Good Research Practice in Non-Clinical Pharmacology and Biomedicine

Preface\nContents\nQuality in Non-GxP Research Environment\n	1 Why Do We Need a Quality Standard in Research?\n	2 Critical Points to Consider Before Implementing a Quality Standard in Research\n		2.1 GxP or Non-GxP Standard Implementation in Research?\n			2.1.1 Diverse Quality Mind-Set\n		2.2 Resource Constraints\n	3 Non-GxP Research Standard Basics\n		3.1 Data Integrity Principles: ALCOA+\n		3.2 Research Quality System Core Elements\n			3.2.1 Management and Governance\n			3.2.2 Secure Research Documentation and Data Management\n			3.2.3 Method and Assay Qualification\n			3.2.4 Material, Reagents and Samples Management\n			3.2.5 Facility, Equipment and Computerized System Management\n			3.2.6 Personnel and Training Records Management\n			3.2.7 Outsourcing/External Collaborations\n		3.3 Risk- and Principle-Based Quality System Assessment Approach\n	4 How Can the Community Move Forward?\n		4.1 Promoting Quality Culture\n			4.1.1 Raising Scientist Awareness, Training and Mentoring\n			4.1.2 Empowering of Associates\n			4.1.3 Incentives for Behaviours Which Support Research Quality\n			4.1.4 Promoting a Positive Error Culture\n		4.2 Creating a Recognized Quality Standard in Research: IMI Initiative - EQIPD\n		4.3 Funders Plan to Enhance Reproducibility and Transparency\n	5 Conclusion\n	References\nGuidelines and Initiatives for Good Research Practice\n	1 Introduction\n	2 Guidelines and Resources Aimed at Improving Reproducibility and Robustness in Preclinical Data\n		2.1 Funders/Granting Agencies/Policy Makers\n		2.2 Publishers/Journal Groups\n		2.3 Summary of Overarching Themes\n	3 Gaps and Looking to the Future\n	References\nLearning from Principles of Evidence-Based Medicine to Optimize Nonclinical Research Practices\n	1 Introduction\n	2 Current Context of Nonclinical, Nonregulated Experimental Pharmacology Study Conduct: Purposes and Processes Across Sectors\n		2.1 Outcomes and Deliverables of Nonclinical Pharmacology Studies in Industry and Academia\n		2.2 Scientific Integrity: Responsible Conduct of Research and Awareness of Cognitive Bias\n		2.3 Initiating a Research Project and Documenting Prior Evidence\n		2.4 Existence and Use of Guidelines\n		2.5 Use of Experimental Bias Reduction Measures in Study Design and Execution\n		2.6 Biostatistics: Access and Use to Enable Appropriate Design of Nonclinical Pharmacology Studies\n		2.7 Data Integrity, Reporting, and Sharing\n	3 Overcoming Obstacles and Further Learning from Principles of Evidence-Based Medicine\n		3.1 Working Together to Improve Nonclinical Data Reliability\n		3.2 Enhancing Capabilities, from Training to Open Access to Data\n	4 Conclusion and Perspectives\n	References\nGeneral Principles of Preclinical Study Design\n	1 An Overview\n	2 General Scientific Methods for Designing In Vivo Experiments\n		2.1 Hypotheses and Effect Size\n		2.2 Groups, Experimental Unit and Sample Size\n		2.3 Measurements and Outcome Measures\n		2.4 Independent Variables and Analysis\n	3 Experimental Biases: Definitions and Methods to Reduce Them\n	4 Experimental Biases: Major Domains and General Principles\n	5 Existing Guidelines and How to Use Them\n	6 Exploratory and Confirmatory Research\n	References\nResolving the Tension Between Exploration and Confirmation in Preclinical Biomedical Research\n	1 Introduction\n	2 Discrimination Between Exploration and Confirmation\n	3 Exploration Must Lead to a High Rate of False Positives\n	4 The Garden of Forking Paths\n	5 Confirmation Must Weed Out the False Positives of Exploration\n	6 Exact Replication Does Not Equal Confirmation\n	7 Design, Analysis, and Interpretation of Exploratory vs Confirmatory Studies\n	8 No Publication Without Confirmation?\n	9 Team Science and Preclinical Multicenter Trials\n	10 Resolving the Tension Between Exploration and Confirmation\n	References\nBlinding and Randomization\n	1 Randomization and Blinding: Need for Disambiguation\n	2 Randomization\n		2.1 Varieties of Randomization\n			2.1.1 Simple Randomization\n			2.1.2 Block Randomization\n			2.1.3 Stratified Randomization\n			2.1.4 The Case of Within-Subject Study Designs\n		2.2 Tools to Conduct Randomization\n		2.3 Randomization: Exceptions and Special Cases\n	3 Blinding\n		3.1 Fit-for-Purpose Blinding\n			3.1.1 Assumed Blinding\n			3.1.2 Partial Blinding\n			3.1.3 Full Blinding\n		3.2 Implementation of Blinding\n	4 Concluding Recommendations\n	References\nOut of Control? Managing Baseline Variability in Experimental Studies with Control Groups\n	1 What Are Control Groups?\n	2 Basic Considerations for Control Groups\n		2.1 Attribution of Animals to Control Groups\n		2.2 What Group Size for Control Groups?\n		2.3 Controls and Blinding\n	3 Primary Controls\n		3.1 Choosing Appropriate Control Treatments: Not All Negative Controls Are Equal\n		3.2 Vehicle Controls\n		3.3 Sham Controls\n		3.4 Non-neutral Control Groups\n		3.5 Controls for Mutant, Transgenic and Knockout Animals\n	4 Positive Controls\n	5 Secondary Controls\n		5.1 Can Baseline Values Be Used as Control?\n		5.2 Historical Control Values\n	6 When Are Control Groups Not Necessary?\n	7 Conclusion\n	References\nQuality of Research Tools\n	1 Introduction\n	2 Drugs in the Twenty-First Century\n		2.1 Chemical Tools Versus Drugs\n	3 First Things First: Identity and Purity\n		3.1 The Case of Evans Blue\n		3.2 Identity and Purity of Research Reagents\n	4 Drug Specificity or Drug Selectivity?\n	5 Species Selectivity\n		5.1 Animal Strain and Preclinical Efficacy Using In Vivo Models\n		5.2 Differences in Sequence of Biological Target\n		5.3 Metabolism\n	6 What We Dose Is Not Always Directly Responsible for the Effects We See\n		6.1 Conditions Where In Vitro Potency Measures Do Not Align\n	7 Chemical Modalities: Not All Drugs Are Created Equal\n	8 Receptor Occupancy and Target Engagement\n	9 Radioligands and PET Ligands as Chemical Tools\n	10 Monoclonal Antibodies as Target Validation Tools\n		10.1 Targets Amenable to Validation by mAbs\n		10.2 The Four Pillars for In Vivo Studies\n		10.3 Quality Control of Antibody Preparation\n		10.4 Isotype\n		10.5 Selectivity\n	11 Parting Thoughts\n	References\nGenetic Background and Sex: Impact on Generalizability of Research Findings in Pharmacology Studies\n	1 Introduction\n	2 Genetic Background: The Importance of Strain and Substrain\n	3 Importance of Including Sex as a Variable\n	4 Pharmacokinetic and Pharmacodynamic Differences Attributable to Sex\n	5 Improving Reproducibility Through Heterogeneity\n	6 Good Research Practices in Pharmacology Include Considerations for Sex, Strain, and Age: Advantages and Limitations\n	7 Conclusions and Recommendations\n	References\nBuilding Robustness into Translational Research\n	1 Introduction\n	2 Homogeneous vs. Heterogeneous Models\n		2.1 Animal Species and Strain\n		2.2 Sex of Animals\n		2.3 Age\n		2.4 Comorbidities\n	3 Translational Bias\n		3.1 Single Versus Multiple Pathophysiologies\n		3.2 Timing of Intervention\n		3.3 Pharmacokinetics and Dosage Choice\n	4 Conclusions\n	References\nMinimum Information and Quality Standards for Conducting, Reporting, and Organizing In Vitro Research\n	1 Introduction: Why Details Matter\n	2 Efforts to Standardize In Vitro Protocols\n		2.1 The MIAME Guidelines\n		2.2 The MIBBI Portal\n		2.3 Protocol Repositories\n	3 The Role of Ontologies for In Vitro Studies\n		3.1 Ontologies for Cells and Cell Lines\n		3.2 The BioAssay Ontology\n		3.3 Applications of the BAO to Bioassay Databases\n	4 Specific Examples: Quality Requirements for In Vitro Research\n		4.1 Chemical Probes\n		4.2 Cell Line Authentication\n		4.3 Antibody Validation\n		4.4 Webtools Without Minimal Information Criteria\n		4.5 General Guidelines for Reporting In Vitro Research\n	5 Open Questions and Remaining Issues\n		5.1 Guidelines vs. Standards\n		5.2 Compliance and Acceptance\n		5.3 Coordinated Efforts\n		5.4 Format and Structured Data\n	6 Concluding Remarks\n	References\nMinimum Information in In Vivo Research\n	1 Introduction\n	2 General Aspects\n	3 Behavioural Experiments\n	4 Anaesthesia and Analgesia\n	5 Ex Vivo Biochemical and Histological Analysis\n	6 Histology\n	7 Ex Vivo Biochemical Analysis\n	8 Perspective\n	References\nA Reckless Guide to P-values\n	1 Introduction\n		1.1 On the Role of Statistics\n	2 All About P-values\n		2.1 Hypothesis Test and Significance Test\n		2.2 Contradictory Instructions\n		2.3 Evidence Is Local; Error Rates Are Global\n		2.4 On the Scaling of P-values\n		2.5 Power and Expected P-values\n	3 Practical Problems with P-values\n		3.1 The Significance Filter Exaggeration Machine\n		3.2 Multiple Comparisons\n		3.3 P-hacking\n		3.4 What Is a Statistical Model?\n	4 P-values and Inference\n	References\nElectronic Lab Notebooks and Experimental Design Assistants\n	1 Paper vs. Electronic Lab Notebooks\n	2 Finding an eLN\n	3 Levels of Quality for eLNs\n	4 Assistance with Experimental Design\n	5 Data-Related Quality Aspects of eLNs\n	6 The LN as the Central Element of Data Management\n	7 Organizing and Documenting Experiments\n	References\nData Storage\n	1 Introduction\n	2 Data Storage Systems\n		2.1 Types of Storage\n		2.2 Features of Storage Systems\n		2.3 Data File Formats\n		2.4 Dataset Structure and Organisation\n	3 Metadata: Data Describing Data\n		3.1 Unique and Persistent Identifiers\n		3.2 The FAIR Principles\n	4 Legal Aspects of Data Storage\n		4.1 Anonymisation of Research Data\n		4.2 Legal Frameworks to Consider\n		4.3 Licencing\n		4.4 Blockchain: A Technical Solution for Legal Requirements\n	5 Overview of Research Data Repositories and Tools\n		5.1 Repositories\n	References\nDesign of Meta-Analysis Studies\n	1 Principles of Systematic Review\n	2 Principles of Meta-Analysis\n	3 Summary\n	References\nPublishers´ Responsibilities in Promoting Data Quality and Reproducibility\n	1 Introduction\n	2 Understanding Researchers´ Problems and Motivations\n		2.1 Understanding Motivations to Share Data\n	3 Raising Awareness and Changing Behaviours\n		3.1 Journal Policies\n			3.1.1 Standardising and Harmonising Journal Research Data Policies\n				Code and Materials Sharing Policies\n		3.2 Effectiveness of Journal Research Data Policies\n	4 Improving the Quality, Transparency and Objectivity of the Peer-Review Process\n		4.1 Implementation of Reporting Guidelines\n		4.2 Editorial and Peer-Review Procedures to Support Transparency and Reproducibility\n		4.3 Image Manipulation and Plagiarism Detection\n	5 Better Scholarly Communication Infrastructure and Innovation\n		5.1 Tackling Publication (Reporting) Bias\n			5.1.1 Journals\n			5.1.2 Preregistration of Research\n				Registered Reports\n				Protocol Publication and Preprint Sharing\n		5.2 Research Data Repositories\n		5.3 Research Data Tools and Services\n		5.4 Making Research Data Easier to Find\n	6 Enhancing Incentives\n		6.1 New Types of Journal and Journal Article\n		6.2 Data and Software Citation\n		6.3 Digital Badges for Transparency: A New Type of Incentive\n			Box 1 Practical Recommendations for Researchers to Support the Publication of Reproducible Research\n	7 Making Research Publishing More Open and Accessible\n		7.1 Open Access and Licencing Research for Reuse\n		7.2 Open Publisher (Meta)Data\n		7.3 Open for Collaboration\n			7.3.1 The Future of Scholarly Communication?\n	References\nQuality Governance in Biomedical Research\n	1 What Is Quality Governance?\n	2 Looking in the Quality Mirror (How to Define the Green Zone)\n		2.1 What Is at Stake?\n		2.2 What Do You Do to Protect and Maximize Your Stakes?\n	3 Fixing Your Quality Image (How to Get in the Green Zone)\n	4 Looking Good! All Done Now? (How to Stay in the Green Zone)\n	5 Conclusion\n	References\nGood Research Practice: Lessons from Animal Care and Use\n	1 Ethical and Legal Framework\n		1.1 Recommendations for the Care and Use of Laboratory Animals\n		1.2 Legislation in the USA\n		1.3 Legislation in the European Union\n		1.4 Legislation in Other Countries\n	2 Implications for Preclinical Data Quality\n		2.1 Oversight Bodies Impact on Preclinical Data Quality\n		2.2 Animal Care and Use Programs Affect Preclinical Data Quality\n		2.3 Health Status Influencing Preclinical Data\n		2.4 The Impact of Housing and Husbandry\n	3 Assessment of Animal Care and Use Programs\n		3.1 Internal Oversight\n		3.2 External Oversight\n		3.3 The AAALAC International Accreditation Process\n		3.4 Assessments by Industry\n	4 Conclusion\n	References\nResearch Collaborations and Quality in Research: Foes or Friends?\n	1 Introduction\n	2 Successful Collaborative Research and High Research Quality Are Interdependent\n	3 Quality of Research and Sustainability\n	4 The Importance of Effective Governance in Collaborative Research\n	5 On Data Sharing, Collaborative Research and Research Quality\n	6 Enlarging the Collaborative Research Environment: Regulators and Patients as Important Partners for Research Quality\n	7 Why Scientists Should Consider Quality as a Key Parameter for Their Collaborative Research from the Very Start\n	8 Conclusions\n	References\nCosts of Implementing Quality in Research Practice\n	1 Introduction\n	2 German Mouse Clinic: ISO 9001\n		2.1 Our Mission\n		2.2 Our Team and Main Stakeholders\n		2.3 Needs Concerning Quality Management and Why ISO 9001\n		2.4 Challenges\n		2.5 Costs\n		2.6 Payoffs/Benefits\n			Box 1 First Third-Party Audit\n			Box 2 Traceability Versus Personalized Data Storage\n			Box 3 Taking Responsibility in Writing Up Publications\n		2.7 Lessons Learned/Outlook\n	3 University of Kentucky Good Research Practice (GRP) Resource Center\n		3.1 Our Mission\n		3.2 Our Stakeholder´s Interests and Concerns\n		3.3 How to Address Data Irreproducibility\n		3.4 Why Build a GLP-Compliant Quality Management System in Academia\n		3.5 Challenges\n		3.6 Costs\n		3.7 Payoffs/Benefits\n		3.8 Lessons Learned/Outlook\n	4 Conclusions: Investing in Quality\n	References