Preclinical Efficacy Using In Vivo Models
5.2 Differences in Sequence of Biological Target
5.3 Metabolism
6 What We Dose Is Not Always Directly Responsible for the Effects We See
6.1 Conditions Where In Vitro Potency Measures Do Not Align
7 Chemical Modalities: Not All Drugs Are Created Equal
8 Receptor Occupancy and Target Engagement
9 Radioligands and PET Ligands as Chemical Tools
10 Monoclonal Antibodies as Target Validation Tools
10.1 Targets Amenable to Validation by mAbs
10.2 The Four Pillars for In Vivo Studies
10.3 Quality Control of Antibody Preparation
10.4 Isotype
10.5 Selectivity
11 Parting Thoughts
References
Genetic Background and Sex: Impact on Generalizability of Research Findings in Pharmacology Studies
1 Introduction
2 Genetic Background: The Importance of Strain and Substrain
3 Importance of Including Sex as a Variable
4 Pharmacokinetic and Pharmacodynamic Differences Attributable to Sex
5 Improving Reproducibility Through Heterogeneity
6 Good Research Practices in Pharmacology Include Considerations for Sex, Strain, and Age: Advantages and Limitations
7 Conclusions and Recommendations
References
Building Robustness into Translational Research
1 Introduction
2 Homogeneous vs. Heterogeneous Models
2.1 Animal Species and Strain
2.2 Sex of Animals
2.3 Age
2.4 Comorbidities
3 Translational Bias
3.1 Single Versus Multiple Pathophysiologies
3.2 Timing of Intervention
3.3 Pharmacokinetics and Dosage Choice
4 Conclusions
References
Minimum Information and Quality Standards for Conducting, Reporting, and Organizing In Vitro Research
1 Introduction: Why Details Matter
2 Efforts to Standardize In Vitro Protocols
2.1 The MIAME Guidelines
2.2 The MIBBI Portal
2.3 Protocol Repositories.
3 The Role of Ontologies for In Vitro Studies
3.1 Ontologies for Cells and Cell Lines
3.2 The BioAssay Ontology
3.3 Applications of the BAO to Bioassay Databases
4 Specific Examples: Quality Requirements for In Vitro Research
4.1 Chemical Probes
4.2 Cell Line Authentication
4.3 Antibody Validation
4.4 Webtools Without Minimal Information Criteria
4.5 General Guidelines for Reporting In Vitro Research
5 Open Questions and Remaining Issues
5.1 Guidelines vs. Standards
5.2 Compliance and Acceptance
5.3 Coordinated Efforts
5.4 Format and Structured Data
6 Concluding Remarks
References
Minimum Information in In Vivo Research
1 Introduction
2 General Aspects
3 Behavioural Experiments
4 Anaesthesia and Analgesia
5 Ex Vivo Biochemical and Histological Analysis
6 Histology
7 Ex Vivo Biochemical Analysis
8 Perspective
References
A Reckless Guide to P-values
1 Introduction
1.1 On the Role of Statistics
2 All About P-values
2.1 Hypothesis Test and Significance Test
2.2 Contradictory Instructions
2.3 Evidence Is Local
Error Rates Are Global
2.4 On the Scaling of P-values
2.5 Power and Expected P-values
3 Practical Problems with P-values
3.1 The Significance Filter Exaggeration Machine
3.2 Multiple Comparisons
3.3 P-hacking
3.4 What Is a Statistical Model?
4 P-values and Inference
References
Electronic Lab Notebooks and Experimental Design Assistants
1 Paper vs. Electronic Lab Notebooks
2 Finding an eLN
3 Levels of Quality for eLNs
4 Assistance with Experimental Design
5 Data-Related Quality Aspects of eLNs
6 The LN as the Central Element of Data Management
7 Organizing and Documenting Experiments
References
Data Storage
1 Introduction
2 Data Storage Systems
2.1 Types of Storage.
2.2 Features of Storage Systems.

Good Research Practice in Non-Clinical Pharmacology and Biomedicine.

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