Good Research Practice in Non-Clinical Pharmacology and Biomedicine.

• Superior document: Handbook of Experimental Pharmacology Series ; v.257
• Author: Bespalov, Anton.
• TeilnehmendeR: Michel, Martin C.; Steckler, Thomas.
• Place / Publishing House: Cham : : Springer International Publishing AG,, 2020.; Ã2020.
• Year of Publication: 2020
• Edition: 1st ed.
• Language: English
• Series: Handbook of Experimental Pharmacology Series
• Physical Description: 1 online resource (424 pages)

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.